Environmental Public Health Tracking: Data Details

Read frequently asked questions about the air quality data

Ozone

Annual days above standard

This measure is the annual number of days with maximum eight-hour average ozone concentration above the National Ambient Air Quality Standard. The measure includes both monitored and modeled data. The monitored data comes from the U.S. Environmental Protection Agency (EPA) Air Quality System (AQS). When AQS data are available from multiple monitors for a given county and day, the highest eight-hour maximum (daily) ozone concentration among all the monitors is selected for purposes of creating daily county-level data. EPA provides modeled estimates of ozone using Downscaler (DS) model, which uses a statistical approach to fuse monitored data in areas where monitors exist, and relies on Community Multi-scale Air Quality (CMAQ) modeled output in areas without monitors. DS modeled estimates are available by census tract centroid; the geographic center of the census tract. Daily county-level modeled estimates are obtained by selecting the maximum value observed among all the census tracts within each county. County-level ozone measures are created using monitor data when available and using modeled estimates for days and locations without such data.

Annual person-days above standard

This measure is the annual number of person-days with maximum eight-hour average ozone concentration above the National Ambient Air Quality Standard. The measure includes both monitored and modeled data. The monitored data comes from the U.S. Environmental Protection Agency (EPA) Air Quality System (AQS). When AQS data are available from multiple monitors for a given county and day, the highest eight-hour maximum (daily) ozone concentration among all the monitors is selected for purposes of creating daily county-level data. EPA provides modeled estimates of ozone using Downscaler (DS) model, which uses a statistical approach to fuse monitored data in areas where monitors exist, and relies on Community Multi-scale Air Quality (CMAQ) modeled output in areas without monitors. DS modeled estimates are available by census tract centroid; the geographic center of the census tract. Daily county-level modeled estimates are obtained by selecting the maximum value observed among all the census tracts within each county. County-level ozone measures are created using monitor data when available and using modeled estimates for days and locations without such data.

Particulate matter less than 2.5 micrometers (PM2.5)

Annual person-days above standard

This measure is the annual number of person-days with particulate matter less than 2.5 micrometers (PM2.5) levels above the National Ambient Air Quality Standard. The measure includes both monitored and modeled data. The monitored data comes from the U.S. Environmental Protection Agency (EPA) Air Quality System (AQS). When AQS data are available from multiple monitors for a given county and day, the highest 24-hour average (daily) PM2.5 concentration among all the monitors is selected for purposes of creating daily county-level data. EPA provides modeled estimates of PM2.5 using Downscaler (DS) model, which uses a statistical approach to fuse monitored data in areas where monitors exist, and relies on Community Multi-scale Air Quality (CMAQ) modeled output in areas without monitors. DS modeled estimates are available by census tract centroid; the geographic center of the census tract. Daily county-level modeled estimates are obtained by selecting the maximum value observed among all the census tracts within each county. County-level PM2.5 measures are created using monitor data when available and using modeled estimates for days and locations without such data.

Average annual concentration (μ/m3)

This measure is the annual average ambient concentration of particulate matter less than 2.5 micrometers (PM2.5) in micrograms per cubic meter (based on seasonal averages and daily measurements). The measure includes both monitored and modeled data. The monitored data comes from the U.S. Environmental Protection Agency's (EPA) Air Quality System (AQS). When AQS data are available from multiple monitors for a given county and day, the highest 24-hour average (daily) PM2.5 concentration among all the monitors is selected for purposes of creating daily county-level data. EPA provides modeled estimates of PM2.5 using Downscaler (DS) model, which uses a statistical approach to fuse monitored data in areas where monitors exist, and relies on Community Multi-scale Air Quality (CMAQ) modeled output in areas without monitors. DS modeled estimates are available by census tract centroid the geographic center of the census tract. Daily county-level modeled estimates are obtained by selecting the maximum value observed among all the census tracts within each county. County-level PM2.5 measures are created using monitor data when available and using modeled estimates for days and locations without such data.

Read frequently asked questions about the alcohol data

Alcohol outlet density

Number of licenses

These data come from the Wisconsin Department of Revenue (DOR) and are based on the liquor licenses issued and reported to the DOR. Data are a point-in-time estimate (that means the data are shared once annually and, at any given time throughout the year, a new license could be issued or an old one not renewed). Data are not suppressed for this measure. This measure represents the number of licenses in a municipality, county, or state in the respective year. Some establishments are issued more than one type of liquor license. The reported number of licenses per geographic region is the number of establishments issued a license. Please note that differences in alcohol outlet density by county or municipality are difficult to interpret. Rural counties may have a higher number of outlets relative to population, but these outlets may be small and serve fewer people than a single outlet in a large city. However, this higher number of outlets relative to the population may also indicate greater accessibility. Moreover, county level data may mask great variations in density for various locations within a given county.

People per license (PPL)

These data come from the Wisconsin Department of Revenue (DOR) and are based on the liquor licenses issued and reported to the DOR. Data are a point-in-time estimate (that means the data are shared once annually and, at any given time throughout the year, a new license could be issued or an old one not renewed). Data are not suppressed for this measure. This measure represents the total population in a given geographic area (state, county, and municipality) divided by the number of establishments with liquor licenses. That is, the number of people per alcohol license by municipality, county, or state in the respective year. Please note that differences in alcohol outlet density by county or municipality are difficult to interpret. Rural counties may have a higher number of outlets relative to population, but these outlets may be small and serve fewer people than a single outlet in a large city. However, this higher number of outlets relative to the population may also indicate greater accessibility. Moreover, county level data may mask great variations in density for various locations within a given county.

Rate of alcohol licensure per 500 people

These data come from the Wisconsin Department of Revenue (DOR) and are based on the liquor licenses issues and reported to the DOR. Data are a point-in-time estimate (that means the data are shared once annually and, at any given time throughout the year, a new license could be issued or an old one not renewed). Data are not suppressed for this measure. This rate represents the number of establishments with a liquor license divided by the total number of people in the geographic region (municipality, county, or state). This rate is expressed as a number per 500 people in the population. Please note that differences in alcohol outlet density by county or municipality are difficult to interpret. Rural counties may have a higher number of outlets relative to population, but these outlets may be small and serve fewer people than a single outlet in a large city. However, this higher number of outlets relative to the population may also indicate greater accessibility. Moreover, rates may mask great variations in density for various locations within a given county.

Number of license types

These data come from the Wisconsin Department of Revenue (DOR) and are based on the liquor licenses issues and reported to the DOR. Data are a point-in-time estimate (that means the data are shared once annually and, at any given time throughout the year, a new license could be issued or an old one not renewed). Data are not suppressed for this measure.

This measure represents the number of alcohol licenses issued by license type by municipality, county, or state in the respective year. Per Wis. Stat. § 125.04(g):

• Type A licenses includes Class "A" retail sale of beer for consumption off the premises (AB); "Class A” retail sale of liquor, including wine, for consumption off the premises (AL); "Class A" retail sale of cider for consumption off the premises (AC); and "Class A” retail sale of beer and liquor, including wine, for consumption off the premises (ALB).
• Type B includes Class “B” retail sale of beer for consumption on or off the premises (BB); "Class B” retail sale of liquor, including wine, for consumption on the premises and wine in original sealed container for consumption off the premises (BL); and "Class B“ beer and liquor (BLB).
• Type C is "Class C” wine for consumption only on the premises and carryout of a single opened and resealed bottle if sold with a meal. For more information on what each class permits visit the State of Wisconsin DOR.

Sub-county (city/town/village)

At the sub-county level, alcohol license data can be viewed by municipality (i.e., city, town, or village). However, it is important to keep in mind that 56 municipalities in Wisconsin overlap county boundaries. For ease of use, we have included these municipalities in each county where they are partially contained. For example, Watertown (city) can be viewed as a sub-county component of either Dodge County or Jefferson County. The following cities, towns, or villages are affected: Abbotsford (city), Appleton (city), Ashland (city), Bayside (village), Belleville (village), Berlin (city), Birnamwood (village), Blanchardville (village), Brodhead (city), Brooklyn (village), Burlington (city), Cambridge (village), Cazenovia (village), Colby (city), Columbus (city), Cuba City (city), De Soto (village), Dorchester (village), Eau Claire (city), Edgerton (city), Genoa City (village), Hartford (city), Harrison (village), Hazel Green (village), Howard (village), Kaukauna (city), Kewaskum (village), Kiel (city), Lac La Belle (village), Livingston (village), Marion (city), Marshfield (city), Menasha (city), Milladore (village), Milwaukee (city), Montfort (village), Mukwonago (village), Muscoda (village), New Auburn (village), Newburg (village), New London (city), Ontario (village), Pulaski (village), Randolph (village), River Falls (city), Rockland (village), Spring Valley (village), Stanley (city), Turtle Lake (village), Unity (village), Viola (village), Watertown (city), Waupun (city), Whitewater (city), Wisconsin Dells (city), and Wrightstown (village).

Alcohol hospitalizations

Number of alcohol-attributable hospitalizations

These data were collected from inpatient hospital discharge records. When Wisconsin residents were treated in neighboring states, data from those states were obtained (where possible). Hospitalization records for 2001-2004 were obtained from Wisconsin and Iowa. Hospitalizations records for 2005-2014 were obtained from Wisconsin, Iowa, and Minnesota. Data are not suppressed for this measure. To determine which hospitalizations were attributable to alcohol, specifications from the Alcohol-Related Disease Impact (ARDI), developed by the Centers for Disease Control and Prevention (CDC) Alcohol Program, were used. These specifications define 54 conditions or groups of conditions and associate each with distinct fractions of cases that are attributable to alcohol. These alcohol-attributable fractions (AAF) were determined by the CDC through direct and indirect measurements. The total number of alcohol-attributable hospitalizations was estimated by using the ARDI specified International Classification of Diseases, 9th and 10th revision (ICD-9 and ICD-10) codes and multiplying by the respective alcohol-attributable fraction.

Crude rate per 100,000

These data were collected from inpatient hospital discharge records. When Wisconsin residents were treated in neighboring states, data from those states were obtained (where possible). Hospitalization records for 2001-2004 were obtained from Wisconsin and Iowa. Hospitalizations records for 2005-2014 were obtained from Wisconsin, Iowa, and Minnesota. Data are not suppressed for this measure. To determine which hospitalizations were attributable to alcohol, specifications from ARDI, developed by the CDC Alcohol Program, were used. These specifications define 54 conditions or groups of conditions and associate each with distinct fractions of cases that are attributable to alcohol. These alcohol-attributable fractions (AAF) were determined by the CDC through direct and indirect measurements. The total number of alcohol-attributable hospitalizations was estimated by using the ARDI specified ICD-9 and ICD-10 codes and multiplying by the respective alcohol-attributable fraction. The crude rate is determined by dividing the total number of alcohol-attributable hospitalizations in the county or state by the respective population. This rate is expressed as a number per 100,000 people. The crude rate does not take into account the differences across counties in age or sex distribution and are therefore potentially subject to biases from these factors.

Suicide deaths

Count

These data were obtained from Wisconsin resident death certificate files. International Classification of Diseases, 10th revision (ICD-10) codes were used to determine suicide deaths. To determine the suicides attributable to alcohol, specifications from ARDI, developed by the CDC Alcohol Program were used. The ARDI specifications define the fraction of deaths by various means that are attributable to alcohol. Suicide by alcohol of those 15 years of age and older was 100% attributable to alcohol and was identified by ICD-10 code X65. Suicide and self-inflicted injury deaths not specifically by alcohol of those 15 years of age and older were considered 23% attributable to alcohol and were identified by the ICD-10 codes X60-X64, X66-X84, and Y87.0. ARDI determined an AAF of 23% for suicide and self-inflicted injury not specifically by alcohol through direct measurement based on a meta-analysis by Smith et al. (1999). Total suicide deaths attributable to alcohol were determined by multiplying the counts of the ICD-10 codes by the respective AAF and summing.

Crude rate

These data were obtained from Wisconsin resident death certificate files. ICD-10 codes were used to determine suicide deaths To determine the suicides attributable to alcohol, specifications from ARDI, developed by the CDC Alcohol Program, were used. The ARDI specifications define the fraction of deaths by various means that are attributable to alcohol. Suicide by alcohol of those 15 years of age and older was 100% attributable to alcohol and was identified by ICD-10 code X65. Suicide and self-inflicted injury deaths not specifically by alcohol of those 15 years of age and older were considered 23% attributable to alcohol and were identified by the ICD-10 codes X60-X64, X66-X84, and Y87.0. ARDI determined an AAF of 23% for suicide and self-inflicted injury not specifically by alcohol through direct measurement based on a meta-analysis by Smith et al. (1999). The crude rate was then determined by dividing the total number of alcohol-attributable suicides in the state by the state population. This rate is expressed as a number per 100,000 people.

Poisoning deaths

Count

These data were obtained from Wisconsin resident death certificate files. ICD-10 codes were used to determine poisoning deaths. Poisoning deaths exclude acute alcohol poisoning and includes only non-ethyl alcohol poisonings. To determine the non-ethyl alcohol poisonings attributable to alcohol, specifications from ARDI, developed by the CDC Alcohol Program, were used. The ARDI specifications define the fraction of deaths by various means that are attributable to alcohol. Non-ethyl alcohol poisonings of those 15 years of age and older was 29% attributable to alcohol and was identified by ICD-10 codes X40-X44,X46-X49, Y10-Y14, and Y16-Y19. ARDI determined an AAF of 29% non-ethyl alcohol poisonings through direct measurement based on a meta-analysis by Smith et al. (1999). Total non-ethyl alcohol poisonings deaths attributable to alcohol were determined by multiplying the counts of the ICD-10 codes by the respective AAF and summing.

Crude rate

These data were obtained from Wisconsin resident death certificate files. ICD-10 codes were used to determine poisoning deaths. Poisoning deaths exclude acute alcohol poisoning and include only non-ethyl alcohol poisonings. To determine the non-ethyl alcohol poisonings attributable to alcohol, specifications from ARDI, developed by the CDC Alcohol Program, were used. The ARDI specifications define the fraction of deaths by various means that are attributable to alcohol. Non-ethyl alcohol poisonings of those 15 years of age and older was 29% attributable to alcohol and was identified by ICD-10 codes X40-X44,X46-X49, Y10-Y14, and Y16-Y19. ARDI determined an AAF of 29% non-ethyl alcohol poisonings through direct measurement based on a meta-analysis by Smith et al. (1999). The crude rate was then determined by dividing the total number of alcohol-attributable non-ethyl alcohol poisonings in the state by the state population. This rate is expressed as a number per 100,000 people.

Unintentional fall deaths

Count

These data were obtained from Wisconsin resident death certificate files. International Classification of Diseases, 10th revision (ICD-10) codes were used to determine unintentional fall deaths. To determine the fall deaths attributable to alcohol, specifications from Alcohol-Related Disease Impact (ARDI), developed by the CDC Alcohol Program, were used. The ARDI specifications define the fraction of deaths by various means that are attributable to alcohol. Unintentional fall deaths of those 15 years of age and older was 32% attributable to alcohol and was identified by ICD-10 codes W00-W19. ARDI determined an AAF of 32% alcohol-attributable falls through direct measurement based on a meta-analysis by Smith et al. (1999). Total fall deaths attributable to alcohol were determined by multiplying the counts of the ICD-10 codes by the respective AAF and summing.

Crude rate

These data were obtained from Wisconsin resident death certificate files. International Classification of Diseases, 10th revision (ICD-10) codes were used to determine unintentional fall deaths. To determine the fall deaths attributable to alcohol, specifications from Alcohol-Related Disease Impact (ARDI), developed by the CDC Alcohol Program, were used. The ARDI specifications define the fraction of deaths by various means that are attributable to alcohol. Unintentional fall deaths of those 15 years of age and older was 32% attributable to alcohol and was identified by ICD-10 codes W00-W19. ARDI determined an AAF of 32% alcohol-attributable falls through direct measurement based on a meta-analysis by Smith et al. (1999). The crude rate was then determined by dividing the total number of alcohol-attributable falls in the state by the state population. This rate is expressed as a number per 100,000 people.

Read frequently asked questions about the asthma data

Asthma emergency department visits

Number of emergency department visits for asthma

These data include emergency department visits for asthma and are collected from emergency room visit discharge records. Emergency department visits resulting in subsequent hospitalization are also included. Federally funded hospitals (for example, Veteran's Administration [VA] hospitals, which are exempt from state reporting requirements) are not included in these data. This measure includes emergency department visits with an ICD-9 code of 493 or ICD-10 code of J45 in the principal diagnosis field. Data are suppressed for counties with fewer than five visits to protect confidentiality. However, counties with zero cases are not suppressed. Please note that counts are a statistically-limited way to consider emergency department visits because they do not allow for accurate comparison between counties. Counties with higher populations, such as Milwaukee, will have more emergency department visits simply because they have more people. An age-adjusted rate is a better measure for true comparison between counties.

Prior to 2018, the county variable in the emergency department (ED) data was assigned based on the zip code of the residence of the patient. In cases where a zip code spanned more than one county, the ED visit was placed into the county with the higher population. Beginning in 2018, geocoded data became available in Wisconsin ED visit data, which allows for a more accurate determination of county of residence. The new block group variable is the geocoded latitude/longitude of the patient’s residential address. The first five digits of the block group variable are used to determine the Wisconsin county. Due to this change, caution should be used when comparing county-level data prior to 2018 with data from 2018 onward.

Crude rates of emergency department visits for asthma per 10,000 people

These data include emergency department visits for asthma and are collected from emergency room visit discharge records. Emergency department visits resulting in subsequent hospitalization are also included. Federally funded hospitals (for example, Veteran's Administration [VA] hospitals, which are exempt from state reporting requirements) are not included in these data. This measure includes emergency department visits with an ICD-9 code of 493 or ICD-10 code of J45 in the principal diagnosis field. Data are suppressed for counties with fewer than five visits to protect confidentiality. However, counties with zero cases are not suppressed. The crude rate is the number of emergency department visits divided by the total number of people in the area of interest (for example, a county). Population of interest is derived from census data. This is expressed as a number per unit population such as "per 10,000 population." Crude rates do not take into account the differences in age distributions across counties and are therefore subject to bias. Use age-adjusted rates for a better standardized measure.

Prior to 2018, the county variable in the emergency department (ED) data was assigned based on the zip code of the residence of the patient. In cases where a zip code spanned more than one county, the ED visit was placed into the county with the higher population. Beginning in 2018, geocoded data became available in Wisconsin ED visit data, which allows for a more accurate determination of county of residence. The new block group variable is the geocoded latitude/longitude of the patient’s residential address. The first five digits of the block group variable are used to determine the Wisconsin county. Due to this change, caution should be used when comparing county-level data prior to 2018 with data from 2018 onward.

Asthma emergency department visits age-adjusted rates per 10,000 people

These data include emergency department visits for asthma and are collected from emergency room visit discharge records. Emergency department visits resulting in subsequent hospitalization are also included. Federally funded hospitals (for example, Veteran's Administration [VA] hospitals, which are exempt from state reporting requirements) are not included in these data. This measure includes emergency department visits with an ICD-9 code of 493 or ICD-10 code of J45 in the principal diagnosis field. Data are suppressed for counties with fewer than five visits to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. An age-adjusted rate is a rate that is statistically modified to eliminate the potential biasing effect of different age distributions across different populations. In other words, the age-adjusted rate accounts for the possibility that emergency department visits for asthma may be more frequent among younger individuals and some counties have more younger individuals than others. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Prior to 2018, the county variable in the emergency department (ED) data was assigned based on the zip code of the residence of the patient. In cases where a zip code spanned more than one county, the ED visit was placed into the county with the higher population. Beginning in 2018, geocoded data became available in Wisconsin ED visit data, which allows for a more accurate determination of county of residence. The new block group variable is the geocoded latitude/longitude of the patient’s residential address. The first five digits of the block group variable are used to determine the Wisconsin county. Due to this change, caution should be used when comparing county-level data prior to 2018 with data from 2018 onward.

Asthma hospitalizations

Number of hospitalizations for asthma

These data include hospitalizations for asthma and are collected from inpatient hospital discharge records. This measure includes hospitalizations with an ICD-9 code of 493 or ICD-10 code of J45 in the principal diagnosis field. Data are suppressed for counties with fewer than five visits to protect confidentiality. However, counties with zero cases are not suppressed. Please note that counts are a statistically limited way to consider emergency department visits because they do not allow for accurate comparison between counties. Counties with higher populations, such as Milwaukee, will have more hospitalizations simply because they have more people. An age-adjusted rate is a better measure for true comparison between counties.

Prior to 2018, the county variable in the hospitalization data was assigned based on the zip code of the residence of the patient. In cases where a zip code spanned more than one county, the hospitalization was placed into the county with the higher population. Beginning in 2018, geocoded data became available in Wisconsin hospitalization data, which allows for a more accurate determination of county of residence. The new block group variable is the geocoded latitude/longitude of the patient’s residential address. The first five digits of the block group variable are used to determine the Wisconsin county. Due to this change, caution should be used when comparing county-level data prior to 2018 with data from 2018 onward.

Crude rates of hospitalizations for asthma per 10,000 people

These data include hospitalizations for asthma and are collected from inpatient hospital discharge records. This measure includes hospitalizations with an ICD-9 code of 493 or ICD-10 code of J45 in the principal diagnosis field. Data are suppressed for counties with fewer than five visits to protect confidentiality. However, counties with zero cases are not suppressed. The crude rate is the number of hospitalizations divided by the total number of people in the population of interest (for example, a county). This is expressed as a number per unit population, such as "per 10,000 population." A crude rate does not take into account the differences in age distributions across counties and are therefore subject to bias. Use age-adjusted rates for a better standardized measure.

Prior to 2018, the county variable in the hospitalization data was assigned based on the zip code of the residence of the patient. In cases where a zip code spanned more than one county, the hospitalization was placed into the county with the higher population. Beginning in 2018, geocoded data became available in Wisconsin hospitalization data, which allows for a more accurate determination of county of residence. The new block group variable is the geocoded latitude/longitude of the patient’s residential address. The first five digits of the block group variable are used to determine the Wisconsin county. Due to this change, caution should be used when comparing county-level data prior to 2018 with data from 2018 onward.

Asthma hospitalizations age-adjusted rates per 10,000 people

These data include hospitalizations for asthma and are collected from inpatient hospital discharge records. This measure includes hospitalizations with an ICD-9 code of 493 or ICD-10 code of J45 in the principal diagnosis field. Data are suppresses data for counties with fewer than five visits per 10,000 to protect confidentiality. However, counties with zero cases are not suppressed. An age-adjusted rate is a rate that is statistically modified to eliminate the potential biasing effect of different age distributions across different populations. In other words, the age-adjusted rate accounts for the possibility that emergency department visits for asthma may be more frequent among younger individuals and some counties have more younger individuals than others. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Prior to 2018, the county variable in the hospitalization data was assigned based on the zip code of the residence of the patient. In cases where a zip code spanned more than one county, the hospitalization was placed into the county with the higher population. Beginning in 2018, geocoded data became available in Wisconsin hospitalization data, which allows for a more accurate determination of county of residence. The new block group variable is the geocoded latitude/longitude of the patient’s residential address. The first five digits of the block group variable are used to determine the Wisconsin county. Due to this change, caution should be used when comparing county-level data prior to 2018 with data from 2018 onward.

Average daily count

These data include hospitalizations for asthma and are collected from inpatient hospital discharge records. This measure includes hospitalizations with an ICD-9 code of 493 or ICD-10 code of J45. Average Daily Count was calculated by dividing the monthly count by the number of days in that respective month.

Read frequently asked questions about the birth defects data

Anencephaly

Count

Data came from the National Birth Defects Prevention Network (NBDPN). NBDPN collects data from a combination of birth certificates, hospital discharge data and fetal death records. Cases were then summed over five-year periods. In 2011, there was a change in the way birth defects were counted, so caution should be used when comparing data from 2004-2010 to data from 2011 onward.

Crude rate per 10,000 live births

Data came from the National Birth Defects Prevention Network (NBDPN). NBDPN collects data from a combination of birth certificates, hospital discharge data and fetal death records. This is a five-year crude rate. Rates were calculated by dividing the count by the number of live births in Wisconsin in those years and multiplied by 10,000. Rates were suppressed if the count was three or less over a five-year time period to avoid unstable rates. However, rates with zero cases are not suppressed.

Cleft lip without cleft palate

Count

Data came from the National Birth Defects Prevention Network (NBDPN). NBDPN collects data from a combination of birth certificates, hospital discharge data and fetal death records. Cases were then summed over five-year periods. In 2011, there was a change in the way birth defects were counted, so caution should be used when comparing data from 2004-2010 to data from 2011 onward.

Crude rate per 10,000 live births

Data came from the National Birth Defects Prevention Network (NBDPN). NBDPN collects data from a combination of birth certificates, hospital discharge data and fetal death records. This is a five-year crude rate. Rates were calculated by dividing the count by the number of live births in Wisconsin in those years and multiplied by 10,000. Rates were suppressed if the count was three or less over a five-year time period to avoid unstable rates. However, rates with zero cases are not suppressed.

Cleft lip with cleft palate

Count

Data came from the National Birth Defects Prevention Network (NBDPN). NBDPN collects data from a combination of birth certificates, hospital discharge data and fetal death records. Cases were then summed over five-year periods. In 2011, there was a change in the way birth defects were counted, so caution should be used when comparing data from 2004-2010 to data from 2011 onward.

Crude rate per 10,000 live births

Data came from the National Birth Defects Prevention Network (NBDPN). NBDPN collects data from a combination of birth certificates, hospital discharge data and fetal death records. This is a five-year crude rate. Rates were calculated by dividing the count by the number of live births in Wisconsin in those years and multiplied by 10,000. Rates were suppressed if the count was three or less over a five-year time period to avoid unstable rates. However, rates with zero cases are not suppressed.

Cleft palate without cleft lip

Count

Data came from the National Birth Defects Prevention Network (NBDPN). NBDPN collects data from a combination of birth certificates, hospital discharge data and fetal death records. Cases were then summed over five-year periods. In 2011, there was a change in the way birth defects were counted, so caution should be used when comparing data from 2004-2010 to data from 2011 onward.

Crude rate per 10,000 live births

Data came from the National Birth Defects Prevention Network (NBDPN). NBDPN collects data from a combination of birth certificates, hospital discharge data and fetal death records. This is a five-year crude rate. Rates were calculated by dividing the count by the number of live births in Wisconsin in those years and multiplied by 10,000. Rates were suppressed if the count was three or less over a five-year time period to avoid unstable rates. However, rates with zero cases are not suppressed.

Gastroschisis

Note: Prior to 10/1/2009, gastroschisis shared an ICD-9 code with omphalocele. Due to the inability to distinguish between those two conditions during that time, gastroschisis data are not available until 2010.

Count

Data came from the National Birth Defects Prevention Network (NBDPN). NBDPN collects data from a combination of birth certificates, hospital discharge data and fetal death records. Cases were then summed over five-year periods. In 2011, there was a change in the way birth defects were counted, so caution should be used when comparing data from 2004-2010 to data from 2011 onward.

Crude rate per 10,000 live births

Data came from the National Birth Defects Prevention Network (NBDPN). NBDPN collects data from a combination of birth certificates, hospital discharge data and fetal death records. This is a five-year crude rate. Rates were calculated by dividing the count by the number of live births in Wisconsin in those years and multiplied by 10,000. Rates were suppressed if the count was three or less over a five-year time period to avoid unstable rates. However, rates with zero cases are not suppressed.

Hypoplastic left heart syndrome

Count

Data came from the National Birth Defects Prevention Network (NBDPN). NBDPN collects data from a combination of birth certificates, hospital discharge data and fetal death records. Cases were then summed over five-year periods. In 2011, there was a change in the way birth defects were counted, so caution should be used when comparing data from 2004-2010 to data from 2011 onward.

Crude rate per 10,000 live births

Data came from the National Birth Defects Prevention Network (NBDPN). NBDPN collects data from a combination of birth certificates, hospital discharge data and fetal death records. This is a five-year crude rate. Rates were calculated by dividing the count by the number of live births in Wisconsin in those years and multiplied by 10,000. Rates were suppressed if the count was three or less over a five-year time period to avoid unstable rates. However, rates with zero cases are not suppressed.

Hypospadias

Count

Data came from the National Birth Defects Prevention Network (NBDPN). NBDPN collects data from a combination of birth certificates, hospital discharge data and fetal death records. Cases were then summed over five-year periods. In 2011, there was a change in the way birth defects were counted, so caution should be used when comparing data from 2004-2010 to data from 2011 onward.

Crude rate per 10,000 male live births

Data came from the National Birth Defects Prevention Network (NBDPN). NBDPN collects data from a combination of birth certificates, hospital discharge data and fetal death records. This is a five-year crude rate. Rates were calculated by dividing the count by the number of live births in Wisconsin in those years and multiplied by 10,000. Rates were suppressed if the count was three or less over a five-year time period to avoid unstable rates. However, rates with zero cases are not suppressed.

Limb deficiencies combined

Count

Data came from the National Birth Defects Prevention Network (NBDPN). NBDPN collects data from a combination of birth certificates, hospital discharge data and fetal death records. Cases were then summed over five-year periods. In 2011, there was a change in the way birth defects were counted, so caution should be used when comparing data from 2004-2010 to data from 2011 onward.

Crude rate per 10,000 live births

Data came from the National Birth Defects Prevention Network (NBDPN). NBDPN collects data from a combination of birth certificates, hospital discharge data and fetal death records. This is a five-year crude rate. Rates were calculated by dividing the count by the number of live births in Wisconsin in those years and multiplied by 10,000. Rates were suppressed if the count was three or less over a five-year time period to avoid unstable rates. However, rates with zero cases are not suppressed.

Tetralogy of fallot

Count

Data came from the National Birth Defects Prevention Network (NBDPN). NBDPN collects data from a combination of birth certificates, hospital discharge data and fetal death records. Cases were then summed over five-year periods. In 2011, there was a change in the way birth defects were counted, so caution should be used when comparing data from 2004-2010 to data from 2011 onward.

Crude rate per 10,000 live births

Data came from the National Birth Defects Prevention Network (NBDPN). NBDPN collects data from a combination of birth certificates, hospital discharge data and fetal death records. This is a five-year crude rate. Rates were calculated by dividing the count by the number of live births in Wisconsin in those years and multiplied by 10,000. Rates were suppressed if the count was three or less over a five-year time period to avoid unstable rates. However, rates with zero cases are not suppressed.

Transposition of the great arteries (vessels)

Count

Data came from the National Birth Defects Prevention Network (NBDPN). NBDPN collects data from a combination of birth certificates, hospital discharge data and fetal death records. Cases were then summed over five-year periods. In 2011, there was a change in the way birth defects were counted, so caution should be used when comparing data from 2004-2010 to data from 2011 onward.

Crude rate per 10,000 live births

Data came from the National Birth Defects Prevention Network (NBDPN). NBDPN collects data from a combination of birth certificates, hospital discharge data and fetal death records. This is a five-year crude rate. Rates were calculated by dividing the count by the number of live births in Wisconsin in those years and multiplied by 10,000. Rates were suppressed if the count was three or less over a five-year time period to avoid unstable rates. However, rates with zero cases are not suppressed.

Trisomy 21 (Down Syndrome)

Crude rate per 10,000 live births

Data came from the National Birth Defects Prevention Network (NBDPN). NBDPN collects data from a combination of birth certificates, hospital discharge data and fetal death records. This is a five-year crude rate. Rates were calculated by dividing the count by the number of live births in Wisconsin in those years and multiplied by 10,000. Rates were suppressed if the count was three or less over a five-year time period to avoid unstable rates. However, rates with zero cases are not suppressed.

Read frequently asked questions about the cancer data

Bladder cancer

Number of new bladder cancer cases: all ages

Counts of new bladder cancer cases are reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin for a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality. However, counties with zero cases are not suppressed.

Age-adjusted incidence rates of bladder cancer per 100,000 people: all ages

Incidence rates are calculated from the annual number of new cases (counts) of bladder cancer reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin during a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Cancer of the brain and central nervous system

Number of new brain and central nervous system cancer: ages 0-19

Counts of new brain and central nervous system cancer cases are reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin for a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality. However, counties with zero cases are not suppressed.

Age-adjusted incidence rates of brain and central nervous system cancer per 1,000,000 people: ages 0-19

Incidence rates are calculated from the annual number of new cases (counts) of cancer of the brain and central nervous system reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin during a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Number of new brain and central nervous system cancer: all ages

Counts of new brain and central nervous system cancer cases are reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin for a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality. However, counties with zero cases are not suppressed.

Age-adjusted incidence rates of brain and central nervous system cancer per 100,000 people: all ages

Incidence rates are calculated from the annual number of new cases (counts) of cancer of the brain and central nervous system reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin during a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Cancer of the esophagus

Number of new esophagus cancer cases: all ages

Counts of new esophagus cancer cases are reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin for a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality. However, counties with zero cases are not suppressed.

Age-adjusted incidence rates of esophagus cancer per 100,000 people: all ages

Incidence rates are calculated from the annual number of new cases (counts) of esophagus cancer reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin during a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Female breast cancer

Number of new female breast cancer cases: ages 0-49

Counts of new female breast cancer cases are reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin for a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality. However, counties with zero cases are not suppressed.

Age-adjusted incidence rates of female breast cancer per 100,000 people: ages 0-49

Incidence rates are calculated from the annual number of new cases (counts) of female breast cancer reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin during a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Number of new female breast cancer cases: ages 50+

Counts of new female breast cancer cases are reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin for a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality. However, counties with zero cases are not suppressed.

Age-adjusted incidence rates of female breast cancer per 100,000 people: ages 50+

Incidence rates are calculated from the annual number of new cases (counts) of female breast cancer reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin during a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Kidney and renal pelvis cancer

Number of new kidney and renal pelvis cancer cases: all ages

Counts of new kidney and renal pelvis cancer cases are reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin for a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality. However, counties with zero cases are not suppressed.

Age-adjusted incidence rates of kidney and renal pelvis cancer per 100,000 people: all ages

Incidence rates are calculated from the annual number of new cases (counts) of kidney renal and pelvis cancer reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin during a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Cancer of the larynx

Number of new larynx cancer cases: all ages

Counts of new larynx cancer cases are reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin for a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality. However, counties with zero cases are not suppressed.

Age-adjusted incidence rates of larynx cancer per 100,000 people: all ages

Incidence rates are calculated from the annual number of new cases (counts) of larynx cancer reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin during a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Leukemia

Number of leukemia: ages 0-19

Counts of new leukemia cases are reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin for a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality. However, counties with zero cases are not suppressed.

Age-adjusted incidence leukemia per 1,000,000 people: ages 0-19

Incidence rates are calculated from the annual number of new cases (counts) of leukemia reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin during a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Number of leukemia: all ages

Counts of new leukemia cases are reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin for a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality. However, counties with zero cases are not suppressed.

Age-adjusted incidence rates of leukemia per 100,000 people: all ages

Incidence rates are calculated from the annual number of new cases (counts) of leukemia reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin during a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Acute lymphocytic leukemia

Number of acute lymphocytic leukemia: all ages

Counts of new acute lymphocytic leukemia cases are reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin for a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality. However, counties with zero cases are not suppressed.

Age-adjusted incidence rates of acute lymphocytic leukemia per 100,000 people: all ages

Incidence rates are calculated from the annual number of new cases (counts) of acute lymphocytic leukemia reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin during a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Acute myeloid leukemia

Number of acute myeloid leukemia: all ages

Counts of new acute myeloid leukemia cases are reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin for a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality. However, counties with zero cases are not suppressed.

Age-adjusted incidence rates of acute myeloid leukemia per 100,000 people: all ages

Incidence rates are calculated from the annual number of new cases (counts) of acute myeloid leukemia reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin during a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Chronic lymphocytic leukemia

Number of chronic lymphocytic leukemia: all ages

Counts of new chronic lymphocytic leukemia cases are reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin for a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality. However, counties with zero cases are not suppressed.

Age-adjusted incidence rates of chronic lymphocytic leukemia per 100,000 people: all ages

Incidence rates are calculated from the annual number of new cases (counts) of chronic lymphocytic leukemia reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin during a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Lung and bronchus cancer

Number of new lung and bronchus cancer cases: all ages

Counts of new lung and bronchus cancer cases are reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin for a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality. However, counties with zero cases are not suppressed.

Age-adjusted incidence rates of lung and bronchus cancer per 100,000 people: all ages

Incidence rates are calculated from the annual number of new cases (counts) of lung and bronchus cancer reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin during a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Liver cancer

Number of new liver cancer cases: all ages

Counts of new liver cancer cases are reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin for a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality. However, counties with zero cases are not suppressed.

Age-adjusted incidence rates of liver cancer per 100,000 people: all ages

Incidence rates are calculated from the annual number of new cases (counts) of liver cancer reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin during a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Non-Hodgkin's lymphoma

Number of new non-Hodgkin's lymphoma cases: all ages

Counts of new non-Hodgkin's lymphoma cases are reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin for a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality. However, counties with zero cases are not suppressed.

Age-adjusted incidence rates of non-Hodgkin's lymphoma per 100,000 people: all ages

Incidence rates are calculated from the annual number of new cases (counts) of non-Hodgkin's lymphoma reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin during a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Melanoma

Number of new melanoma cases: all ages

Counts of new melanoma cases are reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin for a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality. However, counties with zero cases are not suppressed.

Age-adjusted incidence rates of melanoma per 100,000 people: all ages

Incidence rates are calculated from the annual number of new cases (counts) of melanoma reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin during a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Mesothelioma

Number of new mesothelioma cases: all ages

Counts of new mesothelioma cases are reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin for a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality. However, counties with zero cases are not suppressed.

Age-adjusted incidence rates of mesothelioma per 100,000 people: all ages

Incidence rates are calculated from the annual number of new cases (counts) of mesothelioma reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin during a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Cancer of the oral cavity and pharynx

Number of new oral cavity and pharynx cancer cases: all ages

Counts of new oral cavity and pharynx cancer cases are reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin for a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality. However, counties with zero cases are not suppressed.

Age-adjusted incidence rates of oral cavity and pharynx cancer per 100,000 people: all ages

Incidence rates are calculated from the annual number of new cases (counts) of oral cavity and pharynx cancer reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin during a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Cancer of the pancreas

Number of pancreatic cancer cases: all ages

Counts of new pancreatic cancer cases are reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin for a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality. However, counties with zero cases are not suppressed.

Age-adjusted incidence rates of pancreatic cancer per 100,000 people: all ages

Incidence rates are calculated from the annual number of new cases (counts) of pancreatic cancer reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin during a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Thyroid cancer

Number of thyroid cancer cases: all ages

Counts of new thyroid cancer cases are reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin for a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality. However, counties with zero cases are not suppressed.

Age-adjusted incidence rates of thyroid cancer per 100,000 people: all ages

Incidence rates are calculated from the annual number of new cases (counts) of thyroid cancer reported to the Wisconsin Cancer Reporting System by health care providers in Wisconsin during a given year. Data for counties with fewer than six cases are suppressed to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Read frequently asked questions about the carbon monoxide poisoning data

Carbon monoxide poisoning emergency department visits

Number of emergency department visits for carbon monoxide poisoning (counts)

These data include emergency department visits for carbon monoxide poisoning and are collected from emergency room visit discharge records. This measure includes emergency department visits with an ICD-9 code of 986, E868.2, E868.3, E868.8, E868.9, E982.0, or E982.1 or an ICD-10 code of T58.01, T58.04, T58.11, T58.14, T58.2X1, T58.2X4, T58.8X1, T58.8X4, T58.91 or T58.94 in the principal or other diagnosis fields. Records of intentional or purposeful CO poisoning are excluded. These data include three categories of carbon monoxide poisoning causes:

1. Unintentional, non-fire related;
2. Unintentional, fire-related; and
3. Unknown intent.

Data are not suppressed for this measure. Please note that counts are a statistically limited way to consider emergency department visits because they do not allow for accurate comparison between counties. Counties with higher populations, such as Milwaukee, will have more emergency department visits simply because they have more people. An age-adjusted rate is a better measure for true comparison between counties.

Crude rates per 100,000

These data include emergency department visits for carbon monoxide poisoning and are collected from emergency room visit discharge records. This measure includes emergency department visits with an ICD-9 code of 986, E868.2, E868.3, E868.8, E868.9, E982.0, or E982.1 or an ICD-10 code of T58.01, T58.04, T58.11, T58.14, T58.2X1, T58.2X4, T58.8X1, T58.8X4, T58.91 or T58.94 in the principal or other diagnosis fields. Records of intentional or purposeful CO poisoning are excluded. These data include three categories of carbon monoxide poisoning causes:

1. Unintentional, non-fire related;
2. Unintentional, fire-related; and
3. Unknown intent.

Data are suppressed when there are fewer than five visits to improve rate stability. However, rates with zero cases are not suppressed. The crude rate is the number of emergency department visits divided by the total number of people in the area of interest (for example, a county). This is expressed as a number per unit population, such as "per 100,000 population." The crude rate does not take into account the differences in age distributions across counties and are therefore subject to bias. Use age-adjusted rates for a better standardized measure.

Age-adjusted rates per 100,000

These data include emergency department visits for carbon monoxide poisoning and are collected from emergency room visit discharge records. This measure includes emergency department visits with an ICD-9 code of 986, E868.2, E868.3, E868.8, E868.9, E982.0, or E982.1 or an ICD-10 code of T58.01, T58.04, T58.11, T58.14, T58.2X1, T58.2X4, T58.8X1, T58.8X4, T58.91 or T58.94 in the principal or other diagnosis fields. Records of intentional or purposeful CO poisoning are excluded. These data include three categories of carbon monoxide poisoning causes:

1. Unintentional, non-fire related;
2. Unintentional, fire-related; and
3. Unknown intent.

Data are suppressed when there are fewer than five visits to improve rate stability. However, rates with zero cases are not suppressed. An age-adjusted rate is a rate that is statistically modified to eliminate the potential biasing effect of different age distributions across different populations. In other words, the age-adjusted rate accounts for the possibility that emergency department visits for carbon monoxide may be more frequent among younger individuals and some counties have more younger individuals than others. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Carbon monoxide poisoning hospitalizations

Number of hospitalizations for carbon monoxide poisoning (counts)

These data include hospitalizations for carbon monoxide poisoning and are collected from inpatient hospital discharge records. This measure includes hospitalizations with an ICD-9 code of 986, E868.2, E868.3, E868.8, E868.9, E982.0, or E982.1 or an ICD-10 code of T58.01, T58.04, T58.11, T58.14, T58.2X1, T58.2X4, T58.8X1, T58.8X4, T58.91 or T58.94 in the principal or other diagnosis fields. Records of intentional or purposeful CO poisoning are excluded. These data include three categories of carbon monoxide poisoning causes:

1. Unintentional, non-fire related;
2. Unintentional, fire-related; and
3. Unknown intent.

Data are not suppressed for this measure. Please note that counts are a statistically-limited way to consider hospitalizations because they do not allow for accurate comparison between counties. Counties with higher populations, such as Milwaukee, will have more hospitalizations simply because they have more people. An age-adjusted rate is a better measure for true comparison between counties.

Crude rates per 100,000

These data include hospitalizations for carbon monoxide poisoning and are collected from inpatient hospital discharge records. This measure includes hospitalizations with an ICD-9 code of 986, E868.2, E868.3, E868.8, E868.9, E982.0, or E982.1 or an ICD-10 code of T58.01, T58.04, T58.11, T58.14, T58.2X1, T58.2X4, T58.8X1, T58.8X4, T58.91 or T58.94 in the principal or other diagnosis fields. Records of intentional or purposeful CO poisoning are excluded.. These data include three categories of carbon monoxide poisoning causes:

1. Unintentional, non-fire related;
2. Unintentional, fire-related; and
3. Unknown intent.

Data are suppressed when there are fewer than five visits to improve rate stability. However, rates with zero cases are not suppressed. The crude rate is the number of hospitalizations divided by the total number of people in the population of interest (for example, a county). This is expressed as a number per unit population such as "per 10,000 population." A crude rate does not take into account the differences in age distributions across counties and are therefore subject to bias. Use age-adjusted rates for a better standardized measure.

Age-adjusted rates per 100,000

These data include hospitalizations for carbon monoxide poisoning and are collected from inpatient hospital discharge records. This measure includes emergency department visits with an ICD-9 code of 986, E868.2, E868.3, E868.8, E868.9, E982.0, or E982.1 or an ICD-10 code of T58.01, T58.04, T58.11, T58.14, T58.2X1, T58.2X4, T58.8X1, T58.8X4, T58.91 or T58.94 in the principal or other diagnosis fields. Records of intentional or purposeful CO poisoning are excluded. These data include three categories of carbon monoxide poisoning causes:

1. Unintentional, non-fire related;
2. Unintentional, fire-related; and
3. Unknown intent.

Data are suppressed when there are fewer than five visits to improve rate stability. However, rates with zero cases are not suppressed. An age-adjusted rate is a rate that is statistically modified to eliminate the potential biasing effect of different age distributions across different populations. In other words, the age-adjusted rate accounts for the possibility that hospitalizations for asthma may be more frequent among younger individuals and some counties have more younger individuals than others. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Carbon monoxide poisoning mortality

Number of deaths from carbon monoxide poisoning (counts)

These data include deaths from carbon monoxide poisoning and are collected from the National Vital Statistics System from the National Center for Health Statistics. These data include three categories of carbon monoxide poisoning causes:

1. Unintentional, non-fire related;
2. Unintentional, fire-related; and
3. Unknown intent.

Data are suppressed for counties with nine or fewer (including zero) per 100,000 to protect confidentiality. Please note that counts are a statistically limited way to consider deaths because they do not allow for accurate comparison between counties. Counties with higher populations, such as Milwaukee, will have more deaths simply because they have more people. An age-adjusted rate is a better measure for true comparison between counties.

Crude rates per 100,000

These data include deaths from carbon monoxide poisoning and are collected from the National Vital Statistics System from the National Center for Health Statistics. These data include three categories of carbon monoxide poisoning causes:

1. Unintentional, non-fire related;
2. Unintentional, fire-related; and
3. Unknown intent.

Data are suppressed for counties with nine or fewer (including zero) per 100,000 to protect confidentiality. The crude rate is the number of deaths divided by the total number of people in the population of interest (for example, a county). This is expressed as a number per unit population, such as "per 10,000 population." A crude rate does not take into account the differences in age distributions across counties and are therefore subject to bias. Use age-adjusted rates for a better standardized measure.

Age-adjusted rates per 100,000

These data include deaths from carbon monoxide poisoning and are collected from the National Vital Statistics System from the National Center for Health Statistics. These data include three categories of carbon monoxide poisoning causes:

1. Unintentional, non-fire related;
2. Unintentional, fire-related; and
3. Unknown intent.

Data are suppressed for counties with nine or fewer (including zero) per 100,000 to protect confidentiality. An age-adjusted rate is a rate that is statistically modified to eliminate the potential biasing effect of different age distributions across different populations. In other words, the age-adjusted rate accounts for the possibility that carbon monoxide poisoning deaths may be more frequent among younger individuals and some counties have more younger individuals than others. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Flood vulnerability

Number of housing units within FEMA Designated Special Flood Hazard Area (SFHA)

These data provide estimates of the number of housing units within the SFHA. The SFHAs have 1% annual chance of coastal or riverine flooding. The population distribution was derived using 2010 census block group data in conjunction with 2010 LandScan Nighttime Population raster dataset (Bhaduri, et al., 2007), aggregated to the county level using a Geographic Information System (GIS). The ratio of population distribution was used to determine housing unit distribution at census block group level, which was then aggregated to the county level. The 2011 National Flood Hazard Layer, a national-level digital flood hazard database created by Federal Emergency Management Agency (FEMA), was used to calculate the flood hazard area.

Number of people within FEMA Designated Special Flood Hazard Area (SFHA)

These data provide estimates of the number of people within the SFHA. The SFHAs have 1% annual chance of coastal or riverine flooding. In order to estimate population, 2010 census block group data (assuming uniform population distribution among counties) were overlaid with the digital coastal flood hazard database using a Geographic Information System (GIS). Additionally, census block group-level measures were derived from the 2010 LandScan Nighttime Population raster dataset and aggregated to the county level. The 2011 National Flood Hazard Layer, a national-level digital flood hazard database created by Federal Emergency Management Agency (FEMA), was used to calculate the flood hazard area.

Number of square miles within FEMA Designated Special Flood Hazard Area (SFHA)

These data provide areal (square miles) estimates of the SFHA with a 1% annual chance of coastal or riverine flooding, per county. This is sometimes referred to as the “100 year” flood zone. The 2011 National Flood Hazard Layer, a national-level digital flood hazard database created by Federal Emergency Management Agency (FEMA), was used to calculate the flood hazard area.

Percent area (square miles) within FEMA Designated Special Flood Hazard Area (SFHA)

These data provide county estimates of the percentage of total area within the SFHA. The SFHAs have 1% annual chance of coastal or riverine flooding. The 2011 National Flood Hazard Layer, a national-level digital flood hazard database created by Federal Emergency Management Agency (FEMA), was used to calculate the flood hazard area.

Percent of hospital beds within a flood hazard area

Hospital data were obtained from the 2016 American Hospital Association (AHA) Annual Survey. A Geographic Information System (GIS) was used to identify hospitals within a Federal Emergency Management Agency (FEMA)-designated flood hazard zone. The National Flood Hazard Layer (NFHL) does not have national coverage. Therefore, percentage of hospital beds in flood hazard areas is not available for all counties.

Percent of hospitals within a flood hazard area

Hospital data were obtained from the 2016 American Hospital Association (AHA) Annual Survey. A Geographic Information System (GIS) was used to identify hospitals within a Federal Emergency Management Agency (FEMA)-designated flood hazard zone. The National Flood Hazard Layer (NFHL) does not have national coverage. Therefore, percentage of hospital beds in flood hazard areas is not available for all counties.

Households

Number of housing units

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Read more about the Census ACS methodology.

Number of housing units with 10 or more units

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Read more about the Census ACS methodology.

Number of housing units with more people than rooms

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Read more about the Census ACS methodology.

Number of housing units with no vehicle available

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Read more about the Census ACS methodology.

Number of mobile homes housing units

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Read more about the Census ACS methodology.

Number of population living in group quarters

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Read more about the Census ACS methodology.

Number of renter-occupied housing units

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Read more about the Census ACS methodology.

Number of vacant housing units

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Read more about the Census ACS methodology.

Percent of housing units with more people than rooms

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Read more about the Census ACS methodology.

Percent of housing units with no vehicle available

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Read more about the Census ACS methodology.

Percent of mobile homes housing units

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Read more about the Census ACS methodology.

Percent of renter-occupied housing units

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Read more about the Census ACS methodology.

Percent of vacant housing units

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Read more about the Census ACS methodology.

Percent of vacant housing units

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Read more about the Census ACS methodology.

Percent of population living in group quarters

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Read more about the Census ACS methodology.

Internet access

Number of households with a smartphone

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Refer to the technical notes for more information about ACS.

Number of households with a smartphone, but no other device

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Refer to the technical notes for more information about ACS.

Number of households with income less than $20,000 without an Internet subscription

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Refer to the technical notes for more information about ACS.

Number of households with no Internet access

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Refer to the technical notes for more information about ACS.

Number of people age 25+ years, with less than high school education who have a computer without an Internet subscription

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Refer to the technical notes for more information about ACS.

Number of people age 65+ years who have a computer without an Internet subscription

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Refer to the technical notes for more information about ACS.

Number of people with access to a computer with Internet, but no cell phone

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Refer to the technical notes for more information about ACS.

Number of unemployed people age 16+ years who have a computer without an Internet subscription

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Refer to the technical notes for more information about ACS.

Percent of households with a smartphone

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Refer to the technical notes for more information about ACS.

Percent of households with a smartphone, but no other device

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Refer to the technical notes for more information about ACS.

Percent of households with income less than $20,000 without an Internet subscription

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Refer to the technical notes for more information about ACS.

Percent of households with no Internet access

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Refer to the technical notes for more information about ACS.

Percent of people age 25+ years, with less than high school education who have a computer without an Internet subscription

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Refer to the technical notes for more information about ACS.

Percent of people age 65+ years who have a computer without an Internet subscription

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Refer to the technical notes for more information about ACS.

Percent of people with access to a computer with Internet, but no cell phone

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Refer to the technical notes for more information about ACS.

Percent of unemployed people age 16+ years who have a computer without an Internet subscription

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Refer to the technical notes for more information about ACS.

Land cover

Average percent of developed imperviousness

Impervious cover is any surface in the landscape that cannot effectively absorb or infiltrate rainfall. Data was obtained from National Land Cover Database (NLCD) provided by U.S. Department of the Interior, Multi-Resolution Land Characteristics Consortium. The percent of developed imperviousness is a NLCD product that represents the fraction of impervious area in each 30m x 30m pixel. The area-weighted average of each pixel was then calculated for each state, county, and census tract.

Percent of land covered by forest

Data obtained from National Land Cover Database (NLCD) provided by U.S. Department of the Interior, U.S. Geological Survey. Forest is defined as the combination of deciduous forest, evergreen forest, and mixed forest in NLCD gridded data.

Percent of land covered by water

Data obtained from National Land Cover Database (NLCD) provided by U.S. Department of the Interior, Multi-Resolution Land Characteristics Consortium. Percentages include open water and perennial ice/snow in NLCD gridded data. Percentages for each county were estimated as the proportion of grids classified as "Water" among all grids within the county.

Land use

Classification of county from rural to urban (six-category scale)

Data obtained from National Center for Health Statistics (NCHS) Urban-Rural Classification Scheme for Counties. Refer to the file documentation for a description of the NCHS Urban-Rural Classification.

Classification of county from rural to urban (two-category scale)

Data obtained from National Center for Health Statistics (NCHS) Urban-Rural Classification Scheme for Counties. Refer to the file documentation for a description of the NCHS Urban-Rural Classification.

Percent of land used for agriculture

Data obtained from National Land Cover Database (NLCD) provided by U.S. Department of the Interior, Multi-Resolution Land Characteristics Consortium. Agriculture includes the combination of Pasture/Hay and Cultivated Crop Land Use in NLCD gridded data. Percentages for each county were estimated as the proportion of grids classified as "Agricultural Land Use" among all grids within the county.

Percent of land used for development

Data obtained from National Land Cover Database (NLCD) provided by U.S. Department of the Interior, Multi-Resolution Land Characteristics Consortium. Developed land use is the combination of developed-open space, developed-low intensity, developed-medium intensity, and developed-high intensity in NLCD gridded data. Developed, open spaces are areas with a mixture of some constructed materials, but mostly vegetation in the form of lawn grasses. Impervious surfaces account for less than 20% of total cover. These areas most commonly include large-lot single-family housing units, parks, golf courses, and vegetation planted in developed settings for recreation, erosion control, or aesthetic purposes. Percent of developed land use for each county were estimated as the proportion of grids classified as "developed land use" among all grids within the county.

Read frequently asked questions about the Community Design data

Access to parks and public elementary schools

Number of children ages 5 to 9 years living within a half mile of a public elementary school

A list of public schools was provided by the National Center for Educational Statistics. School Description includes regular, special education, and other/alternative; Special School Type includes charter and magnet schools; and Grade-Span includes pre-kindergarten to 4th grade. All half-mile buffers were restricted by county. If a half-mile buffer crossed county or state boundaries, only the proportion of the population residing within the same county as the school contributed to the estimate.

Number of people living within a half mile of a park

Number of people within a buffer of a half mile radius of a park was determined at the census tract level. These estimates are aggregated to county, and state levels. Park data are from NAVTEQ (2010), Esri StreetMap Premium HERE (2016), and PAD-US (2015), providers of Geographic Information Systems (GIS) data. The underlying map database is a compilation of first-hand observation of geographic features and third-party data sources. 2010 and 2015 park data are from different sources; data for these measures are, therefore, not comparable across years. There are no advanced view options for 2015 as the underlying age, race, and race/ethnicity data were unavailable at the time of calculation. If a half- or one-mile buffer crossed a county or state boundary, the population residing within this buffer is estimated and attributed to the county within which the population resides (not the county within which the park is located).

Percent of children ages 5 to 9 years living within a half mile of a public elementary school

A list of public schools was provided by the National Center for Educational Statistics. School Description includes regular, special education, and other/alternative; Special School Type includes charter and magnet schools; and Grade-Span includes pre-kindergarten to 4th grade. All half-mile buffers were restricted by county. If a half-mile buffer crossed county or state boundaries, only the proportion of the population residing within the same county as the school contributed to the estimate.

Percent of people living within a half mile of a park

Percentages of people living within a half mile of the park boundary are calculated for the census tract, county, state, and national levels. The percentage uses the estimated numbers of people as determined via the buffer analysis and then divides this numerator by the total number of people in each geographic unit. Park data are from NAVTEQ (2010), Esri StreetMap Premium HERE (2016), and PAD-US (2015), providers of Geographic Information Systems (GIS) data. The underlying map database is a compilation of first-hand observation of geographic features and third-party data sources. 2010 and 2015 park data are from different sources; data for these measures are, therefore, not comparable across years. There are no advanced view options for 2015 as the underlying age, race, and race/ethnicity data were unavailable at the time of calculation. If a half- or one-mile buffer crossed county or state boundary, the population residing within this buffer is estimated and attributed to the county within which the population resides (not the county within which the park is located).

Commute time

Average one-way commute time (minutes) for workers 16 years and older for all travel modes

Data obtained from the American Community Survey (ACS) by the U.S. Census Bureau. ACS is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. However, because ACS data are based on a sample, they are subject to sampling variability and include a range of uncertainty. This dataset only captures commute time to work, and does not capture the distance of the trip. Individual preference and economics also influences commute choices, not just community design.

Number of workers 16 years and older driving 20+ minutes to work (car, truck, van)

Data obtained from the American Community Survey (ACS) by the U.S. Census Bureau. ACS is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. However, because ACS data are based on a sample, they are subject to sampling variability and include a range of uncertainty. This dataset only captures commute time to work, and does not capture the distance of the trip. Individual preference and economics also influences commute choices, not just community design. Measures are based on the national median commute time for 2011-2015 for each particular mode of transportation. Advanced view options are based on the national 75th (30 minute) and 90th (45 minute) percentile commute times.

Number of workers 16 years and older taking public transportation 45+ minutes to work

Data obtained from the American Community Survey (ACS) by the U.S. Census Bureau. ACS is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. However, because ACS data are based on a sample, they are subject to sampling variability and include a range of uncertainty. This dataset only captures commute time to work, and does not capture the distance of the trip. Individual preference and economics also influences commute choices, not just community design. Measures are based on the national median commute time for 2011-2015 for each particular mode of transportation. Advanced view options are based on the national 75th and 90th percentile commute times.

Number of workers 16 years and older walking 10+ minutes to work

Data obtained from the American Community Survey (ACS) by the U.S. Census Bureau. ACS is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. However, because ACS data are based on a sample, they are subject to sampling variability and include a range of uncertainty. This dataset only captures commute time to work, and does not capture the distance of the trip. Individual preference and economics also influences commute choices, not just community design. Measures are based on the national median commute time for 2011-2015 for each particular mode of transportation. Advanced view options are based on the national 75th and 90th percentile commute times.

Percent of workers 16 years and older driving 20+ minutes to work (car, truck, van)

Data obtained from the American Community Survey (ACS) by the U.S. Census Bureau. ACS is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. However, because ACS data are based on a sample, they are subject to sampling variability and include a range of uncertainty. This dataset only captures commute time to work, and does not capture the distance of the trip. Individual preference and economics also influences commute choices, not just community design. Measures are based on the national median commute time for 2011-2015 for each particular mode of transportation. Advanced view options are based on the national 75th and 90th percentile commute times.

Percent of workers 16 years and older taking public transportation 45+ minutes to work

Data obtained from the American Community Survey (ACS) by the U.S. Census Bureau. ACS is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. However, because ACS data are based on a sample, they are subject to sampling variability and include a range of uncertainty. This dataset only captures commute time to work, and does not capture the distance of the trip. Individual preference and economics also influences commute choices, not just community design. Measures are based on the national median commute time for 2011-2015 for each particular mode of transportation. Advanced view options are based on the national 75th and 90th percentile commute times.

Percent of workers 16 years and older walking 10+ minutes to work

Data obtained from the American Community Survey (ACS) by the U.S. Census Bureau. ACS is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. However, because ACS data are based on a sample, they are subject to sampling variability and include a range of uncertainty. This dataset only captures commute time to work, and does not capture the distance of the trip. Individual preference and economics also influences commute choices, not just community design. Measures are based on the national median commute time for 2011-2015 for each particular mode of transportation. Advanced view options are based on the national 75th and 90th percentile commute times.

Motor vehicle-related fatalities

Number of fatal motor vehicle crashes

Data were obtained from the National Highway Traffic Safety Administration's Fatality Analysis Reporting System (FARS). U.S. Census Bureau intercensal population estimates were used to calculate rates. The number of crashes was calculated by summing the number of distinct fatal crashes from annual FARS datasets for each five-year period. FARS does not capture information on crashes in driveways, parking lots, or off public roadways. Crash data were geocoded to census tracts using 2010 boundaries and aggregated to county or state level. Accidents occurring on census tract boundaries were randomly assigned to an adjacent census tract.

Percent of all fatal motor vehicle crashes involving cyclists or pedestrians

Data were obtained from the National Highway Traffic Safety Administration's Fatality Analysis Reporting System (FARS). U.S. Census Bureau intercensal population estimates were used to calculate rates. Percentages were calculated by dividing the number of distinct crashes involving bicyclists or pedestrians by the total number of distinct fatal crashes, then multiplying by 100.

Rate of fatal motor vehicle crashes per 100,000 population

Data were obtained from the National Highway Traffic Safety Administration's Fatality Analysis Reporting System (FARS). U.S. Census Bureau intercensal population estimates were used to calculate rates. Rates were calculated by dividing the number of fatal motor vehicle crashes by the sum of the intercensal populations for each five-year period, then multiplying by 100,000.

Proximity of population and schools to highways

Number of people living within 150m of a highway

This measure was created using data from the National Center for Education Statistics (NCES) and road network data from 2010, from NAVTEQ, a commercial company. Class 1 and class 2 roads were selected to represent major highways in the United States. The Federal Highway Administration (FHWA) Functional Classification system categorizes interstates as class 1 and freeways and expressways as class 2. Esri ArcGIS 10.1 software was used to calculate the number of schools within a 150m buffer around all class 1 and class 2 roads. Population estimates were based on population counts within census tracts made publicly available by the U.S. Census (2010, 100% count data). The proportion of each census tract included within the buffer area was calculated and summed from the census tract level to the county level.

Number of public schools located within 150m of a highway

This measure was created using data from the National Center for Education Statistics (NCES) and road network data from 2010, from NAVTEQ, a commercial company. Class 1 and class 2 roads were selected to represent major highways in the United States. The Federal Highway Administration (FHWA) Functional Classification system classifies interstates as class 1 and freeways and expressways as class 2. Esri ArcGIS 10.1 software was used to calculate the number of schools within a 150m buffer around all class 1 and class 2 roads. Population estimates were based on population counts within census tracts made publicly available by the U.S. Census (2010, 100% count data). The proportion of each census tract included within the buffer area was calculated and summed from the census tract level to the county level.

Percent of population living within 150m of a highway

This measure was created using data from the National Center for Education Statistics (NCES) and road network data from 2010, from NAVTEQ, a commercial company. Class 1 and class 2 roads were selected to represent major highways in the United States. The Federal Highway Administration (FHWA) Functional Classification system classifies interstates as class 1 and freeways and expressways as class 2. Esri ArcGIS 10.1 software was used to calculate the number of schools within a 150m buffer around all class 1 and class 2 roads. Population estimates were based on population counts within census tracts made publicly available by the U.S. Census (2010, 100% count data). The proportion of each census tract included within the buffer area was calculated and summed from the census tract level to the county level. Percent of population living within 150m of a highway was calculated using the number of people living within 150m of a highway as the numerator and the total population in a county as the denominator.

Percent of public schools located within 150m of a highway

This measure was created using data from the National Center for Education Statistics (NCES) and road network data from 2010, from NAVTEQ, a commercial company. Class 1 and class 2 roads were selected to represent major highways in the United States. The Federal Highway Administration (FHWA) Functional Classification system classifies interstates as class 1 and freeways and expressways as class 2. Esri ArcGIS 10.1 software was used to calculate the number of schools within a 150m buffer around all class 1 and class 2 roads. Population estimates were based on population counts within census tracts made publicly available by the U.S. Census (2010, 100% count data). The proportion of each census tract included within the buffer area was calculated and summed from the census tract level to the county level. Percent of public schools (grades pre-K to 4th) sited within 150m of a highway was calculated using the number of public schools (grades pre-K to 4th) sited within 150m of a highway as the numerator and the total number of public schools in a county as the denominator.

Read frequently asked questions about the COPD data

COPD emergency department visits

Number of emergency department visits for COPD

These data include emergency department visits for COPD and are collected from emergency room visit discharge records, for people over the age of 25. Emergency department visits resulting in subsequent hospitalization are also included. Federally-funded hospitals (for example, Veteran's Administration (VA) hospitals, which are exempt from state reporting requirements) are not included in these data. This measure includes emergency department visits with any of the following ICD-9 codes in the principal diagnosis field: 490, 491, 492, 496, and 493.2. Cases with 493.2 as a primary diagnosis are excluded if none of the secondary diagnosis fields contain 490, 492, or 496. The measure also includes any of the ICD-10 codes J40-J44 in the principal diagnosis field. Data are suppressed for counties with fewer than five visits to protect confidentiality. However, counties with zero cases are not suppressed. Please note that counts are a statistically limited way to consider emergency department visits because they do not allow for accurate comparison between counties. Counties with higher populations, such as Milwaukee, will have more emergency department visits simply because they have more people. An age-adjusted rate is a better measure for true comparison between counties. In 2018 a new block group variable became available, for accuracy the new variable is now used to determine the county instead of the county variable.

Crude rates of emergency department visits for COPD per 10,000 people

These data include emergency department visits for COPD and are collected from emergency room visit discharge records, for people over the age of 25. Emergency department visits resulting in subsequent hospitalization are also included. Federally funded hospitals (for example, Veteran's Administration [VA] hospitals, which are exempt from state reporting requirements) are not included in these data. This measure includes emergency department visits with any of the following ICD-9 codes in the principal diagnosis field: 490, 491, 492, 496, and 493.2, Cases with 493.2 as a primary diagnosis are excluded if none of the secondary diagnosis fields contain 490, 492, or 496. The measure also includes any of the ICD-10 codes J40-J44 in the principal diagnosis field. Data are suppressed for counties with fewer than five visits to protect confidentiality. However, counties with zero cases are not suppressed. The crude rate is the number of emergency department visits divided by the total number of people in the area of interest (for example, a county). Population of interest is derived from census data (2000 standard population). This is expressed as a number per unit population such as "per 10,000 population." Crude rates do not take into account the differences in age distributions across counties and are therefore subject to bias. Use age-adjusted rates for a better standardized measure. In 2018 a new block group variable became available, for accuracy the new variable is now used to determine the county instead of the county variable.

COPD emergency department visits age-specific rates per 10,000 people

These data include emergency department visits for COPD and are collected from emergency room visit discharge records, for people over the age of 25. Emergency department visits resulting in subsequent hospitalization are also included. Federally funded hospitals (for example, Veteran's Administration (VA) hospitals, which are exempt from state reporting requirements) are not included in these data. This measure includes emergency department visits with any of the following ICD-9 codes in the principal diagnosis field: 490, 491, 492, 496, and 493.2. Cases with 493.2 as a primary diagnosis are excluded if none of the secondary diagnosis fields contain 490, 492, or 496. The measure also includes any of the ICD-10 codes J40-J44 in the principal diagnosis field. Data are suppressed for counties with fewer than five visits to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. The age-specific rate is the number of emergency department visits for a certain age group divided by the total number of people in that age group and area of interest (for example, a county). Population of interest is derived from census data (2000 standard population). This is expressed as a number per unit population such as "per 10,000 population." Ages are grouped into the following categories: 0-24 years, 25-44 years, 45-64 years, 65-84 years, and 85+ years. In 2018 a new block group variable became available, for accuracy the new variable is now used to determine the county instead of the county variable.

COPD emergency department visits age-adjusted rates per 10,000 people

These data include emergency department visits for COPD and are collected from emergency room visit discharge records, for people over the age of 25. Emergency department visits resulting in subsequent hospitalization are also included. Federally funded hospitals (for example, Veteran's Administration [VA] hospitals, which are exempt from state reporting requirements) are not included in these data. This measure includes emergency department visits with any of the following ICD-9 codes in the principal diagnosis field: 490, 491, 492, 496, and 493.2. Cases with 493.2 as a primary diagnosis are excluded if none of the secondary diagnosis fields contain 490, 492, or 496. The measure also includes any of the ICD-10 codes J40-J44 in the principal diagnosis field. Data are suppressed for counties with fewer than five visits to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. An age-adjusted rate is a rate that is statistically modified to eliminate the potential biasing effect of different age distributions across different populations. In other words, the age-adjusted rate accounts for the possibility that emergency department visits for COPD may be more frequent among older individuals and the fact that some counties have older populations than others. Direct age-adjustment is conducted using the 2000 U.S. standard population. Note: Due to a number of cases where ethnicity was not collected for discharge records and is thus unknown, there are several years where the ‘combined’ age-adjusted rate for ethnicity is higher than the age-adjusted rates for both Hispanic and non-Hispanic populations. In 2018 a new block group variable became available, for accuracy the new variable is now used to determine the county instead of the county variable.

Average daily count for COPD emergency department visits

These data include emergency department visits for COPD and are collected from emergency room visit discharge records, for people over the age of 25. This measure includes emergency department visits with any of the following ICD-9 codes in the principal diagnosis field: 490, 491, 492, 496, and 493.2. Cases with 493.2 as a primary diagnosis are excluded if none of the secondary diagnosis fields contain 490, 492, or 496. The measure also includes any of the ICD-10 codes J40-J44 in the principal diagnosis field. Average Daily Count was calculated by dividing the monthly count by the number of days in that respective month. In 2018 a new block group variable became available, for accuracy the new variable is now used to determine the county instead of the county variable.

COPD hospitalizations

Number of hospitalizations for COPD

These data include hospitalizations for COPD and are collected from inpatient hospital discharge records, for people over the age of 25. This measure includes hospitalizations with any of the following ICD-9 codes in the principal diagnosis field: 490, 491, 492, 496, and 493.2. Cases with 493.2 as a primary diagnosis are excluded if none of the secondary diagnosis fields contain 490, 492, or 496. The measure also includes any of the ICD-10 codes J40-J44 in the principal diagnosis field. Data are suppressed for counties with fewer than five visits to protect confidentiality. However, counties with zero cases are not suppressed. Please note that counts are a statistically limited way to consider emergency department visits because they do not allow for accurate comparison between counties. Counties with higher populations, such as Milwaukee, will have more hospitalizations simply because they have more people. An age-adjusted rate is a better measure for true comparison between counties. In 2018 a new block group variable became available, for accuracy the new variable is now used to determine the county instead of the county variable.

Crude rates of hospitalizations for COPD per 10,000 people

These data include hospitalizations for COPD and are collected from inpatient hospital discharge records, for people over the age of 25. This measure includes hospitalizations with any of the following ICD-9 codes in the principal diagnosis field: 490, 491, 492, 496, and 493.2. Cases with 493.2 as a primary diagnosis are excluded if none of the secondary diagnosis fields contain 490, 492, or 496. The measure also includes any of the ICD-10 codes J40-J44 in the principal diagnosis field. Data are suppressed for counties with fewer than five visits to protect confidentiality. However, counties with zero cases are not suppressed. The crude rate is the number of hospitalizations divided by the total number of people in the population of interest (for example, a county). Population of interest is derived from census data (2000 standard population). This is expressed as a number per unit population, such as "per 10,000 population." A crude rate does not take into account the differences in age distributions across counties and are therefore subject to bias. Use age-adjusted rates for a better standardized measure. In 2018 a new block group variable became available, for accuracy the new variable is now used to determine the county instead of the county variable.

COPD hospitalizations age-specific rates per 10,000 people

These data include hospitalizations for COPD and are collected from inpatient hospital discharge records, for people over the age of 25. This measure includes hospitalizations with any of the following ICD-9 codes in the principal diagnosis field: 490, 491, 492, 496, and 493.2. Cases with 493.2 as a primary diagnosis are excluded if none of the secondary diagnosis fields contain 490, 492, or 496. The measure also includes any of the ICD-10 codes J40-J44 in the principal diagnosis field. Data are suppresses data for counties with fewer than five visits per 10,000 to protect confidentiality. However, counties with zero cases are not suppressed. The age-specific rate is the number of hospitalizations for a certain age group divided by the total number of people in that age group and area of interest (for example, a county). Population of interest is derived from census data (2000 standard population). This is expressed as a number per unit population such as "per 10,000 population." Ages are grouped into the following categories: 0-24 years, 25-44 years, 45-64 years, 65-84 years, and 85+ years. In 2018 a new block group variable became available, for accuracy the new variable is now used to determine the county instead of the county variable.

COPD hospitalizations age-adjusted rates per 10,000 people

These data include hospitalizations for COPD and are collected from inpatient hospital discharge records, for people over the age of 25. This measure includes hospitalizations with any of the following ICD-9 codes in the principal diagnosis field: 490, 491, 492, 496, and 493.2. Cases with 493.2 as a primary diagnosis are excluded if none of the secondary diagnosis fields contain 490, 492, or 496. The measure also includes any of the ICD-10 codes J40-J44 in the principal diagnosis field. Data are suppressed for counties with fewer than five visits per 10,000 to protect confidentiality. However, counties with zero cases are not suppressed. An age-adjusted rate is a rate that is statistically modified to eliminate the potential biasing effect of different age distributions across different populations. In other words, the age-adjusted rate accounts for the possibility that hospitalizations for COPD may be more frequent among older individuals and the fact that some counties have older populations than others. Direct age-adjustment is conducted using the 2000 U.S. standard population. Due to a number of cases where ethnicity was not collected for discharge records and is thus unknown, there are several years where the "combined" age-adjusted rate for ethnicity is higher than the age-adjusted rates for both Hispanic and non-Hispanic populations. In 2018 a new block group variable became available, for accuracy the new variable is now used to determine the county instead of the county variable.

Average daily count for COPD hospitalizations

These data include hospitalizations for COPD and are collected from inpatient hospital discharge records, for people over the age of 25. This measure includes hospitalizations with any of the following ICD-9 codes in the principal diagnosis field: 490, 491, 492, 496, and 493.2. Cases with 493.2 as a primary diagnosis are excluded if none of the secondary diagnosis fields contain 490, 492, or 496. The measure also includes any of the ICD-10 codes J40-J44 in the principal diagnosis field. Average Daily Count was calculated by dividing the monthly count by the number of days in that respective month. In 2018 a new block group variable became available, for accuracy the new variable is now used to determine the county instead of the county variable.

Demographics

Percent of population aged 5 years and older that speak English less than "very well"

This dataset was collected from the U.S. Census Bureau, American Factfinder, and the American Community Survey (ACS) five-year estimates. ACS is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. However, because ACS data are based on a sample, they are subject to sampling variability and include a range of uncertainty. Read more about the Census ACS methodology. The definition for people who speak English less than “very well” includes people whose native/primary language is any language other than English.

Percent of population aged 65 years and older living alone in a non-family household

This dataset was collected from the U.S. Census Bureau, American Factfinder, and the American Community Survey (ACS) five-year estimates. ACS is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. However, because ACS data are based on a sample, they are subject to sampling variability and include a range of uncertainty. Read more about the Census ACS methodology. A non-family household can be either a person living alone or a householder who shares the housing unit only with non-relatives—for example, boarders or roommates. Non-family households are maintained only by people with no relatives at home.

Percent of population by demographic group

Data provided by the Centers for Disease Control and Prevention (CDC) National Vital Statistic System. Intercensal estimates were used for 2000-2009. Postcensal estimates were used for 2010 onward.

Environmental Quality Index

Air Domain Index

Environmental Quality Index (EQI) and domain-specific indices are available publicly at the U.S. Environmental Protection Agency’s (EPA) Environmental Dataset Gateway. The Air Domain Index is output from a principal component analysis using 43 air pollutant variables, including both criteria and hazardous air pollutants. This index summarizes average air pollutant exposures from 2006–2010 for each county in the United States. Lower index scores indicate higher environmental quality, and higher index scores mean lower environmental quality.

Built Environment Domain Environment Index

Environmental Quality Index (EQI) and domain-specific indices are available publicly at the U.S. Environmental Protection Agency's (EPA) Environmental Dataset Gateway. The Built Environment Domain Index is output from a principal component analysis using 15 built environment quality variables, including street information, business data, highway safety information, walkability score, green space/open land, and U.S. Census data. This index summarizes average built environment quality measures from 2006-2010 for each county in the United States. Lower index scores indicate higher environmental quality, and higher index scores indicate lower environmental quality.

Land Domain Index

Environmental Quality Index (EQI) and domain-specific indices are available publicly at the U.S. Environmental Protection Agency’s (EPA) Environmental Dataset Gateway. The Land Domain Index is output from a principal component analysis using 18 land quality variables, including pesticides, general agriculture, facility counts, mine data, and radon rankings. This index summarizes average land quality measures from 2006–2010 for each county in the United States. Lower index scores indicate higher environmental quality, and higher index scores indicate lower environmental quality.

Overall Environmental Quality Index

Environmental Quality Index (EQI) and domain-specific indices are available publicly at the U.S. Environmental Protection Agency’s (EPA) Environmental Dataset Gateway. The Environmental Quality Index (EQI) presents data in five domains: sociodemographics, air, water, land, and built environments to provide a county-by-county snapshot of overall environmental quality across all U.S. data provided at the county level for all counties (3,143) in the U.S. for the time period 2006–2010. Lower index scores indicate higher environmental quality, and higher index scores indicate lower environmental quality.

Sociodemographic Domain Index

Environmental Quality Index (EQI) and domain-specific indices are available publicly at the U.S. Environmental Protection Agency’s (EPA) Environmental Dataset Gateway. The Sociodemographic Domain Index is output from a principal component analysis using 12 sociodemographic variables, including U.S. Census, Economic Resource Service, election results, and crime data. This index summarizes average sociodemographic measures from 2006–2010 for each county in the United States. Lower index scores indicate higher environmental quality, and higher index scores indicate lower environmental quality.

Water Domain Index

Environmental Quality Index (EQI) and domain-specific indices are available publicly at the U.S. Environmental Protection Agency’s (EPA) Environmental Dataset Gateway. The Water Domain Index is output from a principal component analysis using 51 water quality variables, including water impairment, waste permits, domestic water source, deposition for six pollutants, drought status, 39 chemical contaminants, and one microorganism. This index summarizes average water quality measures from 2006–2010 for each county in the United States. Lower index scores indicate higher environmental quality, and higher index scores indicate lower environmental quality.

Health status

Percent of population aged 5 years and older with a disability

This dataset was collected from the US. Census Bureau, American Factfinder, and the American Community Survey (ACS) five-year estimates. ACS is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. However, because ACS data are based on a sample, they are subject to sampling variability and include a range of uncertainty. Read more about the Census ACS methodology. Because the ACS has replaced the decennial long-form as the source for small area statistics, there is no disability data in the 2010 Census. The Census Bureau collects data on disability primarily through ACS and the Survey of Income and Program Participation (SIPP). The definitions of disability are not always alike so caution should be taken when making comparisons across surveys. Generally, the SIPP estimates of disability prevalence are broader and encompass a greater number of activities on which disability status is assessed. The ACS has a narrower definition but can produce estimates for states, counties, and metropolitan areas.

Households

Percent of housing units with no vehicle available

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Read more about the Census ACS methodology.

Percent of mobile home housing units

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Read more about the Census ACS methodology.

Internet access

Percent of households with no Internet access

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) five-year estimates. Refer to the technical notes for more information about ACS.

Social Vulnerability Index

Percentile rank for household composition/disability, housing/transportation, minority status/language, and socioeconomic vulnerability

Data are provided by CDC/ATSDR's Geospatial Research, Analysis & Service Program and are developed using Census 2010 and American Community Survey (ACS) data. The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every census tract. The SVI ranks each Census tract on 15 social factors, including poverty, lack of vehicle access, and crowded housing. It then groups them into four related themes, as well as an overall ranking. Maps of the four themes are shown in the figure in the SVI fact sheet. For 2010, the household composition/disability percentile does not include disability because the 2010 Census data does not contain data on disabilities.

Overall percentile vulnerability rank

Data are provided by CDC/ATSDR's Geospatial Research, Analysis & Service Program and are developed using Census 2010 and American Community Survey (ACS) data. The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. The SVI ranks each tract on 15 social factors*, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes, as well as an overall ranking. Maps of the four themes are shown in the figure in the SVI fact sheet.

Socioeconomic status

Percent of population aged 16 years old and older who are unemployed

Data provided by the U.S. Census Bureau, American Factfinder, and American Community Survey (ACS) five-year estimates. Read more about the Census ACS methodology. The ACS uses the same employment status concepts as those used in Census 2000. The ACS data, however, are annual averages, whereas the census estimates relate to the period when the census was conducted — primarily from March to June 2000. The labor force questions changed in 2008 therefore use caution when making labor force data comparisons from 2008 or later with data from prior years. Additional information can be found on the Census website. Since employment data from the CPS and the ACS are obtained from respondents in households, they differ from statistics based on reports from individual businesses, farm enterprises, and certain government programs. Some discrepancies between different measures of employment may exist for people holding more than one job, private household workers, unpaid family workers, self-employed people, people less than16 years of age, and people who had a job but were not working at the time of survey. Furthermore, the employment status data in household survey tabulations include people based on place of residence regardless of where they work, whereas establishment data report people at their place of work regardless of where they live. This latter consideration is particularly significant when comparing data for workers who commute between areas and is likely to be more important the smaller the geographic area.

Percent of population aged 25 years of age and older with a high school diploma (or equivalent) or higher

Data provided by the U.S. Census Bureau, American Factfinder, and American Community Survey (ACS) five-year estimates. ACS is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. However, because ACS data are based on a sample, they are subject to sampling variability and include a range of uncertainty. Read more about the Census ACS methodology. For each race, ethnicity, and sex, the total number of people graduating high school or the equivalent were calculated by summing the following eight columns of highest education attained: high school graduate (includes equivalency), some college less than one year, some college one or more years, Associate degree, Bachelor's degree, Master's degree, Professional school degree, and Doctorate degree. Percentages were calculated by dividing this sum by the total population for the corresponding race, ethnicity, and sex.

Percent of population living in poverty

These data are collected from the U.S. Census Bureau, Small Area Income and Poverty estimates. Read more about the Census ACS methodology

Read frequently asked questions about the heart attack data

Heart attack hospitalizations

Number of hospitalizations for heart attack among persons 35 and over

These data include hospitalizations for heart attack and are collected from inpatient hospital discharge records. This measure includes hospitalizations with an ICD-9 code of 410.0-410.92 or an ICD-10 code of I21.0-I22.9 in the principal diagnosis field. Starting in 2015, transfers between hospitals were excluded. Data are suppressed for counties with fewer than six hospitalizations to protect confidentiality. However, counties with zero cases are not suppressed. Please note that counts are a statistically limited way to consider hospitalization data because they do not allow for accurate comparison between counties. Counties with higher populations, such as Milwaukee, will have more hospitalizations simply because they have more people. An age-adjusted rate is a better measure for true comparison between counties.

Crude rates of hospitalizations for heart attack among persons 35 and over per 10,000 people

These data include hospitalizations for heart attack and are collected from inpatient hospital discharge records. This measure includes hospitalizations with an ICD-9 code of 410.0-410.92 or an ICD-10 code of I21.0-I22.9 in the principal diagnosis field. Starting in 2015, transfers between hospitals were excluded. Data are suppressed for counties with fewer than six hospitalizations to protect confidentiality. However, counties with zero cases are not suppressed. The crude rate is the number of hospitalizations divided by the total number of people in the area of interest (for example, a county). This is expressed as a number per unit population, such as "per 10,000 population." A crude rate does not take into account the differences in age distributions across counties and are therefore subject to bias. Use age-adjusted rates for a better standardized measure.

Age-adjusted rates of hospitalization for heart attack among persons 35 and over per 10,000 people

These data include hospitalizations for heart attack and are collected from inpatient hospital discharge records. This measure includes hospitalizations with an ICD-9 code of 410.0-410.92 or an ICD-10 code of I21.0-I22.9 in the principal diagnosis field. Starting in 2015, transfers between hospitals were excluded. Data are suppressed for counties with fewer than six hospitalizations to protect confidentiality and improve rate stability. However, counties with zero cases are not suppressed. An age-adjusted rate is a rate that is statistically modified to eliminate the potential biasing effect of different age distributions across different populations. In other words, the age-adjusted rate accounts for the possibility that heart attacks are more frequent among older individuals and some counties have more older individuals than others. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Read frequently asked questions about the heat and heat-related illness data

Heat-related mortality

Number of heat-related deaths (counts)

These data include heat-related deaths collected from the Multiple Cause Mortality files from CDC’s National Center for Health Statistics. These data include deaths in which excessive heat exposure (ICD-10 code X30) or effects of heat and light (ICD-10 code T67) is reported as either the underlying or contributing cause of death. Deaths due to exposure to excessive heat of man-made origins (W92) are excluded. Only deaths that occurred in the summer months (May through September) are included in this measure. Data are suppressed when the number of deaths is less than 10 (including 0).

Heat-related emergency department visits

Age-adjusted rate of emergency department visits for heat stress per 100,000 population

These data include emergency department (ED) visits for heat stress and are collected from emergency room visit discharge records. ED visits resulting in subsequent hospitalization are also included. This measure includes ED visits with an ICD-9 code of 992.0-992.9 or an ICD-10 code of T67.0-T67.9, X30, or X32 in the principal or other diagnosis fields. ED visits due to exposure to excessive heat of man-made origin (ICD-9 code E900.1 or ICD-10 code W92) are excluded. Data for Wisconsin residents visiting an ED in Iowa and Minnesota are included. Counties with non-zero counts less than 6 and population less than 100,000 are suppressed for this measure.

An age-adjusted rate is statistically modified to eliminate the potential biasing effect of different age distributions across different populations. In other words, the age-adjusted rate accounts for the possibility that ED visits for heat stress may be more frequent among younger individuals and some counties have a higher population of younger individuals than others.

Direct age-adjustment is conducted using the 2000 U.S. standard population. Federally funded hospitals (for example, Veteran’s Administration [VA] hospitals, which are exempt from state reporting requirements) and are not included in these data. Data are based on the number of admissions, not the number of individuals admitted to the hospital.

Prior to 2018, the county variable in the emergency department data was assigned based on the zip code of the residence of the patient. In cases where a zip code spanned more than one county, the ED visit was placed into the county with the higher population. Beginning in 2018, geocoded data became available in Wisconsin ED visit data, which allows for a more accurate determination of county of residence. The new block group variable is the geocoded latitude/longitude of the patient’s residential address. The first five digits of the block group variable are used to determine the Wisconsin county. Due to this change, caution should be used when comparing county-level data prior to 2018 with data from 2018 onward.

Crude rate of emergency department visits for heat stress per 100,000 population

These data include emergency department visits for heat stress and are collected from emergency room visit discharge records. This measure includes emergency department visits with an ICD-9 code of 992.0-992.9, E900.9 or an ICD-10 code of T67.0-T67.9, X30, or X32 in the principal or other diagnosis fields. ED visits due to exposure to excessive heat of man-made origin (ICD-9 code E900.1 or ICD-10 code W92) are excluded. Data for Wisconsin residents hospitalized in Iowa and Minnesota are included. Counties with non-zero counts less than 6 and population less than 100,000 are suppressed for this measure. Federally funded hospitals (for example Veteran’s Administration [VA] hospitals, which are exempt from state reporting requirements) are not included in these data. Data are based on the number of admissions, not the number of individuals admitted to the hospital. The crude rate is the number of ED visits divided by the total number of people in that age category (for example, people aged 65+). This is expressed as a number per unit population such as “per 100,000 people”. The crude rate does not take into account the differences in age distributions across counties and are therefore subject to bias. Use age-adjusted rates for a better standardized measure.

Prior to 2018, the county variable in the emergency department data was assigned based on the zip code of the residence of the patient. In cases where a zip code spanned more than one county, the ED visit was placed into the county with the higher population. Beginning in 2018, geocoded data became available in Wisconsin ED visit data, which allows for a more accurate determination of county of residence. The new block group variable is the geocoded latitude/longitude of the patient’s residential address. The first five digits of the block group variable are used to determine the Wisconsin county. Due to this change, caution should be used when comparing county-level data prior to 2018 with data from 2018 onward.

Number of emergency department visits for heat stress

These data include emergency department visits for heat stress and are collected from emergency room visit discharge records. This measure includes emergency department visits with an ICD-9 code of 992.0-992.9, E900.0 or E900.9 or an ICD-10 code of T67.0-T67.9, X30, or X32 in the principal or other diagnosis fields. ED visits due to exposure to excessive heat of man-made origin (ICD-9 code for E900.1 or ICD-10 code W92) are excluded. Data for Wisconsin residents hospitalized in Iowa and Minnesota are included. Counties with non-zero counts less than 6 and population less than 100,000 are suppressed for this measure. Federally funded hospitals (for example Veteran’s Administration [VA] hospitals, which are exempt from state reporting requirements) are not included in these data. Data are based on the number of admissions, not the number of individuals admitted to the hospital. Please note that counts are a statistically limited way to consider ED visits because they do not allow for accurate comparison between counties. Counties with higher populations, such as Milwaukee, will have more emergency visits simply because they have more people. An age-adjusted rate is a better measure for true comparison between counties.

Prior to 2018, the county variable in the emergency department data was assigned based on the zip code of the residence of the patient. In cases where a zip code spanned more than one county, the ED visit was placed into the county with the higher population. Beginning in 2018, geocoded data became available in Wisconsin ED visit data, which allows for a more accurate determination of county of residence. The new block group variable is the geocoded latitude/longitude of the patient’s residential address. The first five digits of the block group variable are used to determine the Wisconsin county. Due to this change, caution should be used when comparing county-level data prior to 2018 with data from 2018 onward.

Heat-related hospitalizations

Age-adjusted rate of hospitalizations for heat stress per 100,000 population

These data include hospitalizations for heat stress and are collected from inpatient hospital discharge records. This measure includes hospitalizations with an ICD-9 code of 992.0-992.9, E900.0 or E900.9 or an ICD-10 code of T67.0-T67.9, X30, or X32 in the principal or other diagnosis fields. Hospitalizations due to exposure to excessive heat of man-made origin (ICD-9 code for E900.1 or ICD-10 code W92) are excluded. Data for Wisconsin residents hospitalized in Iowa and Minnesota are included. Counties with non-zero counts less than 6 and population less than 100,000 are suppressed for this measure. Federally, funded hospitals (for example Veteran’s Administration [VA] hospitals, which are exempt from state reporting requirements) are not included in these data. Data are based on the number of admissions, not the number of individuals admitted to the hospital. An age-adjusted is statistically modified to eliminate the potential biasing effect of different age distributions across different populations. In other words, the age-adjusted rates accounts for the possibility that hospitalizations for heat stress may be more frequent among younger individuals and some counties have a higher population of younger individuals than others. Direct age-adjustment is conducted using the 2000 US standard population.

Prior to 2018, the county variable in the hospitalization data was assigned based on the zip code of the residence of the patient. In cases where a zip code spanned more than one county, the hospitalization was placed into the county with the higher population. Beginning in 2018, geocoded data became available in Wisconsin hospitalization data, which allows for a more accurate determination of county of residence. The new block group variable is the geocoded latitude/longitude of the patient’s residential address. The first five digits of the block group variable are used to determine the Wisconsin county. Due to this change, caution should be used when comparing county-level data prior to 2018 with data from 2018 onward.

Crude rate of hospitalizations for heat stress per 100,000 population

These data include hospitalizations for heat stress and are collected from inpatient hospital discharge records. This measure includes hospitalizations with an ICD-9 code of 992.0-992.9, E900.0 or E900.9 or an ICD-10 code of T67.0-T67.9, X30, or X32 in the principal or other diagnosis fields. Hospitalizations due to exposure to excessive heat of man-made origin (ICD-9 code for E900.1 or ICD-10 code W92) are excluded. Data for Wisconsin residents hospitalized in Iowa and Minnesota are included. Counties with non-zero counts less than 6 and population less than 100,000 are suppressed for this measure. Federally funded hospitals (for example Veteran’s Administration [VA] hospitals, which are exempt from state reporting requirements) are not included in these data. Data are based on the number of admissions, not the number of individuals admitted to the hospital. The crude rate is the number of hospitalizations divided by the total number of people in the area of interest (for example, a county). This is expressed as a number per unit population, such as “per 100,000 people”. The crude rate does not take into account the differences in age distributions across counties and are therefore subject to bias. Use age-adjusted rates for a better standardized measure.

Prior to 2018, the county variable in the hospitalization data was assigned based on the zip code of the residence of the patient. In cases where a zip code spanned more than one county, the hospitalization was placed into the county with the higher population. Beginning in 2018, geocoded data became available in Wisconsin hospitalization data, which allows for a more accurate determination of county of residence. The new block group variable is the geocoded latitude/longitude of the patient’s residential address. The first five digits of the block group variable are used to determine the Wisconsin county. Due to this change, caution should be used when comparing county-level data prior to 2018 with data from 2018 onward.

Number of hospitalizations for heat stress

These data include hospitalizations for heat stress and are collected from inpatient hospital discharge records. This measure includes hospitalizations with an ICD-9 code of 992.0-992.9, E900.0 or E900.9 or an ICD-10 code of T67.0-T67.9, X30, or X32 in the principal or other diagnosis fields. Hospitalizations due to exposure to excessive heat of man-made origin (ICD-9 code for E900.1 or ICD-10 code W92) are excluded. Counties with non-zero counts less than 6 and population less than 100,000 are suppressed for this measure. Federally funded hospitals (for example Veteran’s Administration [VA] hospitals, which are exempt from state reporting requirements) are not included in these data. Data are based on the number of admissions, not the number of individuals admitted to the hospital. Please note that counts are a statistically limited way to consider hospitalizations because they do not allow for accurate comparison between counties. Counties with higher populations, such as Milwaukee, will have more hospitalizations simply because they have more people. An age-adjusted rate is a better measure for true comparison between counties.

Prior to 2018, the county variable in the hospitalization data was assigned based on the zip code of the residence of the patient. In cases where a zip code spanned more than one county, the hospitalization was placed into the county with the higher population. Beginning in 2018, geocoded data became available in Wisconsin hospitalization data, which allows for a more accurate determination of county of residence. The new block group variable is the geocoded latitude/longitude of the patient’s residential address. The first five digits of the block group variable are used to determine the Wisconsin county. Due to this change, caution should be used when comparing county-level data prior to 2018 with data from 2018 onward.

Historical temperature and Heat Index

Number of extreme heat days

The purpose of this dataset is to keep record of past extreme days.

The CONUS Downscaled Climate Projections calculated temperature and precipitation projections using outputs from 16 global change models at 1/8th degree resolution to generate a single comprehensive dataset. Grid-level data were converted into county-level estimates by processing modeled data. The measure involves calculation of a daily maximum heat index, which takes into account relative humidity and temperature. The measure uses a threshold of 90 degrees Fahrenheit to constitute an extreme heat day. Data were collected only for summer months (May through September). Measures for daily maximum temperature and heat index are available in relative thresholds (90th, 95th, 98th, 99th percentiles) and absolute thresholds (90 ̊F, 95 ̊F, 100 ̊F, 105 ̊F).

Temperature and heat projections

Projected difference in extreme heat days as compared to the historical period

These estimates represent the difference in extreme heat days between the time period selected (2016-2045, 2036-2065, 2070-2099) and the referent period (1976-2005). These measures are derived using Localized Constructed Analogs (LOCA): a statistical downscaling technique that uses historical data derived from CMIP5 simulations to predict future climate scenarios. The county-level temperature estimates, which are available at a 1/16th-degree resolution, are obtained by using LOCA to downscale data from 32 global climate models from 1950-2100 with a historical period from 1950-2005. The resulting models are used to estimate two future scenarios (RCP 4.5 and RCP 8.5) over the period 2006-2100. The process of converting grid-level data to county-level estimates uses a population-weighted centroid approach which may lead to the misclassification of temperature for some areas.

The modeling tools are used to make a suite of future climate changes that illustrate the possibilities that may lie ahead. The scenarios include time series of emissions and concentrations of the full suite of greenhouse gases (GHGs), aerosols, and chemically-active gases, as well as land use. The representative concentration pathway (RCP) is used to represent two of many possible scenarios – in this case, the data on the portal is available for RCP 8.5 to represent high emissions and RCP 4.5 to represent low emissions.

Projected difference was determined by either absolute (i.e., 90 ̊F, 95 ̊F, 100 ̊F, 105 ̊F) or relative (i.e., 99th) thresholds. These thresholds are displayed for both RCP scenarios.

Projected difference in extreme heat nights as compared to the historical period

These estimates represent the difference in extreme heat nights between the time period selected (2016-2045, 2036-2065, 2070-2099) and the referent period (1976-2005). These measures are derived using Localized Constructed Analogs (LOCA): a statistical downscaling technique that uses historical data derived from CMIP5 simulations to predict future climate scenarios. The county-level temperature estimates, which are available at a 1/16th-degree resolution, are obtained by using LOCA to downscale data from 32 global climate models from 1950-2100 with a historical period from 1950-2005. The resulting models are used to estimate two future scenarios (RCP 4.5 and RCP 8.5) over the period 2006-2100. The process of converting grid-level data to county-level estimates uses a population-weighted centroid approach which may lead to the misclassification of temperature for some areas.

The modeling tools are used to make a suite of future climate changes that illustrates the possibilities that may lie ahead. The scenarios include time series of emissions and concentrations of the full suite of greenhouse gases (GHGs), aerosols, and chemically-active gases, as well as land use. The representative concentration pathway (RCP) is used to represent two of many possible scenarios – in this case, the data on the portal is available for RCP 8.5 to represent high emissions and RCP 4.5 to represent low emissions.

Projected difference was determined by either absolute (i.e., 75 ̊F, 80 ̊F, 85 ̊F, 90 ̊F) or relative (i.e., 99th) thresholds. These thresholds are displayed for both RCP scenarios.

Vulnerability and preparedness: heat

Age-adjusted estimates of the percent of adults >= 20 years diagnosed with diabetes

These data are provided by the CDC’s National Diabetes Surveillance System. Prevalence rates by county for adults 20 years and older were estimated using data from CDC’s Behavioral Risk Factor Surveillance Systems and age-adjusted using data from U.S. Census Bureau’s population estimates.

Age-adjusted rate of hospitalizations for heart attack among persons 35 and over per 10,000 population

These data include hospitalizations for heart attack and are collected from inpatient hospital discharge records. This measure includes hospitalizations with an ICD-9 code of 410.0-410.92, prior to October 2015 or an IDC-10 code of 121.0-122.9 from October 2015 and on. Starting in 2015, transfers between hospitals were excluded. Data for Wisconsin residents hospitalized in Iowa and Minnesota are included. Counties with non-zero counts less than 6 and population less than 100,000 are suppressed for this measure. Federally funded hospitals (for example Veteran’s Administration [VA] hospitals, which are exempt from state reporting requirements) are not included in these data. Data are based on the number of admissions, not the number of individuals admitted to the hospital. An age-adjusted rate is a rate that is statistically modified to eliminate the potential biasing effect of different age distributions across different populations. In other words, the age-adjusted rate accounts for the possibility that heart attacks are more frequent among older individuals and some counties have a higher count of older individuals than other. Direct age-adjustment is conducted using the 2000 U.S. standard population.

Median household income

Data are provided by the U.S. Census Bureau, Small Area Income and Poverty Estimates.

Number of hospital beds

Data are obtained from the 2016 American Hospital Association Annual Survey. Facility-level insights are not available for hospitals that did not complete the survey.

Number of hospital beds per 10,000 population

Data are obtained from the 2016 American Hospital Association Annual Survey. Facility-level insights are not available for hospitals that did not complete the survey. The rate of hospital beds per 10,000 was calculated using the total population per county as provided by the U.S. Census Bureau.

Number of hospitals

Data are obtained from the 2016 American Hospital Association Annual Survey. Facility-level insights are not available for hospitals that did not complete the survey.

Number of hospitals per 100,000 population

Data are obtained from the 2016 American Hospital Association Annual Survey. Facility-level insights are not available for hospitals that did not complete the survey. The rate of hospital beds per 10,000 people was calculated using the total population per county as provided by the U.S. Census Bureau.

Number of people living in poverty

Data are provided by the U.S. Census Bureau, Small Area Income and Poverty Estimates. Data are also available for populations aged 0-17 in addition to the rest of the population.

Number of people without health insurance

Data are provided by the U.S. Census Bureau, Small Area Health Insurance Estimates. Data are also available for populations aged 0-18.

Percent of land covered by forest

Data are collected from National Land Cover Database provided by the U.S. Department of the Interior, U.S. Geological Survey. Percentages for each county were estimated as a proportion of gridded data available from the Multi-Resolution Land Characteristics Consortium.

Forest classification is defined in three categories; deciduous forest, evergreen forest, and mixed forest. A deciduous forest is defined as having 75% of trees shedding foliage in the fall. An evergreen forest has 75% of trees maintain their foliage for the full year. In a mixed forest, neither deciduous or evergreen trees make up 75% of tree species.

Percent of land used for development

Data are collected from National Land Cover Database provided by the U.S. Department of the Interior, Multi-Resolution Land Characteristics Consortium. Percentages for each county were estimated as a proportion of gridded data available from the Multi-Resolution Land Characteristics Consortium. Developed Land Use is the combined data from Developed-Open Space, Developed Low-Intensity, Developed-Medium Intensity, and Developed-High Intensity in gridded data from National Land Cover Database.

Developed open spaces are defined as areas with some constructed materials but mostly vegetation (e.g. golf courses).

Percent of population aged 65 years and over living alone in a non-family household

Data are provided by the U.S. Census Bureau, American Factfinder, American Community Survey 5-year estimates. Data are displayed by aggregating census tracts to a minimum of 5,000 people or 200,000 people.

Percent of population living in poverty

Data are provided by the U.S. Census Bureau, Small Area Income and Poverty Estimates. Data are available for those populations aged 0-17.

Percent of population of a race other than white

Data are collected from Census 2000 Summary File 3 created by the U.S. Census Bureau.

Percent of population without health insurance

Data are provided by the U.S. Census Bureau, Small Area Health Insurance Estimates. This data does not include those over 65 years covered by Medicare. Data are available for populations aged 0-18 in addition to the rest of the population.

Read frequently asked questions for the immunizations data

Percent immunized by vaccine

The data source is the Wisconsin Immunization Registry (WIR). The WIR is a confidential database that collects immunization histories for Wisconsin residents. WIR collects the date and type of each vaccine a person receives. WIR receives data from local health departments, public and private health care providers, health maintenance organizations, pharmacies, tribal health centers/clinics, and schools. Reporting to WIR is voluntary except for pharmacists and pharmacies who vaccinate children and for Vaccines for Children (VFC) providers. Percent immunized is calculated by dividing the number of immunized individuals (numerator) by the population in the selected age group and geographic area (denominator). For the non-influenza vaccines, the population (i.e., denominator) is obtained from the WIR and is equal to the total number of individuals with a WIR client record for the selected age group and geographic area. For influenza vaccine, the numerators were obtained from the WIR and the denominators were obtained from population estimates from the Wisconsin Interactive Statistics on Health (WISH).

Note: All measures are based on the routine vaccination recommendations from the Centers for Disease Control and Prevention's (CDC) Advisory Committee on Immunization Practices (ACIP). The details below describe the measures on the Wisconsin Environmental Public Health Tracking Portal and should not be used as descriptions of the ACIP vaccination recommendations. For example, measuring the percentage of children who have received recommended vaccines by age 24 months is a common benchmark for evaluating the vaccination status of young children, but does not reflect or evaluate the exact ages at which each vaccine dose is recommended. For descriptions and details of the ACIP vaccination recommendations see the Child Vaccine Schedule and the Adult Vaccine Schedule.

4:3:1:3:3:1:4 - Series of 7 vaccines

The 4:3:1:3:3:1:4 vaccination series is a series of seven vaccines recommended for children and includes: four or more doses of diphtheria-tetanus-acellular-pertussis (DTaP) vaccine, three or more doses of polio vaccine, one or more doses of measles, mumps, and rubella (MMR) vaccine, three or more doses of Haemophilus influenzae type b (Hib) vaccine, three or more doses of hepatitis B (HepB) vaccine, one or more doses of varicella vaccine, and four or more doses of Pneumococcal conjugate vaccine (PCV) vaccine. Percent immunized is calculated by dividing the number of children who turned 24 months and completed this vaccine series by the total number of children who turned age 24 months (by year and geographic area). Thus, the 2016 data include information on children born during 2014. (Note: Children who completed the series of seven vaccines are also found in the counts of the individual vaccine measures. For example, a child who completed the series of seven vaccines will also be counted as having completed DTaP, Polio, MMR, Hib, HepB, Varicella, and PCV vaccine measures. However, a child who received three doses of the polio vaccine and no other vaccines will be included in the Polio vaccine measure (below) but not in 4:3:1:3:3:1:4 series measure.)

DTaP vaccine (4+ doses)

Diphtheria-tetanus-acellular-pertussis (DTaP) vaccine provides protection from the diseases diphtheria, tetanus and pertussis. DTaP is for use in children. By age 24 months, children are recommended to have received four doses of DTaP vaccine. Percent immunized is calculated by dividing the number of children who turned age 24 months and had received four or more doses of DTaP by the total number of same-aged children in that year and geographic area.

Hepatitis A vaccine (1+ dose)

Hepatitis A vaccine provides protection from hepatitis A. Children are recommended to receive the first dose of the hepatitis vaccine at age 1 and the second dose 6-18 months later. Percent immunized is calculated by dividing the number of children who turned age 24 months and received 1 or more doses of the hepatitis A vaccine by the total number of same-aged children in that year and geographic area.

Hepatitis A vaccine (up to date)

Hepatitis A vaccine provides protection from hepatitis A. Two doses of the hepatitis A vaccine are required for long-term protection and should be given after age 1. Percent immunized is calculated by dividing the number of adults who turned 18-49 years and are up-to-date on the hepatitis A vaccine by the total number of same-aged adults in that year and geographic area.

Hepatitis B vaccine (3+ doses)

Hepatitis B vaccine provides protection from hepatitis B. By age 24 months, children are recommended to have received 3 doses of hepatitis B vaccine. Percent immunized is calculated by dividing the number of children who turned age 24 months and had received three doses of the hepatitis B vaccine by the total number of same-aged children in that year and geographic area.

Hib vaccine (3+ doses)

Haemophilus influenzae type b (Hib) vaccine protects against disease caused by Hib infection. By age 24 months, children are recommended to have received three or four doses of Hib vaccine, depending on the Hib vaccine brand received. Percent immunized is calculated by dividing the number of children who turned age 24 months and had received three or more doses of Hib vaccine by the total number of same-aged children in that year and geographic area.

HPV vaccine (1+ doses)

Human papillomavirus (HPV) vaccine protects against disease caused by HPV, including HPV-related cancers. Before 2017, three doses of HPV vaccine were recommended to be considered completely vaccinated for HPV. To assess the initiation of HPV vaccination, this measure assesses the percent of individuals who received one or more doses of HPV vaccine. Percent immunized is calculated by dividing the number of children who turned age 13-18 years and had received one or more doses of HPV vaccine by the total number of same-aged adolescents in that year and geographic area.

HPV vaccine (3+ doses)

Human papillomavirus (HPV) vaccine protects against disease caused by HPV, including HPV-related cancers. Before 2017, three doses of HPV vaccine were recommended to be considered completely vaccinated for HPV. To assess the completion of HPV vaccination, this measure assesses the percent of individuals who received three or more doses of HPV vaccine. Percent immunized is calculated by dividing the number of children who turned age 13-18 years or 19-26 years and had received three or more doses of HPV vaccine by the total number of same-aged adolescents in that year and geographic area.

Influenza vaccine (1+ doses)

Influenza vaccine protects against influenza disease, commonly called the flu. Every influenza season (approximately August through June) all individuals aged 6 months and older are recommended to receive influenza vaccine. Percent immunized is calculated by dividing the number of people who received one or more doses of influenza vaccine during the selected influenza season by the total number of same-aged people in the selected season, year, and geographic region.

MenACWY (Meningitis) vaccine (1+ doses)

Meningococcal conjugate vaccine ACWY (MenACWY) protects against disease caused by bacteria Neisseria meningitides serogroups A, C, W, and Y. Adolescents are recommended to receive MenACWY vaccine at age 11-12 and a booster dose at age 16 years. However, if the first dose is received at age 16 or older, no booster dose is needed. To assess the initiation of MenACWY vaccination, this measure assesses the percent of individuals who received one or more doses of the MenACWY vaccine. Percent immunized is calculated by dividing the number of individuals aged 13-18 years who received 1 or more doses of the MenACWY vaccine by the total number of same-aged individuals in the selected year and geographic area.

MenACWY (Meningitis) vaccine (up-to-date)

Meningococcal conjugate vaccine ACWY (MenACWY) protects against disease caused by bacteria Neisseria meningitides serogroups A, C, W, and Y. Adolescents are recommended to receive MenACWY vaccine at age 11-12 and a booster dose at age 16 years. However, if the first dose is received at age 16 or older, no booster dose is needed. To assess the completion of MenACWY vaccination, this measure assesses the number of individuals who were up-to-date (had received a booster dose or a first dose at age 16 years or older) with MenACWY vaccine. Percent immunized is calculated by dividing the number of individuals aged 17-18 years who were up-to-date with the MenACWY vaccine by the total number of same-aged individuals in the selected year and geographic area.

MMR vaccine (1+ doses)

Measles, mumps, and rubella (MMR) vaccine protects against the diseases measles, mumps, and rubella. By age 24 months, children are recommended to have received one dose of MMR vaccine. Percent immunized is calculated by dividing the number of children who turned age 24 months and had received one or more doses of MMR vaccine by the total number of same-aged children in that year and geographic area.

PCV vaccine (4+ doses)

Pneumococcal conjugate vaccine (PCV) protects against diseases caused by Streptococcus pneumoniae bacteria. Before 2010, the only PCV vaccine that was available was PCV7, which protects against 7 serotypes of S. pneumoniae. In 2010, PCV13 vaccine was introduced and protects against 13 serotypes S. pneumoniae. Children assessed during the earliest years displayed for this measure might have received either PCV7 or PCV13 or both. By age 24 months, children are recommended to have received 4 doses of PCV vaccine. Percent immunized is calculated by dividing the number of children who turned age 24 months and had received 4 or more doses of PCV vaccine by the total number of same-aged children in that year and geographic area.

PCV13 vaccine (1+ doses)

Pneumococcal conjugate vaccine (PCV13) protects against disease caused by 13 serotypes of Streptococcus pneumoniae. One dose of PCV13 vaccine is recommended for all adults aged 65 years and older. Percent immunized is calculated by dividing the number of adults who received one or more doses of the PCV13 vaccine on or after their 65th birthday by the total number of same-aged adults in that year and geographic area.

PPSV23 vaccine (1+ doses)

Pneumococcal polysaccharide vaccine (PPSV23) protects against disease caused by 23 serotypes of Streptococcus pneumoniae bacteria. One dose of PPSV23 vaccine is routinely recommended for adults aged 65 and older. Percent immunized is calculated by dividing the number of adults who received one or more doses of the PPSV23 vaccine on or after their 65th birthday by the total number of same-aged adults in that year and geographic area.

Polio vaccine (3+ doses)

Polio vaccine protects against polio. By age 24 months, children are recommended to have received 3 doses of polio vaccine. Percent immunized is calculated by dividing the number of children who turned age 24 months and had received three or more doses of polio vaccine by the total number of same-aged children in that year and geographic area.

Tdap vaccine (1+ doses)

Tetanus-diphtheria-acellular-pertussis (Tdap) vaccine provides protection from the diseases diphtheria, tetanus and pertussis. Tdap is for use in adolescents and adults: one dose is recommended during a lifetime (except for pregnant women who are recommended to receive Tdap during every pregnancy). Percent immunized is calculated by dividing the number of individuals who turned age 13-18 years or age 19-64 years who had received one or more doses of Tdap by the total number of same-aged individuals in that year and geographic area.

Varicella vaccine (1+ doses)

Varicella vaccine protects against chickenpox disease. By age 24 months, children are recommended to have received one dose of varicella vaccine. Percent immunized is calculated by dividing the number of children who turned age 24 months and who received one or more doses of varicella vaccine by the total number of same-aged children in that year and geographic area.

Zoster complete

Zoster vaccine protects against herpes zoster (shingles) disease. Due to a change in zoster vaccine recommendations which occurred at the end of 2017, Zoster Complete data begins in 2018. Currently, adults aged 50 years and older are recommended to receive two doses of Shingrix zoster vaccine, regardless of prior zoster vaccine history. Zoster Complete percent immunized is calculated by dividing the number of adults aged 50 years or older who received both doses of zoster vaccine by the total number of same-aged adults in that year and geographic area.

Read frequently asked questions about adult and childhood lead poisoning data

Adult lead poisoning

Number of adults with blood lead levels at or over 5, 10, 25 and 40 μg/dL

Wisconsin blood lead testing data from adults (age 16 and over) are reported to the Wisconsin Adult Lead Program. Lead poisoning is defined as an adult with a venous blood lead level (BLL) greater than or equal to 5 micrograms per deciliter (μg/dL). Only venous blood lead analysis is used in adult blood lead surveillance. Data are de-duplicated within a given year such that they contain the highest venous test. Meaning if an adult had five tests in 2015, for example, only the test with the highest BLL would be counted and reflected in the portal data.

All unique adults with elevated blood lead levels are reported in this measure.

To protect confidentiality, data are suppressed for counties with fewer than five adults with elevated blood lead levels.

Rate of adults with blood lead levels at or over 5, 10, 25 and 40 μg/dL per 100,000 employed

Wisconsin blood lead testing data from adults (age 16 and over) are reported to the Wisconsin Adult Lead Program. Lead poisoning is defined as an adult with a venous blood lead level (BLL) greater than or equal to 5 micrograms per deciliter (μg/dL). Only venous blood lead analysis is used in adult blood lead surveillance. Data are de-duplicated within a given year such that they contain the highest venous test. Meaning if an adult had five tests in 2015, for example, only the test with the highest BLL would be counted and reflected in the portal data.

All unique adults with elevated blood lead levels are reported in this measure. Since the majority of lead poisoning in adults is work-related, rates are calculated as the number of adults at each of the blood lead levels divided by the total number of employed adults in Wisconsin or the specified county and multiplied by 100,000. The total number of adults (numerator) at each of the blood lead levels is the total count of adults at each blood lead levels that is reported to the Wisconsin Adult Lead Program for the reporting year. For example, the numerator for adults with a BLL of 5 μg/dL in 2021 is the total number of adults with a blood test at this level during this year. The total number of employed adults (denominator) was obtained from the Bureau of Labor Statistics.

To protect confidentiality, data are suppressed for counties with fewer than five adults with elevated blood lead levels.

Number of newly recorded adults with blood lead levels at or over 5, 10, 25 and 40 μg/dL

Wisconsin blood lead testing data from adults (age 16 and over) are reported to the Wisconsin Adult Lead Program. Lead poisoning is defined as an adult with a venous blood lead level (BLL) greater than or equal to 5 micrograms per deciliter (μg/dL). Only venous blood lead analysis is used in adult blood lead surveillance. Data are de-duplicated within a given year such that they contain the highest venous test. Meaning if an adult had five tests in 2015, for example, only the test with the highest BLL would be counted and reflected in the portal data.

Only adults with newly recorded elevated blood lead levels are reported in this measure. An adult is considered newly recorded if they have not had any tests above or equal to 5 μg/dL in the previous year. For example, if a person had a BLL above 5 μg/dL in 2015 and another in 2016, only the 2015 test would be counted and reflected in this measure. However, if the same person had no records of an elevated blood lead level in 2017, but had an elevated blood lead level in 2018, they would be counted as newly recorded in 2018.

To protect confidentiality, data are suppressed for counties with fewer than five adults with newly recorded elevated blood lead levels.

Rate of newly recorded adults with blood lead levels at or over 5, 10, 25 and 40 μg/dL per 100,000 employed

Wisconsin blood lead testing data from adults (age 16 and over) are reported to the Wisconsin Adult Lead Program. Lead poisoning is defined as an adult with a venous blood lead level (BLL) greater than or equal to 5 micrograms per deciliter (μg/dL). Only venous blood lead analysis is used in adult blood lead surveillance. Data are de-duplicated within a given year such that they contain the highest venous test. Meaning if an adult had five tests in 2015, for example, only the test with the highest BLL would be counted and reflected in the portal data.

Only adults with newly recorded elevated blood lead levels are reported in this measure. An adult is considered newly recorded if they have not had any tests above or equal to 5 μg/dL in the previous year. For example, if a person had a BLL above 5 μg/dL in 2015 and another in 2016, only the 2015 test would be counted and reflected in this measure. However, if the same person had no records of an elevated blood lead level in 2017, but had an elevated blood lead level in 2018, they would be counted as newly recorded in 2018. Since the majority of lead poisoning in adults is work related, the rate for each blood lead level is calculated as the number of newly recorded adults divided by the total number of employed adults and multiplied by 100,000. The total number of adults (numerator) at each of the blood lead levels is the total count of adults at each blood lead levels that is reported to the Wisconsin Adult Lead Program for the reporting year. For example, the numerator for adults with a BLL of 5 μg/dL in 2021 is the total number of adults with a blood test at this level during this year. The total number of employed adults (denominator) was obtained from the Bureau of Labor Statistics.

To protect confidentiality, data are suppressed for counties with fewer than five adults with newly recorded elevated blood lead levels.

Childhood lead poisoning

Number of children tested positive for childhood lead poisoning

Wisconsin blood lead testing data from children less than six years of age are reported to the Wisconsin Childhood Lead Poisoning Prevention Program. Lead poisoning is defined as a child with a capillary or venous blood lead level (BLL) greater than or equal to 5 micrograms per deciliter (μg/dL). Data are de-duplicated within a given year such that they contain the most recent confirmatory (venous) test following an elevated screening (capillary) test. If no confirmatory test for the individual is available, the most recent screening test result is used. If a child had five tests in 2005, for example, only one test would be counted and reflected in the portal data. However, a child could appear in multiple time periods. For example, if I child was tested in 2005 at age three, the child could be retested in 2006 at age four. Both tests would be counted and reflected in the portal. The code "NPT" is used to indicate a poisoning case where the child was not previously tested and "PT" is used to indicate poisoning cases where the child was previously tested. The number of children poisoned is a count value and is not the best manner to use for comparisons between counties or regions such as census tract. Some areas of the state will have higher numbers of poisonings simply because there are more people there. The percent of children poisoned is a better measure for comparison between geographic areas. To protect confidentiality, data are suppressed for counties with fewer than five children tested. However, those data are not suppressed if 100 or more children are tested. When viewing data downloaded from the portal, note that suppression is indicated by a value of -5. The Census tract refers to the child’s home address, not the place where the child was tested.

Number of children tested for childhood lead poisoning

Wisconsin blood lead testing data from children less than six years of age are reported to the Wisconsin Childhood Lead Poisoning Prevention Program. This measure is a count of all children tested. Children may be tested using a capillary or venous BLL with preference given to the latter when available. Children who received multiple tests are only counted once per year. To protect confidentiality, data are suppressed for census tracts or counties if fewer than five children are poisoned. However, those data are not suppressed if 100 or more children are tested. When viewing data downloaded from the portal, note that suppression is indicated by a value of -5.

Percent of children with childhood lead poisoning (among those tested)

Wisconsin blood lead testing data from children less than six years of age are reported to the Wisconsin Childhood Lead Poisoning Prevention Program. Lead poisoning is defined as a child with a capillary or venous BLL greater than or equal to 5 micrograms per deciliter (μg/dL). Data are de-duplicated such that they contain the most recent confirmatory (venous) test following an elevated screening (capillary) test. If no confirmatory test for the individual is available, the most recent screening test result is used. The code "NPT" is used to indicate a poisoning case where the child was not previously tested and "PT" is used to indicate poisoning cases where the child was previously tested. The percent of children poisoned is calculated as the number of children poisoned divided by the number of children tested. This measure is the most accurate one to use for comparisons between geographic areas of the state as it accounts, to a large degree, for differences in population size between regions. Please note that the overall statewide age-specific percentage of poisoned children provided for reference may be greater than the sum of the percentages of poisoned children across all counties due to a small subset of blood lead test results missing county of residence information. Similarly, the percentages of poisoned children across all counties, may be greater than the percentages of poisoned children across all census tracts due to a small subset of blood lead test results missing census tract residence information. To protect confidentiality, data are suppressed for census tracts or counties if fewer than five children are poisoned. However, those data are not suppressed if 100 or more children are tested. When viewing data downloaded from the portal, note that suppression is indicated by a value of -5.

Read frequently asked questions about the Lyme disease data

Cases

Total count of Lyme disease cases

These data are obtained from the Wisconsin Electronic Disease Surveillance System (WEDSS). WEDSS is a secure, web-based system used by public health staff, infection control practitioners, clinical laboratories, clinics, and other disease reporters to report communicable diseases. "Month" indicates the month of illness onset. County-level data are based on the county of residence of the case; some infections may have been acquired during travel to other areas. The entire state of Wisconsin is considered to be endemic for Lyme disease; thus, any Wisconsin resident is considered to be "exposed." "Confirmed cases" of Lyme disease include:

1. Those with an erythema migrans (EM) rash that is greater or equal to 5 cm in diameter and diagnosed by a medical professional OR
2. Those with at least one non-EM confirmatory sign or symptom indicating late manifestation of disease (arthritis, Bell's palsy or other cranial neuritis, encephalomyelitis, lymphocytic meningitis, radiculoneuropathy, or 2nd or 3rd degree atrioventricular block) that also has laboratory evidence of infection that meets criteria.

Non-confirmatory signs and symptoms include fever, sweats, chills, fatigue, neck pain, arthralgias, myalgias, fibromyalgia syndromes, cognitive impairment, headache, paresthesias, visual/auditory impairment, peripheral neuropathy, encephalopathy, palpitations, bradycardia, bundle branch block, myocarditis, or other rash. In 2008, the national surveillance case definition for Lyme disease introduced probable cases. Probable cases are any other physician-diagnosed Lyme disease cases with laboratory evidence of infection and non-confirmatory signs and symptoms. In 2012, to address the increased burden of Lyme disease reporting on local health departments and medical care providers, criteria in Wisconsin for reporting Lyme disease were revised so that only cases with an EM rash required public health follow up. Public health follow up involves a complete case investigation to collect information on other clinical signs and symptoms, possible exposures, and treatment. As an alternative to follow-up for every suspect Lyme disease case, a statistical method was implemented to estimate statewide cases based on the number of total laboratory reports for each year since 2012. The total state case count measure since 2012 is the sum of confirmed, probable, and estimated cases. However, the total state case count before 2012 and all total county case counts do not include estimated cases. For the total state case count, the data include only confirmed cases from 1991-2007; confirmed and probable cases from 2008-2011; and confirmed, probable, and estimated cases from 2012 to present. Note: In 2017, additional categories were introduced to allow for non-binary gender breakdowns of Lyme disease cases. Due to data limitations, it is likely that there is an underrepresentation of individuals in the non-binary gender categories.

Incidence

Crude rates of confirmed cases per 100,000 population

These data are obtained from the Wisconsin Electronic Disease Surveillance System (WEDSS). WEDSS is a secure, web-based system used by public health staff, infection control practitioners, clinical laboratories, clinics, and other disease reporters to report communicable diseases. County-level data are based on the county of residence of the case; some infections may have been acquired during travel to other areas. The national surveillance case definition was revised in 2008 to include probable cases. In years prior to 2008, the incidence rate was calculated using confirmed cases as the numerator. Incidence rates after 2008 were calculated using confirmed and probable cases as the numerator. The incidence, or crude rate, is then calculated by dividing the numerator by the total number of people in the population of interest (for example, a county). Population estimates are derived from the U.S. Census. This is expressed as a number per unit population such as "per 100,000 population." Note: a crude rate does not take into account the differences in age distributions across counties and are therefore subject to bias. For example, as Lyme disease is less common among working age individuals, areas of the state with more working age individuals could appear, artificially, to have fewer cases generally. For more information on age-adjustment and biases see our glossary of terms.

Read frequently asked questions about the oral health data

Dental licensure

Total population per dentist

These data come from the Oral Health Program in the Wisconsin Department of Health Services. This measure is a representation of the number of licensed dentists in a geographic area. The measure is presented as a ratio of the population to the number of licensed dentists within the same geography. Within the respective geographies, the 2010 Wisconsin census population was used as the numerator and the count of licensed dentists in the queried year was used as the denominator.

Total population per dental hygienist

These data come from the Oral Health Program in the Wisconsin Department of Health Services. This measure is a representation of the number of licensed dental hygienists in a geographic area. The measure is presented as a ratio of the population to the number of licensed dental hygienists within the same geography. Within the respective geographies, the 2010 Wisconsin census population was used as the numerator, and the count of licensed dental hygienists in the queried year was used as the denominator.

Emergency department visits (non-traumatic)

Counts

These data are collected from emergency room visit records. Patient visits with an ICD-9 code of a primary dental diagnosis that was considered preventable and non-traumatic are collected for this measure. This measure includes cases with an ICD-9 code of 520.6, 521.00-521.09, 521.9, 522.0, 522.1, 522.4-522.7, 522.9, 523.00, 523.01, 523.10, 523.11, 523.3-523.6, 523.9, 525.8, 525.9, 528.3, and 528.9. It also includes cases with an ICD-10 code of K00.6, K01.0, K01.1, K02.3, K02.51, K02.61-K02.63, K02.7, K02.9, K03.6, K03.89, K03.9-K04.1, K04.4-K04.7, K04.90, K04.99, K05.00, K05.01, K05.10, K05.11, K05.20-K05.22, K05.30-K05.32, K05.4, K05.6, K08.8, K08.81, K08.82, K08.89, K08.9, K12.2, K13.70, and K13.79. Effective October 1, 2018, the ICD-10 code of K08.8 was deleted and has been replaced by K08.81, K08.82, or K08.89. Please keep this in mind when interpreting the data. Patients with the diagnosis of disturbances in tooth eruption (ICD-9 code 520.6 and ICD-10 codes K00.6, K01.0, and K01.1) were only included if they fell in the 15 to 30 year age range. This permitted those that may seek emergency care due to impacted third molars to be included. Patients who concomitantly had an ICD-9 code or an E-code in the 800 to 900 range or an ICD-10 code or an E-code in the S00-S99 or T07-T88 range associated with their dental diagnosis were excluded. An E-code and ICD-9 code in the 800 to 900 range and an E-code and ICD-10 code in the S00-S99 and T07-T88 range denotes that the patient's diagnosis was associated with an unintentional or intentional injury or poisoning, and since the target population should include only those with non-traumatic dental diagnoses, this group was excluded. Data for counties with fewer than ten visits are suppressed to protect confidentiality. However, counties with zero cases are not suppressed. Please note that counts are a statistically-limited way to consider emergency department visits because they do not allow for accurate comparison between counties. Counties with higher populations, such as Milwaukee, will have more emergency department visits simply because they have more people. An age-adjusted rate is a better measure for true comparison between counties.

Crude rate (per 10,000 population)

These data are collected from emergency room visit records. Patient visits with an ICD-9 code of a primary dental diagnosis that was considered preventable and non-traumatic are collected for this measure. This measure includes cases with an ICD-9 code of 520.6, 521.00-521.09, 521.9, 522.0, 522.1, 522.4-522.7, 522.9, 523.00, 523.01, 523.10, 523.11, 523.3-523.6, 523.9, 525.8, 525.9, 528.3, and 528.9. It also includes cases with an ICD-10 code of K00.6, K01.0, K01.1, K02.3, K02.51, K02.61-K02.63, K02.7, K02.9, K03.6, K03.89, K03.9-K04.1, K04.4-K04.7, K04.90, K04.99, K05.00, K05.01, K05.10, K05.11, K05.20-K05.22, K0.5.30-K05.32, K05.4, K05.6, K08.8, K08.81, K08.82, K08.89, K08.9, K12.2, K13.70, and K13.79. Effective October 1, 2018, the ICD-10 code of K08.8 was deleted and has been replaced by K08.81, K08.82, or K08.89. Please keep this in mind when interpreting the data. Patients with the diagnosis of disturbances in tooth eruption (ICD-9 code 520.6 and ICD-10 codes K00.6, K01.0, and K01.1) were only included if they fell in the 15 to 30 year age range. This permitted those that may seek emergency care due to impacted third molars to be included. Patients who concomitantly had an ICD-9 code or an E-code in the 800 to 900 range or an ICD-10 code or an E-code in the S00-S99 and T07-T88 range associated with their dental diagnosis were excluded. An E-code and ICD-9 code in the 800 to 900 range and an E-code and ICD-10 code in the S00-99 and T07-T88 range denotes that the patient's diagnosis was associated with an unintentional or intentional injury or poisoning, and since the target population should include only those with non-traumatic dental diagnoses, this group was excluded. Data for counties with fewer than ten visits per 10,000 are suppressed to protect confidentiality. However, counties with zero cases are not suppressed. The crude rate is the number of emergency department visits divided by the total number of people in the area of interest (for example, a county). This is expressed as a number per unit population such as "per 10,000 population." The crude rate does not take into account the differences in age distributions across counties and is therefore subject to bias.

Medicaid

Percent of Medicaid members with any utilization

These data come from the Division of Medicaid Services and Oral Health Program in the Wisconsin Department of Health Services. The percent of Medicaid members receiving a dental service (any utilization) within a given year is obtained by dividing the number of continuously enrolled Medicaid members who received any dental service in a given age category (indicated by at least one of the codes D0100 to D9999) by the total number of Medicaid members in that age category who have been continuously enrolled for 90 days within the reporting period in a full benefit plan. The percent of Medicaid members is separated into the following age categories: 0-2 years, 3-20 years, 21-64 years, 65+ years, and all ages. If the percent is based on a denominator less than or equal to five, it is suppressed. The county represents the Medicaid member’s county of residence. Counties classified as “other” (which includes tribal areas) were included in the statewide estimate but not as separate counties.

Percent of Medicaid members seeking preventive care

These data come from the Division of Medicaid Services and Oral Health Program in the Wisconsin Department of Health Services. The percent of Medicaid members receiving a preventive dental service within the given year is obtained by dividing the number of continuously enrolled Medicaid members who received preventive care in a given age category (indicated by at least one of the codes D0100 to D1999) by the total number of Medicaid members in that age category who have been continuously enrolled for 90 days within the reporting period in a full benefit plan. The percent of Medicaid members is separated into the following age categories: 0-2 years, 3-20 years, 21-64 years, 65+ years, and all ages. If the percent is based on a denominator less than or equal to five, it is suppressed. The county represents the Medicaid member’s county of residence. Counties classified as “other” (which includes tribal areas) were included in the statewide estimate but not as separate counties.

Percent of Medicaid members seeking post-ED visit follow-up care

These data come from the Division of Medicaid Services and Oral Health Program in the Wisconsin Department of Health Services. The values represent the percent of Medicaid members who had a dental visit within 30 days of a non-traumatic dental-related emergency department visit. It is obtained by dividing the number of continuously enrolled Medicaid members who visited a dental provider within 30 days of a dental-related emergency department visit by the total number of Medicaid members continuously enrolled for 90 days within the reporting period in a full benefit plan who had a dental-related emergency department visit. If the percent is based on a denominator less than or equal to five, it is suppressed. The county represents the Medicaid member’s county of residence. Counties classified as “other” (which includes tribal areas) were included in the statewide estimate but not as separate counties.

Enrolled dentist counts by Medicaid members served

These data come from the Division of Medicaid Services and Oral Health Program in the Wisconsin Department of Health Services. The values represent the number of Medicaid continuously enrolled dentists by the number of Medicaid members they served (0 members, 1-49 members, 50-100 members, and 100+ members) as well as a total count of all Medicaid continuously enrolled dentists during the given year. The county represents the dentist’s county of practice. Due to the different ways these counts are obtained and limitations in geocoding, there may be a discrepancy between the number of Medicaid continuously enrolled dentists in a given county and the total number of licensed dentists (available in the licensure data under the Oral Health topic) in that county.

Percent of enrolled dentists seeing Medicaid members

These data come from the Division of Medicaid Services and Oral Health Program in the Wisconsin Department of Health Services. The values represent the percent of dentists continuously enrolled during the reporting period who saw at least one Medicaid member within the given year. This figure is obtained by adding the number of dentists who saw 1-49 members, 50-100 members, and 100+ members together and dividing it by the total number of Medicaid continuously enrolled dentists. The county represents the dentist’s county of practice.

Enrolled hygienist counts by Medicaid members served

These data come from the Division of Medicaid Services and Oral Health Program in the Wisconsin Department of Health Services. The values represent the number of Medicaid continuously enrolled hygienists by the number of Medicaid members they served (0 members, 1-49 members, 50-100 members, and 100+ members) as well as a total count of all Medicaid continuously enrolled hygienists during the given year. The county represents the hygienist’s county of practice. Due to the different ways these counts are obtained and limitations in geocoding, there may be a discrepancy between the number of Medicaid continuously enrolled hygienists in a given county and the total number of licensed hygienists (available in the licensure data under the Oral Health topic) in that county.

Percent of enrolled hygienists seeing Medicaid members

These data come from the Division of Medicaid Services and Oral Health Program in the Wisconsin Department of Health Services. The values represent the percent of hygienists continuously enrolled during the reporting period who saw at least one Medicaid recipient within the given year. This figure is obtained by adding the number of hygienists who saw 1-49 members, 50-100 members, and 100+ members together and dividing it by the total number of Medicaid continuously enrolled hygienists. The county represents the hygienists’ county of practice.

Population on fluoridated public water system

Percent of population

These data are collected from the Water Fluoridation Reporting System (WFRS). Data are based on samples taken from active public water systems and do not reflect data from private wells. The data represent the percent of the population on public drinking water that have access to fluoridated water, regardless of whether it is at the recommended level.

Third grade survey

Percent of children who experience caries

These data are from the "Healthy Smiles, Healthy Growth" statewide survey administered by the Wisconsin Department of Health Services. Data are collected during the school year (e.g., 2000-01). The survey included a representative sample of third grade students in Wisconsin public schools. The caries experienced measure captures children with either treated decay, untreated decay, or both.

Percent of children who need treatment

These data are from the "Healthy Smiles, Healthy Growth" statewide survey administered by the Wisconsin Department of Health Services. Data are collected during the school year (e.g., 2000-01). The survey included a representative sample of third grade students in Wisconsin public schools. The need treatment measure captures children that exhibited a dental condition that needed to be addressed by a dentist.

Percent of children with sealants

These data are from the "Healthy Smiles, Healthy Growth" statewide survey administered by the Wisconsin Department of Health Services. Data are collected during the school year (e.g., 2000-01). The survey included a representative sample of third grade students in Wisconsin public schools. The sealants measure captures children with the presence of at least one sealant on a permanent molar tooth.

Percent of children with untreated decay

These data are from the "Healthy Smiles, Healthy Growth" statewide survey administered by the Wisconsin Department of Health Services. Data are collected during the school year (e.g., 2000-01). The survey included a representative sample of third grade students in Wisconsin public schools. The untreated decay measure captures children with the presence of a dental cavity where the breakdown of the enamel surface is readily observed.

Demographics

Number of people aged 65 years and older living alone in a non-family household

This data was collected from the U.S. Census Bureau, American Factfinder, and the American Community Survey (ACS) five-year estimates. ACS is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. However, because ACS data are based on a sample, they are subject to sampling variability and include a range of uncertainty. Read more about the Census ACS methodology. A non-family household can be either a person living alone or a householder who shares the housing unit only with non-relatives—for example, boarders or roommates. Non-family households are maintained only by people with no relatives at home.

Number of people aged 5 years and over that speak English less than "very well"

This data was collected from the U.S. Census Bureau, American Factfinder, and the American Community Survey (ACS) five-year estimates. ACS is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. However, because ACS data are based on a sample, they are subject to sampling variability and include a range of uncertainty. Read more about the Census ACS methodology. The data for people who speak English less than “very well” includes people whose native/primary language is any language other than English.”

Number of people by demographic group

Data provided by the Centers for Disease Control and Prevention (CDC) National Vital Statistic System. Intercensal estimates were used for 2000-2009. Postcensal estimates were used for 2010 onward.

Number of single-parent households

This data was collected from the U.S. Census Bureau, American Factfinder, and the American Community Survey (ACS) five-year estimates. ACS is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. However, because ACS data are based on a sample, they are subject to sampling variability and include a range of uncertainty. Read more about the Census ACS methodology.

Percent of people aged 65 years and older living alone in a non-family household

This data was collected from the U.S. Census Bureau, American Factfinder, and the American Community Survey (ACS) five-year estimates. ACS is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. However, because ACS data are based on a sample, they are subject to sampling variability and include a range of uncertainty. Read more about the Census ACS methodology. A non-family household can be either a person living alone or a householder who shares the housing unit only with non-relatives—for example, boarders or roommates. Non-family households are maintained only by people with no relatives at home.

Percent of people aged 5 years and older that speak English less than "very well"

This data was collected from the U.S. Census Bureau, American Factfinder, and the American Community Survey (ACS) five-year estimates. ACS is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. However, because ACS data are based on a sample, they are subject to sampling variability and include a range of uncertainty. Read more about the Census ACS methodology The data for people who speak English less than “very well” includes people whose native/primary language is any language other than English.”

Percent of people by demographic group

Data provided by the Centers for Disease Control and Prevention (CDC) National Vital Statistic System. Intercensal estimate were used for 2000-2009. Postcensal estimates were used for 2010 onward.

Percent of single-parent households

This data was collected from the U.S. Census Bureau, American Factfinder, and the American Community Survey (ACS) five-year estimates. ACS is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. However, because ACS data are based on a sample, they are subject to sampling variability and include a range of uncertainty. Read more about the Census ACS methodology.

Health status

Age-adjusted estimates of the percent of adults >= 20 years diagnosed with diabetes

This data was provided by CDC's National Diabetes Surveillance System. Prevalence rates by county were estimated using data from CDC's Behavioral Risk Factor Surveillance System (BRFSS) and data from the U.S. Census Bureau's Population Estimates Program. Prevalence rates are age adjusted and calculated for adults 20 years and older to be consistent with population estimates from the U.S. Census Bureau.

Number of people without health insurance

This data was collected from the U.S. Census Bureau, Small Area Health Insurance Estimates. Data were downloaded for 2005 and 2006, 13 fields were extracted, and the 2005 and 2006 data were concatenated. Read more about the Census's methodology.

Percent of population aged 5 years and over with a disability

This data was collected from the US. Census Bureau, American Factfinder, and the American Community Survey (ACS) 5 year estimates. ACS is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. However, because ACS data are based on a sample, they are subject to sampling variability and include a range of uncertainty. Read more about the Census ACS methodology. Because the ACS has replaced the decennial long-form as the source for small area statistics, there is no disability data in the 2010 Census. The Census Bureau collects data on disability primarily through the American Community Survey (ACS) and the Survey of Income and Program Participation (SIPP). The definitions of disability are not always alike so caution should be taken when making comparisons across surveys. Generally, the SIPP estimates of disability prevalence are broader and encompass a greater number of activities on which disability status is assessed. The ACS has a more narrow definition but is capable of producing estimates for states, counties, and metropolitan areas.

Percent of population without health insurance

This data was collected from the U.S. Census Bureau, Small Area Health Insurance Estimates. Data were downloaded for 2005 and 2006, 13 fields were extracted, and the 2005 and 2006 data were concatenated. Read more about the Census's methodology.

Social Vulnerability Index (ATSDR)

Household composition/disability percentile rank

Data are provided by CDC/ATSDR's Geospatial Research, Analysis & Service Program and are developed using Census 2010 and American Community Survey (ACS) data. The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every Census tract. The SVI ranks each Census tract on 15 social factors, including poverty, lack of vehicle access, and crowded housing. It then groups them into four related themes, as well as an overall ranking. Maps of the four themes are shown in the figure in the SVI fact sheet. For 2010, the household composition/disability percentile does not include disability because the 2010 Census data does not contain data on disabilities.

Housing/transportation percentile rank

Data are provided by CDC/ATSDR's Geospatial Research, Analysis & Service Program and are developed using Census 2010 and American Community Survey (ACS) data. The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every Census tract. The SVI ranks each tract on 15 social factors, including poverty, lack of vehicle access, and crowded housing. It then groups them into four related themes, as well as an overall ranking. Maps of the four themes are shown in the figure in the SVI fact sheet.

Minority status/language percentile rank

Data are provided by CDC/ATSDR's Geospatial Research, Analysis & Service Program and are developed using Census 2010 and American Community Survey (ACS) data. The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every Census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. The SVI ranks each tract on 15 social factors*, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes, as well as an overall ranking. Maps of the four themes are shown in the figure in the SVI fact sheet.

Overall percentile vulnerability rank

Data are provided by CDC/ATSDR's Geospatial Research, Analysis & Service Program and are developed using Census 2010 and American Community Survey (ACS) data. The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every Census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. The SVI ranks each tract on 15 social factors*, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes, as well as an overall ranking. Maps of the four themes are shown in the figure in the SVI fact sheet.

Socioeconomic percentile vulnerability rank

Data are provided by CDC/ATSDR's Geospatial Research, Analysis & Service Program and are developed using Census 2010 and American Community Survey (ACS) data. The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every Census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. The SVI ranks each tract on 15 social factors*, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes, as well as an overall ranking. Maps of the four themes are shown in the figure in the SVI fact sheet.

Socioeconomic status

Median household income

Median household income data are collected from the U.S. Census Bureau, Small Area Income and Poverty Estimates.

Number of people aged 16 years and older who are unemployed

Data provided by the U.S. Census Bureau, American Factfinder, and American Community Survey (ACS) 5-year estimates. Read more about the Census ACS methodology. The ACS uses the same employment status concepts as those used in Census 2000. The ACS data, however, are annual averages, whereas the census estimates relate to the period of time when the census was conducted — primarily from March to June 2000. The labor force questions changed in 2008 therefore you should use caution when making labor force data comparisons from 2008 or later with data from prior years. Additional information can be found on the Census website. Since employment data from the CPS and the ACS are obtained from respondents in households, they differ from statistics based on reports from individual businesses, farm enterprises, and certain government programs. Some discrepancies between different measures of employment may exist for people holding more than one job, private household workers, unpaid family workers, self-employed people, people less than 16 years of age, and people who had a job but were not working at the time of survey. Furthermore, the employment status data in household survey tabulations include people on the basis of place of residence regardless of where they work, whereas establishment data report people at their place of work regardless of where they live. This latter consideration is particularly significant when comparing data for workers who commute between areas and is likely to be more important the smaller the geographic area.

Number of people aged 25 years and over with high school diploma (or equivalent) or higher

Data provided by the U.S. Census Bureau, American Factfinder, American Community Survey (ACS) 5-year estimates. ACS is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. However, because ACS data are based on a sample, they are subject to sampling variability and include a range of uncertainty. Read more about the Census ACS methodology. For each race, ethnicity, and gender, the total number of people graduating high school or the equivalent were calculated by summing the following eight columns of highest education attained: high school graduate (includes equivalency), some college less than 1 year, some college 1 or more years, Associate degree, Bachelor's degree, Master's degree, Professional school degree, and Doctorate degree. Percentages were calculated by dividing this sum by the total population for the corresponding race, ethnicity, and gender.

Number of people living in poverty

These data are collected from the U.S. Census Bureau, Small Area Income and Poverty estimates. Read more about the Census ACS methodology.

Percent of population aged 16 years and older who are unemployed

Data provided by the U.S. Census Bureau, American Factfinder, and American Community Survey (ACS) 5-year estimates. Read more about the Census ACS methodology. The ACS uses the same employment status concepts as those used in Census 2000. The ACS data, however, are annual averages, whereas the census estimates relate to the period of time when the census was conducted — primarily from March to June 2000. The labor force questions changed in 2008 therefore you should use caution when making labor force data comparisons from 2008 or later with data from prior years. Additional information can be found on the Census website. Since employment data from the CPS and the ACS are obtained from respondents in households, they differ from statistics based on reports from individual businesses, farm enterprises, and certain government programs. Some discrepancies between different measures of employment may exist for people holding more than one job, private household workers, unpaid family workers, self-employed people, people less than 16 years of age, and people who had a job but were not working at the time of survey. Furthermore, the employment status data in household survey tabulations include people on the basis of place of residence regardless of where they work, whereas establishment data report people at their place of work regardless of where they live. This latter consideration is particularly significant when comparing data for workers who commute between areas and is likely to be more important the smaller the geographic area.

Percent of population aged 25 years and over with high school diploma (or equivalent) or higher

Data provided by the U.S. Census Bureau, American Factfinder, and American Community Survey (ACS) 5-year estimates. ACS is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. However, because ACS data are based on a sample, they are subject to sampling variability and include a range of uncertainty. Read more about the Census ACS methodology. For each race, ethnicity, and gender, the total number of people graduating high school or the equivalent were calculated by summing the following eight columns of highest education attained: high school graduate (includes equivalency), some college less than 1 year, some college 1 or more years, Associate degree, Bachelor's degree, Master's degree, Professional school degree, and Doctorate degree. Percentages were calculated by dividing this sum by the total population for the corresponding race, ethnicity, and gender.

Percent of population living in poverty

These data are collected from the U.S. Census Bureau, Small Area Income and Poverty estimates. Read more about the Census ACS methodology.

Read frequently asked questions about the flooding and precipitation data

Historical precipitation

Number of extreme precipitation days

This measure represents the number of extreme precipitation days per year. The raw data are collected from the North American Land Data Assimilation Systems (NLDAS). NLDAS takes measures via radar of land surface variables. These variables are then used to calculate precipitation estimates for how much precipitation fell within a 24-hour period. Precipitation data collected from radar are presented as a grid of latitude and longitude. CDC places county lines over the latitudinal and longitudinal grids produced by NLDAS in order to estimate precipitation within county boundaries. The data are available in absolute threshold (>.01”, 1”, 2”, 3”) and relative threshold (90th, 95th, 98th, 99th percentile).

Precipitation and flooding projections

Projected annual precipitation intensity

The CONUS Downscaled Climate Projections calculated temperature and precipitation projections using outputs from 16 global change models at 1/8th degree resolution to generate a single comprehensive dataset. Grid-level data was transformed into county-level data estimates to determine population exposure to extreme precipitation. Population within each grid-level was determined using the 2010 U.S. Census. Geo-imputation was used to convert grid-level meteorological data into county-level data. Grid polygons with population information were converted to centroids which were then related to the counties in the U.S. in order to determine which counties and populations are most likely to be affected by future precipitation levels.

Two story-lines are used to predict future extreme precipitation days – the A2 and B1 scenarios; the Intergovernmental Panel for Climate Change (IPCC) created the scenarios in order to predict the effects of climate change depending on potential global trends. The A2 scenario shows economic development to be regionally orientated with high population growth. The B1 scenario represents a trend towards a global service and information economy with reductions in material industry and therefore a decrease in emissions. In the B1 scenario, eco-friendlier technologies have been introduced, and the population growth is slow.

Precipitation intensity as determined by cumulative precipitation divided by the number of wet days (where precipitation is >.01”). Precipitation intensity is predicted for both the A2 (high emissions) and B1 (low emissions) scenarios.

Projected number of future extreme precipitation days

The CONUS Downscaled Climate Projections calculated temperature and precipitation projections using outputs from 16 global change models at 1/8th degree resolution to generate a single comprehensive dataset. Grid-level data was transformed into county-level data estimates to determine population exposure to extreme precipitation. Population within each grid-level was determined using the 2010 U.S. Census. Geo-imputation was used to convert grid-level meteorological data into county-level data. Grid polygons with population information were converted to centroids which were then related to the counties in the U.S. in order to determine which counties and populations are most likely to be affected by future precipitation levels.

Two story-lines are used to predict future extreme prediction days – the A2 and B1 scenarios; the Intergovernmental Panel for Climate Change (IPCC) created the scenarios in order to predict the effects of climate change depending on potential global trends. The A2 scenario shows economic development to be regionally orientated with high population growth. The B1 scenario represents a trend towards a global service and information economy with reductions in material industry and therefore a decrease in emissions. In the B1 scenario, eco-friendlier technologies have been introduced, and the population growth is slow.

Extreme precipitation days were determined by absolute (>.01”, 1”, 2”, 3”) or relative (90th, 95th, 98th) thresholds. Extreme precipitation days are determined for both the A2 (high emissions) and B1 (low emissions) scenarios.

Projected ratio of precipitation falling as rain to that falling as snow

The CONUS Downscaled Climate Projections calculated temperature and precipitation projections using outputs from 16 global change models at 1/8th degree resolution to generate a single comprehensive dataset. Grid-level data was transformed into county-level data estimates to determine population exposure to extreme precipitation. Population within each grid-level was determined using the 2010 U.S. Census. Geo-imputation was used to convert grid-level meteorological data into county-level data. Grid polygons with population information were converted to centroids which were then related to the counties in the U.S. in order to determine which counties and populations are most likely to be affected by future precipitation levels.

Two story-lines are used to predict future extreme precipitation days – the A2 and B1 scenarios; the Intergovernmental Panel for Climate Change (IPCC) created the scenarios in order to predict the effects of climate change depending on potential global trends. The A2 scenario shows economic development to be regionally orientated with high population growth. The B1 scenario represents a trend towards a global service and information economy with reductions in material industry and therefore a decrease in emissions. In the B1 scenario, eco-friendlier technologies have been introduced, and the population growth is slow.

The ratio of precipitation was defined as the ratio of the precipitation >.01” with daily maximum temperature above freezing to that at or below freezing. Precipitation ratio is predicted for both the A2 (high emissions) and B1 (low emissions) scenarios.

Vulnerability and preparedness

Number of housing units within FEMA-designated flood hazard area

This is an estimate of the number of housing units within the special flood hazard area by county. Data and calculation of special flood hazard areas are provided by the Federal Emergency Management Agency (FEMA) using 2011 National Flood Hazard Layer data. Data are defined by 1% annual chance of coastal or riverine flooding. This is also known as the 100-year flood zone, in which a flood even has a 1 in 100 chance of being equaled or exceeded in any given year. The population distribution was obtained from 2010 census block group data along with 2010 LandScan U.S. population data.

Number of people within FEMA-designated flood hazard area

The number of people in a flood hazard area is determined by 2010 U.S. census block group data in conjunction with 2010 LandScan U.S. population data.

Number of square miles within FEMA-designated flood hazard area

This is an estimate of the square miles of area within the special flood hazard area by county. Data and calculation of special flood hazard areas are provided by the Federal Emergency Management Agency (FEMA) using 2011 National Flood Hazard Layer data. Data are defined by 1% annual chance of coastal or riverine flooding. This is also known as the 100-year flood zone, in which a flood event had a 1 in 100 chance of being equaled or exceeded in any given year.

Percent area within FEMA-designated flood hazard area

This is a county estimate of the percentage of total area within the special flood hazard area. Data and calculation of special flood hazard area are provided by the Federal Emergency Management Agency (FEMA) using 2011 National Flood Hazard Layer data. Data are defined by 1% annual chance of coastal or riverine flooding. This is also known as the 100-year flood zone, in which a flood event had a 1 in 100 chance of being equaled or exceeded in any given year.

Percent of hospital beds within flood hazard area (no data available for Wisconsin)

This is an estimation of the percentage of hospital beds within the special flood hazard area. Hospital data is collected from the 2016 American Hospital Association (AHA) survey. A geographic information system was used to identify hospitals within a Federal Emergency Management Agency (FEMA) designated flood hazard zone.

Percent of Hospitals within flood hazard area (no data available for Wisconsin)

This is an estimation of the percentage of hospitals within the special flood hazard area. Hospital data is collected from the 2016 American Hospital Association (AHA) survey. A geographic information system was used to identify hospitals within a Federal Emergency Management Agency (FEMA) designated flood hazard zone.

Read frequently asked questions about the reproductive outcomes data

Fertility

Total fertility rate per 1,000 women of reproductive age

These data are collected from birth certificates and are provided by the Centers for Disease Control and Prevention's National Center for Health Statistics Vital Statistics System. This measure is an estimate of the average number of children a hypothetical cohort of 1,000 women would birth if the age-specific birth rates were observed in a given year. It is calculated by multiplying the sum of these age-specific fertility rates by five. Prior to 2008, the National Environmental Public Health Tracking Network suppressed data for counties with fewer than six cases or events per 100,000 to protect confidentiality. However, counties with zero cases were not suppressed. Since 2008, data has been suppressed for all counties with fewer than ten cases or events.

Infant mortality

Average annual number of infant deaths

These data are collected from linked birth and death certificate data provided by the Centers for Disease Control and Prevention's National Center for Health Statistics. This measure is a count of deaths which occurred in infants younger than one year of age; the presented data are averages over a five year time period. Prior to 2008, the National Environmental Public Health Tracking Network suppressed data for counties with fewer than six cases per 100,000 to protect confidentiality. However, counties with zero cases were not suppressed. Since 2008, data has been suppressed for all counties with fewer than 10 cases. Please note that counts are a statistically limited way to consider deaths because they do not allow for accurate comparison between counties. Counties with higher populations, such as Milwaukee, will have more deaths simply because they have more people. A rate is a better measure for true comparison between counties.

Average annual number of neonatal deaths

These data are collected from linked birth and death certificates provided by the Centers for Disease Control and Prevention's National Center for Health Statistics. This measure is a count of deaths which occurred in infants younger than 28 days old; the presented data are averages over a five-year time period. Prior to 2008, the National Environmental Public Health Tracking Network suppressed data for counties with fewer than six cases per 100,000 to protect confidentiality. However, counties with zero cases were not suppressed. Since 2008, data has been suppressed for all counties with fewer than 10 cases. Please note that counts are a statistically limited way to consider deaths because they do not allow for accurate comparison between counties. Counties with higher populations, such as Milwaukee, will have more deaths simply because they have more people. A rate is a better measure for true comparison between counties.

Average annual number of perinatal deaths

These data are collected from linked birth and death certificate data provided by the Centers for Disease Control and Prevention's National Center for Health Statistics. This measure is a count of deaths which occurred in fetuses and infants from between 28 days of gestation to seven days after birth; the presented data are averages over a five-year time period. Prior to 2008, the National Environmental Public Health Tracking Network suppressed data for counties with fewer than six cases per 100,000 to protect confidentiality. However, counties with zero cases were not suppressed. Since 2008, data has been suppressed for all counties with fewer than 10 cases. Please note that counts are a statistically limited way to consider deaths because they do not allow for accurate comparison between counties. Counties with higher populations, such as Milwaukee, will have more deaths simply because they have more people. A rate is a better measure for true comparison between counties.

Average annual number of postneonatal deaths

These data are collected from linked birth and death certificate data provided by the Centers for Disease Control and Prevention's National Center for Health Statistics. This measure is a count of deaths which occurred in infants 28 days to less than one year of age; the presented data are averages over a five-year time period. Prior to 2008, the National Environmental Public Health Tracking Network suppressed data for counties with fewer than six cases per 100,000 to protect confidentiality. However, counties with zero cases were not suppressed. Since 2008, data has been suppressed for all counties with fewer than 10 cases. Please note that counts are a statistically limited way to consider deaths because they do not allow for accurate comparison between counties. Counties with higher populations, such as Milwaukee, will have more deaths simply because they have more people. A rate is a better measure for true comparison between counties.

Infant mortality rate per 1,000 live births

These data are collected from linked birth and death certificate data provided by the Centers for Disease Control and Prevention's National Center for Health Statistics. This measure includes the number of deaths which occurred in infants younger than one year of age divided by all live births; the presented data are averages over a five-year time period. Prior to 2008, the National Environmental Public Health Tracking Network suppressed data for counties with fewer than six cases per 100,000 to protect confidentiality. However, counties with zero cases were not suppressed. Since 2008, data has been suppressed for all counties with fewer than 10 cases.

Neonatal mortality rate per 1,000 live births

These data are collected from linked birth and death certificate data provided by the Centers for Disease Control and Prevention's National Center for Health Statistics. This measure includes the number of deaths which occurred in infants younger than 28 days old divided by all live births; the presented data are averages over a five-year time period. Prior to 2008, the National Environmental Public Health Tracking Network suppressed data for counties with fewer than six cases per 100,000 to protect confidentiality. However, counties with zero cases were not suppressed. Since 2008, data has been suppressed for all counties with fewer than 10 cases.

Perinatal mortality rate per 1,000 live births

These data are collected from linked birth and death certificate data provided by the Centers for Disease Control and Prevention's National Center for Health Statistics. This measure includes the number of deaths which occurred in fetuses and infants from between 28 weeks of gestation to seven days after birth; the presented data are averages over a five-year time period. Prior to 2008, the National Environmental Public Health Tracking Network suppressed data for counties with fewer than six cases per 100,000 to protect confidentiality. However, counties with zero cases were not suppressed. Since 2008, data has been suppressed for all counties with fewer than 10 cases.

Postneonatal mortality rate per 1,000 live births

These data are collected from linked birth and death certificate data provided by the Centers for Disease Control and Prevention's National Center for Health Statistics. This measure includes the number of deaths which occurred in infants 28 days to less than one year of age divided by all live births; the presented data are averages over a five-year time period. Prior to 2008, the National Environmental Public Health Tracking Network suppressed data for counties with fewer than six cases per 100,000 to protect confidentiality. Since 2008, data has been suppressed for all counties with fewer than 10 cases.

Low birthweight (<2,500 grams)

Number of live term singleton births (counts)

These data are collected from birth certificates and provided by the Centers for Disease Control and Prevention's National Center for Health Statistics National Vital Statistics System. This measure is a count of live singleton births at term (≤ 37 weeks of gestation) with a birthweight less than 2,500 grams. Gestational age is determined by an algorithm that uses the clinician's estimate of gestational age and the mother's last reported normal menses. Prior to 2008, the National Environmental Public Health Tracking Network suppressed data for counties with fewer than six cases per 100,000 to protect confidentiality. However, counties with zero cases were not suppressed. Since 2008, data has been suppressed for all counties with fewer than 10 cases or events. Please note that counts are a statistically limited way to consider low birthweight outcomes because they do not allow for accurate comparison between counties. Counties with higher populations, such as Milwaukee, will have more cases simply because they have more people. A rate is a better measure for true comparison between counties.

Percent of live term singleton births

These data are are collected from birth certificates and provided by the Centers for Disease Control and Prevention's National Center for Health Statistics National Vital Statistics System. This measure includes the number of live singleton births at term (≤ 37 weeks of gestation) with a birthweight less than 2,500 grams divided by the total number of live singleton births. Low birthweight is expressed as a percentage of all live births. Gestational age is determined by an algorithm that uses the clinician's estimate of gestational age and the mother's last reported normal menses. Prior to 2008, the National Environmental Public Health Tracking Network suppressed data for counties with fewer than six cases per 100,000 to protect confidentiality. However, counties with zero cases were not suppressed. Since 2008, data has been suppressed for all counties with fewer than 10 cases.

Preterm (37 Weeks Gestation)

Number of Live Singleton Births (Counts)

These data are collected from birth certificates provided by the Center for Disease Control and Prevention's National Center for Health Statistics National Vital Statistics System. This measure is a count of live singleton births before 37 weeks of gestation. Gestational age is determined by an algorithm that uses the clinician's estimate of gestational age and the mother's last reported normal menses. Prior to 2008, the National Environmental Public Health Tracking Network suppressed data for counties with fewer than six cases per 100,000 to protect confidentiality. However, counties with zero cases were not suppressed. Since 2008, data has been suppressed for all counties with fewer than 10 cases. Please note that counts are a statistically limited way to consider prematurity because they do not allow for accurate comparison between counties. Counties with higher populations, such as Milwaukee, will have more deaths simply because they have more people. A rate is a better measure for true comparison between counties.

Percent of Live Singleton Births

These data are collected from birth certificates and provided by the Center for Disease Control and Prevention's National Center for Health Statistics National Vital Statistics System. This measure includes the number of live singleton births before 37 weeks of gestation divided by the total number of live singleton births. Prematurity is expressed as a percentage of all live births. Gestational age is determined by an algorithm that uses the clinician's estimate of gestational age and the mother's last reported normal menses. Prior to 2008, the National Environmental Public Health Tracking Network suppressed data for counties with fewer than six cases per 100,000 to protect confidentiality. However, counties with zero cases were not suppressed. Since 2008, data has been suppressed for all counties with fewer than 10 cases.

Sex Ratio

Male to Female Sex Ratio at Birth (Term Singleton Only)

These data are collected from birth certificates and provided by the Center for Disease Control and Prevention's National Center for Health Statistics National Vital Statistics System. This measure is calculated by dividing male births by female births; only live singleton term (≤ 37 weeks of gestation) births are included. A rate of 1.000 indicates that an equal number of male and female infants were born in a given year.

Very Low Birthweight (<1,500 Grams)

Average Annual Number of Live Singleton Births

These data are collected from birth certificates and provided by the Center for Disease Control and Prevention's National Center for Health Statistics National Vital Statistics System. This measure is a count of live singleton births at term (≤ 37 weeks of gestation) with a birthweight less than 1,500 grams. Gestational age is determined by an algorithm that uses the clinician's estimate of gestational age and the mother's last reported normal menses. This measure is presented as an average over a five-year period. Prior to 2008, the National Environmental Public Health Tracking Network suppressed data for counties with fewer than six cases per 100,000 to protect confidentiality. However, counties with zero cases were not suppressed. Since 2008, data has been suppressed for all counties with fewer than 10 cases. Please note that counts are a statistically limited way to consider low birthweight outcomes because they do not allow for accurate comparison between counties. Counties with higher populations, such as Milwaukee, will have more cases simply because they have more people. A rate is a better measure for true comparison between counties.

Average Annual Percent of Live Singleton Births

These data are collected from birth certificates and provided by the Center for Disease Control and Prevention's National Center for Health Statistics National Vital Statistics System. This measure includes the number of live singleton births at term (≤ 37 weeks of gestation) with a birthweight less than 1,500 grams divided by the total number of singleton infants live born. Gestational age is determined by an algorithm that uses the clinician's estimate of gestational age and the mother's last reported normal menses. This is expressed as an average percentage over a five-year period. Prior to 2008, the National Environmental Public Health Tracking Network suppressed data for counties with fewer than six cases per 100,000 to protect confidentiality. However, counties with zero cases were not suppressed. Since 2008, data has been suppressed for all counties with fewer than 10 cases.

Very Preterm (<32 Weeks Gestation)

Average Annual Number of Live Singleton Births

These data are collected from birth certificates and provided by the Centers for Disease Control and Prevention's National Center for Health Statistics National Vital Statistics System. This measure is a count of live singleton births before 32 weeks of gestation; it is presented as an annual average over a five-year period. Gestational age is determined by an algorithm that uses the clinician's estimate of gestational age and the mother's last reported normal menses. Prior to 2008, the National Environmental Public Health Tracking Network suppressed data for counties with fewer than six cases per 100,000 to protect confidentiality. However, counties with zero cases were not suppressed. Since 2008, data has been suppressed for all counties with fewer than 10 cases. Please note that counts are a statistically limited way to consider prematurity because they do not allow for accurate comparison between counties. Counties with higher populations, such as Milwaukee, will have more cases simply because they have more people. A rate is a better measure for true comparisons between counties.

Average Annual Percent of Live Singleton Births

These data are collected from birth certificates and provided by the Centers for Disease Control and Prevention's National Center for Health Statistics National Vital Statistics System. This measure includes the number of live singleton births before 32 weeks of gestation divided by the total number of live singleton births. It is expressed as a percentage of all live births. Gestational age is determined by an algorithm that uses the clinician's estimate of gestational age and the mother's last reported normal menses. Prior to 2008, the National Environmental Public Health Tracking Network suppressed data for counties with fewer than six cases per 100,000 to protect confidentiality. However, counties with zero cases were not suppressed. Since 2008, data has been suppressed for all counties with fewer than 10 cases.

Smoking Prevalence

These data come from the Wisconsin Behavioral Risk Factor Survey (BRFS), which is part of the Behavioral Risk Factor Surveillance System (BRFSS), a telephone survey of adults coordinated by the Centers for Disease Control and Prevention. These data are collected using a sampling of cell phone and landline calls to conduct interviews with more than 400,000 adults ages 18 and older every year since 2011. This questionnaire establishes individuals’ health-related risk behaviors, chronic health conditions, and use of preventative services. Cigarette smoking status and frequency were self-reported by the individual. A “current smoker” is defined as an adult (18+) who has smoked 100 or more cigarettes in their lifetime and who smoked some days or every day in the last 30 days. A “former smoker” is defined as an adult (18+) who has smoked 100 or more cigarettes in their lifetime and who has not smoked cigarettes in the last 30 days. “Never smoker” is defined as a person who has never smoked cigarettes.

Other combustible tobacco products, such as cigars, cigarillos, and pipes are not included in these measures. These prevalence data are represented as a percent. To calculate the prevalence, the number of current smokers, former smokers, or never smokers is the numerator and is divided by the rest of the population in the county of interest (the denominator).

Read frequently asked questions about the toxic air emissions data

Facilities Reported

These data come from the Wisconsin Department of Natural Resources (DNR) and represent the number of industrial facilities in a geographic area that have reached or exceeded the reportable level of the contaminants of interest. Note: Facilities from years 2000-2014 that were missing addresses were geocoded retrospectively using the most recent years of data and the facility identification number (FID). This allowed for facilities and reported emissions to be tied to locations at the county and census tract level more accurately. To learn more, visit the Department of Natural Resources website.

Acrolein

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for acrolein is 75 pounds per year (0.0375 tons/year). These data represent the sum total of acrolein emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect pollution sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more, visit the CDC website.

Acrylonitrile

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for acrylonitrile is 13.1 pounds per year (0.00655 tons/year). These data represent the sum total of acrylonitrile emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Ammonia

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for ammonia is 4,097 pounds per year (2.0485 tons/year). These data represent the sum total of ammonia emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Arsenic

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for arsenic, including elemental and inorganic compounds, is 0.207 pounds per year (0.0001035 tons/year). These data represent the sum total of arsenic emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Benzene

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for benzene is 114 pounds per year (0.057 tons/year). These data represent the sum total of benzene emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Benzidine

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for benzidine is 0.0133 pounds per year (0.00000665 tons/year). These data represent the sum total of benzidine emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Beryllium

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for beryllium, including beryllium compounds, is 0.37 pounds per year (0.000185 tons/year). These data represent the sum total of beryllium emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

1,3-Butadiene (Butadiene - 1,3)

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for 1,3-Butadiene is 3.17 pounds per year (0.001585 tons/year). These data represent the sum total of 1,3-Butadiene emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Cadmium

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for cadmium, including cadmium compounds, is 0.494 pounds per year (0.000247 tons/year). These data represent the sum total of cadmium emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Chlorine

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for chlorine is 341 pounds per year (0.1705 tons/year). These data represent the sum total of chlorine emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of chlorine emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county and census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Chromium (Metals and Other)

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for chromium (metals and other) is 118 pounds per year (0.059 tons/year). Chromium (metals and other) includes chromium metals and compounds other than chromium (VI). These data represent the sum total of chromium (metal and other) emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Chromium (VI)

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Chromium (VI) includes chromium (VI) compounds and particulates, chromic acid mists, and dissolved chromium (VI) aerosols. Among this combination of compounds, the lowest reportable level is 0.074 pounds per year (0.000037 tons/year). The measures on the portal use this value as the threshold for combined chromium (VI). These data represent the sum total of chromium (VI) emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Ethanolamine

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for ethanolamine is 1,763 pounds per year (0.8815 tons/year). These data represent the sum total of ethanolamine emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Ethylbenzene

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for ethylbenzene is 6,000 pounds per year (3 tons/year). These data represent the sum total of ethyl benzene emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

2-Ethoxyethanol

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for 2-ethoxyethanol (also known as ethylene glycol monoethyl ether, EGEE, or Cellosolve) is 4,336 pounds per year (2.168 tons/year). These data represent the sum total of 2-ethoxyethanol emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Ethylene Oxide

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for ethylene oxide is 10.1 pounds per year (0.00505 tons/year). These data represent the sum total of ethylene oxide emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Formaldehyde

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wis. Admin. Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for formaldehyde is 68.3 pounds per year (0.03415 tons/year). These data represent the sum total of formaldehyde emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Hydrogen Sulfide

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wis. Admin. Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for hydrogen sulfide is 3,279 pounds per year (1.6395 tons/year). These data represent the sum total of hydrogen sulfide emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Lead

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wis. Admin. Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for lead, including lead (metal) and lead compounds, is 400 pounds per year (0.2 tons/year). These data represent the sum total of lead emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Mercury (Alkyl)

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for alkyl mercury compounds is 2.35 pounds per year (0.001175 tons/year). These data represent the sum total of mercury (alkyl) emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Mercury (Aryl)

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for aryl mercury compounds is 23.5 pounds per year (0.01175 tons/year). These data represent the sum total of mercury (aryl) emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Mercury (Inorganic Forms)

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for inorganic forms of mercury, including metallic mercury, is 5.88 pounds per year (0.00294 tons/year). These data represent the sum total of mercury (inorganic forms) emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude less than the reportable level, from indirect sources, and emissions from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Methylene Chloride

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for methylene chloride (also known as dichloromethane) is 1,890 pounds per year (0.945 tons/year). These data represent the sum total of methylene chloride emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county and census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Naphthalene

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for naphthalene is 6,000 pounds per year (3 tons/year). These data represent the sum total of naphthalene emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Nitric Acid

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for nitric acid is 1,213 pounds per year (0.6065 tons/year). These data represent the sum total of nitric acid emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Perchloroethylene (PERC)

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for perchloroethylene (also known as tetrachloroethylene) is 151 pounds per year (0.0755 tons/year). These data represent the sum total of perchloroethylene emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Phenol

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for phenol is 4,528 pounds per year (2.264 tons/year). These data represent the sum total of phenol emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Phosphoric Acid

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for phosphoric acid is 235 pounds per year (0.1175 tons/year). These data represent the sum total of phosphoric acid emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Stoddard Solvent

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for Stoddard solvent (also known as mineral spirits) is 6,000 pounds per year (3 tons/year). These data represent the sum total of Stoddard solvent emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Styrene (Monomer)

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for styrene (monomer) is 6,000 pounds per year (3 tons/year). These data represent the sum total of styrene (monomer) emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Sulfuric Acid

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for sulfuric acid is 235 pounds per year (0.1175 tons/year). These data represent the sum total of sulfuric acid emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Tetrachlorodibenzo-p-dioxin-2,3,7,8 (TCDD)

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for TCDD (2,3,7,8-Tetrachlorodibenzo-p-dioxin) is 0.00005 pounds per year (0.000000025 tons/year). These data represent the sum total of TCDD emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Toluene

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for toluene (also known as toluol) is 6,000 pounds per year (3 tons/year). These data represent the sum total of toluene emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Trichloroethylene

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wis. Admin. Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for trichloroethylene (also known as trichloroethene) is 444 pounds per year (0.222 tons/year). These data represent the sum total of trichloroethylene emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Triethanolamine

These data comes from the Wisconsin Department of Natural Resources (DNR). Under Wis. Admin. Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for triethanolamine is 1,176 pounds per year (0.588 tons/year). These data represent the sum total of triethanolamine emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the OSHA website.

Vinyl Chloride

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for vinyl chloride is 101 pounds per year (0.0505 tons/year). These data represent the sum total of vinyl chloride emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Xylene

These data come from the Wisconsin Department of Natural Resources (DNR). Under Wisconsin Administrative Code NR 438.03 (Air Contaminant Emissions Inventory Reporting Requirements), industrial facilities in Wisconsin must report air contaminant emissions when emissions exceed reportable levels. Since 2004, the reportable level for xylene, including mixtures and isomers, xylol, and dimethyl benzene, is 6,000 pounds per year (3 tons/year). These data represent the sum total of xylene emissions from all facilities that exceeded the reportable level, within the chosen year and geographic boundary. Note that the data capture only reportable levels of contaminant emissions. The data exclude emissions less than the reportable level, from indirect sources, and from non-industrial sources. State data are greater than summed county or census tract data because of limitations in geocoding. To learn more about this compound and its health effects, visit the CDC website.

Read frequently asked questions about the public water quality data

Public water use

Number of people receiving water from community water systems

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors the number of people receiving water from community water systems. A community water system is a public water system which supplies water to the same population year-round.

Arsenic

Number of community water systems by maximum concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. In 2001, EPA reduced the regulatory drinking water Maximum Contaminant Level (MCL) for arsenic from 0.05 ppm to 0.01 ppm. The current MCL is also represented as 10 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of community water systems by maximum arsenic concentration in μg/L. Cut points are 0-5, >5-10, >10-20, >20-30, and >30 μg/L arsenic. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of community water systems by mean concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. In 2001, EPA reduced the regulatory drinking water Maximum Contaminant Level (MCL) for arsenic from 0.05 ppm to 0.01 ppm. The current MCL is also represented as 10 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of community water systems by mean (average) arsenic concentration in μg/L. Cut points are 0-5, >5-10, >10-20, >20-30, and >30 μg/L arsenic. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of people served by community water systems by maximum concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. In 2001, EPA reduced the regulatory drinking water Maximum Contaminant Level (MCL) for arsenic from 0.05 ppm to 0.01 ppm. The current MCL is also represented as 10 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of people served by community water systems by maximum arsenic concentration in μg/L. Cut points are 0-5, >5-10, >10-20, >20-30, and >30 μg/L arsenic. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of people served by community water systems by mean concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. In 2001, EPA reduced the regulatory drinking water Maximum Contaminant Level (MCL) for arsenic from 0.05 ppm to 0.01 ppm. The current MCL is also represented as 10 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of people served by community water systems by mean (average) arsenic concentration in μg/L. Cut points are 0-5, >5-10, >10-20, >20-30, and >30 μg/L arsenic. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Atrazine

Number of community water systems by maximum concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for atrazine is 3 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of community water systems by maximum atrazine concentration in μg/L. Cut points are 0-1, >1-3, >3-4, and >4 μg/L atrazine. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of community water systems by mean concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for atrazine is 3 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of community water systems by mean (average) atrazine concentration in μg/L. Cut points are 0-1, >1-3, >3-4, and >4 μg/L atrazine. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of community water systems by mean concentrations (μg/L), quarterly

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for atrazine is 3 micrograms per liter (μg/L). These data illustrate the quarterly distribution of the number of community water systems by mean (average) atrazine concentration in μg/L. Cut points are 0-1, >1-3, >3-4, and >4 μg/L atrazine. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of people served by community water systems by maximum concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for atrazine is 3 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of people served by community water systems by maximum atrazine concentration in μg/L. Cut points are 0-1, >1-3, >3-4, and >4 μg/L atrazine. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of people served by community water systems by mean concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for atrazine is 3 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of people served by community water systems by mean (average) atrazine concentration in μg/L. Cut points are 0-1, >1-3, >3-4, and >4 μg/L atrazine. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of people served by community water systems by mean concentrations (μg/L), quarterly

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for atrazine is 3 micrograms per liter (μg/L). These data illustrate the quarterly distribution of the number of people served by community water systems by mean (average) atrazine concentration in μg/L. Cut points are 0-1, >1-3, >3-4, and >4 μg/L atrazine. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

DEHP

Number of community water systems by maximum concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for di(2-ethylhexyl) phthalate (DEHP) is 6 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of community water systems by maximum DEHP concentration in μg/L. Cut points are 0-2, >2-4, >4-6, >6-10, and >10 μg/L DEHP. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of community water systems by mean concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for di(2-ethylhexyl) phthalate (DEHP) is 6 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of community water systems by mean (average) DEHP concentration in μg/L. Cut points are 0-2, >2-4, >4-6, >6-10, and >10 μg/L DEHP. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of people served by community water systems by maximum concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for di(2-ethylhexyl) phthalate (DEHP) is 6 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of people served by community water systems by maximum DEHP concentration in μg/L. Cut points are 0-2, >2-4, >4-6, >6-10, and >10 μg/L DEHP. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of people served by community water systems by mean concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for di(2-ethylhexyl) phthalate (DEHP) is 6 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of people served by community water systems by mean (average) DEHP concentration in μg/L. Cut points are 0-2, >2-4, >4-6, >6-10, and >10 μg/L DEHP. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

HAA5

Number of community water systems by maximum concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for haloacetic acids (HAA5) is 60 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of community water systems by maximum HAA5 concentration in μg/L. Cut points are 0-15, >15-30, >30-45, >45-60, >60-75, and >75 μg/L HAA5. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of community water systems by mean concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for haloacetic acids (HAA5) is 60 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of community water systems by mean (average) HAA5 concentration in μg/L. Cut points are 0-15, >15-30, >30-45, >45-60, >60-75, and >75 μg/L HAA5. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of community water systems by mean concentrations (μg/L), quarterly

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for haloacetic acids (HAA5) is 60 micrograms per liter (μg/L). These data illustrate the quarterly distribution of the number of community water systems by mean (average) HAA5 concentration in μg/L. Cut points are 0-15, >15-30, >30-45, >45-60, >60-75, and >75 μg/L HAA5. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of people served by community water systems by maximum concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for haloacetic acids (HAA5) is 60 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of people served by community water systems by maximum HAA5 concentration in μg/L. Cut points are 0-15, >15-30, >30-45, >45-60, >60-75, and >75 μg/L HAA5. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of people served by community water systems by mean concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for haloacetic acids (HAA5) is 60 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of people served by community water systems by mean (average) HAA5 concentration in μg/L. Cut points are 0-15, >15-30, >30-45, >45-60, >60-75, and >75 μg/L HAA5. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of people served by community water systems by mean concentrations (μg/L), quarterly

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for haloacetic acids (HAA5) is 60 micrograms per liter (μg/L). These data illustrate the quarterly distribution of the number of people served by community water systems by mean (average) HAA5 concentration in μg/L. Cut points are 0-15, >15-30, >30-45, >45-60, >60-75, and >75 μg/L HAA5. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Nitrate

Number of community water systems by maximum concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for nitrate is 10 parts per million (or 10 milligrams per liter [mg/L]). These data illustrate the yearly distribution of the number of community water systems by maximum nitrate concentration in mg/L. Cut points are 0-3, >3-5, >5-10, >10-20, and >20 mg/L nitrate. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of community water systems by mean concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for nitrate is 10 parts per million (or 10 milligrams per liter [mg/L]). These data illustrate the quarterly distribution of the number of community water systems by mean (average) nitrate concentration in mg/L. Cut points are 0-3, >3-5, >5-10, >10-20, and >20 mg/L nitrate. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of community water systems by mean concentrations (μg/L), quarterly

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for nitrate is 10 parts per million (or 10 milligrams per liter [mg/L]). These data illustrate the quarterly distribution of the number of community water systems by mean (average) nitrate concentration in mg/L. Cut points are 0-3, >3-5, >5-10, >10-20, and >20 mg/L nitrate. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of people served by community water systems by maximum concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for nitrate is 10 parts per million (or 10 milligrams per liter [mg/L]). These data illustrate the yearly distribution of the number of people served by community water systems by maximum nitrate concentration in mg/L. Cut points are 0-3, >3-5, >5-10, >10-20, and >20 mg/L nitrate. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of people served by community water systems by mean concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for nitrate is 10 parts per million (or 10 milligrams per liter [mg/L]). These data illustrate the yearly distribution of the number of people served by community water systems by mean (average) nitrate concentration in mg/L. Cut points are 0-3, >3-5, >5-10, >10-20, and >20 mg/L nitrate. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of people served by community water systems by mean concentrations (μg/L), quarterly

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for nitrate is 10 parts per million (or 10 milligrams per liter [mg/L]). These data illustrate the quarterly distribution of the number of people served by community water systems by mean (average) nitrate concentration in mg/L . Cut points are 0-3, >3-5, >5-10, >10-20, and >20 mg/L nitrate. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

PCE

Number of community water systems by maximum concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for tetrachloroethylene (PCE) is 5 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of community water systems by maximum PCE concentration in μg/L. Cut points are 0-1, >1-2, >2-5, and >5 μg/L PCE. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of community water systems by mean concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for tetrachloroethylene (PCE) is 5 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of community water systems by mean (average) PCE concentration in μg/L. Cut points are 0-1, >1-2, >2-5, and >5 μg/L PCE. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of people served by community water systems by maximum concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for tetrachloroethylene (PCE) is 5 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of people served by community water systems by maximum PCE concentration in μg/L. Cut points are 0-1, >1-2, >2-5, and >5 μg/L PCE. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of people served by community water systems by mean concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for tetrachloroethylene (PCE) is 5 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of people served by community water systems by mean (average) PCE concentration in μg/L. Cut points are 0-1, >1-2, >2-5, and >5 μg/L PCE. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Radium

Number of community water systems by maximum concentrations (pCi/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for radium is 5 picocuries per liter (pCi/L). These data illustrate the yearly distribution of the number of community water systems by maximum radium concentration in pCi/L. Cut points are 0-3, >3-5, >5-10, and >10 pCi/L radium. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of community water systems by mean concentrations (pCi/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for radium is 5 picocuries per liter (pCi/L). These data illustrate the yearly distribution of the number of community water systems by mean (average) radium concentration in pCi/L. Cut points are 0-3, >3-5, >5-10, and >10 pCi/L radium. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of people served by community water systems by maximum concentrations (pCi/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for radium is 5 picocuries per liter (pCi/L). These data illustrate the yearly distribution of the number of people served by community water systems by maximum radium concentration in pCi/L. Cut points are 0-3, >3-5, >5-10, and >10 pCi/L radium. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of people served by community water systems by mean concentrations (pCi/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for radium is 5 picocuries per liter (pCi/L). These data illustrate the yearly distribution of the number of people served by community water systems by mean (average) radium concentration in pCi/L. Cut points are 0-3, >3-5, >5-10, and >10 pCi/L radium. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

TCE

Number of community water systems by maximum concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for trichloroethylene (TCE) is 5 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of community water systems by maximum TCE concentration in μg/L. Cut points are 0-1, >1-2, >2-5, and >5 μg/L TCE. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of community water systems by mean concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for trichloroethylene (TCE) is 5 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of community water systems by mean (average) TCE concentration in μg/L. Cut points are 0-1, >1-2, >2-5, and >5 μg/L TCE. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of people served by community water systems by maximum concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for trichloroethylene (TCE) is 5 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of people served by community water systems by maximum TCE concentration in μg/L. Cut points are 0-1, >1-2, >2-5, and >5 μg/L TCE. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of people served by community water systems by mean concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for trichloroethylene (TCE) is 5 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of people served by community water systems by mean (average) TCE concentration in μg/L. Cut points are 0-1, >1-2, >2-5, and >5 μg/L TCE. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

TTHM

Number of community water systems by maximum concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for trihalomethanes (TTMH) is 80 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of community water systems by maximum TTHM concentration in μg/L. Cut points are 0-20, >20-40, >40-60, >60-80, >80-100, and >100 μg/L TTHM. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of community water systems by mean concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for trihalomethanes (TTMH) is 80 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of community water systems by mean (average) TTHM concentration in μg/L. Cut points are 0-20, >20-40, >40-60, >60-80, >80-100, and >100 μg/L TTHM. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of community water systems by mean concentrations (μg/L), quarterly

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for trihalomethanes (TTMH) is 80 micrograms per liter (μg/L). These data illustrate the quarterly distribution of the number of community water systems by mean (average) TTHM concentration in μg/L. Cut points are 0-20, >20-40, >40-60, >60-80, >80-100, and >100 μg/L TTHM. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of people served by community water systems by maximum concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for trihalomethanes (TTMH) is 80 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of people served by community water systems by maximum TTHM concentration in μg/L. Cut points are 0-20, >20-40, >40-60, >60-80, >80-100, and >100 μg/L TTHM. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of people served by community water systems by mean concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for trihalomethanes (TTMH) is 80 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of people served by community water systems by mean (average) TTHM concentration in μg/L. Cut points are 0-20, >20-40, >40-60, >60-80, >80-100, and >100 μg/L TTHM. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of people served by community water systems by mean concentrations (μg/L), quarterly

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for trihalomethanes (TTMH) is 80 micrograms per liter (μg/L). These data illustrate the quarterly distribution of the number of people served by community water systems by mean (average) TTHM concentration in μg/L. Cut points are 0-20, >20-40, >40-60, >60-80, >80-100, and >100 μg/L TTHM. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Uranium

Number of community water systems by maximum concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for uranium is 30 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of community water systems by maximum uranium concentration in μg/L. Cut points are 0-5, >5-15, >15-30, and >30 μg/L uranium. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of community water systems by mean concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current maximum contaminant level (MCL) for uranium is 30 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of community water systems by mean (average) uranium concentration in μg/L. Cut points are 0-5, >5-15, >15-30, and >30 μg/L uranium. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of people served by community water systems by maximum concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for uranium is 30 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of people served by community water systems by maximum uranium concentration in μg/L. Cut points are 0-5, >5-15, >15-30, and >30 μg/L uranium. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.

Number of people served by community water systems by mean concentrations (μg/L)

These data are from the Wisconsin Department of Natural Resources (DNR) online public water query system. The DNR monitors for substances in accordance with the federal Safe Drinking Water Act standards. Drinking water concentrations are based on samples taken from public community water systems. A community water system is a public water system which supplies water to the same population year-round. A water sample is designated as "non-detect" when the laboratory analysis cannot detect the analyte being measured. The current Maximum Contaminant Level (MCL) for uranium is 30 micrograms per liter (μg/L). These data illustrate the yearly distribution of the number of people served by community water systems by mean uranium concentration in μg/L. Cut points are 0-5, >5-15, >15-30, and >30 μg/L uranium. Measures do not account for the variability in sampling, numbers of sampling repeats, and variability within systems. Concentrations in drinking water cannot be directly converted to exposure, because water consumption varies by climate, level of physical activity, and between people.