This web map is part of the Centers for Disease Control and Prevention (CDC) PLACES. It provides model-based estimates of current asthma prevalence among adults aged 18 years and old at county, place, census tract and ZCTA levels in the United States. PLACES is an expansion of the original 500 Cities Project and a collaboration between the CDC, the Robert Wood Johnson Foundation, and the CDC Foundation. Data sources used to generate these estimates include the Behavioral Risk Factor Surveillance System (BRFSS), Census 2020 population counts or Census annual county-level population estimates, and the American Community Survey (ACS) estimates. For detailed methodology see www.cdc.gov/places. For questions or feedback send an email to places@cdc.gov.Measure name used for current asthma is CASTHMA.

Table contains estimated percentage of adults ages 18 years and older who report ever being diagnosed with asthma by a healthcare provider. Data are at zip code level. Data are downloaded from the AskCHIS Neighborhood Edition and are not direct estimates. For more information on the methodology used to calculate estimates, please visit healthpolicy.ucla.edu. Data for zip codes 94305 and 95053 are not available. Source: California Health Interview Survey, AskCHIS Neighborhood Edition, 2018 CHIS data. Exported on June 1, 2022.METADATA:notes (String): Lists table title, notes, sourceszip_code (Numeric): Geography IDestimate (Numeric): Estimate of adults with asthmaunit (String): Unit used for the estimate (Percent)CI (Numeric): 95% confidence interval for the estimate

This map shows the asthma hospitalization rate per 10,000 for ages 0 to 17 by county. Counties are shaded based on quartile distribution. The lighter shaded counties have lower asthma hospitalization rates. The darker shaded counties have higher asthma hospitalization rates. New York State Community Health Indicator Reports (CHIRS) were developed in 2012, and are updated annually to consolidate and improve data linkages for the health indicators included in the County Health Assessment Indicators (CHAI) for all communities in New York. The CHIRS present data for more than 300 health indicators that are organized by 15 different health topics. Data if provided for all 62 New York State counties, 11 regions (including New York City), the State excluding New York City, and New York State. For more information, check out: http://www.health.ny.gov/statistics/chac/indicators/. The "About" tab contains additional details concerning this dataset.

Find data on pediatric asthma in Massachusetts. This dataset provides the number of cases and prevalence of asthma among students, grades K-8, in Massachusetts.

These data map the rate of asthma among individuals 18 years of age and older at the census tract level. Provided by the CDC Population Level Analysis and Community Estimates (PLACES). For more information, please visit https://chronicdata.cdc.gov/500-Cities-Places/PLACES-Local-Data-for-Better-Health-Census-Tract-D/cwsq-ngmh.

This story map contains details about the prevalence of asthma in youth in King County. It has been developed for the Determinant of Equity - Health and Human Services. It includes information about Asthma Prevalence in Youth equity indicator. This presentation includes charts, maps, and a narrative describing this indicator.

The data for the Asthma Prevalence (Youth) dataset was compiled from Medicaid claims data by the Washington state Health Care Authority (HCA).

For more information about King County's equity efforts, please see:

Equity, Racial & Social Justice Vision Ordinance 16948 describing the determinates of equity Determinants of Equity and Data Tool

Adult respondents ages 18+ who were ever diagnosed with asthma by a doctor. Years covered are from 2013-2014 by zip code. Data taken from the California Health Interview Survey Neighborhood Edition (AskCHIS NE) (http://askchisne.ucla.edu/), downloaded February 2018. AskCHIS Neighborhood Edition is an online data dissemination and visualization platform that provides health estimates at sub-county geographic regions. Estimates are powered by data from The California Health Interview Survey (CHIS). CHIS is conducted by The UCLA Center for Health Policy Research, an affiliate of UCLA Fielding School of Public Health.Health estimates available in AskCHIS NE (Neighborhood Edition) are model-based small area estimates (SAEs).SAEs are not direct estimates (estimates produced directly from survey data, such as those provided through AskCHIS).CHIS data and analytic results are used extensively in California in policy development, service planning and research, and is recognized and valued nationally as a model population-based health survey.Before using estimates from AskCHIS NE, it is recommended that you read more about the methodology and data limitations at: http://healthpolicy.ucla.edu/Lists/AskCHIS%20NE%20Page%20Content/AllItems.aspx. You can go to http://askchisne.ucla.edu/ to create your own account.Produced by The California Health Interview Survey and The UCLA Center for Health Policy Research and compiled by the Los Angeles County Department of Public Health. "Field Name = Field Definition "Zipcode" = postal zip code in the City of Los Angeles “Percent” = estimated percentage of adult respondents ages 18+ who were ever diagnosed with asthma by a doctor"LowerCL" = the lower 95% confidence limit represents the lower margin of error that occurs with statistical sampling"UpperCL" = the upper 95% confidence limit represents the upper margin of error that occurs in statistical sampling "Population" = estimated population 18 and older (denominator) residing in the zip code Notes: 1) Zip codes are based on the Los Angeles Housing Department Zip Codes Within the City of Los Angeles map (https://media.metro.net/about_us/pla/images/lazipcodes.pdf).2) Zip codes that did not have data available (i.e., null values) are not included in the dataset; there are additional zip codes that fall within the City of Los Angeles.3) Zip code boundaries do not align with political boundaries. These data are best viewed with a City of Los Angeles political boundary file (i.e., City of Los Angeles jurisdiction boundary, City Council boundary, etc.) FAQS: 1. Which cycle of CHIS does AskCHIS Neighborhood Edition provide estimates for?All health estimates in this version of AskCHIS Neighborhood Edition are based on data from the 2013-2014 California Health Interview Survey. 2. Why do your population estimates differ from other sources like ACS? The population estimates in AskCHIS NE represent the CHIS 2013-2014 population sample, which excludes Californians living in group quarters (such as prisons, nursing homes, and dormitories). 3. Why isn't there data available for all ZIP codes in Los Angeles?While AskCHIS NE has data on all ZCTAs (Zip Code Tabulation Areas), two factors may influence our ability to display the estimates:A small population (under 15,000): currently, the application only shows estimates for geographic entities with populations above 15,000. If your ZCTA has a population below this threshold, the easiest way to obtain data is to combine it with a neighboring ZCTA and obtain a pooled estimate.A high coefficient of variation: high coefficients of variation denote statistical instability.

Analysis of ‘Asthma ED Visit Rates by ZIP 2012’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/f4c0bf41-33a1-42a9-a345-374e21ecc549 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

This map contains counts & rates (per 10,000) of asthma (ICD9-CM ,493.0-493.9) emergency department visits among California residents by ZIP Code and age group (all ages, 0-17, 18+) for 2012. For more information please go to www.californiabreathing.org. Rates are not provided for ZIP Codes where the number of events was less than 12.

--- Original source retains full ownership of the source dataset ---

These data represent the predicted (modeled) prevalence of Asthma among adults (Age 18+) for each census tract in Colorado. Asthma is defined as ever being diagnosed with Asthma by a doctor, nurse, or other health professional, and still having the condition.The estimate for each census tract represents an average that was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).CDPHE used a model-based approach to measure the relationship between age, race, gender, poverty, education, location and health conditions or risk behavior indicators and applied this relationship to predict the number of persons' who have the health conditions or risk behavior for each census tract in Colorado. We then applied these probabilities, based on demographic stratification, to the 2013-2017 American Community Survey population estimates and determined the percentage of adults with the health conditions or risk behavior for each census tract in Colorado.The estimates are based on statistical models and are not direct survey estimates. Using the best available data, CDPHE was able to model census tract estimates based on demographic data and background knowledge about the distribution of specific health conditions and risk behaviors.The estimates are displayed in both the map and data table using point estimate values for each census tract and displayed using a Quintile range. The high and low value for each color on the map is calculated based on dividing the total number of census tracts in Colorado (1249) into five groups based on the total range of estimates for all Colorado census tracts. Each Quintile range represents roughly 20% of the census tracts in Colorado. No estimates are provided for census tracts with a known population of less than 50. These census tracts are displayed in the map as "No Est, Pop < 50."No estimates are provided for 7 census tracts with a known population of less than 50 or for the 2 census tracts that exclusively contain a federal correctional institution as 100% of their population. These 9 census tracts are displayed in the map as "No Estimate."

These data contain the Age-Adjusted Colorado Census Tract Rate of Asthma-Related Hospital Discharges (2015-2019) and Inpatient Hospitalizations per 100,000 persons based on the ICD-10 Code of J45-J46. The rates are calculated using the geocoded billing address of discharged individuals found in the dataset with the selected ICD-10 Codes and 2015-2019 Population Estimates from the American Community Survey. These data are from the Colorado Hospital Association's Hospital Discharge Dataset and are published annually by the Colorado Department of Public Health and Environment.

This map shows the percent of a weighted number of respondents who answer “yes” both to both of the following questions: “Have you ever been told by a doctor, nurse, or other health professional that you have asthma?” and the question “Do you still have asthma?”As stated by the CDC in the methodology:Asthma prevalence describes the size of a state’s population with asthma as well as the overall asthma prevalence relative to other chronic conditions. The greater the prevalence of asthma, the greater the likelihood of adverse outcomes from asthma including emergency department visits, hospitalizations, and death. Compared with persons without asthma, persons with asthma have more days of activity limitation and missed school and missed work and are more likely to report comorbid depression.Who is included in this survey?Weighted number of respondents to BRFSS (or National Survey of Children’s Health) excluding “don’t know” and “refused” responses to the question “Have you ever been told you have asthma?”Data SourceCDC's 2017 500 Cities ProjectArcGIS Living Atlas of the World contains multiple years of 500 Cities CDC layers, which can be found here. For more information about the methodology, visit https://www.cdc.gov/500cities or contact 500Cities@cdc.gov.

These data represent the predicted (modeled) prevalence of Asthma among adults (Age 18+) for each census tract in Colorado. Asthma is defined as ever being diagnosed with Asthma by a doctor, nurse, or other health professional, and still having the condition.The estimate for each census tract represents an average that was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).CDPHE used a model-based approach to measure the relationship between age, race, gender, poverty, education, location and health conditions or risk behavior indicators and applied this relationship to predict the number of persons' who have the health conditions or risk behavior for each census tract in Colorado. We then applied these probabilities, based on demographic stratification, to the 2013-2017 American Community Survey population estimates and determined the percentage of adults with the health conditions or risk behavior for each census tract in Colorado.The estimates are based on statistical models and are not direct survey estimates. Using the best available data, CDPHE was able to model census tract estimates based on demographic data and background knowledge about the distribution of specific health conditions and risk behaviors.The estimates are displayed in both the map and data table using point estimate values for each census tract and displayed using a Quintile range. The high and low value for each color on the map is calculated based on dividing the total number of census tracts in Colorado (1249) into five groups based on the total range of estimates for all Colorado census tracts. Each Quintile range represents roughly 20% of the census tracts in Colorado. No estimates are provided for census tracts with a known population of less than 50. These census tracts are displayed in the map as "No Est, Pop < 50."No estimates are provided for 7 census tracts with a known population of less than 50 or for the 2 census tracts that exclusively contain a federal correctional institution as 100% of their population. These 9 census tracts are displayed in the map as "No Estimate."

This layer maps the percentile of asthma incidence among California census tracts as calculated by the Communities Environmental Health Screening Tool (CalEnviroScreen) created by the Office of Environmental Health Hazard Assessment (OEHHA) on behalf of the California Environmental Protection Agency (CalEPA)CalEnviroScreen is a screening methodology that can be used to help identify California communities that are disproportionately burdened by multiple sources of pollution. The methodology is described in the current version (3.0) report and supporting documents available at http://oehha.ca.gov/calenviroscreen/report/calenviroscreen-30 .

These data contain the Age-Adjusted Colorado County Rate of Asthma-Related Hospital Discharges (2015-2019) and Inpatient Hospitalizations per 100,000 persons based on the ICD-10 Code of J45-J46. The rates are calculated using the geocoded billing address of discharged individuals found in the dataset with the selected ICD-10 Codes and 2013-2017 Population Estimates from the American Community Survey. These data are from the Colorado Hospital Association's Hospital Discharge Dataset and are published annually by the Colorado Department of Public Health and Environment.

Asthma is a life-threatening disease that sends many sufferers to the hospital each year. Use the map to discover asthma hospitalization count, crude rate and age-adjusted rate in Maryland for 2000 - 2009.Provided by the Maryland Department of Health (MDH)

This is a dataset of over 70,000 United States Census tracts enriched with over 25 demographic and environmental variables. These tracts cover the conterminous United States. The tract-level data were used to calculate and map climate resiliency indices.Data SourcesThis data product were first published in January 2022.United States (US) Census Bureau: American Community Survey (ACS) layers for all demographic and housing variables, TIGER/Line Shapefiles USA 2021 for national roads,US Centers for Disease Control and Prevention (CDC) for Daily Census Tract-Level PM2.5 Concentrations, 2016,US CDC PLACES: Local Data for Better Health for Current Asthma Prevalence,US Forest Service Wildfire Risk to Communities layers for Average Wildfire Exposure Type, Average Wildfire Risk to Homes, Average Housing Density, and Wildfire Hazard Potential.Processing NotesThe polygon features underwent several processing steps as part of the enrichment process. The tools used were dependent on the type of input data.All table joins used the attribute GEOID as a unique identifier for tracts.PM2.5 Concentrations were provided as coordinates for census tract centroids as well as census tract FIPS which was joined to polygon GEOIDs.The Zonal Statistics as Table geoprocessing tool was used on raster data types including Wildfire Exposure Type, Risk to Potential Structures, and Wildfire Hazard Potential inputs. Mean values for these inputs was calculated using the census tract as the zone and the raster as the value. Output was then joined back to the features.The Join Field geoprocessing tool was used with ACS input variables.The Egress Score was derived by intersecting TIGER/Line roads with tract boundaries. Roads were first filtered to include only Primary, Secondary, and Local roads. The number of intersections per tract was counted and normalized by the area of the tract. The inverse of this measure is called "Egress Score" and is used as a proxy for ranking tracts based on the number of routes into or out of each tract.*Note: This measure is intended for planning purposes only and should not be used for tactical decision making.Process OverviewFor every census tract, a Z-score was calculated that compares the value of each variable for the tract to the mean value for all tracts in the same county and is expressed as standard deviation from that mean. The Z-scores were than standardized into breaks ranging from 1 to 5 and averaged to create an overall wildfire resiliency index (WRI) for each tract. The WRIs and methodology were developed in collaboration with partners at the Centers for Disease Control and Prevention, UC Davis Department of Public Health, and the US Forest Service's Fire Lab.The tract Egress Score was derived by intersecting US Census Bureau TIGER/Line feature data with census tract polygon features to generate multipoint features. Because the TIGER/Line data may contain multiple coincident road segments that represent different road names, the multipoint features were dissolved using the unique GEOID and generated as point features. This result was summarized on GEOID and counted. The intersection point counts were joined back to the original tract features using GEOID. The counts were normalized by the area of the tracts and the reciprocal was calculated to get the Egress Score for the tract, higher Egress Score means fewer roads intersecting the tract and greater benefit from the intervention.Related WRI maps include “Where Will Better Air Filtration Improve Wildfire Resilience?”, “Where Will Home Hardening Improve Wildfire Resilience?”, and “Where Will Better Evacuation Routes Improve Wildfire Resilience?”.