The MarketScan health claims database is a compilation of nearly 110 million patient records with information from more than 100 private insurance carriers and large self-insuring companies. Public forms of insurance (i.e., Medicare and Medicaid) are not included, nor are small (< 100 employees) or medium (1000 employees). We excluded the relatively few (n=6735) individuals over 65 years of age because Medicare is the primary insurance of U.S. adults over 65. The EQI was constructed for 2000-2005 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Gray, C., D. Lobdell, K. Rappazzo, Y. Jian, J. Jagai, L. Messer, A. Patel, S. Deflorio-Barker, C. Lyttle, J. Solway, and A. Rzhetsky. Associations between environmental quality and adult asthma prevalence in medical claims data. ENVIRONMENTAL RESEARCH. Elsevier B.V., Amsterdam, NETHERLANDS, 166: 529-536, (2018).

This dataset contains counts and rates (per 10,000 residents) of asthma hospitalizations among Californians statewide and by county. The data are stratified by age group (all ages, 0-17, 18+, 0-4, 5-17, 18-64, 65+) and race/ethnicity (white, black, Hispanic, Asian/Pacific Islander, American Indian/Alaskan Native). The data are derived from the Department of Health Care Access and Information Patient Discharge Data. These data include hospitalizations from all licensed hospitals in California. These data are based only on primary discharge diagnosis codes. On October 1, 2015, diagnostic coding for asthma transitioned from ICD-9-CM (493) to ICD-10-CM (J45). Because of this change, CDPH and CDC do not recommend comparing data from 2015 (or earlier) to 2016 (or later). NOTE: Rates are calculated from the total number of asthma hospitalizations (not the unique number of individuals).

Note: This dataset is historical only and there are not corresponding datasets for more recent time periods. For that more-recent information, please visit the Chicago Health Atlas at https://chicagohealthatlas.org. This dataset contains the annual number of hospital discharges, crude hospitalization rates with corresponding 95% confidence intervals, and age-adjusted hospitalization rates (per 10,000 children and adults aged 5 to 64 years) with corresponding 95% confidence intervals, for the years 2000 – 2011, by Chicago U.S. Postal Service ZIP code or ZIP code aggregate. See the full dataset description for more information at http://bit.ly/PKI8p0.

This dataset contains counts and rates (per 10,000 residents) of asthma emergency department (ED) visits among Californians. The table “Asthma Emergency Department Visit Rates by County” contains statewide and county-level data stratified by age group (all ages, 0-17, 18+, 0-4, 5-17, 18-64, 65+) and race/ethnicity (white, black, Hispanic, Asian/Pacific Islander, American Indian/Alaskan Native). The table “Asthma Emergency Department Visit Rates by ZIP Code” contains zip-code level data stratified by age group (all ages, 0-17, 18+). The data are derived from the Department of Health Care Access and Information emergency department database. These data include emergency department visits from all licensed hospitals in California. These data are based only on primary discharge diagnosis codes. On October 1, 2015, diagnostic coding for asthma transitioned from ICD9-CM (493) to ICD10-CM (J45). Because of this change, CDPH and CDC do not recommend comparing data from 2015 (or earlier) to 2016 (or later). NOTE: Rates are calculated from the total number of asthma emergency department visits (not the unique number of individuals).

The highest prevalence of current asthma among U.S. children was reported in Connecticut, where 10.6 percent of all children were estimated to currently suffer from asthma. This statistic represents the prevalence of current asthma among children in the United States in 2022, by state.

Colorado county-level and state data on rates of hospitalizations among Colorado residents for multiple years as published by the Colorado Environmental Public Health Tracking project. Current years published include 2004-2018.Numerator/denominator informationEvent/numerator data:Hospital discharges, Hospital Discharge Data Set, Colorado Hospital Association.Emergency department discharges, Emergency Department Discharge Data Set, Colorado Hospital Association.Population/denominator data:Midyear resident population estimates. Source: State Demography Office, Colorado Department of Local Affairs.Interpreting the dataWhat these data tell us:These data tell us rates of hospitalizations and emergency department visits among Colorado residents over time and across counties. The rate is the number of hospitalizations or emergency department visits per state or county population in a calendar year.What these data do not tell us:These data do not tell us the number of people who currently have or experience each condition. The data may reflect more severe cases of each condition since people who are hospitalized or admitted to the emergency room often have a more severe illness.Comparisons of these rates of hospitalization and emergency department visits to environmental measures should be done with caution.Elevated rates of hospitalizations and emergency department visits in a geographic area with higher than average environmental exposure do not necessarily indicate that the environmental exposure is causing the higher rate.There may be other factors that lead to increased disease rates within a geographic area. Rates may differ due to factors such as access to medical care which can affect the likelihood of a person being hospitalized for asthma.Calculation methodsCase definition for hospitalizations and emergency department visits occurring:before October 1, 2015 are based on diagnosis codes from the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9CM).on or after October 1, 2015 are based on diagnosis codes from the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10CM).Age-specific rates in each age group and geographic population are calculated:per 10,000 population for asthma, chronic obstructive pulmonary disease (COPD), and heart attack.per 100,000 population for carbon monoxide poisoning and heat-related illness.Age-adjusted rates are calculated:per 10,000 population for asthma, chronic obstructive pulmonary disease, and heart attackper 100,000 population for carbon monoxide poisoning and heat-related illness.Rates are adjusted for differences across age and sex by the direct method using the Year 2000 U.S. Standard Population.Limitations of the dataThe hospital and emergency department visits datasets do not include all cases. Those who do not receive medical care, receive medical treatment in outpatient settings (other than emergency department), or die without being admitted to a hospital are not included in these datasets. Differences in rates by year or county may reflect differences or changes in medical coding or billing for hospitalizations and emergency department visits, or changes in access to medical care. Although exact duplicate records are excluded, the measures are based upon events, not individuals. If the same person is admitted to the hospital or emergency department multiple times for the same condition in the same year, these events would be counted as separate events, even though it was the same person. If people are being counted more than once, the reported rate may be higher than the true rate. Reporting rates at the state and county level is a broad measure. This means the data will not show the true disease burden at a more local level, such as the neighborhood. These data are not geographically specific enough to be linked with many types of environmental exposure, which may vary across the county.Data not includedThese data do not include hospital or emergency department discharges from Federal facilities in Colorado, such as U.S. Department of Veterans Affairs Medical Centers.

This data shows healthcare utilization for asthma by Allegheny County residents 18 years of age and younger. It counts asthma-related visits to the Emergency Department (ED), hospitalizations, urgent care visits, and asthma controller medication dispensing events. The asthma data was compiled as part of the Allegheny County Health Department’s Asthma Task Force, which was established in 2018. The Task Force was formed to identify strategies to decrease asthma inpatient and emergency utilization among children (ages 0-18), with special focus on children receiving services funded by Medicaid. Data is being used to improve the understanding of asthma in Allegheny County, and inform the recommended actions of the task force. Data will also be used to evaluate progress toward the goal of reducing asthma-related hospitalization and ED visits. Regarding this data, asthma is defined using the International Classification of Diseases, Tenth Revision (IDC-10) classification system code J45.xxx. The ICD-10 system is used to classify diagnoses, symptoms, and procedures in the U.S. healthcare system. Children seeking care for an asthma-related claim in 2017 are represented in the data. Data is compiled by the Health Department from medical claims submitted to three health plans (UPMC, Gateway Health, and Highmark). Claims may also come from people enrolled in Medicaid plans managed by these insurers. The Health Department estimates that 74% of the County’s population aged 0-18 is represented in the data. Users should be cautious of using administrative claims data as a measure of disease prevalence and interpreting trends over time. Missing from the data are the uninsured, members in participating plans enrolled for less than 90 continuous days in 2017, children with an asthma-related condition that did not file a claim in 2017, and children participating in plans managed by insurers that did not share data with the Health Department. Data users should also be aware that diagnoses may also be subject to misclassification, and that children with an asthmatic condition may not be diagnosed. It is also possible that some children may be counted more than once in the data if they are enrolled in a plan by more than one participating insurer and file a claim on each policy in the same calendar year.

This dataset contains the estimated percentage of Californians with asthma (asthma prevalence). Two types of asthma prevalence are included: 1) lifetime asthma prevalence describes the percentage of people who have ever been diagnosed with asthma by a health care provider, 2) current asthma prevalence describes the percentage of people who have ever been diagnosed with asthma by a health care provider AND report they still have asthma and/or had an asthma episode or attack within the past 12 months. The tables “Lifetime Asthma Prevalence by County” and “Current Asthma Prevalence by County” are derived from the California Health Interview Survey (CHIS) and include data stratified by county and age group (all ages, 0-17, 18+, 0-4, 5-17, 18-64, 65+) reported for 2-year periods. The table “Asthma Prevalence, Adults (18 and older)” is derived from the California Behavioral Risk Factor Surveillance System (BRFSS) and includes statewide data on adults reported by year.

BackgroundWorldwide, asthma is a leading cause of morbidity, mortality and economic burden, with significant gender and racial disparities. However, little attention has been given to the independent role of age on lifetime asthma severity and hospitalization. We aimed to assess the effect of age, gender, race and ethnicity on indicators of asthma severity including asthma related hospitalization, mortality, hospital cost, and the rate of respiratory failure.MethodsWe analyzed the 2011 and 2012 Healthcare Cost and Utilization Project- National Inpatient Sample (NIS). We validated and extended those results using the National Heart, Lung, and Blood Institute-Severe Asthma Research Program (SARP; 2002–2011) database. Severe asthma was prospectively defined using the stringent American Thoracic Society (ATS) definition.ResultsHospitalization for asthma was reported in 372,685 encounters in 2012 and 368,528 in 2011. The yearly aggregate cost exceeded $2 billion. There were distinct bimodal distributions for hospitalization age, with an initial peak at 5 years and a second at 50 years. Likewise, this bimodal age distribution of patients with severe asthma was identified using SARP. Males comprised the majority of individuals in the first peak, but women in the second. Aggregate hospital cost mirrored the bimodal peak distribution. The probability of respiratory failure increased with age until the age of 60, after which it continued to increase in men, but not in women.ConclusionsSevere asthma is primarily a disease of young boys and middle age women. Greater understanding of the biology of lung aging and influence of sex hormones will allow us to plan for targeted interventions during these times in order to reduce the personal and societal burdens of asthma.

Objective: For medically treated asthma, we estimated prevalence, medical and absenteeism costs, and projected medical costs from 2015 to 2020 for the entire population and separately for children in the 50 US states and District of Columbia (DC) using the most recently available data. Methods: We used multiple data sources, including the Medical Expenditure Panel Survey, U.S. Census Bureau, Kaiser Family Foundation, Medical Statistical Information System, and Current Population Survey. We used a two-part regression model to estimate annual medical costs of asthma and a negative binomial model to estimate annual school and work days missed due to asthma. Results: Per capita medical costs of asthma ranged from $1,860 (Mississippi) to $2,514 (Michigan). Total medical costs of asthma ranged from $60.7 million (Wyoming) to $3.4 billion (California). Medicaid costs ranged from $4.1 million (Wyoming) to $566.8 million (California), Medicare from $5.9 million (DC) to $446.6 million (California), and costs paid by private insurers ranged from $27.2 million (DC) to $1.4 billion (California). Total annual school and work days lost due to asthma ranged from 22.4 thousand (Wyoming) to 1.5 million days (California) and absenteeism costs ranged from $4.4 million (Wyoming) to $345 million (California). Projected increase in medical costs from 2015 to 2020 ranged from 9% (DC) to 34% (Arizona). Conclusion: Medical and absenteeism costs of asthma represent a significant economic burden for states and these costs are expected to rise. Our study results emphasize the urgency for strategies to strengthen state level efforts to prevent and control asthma attacks.

Data is CMAQ (Community Multiscale Air Quality) air quality modeling data contained in 12km grids (covering the eastern US) and 36 km grids (covering the entire US) for the years 2004, 2005, and 2006. Each CMAQ grid contains a concentration value for an air pollutant (fine particulate matter - PM2.5), and this concentration value can be used to determine the impact of air pollution concentrations on hospital emergency department admissions for asthma, and hospital inpatient admissions for asthma, myocardial infraction (MI), and heart failure (HF) in Baltimore Maryland. CMAQ data is in both .IOAPI (Input/Output Applications Programming Interface) format and .csv (comma-separated value) format. Health data was used in this analysis, but the health data cannot be released because it contains personally identifiable information (PII) on living individuals, and is protected by the Privacy Act of 1974 (as amended), the Health Insurance Portability and Accountability Act (HIPPA) of 1996 (as amended), and is exempt from Freedom of Information Act (FOIA) requests. The health dataset contains information about human research subjects, and access to it was limited by the Institutional Review Board (IRB) decision of 19 February 2014 (Protocol #13-76), and updated on 8 December 2016. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: The following folder has been set aside to access this (CMAQ_Data-only) data: ftp://newftp.epa.gov/EPADataCommons/ORD/NERL_SED/EHCAB/Hall_ORD-028187/. Format: Data is CMAQ (Community Multiscale Air Quality) air quality modeling data contained in 12km grids (covering the eastern US) and 36 km grids (covering the entire US) for the years 2004, 2005, and 2006. Each CMAQ grid contains a concentration value for an air pollutant (fine particulate matter - PM2.5), and this concentration value can be used to determine the impact of air pollution concentrations on hospital emergency department admissions for asthma, and hospital inpatient admissions for asthma, myocardial infraction (MI), and heart failure (HF) in Baltimore Maryland. CMAQ data is in both .IOAPI (Input/Output Applications Programming Interface) format and .csv (comma-separated value) format. Health data was used in this analysis, but the health data cannot be released because it contains personally identifiable information (PII) on living individuals, and is protected by the Privacy Act of 1974 (as amended), the Health Insurance Portability and Accountability Act (HIPPA) of 1996 (as amended), and is exempt from Freedom of Information Act (FOIA) requests. The health dataset contains information about human research subjects, and access to it was limited by the Institutional Review Board (IRB) decision of 19 February 2014 (Protocol #13-76), and updated on 8 December 2016. This dataset is associated with the following publication: Braggio, J., E. Hall, S. Weber, and A. Huff. Contribution of Satellite-Derived Aerosol Optical Depth PM2.5 Bayesian Concentration Surfaces to Respiratory-Cardiovascular Chronic Disease Hospitalizations in Baltimore, Maryland. ATMOSPHERE. MDPI AG, Basel, SWITZERLAND, 11(2): 209, (2020).

This table contains 90 series, with data for years 2002 - 2002 (not all combinations necessarily have data for all years), and was last released on 2010-03-30. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...) Sex (2 items: Males; Females ...) Age group (3 items: 11 years; 13 years; 15 years ...) Asthma related health problems (5 items: Has a doctor ever told you that you have asthma; In the last 12 months; have you ever had episodes of wheezing (whistling in the chest); In the last 12 months; other than a cold; have you had a dry cough at night; In the last 12 months; did you wheeze or cough during or after a sport or active play ...) Student response (3 items: Yes; Don't know; No ...).

Autoimmunity prevalence, as measured by antinuclear antibodies (ANA), is increasing in U.S. adolescents. Improved hygiene and cleaner environments in childhood may reduce exposure to infections and other immune challenges, resulting in improper immune responses to later-life exposures. We examined associations of hygiene hypothesis indicators, including asthma, allergies, and antibodies to infectious agents, with ANA prevalence, measured by HEp-2 immunofluorescence, in adolescents (aged 12-19 years) over a 25-year time span in the National Health and Nutrition Examination Survey (NHANES) (N=2,709), adjusting for age, sex, race/ethnicity, body mass index, education and survey cycle, overall and within individual time periods, using logistic regression. Prevalence of ANA in adolescents increased from 5.0% in 1988-1991 to 12.8% in 2011-2012. ANA were positively associated with diagnosis of asthma in early childhood (OR: 2.07, CI: 1.09–3.99) and the effect estimate for current hay fever was elevated but not statistically significant (OR: 1.55, CI: 0.85–2.84). Fewer than 2% of those with ANA in 1988-1991 had been diagnosed with asthma, compared with 18% in 1999-2000, and 27% in 2003-2004 and 2011-2012. ANA trended negatively with Helicobacter pylori antibodies (OR: 0.49, CI: 0.24–0.99). ANA may be useful as an additional indicator of inadequate immune education in adolescence, a critical period of growth and development.