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.

The "Asthma Disease Prediction"😮‍💨 dataset is a comprehensive collection of anonymized health records and patient data, meticulously curated for predictive modeling and research purposes. It includes vital patient information, environmental factors, and medical history, enabling the development of advanced machine learning models to forecast asthma onset, severity, and treatment outcomes. This dataset serves as a valuable resource for improving early diagnosis and management of asthma, ultimately enhancing the quality of care for affected individuals🤩.

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 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.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

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).

Asthma Detection Dataset: Version 2 Overview The Asthma Detection Dataset: Version 2 is a specialized collection of audio samples designed to facilitate research in diagnosing asthma and other lung conditions using machine learning (ML) and deep learning (DL) techniques. This dataset is self-created and carefully curated, consisting of sound files segmented into 1.5-5 seconds to ensure consistency and manageability for analysis. The dataset is ideal for developing and testing ML models aimed at detecting asthma through the analysis of lung sounds. Dataset Composition The dataset comprises a total of 1,211 audio samples, divided into five distinct classes representing various lung conditions: • Asthma: 288 samples • Bronchial: 104 samples • COPD: 401 samples • Healthy: 133 samples • Pneumonia: 255 samples Each folder in the dataset corresponds to one of these classes, containing audio recordings specific to that condition. The distribution of samples is balanced to allow effective training and evaluation of ML models. Methodology Data Collection and Preprocessing The dataset was generated by processing and splitting recordings from two standard audio datasets, as well as additional recordings from individual patients. Each recording was divided into smaller segments, each lasting between 1.5-5 seconds, to standardize the input data for ML models. The data were collected from the following sources: 1. The Respiratory Sound Database: Approximately 170 samples were obtained from this database and preprocessed to remove noise and segment into 5-6 second intervals. The original database can be accessed at DOI: 10.1016/j.bbe.2020.11.003. 2. ICBHI (International Conference on Biomedical and Health Informatics) Dataset: 212 samples were sourced from this dataset, known for its comprehensive collection of respiratory sounds. These were also preprocessed and segmented. The dataset is available at PhysioNet ICBHI dataset. 3. Additional Samples from 22 Patients: The remaining samples were collected from 18 individual patients, including both normal (healthy) individuals and those diagnosed with various respiratory conditions such as asthma, bronchitis, COPD, and pneumonia. The following preprocessing steps were applied: • Data Cleaning: Noise reduction and cleaning were performed using WavePad software to enhance the clarity of the lung sounds. Irrelevant, incomplete, or noisy entries were removed. • Amplification: The volume of the recordings was adjusted using the amplify function in WavePad to ensure consistent loudness across all samples. • Segmentation: Audio files were segmented to ensure uniformity in duration, facilitating consistent analysis. All audio files were standardized to 5-6 seconds in length. Applications This dataset is suitable for a wide range of research applications, including but not limited to: • Developing ML and DL models for the diagnosis of asthma and other lung conditions. • Comparative studies on the efficacy of different classification algorithms in identifying lung conditions from audio data. • Exploratory analysis to understand the unique acoustic patterns associated with different lung diseases. Citation If you use this dataset in your research, please cite the following study: Tawfik, M., Al-Zidi, N. M., Fathail, I., & Nimbhore, S. (2022). Asthma Detection System: Machine and Deep Learning-Based Techniques. In Artificial Intelligence and Sustainable Computing: Proceedings of ICSISCET 2021 (pp. 207-218). Singapore: Springer Nature Singapore. Acknowledgements We acknowledge the contributions of the Respiratory Sound Database and the ICBHI dataset for providing valuable data. We also thank the patients who participated in this study. Tools such as WavePad were instrumental for data preprocessing. Additionally, we appreciate the Kaggle community for providing a platform to share and collaborate on valuable datasets. Conclusion The Asthma Detection Dataset: Version 2 provides a robust foundation for developing ML and DL models aimed at detecting asthma using lung sounds. By leveraging this dataset, researchers can contribute to advancing the field of respiratory health diagnostics, potentially leading to improved outcomes for patients with asthma and other lung conditions.

Asthma ED Visit Rates by ZIP

Counts and Rates by Age Group in California

By Health [source]

About this dataset

This dataset presents a comprehensive look into the prevalence of asthma among Californian residents in terms of emergency department visits. Using age-adjusted rates and county FIPS codes, it offers an accurate snapshot of the prevalence rates per 10,000 people and provides key insights into how this condition affects certain age groups by ZIP Code. With its easy to use associated map view, this dataset allows users to quickly gain deeper knowledge about this important health issue and craft meaningful solutions to address it

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How to use the dataset

This dataset contains counts and rates of asthma related emergency department visits by ZIP Code and age group in California. This data can be useful when doing research on asthma related trends or attempting to find correlations between environmental factors, prevalence of disease and geography.

• Select a year for analysis - the latest year for which data is available is the default selection, but other years are also listed in the dropdown menu.
• Select an Age Group to analyze - use the provided dropdown menus to select one or more age groups (all ages, 0-17, 18+) if you wish to analyze two different age groups in your analysis.
• Define a geographical area by selecting a ZIP code or County Fips code from which you wish to obtain your dataset from based on its availability or importance in your research question .
• View and download relevant data - after selecting all of the desired criteria (year,Age group(s), ZIP code/County FIPS Code) click “View Data” then “Download” at the bottom right corner of window that opens up
  5 Analyze information found - use software such as Microsoft Excel or open source programs like Openoffice Calc to gain insight into your downloaded dataset through statistics calculations, graphs etc.. In particular look out for anomalies that could signify further investigation

Research Ideas

• Identifying the geographic clusters of asthma sufferers by analyzing the rate of emergency department visits with geographic mapping.
• Developing outreach initiatives to areas with a high rate of ED visits for asthma to provide education, interventions and resources designed towards increasing preventive care and reducing preventable complications due to lack of access or knowledge about available services in these communities.
• Assessing disparities in ED visit rates for asthma between age groups as well as between urban and rural areas or different socio-economic groups within counties or ZIP codes in order to identify areas where there is a need for increased interventions, services and other resources related to asthma care in order to reduce the burden or severity of this chronic condition among particularly vulnerable population groups

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: Asthma_Emergency_Department_Visit_Rates_by_ZIP_Code.csv | Column name | Description | |:----------------------|:------------------------------------------------------------------------------------------------------------------| | Year | The year the data was collected. (Integer) | | ZIP code | The ZIP code of the area the data was collected from. (String...

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of asthma (in persons of all ages). Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to asthma (in persons of all ages).This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.The percentage of each MSOA’s population (all ages) with asthma was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of registered patients that have that illness The estimated percentage of each MSOA’s population with asthma was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with asthma, within the relevant age range.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have asthmaB) the NUMBER of people within that MSOA who are estimated to have asthmaAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA that are estimated to have asthma, compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people suffer from asthma, and where those people make up a large percentage of the population, indicating there is a real issue with asthma within the population and the investment of resources to address that issue could have the greatest benefits.LIMITATIONS1. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of asthma, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of asthma.TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:Health and wellbeing statistics (GP-level, England): Missing data and potential outliersLevels of obesity, inactivity and associated illnesses (England): Missing dataDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

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 1,000,000 residents) of asthma deaths among Californians statewide and by county. The data are stratified by age group (all ages, 0-17, 18+) and reported for 3-year periods. The data are derived from the California Death Statistical Master Files, which contain information collected from death certificates. All deaths with asthma coded as the underlying cause of death (ICD-10 CM J45 or J46) are included.

This synthetic dataset simulates health records of individuals with varying levels of asthma severity. It is designed to support predictive modeling, classification, and exploratory analysis in the healthcare domain.

The dataset contains patient-level data such as demographics, lifestyle factors, environmental exposures, and medical indicators that are known to influence asthma risk and severity.

Use cases include: - Asthma severity prediction - Health risk scoring - Impact analysis of factors like pollution, BMI, or smoking - Educational machine learning tasks

Since the data is fully synthetic, it is safe for public use and contains no personal or sensitive information.

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).

Asthma is a chronic disease that affects the airways that carry oxygen in and out of the lungs. If a person has asthma, the inside of these airways is irritated and swollen. Asthma can cause shortness of breath, wheezing, coughing, and tightness in the chest. For some people, asthma symptoms only appear when they are exposed to something that irritates their breathing such as cigarette smoke, dust, or pet dander. Others have a kind of asthma that makes breathing difficult all of the time.

The data contained in this dataset is collected by the Iowa Hospital Association on behalf of the Iowa Department of Public Health in accordance with Iowa Code section 135.166. This dataset contains confidential information. Small numbers suppression has been applied to data available to the public. The measures include Emergency Department encounters that result in inpatient admissions as well as Emergency Department only encounters.

This dataset reports the crude rate of emergency hospital admissions for asthma among children and young people aged under 19. It provides a measure of the burden of acute asthma episodes requiring urgent medical care and serves as an important indicator of respiratory health and healthcare access for this age group.

Rationale Reducing hospital admissions caused by asthma in children and young people is a key public health objective. High admission rates may reflect poor asthma control, environmental triggers, or gaps in primary care and early intervention. Monitoring this indicator supports efforts to improve asthma management and reduce preventable hospitalisations.

Numerator The numerator is the number of emergency hospital admissions for individuals aged under 19 with a primary diagnosis of asthma, identified using ICD-10 codes J45 (Asthma) and J46 (Status asthmaticus). Data are sourced from the Secondary Uses Service (SUS).

Denominator The denominator is the total population of children and young people aged under 19, based on 2021 Census data.

Caveats The data reflect episodes of admission rather than individual patients, meaning multiple admissions by the same person are counted separately. Hospital admission rates may also be influenced by local variations in referral and admission practices, as well as differences in asthma prevalence. NHS England has identified a data quality issue, though further detail is not specified in this summary.

External References Fingertips Public Health Profiles – Asthma Admissions (Under 19)

Localities ExplainedThis dataset contains data based on either the resident locality or registered locality of the patient, a distinction is made between resident locality and registered locality populations:Resident Locality refers to individuals who live within the defined geographic boundaries of the locality. These boundaries are aligned with official administrative areas such as wards and Lower Layer Super Output Areas (LSOAs).Registered Locality refers to individuals who are registered with GP practices that are assigned to a locality based on the Primary Care Network (PCN) they belong to. These assignments are approximate—PCNs are mapped to a locality based on the location of most of their GP surgeries. As a result, locality-registered patients may live outside the locality, sometimes even in different towns or cities.This distinction is important because some health indicators are only available at GP practice level, without information on where patients actually reside. In such cases, data is attributed to the locality based on GP registration, not residential address.

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

This dataset reports the percentage of patients aged 6 years and over who have asthma recorded on their general practice disease register. It provides a measure of asthma prevalence across the population, based on routinely collected data from the Quality and Outcomes Framework (QOF). The indicator helps to monitor the burden of asthma and supports healthcare planning and resource allocation.

Rationale

The rationale for this indicator is to support efforts to reduce the prevalence of asthma among individuals aged 6 years and over. By identifying and tracking cases, healthcare providers can better target interventions and improve management of this chronic respiratory condition.

Numerator

The numerator is the number of patients aged 6 years and over who are recorded as having asthma on the practice disease register. This excludes patients who have not been prescribed any asthma-related medication in the previous twelve months.

Denominator

The denominator is the total number of patients aged 6 years and over registered with the practice. This data is sourced from the Quality and Outcomes Framework (QOF), NHS Digital.

Caveats

Patients who have not been prescribed any asthma-related drugs in the past twelve months are excluded from the numerator, which may affect prevalence estimates. The data is dependent on accurate and up-to-date coding within GP systems.

External references

OHID Fingertips: Prevalence of Asthma (6yrs+)

External References Fingertips Public Health Profiles – Self-Harm Admissions (10–24 years)

Localities ExplainedThis dataset contains data based on either the resident locality or registered locality of the patient, a distinction is made between resident locality and registered locality populations:Resident Locality refers to individuals who live within the defined geographic boundaries of the locality. These boundaries are aligned with official administrative areas such as wards and Lower Layer Super Output Areas (LSOAs).Registered Locality refers to individuals who are registered with GP practices that are assigned to a locality based on the Primary Care Network (PCN) they belong to. These assignments are approximate—PCNs are mapped to a locality based on the location of most of their GP surgeries. As a result, locality-registered patients may live outside the locality, sometimes even in different towns or cities.This distinction is important because some health indicators are only available at GP practice level, without information on where patients actually reside. In such cases, data is attributed to the locality based on GP registration, not residential address.

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

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 table contains 267456 series, with data for years 2000 - 2000 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (not all combinations are available): Geography (199 items: Canada; Newfoundland and Labrador; Health and Community Services Eastern Region; Newfoundland and Labrador (Peer group D); Health and Community Services St. John's Region; Newfoundland and Labrador (Peer group H) ...), Age group (14 items: Total; 12 years and over; 15-19 years; 12-14 years; 12-19 years ...), Sex (3 items: Both sexes; Females; Males ...), Asthma (4 items: Total population for the variable asthma; Without asthma; Asthma; not stated; With asthma ...), Characteristics (8 items: Number of persons; Coefficient of variation for number of persons; High 95% confidence interval - number of persons; Low 95% confidence interval - number of persons ...).

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.

The dataset currently contains (1) a table of time periods (defined with start and end dates) during which a patient had any diagnosis of asthma; (2) and table of time periods for asthma severity level (based on prescriptions), asthma exacerbations, asthma-related hospital episodes, and asthma control; and (3) a table for other asthma-related data represented as events (e.g., lung function, blood tests, and A&E visit).

Includes an e-cohort of most people with a history of asthma in Wales, derived from the SAIL Databank's core datasets. Individuals are identified from the Welsh Longitudinal General Practice (WLGP) using several case definitions (e.g., having ever GP asthma diagnosis, asthma treatment in the last 12 months, or both). Data for each patient include essential research-ready asthma-related variables derived from primary and secondary care data, such as asthma treatment step, asthma severity, asthma exacerbations, and asthma-related death. The case definitions and some clinical variables are represented as clinical states. Additional case definitions and variables are actively being added. The WAO dataset is intended to support a wide range of cross-sectional and longitudinal epidemiological asthma studies as well as asthma surveillance, service planning, and health policy.

This is a source dataset for a Let's Get Healthy California indicator at https://letsgethealthy.ca.gov/. This dataset contains counts and rates (per 10,000 residents) of asthma (ICD9-CM, 493.0-493.9) emergency department visits among California residents by County and age group (all ages, 0-17, 18+). The data are derived from the Department of Health Care Access and Information emergency department databases. These data include emergency department visits from all licensed hospitals in California. These data are based only on primary discharge diagnosis codes (ICD9-CM). Starting in 2019, HCAI classified non-Hispanic individuals who identified with two or more races as "Multiracial." Previously these were assigned to a single other race. NOTE: Rates are calculated from the total number of Asthma ED Visits (not the unique number of individuals).