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 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.

This dataset contains year, state and district wise number of Asthma Cases in children of age group 0-5 years

Note: Asthma is a condition in which your airways narrow and swell and may produce extra mucus. This can make breathing difficult and trigger coughing, a whistling sound (wheezing) when you breathe out and shortness of breath. For some people, asthma is a minor nuisance.

Analysis of ‘Air Pollution Effects: Asthma Prevalence by Year ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/asthma-prevalencee on 13 February 2022.

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

About this dataset

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.

Source: https://www.cdph.ca.gov/Programs/CCDPHP/DEODC/EHIB/CPE/Pages/CaliforniaBreathing.aspx
Last updated at https://data.chhs.ca.gov : 2020-07-31
License: https://data.chhs.ca.gov/pages/terms

This dataset was created by California Health and Human Services and contains around 0 samples along with 95% Ci Lower Limit, Measure, technical information and other features such as: - Unnamed: 7 - Unnamed: 9 - and more.

How to use this dataset

• Analyze Unnamed: 6 in relation to Unnamed: 8
• Study the influence of Unnamed: 5 on 95% Ci Upper Limit
• More datasets

Acknowledgements

If you use this dataset in your research, please credit California Health and Human Services

Start A New Notebook!

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

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.

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.

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.

from 24-march – 30-June 2021.

Analysis of ‘Childhood Asthma Healthcare Utilization’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/54c9bd57-5f60-47f8-aaa2-a3066a52ed86 on 26 January 2022.

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

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.

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

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.

Title: Dataset for IoT-Based Remote Health Monitoring System for Asthma Patients

Description: The sensor data from an Internet of Things-based remote health monitoring system created for people with asthma is included in this collection. The information includes both environmental and health factors, giving details on the patients' health and indoor environments. The dataset is a useful tool for researching the efficiency of the monitoring system and examining asthma patients' medical problems.

Environmental Data: The file contains measurements of the room's temperature, humidity, and dust density. A DHT11 sensor was used to measure temperature and humidity, while an optical dust sensor was used to measure dust concentration. Over the course of a month, the data was gathered every 15 minutes.

Health Data: The dataset also includes health parameters including body temperature, oxygen saturation, and heart rate (measured in beats per minute, or BPM). A MAX30100 sensor was used to measure the heart rate and the amount of oxygen in the blood, while a DS18B20 sensor was used to gauge the body temperature. Over the course of one week, measurements for each of these parameters were conducted at regular intervals on one patient.

Data Analysis: A thorough data analysis, including descriptive analysis, graphical representation, and statistical testing, has been performed on the dataset. For both environmental and health factors, descriptive analysis involves computing a number of statistical measures, including mean, standard deviation, minimum, maximum, and percentage of data outside permissible limits. To see the trends and patterns in the data, graphs were created, including line plots, box plots, and time series plots. In order to investigate the variations and importance of health markers throughout various time periods, statistical tests like ANOVA were carried out.

Data Reproducibility: Researchers may use the technique described in the corresponding paper to replicate the data. This involves connecting the sensors (DHT11, MAX30100, DS18B20) for data collecting, setting up the IoT-based monitoring system using NodeMCU and Arduino microcontrollers, and employing the relevant software platforms (Blynk, Thingspeak) for real-time monitoring and data visualization. The corresponding author will provide more thorough instructions upon request.

Childhood Allergies: Prevalence, Diagnosis, and Treatment Outcomes

Investigating Allergy Prevalence, Treatment Outcomes, and Patient Demographics

By [source]

About this dataset

This dataset contains the power to help us better understand the prevalence and treatment outcomes of childhood allergies over an extended period of time. Not only does it publicize the number of individuals currently suffering from asthma, atopic dermatitis, allergic rhinitis and food allergies through retrospective data as reported by healthcare providers - but it also features a set of columns which allow us to gain valuable insights into how these outcomes differ across different demographics such as gender, race and ethnicity. By further examining this data, we can start to recognize patterns in trends among the diagnosed cases - paving way for new treatments and prevention strategies that could prevent severe allergic reactions for many children all around the world

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

• Assess what kind of questions you want to answer using this data - do you want to focus on one particular type of allergy or analyze them together? Do you want a descriptive analysis or would an analysis that looks for correlations between conditions be more appropriate?

• Once you have determined your research question(s), identify what variables from the dataset are pertinent to your inquiry and assess any outliers that might need further investigation or filtering out during your analysis. Also consider any independent variables or confounding factors which might affect your results as well as any existing hypotheses related to the topic that might help guide your research project expectations

• Be aware of potential sources of bias when using self-reported healthcare provider information such as difficulties in disease identification (i.e allergies may be misdiagnosed). Additionally note that many allergy cases may go unreported/unrecorded due issues such as lack access/awareness about healthcare etc). A good way combat bias is by sample size - use largest possible datasets whenever available!

• Begin collecting relevant data from columns pertaining medical history (allergy diagnosis start & end date etc.), patient demographic information (gender factor ,ethnicity factor etc.), treatment trends & outcomes( first Asthma RX date , last asthma RX date , NUM asthma rx etc ). To get the most insights outta thisdata all these factors must be taken into account – if there isn’t enough evidence then explore other reliable sources too

• Structure & organize collected data so they can me easily accessed later – maybe create separate sheets/tabs with different categories i.e patient/treatment information OR create individual sheets for each subject depending upon how much info needs collecting .Designing formulaic functions will not only make life easier but critically save time & energy when it comes analyzing vast amounts data stored within workbook ! Remember larger sample sizes provide more

Research Ideas

• Use the dataset to identify risk factors or patterns in childhood allergies that can inform preventative and treatment measures.
• Investigate the correlation between demographic characteristics (e.g., age, gender) and diagnosis or severity of childhood allergies by using cross-tabs or other statistical techniques on the data provided in this dataset.
• Analyze longitudinal trends in treatment outcomes for various types of childhood allergy, such as asthma, atopic dermatitis and food allergy by comparing patient results over time (i.e., looking at pre-treatment diagnosis and post-treatment diagnoses)

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: food-allergy-analysis-Zenodo.csv | Column name | Description | |:----------------------------|:--------------------------------------------------------------| | BIRTH_YEAR | Year of birth of the patient. (Integer) | | GENDER_FACTOR ...

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of obesity, inactivity and inactivity/obesity-related illnesses. Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.The analysis incorporates data relating to the following:Obesity/inactivity-related illnesses (asthma, cancer, chronic kidney disease, coronary heart disease, depression, diabetes mellitus, hypertension, stroke and transient ischaemic attack)Excess weight in children and obesity in adults (combined)Inactivity in children and adults (combined)The analysis was designed with the intention that this dataset could be used to identify locations where investment could encourage greater levels of activity. In particular, it is hoped the dataset will be used to identify locations where the creation or improvement of accessible green/blue spaces and public engagement programmes could encourage greater levels of outdoor activity within the target population, and reduce the health issues associated with obesity and inactivity.ANALYSIS METHODOLOGY1. Obesity/inactivity-related illnessesThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to:- Asthma (in persons of all ages)- Cancer (in persons of all ages)- Chronic kidney disease (in adults aged 18+)- Coronary heart disease (in persons of all ages)- Depression (in adults aged 18+)- Diabetes mellitus (in persons aged 17+)- Hypertension (in persons of all ages)- Stroke and transient ischaemic attack (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.For each of the above illnesses, the percentage of each MSOA’s population with that illness 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 patients registered with each GP that have that illness The estimated percentage of each MSOA’s population with each illness 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 each illness, within the relevant age range.For each illness, 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 that illnessB) the NUMBER of people within that MSOA who are estimated to have that illnessAn 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 predicted to have that illness, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from an illness, and where those people make up a large percentage of the population, indicating there is a real issue with that illness within the population and the investment of resources to address that issue could have the greatest benefits.The scores for each of the 8 illnesses were added together then converted to a relative score between 1 – 0 (1 = worst, 0 = best), to give an overall score for each MSOA: a score close to 1 would indicate that an area has high predicted levels of all obesity/inactivity-related illnesses, and these are areas where the local population could benefit the most from interventions to address those illnesses. A score close to 0 would indicate very low predicted levels of obesity/inactivity-related illnesses and therefore interventions might not be required.2. Excess weight in children and obesity in adults (combined)For each MSOA, the number and percentage of children in Reception and Year 6 with excess weight was combined with population data (up to age 17) to estimate the total number of children with excess weight.The first part of the analysis detailed in section 1 was used to estimate the number of adults with obesity in each MSOA, based on GP-level statistics.The percentage of each MSOA’s adult population (aged 18+) with obesity was estimated, using GP-level data (see section 1 above). 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 adult patients registered with each GP that are obeseThe estimated percentage of each MSOA’s adult population with obesity was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of adults in each MSOA with obesity.The estimated number of children with excess weight and adults with obesity were combined with population data, to give the total number and percentage of the population with excess weight.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 excess weight/obesityB) the NUMBER of people within that MSOA who are estimated to have excess weight/obesityAn 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 predicted to have excess weight/obesity, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from excess weight/obesity, and where those people make up a large percentage of the population, indicating there is a real issue with that excess weight/obesity within the population and the investment of resources to address that issue could have the greatest benefits.3. Inactivity in children and adultsFor each administrative district, the number of children and adults who are inactive was combined with population data to estimate the total number and percentage of the population that are inactive.Each district was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that district who are estimated to be inactiveB) the NUMBER of people within that district who are estimated to be inactiveAn 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 district predicted to be inactive, compared to other districts. In other words, those are areas where a large number of people are predicted to be inactive, and where those people make up a large percentage of the population, indicating there is a real issue with that inactivity within the population and the investment of resources to address that issue could have the greatest benefits.Summary datasetAn average of the scores calculated in sections 1-3 was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer the score to 1, the greater the number and percentage of people suffering from obesity, inactivity and associated illnesses. I.e. these are areas where there are a large number of people (both children and adults) who are obese, inactive and suffer from obesity/inactivity-related illnesses, and where those people make up a large percentage of the local population. These are the locations where interventions could have the greatest health and wellbeing benefits for the local population.LIMITATIONS1. For data recorded at the GP practice level, 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 ‘Levels of obesity, inactivity and associated illnesses: Summary (England). Areas with data missing’ 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, 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

Originally, the dataset come from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to gather data on the health status of U.S. residents. As the CDC describes: "Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as the District of Columbia and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world.". The most recent dataset (as of February 15, 2022) includes data from 2020. It consists of 401,958 rows and 279 columns. The vast majority of columns are questions asked to respondents about their health status, such as "Do you have serious difficulty walking or climbing stairs?" or "Have you smoked at least 100 cigarettes in your entire life? [Note: 5 packs = 100 cigarettes]".

To improve the efficiency and relevance of our analysis, we removed certain attributes from the original BRFSS dataset. Many of the 279 original attributes included administrative codes, metadata, or survey-specific variables that do not contribute meaningfully to heart disease prediction—such as respondent IDs, timestamps, state-level identifiers, and detailed lifestyle questions unrelated to cardiovascular health. By focusing on a carefully selected subset of 18 attributes directly linked to medical, behavioral, and demographic factors known to influence heart health, we streamlined the dataset. This not only reduced computational complexity but also improved model interpretability and performance by eliminating noise and irrelevant information. All predicting variables could be divided into 4 broad categories:

1. Demographic factors: sex, age category (14 levels), race, BMI (Body Mass Index)

2. Diseases: weather respondent ever had such diseases as asthma, skin cancer, diabetes, stroke or kidney disease (not including kidney stones, bladder infection or incontinence)

3. Unhealthy habits:

   • Smoking - respondents that smoked at least 100 cigarettes in their entire life (5 packs = 100 cigarettes)
   • Alcohol Drinking - heavy drinkers (adult men having more than 14 drinks per week and adult women having more than 7 drinks per week

4. General Health:

   • Difficulty Walking - weather respondent have serious difficulty walking or climbing stairs
   • Physical Activity - adults who reported doing physical activity or exercise during the past 30 days other than their regular job
   • Sleep Time - respondent’s reported average hours of sleep in a 24-hour period
   • Physical Health - number of days being physically ill or injured (0-30 days)
   • Mental Health - number of days having bad mental health (0-30 days)
   • General Health - respondents declared their health as ’Excellent’, ’Very good’, ’Good’ ,’Fair’ or ’Poor’

Below is a description of the features collected for each patient:

Environmental exposures and socioeconomic status (SES) are associated with asthma and chronic obstructive pulmonary disease (COPD) morbidity and mortality. Despite efforts to reduce the impact of environmental exposures through regulation and education, knowledge gaps remain. We sought to understand how adults with asthma and COPD perceive and seek information about environmental factors, and how these responses varied by disease or socioeconomic characteristics. Participants with self-reported asthma or COPD enrolled in a digital platform for respiratory disease self-management, consisting of sensors to track medication use and a companion smartphone app, completed an electronic survey exploring perceptions of environmental factors. Using mixed-method analyses, we evaluated differences in responses by disease (asthma vs. COPD), education (≤ vs. > some college), annual household income (< vs. ≥ $50,000), and mean annual residential air pollutant exposure (> vs. ≤80th percentile). Survey responses from 698 participants [500 asthma (72%) and 198 COPD (28%)] were analyzed. A high percentage of participants perceived that environmental factors could influence their symptoms, including: pollen (93% for asthma vs. 86% for COPD), mold (89 vs. 85%), second-hand smoke (89 vs. 83%), and air pollution (84% for both). Participants reported seeking environmental information daily from an average of three sources, preferring mobile apps and television (TV) programs. Significant differences were identified by disease.ConclusionParticipants with asthma and COPD perceive a relationship between their respiratory symptoms and their environment and regularly seek out environmental information. This information can help inform digital health development for respiratory education and self-management.

Context

The respiratory disease causes an immense health burden. It is estimated that worldwide 235 million people suffer from asthma, more than 200 million people have chronic obstructive pulmonary disease (COPD), 65 million endure moderate-to-severe COPD, 1–6% of the adult population (more than 100 million people) experience sleep-disordered breathing, 9.6 million people develop tuberculosis (TB) annually, millions live with Pulmonary Hypertension and more than 50 million people struggle with occupational lung diseases,more than 1 billion people suffering from chronic respiratory conditions. At least 2 billion people are exposed to the toxic effects of biomass fuel consumption, 1 billion are exposed to outdoor air pollution and 1 billion are exposed to tobacco smoke. Each year, 4 million people die prematurely from chronic respiratory disease.To analyze pulmonary diseases we collected the data from the local health department of Albuquerque,NM,US. The Data containing different attributes to identify the disease and nature.

Content

This data was collected from public health department to identify different chronic respiratory diseases across the state of NM,US. This data consists of different attributes like Name,age,sex,diseases,treatment and nature.Here Name,Sex,Diseases,Treatment and Nature are string values and some of them like Sex and nature are categorical values.This data contains 37000+ records till now and it has been updated regularly in quarterly basis.

Acknowledgements

We would like to thank public health department of New Mexico for their cooperation and consider our request.

Each year, there are audits to assess maternal & foetal outcomes across the UK. In 2016-18, 217 women died during or up to six weeks after pregnancy, from causes associated with their pregnancy, among 2,235,159 women giving birth in the UK. 9.7 women per 100k died during pregnancy or up to six weeks after childbirth or the end of pregnancy. There was an increase in the overall maternal death rate in the UK between 2013-15 & 2016-18. Assessors judged that 29% of women who died had good care. However, improvements in care which may have made a difference to the outcome were identified for 51% of women who died. Birmingham has a higher than average maternal & foetal death rate. This dataset includes detailed information about the reasons pregnant women seek acute care, & their care pathways & outcomes. PIONEER geography: The West Midlands (WM) has a population of 5.9m & includes a diverse ethnic, socio-economic mix. There is a higher than average % of minority ethnic groups. WM has the youngest population in the UK with a higher than average birth rate. There are particularly high rates of physical inactivity, obesity, smoking & diabetes. 51.2% of babies born in Birmingham have at least one parent born outside of the UK, this compares with 34.7% for England. Each day >100k people are treated in hospital, see their GP or are cared for by the NHS. EHR: University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. Scope: Pregnant or post-partum women from 2015 onwards who attended A&E in Queen Elizabeth hospital. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics (including gestation & postpartum period), co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (admissions, wards, practitioner changes & discharge outcomes), presenting complaints, physiology readings (temperature, blood pressure, NEWS2, SEWS, AVPU), referrals, all prescribed & administered treatments & all outcomes. Available supplementary data: More extensive data including granular serial physiology, bloods, conditions, interventions, treatments. Ambulance, 111, 999 data, synthetic data. Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

GW642444 (the study medicine) is a new experimental medicine for treating asthma and chronic obstructive pulmonary disease (COPD). People with asthma and COPD suffer from breathlessness because the small tubes (bronchioles) that carry air in and out of the lungs become narrow. We hope that the study medicine will work by relaxing the muscles in the airways and help to keep the airways open, and make breathing easier. The study medicine might improve on available treatments by having fewer side-effects, and by working faster and for longer, so that patients could take it once daily, instead of twice daily. When a medicine is made into a form ready to be given to patients, inactive ingredients are often added. Inactive ingredients might be used to help a medicine work better, to make it easier to produce the medicine, or to make it easier to get an accurate dose of medicine. In this study, the study medicine contains the inactive ingredient magnesium stearate. We need to check that the study medicine doesn't cause problems when inhaled along with magnesium stearate. So, we're doing this study in healthy people to find out the answers to these questions. 1)Do repeated once-daily doses of the study medicine cause any important side effects when inhaled, along with magnesium stearate, as a fine powder? 2)How much of the study medicine gets into the bloodstream and how long does the body take to get rid of it?

The State Snapshots provide graphical representations of State-specific health care quality information, including strengths, weaknesses, and opportunities for improvement. The goal is to help State officials and their public- and private-sector partners better understand health care quality and disparities in their State. State-level information used to create the State Snapshots is based on data collected for the National Healthcare Quality Report (NHQR). The State Snapshots include summary measures of quality of care and States' performances relative to all States, the region, and best performing States by overall health care quality, types of care (preventive, acute, and chronic), settings of care (hospitals, ambulatory care, nursing home, and home health), and clinical conditions (cancer, diabetes, heart disease, maternal and child health, and respiratory diseases). Special focus areas on diabetes, asthma, Healthy People 2010, clinical preventive services, disparities, payer, and variation over time are also featured. The Agency for Healthcare Research and Quality (AHRQ) has released the State Snapshots each year in conjunction with the 2004 NHQR through the 2009 NHQR.

Background\ud Transforming the delivery of care for people with Long Term Conditions (LTCs) requires understanding about how health care policies in England and historical patterns of service delivery have led to different models of chronic disease management (CDM). It is also essential in this transformation to analyse and critique the models that have emerged to provide a more detailed evidence base for future decision making and better patient care. Nurses have made, and continue to make, a particular contribution to the management of chronic diseases. In the context of this study, there is a particular focus on the origins of each CDM model examined, the processes by which nursing care is developed, sustained and mainstreamed, and the outcomes of each case study as\ud experienced by service users and carers.\ud Aims\ud To explore, identify and characterise the origins, processes and outcomes of effective CDM models and the nursing contribution to such models using a whole systems approach\ud Methods\ud The study was divided into three phases:\ud Phase 1: Systematic mapping of published and web-based literature.\ud Phase 2: A consensus conference of nurses working within CDM. Sampling criteria were derived from the conference and selected nurses attended a follow up workshop where case study sites were identified.\ud Phase 3: Multiple case study evaluation\ud Sample: 7 case studies representing 4 CDM models. These were: i) public health nursing model; ii) primary care nursing model; iii) condition specific nurse specialist model; iv) community matron model.\ud Methods: Evaluative case study design with the unit of analysis the CDM model (Yin, 2003):\ud • semi-structured interviews with practitioners, patients, their carers, managers and commissioners\ud • documentary analysis\ud • psycho-social and clinical outcome data from specific conditions\ud • children and young people: focus groups, age-specific survey tools.\ud Benchmarking outcomes: Adults benchmarked against the Health Outcomes Data Repository (HODaR) dataset (Currie et al, 2005). Young people were benchmarked against the Health Behaviour of School aged Children Survey (Currie et al, 2008).\ud Cost analysis: Due to limitations in the available data, a simple costing exercise was undertaken to ascertain the per patient cost of the nurse contribution to CDM in each of the models, and to explore patterns of health and social care utilisation.\ud Analysis: A whole system methodology was used to establish the principles of CDM. i) The causal system is a “network of causal relationships” and focuses on long term trends and processes. ii) The data system recognises that for many important areas there is very little data. Where a particular explanatory factor is important but precise data are lacking, a range of methods should be\ud employed to illuminate each factor as much as possible. iii) The organisational whole system emphasises how various parts of the health and social care system function together as a single system rather than as parallel systems. iv) The patient experience recognises that the whole system comes together and is embodied in the experience of each patient.\ud Key findings\ud While all the models strove to be patient centred in their implementation, all were linked at a causal level to disease centric principles of care which dominated the patient experience.\ud Public Health Model\ud • The users (both parents and children) experienced a well organised and coordinated service that is crossing health and education sectors.\ud • The lead school nurse has provided a vision for asthma management in school-aged children. This has led to the implementation of the school asthma strategy, and the ensuing impacts including growing awareness, prevention of hospital admissions, confidence in schools about asthma management and healthier children.\ud Primary Care Model\ud • GP practices are providing planned and routine management of chronic disease, tending to focus on single diseases treated in isolation. Care is geared to the needs of the uncomplicated stable patient.\ud • More complex cases tend to be escalated to secondary care where they may remain even after the patient has stabilised.\ud • Patients with multiple diagnoses continue to experience difficulty in accessing services or practice that is designed to provide a coherent response to the idiosyncratic range of diseases with which they present.\ud This is as true for secondary care as for primary care.\ud • While the QOF system has clearly been instrumental in developing and sustaining a primary care nursing model of CDM, it has also limited the scope of the model to single diseases recordable on a register, rather than focus on patient centred care needs.\ud Nurse Specialist Model\ud • The model works under a disease focused system underpinned by evidence based medicine exemplified by NICE guidelines and NSF’s.\ud • The model follows a template drawn from medicine and sustai...