The Health Index is an Experimental Statistic to measure a broad definition of health, in a way that can be tracked over time and compared between different areas. These data are the provisional results of the Health Index for upper-tier local authorities in England, 2015 to 2018, to illustrate the type of analysis the Health Index can enable.

In 2023, Singapore ranked first with a health index score of ****, followed by Japan and South Korea. The health index measures the extent to which people are healthy and have access to the necessary services to maintain good health, including health outcomes, health systems, illness and risk factors, and mortality rates. The statistic shows the health and health systems ranking of countries worldwide in 2023, by their health index score.

These indicators are presented by Public Health — Seattle & King County, in conjunction with the King County Hospitals for a Healthier Community (HHC). The data offer a comprehensive overview of demographics, health, and health behaviors among King County residents. Users can search by key word or topic area to filter the table of contents displayed below. After clicking on an indicator, a summary tab will open and users can click on additional tabs to explore data analyzed by demographic characteristics, see how rates have changed over time, and view data for cities/neighborhoods. Most indicators are interactive and users can hover over maps or charts to find more information. The data presented on this website may be reproduced without permission. Please use the following citation when reproducing: "Retrieved (date) from Public Health – Seattle & King County, Community Health Indicators. www.kingcounty.gov/chi"

Graph and download economic data for Producer Price Index by Commodity: Health Care Services (WPU51) from Jun 2009 to Sep 2025 about healthcare, health, services, commodities, PPI, inflation, price index, indexes, price, and USA.

Descriptions of the variables are shown. Slight differences in totals within categories compared to all are due to the small percentages (<2%) of participants for whom either age, gender or type of MS was missing.

The NYSE Health Care Index tracks the performance of the equity components on the New York Stock Exchange that offer goods and services in the health care industry. Between January 2004 and February 2025, the NYSE Health Care Index increased overall and reached a value of *********.

ENVIRONMENTAL HEALTH HAZARD INDEXSummary

The environmental health hazard exposure index summarizes potential exposure to harmful toxins at a neighborhood level. Potential health hazards exposure is a linear combination of standardized EPA estimates of air quality carcinogenic (c), respiratory (r) and neurological (n) hazards with i indexing census tracts.

Where means and standard errors are estimated over the national distribution.

InterpretationValues are inverted and then percentile ranked nationally. Values range from 0 to 100. The higher the index value, the less exposure to toxins harmful to human health. Therefore, the higher the value, the better the environmental quality of a neighborhood, where a neighborhood is a census block-group.

Data Source: National Air Toxics Assessment (NATA) data, 2014. Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 13.

References: https://www.epa.gov/ttn/atw/natamain/

To learn more about the Environmental Health Hazard Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 07/2020

Diabetes Health Indicators Dataset

Overview

This dataset contains 100,000 patient records designed for diabetes risk prediction, analysis, and machine learning applications. The dataset is clean, preprocessed, and ready for use in classification, regression, feature engineering, statistical analysis, and data visualization.

• Rows: 100,000
• Columns: 35+
• File: diabetes_dataset.csv

Dataset Description

The dataset includes patient profiles with features based on demographics, lifestyle habits, family history, and clinical measurements that are well-established indicators of diabetes risk. All data is generated using statistical distributions inspired by real-world medical research, ensuring privacy preservation while reflecting realistic health patterns.

Features

Data Quality

• ✅ Complete: No missing values or duplicates
• ✅ Clean: All values fall within medically realistic ranges
• ✅ Balanced Features: Distribution matches realistic population health patterns
• ✅ Target Distribution: ~20–25% diagnosed cases (balanced for ML classification)

Use Cases

• 🩺 Binary Classification → Predict diagnosed_diabetes (Yes/No)
• 🧮 Multiclass Classification → Predict diabetes_stage
• 📊 Regression → Predict glucose_fasting, hba1c, or diabetes_risk_score
• 🔍 EDA & Visualization → Explore lifestyle and clinical health patterns
• 🧠 Machine Learning → Train ML/DL models for healthcare prediction tasks
• 📈 Statistical Testing → Hypothesis testing on health indicators

Percent of population 18-64 years of age with no health insurance coverage by race/ethnicity in New Orleans and the United States

Indicators that describe the occupational health status of the working population in Connecticut; Data typically has three-year lag period. Data available from 2000 through 2021. Data for 2021 is complete and a few measures for 2022 are available. Blank Value Cells = Not Calculated, suppressed or invalid.

These products represent crop health indices derived from the Versatile Soil Moisture Budget (VSMB) model using crop specific coefficients and station based precipitation and temperature measurements to simulate crop growth. The VSMB model simulates soil moisture dynamics and water stress conditions based on water availability in the soil profile and simulated evapotranspiration during the crop growing season. Crop phenological stages, which are related to crop water use, are determined by a biometeorlogical time scale model (Robertson, 1968) for cool season crops (wheat, barley etc.) and a Crop Heat Unit (Brown and Bootsma, 1993) algorithm for warm season crops (corn and soybean etc.).

Age-standardised rates based on data from the European Health Interview Survey (EHIS), 2019 to 2020, for the UK by sex and country.

The California Healthy Places Index 3.0 data file was acquired on 04/25/22 from the Public Health Institute on behalf of the Public Health Alliance of Southern California.According to the Public Health Institute, "The HPI tool evaluates the relationship between 23 identified key drivers of health and life expectancy at birth -- which can vary dramatically by neighborhood. Based on that analysis, it produces a score ranking from 1 to 99 that shows the relative impact of conditions in a selected area compared to all other such places in the state." The HPI score is divided across four quartiles. (The Enhanced HPI 3.0: Advancing Health Equity Through High-Quality Data)Potential indicators assigned to eight policy action areas (domains):EconomicsEducationHealthcare accessHousingNeighborhood ConditionsClean EnvironmentSocial EnvironmentTransportationAn HPI score, domains, and individual indicator values and their percentile rankings are presented in the table.For more information, visit the California Healthy Places Index website at https://www.healthyplacesindex.org/ProcessConverted the XLSX file received from the Public Health Institute to a file geodatabase table. Filtered the statewide data to Los Angeles County only. The filtered dataset retains the original default HPI score rank, which is based on conditions across statewide census tracts. Edited field alias names for readability. Joined table to CENSUS_TRACTS_2010 from the Los Angeles County eGIS Data Repository. Exported to new file geodatabase feature class.

WHO Malaysia Health Indicators

Malaria, HIV/STIs, Suicide, CVD, Mortality, and more

By Humanitarian Data Exchange [source]

About this dataset

This dataset contains a range of indicators related to health, health systems, and sustainable development from the World Health Organization's data portal. It covers topics ranging from mortality and global health estimates to essential health technologies, youth engagement, mental health initiatives, and infectious diseases. With data points including publich state codes and display values, this dataset provides detailed insight into how healthcare is managed all around the globe. From tracking malaria outbreaks to exploring various international agreements on public healthcare initiatives, this dataset offers a wide array of powerful information for machine learning projects that are designed to improve our understanding of global healthcare trends. Explore the correlations between different countries' universal healthcare coverage measures or investigate any discrepancies between developed and developing nations - unlock deeper insights with the WHO's extensive data!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Getting Started: First, you need to download the dataset from Kaggle. Once you have it saved in your computer, open it with a spreadsheet software such as Excel or Google Sheets.

Exploring the Data: The dataset contains columns that offer information about indicators related to health in Malaysia including mortality rates, prevention programs and providers, financing information, human resource information, and more. To explore particular aspects of this data you should filter the rows using any of these column values. For example if you want results for a specific year or region you can filter by ‘year’ or ‘region’ accordingly. It’s important to note that some columns have relation between them (e.g., country code corresponds with country display name).

Data Outputs:
Using this dataset allows users to generate visual representations such as graphs which can help display trends over time regarding our stability goals concerning human resources funding rates or pregnancies outcomes among other variables included in our report summary outputs on WHO dashboard at global level specifically representing data coming from our members countries likeMalaysia making sense out these actions performed by several governments highlights where we still have areas lacking risk mitigation efforts and core elements when tryingto achieve better life quality around world aiming better efficiency through good governance practices supported on demand reduction strategies coming from healthcare professionals expertise frame work .

Conclusion:

Research Ideas

• Analysis of health coverage and services in Malaysia, allowing comparison between different public health organizations and the effect of specific prevention programs.
• Identification of gaps between existing healthcare access and provide a standardized data-driven reference point to ensure equitable access across different regions in the country.
• Creation of interactive geographical dashboards that display comparisons among relevant indicators, providing visual representation on how to best target distribution resources for optimal impact

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: rsud-service-organization-and-delivery-prevention-programs-and-providers-indicators-for-malaysia-38.csv | Column name | Description | |:--------------------------------------|:----------------------------------------------------------------| | GHO (CODE) | The Global Health Observatory code for the indicator. (String) | | GHO (DISPLAY) | The name of the indicator. (String) | | GHO (URL) | The URL for the indicator. (URL) | | PUBLISHSTATE (CODE) | The code for the publishing state of the indicator. (String) | | PUBLISHSTATE (DISPLAY) | The name of the publishing state of the indicator. (String) | | PUBLISHSTATE (URL) | The URL for the publishing state of the indicator. (URL) | | YEAR (CODE) | The code for...

Health index

The Community Health and Equity Index was developed by Raimi + Associates to compare health conditions, vulnerabilities, and cumulative burdens across the City of Los Angeles. The Index standardizes demographic, socio-economic, health conditions, land use, transportation, food environment, crime, and pollution burden variables, and then averages them together, yielding a score on a scale of 0-100. Lower values indicate better community health.Variables used in the index include: Hardship Index, Life Expectancy, Health Variables (Heart Disease Mortality, Emergency Department Visits for Heart Attacks, Respiratory Disease Mortality, Diabetes Mortality, Stroke Mortality, Childhood Obesity, Percentage of Low Birth Weight Infants, Number of Emergency Department Visits for Asthma for Under 17 and 18+ age groups), Walkability Index, Complete Communities Index (amenities and establishments serving the community), Transportation Index, Modified Retail Food Environment Index, Crime Rate (Violent Crimes, Property Crimes), and Pollution Burden (Pollution Exposure, Environmental Effects).Variables were assigned weights and averaged together. Weights were assigned based on the weights used in the 2013 Health Atlas. For more information, see page 181 of the 2013 Health Atlas, which is available as a PDF on the Los Angeles City Planning website, https://planning.lacity.gov.

The Case Mix Index (CMI) is the average relative DRG weight of a hospital’s inpatient discharges, calculated by summing the Medicare Severity-Diagnosis Related Group (MS-DRG) weight for each discharge and dividing the total by the number of discharges. The CMI reflects the diversity, clinical complexity, and resource needs of all the patients in the hospital. A higher CMI indicates a more complex and resource-intensive case load. Although the MS-DRG weights, provided by the Centers for Medicare & Medicaid Services (CMS), were designed for the Medicare population, they are applied here to all discharges regardless of payer. Note: It is not meaningful to add the CMI values together.

This dataset combines multiple open data sets for Covid-19 cases and deaths (kaggle1), Death causes (ourworldindata1, ourworldindata2, ourworldindata3, Food sources (FAO1), Health Care System (WHO1, WHO2, WHO3), TB vaccine status (BCG1) School closures (UNESCO1), and People/Society facts (CIA1).

Graph and download economic data for Economic Policy Uncertainty Index: Categorical Index: Health care (EPUHEALTHCARE) from Jan 1985 to Oct 2025 about healthcare, uncertainty, health, World, and indexes.