Key health nutrition & population statistics gathered from the World Bank, gathered from various international sources.

Data includes:

• Background
  • Economy
  • Education
  • Labor force
  • Poverty
• Cause of deaths
• Health financing
• HIV/AIDS
• Immunization
• Infectious diseases
• Medical resources and usage
• Non-communicable diseases
• Nutrition
• Population dynamics
  • Other demographic Indicators
  • Population
  • Population by age group
• Public Sector
  • Policy & institutions
• Reproductive health
• Water and sanitation

Public Health Indicators in Chicago

Natality, Mortality, Infectious Disease, Lead Poisoning and Economic Status

By City of Chicago [source]

About this dataset

This public health dataset contains a comprehensive selection of indicators related to natality, mortality, infectious disease, lead poisoning, and economic status from Chicago community areas. It is an invaluable resource for those interested in understanding the current state of public health within each area in order to identify any deficiencies or areas of improvement needed.

The data includes 27 indicators such as birth and death rates, prenatal care beginning in first trimester percentages, preterm birth rates, breast cancer incidences per hundred thousand female population, all-sites cancer rates per hundred thousand population and more. For each indicator provided it details the geographical region so that analyses can be made regarding trends on a local level. Furthermore this dataset allows various stakeholders to measure performance along these indicators or even compare different community areas side-by-side.

This dataset provides a valuable tool for those striving toward better public health outcomes for the citizens of Chicago's communities by allowing greater insight into trends specific to geographic regions that could potentially lead to further research and implementation practices based on empirical evidence gathered from this comprehensive yet digestible selection of indicators

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How to use the dataset

In order to use this dataset effectively to assess the public health of a given area or areas in the city: - Understand which data is available: The list of data included in this dataset can be found above. It is important to know all that are included as well as their definitions so that accurate conclusions can be made when utilizing the data for research or analysis. - Identify areas of interest: Once you are familiar with what type of data is present it can help to identify which community areas you would like to study more closely or compare with one another. - Choose your variables: Once you have identified your areas it will be helpful to decide which variables are most relevant for your studies and research specific questions regarding these variables based on what you are trying to learn from this data set.
- Analyze the Data : Once your variables have been selected and clarified take right into analyzing the corresponding values across different community areas using statistical tests such as t-tests or correlations etc.. This will help answer questions like “Are there significant differences between two outputs?” allowing you to compare how different Chicago Community Areas stack up against each other with regards to public health statistics tracked by this dataset!

Research Ideas

• Creating interactive maps that show data on public health indicators by Chicago community area to allow users to explore the data more easily.
• Designing a machine learning model to predict future variations in public health indicators by Chicago community area such as birth rate, preterm births, and childhood lead poisoning levels.
• Developing an app that enables users to search for public health information in their own community areas and compare with other areas within the city or across different cities in the US

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: public-health-statistics-selected-public-health-indicators-by-chicago-community-area-1.csv | Column name | Description | |:-----------------------------------------------|:--------------------------------------------------------------------------------------------------| | Community Area | Unique identifier for each community area in Chicago. (Integer) | | Community Area Name | Name of the community area in Chicago. (String) | | Birth Rate | Number of live births per 1,000 population. (Float) | | General Fertility Rate | Number of live births per 1,000 women aged 15-44. (Float) ...

Interactive Summary Health Statistics for Adults provide annual estimates of selected health topics for adults aged 18 years and over based on final data from the National Health Interview Survey.

In the contemporary world, mental health stands out as a significant and increasingly discussed issue. Recognizing its importance, efforts have been made to delve deeper into understanding mental health by collecting relevant data. This dataset, obtained from data.gov, has undergone a meticulous cleaning process, with a specific emphasis on extracting information related to mental health. The curated dataset presents key details, including the location, date, and confidence level associated with individuals' mental health records.

It is essential to acknowledge data.gov for providing this dataset, as it serves as a valuable resource in our quest to unravel more about mental health. The commitment to focusing on mental health-related information within the dataset enhances its relevance and utility in addressing the ongoing challenges and concerns related to mental well-being.

This data set contains various information about individuals' sleep habits and physical activities. The data provides important indicators of individuals' overall health and quality of life. Below is detailed information about the columns in the data set and their contents:

User ID: An individual's unique identification number.

Age: The age of the individual.

Gender: The sex of the individual ('f' female, 'm' male)

Sleep Quality: The quality of an individual's sleep (a scale of 1-10, with 10 indicating the highest quality)

Bedtime: The individual's bedtime (in 24-hour format)

Wake-up Time: The individual's wake-up time (in 24-hour format)

Daily Steps: Number of steps per day

Calories Burned: The amount of calories burned per day

Physical Activity Level: The individual's physical activity level (low, medium, high)

Dietary Habits: Dietary habits of the individual (healthy, medium, unhealthy)

Sleep Disorders: Whether the individual has sleep disorders (yes, no)

Medication Usage: Whether the individual uses medication for sleep disorders (yes, no)

Explanation: These data are imaginary data. It was created entirely for the purpose of improving users, it has nothing to do with reality.

A 2025 survey found that over half of U.S. individuals indicated the cost of accessing treatment was the biggest problem facing the national healthcare system. This is much higher than the global average of 33 percent and is in line with the high cost of health care in the U.S. compared to other high-income countries. Bureaucracy along with a lack of staff were also considered to be pressing issues.

The Health Information National Trends Survey (HINTS) is a biennial, cross-sectional survey of a nationally-representative sample of American adults that is used to assess the impact of the health information environment. The survey provides updates on changing patterns, needs, and information opportunities in health; Identifies changing communications trends and practices; Assesses cancer information access and usage; Provides information about how cancer risks are perceived; and Offers a testbed to researchers to test new theories in health communication.

Health, United States is the report on the health status of the country. Every year, the report presents an overview of national health trends organized around four subject areas: health status and determinants, utilization of health resources, health care resources, and health care expenditures and payers.

This dataset contains model-based ZIP Code Tabulation Area (ZCTA) level estimates for the PLACES project by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. It represents a first-of-its kind effort to release information uniformly on this large scale. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2019 or 2018 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 or 2014–2018 estimates. The 2021 release uses 2019 BRFSS data for 22 measures and 2018 BRFSS data for 7 measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours a night). Seven measures are based on the 2018 BRFSS data because the relevant questions are only asked every other year in the BRFSS. This data only covers the health of adults (people 18 and over) in East Baton Rouge Parish. All estimates lie within a 95% confidence interval.

This is a monthly report on publicly funded community services for people of all ages using data from the Community Services Data Set (CSDS) reported in England for August 2025. It has been developed to help achieve better outcomes and provide data that will be used to commission services in a way that improves health, reduces inequalities, and supports service improvement and clinical quality. These statistics are classified as experimental and should be used with caution. Experimental statistics are new official statistics undergoing evaluation. More information about experimental statistics can be found on the UK Statistics Authority website (linked at the bottom of this page). A provisional data file for September 2025 is now included in this publication. Please note this is intended as an early view until providers submit a refresh of their data, which will be published next month.

Zip Code, Life expectancy; Cancer deaths per 100,000 people; Heart disease deaths per 100,000 people; Alzheimer’s disease deaths per 100,000 people; Stroke deaths per 100,000 people; Chronic lower respiratory disease deaths per 100,000 people; Unintentional injury deaths per 100,000 people; Diabetes deaths per 100,000 people; Influenza and pneumonia deaths per 100,000 people; Hypertension deaths per 100,000 people. Percentages unless otherwise noted. Source information provided at: https://www.sccgov.org/sites/phd/hi/hd/Documents/City%20Profiles/Methodology/Neighborhood%20profile%20methodology_082914%20final%20for%20web.pdf

TD: Out-of-Pocket Health Expenditure: % of Private Expenditure on Health data was reported at 86.329 % in 2014. This stayed constant from the previous number of 86.329 % for 2013. TD: Out-of-Pocket Health Expenditure: % of Private Expenditure on Health data is updated yearly, averaging 92.829 % from Dec 1995 (Median) to 2014, with 20 observations. The data reached an all-time high of 96.483 % in 1995 and a record low of 85.859 % in 2010. TD: Out-of-Pocket Health Expenditure: % of Private Expenditure on Health data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chad – Table TD.World Bank.WDI: Social: Health Statistics. Out of pocket expenditure is any direct outlay by households, including gratuities and in-kind payments, to health practitioners and suppliers of pharmaceuticals, therapeutic appliances, and other goods and services whose primary intent is to contribute to the restoration or enhancement of the health status of individuals or population groups. It is a part of private health expenditure.; ; World Health Organization Global Health Expenditure database (see http://apps.who.int/nha/database for the most recent updates).; Weighted average;

Health Nutrition and Population Statistics database provides key health, nutrition and population statistics gathered from a variety of international and national sources. Themes include global surgery, health financing, HIV/AIDS, immunization, infectious diseases, medical resources and usage, noncommunicable diseases, nutrition, population dynamics, reproductive health, universal health coverage, and water and sanitation.

Vital statistics, health practitioner, health facilities, mental health, substance abuse, and disabilities data for North Carolina and counties.

Age-adjustment mortality rates are rates of deaths that are computed using a statistical method to create a metric based on the true death rate so that it can be compared over time for a single population (i.e. comparing 2006-2008 to 2010-2012), as well as enable comparisons across different populations with possibly different age distributions in their populations (i.e. comparing Hispanic residents to Asian residents). Age adjustment methods applied to Montgomery County rates are consistent with US Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS) as well as Maryland Department of Health and Mental Hygiene’s Vital Statistics Administration (DHMH VSA). PHS Planning and Epidemiology receives an annual data file of Montgomery County resident deaths registered with Maryland Department of Health and Mental Hygiene’s Vital Statistics Administration (DHMH VSA). Using SAS analytic software, MCDHHS standardizes, aggregates, and calculates age-adjusted rates for each of the leading causes of death category consistent with state and national methods and by subgroups based on age, gender, race, and ethnicity combinations. Data are released in compliance with Data Use Agreements between DHMH VSA and MCDHHS. This dataset will be updated Annually.

The SWAN Public Use Datasets provide access to longitudinal data describing the physical, biological, psychological, and social changes that occur during the menopausal transition. Data collected from 3,302 SWAN participants from Baseline through the 10th Annual Follow-Up visit are currently available to the public. Registered users are able to download datasets in a variety of formats, search variables and view recent publications.

The outbreak of Coronavirus (COVID-19) has led to unprecedented changes in the work and behaviour of GP practices and consequently the data in this publication may have been impacted, including indicators and contextual data from patients registered at a GP Practice. The data is extracted through the General Practice Extraction Service (GPES) therefore the burden of the Coronavirus (COVID-19) outbreak has not affected the collection of data for this publication. Caution should be taken in drawing any conclusions from this data without due consideration of the circumstances both locally and nationally as of 1 January 2020 and NHS Digital would recommend that any use of this data is accompanied by an appropriate caveat. Revised indicators in this publication are not comparable with previous versions due to a change in the indicator definitions. • Two indicators for Colorectal Screening indicators were replaced due to a change in criteria. • Two indicators for Palliative Care indicators were replaced due to a change in criteria. • Eighteen indicators new indicators were introduced, fourteen of which relate to patients with Autism. More information on these changes can be found in the Data Quality section of this publication. The aim of this publication is to provide information about the key differences in healthcare between people with a learning disability and those without. It contains aggregated data on key health issues for people who are recorded by their GP as having a learning disability, and comparative data about a control group who are recorded by their GP as not having a learning disability. Data has been collected from participating practices using EMIS, Cegedim Healthcare Systems (formerly Vision) and EVA Health Technologies (formerly Microtest) GP systems. Please note: on 6 January 2021, the Health and Care of People with Learning Disabilities 2016-17 to 2020-21: CCG coverage.csv has been updated to include the numerator and denominator used to calculate the coverage. this is in addition to the previous content which remains unchanged.

Number and percentage of persons for mental health indicators, by age group and sex.