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) ...

US: Improved Sanitation Facilities: Urban: % of Urban Population with Access data was reported at 100.000 % in 2015. This stayed constant from the previous number of 100.000 % for 2014. US: Improved Sanitation Facilities: Urban: % of Urban Population with Access data is updated yearly, averaging 99.900 % from Dec 1990 (Median) to 2015, with 26 observations. The data reached an all-time high of 100.000 % in 2015 and a record low of 99.800 % in 1996. US: Improved Sanitation Facilities: Urban: % of Urban Population with Access data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Access to improved sanitation facilities, urban, refers to the percentage of the urban population using improved sanitation facilities. Improved sanitation facilities are likely to ensure hygienic separation of human excreta from human contact. They include flush/pour flush (to piped sewer system, septic tank, pit latrine), ventilated improved pit (VIP) latrine, pit latrine with slab, and composting toilet.; ; WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation (http://www.wssinfo.org/).; Weighted average;

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.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

In the United States in 2022, the majority of diagnostic vendors only shared data to health information exchanges (HIE) on a regional or state level. While around ** percent said they contributed data to a private HIE.

[ARCHIVED] Community Counts data is retained for archival purposes only, such as research, reference and record-keeping. This data has not been maintained or updated. Users looking for the latest information should refer to Statistics Canada’s Census Program (https://www12.statcan.gc.ca/census-recensement/index-eng.cfm?MM=1) for the latest data, including detailed results about Nova Scotia. This table reports health disorders by age group. Geographies available: county, district health authorities

IL: People Using Basic Sanitation Services: Rural: % of Rural Population data was reported at 100.000 % in 2015. This stayed constant from the previous number of 100.000 % for 2014. IL: People Using Basic Sanitation Services: Rural: % of Rural Population data is updated yearly, averaging 100.000 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 100.000 % in 2015 and a record low of 100.000 % in 2015. IL: People Using Basic Sanitation Services: Rural: % of Rural Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Israel – Table IL.World Bank: Health Statistics. The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, compositing toilets or pit latrines with slabs.; ; WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).; Weighted Average;

County Health Status Profiles is an annually published report for the State of California by the California Department of Public Health in collaboration with the California Conference of Local Health Officers. Health indicators are measured for 58 counties and California statewide that can be directly compared to national standards and populations of similar composition. Where available, the measurements are ranked and compared with target rates established for Healthy People National Objectives.

For tables where the health indicator denominator and numerator are derived from the same data source, the denominator excludes records for which the health indicator data is missing and unable to be imputed.

For more information see the County Health Status Profiles report.

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. Search, visualize, and download these and other estimates from a wide range of health topics with the NCHS Data Query System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.

Provides a reference for the comparison of key figures between the constituent countries, and between the UK as a whole and other nation states. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: United Kingdom Health Statistics

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.

The following datasets are based on the children and youth (under age 21) beneficiary population and consist of aggregate Mental Health Service data derived from Medi-Cal claims, encounter, and eligibility systems. These datasets were developed in accordance with California Welfare and Institutions Code (WIC) § 14707.5 (added as part of Assembly Bill 470 on 10/7/17). Please contact BHData@dhcs.ca.gov for any questions or to request previous years’ versions of these datasets. Note: The Performance Dashboard AB 470 Report Application Excel tool development has been discontinued. Please see the Behavioral Health reporting data hub at https://behavioralhealth-data.dhcs.ca.gov/ for access to dashboards utilizing these datasets and other behavioral health data.

CH: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data was reported at 6.600 NA in 2016. This records a decrease from the previous number of 6.700 NA for 2015. CH: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data is updated yearly, averaging 7.300 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 9.100 NA in 2000 and a record low of 6.600 NA in 2016. CH: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Switzerland – Table CH.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

List of footnotes, notes, and source information for NHIS Adult Summary Statistics. Each row of this dataset contains the accompanying text for a footnote found in the NHIS Adults Summary Statistics Dataset.

Bangladesh BD: Cause of Death: by Communicable Diseases & Maternal, Prenatal & Nutrition Conditions: % of Total data was reported at 22.613 % in 2019. This records a decrease from the previous number of 28.808 % for 2015. Bangladesh BD: Cause of Death: by Communicable Diseases & Maternal, Prenatal & Nutrition Conditions: % of Total data is updated yearly, averaging 30.893 % from Dec 2000 (Median) to 2019, with 4 observations. The data reached an all-time high of 50.247 % in 2000 and a record low of 22.613 % in 2019. Bangladesh BD: Cause of Death: by Communicable Diseases & Maternal, Prenatal & Nutrition Conditions: % of Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Social: Health Statistics. Cause of death refers to the share of all deaths for all ages by underlying causes. Communicable diseases and maternal, prenatal and nutrition conditions include infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting.;Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death;Weighted average;

The MN Public Health Data Access portal, maintained by the Minnesota Department of Health (MDH), provides data on over 20 health and environment topics. Data are accessible through charts, tables, and maps, and also may be downloaded from MDH's website. Users may use these data to inform state and local planning and policy, grant writing, research, and more.

Key Health App StatisticsTop Health AppsHealth & Fitness App Market LandscapeHealth App RevenueHealth Revenue by AppHealth App UsageHealth App Market ShareHealth App DownloadsKeeping track of...

Comprehensive Mental Health Insights: A Diverse Dataset of 1000 Individuals Across Professions, Countries, and Lifestyles

This dataset provides a rich collection of anonymized mental health data for 1000 individuals, representing a wide range of ages, genders, occupations, and countries. It aims to shed light on the various factors affecting mental health, offering valuable insights into stress levels, sleep patterns, work-life balance, and physical activity.

Key Features: Demographics: The dataset includes individuals from various countries such as the USA, India, the UK, Canada, and Australia. Each entry captures key demographic information such as age, gender, and occupation (e.g., IT, Healthcare, Education, Engineering).

Mental Health Conditions: The dataset contains data on whether the individuals have reported any mental health issues (Yes/No), along with the severity of these conditions categorized into Low, Medium, or High.

Consultation History: For individuals with mental health conditions, the dataset notes whether they have consulted a mental health professional.

Stress Levels: Each individual’s stress level is classified as Low, Medium, or High, providing insights into how different factors such as work hours or sleep may correlate with mental well-being.

Lifestyle Factors: The dataset includes information on sleep duration, work hours per week, and weekly physical activity hours, offering a detailed picture of how lifestyle factors contribute to mental health.

This dataset can be used for research, analysis, or machine learning models to predict mental health trends, uncover correlations between work-life balance and mental well-being, and explore the impact of stress and physical activity on mental health.

This dataset was originally collected for a data science and machine learning project that aimed at investigating the potential correlation between the amount of time an individual spends on social media and the impact it has on their mental health.

The project involves conducting a survey to collect data, organizing the data, and using machine learning techniques to create a predictive model that can determine whether a person should seek professional help based on their answers to the survey questions.

This project was completed as part of a Statistics course at a university, and the team is currently in the process of writing a report and completing a paper that summarizes and discusses the findings in relation to other research on the topic.

The following is the Google Colab link to the project, done on Jupyter Notebook -

https://colab.research.google.com/drive/1p7P6lL1QUw1TtyUD1odNR4M6TVJK7IYN

The following is the GitHub Repository of the project -

https://github.com/daerkns/social-media-and-mental-health

Libraries used for the Project -

Pandas
Numpy
Matplotlib
Seaborn
Sci-kit Learn

VAMC-level statistics on the prevalence, mental health utilization, non-mental health utilization, mental health workload, and psychological testing of Veterans with a possible or confirmed diagnosis of mental illness. Information prepared by the VA Northeast Program Evaluation Center (NEPEC) for fiscal year 2015. This dataset is no longer supported and is provided as-is. Any historical knowledge regarding meta data or it's creation is no longer available. All known information is proved as part of this data set.

Food Safety and Public Health Statistics (2021-2024)