This dataset provides comprehensive statistics on global health, focusing on various diseases, treatments, and outcomes. The data spans multiple countries and years, offering valuable insights for health research, epidemiology studies, and machine learning applications. The dataset includes information on the prevalence, incidence, and mortality rates of major diseases, as well as the effectiveness of treatments and healthcare infrastructure.

Dataset Use Cases:

This dataset can be used for:

• Healthcare Policy Analysis: Understanding which diseases are most prevalent and which countries require more investment in healthcare infrastructure.
• Epidemiological Studies: Studying the correlation between disease prevalence and socio-economic factors like income, education, and urbanization.
• Machine Learning Models: Training predictive models to forecast disease trends, mortality rates, and treatment effectiveness based on historical data.
• Global Health Research: Identifying regions that need targeted interventions or public health campaigns.

These data represent prevalence estimates of select chronic conditions from the National Health and Nutrition Examination Survey (NHANES). This version of the dataset is specific for use by the NCHS DQS. 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.

2002-2010. BRFSS SMART County Prevalence land line only data. The Selected Metropolitan Area Risk Trends (SMART) project uses the Behavioral Risk Factor Surveillance System (BRFSS) to analyze the data of selected counties with 500 or more respondents. BRFSS data can be used to identify emerging health problems, establish and track health objectives, and develop and evaluate public health policies and programs. BRFSS is a continuous, state-based surveillance system that collects information about modifiable risk factors for chronic diseases and other leading causes of death. Data will be updated annually as it becomes available. Detailed information on sampling methodology and quality assurance can be found on the BRFSS website (http://www.cdc.gov/brfss). Methodology: http://www.cdc.gov/brfss/factsheets/pdf/DBS_BRFSS_survey.pdf Glossary: https://chronicdata.cdc.gov/Behavioral-Risk-Factors/Behavioral-Risk-Factor-Surveillance-System-BRFSS-H/iuq5-y9ct/data

1995-2010. BRFSS land line only prevalence data. BRFSS is a continuous, state-based surveillance system that collects information about modifiable risk factors for chronic diseases and other leading causes of death. Data will be updated annually as it becomes available. Detailed information on sampling methodology and quality assurance can be found on the BRFSS website (http://www.cdc.gov/brfss). Methodology: http://www.cdc.gov/brfss/factsheets/pdf/DBS_BRFSS_survey.pdf Glossary: https://chronicdata.cdc.gov/Behavioral-Risk-Factors/Behavioral-Risk-Factor-Surveillance-System-BRFSS-H/iuq5-y9ct/data

2011 to present. BRFSS combined land line and cell phone prevalence data. BRFSS is a continuous, state-based surveillance system that collects information about modifiable risk factors for chronic diseases and other leading causes of death. Data will be updated annually as it becomes available. Detailed information on sampling methodology and quality assurance can be found on the BRFSS website (http://www.cdc.gov/brfss). Methodology: http://www.cdc.gov/brfss/factsheets/pdf/DBS_BRFSS_survey.pdf Glossary: https://data.cdc.gov/Behavioral-Risk-Factors/Behavioral-Risk-Factor-Surveillance-System-BRFSS-H/iuq5-y9ct

🔗 Related Datasets

• Behavioral Risk Factor Surveillance System (BRFSS) Prevalence Data (2010 and prior) — HealthData.Gov
• Behavioral Risk Factor Surveillance System (BRFSS) Age-Adjusted Prevalence Data (2011 to present) — HealthData.Gov

Prevalence data: 40.1 million Americans living with diabetes (29.1M diagnosed, 11M undiagnosed). 115 million adults living with prediabetes. Total economic cost: $412.9 billion.

BackgroundThe prospect of eliminating onchocerciasis from Africa by mass treatment with ivermectin has been rejuvenated following recent successes in foci in Mali, Nigeria and Senegal. Elimination prospects depend strongly on local transmission conditions and therefore on pre-control infection levels. Pre-control infection levels in Africa have been mapped largely by means of nodule palpation of adult males, a relatively crude method for detecting infection. We investigated how informative pre-control nodule prevalence data are for estimating the pre-control prevalence of microfilariae (mf) in the skin and discuss implications for assessing elimination prospects.Methods and FindingsWe analyzed published data on pre-control nodule prevalence in males aged ≥20 years and mf prevalence in the population aged ≥5 years from 148 African villages. A meta-analysis was performed by means of Bayesian hierarchical multivariate logistic regression, accounting for measurement error in mf and nodule prevalence, bioclimatic zones, and other geographical variation. There was a strong positive correlation between nodule prevalence in adult males and mf prevalence in the general population. In the forest-savanna mosaic area, the pattern in nodule and mf prevalence differed significantly from that in the savanna or forest areas.SignificanceWe provide a tool to convert pre-control nodule prevalence in adult males to mf prevalence in the general population, allowing historical data to be interpreted in terms of elimination prospects and disease burden of onchocerciasis. Furthermore, we identified significant geographical variation in mf prevalence and nodule prevalence patterns warranting further investigation of geographical differences in transmission patterns of onchocerciasis.

TD: Prevalence of Anemia among Non-Pregnant Women: % of Women Aged 15-49 data was reported at 45.900 % in 2023. This records an increase from the previous number of 45.600 % for 2022. TD: Prevalence of Anemia among Non-Pregnant Women: % of Women Aged 15-49 data is updated yearly, averaging 50.100 % from Dec 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 56.300 % in 2000 and a record low of 45.400 % in 2021. TD: Prevalence of Anemia among Non-Pregnant Women: % of Women Aged 15-49 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. Prevalence of anemia, non-pregnant women, is the percentage of non-pregnant women whose hemoglobin level is less than 120 grams per liter at sea level.;Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO);Weighted average;

TD: Prevalence of Anemia among Pregnant Women: % data was reported at 51.200 % in 2023. This records an increase from the previous number of 51.100 % for 2022. TD: Prevalence of Anemia among Pregnant Women: % data is updated yearly, averaging 56.600 % from Dec 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 61.200 % in 2000 and a record low of 51.100 % in 2022. TD: Prevalence of Anemia among Pregnant Women: % 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. Prevalence of anemia, pregnant women, is the percentage of pregnant women whose hemoglobin level is less than 110 grams per liter at sea level.;Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO);Weighted average;

TD: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 5.800 % in 2021. This records an increase from the previous number of 3.900 % for 2011. TD: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 4.850 % from Dec 2011 (Median) to 2021, with 2 observations. The data reached an all-time high of 5.800 % in 2021 and a record low of 3.900 % in 2011. TD: Diabetes Prevalence: % of Population Aged 20-79 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. Diabetes prevalence refers to the percentage of people ages 20-79 who have type 1 or type 2 diabetes. It is calculated by adjusting to a standard population age-structure.;International Diabetes Federation, Diabetes Atlas.;Weighted average;

Global Mental Health Disorders

Prevalence of Common Mental Health Conditions, 2005-2017

By Amit [source]

About this dataset

This dataset contains valuable information about the prevalence of mental health disorders including schizophrenia, bipolar disorder, eating disorders, anxiety disorders, drug use disorders, depression, and alcohol use disorders from various countries across the globe. Mental health is a critical and complex issue which touches us all and this dataset allows a deeper dive into the quantitative understanding of its prevalence and geographical distribution. With this data at hand one can gain insight on questions such as: which countries have rates of mental illness that are higher or lower than average? Which regions are disproportionately dealing with certain types of mental health disruptions? Who is struggling with particular types of illnesses? This data provides answers to those inquiries as well as helping us gain a better understanding of how we can take action towards increasing global awareness, prevention efforts, and access to vital resources that help individuals become healed and empowered

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How to use the dataset

This dataset provides information on the prevalence of mental health disorders globally, with data collected from various countries in a given year. It includes statistics on several types of mental health disorders, such as schizophrenia, bipolar disorder, eating disorders, anxiety disorders, drug use disorders and depression.

Using this dataset can provide useful insights into the prevalence of mental health conditions worldwide. This could be used to better understand how different countries are affected by mental health issues and to identify areas that may need more help or attention. The data is broken down by country or region and year to allow for a better understanding of trends over time.

To use this dataset effectively for research or data analysis purposes it is important to first familiarize yourself with the columns available in the dataset: Entity (country/region), Code (country code), Year (year in which the data was collected), Schizophrenia (%) , Bipolar Disorder (%) , Eating Disorders (%) , Anxiety Disorders (%) , Drug Use Disorders (%) , Depression (%) and Alcohol Use Disorders (%). Each column represents a specific type of mental health disorder and provides information on its prevalence rate in each country/region during that calendar year.

Once you have an understanding of these columns you can begin analyzing the data to gain further insights into global trends related to these mental health conditions. You might perform descriptive analyses such as finding average percentages across different groups (e.g., genders) or time periods, as well as performing inferential analyses like assessing relationships between different variables within your data set (e.g., correlation). Additionally you could create visualizations such as charts, maps or other graphics that help make sense out of large amounts of statistical information easily accessible to a wider audience

Research Ideas

• Creating age-group specific visualizations and infographics that compare the prevalence of mental health disorders in different countries or regions to better understand how the issue of depression or anxiety intersects with factors such as gender, culture, or socioeconomic status.
• Creating a global map visualization that shows the prevalence of different mental health disorders in different countries/regions to demonstrate disparities between places and provide a way for policy makers to better target areas most affected by these issues.
• Developing data visualizations exploring relationships between demographic variables (e.g., gender, age) and prevalence of mental health disorder types such as depression or anxiety disorders in order to gain insight into possible correlations between them

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: Mental health Depression disorder Data.csv | Column name | Description | |:------------------------------|:--------------------------------------------------------------------------------------| | Entity | The name of the country or region. (String) | | Code ...

TD: Contraceptive Prevalence: Modern Methods: % of Women Aged 15-49 data was reported at 6.700 % in 2019. This records an increase from the previous number of 5.000 % for 2015. TD: Contraceptive Prevalence: Modern Methods: % of Women Aged 15-49 data is updated yearly, averaging 9.900 % from Dec 1997 (Median) to 2019, with 6 observations. The data reached an all-time high of 9.900 % in 2004 and a record low of 1.200 % in 1997. TD: Contraceptive Prevalence: Modern Methods: % of Women Aged 15-49 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. Contraceptive prevalence, any modern method is the percentage of married women ages 15-49 who are practicing, or whose sexual partners are practicing, at least one modern method of contraception. Modern methods of contraception include female and male sterilization, oral hormonal pills, the intra-uterine device (IUD), the male condom, injectables, the implant (including Norplant), vaginal barrier methods, the female condom and emergency contraception.;Household surveys, United Nations (UN), note: Household surveys, including Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by United Nations Population Division., publisher: UN Population Division;Weighted average;

RS: Contraceptive Prevalence: Any Methods: % of Women Aged 15-49 data was reported at 58.400 % in 2014. This records a decrease from the previous number of 60.800 % for 2010. RS: Contraceptive Prevalence: Any Methods: % of Women Aged 15-49 data is updated yearly, averaging 58.400 % from Dec 1970 (Median) to 2014, with 5 observations. The data reached an all-time high of 60.800 % in 2010 and a record low of 41.200 % in 2006. RS: Contraceptive Prevalence: Any Methods: % of Women Aged 15-49 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Serbia – Table RS.World Bank.WDI: Health Statistics. Contraceptive prevalence rate is the percentage of women who are practicing, or whose sexual partners are practicing, any form of contraception. It is usually measured for women ages 15-49 who are married or in union.; ; UNICEF's State of the World's Children and Childinfo, United Nations Population Division's World Contraceptive Use, household surveys including Demographic and Health Surveys and Multiple Indicator Cluster Surveys.; Weighted average; Contraceptive prevalence amongst women of reproductive age is an indicator of women's empowerment and is related to maternal health, HIV/AIDS, and gender equality.

TD: Contraceptive Prevalence: Any Methods: % of Women Aged 15-49 data was reported at 8.100 % in 2019. This records an increase from the previous number of 5.700 % for 2015. TD: Contraceptive Prevalence: Any Methods: % of Women Aged 15-49 data is updated yearly, averaging 11.100 % from Dec 1997 (Median) to 2019, with 6 observations. The data reached an all-time high of 11.100 % in 2004 and a record low of 4.100 % in 1997. TD: Contraceptive Prevalence: Any Methods: % of Women Aged 15-49 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. Contraceptive prevalence, any method is the percentage of married women ages 15-49 who are practicing, or whose sexual partners are practicing, any method of contraception (modern or traditional). Modern methods of contraception include female and male sterilization, oral hormonal pills, the intra-uterine device (IUD), the male condom, injectables, the implant (including Norplant), vaginal barrier methods, the female condom and emergency contraception. Traditional methods of contraception include rhythm (e.g., fertility awareness based methods, periodic abstinence), withdrawal and other traditional methods.;Household surveys, United Nations (UN), note: Household surveys, including Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by United Nations Population Division., publisher: UN Population Division;Weighted average;

The data provided here comes grouped by the indicator domain: Hospital admissions for Drug Use, Obesity and Smoking to 2022/23. Note: Obesity admissions for 2022/23 include measures where OPCS codes have been aligned with the National Obesity Audit. Note: There has been a methodology change for hospital admissions attributable to smoking and we have used this methodology to back date the time series within this publication. Note: Alcohol data is available from OHID (please see link below). Prescriptions covering Alcohol, Obesity and Smoking to 2022/23. Affordability and expenditure covering Alcohol and Smoking to 2023. Unchanged in this release but to be updated during 2024: Deaths covering Smoking only to 2019.

2011 to present. BRFSS combined land line and cell phone age-adjusted prevalence data. The BRFSS is a continuous, state-based surveillance system that collects information about modifiable risk factors for chronic diseases and other leading causes of death. Data will be updated annually as it becomes available.

Detailed information on sampling methodology and quality assurance can be found on the BRFSS website (http://www.cdc.gov/brfss). Methodology: http://www.cdc.gov/brfss/factsheets/pdf/DBS_BRFSS_survey.pdf Glossary: https://data.cdc.gov/Behavioral-Risk-Factors/Behavioral-Risk-Factor-Surveillance-System-BRFSS-H/iuq5-y9ct

This data table provides a collection of information from peer-reviewed autism prevalence studies. Information reported from each study includes the autism prevalence estimate and additional study characteristics (e.g., case ascertainment and criteria). A PubMed search was conducted to identify studies published at any time through September 2020 using the search terms: autism (title/abstract) OR autistic (title/abstract) AND prevalence (title/abstract). Data were abstracted and included if the study fulfilled the following criteria: • The study was published in English; • The study produced at least one autism prevalence estimate; and • The study was population-based (any age range) within a defined geographic area.

Percentages are weighted to population characteristics. Data are not available if it did not meet BRFSS stability requirements.For more information on these requirements, as well as risk factors and calculated variables, see the Technical Documents and Survey Data for a specific year - http://www.cdc.gov/brfss/annual_data/annual_data.htm.Recommended citation: Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, [appropriate year].

This table provides county-level prevalence for 2018 for seven US states using linked statewide health and education data. For full methods see: Shaw KA, Williams S, Hughes MM, Warren Z, Bakian AV, Durkin MS, et al. Statewide county-level autism spectrum disorder prevalence estimates — seven U.S. states, 2018. Annals of Epidemiology. 2023 Jan 18; Available from: https://www.sciencedirect.com/science/article/pii/S1047279723000182

TD: Prevalence of Current Tobacco Use: Males: % of Male Adults data was reported at 13.200 % in 2022. This records a decrease from the previous number of 13.400 % for 2021. TD: Prevalence of Current Tobacco Use: Males: % of Male Adults data is updated yearly, averaging 15.700 % from Dec 2000 (Median) to 2022, with 8 observations. The data reached an all-time high of 18.500 % in 2000 and a record low of 13.200 % in 2022. TD: Prevalence of Current Tobacco Use: Males: % of Male Adults 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. The percentage of the male population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population.;Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho;Weighted average;