Dataset Overview

The dataset provides a comprehensive look at HIV/AIDS adult prevalence rates, the number of people living with HIV, and annual deaths across different countries. It is based on publicly available data sources such as the CIA World Factbook, UNAIDS AIDS Info, and other global health organizations. The dataset primarily focuses on adult HIV prevalence (ages 15–49) and includes estimates from recent years (e.g., 2023–2024).

Data Science Applications

This dataset can be used for: - Epidemiological Analysis: Understanding the regional distribution of HIV/AIDS and identifying high-prevalence areas. - Predictive Modeling: Developing machine learning models to predict HIV prevalence trends or identify risk factors. - Resource Allocation: Informing policymakers about regions requiring urgent intervention or resource allocation. - Health Outcome Monitoring: Tracking progress in combating HIV/AIDS over time. - Social Determinants Research: Analyzing the relationship between socio-economic factors and HIV prevalence.

Column Descriptors

1. Country/Region: The geographical area being analyzed.
2. Adult Prevalence (%): Percentage of adults aged 15–49 living with HIV.
3. Number of People with HIV/AIDS: Absolute count of individuals living with HIV in the region.
4. Annual Deaths from HIV/AIDS: Number of deaths attributed to HIV/AIDS annually.
5. Year of Estimate: The year when the data was collected or estimated.

Ethically Mined Data

The dataset is ethically sourced from publicly available and credible platforms such as the CIA World Factbook, UNAIDS, and WHO. These organizations ensure transparency and ethical standards in data collection, protecting individual privacy while providing aggregate statistics for research purposes.

Acknowledgments

1. Data Source Platforms:
   • CIA World Factbook
   • UNAIDS AIDS Info
   • WHO Global Health Observatory
2. Dataset Visualization Image:
   • Created using DALL-E 3 for illustrative purposes.
3. Research Support:
   • Contributions from platforms like ResearchGate, NIMH, and others for insights into data science applications in HIV research.

This dataset serves as a valuable tool for researchers, policymakers, and public health professionals in addressing the global challenge of HIV/AIDS.

Context

In the time of epidemics, what is the status of HIV AIDS across the world, where does each country stands, is it getting any better. The data set should be helpful to explore much more about above mentioned factors.

Content

The data set contains data on

1. No. of people living with HIV AIDS
2. No. of deaths due to HIV AIDS
3. No. of cases among adults (19-45)
4. Prevention of mother-to-child transmission estimates
5. ART (Anti Retro-viral Therapy) coverage among people living with HIV estimates
6. ART (Anti Retro-viral Therapy) coverage among children estimates

Acknowledgements / Data Source

• WHO : https://www.who.int/hiv/data/en/
• UNESCO : https://data.unicef.org/resources/dataset/hiv-aids-statistical-tables/

Collection methodology

https://github.com/imdevskp/hiv_aids_who_unesco_data_cleaning

Cover Photo

Photo by Anna Shvets from Pexels https://www.pexels.com/photo/red-ribbon-on-white-surface-3900425/

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Statistics relating to HIV infection

This dataset contains death counts, crude rates and adjusted rates for selected causes of death by county and region. For more information, check out: http://www.health.ny.gov/statistics/vital_statistics/, or go to the "About" tab.

Dataset refers to the Statistics Relating to Notification of HIV Aids Cases and Deaths in Mauritius for the year 2000 to 2023

HIV/AIDS yearly statistics in Hong Kong 1984 - 2023

This data set includes tables on persons living with HIV/AIDS, newly diagnosed HIV cases and all cause deaths in HIV/AIDS cases by gender, age, race/ethnicity and transmission category.

In all tables, cases are reported as of December 31 of the given year, as reported by December 31, 2024, to allow a minimum of 12 months reporting delay.

Gender is determined by both current gender and sex at birth variables; transgender values are assigned when current gender is identified as "Transgender" or when a discrepancy is identified between a person's sex at birth and their current gender (e.g., cases where sex at birth is "Male" and current gender is "Female" will become Transgender: Male to Female.) Prior to 2003, Asian and Native Hawaiian/Pacific Islanders were classified as one combined group. In order to present these race/ethnicities separately, living cases recorded under this combined classification were split and redistributed according to their expected proportional population representation estimated from post-2003 data.

This dataset contains death counts and crude rates by region, age group, and selected cause of death. For more information, check out: http://www.health.ny.gov/statistics/vital_statistics/, or go to the "About" tab.

https://data.gov.hk/en-data/dataset/hk-dh-dh_spp-dh-spp-hiv-aids-1984-to-2022-yearly-figures This dataset can be sourced from the data.gov.hk website, which was provided to them by the department of health. The category of the dataset is Health and it is in a csv file format. It was last updated on 09/01/2024. Desciption: HIV/AIDS yearly statistics in Hong Kong 1984 - 2022.

Effect of suicide rates on life expectancy dataset

Abstract
In 2015, approximately 55 million people died worldwide, of which 8 million committed suicide. In the USA, one of the main causes of death is the aforementioned suicide, therefore, this experiment is dealing with the question of how much suicide rates affects the statistics of average life expectancy.
The experiment takes two datasets, one with the number of suicides and life expectancy in the second one and combine data into one dataset. Subsequently, I try to find any patterns and correlations among the variables and perform statistical test using simple regression to confirm my assumptions.

Data

The experiment uses two datasets - WHO Suicide Statistics[1] and WHO Life Expectancy[2], which were firstly appropriately preprocessed. The final merged dataset to the experiment has 13 variables, where country and year are used as index: Country, Year, Suicides number, Life expectancy, Adult Mortality, which is probability of dying between 15 and 60 years per 1000 population, Infant deaths, which is number of Infant Deaths per 1000 population, Alcohol, which is alcohol, recorded per capita (15+) consumption, Under-five deaths, which is number of under-five deaths per 1000 population, HIV/AIDS, which is deaths per 1 000 live births HIV/AIDS, GDP, which is Gross Domestic Product per capita, Population, Income composition of resources, which is Human Development Index in terms of income composition of resources, and Schooling, which is number of years of schooling.

LICENSE

THE EXPERIMENT USES TWO DATASET - WHO SUICIDE STATISTICS AND WHO LIFE EXPECTANCY, WHICH WERE COLLEECTED FROM WHO AND UNITED NATIONS WEBSITE. THEREFORE, ALL DATASETS ARE UNDER THE LICENSE ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 3.0 IGO (https://creativecommons.org/licenses/by-nc-sa/3.0/igo/).

[1] https://www.kaggle.com/szamil/who-suicide-statistics

[2] https://www.kaggle.com/kumarajarshi/life-expectancy-who

This shapefile provides HIV statistics by state that can be used in conjunction with the co-morbidities risk profile to provide more nuance on levels of risk by state. Note that values of 0 mean there is no data for that particular state.The source of data for HIV prevalence rates is the Nigeria Institute for Health Metrics and Evaluation (IHME), HIV Prevalence Geospatial Estimates 2000-2017.

Source: The World Bank Last Updated: 10/26/2023 Database: World Development Indicators Series: Prevalence of HIV, total (% of population ages 15-49) Adults (ages 15+) and children (ages 0-14) newly infected with HIV Adults (ages 15-49) newly infected with HIV Antiretroviral therapy coverage (% of people living with HIV) Antiretroviral therapy coverage for PMTCT (% of pregnant women living with HIV) Children (0-14) living with HIV Children (ages 0-14) newly infected with HIV Incidence of HIV, ages 15-24 (per 1,000 uninfected population ages 15-24) Incidence of HIV, ages 15-49 (per 1,000 uninfected population ages 15-49) Incidence of HIV, all (per 1,000 uninfected population) Prevalence of HIV, female (% ages 15-24) Prevalence of HIV, male (% ages 15-24) Women's share of population ages 15+ living with HIV (%) Young people (ages 15-24) newly infected with HIV

https://datacatalog.worldbank.org/public-licenses#cc-by

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_6f9349bb503b5790e380d03f59dd0e34/view

This dataset provides detailed insights into the prevalence of HIV/AIDS among adults (ages 15–49) across various countries and regions. The data is primarily sourced from the CIA World Factbook and the UNAIDS AIDSinfo platform and reflects the most recent available estimates as of 2022–2024.

What’s Included:

Country/Region – The name of each nation or area.

Adult Prevalence of HIV/AIDS (%) – The percentage of adults estimated to be living with HIV.

Number of People with HIV/AIDS – Estimated count of people infected in each country.

Annual Deaths from HIV/AIDS – Estimated number of HIV/AIDS-related deaths per year.

Year of Estimate – The year the data was reported or estimated.

Key Highlights:

Global Prevalence: Around 0.7% of the global population was living with HIV in 2022, affecting nearly 39 million people.

Hotspots: The epidemic is most severe in Southern Africa, with countries like Eswatini, Botswana, Lesotho, and Zimbabwe reporting adult prevalence rates above 20%.

High Burden Countries:

South Africa: 17.3% prevalence, approximately 9.2 million infected

Tanzania: approximately 7.49 million

Mozambique: approximately 2.48 million

Nigeria: approximately 2.45 million (1.3% prevalence)

Notes:

Data may vary in accuracy and is subject to ongoing updates and verification.

Some entries include a dash ("-") where data was not published or available.

Countries with over 1% adult prevalence are categorized under Generalized HIV Epidemics (GHEs) by UNAIDS.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_6d8bcb5f8e9cf2616b758c53095768fb/view

United States US: Prevalence of HIV: Total: % of Population Aged 15-49 data was reported at 0.500 % in 2014. This stayed constant from the previous number of 0.500 % for 2013. United States US: Prevalence of HIV: Total: % of Population Aged 15-49 data is updated yearly, averaging 0.500 % from Dec 2008 (Median) to 2014, with 7 observations. The data reached an all-time high of 0.500 % in 2014 and a record low of 0.500 % in 2014. United States US: Prevalence of HIV: Total: % of Population Aged 15-49 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. Prevalence of HIV refers to the percentage of people ages 15-49 who are infected with HIV.; ; UNAIDS estimates.; Weighted Average;

Adjusted additionally for age, region of origin, delayed entry into care (≥3 mo between HIV diagnosis and first clinic visit), and HIV-exposure group. Late presentation: presenting for care with a CD4 count below 350/mm3 or presenting with an AIDS defining event regardless of the CD4 count, in the 6 mo following presentation. Advanced disease: presenting for care with a CD4 count below 200/mm3or presenting with an AIDS defining event, regardless of CD4 cell count, in the 6 mo following presentation.aFigures are n (%) of clinical events (AIDS/deaths) in late presenters or late presenters with advanced disease.

BackgroundSince the first HIV/AIDS case appeared in 1980s, HIV/AIDS has been the focus of international attention. As a major public health problem, there are epidemiological uncertainties about the future of HIV/AIDS. It is important to monitor the global statistics of HIV/AIDS prevalence, deaths, disability adjusted life years (DALYs), and risk factors for adequate prevention and control.MethodsThe Global Burden of Disease Study 2019 database was used to analyze the burden of HIV/AIDS in 1990–2019. By extracting global, regional, and national data on HIV/AIDS prevalence, deaths, and DALYs, we described the distribution by age and sex, explored the risk factors, and analyzed the trends in HIV/AIDS.ResultsIn 2019, there were 36.85 million HIV/AIDS cases (95% UI: 35.15–38.86 million), 863.84 thousand deaths (95% UI: 78.61–99.60 thousand), and 47.63 million (95% UI: 42.63–55.65 million) DALYs. The global age-standardized HIV/AIDS prevalence, death, and DALY rates were 454.32 (95% UI: 433.76–478.59), 10.72 (95% UI: 9.70–12.39), and 601.49 (95% UI: 536.16–703.92) per 100,000 cases, respectively. In 2019, the global age-standardized HIV/AIDS prevalence, death, and DALY rates increased by 307.26 (95% UI: 304.45–312.63), 4.34 (95% UI: 3.78–4.90), and 221.91 (95% UI: 204.36–239.47) per 100,000 cases, respectively, compared to 1990. Age-standardized prevalence, death, and DALY rates decreased in high sociodemographic index (SDI) areas. High age-standardized rates were observed in low sociodemographic index areas, while low age-standardized rates were observed in high sociodemographic index areas. In 2019, the high age-standardized prevalence, death, and DALY rates were predominant in Southern Sub-Saharan Africa, and global DALYs peaked in 2004 and subsequently decreased. The highest global HIV/AIDS DALYs were in the 40–44 age group. The main risk factors affecting HIV/AIDS DALY rates included behavioral risks, drug use, partner violence, and unsafe sex.ConclusionsHIV/AIDS disease burden and risk factors vary by region, sex, and age. As access to health care increases across countries and treatment for HIV/AIDS infection improves, the HIV/AIDS disease burden is concentrated in areas with low SDIs, particularly in South Africa. Regional differences should be fully considered to target optimal prevention strategies and treatment options based on risk factors.

Data Series: Number of new HIV infections per 1,000 uninfected population, by sex and age Indicator: III.8 - Number of new HIV infections per 1,000 uninfected population, by sex, age and key populations Source year: 2023 This dataset is part of the Minimum Gender Dataset compiled by the United Nations Statistics Division. Domain: Health and related services