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.

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: https://en.wikipedia.org/wiki/HIV_adult_prevalence_rate 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 UNAIDS AIDS info 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, ~9.2 million infected.

🇹🇿 Tanzania: ~7.49 million.

🇲🇿 Mozambique: ~2.48 million.

🇳🇬 Nigeria: ~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 🚨.

📚 Data Sources: CIA World Factbook 🌐

UNAIDS AIDS Info 💉

Wikipedia 🧠 (used as a collection and compilation point, not primary source)

https://en.wikipedia.org/wiki/HIV_adult_prevalence_rate

Human Immunodeficiency Virus (HIV) remains a significant public health concern, with adults being at greater risk. Thus, understanding the dynamics of HIV transmission is crucial for effective prevention and control strategies, hence the need for a continuous clinical survey of the patients’ records of diagnosis and treatment for HIV. The data include the quarterly records of 138 adults diagnosed with HIV at Osun State University Teaching Hospital, Nigeria which involves the number of adults tested positive and negative for each of the endogenous variables discussed below. Information was sought using a convenient sampling method, which entails careful selection of individual records based on availability. The data was grouped into quarterly records of the diagnosed adults, with an average age ranging between 26 years and 52 years, and spread between the years 2008 and 2021. The records comprise 72 Females and 66 Males while the presence of each symptom is coded as 1 and the absence coded as 0. The endogenous variables observed in the clinical records of the surveyed patients are Fever (F), Diarrhea (D), Abdominal pain (AP), Skin rash (SR), Mouth sour (MS), Cellulitis (C), Coughing with sputum (CS), Loss of appetite (LA), Genital infections (GI), Medical fitness (MF), Headache (H), Catarrh (CA), Weight Loss (WL), Excessive Sweat (ES), Mouth Sour (MS), and Body weakness (BW). The impacts of these aforementioned factors would be examined on the spread of HIV. The clinical survey revealed that 77 individuals (55.80%) did not experience fever, while 61 (44.20%) did. Diarrhea was reported by 39 participants (28.26%), leaving 99 (71.74%) without this symptom. Abdominal pain and cellulitis were both reported by only 4 individuals (2.90%), with 134 participants (97.10%) indicating no occurrences of these symptoms. In terms of medical fitness, 110 individuals (79.71%) reported no fitness issues, whereas 28 (20.29%) reported having some. Cough with sputum affected 50 participants (36.23%), while 88 (63.77%) did not report this symptom. Headaches were almost universally absent, with 137 individuals (99.28%) not experiencing any. Catarrh was present in 14 participants (10.14%), with 124 (89.86%) reporting no instances. Loss of appetite was reported by 5 individuals (3.62%), and skin rashes were observed in 28 participants (20.29%). Weight loss affected 49 individuals (35.51%), and excessive sweating was reported by 137 participants (99.28%). Mouth soreness was noted in 27 participants (19.57%), while genital infections were reported by 6 individuals (4.35%). Body weakness was reported by 49 participants (35.51%). In the age distribution, 56 individuals (40.58%) fall into the young adult’s category while 82 individuals (59.42%) are categorized as older adults. Notably, all participants in the study were confirmed to be HIV positive, emphasizing a focused analysis of this group’s health characteristics.

Much of the information on national HIV prevalence in Tanzania derives from surveillance of HIV in special populations, such as women attending antenatal clinics and blood donors. For example, Mainland Tanzania currently maintains a network of 134 antenatal care (ANC) sites from which HIV prevalence estimates are generated. However, these surveillance data do not provide an estimate of the HIV prevalence among the general population. HIV prevalence is higher among individuals who are employed (6 percent) than among those who are not employed (3 percent) and is higher in urban areas than in rural areas (7percent and 4 percent, respectively). In Mainland Tanzania, HIV prevalence is markedly higher than in Zanzibar (5 percent versus 1 percent). Differentials by region are large. Among regions on the Mainland,Njombe has the highest prevalence estimate (15 percent), followed by Iringa and Mbeya (9 percent each);Manyara and Tanga have the lowest prevalence (2 percent). Among the five regions that comprise Zanzibar, all have HIV prevalence estimates at 1 percent or below. Consistent with the overall national estimate among men and women, HIV prevalence is higher among women than men in nearly all regions of Tanzania.

This indicator provides information about the rate of persons living with HIV (persons per 100,000 population).Human immunodeficiency virus (HIV) infection remains a significant public health concern, with more than 59,000 Los Angeles County residents estimated to be currently living with HIV. Certain communities, such as low-income communities, communities of color, and sexual and gender minority communities, bear a disproportionate burden of this epidemic. The Ending the HIV Epidemic national initiative strives to eliminate the US HIV epidemic by 2030, focusing on four key strategies: Diagnose, Treat, Prevent, and Respond. Achieving this goal requires a collaborative effort involving cities, community organizations, faith-based institutions, healthcare professionals, and businesses. Together, they can create an environment that promotes prevention, reduces stigma, and empowers individuals to safeguard themselves and their partners from HIV. Stakeholders can advance health equity by focusing on the most affected communities and sub-populations.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

People aged 15 to 59 years seen at HIV services in the UK, expressed as a rate per 1,000 population.Data is presented by area of residence, and exclude people diagnosed with HIV in England who are resident in Wales, Scotland, Northern Ireland or abroad.RationaleThe geographical distribution of people seen for HIV care and treatment is not uniform across or within regions in England. Knowledge of local diagnosed HIV prevalence and identification of local risk groups can be used to help direct resources for HIV prevention and treatment.In 2008, http://www.bhiva.org/HIV-testing-guidelines.aspx recommended that Local Authority and NHS bodies consider implementing routine HIV testing for all general medical admissions as well as new registrants in primary care where the diagnosed HIV prevalence exceeds 2 in 1,000 population aged 15 to 59 years.In 2017, guidelines were updated by https://www.nice.org.uk/guidance/NG60 which is co-badged with Public Health England. This guidance continues to define high HIV prevalence local authorities as those with a diagnosed HIV prevalence of between 2 and 5 per 1,000 and extremely high prevalence local authorities as those with a diagnosed HIV prevalence of 5 or more per 1,000 people aged 15 to 59 years.When this is applied to national late HIV diagnosis data, it shows that two-thirds of late HIV diagnoses occur in high-prevalence and extremely-high-prevalence local authorities. This means that if this recommendation is successfully applied in high and extremely-high-prevalence areas, it could potentially affect two-thirds of late diagnoses nationally.Local authorities should find out their diagnosed prevalence published in UKHSA's http://fingertips.phe.org.uk/profile/sexualhealth , as well as that of surrounding areas and adapt their strategy for HIV testing using the national guidelines.Commissioners can use these data to plan and ensure access to comprehensive and specialist local HIV care and treatment for HIV diagnosed individuals according to the http://www.medfash.org.uk/uploads/files/p17abl6hvc4p71ovpkr81ugsh60v.pdf and http://www.bhiva.org/monitoring-guidelines.aspx .Definition of numeratorThe number of people (aged 15 to 59 years) living with a diagnosed HIV infection and accessing HIV care at an NHS service in the UK and who are resident in England.Definition of denominatorResident population aged 15 to 59.The denominators for 2011 to 2023 are taken from the respective 2011 to 2023 Office for National Statistics (ONS) revised population estimates from the 2021 Census.Further details on the ONS census are available from the https://www.ons.gov.uk/census .CaveatsData is presented by geographical area of residence. Where data on residence were unavailable, residence have been assigned to the local health area of care.Every effort is made to ensure accuracy and completeness of the data, including web-based reporting with integrated checks on data quality. The overall data quality is high as the dataset is used for commissioning purposes and for the national allocation of funding. However, responsibility for the accuracy and completeness of data lies with the reporting service.Data is as reported but rely on ‘record linkage’ to integrate data and ‘de-duplication’ to prevent double counting of the same individual. The data may not be representative in areas where residence information is not known for a significant proportion of people accessing HIV care.Data supplied for previous years are updated on an annual basis due to clinic or laboratory resubmissions and improvements to data cleaning. Data may therefore differ from previous publications.Values are benchmarked against set thresholds and categorised into the following groups: <2 (low), 2 to 5 (high) and≥5 (extremely high). These have been determined by developments in national testing guidelines.The data reported in 2020 and 2021 is impacted by the reconfiguration of sexual health services during the national response to COVID-19.

This is a dataset of clinical HIV sequences, including a method of decoding the evolutionary pathways by which HIV evolves drug resistance. "Fitness landscape" describing how HIV proteins can evolve, is shown as a kinetic network. Drug resistance is a major problem in the treatment of AIDS, due to the very high mutation rate of human immunodeficiency virus (HIV) and subsequent rapid development of resistance to new drugs. Identification of mutations associated with drug resistance is critical for both individualized treatment selection and new drug design. We have performed an automated mutation analysis of HIV Type 1 (HIV-1) protease and reverse transcriptase (RT) from approximately 50,000 AIDS patient plasma samples sequenced by Specialty Laboratories Inc. from 1999 to mid-2002. This dataset provides a nearly complete mutagenesis of HIV protease and enables the calculation of statistically significant Ka/Ks values for each individual amino acid mutation in protease and RT. Positive selection (i.e., Ka/Ks>1 indicating increased reproductive fitness) detected 19 of 23 known drug-resistant mutation positions in protease and 20 of 34 such positions in RT. We also discovered 163 new amino acid mutations in HIV protease and RT that are strong candidates for drug resistance or fitness. Our results match available independent data on protease mutations associated with specific drug treatments and mutations with positive reproductive fitness, with high statistical significance (the P values for the observed matches to occur by random chance are 1e-5.2 and 1e-16.6, respectively). Our data indicate that positive selection mapping is an analysis that can yield powerful insights from high-throughput sequencing of rapidly mutating pathogens. This database has been made possible by the generous contribution of HIV sequence chromatograms by Specialty Laboratories, Inc.

Introduction

• HIV (human immunodeficiency virus) is a virus that attacks the body's immune system. If HIV is not treated, it can lead to AIDS (acquired immunodeficiency syndrome) which currently has no cure. Once people get HIV, they have it for life. But with proper medical care, HIV can be controlled. Symptoms: Influenza-like illness; Fatigue… Treatments: Management of HIV/AIDS Type of infectious agent: Virus (Human Immunodeficiency Virus) • AIDS (acquired immune deficiency syndrome) is the name used to describe a number of potentially life-threatening infections and illnesses that happen when one’s immune system has been severely damaged by the HIV virus. While AIDS cannot be transmitted from 1 person to another, the HIV virus can.

Dataset

The data set contains data of the following:- 1. The top causes of deaths in the world 2. Total number of deaths due to HIV/AIDS 3. ART (Anti Retro-viral Therapy) coverage among people living with HIV 4. Knowledge among young citizens (15-24years) about HIV/AIDS 5. Population of HIV/AIDS patients living with TB and their death rate 6. Life expectancy rate among HIV/AIDS patients 7. HIV/AIDS Patients in different age groups 8. Women population living with HIV 9. Young women in India having the knowledge of HIV/AIDS 10. HIV/AIDS deaths in Indian states

Data was scrapped from the official website of UNICEF -https://data.unicef.org/ and https://data.gov.in/

Ask Phase

• Data gives the trend of increasing no. of HIV/AIDS patients across the world • The information available for each country is percentage of total Global AIDS patients • Time period traced is 2000-2019 • Key Questions to answer:  Which countries and regions are affected the most?  How are the different age groups affected?  How much is the ART (Anti Retro-viral Therapy) coverage among the patients and what is the life expectancy rate?  What percentage of the population is aware of the prevention and causes of HIV/AIDS

Prepare phase.

• By tabulating and filtering the data the required data was obtained to bring out observations. • Data was formatted to the desired format to perform further calculations. • Sorting of data region wise. • Columns with inconsistent and empty cells were deleted. • The data of India was extracted for further analysis • Duplicate entries and undesired data was removed

Process phase

For cleaning the dataset for further analysis MS Excel was used due to small data. • Used sumifs() functions to aggregate the data region wise • Used sumif() to segregate the no. of patients within different age groups • Used sumifs() to find the total number of TB patients among HIV deaths. • Used countif() to find the percentage of male and female patients. • Sorted data to find the top and bottom nation with most and least HIV/AIDS patients

Analyze phase

• Formed the following pivot tables to answer key target questions  Year v/s number of death rates  Country v/s death numbers to bring out nation wise deaths  Causes of death v/s the number of deaths to bring at which position AIDS causes causality  Year v/s percentage of life expectancy to observe the pattern of no. of survivors

Visualization phase

The data was visualized using Tableau.

Presentaion

The final presentation was prepared by accumulating all observations and inferences which is linked below https://docs.google.com/presentation/d/1NEX10Vz5u5Va3CrTLVbvsUHZjO-fn8EOeiOHkP03T3Q/edit?usp=sharing

BackgroundWashington DC has a high burden of HIV with a 2.0% HIV prevalence. The city is a national and international hub potentially containing a broad diversity of HIV variants; yet few sequences from DC are available on GenBank to assess the evolutionary history of HIV in the US capital. Towards this general goal, here we analyze extensive sequence data and investigate HIV diversity, phylodynamics, and drug resistant mutations (DRM) in DC.MethodsMolecular HIV-1 sequences were collected from participants infected through 2015 as part of the DC Cohort, a longitudinal observational study of HIV+ patients receiving care at 13 DC clinics. Sequences were paired with Cohort demographic, risk, and clinical data and analyzed using maximum likelihood, Bayesian and coalescent approaches of phylogenetic, network and population genetic inference. We analyzed 601 sequences from 223 participants for int (~864 bp) and 2,810 sequences from 1,659 participants for PR/RT (~1497 bp).ResultsNinety-nine and 94% of the int and PR/RT sequences, respectively, were identified as subtype B, with 14 non-B subtypes also detected. Phylodynamic analyses of US born infected individuals showed that HIV population size varied little over time with no significant decline in diversity. Phylogenetic analyses grouped 13.5% of the int sequences into 14 clusters of 2 or 3 sequences, and 39.0% of the PR/RT sequences into 203 clusters of 2–32 sequences. Network analyses grouped 3.6% of the int sequences into 4 clusters of 2 sequences, and 10.6% of the PR/RT sequences into 76 clusters of 2–7 sequences. All network clusters were detected in our phylogenetic analyses. Higher proportions of clustered sequences were found in zip codes where HIV prevalence is highest (r = 0.607; P

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.

Background: Increasing rates of HIV/AIDS in Eastern Europe and Central Asia contrast global trends, but the scope of HIV/AIDS research originating from Russian Federation and countries of the former Soviet Union has not been quantified.Methods: We searched six major scientific databases in Russian and English languages with medical subject heading terms “HIV” or “AIDS” and “Russia” or “Soviet Union” from 1991 to 2016. Each abstract indexed was reviewed and tagged for 25 HIV/AIDS research themes, location of research focus and first author.Results and Discussion: A total of 2,868 articles were included; 2,156 (75.1%) and 712 (24.8%) described research in the Russian Federation and countries of the former Soviet Union, respectively. There were 15 publications per million population in Russian Federation. Federal districts of the Russian Federation with the highest rates of HIV had the most limited publications. An interactive web-map with time-lapse features and links to primary literature was created using ArcGIS® technology [http://arcg.is/2FUIJ5v].Conclusion: We found a lower than expected publication rate in the Russian Federation relative to rising HIV prevalence. The greatest deficits were in the most HIV burdened regions in the Russian Federation. Our findings highlight opportunities for new research strategies and public health efforts among key populations and subnational regions.

Background The standard of care for HCV Hepatitis is the combination of interferon (IFN) plus Ribavirin. In HIV patients the use of this combination therapy may induce drug interactions, and reduces the adherence to HAART. The aim of this study is to evaluate safety and efficacy of a 48 weeks daily dose IFN schedule. Methods We evaluated 50 coinfected patients; alpha IFN 2a was administered at a dose of 3 MU daily. The baseline values were the following : CD4+ 515 cells/mmc (mean); HIV-RNA <50 copies/ml in all patients; HCV-RNA 28, 3 × 106 copies/ml. Results At 48 weeks, 10 patients (20%) achieved a biochemical and virological response according to an intention to treat analysis. Twenty four patients (48%) underwent a drop-out mainly by side effects related to overlapping toxicity of interferon and antiretroviral therapy. All the patients, who responded to the treatment, showed a fast relapse one month after the end of treatment. Conclusion Although our results demonstrated a very poor outcome and a bad tolerance to interferon monotherapy, this approach should not be dropped out, mainly in patients at high risk for side effects and in those with cirrhosis who do not tolerate or are at increased risk for the use of ribavirin.

Certain subpopulations like female sex workers (FSW), men who have sex with men (MSM), and people who inject drugs (PWID) often have higher prevalence of HIV/AIDS and are difficult to map directly due to stigma, discrimination, and criminalization. Fine-scale mapping of those populations contributes to the progress toward reducing the inequalities and ending the AIDS epidemic. In 2016 and 2017, the PLACE surveys were conducted at 3290 venues in 20 out of the total 28 districts in Malawi to estimate the FSW sizes. These venues represent a presence-only dataset where, instead of knowing both where people live and do not live (presence–absence data), only information about visited locations is available. In this study, we develop a Bayesian model for presence-only data and utilize the PLACE data to estimate the FSW size and uncertainty interval at a1.5×1.5-km resolution for all of Malawi. The estimates can also be aggregated to any desirable level (city/district/region) for implementing targeted HIV prevention and treatment programs in FSW communities, which have been successful in lowering the incidence of HIV and other sexually transmitted infections. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Context

HealthStats provides key health, nutrition and population statistics gathered from a variety of international sources. Themes include population dynamics, nutrition, reproductive health, health financing, medical resources and usage, immunization, infectious diseases, HIV/AIDS, DALY, population projections and lending. HealthStats also includes health, nutrition and population statistics by wealth quintiles.

Content

This dataset includes 345 indicators, such as immunization rates, malnutrition prevalence, and vitamin A supplementation rates across 263 countries around the world. Data was collected on a yearly basis from 1960-2016.

Inspiration

• In your opinion, what are some of the more surprising indicators? Are there any you would consider adding?
• Is there a relationship between condom use and rates of children born with HIV? How do these rates compare over time?
• Which countries have the highest consumption of iodized salt? Has this indicator changed over time, and if so, in which countries? Are there any other indicators that seem to correlate with this one?

Acknowledgements

Data was acquired from the World Bank, and can be accessed in multiple formats here.

The prevalence of CRF07_BC is 39.7% and has become the most infectious HIV strain in China. To study the transmission and diffusion trajectory of CRF07_BC in China and to prevent further expansion of its transmission. A total of 16,635 sequences of the CRF07_BC pol gene were collected from 1997-2020. We characterized the gene subtypes according to a phylogenetic tree analysis. A 0.50% molecular network was constructed to analyze the transmission relationship among different provinces for CRF07_BC and its two epidemic clusters. Spatial and temporal propagation characteristics were analyzed according to phylogeographic analysis. Finally, we evaluated the differences in transmission of CRF07_BC-O, and CRF07_BC-N. Our dataset included 8,816 sequences of CRF07_BC-N and 7,819 sequences of CRF07_BC-O. There were 7,132 CRF07_BC sequences in the molecular network, and the rate of clustered was 42.9%. Compared to CRF07_BC-O, CRF07_BC-N showed significantly (P<0.001) higher transmission-specific rates. CRF07_BC originated among injecting drug users (IDUs), and spread to men who have sex with men (MSMs) and heterosexual individuals (HETs), while MSMs also transmitted directly to HETs. CRF07_BC-O and CRF07_BC-N were prevalent in Xinjiang and Sichuan, respectively, before spreading interprovincially. In modern China, CRF07_BC-N occurs in five of the major economic zones. The CRF07_BC strain, which has contributed to the highest number of HIV infections in China, is divided into two epidemic clusters. Compared with CRF07_BC-O, risk of transmission is much greater in CRF07_BC-N, which is predominantly prevalent in economically developed provinces, and both MSMs and IDUs have transmitted this epidemic cluster to HETs. High-resolution, large-scale monitoring is a useful tool in assessing the trend and spread of the HIV epidemic. The rapidly developing economy of China requires an equally rapid response to the prevention and control of infectious diseases.

Description: The adult and youth data of the SABSSM 2002 study cover information from adults and youths 15 years and older on topics ranging from biographical information, media and communication, male circumcision, marital status and marriage practice, partner and partner characteristics, sexual behaviour and practices, voluntary counseling and testing (VCT), sexual orientation, interpersonal communication, practices around widowhood, knowledge and perceptions of HIV and AIDS, stigma, hospitalisation and health status. The data set consists of 643 variables and 9788 cases. Abstract: Background: This is the first in a series of national HIV household surveys conducted in South Africa. The survey was commissioned by the Nelson Mandela Children's Fund and the Nelson Mandela Foundation. The key aims were to determine the HIV prevalence in the general population, identify risk factors that increase vulnerability of South Africans to HIV infections, to identify the contexts within which sexual behaviour occurs and the obstacles to risk reduction and to determine the level of exposure of all sectors of society to current prevention. The Nelson Mandela Children's Fund requested the HSRC to assess the impact of current HIV and AIDS education and awareness programmes designed to slow down the epidemic, including infection rates, stigma, care and support for affected individuals and families. Methodology: Sampling methods: multi-stage cluster stratified sample stratified by province, settlement geography (geotype) and predominant race group in each area. A systematic sample of 15 households was drawn from each of 1 000 census enumeration areas (EAs). In each household, one person was randomly selected in each of four mutually exclusive age groups (2-11 years; 12-14 years; 15-24 years; 25+ years). Field workers administered questionnaires to selected respondents and also collected oral fluid specimens for HIV testing. Results: This study sampled a cross-section of 9 963 South Africans aged two years and older. HIV is a generalised epidemic in South Africa that extends to all age groups, geographic areas and race groups. It showed 11.4 % were HIV positive, 15.6 per cent of them aged between 15 and 49. Women (12.8% HIV positive) were more at risk of infection than men (9.5% HIV positive). Urban informal settlements have the highest incidence of HIV infection (21.3%). Free State showed the highest prevalence (14.9%) with Eastern Cape having the lowest (6.6%). Higher rates of infection (5.6%) are also found in children aged 2-14 and Africans (10.2%). Awareness of HIV status was low. Only 18.9% reported that they were previously tested. Fewer women (3.9%) reported more than one sexual partner as compared to men (13.5%). Condom use at last sex was low among both women (24.7%) and men (30.3%). Knowledge of HIV and AIDS is generally high, with sexual behaviour changes taking root in encouragingly low numbers of sexual partners and high levels of abstinence among the youth. There is still great uncertainty of the relationship between HIV and AIDS and popular myths. South Africans from all walks of life are at risk. In particular, wealthy Africans have the same levels of risk as poorer Africans - whereas in other race groups, poorer people are more vulnerable to infection. Conclusions: The study recommended the expansion of voluntary counselling and testing. Prevention programmes ought to focus on reduction on multiple partners and increased condom use. It further recommended, inter alia, that HIV/AIDS prevention programmes be intensified for people living in informal settlements, campaigns be implemented using mass media to address myths and misconceptions and that information needs in rural communities and poorer households due to lack of access to mass media channels, should be attended to.

Data Dictionary JANUARY, 2020 Gender Inequality & HIV/AIDS

Country The country the data corresponds to.The data is a subset of UNICEF’s ‘Key HIV epidemiology indicators for children and adolescents aged 10-19, 1990-2019.’This UNICEF data is sourced from UNAIDS 2020 estimates, which provide ‘modeled estimates using the best available epidemiological and programmatic data to track the HIV epidemic’. Modeled estimates are used because counting the true numbers would require regularly testing entire populations for HIV, and investigating all deaths, which is ‘logistically impossible and ethically problematic.’ For more information on the methodology behind these estimates, see the full UNAIDS 2020 report.

UNICEF Region The region the country belongs to - this dataset includes countries from Eastern & Southern Africa, and West & Central Africa.

Year The year the estimates corresponds to.

Sex Whether the estimates refer to men or women.

Age The age group that the estimates refer to - this dataset contains only estimates for adolescent women and men between the ages of 10-19.

Estimated incidence rate of new HIV infection per 1000 uninfected population The estimated number of new HIV infections, for every 1000 uninfected people in the relevant group. Note - some fields were displayed as ‘<0.01’ in the original data, however these have been rounded up to 0.01 in order to make the field numeric.

Estimated number of annual AIDS related deaths The estimated number of annual AIDS related deaths in the relevant group, to the nearest 100. Note - in the original data, values below 500 were split into the following groups; <500, <200, and <100. To make the field numeric, these have been rounded to 500, 200, and 100 respectively.

Estimated number of annual new HIV infections The estimated number of new annual HIV infections in the relevant group. Note - in the original data, values below 500 were split into the following groups; <500, <200, and <100. To make the field numeric, these have been rounded to 500, 200, and 100 respectively.

The estimated number of people living with HIV in the relevant group. Note - in the original data, values below 500 were split into the following groups; <500, <200, and <100. To make the field numeric, these have been rounded to 500, 200, and 100 respectively.

Estimated rate of annual AIDS related deaths per 100,000 population The estimated number of annual AIDS related deaths, for every 100,000 people in the relevant group. Note - some fields were displayed as ‘<0.01’ in the original data, however these have been rounded up to 0.01 in order to make the field numeric.

Data Source: UNICEF ‘Key HIV epidemiology indicators for children and adolescents aged 10-19, 1990-2019

This collection consists of geospatial data layers and summary data at the country and country sub-division levels that are part of USAID's Demographic Health Survey Spatial Data Repository. This collection includes geographically-linked health and demographic data from the DHS Program and the U.S. Census Bureau for mapping in a geographic information system (GIS). The data includes indicators related to: fertility, family planning, maternal and child health, gender, HIV/AIDS, literacy, malaria, nutrition, and sanitation. Each set of files is associated with a specific health survey for a given year for over 90 different countries that were part of the following surveys:Demographic Health Survey (DHS)Malaria Indicator Survey (MIS)Service Provisions Assessment (SPA)Other qualitative surveys (OTH)Individual files are named with identifiers that indicate: country, survey year, survey, and in some cases the name of a variable or indicator. A list of the two-letter country codes is included in a CSV file.Datasets are subdivided into the following folders:Survey boundaries: polygon shapefiles of administrative subdivision boundaries for countries used in specific surveys. Indicator data: polygon shapefiles and geodatabases of countries and subdivisions with 25 of the most common health indicators collected in the DHS. Estimates generated from survey data.Modeled surfaces: geospatial raster files that represent gridded population and health indicators generated from survey data, for several countries.Geospatial covariates: CSV files that link survey cluster locations to ancillary data (known as covariates) that contain data on topics including population, climate, and environmental factors.Population estimates: spreadsheets and polygon shapefiles for countries and subdivisions with 5-year age/sex group population estimates and projections for 2000-2020 from the US Census Bureau, for designated countries in the PEPFAR program.Workshop materials: a tutorial with sample data for learning how to map health data using DHS SDR datasets with QGIS. Documentation that is specific to each dataset is included in the subfolders, and a methodological summary for all of the datasets is included in the root folder as an HTML file. File-level metadata is available for most files. Countries for which data included in the repository include: Afghanistan, Albania, Angola, Armenia, Azerbaijan, Bangladesh, Benin, Bolivia, Botswana, Brazil, Burkina Faso, Burundi, Cape Verde, Cambodia, Cameroon, Central African Republic, Chad, Colombia, Comoros, Congo, Congo (Democratic Republic of the), Cote d'Ivoire, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Eswatini (Swaziland), Ethiopia, Gabon, Gambia, Ghana, Guatemala, Guinea, Guyana, Haiti, Honduras, India, Indonesia, Jordan, Kazakhstan, Kenya, Kyrgyzstan, Lesotho, Liberia, Madagascar, Malawi, Maldives, Mali, Mauritania, Mexico, Moldova, Morocco, Mozambique, Myanmar, Namibia, Nepal, Nicaragua, Niger, Nigeria, Pakistan, Papua New Guinea, Paraguay, Peru, Philippines, Russia, Rwanda, Samoa, Sao Tome and Principe, Senegal, Sierra Leone, South Africa, Sri Lanka, Sudan, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, Uzbekistan, Viet Nam, Yemen, Zambia, Zimbabwe