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.

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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HIV/AIDS data from the HIV Surveillance Annual Report Data reported to the HIV Epidemiology Program by March 31, 2022. All data shown are for people ages 18 and older. Borough-wide and citywide totals may include cases assigned to a borough with an unknown UHF or assigned to NYC with an unknown borough, respectively. Therefore, UHF totals may not sum to borough totals and borough totals may not sum to citywide totals.""

Contents

HIV/AIDS** data from the HIV Surveillance Annual Report * Note: Data reported to the HIV Epidemiology and Field Services Program by June 30, 2016. All data shown are for people ages 13 and older. Borough-wide and citywide totals may include cases assigned to a borough with an unknown UHF or assigned to NYC with an unknown borough, respectively. Therefore, UHF totals may not sum to borough totals and borough totals may not sum to citywide totals."

Dataset has 18 features including:

Year, Borough, UHF, Gender, Age, Race, HIV diagnoses, HIV diagnosis rate, Concurrent diagnoses, % linked to care within 3 months, AIDS diagnoses, AIDS diagnosis rate, PLWDHI prevalence, % viral suppression, Deaths, Death rate, HIV-related death rate, Non-HIV-related death 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 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.

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

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

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

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.

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). There is currently no effective 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

AIDS (acquired immune deficiency syndrome) is the name used to describe a number of potentially life-threatening infections and illnesses that happen when your immune system has been severely damaged by the HIV virus. While AIDS cannot be transmitted from 1 person to another, the HIV virus can.

@article{owidhivaids, author = {Max Roser and Hannah Ritchie}, title = {HIV / AIDS}, journal = {Our World in Data}, year = {2018}, note = {https://ourworldindata.org/hiv-aids} }

Percentages of MSM newly diagnosed with HIV infection by age and race/ethnicity, 2016, Santa Clara County. Source: Santa Clara County Public Health Department, enhanced HIV/AIDS reporting system (eHARS), data as of 4/30/2017. METADATA:Notes (String): Lists table title, notes and sourcesCategory (String): Lists the category representing the data: Age group: 13-24, 25-29, 30-39, 40-49, 50 and older; race/ethnicity:Asian/Pacific Islander, Black/African American, Latino, White (non-Hispanic White only), Other/Unknown.Percentage (Numeric): Percentage of MSM diagnosed with HIV in a particular category among all MSM diagnoses

The ultimate goal of HIV treatment is to achieve viral suppression, which means the amount of HIV in the body is very low or undetectable. This is important for people with HIV to stay healthy, have improved quality of life, and live longer. People living with HIV who maintain viral suppression have effectively no risk of passing HIV to others. Texas DSHS is the source of this data. Diagnosed- received a diagnosis of HIV Linked to care-visited an HIV heath care provider within 1 month (30 days) after learning they were HIV positive Received- or were retained in care** received medical care for HIV infection Viral suppression- their HIV “viral load” – the amount of HIV in the blood – was at a very low level.

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.

This study sought to construct and modify a culturally-based secondary prevention intervention targeted toward HIV-positive black young men who have sex with men. The feasibility and acceptability of the intervention were explored in Trial 1 and Trial 2; the potential efficacy of the intervention was assessed in Trial 2. Primary outcomes examined were health promotion behaviors (i.e., treatment adherence, sexual risk reduction, reduction in substance use behaviors, and HIV status disclosure). Psychosocial factors (i.e., self-esteem, critical consciousness, and socio-political awareness) were examined as secondary outcomes.

CEPHIA HIV Recency Assay Data

Global Assay Results and Participant Characteristics

By [source]

About this dataset

The Consortium for the Evaluation and Performance of HIV Incidence Assays (CEPHIA) is continually striving to deepen understanding of HIV epidemiology around the world. By collecting and testing samples from collaborations across the globe they are able to monitor the accuracy and precision of HIV recency assays. This dataset contains assay results plus corresponding participant characteristics, enabling researchers to gain knowledge about both incidence rates as well as long-term dynamics in different cohorts throughout numerous countries.

This data set provides key information such as assay type, specimen type, testing laboratory, participant demographic factors (e.g., sex and country), HIV status at visit time, cohort entry HIV status, elite controller status over time and antiretroviral use history (both current ART treatment & past first treatment episode). Plus viral load test results with related information such as closest measure to visit date offset , sensitivity level , EDDI interval size and number of days since EDDI for enhanced analysis capabilities. All together these variables make this a powerful tool allowing you to probe a myriad of questions ranging from understanding how incidence changes over time by population or country & reducing infection levels in especially vulnerable communities through to exploring potential interactions between other factors such as wealth or gender based disparities in those affected by this virus

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides a comprehensive look at CEPHIA collaborations across the world to evaluate the accuracy of HIV recency assays. It contains information on assay results and participant characteristics such as HIV status, HIV subtype, country of origin and demographics. The data can be used to gain insight into global trends in HIV incidence and dynamics.

To get started with this dataset, explore the different columns available to you such as assay, cephia_panel, testing_laboratory, etc. These will give an indication of what kind of assay was used, where it was conducted and what samples were tested. Then look at the other columns which provide more detailed information about each participant such as their HIV subtype, HIV status at visit and visit date.

Once you have familiarized yourself with the column titles, start by selecting only those that are relevant for your analysis - there is no need to include all columns if they don't add value your analysis. This will reduce clutter and make analysing your data much easier.

Finally if you have any questions or would like further explanation on any aspect of this dataset please refer to CEPHIA's website or contact them directly for help!

Research Ideas

• Using the HIV subtype and HIV treatment information, researchers can develop and evaluate models that predict treatment effectiveness for different types of HIV.
• Examining the viral load closest to a certain visit date, as well as the viral load type used, allows researchers to better understand the dynamics of viral load within cohorts.
• Analyzing designated-elite controllers during visits can help characterize and track times where a person is intermittently controlling their infection without medication over time allowing investigators to investigate how this occurs in different patient populations with different responses to medications

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: cephia_public_use_dataset_20210604.csv | Column name | Description | |:--------------------------------------------|:--------------------------------------------------------------------------------------------------| | assay | The type of assay used to test the specimen. (String) | | cephia_panel...

Data set for Prevalence and correlates of lifetime and recent HIV testing among men who have sex with men (MSM) who use mobile geo-social networking applications in Greater Tokyo.

This dataset helps to investigate the Spatial Accessibility to HIV Testing, Treatment, and Prevention Services in Illinois and Chicago, USA. The main components are: population data, healthcare data, GTFS feeds, and road network data. The core components are: 1) GTFS which contains GTFS (General Transit Feed Specification) data which is provided by Chicago Transit Authority (CTA) from Google's GTFS feeds. Documentation defines the format and structure of the files that comprise a GTFS dataset: https://developers.google.com/transit/gtfs/reference?csw=1. 2) HealthCare contains shapefiles describing HIV healthcare providers in Chicago and Illinois respectively. The services come from Locator.HIV.gov. 3) PopData contains population data for Chicago and Illinois respectively. Data come from The American Community Survey and AIDSVu. AIDSVu (https://map.aidsvu.org/map) provides data on PLWH in Chicago at the census tract level for the year 2017 and in the State of Illinois at the county level for the year 2016. The American Community Survey (ACS) provided the number of people aged 15 to 64 at the census tract level for the year 2017 and at the county level for the year 2016. The ACS provides annually updated information on demographic and socio economic characteristics of people and housing in the U.S. 4) RoadNetwork contains the road networks for Chicago and Illinois respectively from OpenStreetMap using the Python osmnx package. The abstract for our paper is: Accomplishing the goals outlined in “Ending the HIV (Human Immunodeficiency Virus) Epidemic: A Plan for America Initiative” will require properly estimating and increasing access to HIV testing, treatment, and prevention services. In this research, a computational spatial method for estimating access was applied to measure distance to services from all points of a city or state while considering the size of the population in need for services as well as both driving and public transportation. Specifically, this study employed the enhanced two-step floating catchment area (E2SFCA) method to measure spatial accessibility to HIV testing, treatment (i.e., Ryan White HIV/AIDS program), and prevention (i.e., Pre-Exposure Prophylaxis [PrEP]) services. The method considered the spatial location of MSM (Men Who have Sex with Men), PLWH (People Living with HIV), and the general adult population 15-64 depending on what HIV services the U.S. Centers for Disease Control (CDC) recommends for each group. The study delineated service- and population-specific accessibility maps, demonstrating the method’s utility by analyzing data corresponding to the city of Chicago and the state of Illinois. Findings indicated health disparities in the south and the northwest of Chicago and particular areas in Illinois, as well as unique health disparities for public transportation compared to driving. The methodology details and computer code are shared for use in research and public policy.

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.