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 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.

In 2024, the majority of reported HIV cases in the Philippines were among males in comparison to females. Overall, the number of newly diagnosed cases has gradually increased since 2012.

In 2020, 1.7 million people in Nigeria were living with HIV. The prevalence rate of HIV was 1.3 percent and it was the highest among the female adult population, at 1.6 percent. Among male adults, the prevalence rate stood at one percent.

Men had a higher number of reported HIV infection cases in Sweden in 2024, which amounted to ***, while there were *** reported infection cases among women. Therefore, the total amount of reported infection cases was *** in 2024.

In 2023, the estimated number of HIV diagnoses for males aged 13 years and over was 31,500. This statistic shows the estimated number of HIV (human immunodeficiency virus) diagnoses in the U.S. from 2019 to 2023, by sex assigned at birth and age.

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

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Abstract A prevalence study was conducted to compare quality of life and food insecurity in men and women living with HIV/AIDS. The sample comprised 481 HIV-infected individuals undergoing antiretroviral therapy at a referral hospital in the State of Paraíba, Brazil. Food insecurity and quality of life were assessed using the Brazilian Household Food Insecurity Scale and WHOQOL-HIV Bref, respectively. The results were presented as absolute and relative frequencies and gender differences were tested using the chi-squared test adopting a significance level of 0.05. The findings showed that 40.1% of the sample were women. A higher percentage of women than men had a low income and low education level (65.8% and 72.5%, respectively). Prevalence of food security was lower in women than in men (29.0% compared to 42.7%), and a higher percentage of women than men reported below average quality of life (54.9% compared to 44.4%). The findings reveal that, besides the usual difficulties faced by HIV-infected patients, this group showed a significant level of gender inequality. The management of HIV patient care should consider these important findings, promoting access to care and support services and gender equality so that women can live fairer and more equal lives.

Trinidad and Tobago TT: Contraceptive Prevalence: Any Methods: % of Women Aged 15-49 data was reported at 40.300 % in 2011. This records a decrease from the previous number of 42.500 % for 2006. Trinidad and Tobago TT: Contraceptive Prevalence: Any Methods: % of Women Aged 15-49 data is updated yearly, averaging 43.050 % from Dec 1971 (Median) to 2011, with 6 observations. The data reached an all-time high of 60.200 % in 1977 and a record low of 38.200 % in 2000. Trinidad and Tobago TT: 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 Trinidad and Tobago – Table TT.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.

In 2023, India reported an estimate of ** thousand new cases of HIV infections across the country. Male population newly infected with HIV amounted to just above ** thousand, accounting for approximately ** percent of HIV-infected population in India.

Context

The New York City Department of Health and Mental Hygiene publishes mid-year and annual HIV surveillance reports each year. This dataset is taken from these reports and includes data gathered from 2011 to June 30, 2016.

Content

This dataset includes HIV infections and AIDS diagnoses, viral suppression in persons living with diagnosed HIV infection (PLWDHI), deaths of those with diagnosed HIV infection, and other statistics from 2011 to 2015 in New York City boroughs.

Purpose

The data contained here shows trends in age, gender, and geographic demographics over time for HIV infections in NYC, and this can be used to visualize the prevalence of the virus in the city.

Acknowledgement

This data was pulled from NYC's OpenData at https://data.cityofnewyork.us/Health/DOHMH-HIV-AIDS-Annual-Report/fju2-rdad .

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

Sweden SE: Prevalence of HIV: Female: % Aged 15-24 data was reported at 0.100 % in 2016. This stayed constant from the previous number of 0.100 % for 2015. Sweden SE: Prevalence of HIV: Female: % Aged 15-24 data is updated yearly, averaging 0.100 % from Dec 1990 (Median) to 2016, with 27 observations. The data reached an all-time high of 0.100 % in 2016 and a record low of 0.100 % in 2016. Sweden SE: Prevalence of HIV: Female: % Aged 15-24 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sweden – Table SE.World Bank: Health Statistics. Prevalence of HIV, female is the percentage of females who are infected with HIV. Youth rates are as a percentage of the relevant age group.; ; UNAIDS estimates.; Weighted average; In many developing countries most new infections occur in young adults, with young women especially vulnerable.

Slovenia SI: Prevalence of HIV: Female: % Aged 15-24 data was reported at 0.100 % in 2022. This stayed constant from the previous number of 0.100 % for 2021. Slovenia SI: Prevalence of HIV: Female: % Aged 15-24 data is updated yearly, averaging 0.100 % from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 0.100 % in 2022 and a record low of 0.100 % in 2022. Slovenia SI: Prevalence of HIV: Female: % Aged 15-24 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Slovenia – Table SI.World Bank.WDI: Social: Health Statistics. Prevalence of HIV, female is the percentage of females who are infected with HIV. Youth rates are as a percentage of the relevant age group.;UNAIDS estimates.;Weighted average;In many developing countries most new infections occur in young adults, with young women especially vulnerable.

IntroductionSub-Saharan Africa bears more than two-thirds of the worldwide burden of HIV; however, data among transgender women from the region are sparse. Transgender women across the world face significant vulnerability to HIV. This analysis aimed to assess HIV prevalence as well as psychosocial and behavioral drivers of HIV infection among transgender women compared with cisgender (non-transgender) men who have sex with men (cis-MSM) in 8 sub-Saharan African countries.Methods and findingsRespondent-driven sampling targeted cis-MSM for enrollment. Data collection took place at 14 sites across 8 countries: Burkina Faso (January–August 2013), Côte d’Ivoire (March 2015–February 2016), The Gambia (July–December 2011), Lesotho (February–September 2014), Malawi (July 2011–March 2012), Senegal (February–November 2015), Swaziland (August–December 2011), and Togo (January–June 2013). Surveys gathered information on sexual orientation, gender identity, stigma, mental health, sexual behavior, and HIV testing. Rapid tests for HIV were conducted. Data were merged, and mixed effects logistic regression models were used to estimate relationships between gender identity and HIV infection. Among 4,586 participants assigned male sex at birth, 937 (20%) identified as transgender or female, and 3,649 were cis-MSM. The mean age of study participants was approximately 24 years, with no difference between transgender participants and cis-MSM. Compared to cis-MSM participants, transgender women were more likely to experience family exclusion (odds ratio [OR] 1.75, 95% CI 1.42–2.16, p

Brazil BR: Prevalence of HIV: Female: % Aged 15-24 data was reported at 0.200 % in 2017. This stayed constant from the previous number of 0.200 % for 2016. Brazil BR: Prevalence of HIV: Female: % Aged 15-24 data is updated yearly, averaging 0.200 % from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 0.200 % in 2017 and a record low of 0.100 % in 1991. Brazil BR: Prevalence of HIV: Female: % Aged 15-24 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Social: Health Statistics. Prevalence of HIV, female is the percentage of females who are infected with HIV. Youth rates are as a percentage of the relevant age group.;UNAIDS estimates.;Weighted average;In many developing countries most new infections occur in young adults, with young women especially vulnerable.

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

Abstract

The 2005 Guyana HIV/AIDS Indicator Survey (GAIS) is the first household-based, comprehensive survey on HIV/AIDS to be carried out in Guyana. The 2005 GAIS was implemented by the Guyana Responsible Parenthood Association (GRPA) for the Ministry of Health (MoH). ORC Macro of Calverton, Maryland provided technical assistance to the project through its contract with the U.S. Agency for International Development (USAID) under the MEASURE DHS program. Funding to cover technical assistance by ORC Macro and for local costs was provided in their entirety by USAID/Washington and USAID/Guyana.

The 2005 GAIS is a nationally representative sample survey of women and men age 15-49 initiated by MoH with the purpose of obtaining national baseline data for indicators on knowledge/awareness, attitudes, and behavior regarding HIV/AIDS. The survey data can be effectively used to calculate valuable indicators of the President’s Emergency Plan for AIDS Relief (PEPFAR), the Joint United Nations Program on HIV/AIDS (UNAIDS), the United Nations General Assembly Special Session (UNGASS), the United Nations Children Fund (UNICEF) Orphan and Vulnerable Children unit (OVC), and the World Health Organization (WHO), among others. The overall goal of the survey was to provide program managers and policymakers involved in HIV/AIDS programs with information needed to monitor and evaluate existing programs; and to effectively plan and implement future interventions, including resource mobilization and allocation, for combating the HIV/AIDS epidemic in Guyana.

Other objectives of the 2005 GAIS include the support of dissemination and utilization of the results in planning, managing and improving family planning and health services in the country; and enhancing the survey capabilities of the institutions involved in order to facilitate the implementation of surveys of this type in the future.

The 2005 GAIS sampled over 3,000 households and completed interviews with 2,425 eligible women and 1,875 eligible men. In addition to the data on HIV/AIDS indicators, data on the characteristics of households and its members, malaria, infant and child mortality, tuberculosis, fertility, and family planning were also collected.

Geographic coverage

National

Analysis unit

• Individuals;
• Households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the 2005 GAIS is to provide estimates with acceptable precision for important population characteristics such as HIV/AIDS related knowledge, attitudes, and behavior. The population to be covered by the 2005 GAIS was defined as the universe of all women and men age 15-49 in Guyana.

The major domains to be distinguished in the tabulation of important characteristics for the eligible population are: • Guyana as a whole • The urban area and the rural area each as a separate major domain • Georgetown and the remainder urban areas.

Administratively, Guyana is divided into 10 major regions. For census purposes, each region is further subdivided in enumeration districts (EDs). Each ED is classified as either urban or rural. There is a list of EDs that contains the number of households and population for each ED from the 2002 census. The list of EDs is grouped by administrative units as townships. The available demarcated cartographic material for each ED from the last census makes an adequate sample frame for the 2005 GAIS.

The sampling design had two stages with enumeration districts (EDs) as the primary sampling units (PSUs) and households as the secondary sampling units (SSUs). The standard design for the GAIS called for the selection of 120 EDs. Twenty-five households were selected by systematic random sampling from a full list of households from each of the selected enumeration districts for a total of 3,000 households. All women and men 15-49 years of age in the sample households were eligible to be interviewed with the individual questionnaire.

The database for the recently completed 2002 Census was used as a sampling frame to select the sampling units. In the census frame, EDs are grouped by urban-rural location within the ten administrative regions and they are also ordered in each administrative unit in serpentine fashion. Therefore, this stratification and ordering will be also reflected in the 2005 GAIS sample.

Based on response rates from other surveys in Guyana, around 3,000 interviews of women and somewhat fewer of men expected to be completed in the 3,000 households selected.

Several allocation schemes were considered for the sample of clusters for each urban-rural domain. One option was to allocate clusters to urban and rural areas proportionally to the population in the area. According to the census, the urban population represents only 29 percent of the population of the country. In this case, around 35 clusters out of the 120 would have been allocated to the urban area. Options to obtain the best allocation by region were also examined. It should be emphasized that optimality is not guaranteed at the regional level but the power for analysis is increased in the urban area of Georgetown by departing from proportionality. Upon further analysis of the different options, the selection of an equal number of clusters in each major domain (60 urban and 60 rural) was recommended for the 2005 GAIS. As a result of the nonproportionalallocation of the number of EDs for the urban-rural and regional domains, the household sample for the 2005 GAIS is not a self-weighted sample.

The 2005 GAIS sample of households was selected using a stratified two-stage cluster design consisting of 120 clusters. The first stage-units (primary sampling units or PSUs) are the enumeration areas used for the 2002 Population and Housing Census. The number of EDs (clusters) in each domain area was calculated dividing its total allocated number of households by the sample take (25 households for selection per ED). In each major domain, clusters are selected systematically with probability proportional to size.

The sampling procedures are more fully described in "Guyana HIV/AIDS Indicator Survey 2005 - Final Report" pp.135-138.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two types of questionnaires were used in the survey, namely: the Household Questionnaire and the Individual Questionnaire. The contents of these questionnaires were based on model questionnaires developed by the MEASURE DHS program. In consultation with USAID/Guyana, MoH, GRPA, and other government agencies and local organizations, the model questionnaires were modified to reflect issues relevant to HIV/AIDS in Guyana. The questionnaires were finalized around mid-May.

The Household Questionnaire was used to list all the usual members and visitors in the selected households. For each person listed, information was collected on sex, age, education, and relationship to the head of the household. An important purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview.

The Household Questionnaire also collected non-income proxy indicators about the household's dwelling unit, such as the source of water; type of toilet facilities; materials used for the floor, roof and walls of the house; and ownership of various durable goods and land. As part of the Malaria Module, questions were included on ownership and use of mosquito bednets.

The Individual Questionnaire was used to collect information from women and men age 15-49 years and covered the following topics: • Background characteristics (age, education, media exposure, employment, etc.) • Reproductive history (number of births and—for women—a birth history, birth registration, current pregnancy, and current family planning use) • Marriage and sexual activity • Husband’s background • Knowledge about HIV/AIDS and exposure to specific HIV-related mass media programs • Attitudes toward people living with HIV/AIDS • Knowledge and experience with HIV testing • Knowledge and symptoms of other sexually transmitted infections (STIs) • The malaria module and questions on tuberculosis

Cleaning operations

The processing of the GAIS questionnaires began in mid-July 2005, shortly after the beginning of fieldwork and during the first visit of the ORC Macro data processing specialist. Questionnaires for completed clusters (enumeration districts) were periodically submitted to GRPA offices in Georgetown, where they were edited by data processing personnel who had been trained specifically for this task. The concurrent processing of the data—standard for surveys participating in the DHS program—allowed GRPA to produce field-check tables to monitor response rates and other variables, and advise field teams of any problems that were detected during data entry. All data were entered twice, allowing 100 percent verification. Data processing, including data entry, data editing, and tabulations, was done using CSPro, a program developed by ORC Macro, the U.S. Bureau of Census, and SERPRO for processing surveys and censuses. The data entry and editing of the questionnaires was completed during a second visit by the ORC Macro specialist in mid-September. At this time, a clean data set was produced and basic tables with the basic HIV/AIDS indicators were run. The tables included in the current report were completed by the end of November 2005.

Response rate

• From a total of 3,055 households in the sample, 2,800 were occupied. Among these households, interviews were completed in 2,608, for a response rate of 93 percent. • A total of 2,776 eligible women were identified and

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