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 January 9, 2019, 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.

Context :

Dataset contains healthcare statistics and categorical information about patients who have been diagnosed with AIDS. This dataset was initially published in 1996.

Attribute Information :

• time: time to failure or censoring
• trt: treatment indicator (0 = ZDV only; 1 = ZDV + ddI, 2 = ZDV + Zal, 3 = ddI only)
• age: age (yrs) at baseline
• wtkg: weight (kg) at baseline
• hemo: hemophilia (0=no, 1=yes)
• homo: homosexual activity (0=no, 1=yes)
• drugs: history of IV drug use (0=no, 1=yes)
• karnof: Karnofsky score (on a scale of 0-100)
• oprior: Non-ZDV antiretroviral therapy pre-175 (0=no, 1=yes)
• z30: ZDV in the 30 days prior to 175 (0=no, 1=yes)
• preanti: days pre-175 anti-retroviral therapy
• race: race (0=White, 1=non-white)
• gender: gender (0=F, 1=M)
• str2: antiretroviral history (0=naive, 1=experienced)
• strat: antiretroviral history stratification (1='Antiretroviral Naive',2='> 1 but <= 52 weeks of prior antiretroviral therapy',3='> 52 weeks)
• symptom: symptomatic indicator (0=asymp, 1=symp)
• treat: treatment indicator (0=ZDV only, 1=others)
• offtrt: indicator of off-trt before 96+/-5 weeks (0=no,1=yes)
• cd40: CD4 at baseline
• cd420: CD4 at 20+/-5 weeks
• cd80: CD8 at baseline
• cd820: CD8 at 20+/-5 weeks
• infected: is infected with AIDS (0=No, 1=Yes)

Additional Variable Information :

• Personal information (age, weight, race, gender, sexual activity)
• Medical history (hemophilia, history of IV drugs)
• Treatment history (ZDV/non-ZDV treatment history)
• Lab results (CD4/CD8 counts)

Citation :

https://classic.clinicaltrials.gov/ct2/show/NCT00000625

Acknowledgment :

Creators :

1. S. Hammer
2. D. Katzenstein
3. M. Hughes
4. H. Gundacker
5. R. Schooley
6. R. Haubrich
7. W. K.
8. M. Lederman
9. J. Phair
10. M. Niu
11. M. Hirsch
12. T. Merigan

Donor :

https://archive.ics.uci.edu/dataset/890/aids+clinical+trials+group+study+175

United States US: Prevalence of HIV: Total: % of Population Aged 15-49 data was reported at 0.500 % in 2014. This stayed constant from the previous number of 0.500 % for 2013. United States US: Prevalence of HIV: Total: % of Population Aged 15-49 data is updated yearly, averaging 0.500 % from Dec 2008 (Median) to 2014, with 7 observations. The data reached an all-time high of 0.500 % in 2014 and a record low of 0.500 % in 2014. United States US: Prevalence of HIV: Total: % of Population Aged 15-49 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Prevalence of HIV refers to the percentage of people ages 15-49 who are infected with HIV.; ; UNAIDS estimates.; Weighted Average;

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

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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Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_250a8096f848835cb4a835d31fcd8ffd/view

No. of Deaths: Caused by: HIV Disease (Aids) data was reported at 547.000 Person in Sep 2024. This records a decrease from the previous number of 557.000 Person for Jun 2024. No. of Deaths: Caused by: HIV Disease (Aids) data is updated quarterly, averaging 558.000 Person from Mar 2017 (Median) to Sep 2024, with 30 observations. The data reached an all-time high of 659.000 Person in Mar 2018 and a record low of 461.000 Person in Sep 2020. No. of Deaths: Caused by: HIV Disease (Aids) data remains active status in CEIC and is reported by National Administrative Department of Statistics. The data is categorized under Global Database’s Colombia – Table CO.G012: Number of Deaths: Cause of Death.

HIV/AIDS yearly statistics in Hong Kong 1984 - 2023

There are 175 HIV Testing and Counseling sitesin Nepal that include 39 non-government sites and 136 government sites operating in the country also maintaining their linkages with KPs as well as with ART sites as well as PMTCT sites. The trend of programmatic data of people who were tested and counseled over the last three years is showed in the given dataset. Source: NCASC

Incidence of HIV: per 1,000 Uninfected Population data was reported at 0.040 Ratio in 2020. This records a decrease from the previous number of 0.050 Ratio for 2019. Incidence of HIV: per 1,000 Uninfected Population data is updated yearly, averaging 0.080 Ratio from Dec 1990 (Median) to 2020, with 31 observations. The data reached an all-time high of 0.100 Ratio in 1990 and a record low of 0.040 Ratio in 2020. Incidence of HIV: per 1,000 Uninfected Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Canada – Table CA.World Bank.WDI: Social: Health Statistics. Number of new HIV infections among uninfected populations expressed per 1,000 uninfected population in the year before the period.;UNAIDS estimates.;Weighted average;This is the Sustainable Development Goal indicator 3.3.1 [https://unstats.un.org/sdgs/metadata/].

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

Rate: Number of deaths (per 100,000) due to HIV disease

Definition: Deaths with human immunodeficiency virus (HIV) disease as the underlying cause (ICD-10 codes: B20-B24).

Data Sources:

(1) Centers for Disease Control and Prevention, National Center for Health Statistics

(2) Death Certificate Database, Office of Vital Statistics and Registry, New Jersey Department of Health

(3) Population Estimates, State Data Center, New Jersey Department of Labor and Workforce Development

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 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.

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 .

Geographic coverage

The survey was conducted in two local communities in the Free State province, one urban (Welkom) and one rural (Qwaqwa), in which the HIV/AIDS epidemic is particularly rife. Welkom and Qwaqwa are situated in the Lejweleputswa and Thabo Mofutsanyane districts of the Free State province.

Analysis unit

Households

Universe

All memebers of the Household

Kind of data

Sample survey data [ssd]

Sampling procedure

The household impact of HIV/AIDS was assessed by means of a cohort study of households affected by the disease. The survey was conducted in two local communities in the Free State province, one urban (Welkom) and one rural (Qwaqwa), in which the HIV/AIDS epidemic is particularly rife. Welkom and Qwaqwa are situated in the Lejweleputswa and Thabo Mofutsanyane districts of the Free State province.

Affected households were sampled purposively via NGOs and other organizations involved in AIDS counselling and care and at baseline included at least one person known to be HIV-positive or known to have died from AIDS in the past six months. Informed consent was obtained from the infected individual(s) or their caregivers (in the case of minors). In order to explore the socio-economic impact on affected households of repeated occurrences of HIV/AIDS-related morbidity or mortality, a distinction is made between affected households in general and affected households that have experienced morbidity or mortality more frequently. Non-affected households represent households living in close proximity to affected households. These households at baseline did not include persons suffering from tuberculosis or pneumonia. The incidence of morbidity and mortality is considerably higher in affected households.

Affected households were sampled purposively via NGOs and other organizations involved in AIDS counselling and care and at baseline included at least one person known to be HIV-positive or known to have died from AIDS in the past six months. Informed consent was obtained from the infected individual(s) or their caregivers (in the case of minors). In order to explore the socio-economic impact on affected households of repeated occurrences of HIV/AIDS-related morbidity or mortality, a distinction is made between affected households in general and affected households that have experienced morbidity or mortality more frequently. Non-affected households represent households living in close proximity to affected households. These households at baseline did not include persons suffering from tuberculosis or pneumonia. The incidence of morbidity and mortality is considerably higher in affected households.

Mode of data collection

Face-to-face [f2f]

Research instrument

Household Questionnaire

Response rate

During the first wave of interviews a total of 404 interviews were conducted. During the second wave of data collection, interviews were conducted with 385 households, which translates into an attrition rate of 4.7% (19 households). During wave III, a total of 354 households were interviewed, with 31 households not being reinterviewed (7.7% of the original sample). In wave IV, 55 new households wererecruited into the study, with particular emphasis on an effort to recruit child-headed households into the survey insofar as the sample to date did not include any such households. During waves IV, V and VI a total of 3, 13 and 9 households respectively could not be re-interviewed.

The payment of a minimal participation fee (R150 per household per survey visit) to those households interviewed in each wave, following the interview and distributed in the form of food parcels, contributed to ensuring sustainability of the sample over the three-year period. The dataset includes data for 331 households interviewed in each of the six rounds of interviews. In almost 90 percent of cases the reasons for attrition are related to migration, given that this study did not intend to follow those households that move outside of the two immediate study areas, i.e. Welkom and Qwaqwa. In the majority of cases, attrition can be ascribed to the failure to establish the current whereabouts of the particular household during follow-up, while in a third of cases it could be established that the household had moved to another country, another province, or another town in the Free State province. Less than ten percent of households had refused to participate in subsequent waves. The reasons for attrition in the original sample illustrate the manner in which migration and the disintegration of households, which are important effects of the epidemic, can act to erode the sample population.

CA: Incidence of Tuberculosis: per 100,000 People data was reported at 5.800 Ratio in 2023. This stayed constant from the previous number of 5.800 Ratio for 2022. CA: Incidence of Tuberculosis: per 100,000 People data is updated yearly, averaging 5.500 Ratio from Dec 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 6.500 Ratio in 2001 and a record low of 4.600 Ratio in 2010. CA: Incidence of Tuberculosis: per 100,000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Canada – Table CA.World Bank.WDI: Social: Health Statistics. Incidence of tuberculosis is the estimated number of new and relapse tuberculosis cases arising in a given year, expressed as the rate per 100,000 population. All forms of TB are included, including cases in people living with HIV. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously.;World Health Organization, Global Tuberculosis Report.;Weighted average;Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the World Health Organization. This is the Sustainable Development Goal indicator 3.3.2[https://unstats.un.org/sdgs/metadata/].

Note: This dataset is historical only and STI surveillance data do not change once finalized. For the most recent STI data, please refer to the CDPH Health Atlas (chicagohealthatlas.org), the annual HIV/STI surveillance report, and the Getting to Zero Illinois HIV Dashboard (gtzillinois.hiv).

The annual number of newly reported, laboratory-confirmed cases of gonorrhea (Neisseria gonorrhoeae) among males aged 15-44 years and annual gonorrhea incidence rate (cases per 100,000 males aged 15-44 years) with corresponding 95% confidence intervals by Chicago community area, for years 2000 – 2014. See the full description by clicking on the maroon "About" button on the right-hand side of the screen, and click on the PDF under "Attachments".

Note: This dataset is historical only and STI surveillance data do not change once finalized. For the most recent STI data, please refer to the CDPH Health Atlas (chicagohealthatlas.org), the annual HIV/STI surveillance report, and the Getting to Zero Illinois HIV Dashboard (gtzillinois.hiv).

The annual number of newly reported, laboratory-confirmed cases of gonorrhea (Neisseria gonorrhoeae) among females aged 15-44 years and annual gonorrhea incidence rate (cases per 100,000 females aged 15-44 years) with corresponding 95% confidence intervals by Chicago community area, for years 2000 – 2014. See the full description by clicking on the maroon "About" button on the right-hand side of the screen, and click on the PDF under "Attachments".