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

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.

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

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;

Analysis of ‘HIV AIDS Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/imdevskp/hiv-aids-dataset on 13 February 2022.

--- Dataset description provided by original source is as follows ---

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/

Similar Datasets

• COVID-19 - https://www.kaggle.com/imdevskp/corona-virus-report
• MERS - https://www.kaggle.com/imdevskp/mers-outbreak-dataset-20122019
• Ebola Western Africa 2014 Outbreak - https://www.kaggle.com/imdevskp/ebola-outbreak-20142016-complete-dataset
• H1N1 | Swine Flu 2009 Pandemic Dataset - https://www.kaggle.com/imdevskp/h1n1-swine-flu-2009-pandemic-dataset
• SARS 2003 Pandemic - https://www.kaggle.com/imdevskp/sars-outbreak-2003-complete-dataset
• HIV AIDS - https://www.kaggle.com/imdevskp/hiv-aids-dataset

--- Original source retains full ownership of the source dataset ---

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/

Similar Datasets

• COVID-19 - https://www.kaggle.com/imdevskp/corona-virus-report
• MERS - https://www.kaggle.com/imdevskp/mers-outbreak-dataset-20122019
• Ebola Western Africa 2014 Outbreak - https://www.kaggle.com/imdevskp/ebola-outbreak-20142016-complete-dataset
• H1N1 | Swine Flu 2009 Pandemic Dataset - https://www.kaggle.com/imdevskp/h1n1-swine-flu-2009-pandemic-dataset
• SARS 2003 Pandemic - https://www.kaggle.com/imdevskp/sars-outbreak-2003-complete-dataset
• HIV AIDS - https://www.kaggle.com/imdevskp/hiv-aids-dataset

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.

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

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 .

Local Law 14 (2016) requires that NYCDOE provide citywide Health Education data, disaggregated by community school district, city council district and each individual school. This provides information about the number and percent of students scheduled for at least one semester of health education as reported through the STARS database. NYSED mandates HIV/AIDS instruction with a set number of lessons for every student, every year. There are five required lessons per year for grade levels of K-6 and six required lessons for grade level 7-8; teachers must use the NYCDOE HIV\AIDS curriculum.

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.

The Find Ryan White HIV/AIDS Medical Care Providers tool is a locator that helps people living with HIV/AIDS access medical care and related services. Users can search for Ryan White-funded medical care providers near a specific complete address, city and state, state and county, or ZIP code. Search results are sorted by distance away and include the Ryan White HIV/AIDS facility name, address, approximate distance from the search point, telephone number, website address, and a link for driving directions. HRSA's Ryan White program funds an array of grants at the state and local levels in areas where most needed. These grants provide medical and support services to more than a half million people who otherwise would be unable to afford care.

This dataset contains surveillance study estimates for population size, HIV prevalence, and ART coverage among female sex workers (FSW), men who have sex with men (MSM), people who inject drugs (PWID), and transgender men and women (TGM/W) from 2010-2023. It was created to support the UNAIDS Estimates Key Population Workbook for use by HIV estimates teams in sub-Saharan Africa. Key population surveillance reports, including Ministry of Health-led biobehavioural surveys, mapping studies, and academic studies were used to populate the database.

The dataset was populated using existing key population size estimate databases including:

UNAIDS Key Population Atlas

US Centers for Disease Control and Prevention surveillance database

Global Fund against HIV/AIDS, TB, and Malaria surveillance database

Global.HIV database

Systematic review databases among MSM (Stannah et al, 2019 and Stannah et al., 2023) and PWID (Degenhardt et al., 2023)

and was additionally supplemented by a literature review of peer-reviewed and grey literature sources.

The data can be explored in this web application and the accompanying manuscript can be found here

This data set provides an estimate of the number of people living with Human Immunodeficiency Virus (HIV) Disease at the end of each year for 2012 through 2016 and the number of these persons who have injection drug use identified as the primary risk for having acquired the infection. The data sets also provides the number of new diagnoses of HIV Disease by county among all persons and among those with injection drug identified as the primary risk. These data are derived through HIV surveillance activities of the Pennsylvania Department of Health. Laboratories and providers are required to report HIV test results for all individuals with a result that indicates the presence of HIV infection. These include detectable viral load results and CD4 results below 200 cells. These data are reported electronically to the Pennsylvania National Electronic Disease Surveillance System. The most recent patient address information obtained from all reports (both HIV and non-HIV reports) is used to identify last known county of residence in 2016. Cases are also matched to lists that identify individuals who have been reported to be living outside of Pennsylvania by the US Centers for Disease Control and Prevention (CDC) to remove cases that are presumed to have moved from Pennsylvania. Address data for Philadelphia County cases are extracted from the Pennsylvania enhanced HIV/AIDS Reporting System.

IDU: use of non-prescribed injection drugs (e.g., heroin, fentanyl, cocaine, etc.)

HIV Disease: Confirmed infection with the Human Immunodeficiency Virus (HIV). Acquired Immunodeficiency Syndrome (AIDS) is a stage of HIV Disease marked by a low CD4 count and/or certain co-morbid conditions.

Series Name: Number of new HIV infections per 1 000 uninfected population by sex and age (per 1 000 uninfected population)Series Code: SH_HIV_INCDRelease Version: 2021.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 3.3.1: Number of new HIV infections per 1,000 uninfected population, by sex, age and key populationsTarget 3.3: By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseasesGoal 3: Ensure healthy lives and promote well-being for all at all agesFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Analytical dataset corresponding to publication "HIV prevalence and awareness among adults presenting for enrolment into a study of people at risk for HIV in Kisumu County, Western Kenya" Published in PlosOne.

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.

Every year, UNAIDS releases updated estimates of the number of people living with HIV and AIDS and the mortality impact of the epidemic, while WHO releases data on the number of people on treatment and the number needing treatment. This dataset, from CGD senior fellow Mead Over and Owen McCarthy, is a compilation of selected variables from these published sources as well as from the World Bank Development Indicators and the International Monetary Fund’s estimates of economic quantities such as Gross Domestic Product and central government health expenditures. The data are in the format developed by the Stata statistical software corporation and are intended for use with the AIDSCost package for the purpose of projecting the future budgetary cost of scaling up AIDS treatment. Instructions on how to download, access, and use the AIDSCost package are included in the users' manual. The authors encourage comments on their blog or as an e-mail to them, which will be considered for posting. (CGD’s HIV/AIDS Monitor Initiative provides access to data on past AIDS funding PEPFAR, the World Bank and the Global Fund.)