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.

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.

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;

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.

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

Malawi MW: Antiretroviral Therapy Coverage: % of People Living with HIV data was reported at 71.000 % in 2017. This records an increase from the previous number of 66.000 % for 2016. Malawi MW: Antiretroviral Therapy Coverage: % of People Living with HIV data is updated yearly, averaging 20.500 % from Dec 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 71.000 % in 2017 and a record low of 0.000 % in 2003. Malawi MW: Antiretroviral Therapy Coverage: % of People Living with HIV data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Malawi – Table MW.World Bank.WDI: Health Statistics. Antiretroviral therapy coverage indicates the percentage of all people living with HIV who are receiving antiretroviral therapy.; ; UNAIDS estimates.; Weighted average;

aCases of HIV infection newly diagnosed HIV infection in Australia in 2010 including cases previously diagnosed overseas. All new diagnoses inAustralia in 2010, reported by 31 March 2012. All new diagnoses includes cases previously diagnosed overseas, some of whom have received treatment for HIV infection. The heterosexual contact category includes 53M, 80F, total 133 cases from high prevalence countries and 42M, 12F, total 54 cases whose exposure was attributed to heterosexual contact with a partner from a high prevalence country.bThis dataset is not nationally representative; it includes data from 4 of the 13 provinces and territories and one of these jurisdictions was incomplete (nominal cases only). Across all 13 P/Ts, a total of 2,358 HIV cases were reported in 2010.cHIV surveillance includes the whole country except Sardegna region (coverage 97.8%). Data reported by 31 December 2010.dData Source: New HIV Diagnoses Information System (SINIVIH in Spanish). In 2011, SINIVIH was implemented in 17 out of 19 Autonomous Region, and coverage was 71% of the total Spanish population. While information on first CD4 count after diagnosis is available in all Regions, information on DATE of CD4 count determination is currently available only in seven (Aragon, Asturias, Canary Islands, Castile-Leon, Madrid, Murcia and Navarre). This seven regions provided 1519 (52.2%) of the total 2907 new HIV diagnoses notified in 2010. Information on date of CD4 count was missing in 248 (16%) of the 1519 new HIV diagnoses. Information on viral load determination within 3 months after HIV diagnosis is not collected.eIncludes cases of HIV infection diagnosed in 14 jurisdictions of the United States and reported by 31 December 2011. Estimated numbers resulted from statistical adjustment that accounted for missing risk-factor information, but not for reporting delays and incomplete reporting. Age group 10–19 includes 13–19 only.fMSM, men who have sex with men; IDU, injection drug use.

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

Predominantly heterosexual HIV-1 epidemics like those in sub-Saharan Africa continue to have high HIV incidence in young people. We used a stochastic, agent-based model for age-disparate networks to test the hypothesis that focusing uptake and retention of ART among youth could enhance the efficiency of treatment as prevention (TasP) campaigns. We used the model to identify strategies that reduce incidence to negligible levels (i.e., < 0.1 cases/100 person-years) 20–25 years after initiation of a targeted TasP campaign. The model was parameterized using behavioral, demographic, and clinical data from published papers and national reports. To keep a focus on the underlying age effects we model a generalized heterosexual population with average risks (i.e., no MSM, no PWIDs, no sex workers) and no entry of HIV+ people from other regions. The model assumes that most people (default 95%, range in variant simulations 60–95%) are “linkable”; i.e., could get linked to effective care given sufficient resources. To simplify the accounting, we assume a rapid jump in the number of people receiving treatment at the start of the TasP campaign, followed by a 2% annual increase that continues until all linkable HIV+ people have been treated. Under historical scenarios of CD4-based targeted ART allocation and current policies of untargeted (random) ART allocation, our model predicts that viral replication would need to be suppressed in 60–85% of infected people at the start of the TasP campaign to drive incidence to negligible levels. Under age-based strategies, by contrast, this percentage dropped by 18–54%, depending on the strength of the epidemic and the age target. For our baseline model, targeting those under age 30 halved the number of people who need to be treated. Age-based targeting also minimized total and time-discounted AIDS deaths over 25 years. Age-based targeting yielded benefits without being highly exclusive; in a model in which 60% of infected people were treated, ~87% and ~58% of those initiating therapy during a campaign targeting those

Georgia GE: Antiretroviral Therapy Coverage: % of People Living with HIV data was reported at 39.000 % in 2017. This records an increase from the previous number of 36.000 % for 2016. Georgia GE: Antiretroviral Therapy Coverage: % of People Living with HIV data is updated yearly, averaging 11.500 % from Dec 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 39.000 % in 2017 and a record low of 0.000 % in 2003. Georgia GE: Antiretroviral Therapy Coverage: % of People Living with HIV data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Georgia – Table GE.World Bank.WDI: Health Statistics. Antiretroviral therapy coverage indicates the percentage of all people living with HIV who are receiving antiretroviral therapy.; ; UNAIDS estimates.; Weighted average;

Description: The Adult data set contains information on: biographical data, media, communication and norms, knowledge and perceptions of HIV/AIDS, male circumcision, sexual debut, partners and partner characteristics, condoms, vulnerability, HIV testing, alcohol and substance use, general perceptions about government, health and violence in the community. The data set contains 879 variables and 30563 cases. Abstract: South Africa continues to have the largest number of people living with HIV/AIDS in the World. This study intends to understand the determinants that lead South Africans to be vulnerable and susceptible to HIV. This is the fourth in a series of household surveys conducted by Human Sciences Research council (HSRC), that allow for tracking of HIV and associated determinants over time using a slightly same methodology used in 2002 and 2008 survey, making it the fourth national-level repeat survey. The 2002 and 2005 surveys included individuals aged 2+ years living in South Africa while 2008 and 2012 survey included individuals of all ages living in South Africa, including infants less than 2 years of age. The 2008 study included only four people per household, while in 2012 all members of the households participated. The interval of three years since 2002 allows for an exploration of shifts over time against a complex of demographic and other variables, as well as allowing for investigation of the new areas. The surveys provide the nationally representative HIV incidence estimates showing changes over time. The 2012 study key objectives were: to determine the proportion of PLHIV who are on Antiretroviral treatment (ART) in South Africa; to determine the prevalence and incidence of HIV infection in South Africa in relation to social and behavioural determinants; to determine the proportion of males in South Africa who are circumcised; to investigate the link between social values, and cultural determinants and HIV infection in South Africa; to determine the extent to which mother-child pairs include HIV-negative mothers and HIV-positive infants; to describe trends in HIV prevalence, HIV incidence, and risk behaviour in South Africa over the period 2002 to 2012 collect data on the health conditions of South Africans; and contribute to the analysis of the impact of HIV/AIDS on society. In 2012, of the 15000 selected households or visiting points, 11079 agreed to participate in the survey, 42950 individuals (all household members were included) were eligible to be interviewed, and 38431 individuals completed the interview. Of the 38431 eligible individuals, 28997 agreed to provide a blood specimen for HIV testing and were anonymously linked to the behavioural questionnaires. The household response rate was 87.2% , the individual response rate was 89.5% and the overall response rate for HIV testing was 67.5% Clinical measurements Face-to-face interview Focus group Observation South African population. This project used the updated 2007-2011 HSRC's master sample. Aerial photographs drawn from Google Earth were utilised to ensure that the most up-to-date information was available sample. the master sample is defined as a selection, for the purpose of repeated community or household surveys, of a probability sample of census enumeration areas throughout South Africa that are representative of the country's provincial, settlement and racial diversity. The sampling frame that was used in the design of the Master Sample was the 2001 census Enumerator Areas (EAs) from Statistics South Africa (Stats SA). The target population for this study were all people in South Africa, excluding persons in so-called special institutions (e.g. hospitals, military camps, old age homes, schools and university hostels). The EAs were used as the Primary Sampling Units (PSUs) and the Secondary Sampling Units (SSUs) were the visiting points (VPs) or households (HHs). The Ultimate Sampling Units (USUs) were the individuals eligible to be selected for the survey. Any member of the household "who slept here last night", including visitors was an eligible household member for the interview. This sampling approach was used in the 2001 census and is a standard demographic household survey procedure. The sample was designed with two main explicit strata, the provinces and the geography types (geotype) of the EA. In the 2001 census, the four geotypes were urban formal, urban informal, rural formal (including commercial farms) and tribal areas (rural informal) (i.e. the deep rural areas). In the formal urban areas, race was used as a third stratification variable. What this means is that the Master Sample was designed to allow reporting of results (i.e. reporting domain) at a provincial, geotype and race level. A reporting domain is defined as that domain at which estimates of a population characteristic or variable should be of an acceptable precision for the presentation of survey results. A visiting point is defined as a separate (non-vacant) residential stand, address, structure, and flat in a block of flats or homestead. The 2001 estimate of visiting points was used as the Measure of Size (MOS) in the drawing of the sample. A maximum of four visits were made to each VP to optimise response. Fieldworkers enumerated household members, using a random number generator to select the respondent and then preceded with the interview. All people in the households, resident at the visiting point were invited to participate in the study. These individuals constituted the USUs of this study. Having completed the sample design, the sample was drawn with 1 000 PSUs or EAs being selected throughout South Africa. These PSUs were allocated to each of the explicit strata. With a view to obtaining an approximately self-weighting sample of visiting points (i.e. SSUs), (a) the EAs were drawn with probability proportional to the size of the EA using the 2001 estimate of the number of visiting points in the EA database as a measure of size (MOS) and (b) to draw an equal number of visiting points (i.e. SSUs) from each drawn EA. An acceptable precision of estimates per reporting domain requires that a sample of sufficient size be drawn from each of the reporting domains. Consequently, a cluster of 15 VP was systematically selected on the aerial photography produced for each of the EAs in the master sample. Since it is not possible to determine on an aerial photograph whether a 'dwelling unit' is indeed a residential structure or whether it was occupied (i.e. people sleeping there), it was decided to form clusters of 15 dwelling units per PSU, allowing on average for one invalid dwelling unit in the cluster of 15 dwelling units. Previous experience at Statistics SA indicated a sample size of 10 households per PSU to be very efficient, balancing cost and efficiency. The VP questionnaire was administered by the fieldworker, and in follow-up, participant selection was made by the supervisor. Participants aged 12 years and older who consented were all interviewed and also asked to provide dried blood spots (DBS) specimens for HIV testing. In case of 0-11 years, parents/guardians were interviewed but DBS specimens were obtained from the children. The sample size estimate for the 2012 survey was guided by the (1) requirement for measuring change over time in order to detect a change in HIV prevalence of 5 percentage points in each of the main reporting domains, namely gender, age-group, race, locality type, and province (5% level of significance, 80% power, two-sided test), and (2) the requirement of an acceptable precision of estimates per reporting domain; that is, to be able to estimate HIV prevalence in each of the main reporting domains with a precision level of less than ± 4%, which is equivalent to the expected width of the 95% confidence interval (z-score at the 95% level for two-sided test). A design effect of 2 was assumed. Overall, a total of 38 431 interviewed participants composed of 29.7% children (0-14 years), 19.3% youths (15-24 years), 35.6% adults (25-49 years), and 15.4% adults (50+ years ) were interviewed. The sample was designed with the view to enable reporting of the results on province level, on geography type area and on race of the respondent. The total sample size was limited by financial constraints, but based on other HSRC experience in sample surveys it was decided to aim at obtaining a minimum of 1 200 households per race group. The number of respondents per household for the study was expected to vary between one and three (one respondent in each of the three age groups). More females (70.3%) than males (64.2%) were tested for HIV. The 15-24 year's age group was the most compliant (71.6%), and less than 2 years the least (51.6%). The highest testing response rate was found in rural formal settlements (80.8%) and the least in urban formal areas (59.7%).

Note. Data include persons with diagnosed HIV infection regardless of stage of disease at diagnosis. HIV diagnosis data were statistically adjusted for missing transmission category, but not for reporting delays or incomplete reporting.MSM, men who reported ever having had sexual contact with other men.aRates are per 100,000 population.Diagnoses of HIV infection among black/African American MSM and non-MSM, by age at diagnosis, 2005–2009—17 areas.

The Philippines reported about ****** HIV cases, an increase from the previous year. The number of reported HIV cases has gradually increased since 2012, aside from a significant dip in 2020. The state of HIV As the monthly average number of people newly diagnosed with HIV increases, the risk it poses threatens the lives of Filipinos. HIV is a sexually transmitted infection that attacks the body’s immune system, with more males being diagnosed than females. In 2022, the majority of people newly diagnosed with HIV were those between the age of 25 and 34 years, followed by those aged 15 and 24. There is still no cure for HIV and without treatment, it could lead to other severe illnesses such as tuberculosis and cancers such as lymphoma and Kaposi’s sarcoma. However, HIV is now a manageable chronic illness that can be treated with proper medication. What are the leading causes of death in the Philippines? Between January and September 2024, preliminary figures have shown that ischaemic heart disease was the leading cause of death in the Philippines. The prevalence of heart diseases in the nation has been closely attributed to the Filipino diet, which was described as having a high fat, high cholesterol, and high sodium content. In addition, acute respiratory infections and hypertension also registered the highest morbidity rate among leading diseases in the country in 2021.

Description: This data set contains responses from individuals who are 12 to 14 years old who self-reported on the indicators related to HIV/AIDS behaviour and testing. The respondents' biographical data, school attendance, questions on media, communication and norms, knowledge and perceptions of HIV and AIDS, home environment, care and protection at school, sexual debut, attitudes and knowledge towards sexual roles, health questions, male circumcision, crime and social norms were included. The data set contains 227 variables and 2273 cases. Refer to the user guide for information regarding guidance relating to data analysis. Subsequent to the dissemination of version 1 of the Child 12-14 data set the skip patterns for the data set were corrected and is disseminated as Version 2. Abstract: South Africa continues to have the largest number of people living with HIV/AIDS in the World. This study intends to understand the determinants that lead South Africans to be vulnerable and susceptible to HIV. This is the fourth in a series of household surveys conducted by Human Sciences Research council (HSRC), that allow for tracking of HIV and associated determinants over time using a slightly same methodology used in 2002 and 2008 survey, making it the fourth national-level repeat survey. The 2002 and 2005 surveys included individuals aged 2+ years living in South Africa while 2008 and 2012 survey included individuals of all ages living in South Africa, including infants less than 2 years of age. The 2008 study included only four people per household, while in 2012 all members of the households participated. The interval of three years since 2002 allows for an exploration of shifts over time against a complex of demographic and other variables, as well as allowing for investigation of the new areas. The surveys provide the nationally representative HIV incidence estimates showing changes over time. The 2012 study key objectives were: to determine the proportion of PLHIV who are on Antiretroviral treatment (ART) in South Africa; to determine the prevalence and incidence of HIV infection in South Africa in relation to social and behavioural determinants; to determine the proportion of males in South Africa who are circumcised; to investigate the link between social values, and cultural determinants and HIV infection in South Africa; to determine the extent to which mother-child pairs include HIV-negative mothers and HIV-positive infants; to describe trends in HIV prevalence, HIV incidence, and risk behaviour in South Africa over the period 2002 to 2012 collect data on the health conditions of South Africans; and contribute to the analysis of the impact of HIV/AIDS on society. In 2012, of the 15000 selected households or visiting points, 11079 agreed to participate in the survey, 42950 individuals (all household members were included) were eligible to be interviewed, and 38431 individuals completed the interview. Of the 38431 eligible individuals, 28997 agreed to provide a blood specimen for HIV testing and were anonymously linked to the behavioural questionnaires. The household response rate was 87.2% , the individual response rate was 89.5% and the overall response rate for HIV testing was 67.5% Clinical measurements Face-to-face interview Focus group Observation South African population.

Description: The data set contains the data of the parents or guardians of children aged 0 to 11 years. Some of the questions included were the child's biographical data, health status and health questions, male circumcision, education of the child on life issues, infant and child feeding practices as well as school attendance and immunisation records. The data set contains 275 variables and 9667 cases. Refer to the user guide for information regarding guidance relating to data analysis. Abstract: South Africa continues to have the largest number of people living with HIV/AIDS in the World. This study intends to understand the determinants that lead South Africans to be vulnerable and susceptible to HIV. This is the fourth in a series of household surveys conducted by Human Sciences Research council (HSRC), that allow for tracking of HIV and associated determinants over time using a slightly same methodology used in 2002 and 2008 survey, making it the fourth national-level repeat survey. The 2002 and 2005 surveys included individuals aged 2+ years living in South Africa while 2008 and 2012 survey included individuals of all ages living in South Africa, including infants less than 2 years of age. The 2008 study included only four people per household, while in 2012 all members of the households participated. The interval of three years since 2002 allows for an exploration of shifts over time against a complex of demographic and other variables, as well as allowing for investigation of the new areas. The surveys provide the nationally representative HIV incidence estimates showing changes over time. The 2012 study key objectives were: to determine the proportion of PLHIV who are on Antiretroviral treatment (ART) in South Africa; to determine the prevalence and incidence of HIV infection in South Africa in relation to social and behavioural determinants; to determine the proportion of males in South Africa who are circumcised; to investigate the link between social values, and cultural determinants and HIV infection in South Africa; to determine the extent to which mother-child pairs include HIV-negative mothers and HIV-positive infants; to describe trends in HIV prevalence, HIV incidence, and risk behaviour in South Africa over the period 2002 to 2012 collect data on the health conditions of South Africans; and contribute to the analysis of the impact of HIV/AIDS on society. In 2012, of the 15000 selected households or visiting points, 11079 agreed to participate in the survey, 42950 individuals (all household members were included) were eligible to be interviewed, and 38431 individuals completed the interview. Of the 38431 eligible individuals, 28997 agreed to provide a blood specimen for HIV testing and were anonymously linked to the behavioural questionnaires. The household response rate was 87.2% , the individual response rate was 89.5% and the overall response rate for HIV testing was 67.5% From the total of 38431 (89.5%) individuals who completed the interview, 2295 (5.3%) refused to be interviewed, 2224(5.2%) were absent from the household and 2224 (5.2%) were classified as missing/other. Clinical measurements Face-to-face interview Focus group Observation South African population. This project used the updated 2007-2011 HSRC's master sample. Aerial photographs drawn from Google Earth were utilised to ensure that the most up-to-date information was available sample. the master sample is defined as a selection, for the purpose of repeated community or household surveys, of a probability sample of census enumeration areas throughout South Africa that are representative of the country's provincial, settlement and racial diversity. The sampling frame that was used in the design of the Master Sample was the 2001 census Enumerator Areas (EAs) from Statistics South Africa (Stats SA). The target population for this study were all people in South Africa, excluding persons in so-called special institutions (e.g. hospitals, military camps, old age homes, schools and university hostels). The EAs were used as the Primary Sampling Units (PSUs) and the Secondary Sampling Units (SSUs) were the visiting points (VPs) or households (HHs). The Ultimate Sampling Units (USUs) were the individuals eligible to be selected for the survey. Any member of the household "who slept here last night", including visitors was an eligible household member for the interview. This sampling approach was used in the 2001 census and is a standard demographic household survey procedure. The sample was designed with two main explicit strata, the provinces and the geography types (geotype) of the EA. In the 2001 census, the four geotypes were urban formal, urban informal, rural formal (including commercial farms) and tribal areas (rural informal) (i.e. the deep rural areas). In the formal urban areas, race was used as a third stratification variable. What this means is that the Master Sample was designed to allow reporting of results (i.e. reporting domain) at a provincial, geotype and race level. A reporting domain is defined as that domain at which estimates of a population characteristic or variable should be of an acceptable precision for the presentation of survey results. A visiting point is defined as a separate (non-vacant) residential stand, address, structure, and flat in a block of flats or homestead. The 2001 estimate of visiting points was used as the Measure of Size (MOS) in the drawing of the sample. A maximum of four visits were made to each VP to optimise response. Fieldworkers enumerated household members, using a random number generator to select the respondent and then preceded with the interview. All people in the households, resident at the visiting point were invited to participate in the study. These individuals constituted the USUs of this study. Having completed the sample design, the sample was drawn with 1 000 PSUs or EAs being selected throughout South Africa. These PSUs were allocated to each of the explicit strata. With a view to obtaining an approximately self-weighting sample of visiting points (i.e. SSUs), (a) the EAs were drawn with probability proportional to the size of the EA using the 2001 estimate of the number of visiting points in the EA database as a measure of size (MOS) and (b) to draw an equal number of visiting points (i.e. SSUs) from each drawn EA. An acceptable precision of estimates per reporting domain requires that a sample of sufficient size be drawn from each of the reporting domains. Consequently, a cluster of 15 VP was systematically selected on the aerial photography produced for each of the EAs in the master sample. Since it is not possible to determine on an aerial photograph whether a 'dwelling unit' is indeed a residential structure or whether it was occupied (i.e. people sleeping there), it was decided to form clusters of 15 dwelling units per PSU, allowing on average for one invalid dwelling unit in the cluster of 15 dwelling units. Previous experience at Statistics SA indicated a sample size of 10 households per PSU to be very efficient, balancing cost and efficiency. The VP questionnaire was administered by the fieldworker, and in follow-up, participant selection was made by the supervisor. Participants aged 12 years and older who consented were all interviewed and also asked to provide dried blood spots (DBS) specimens for HIV testing. In case of 0-11 years, parents/guardians were interviewed but DBS specimens were obtained from the children. The sample size estimate for the 2012 survey was guided by the (1) requirement for measuring change over time in order to detect a change in HIV prevalence of 5 percentage points in each of the main reporting domains, namely gender, age-group, race, locality type, and province (5% level of significance, 80% power, two-sided test), and (2) the requirement of an acceptable precision of estimates per reporting domain; that is, to be able to estimate HIV prevalence in each of the main reporting domains with a precision level of less than ± 4%, which is equivalent to the expected width of the 95% confidence interval (z-score at the 95% level for two-sided test). A design effect of 2 was assumed. Overall, a total of 38 431 interviewed participants composed of 29.7% children (0-14 years), 19.3% youths (15-24 years), 35.6% adults (25-49 years), and 15.4% adults (50+ years ) were interviewed. The sample was designed with the view to enable reporting of the results on province level, on geography type area and on race of the respondent. The total sample size was limited by financial constraints, but based on other HSRC experience in sample surveys it was decided to aim at obtaining a minimum of 1 200 households per race group. The number of respondents per household for the study was expected to vary between one and three (one respondent in each of the three age groups). More females (70.3%) than males (64.2%) were tested for HIV. The 15-24 year's age group was the most compliant (71.6%), and less than 2 years the least (51.6%). The highest testing response rate was found in rural formal settlements (80.8%) and the least in urban formal areas (59.7%).

Japan JP: Antiretroviral Therapy Coverage: % of People Living with HIV data was reported at 82.000 % in 2017. This stayed constant from the previous number of 82.000 % for 2016. Japan JP: Antiretroviral Therapy Coverage: % of People Living with HIV data is updated yearly, averaging 57.500 % from Dec 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 82.000 % in 2017 and a record low of 25.000 % in 2000. Japan JP: Antiretroviral Therapy Coverage: % of People Living with HIV data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank.WDI: Health Statistics. Antiretroviral therapy coverage indicates the percentage of all people living with HIV who are receiving antiretroviral therapy.; ; UNAIDS estimates.; Weighted average;

Laos LA: Antiretroviral Therapy Coverage: % of People Living with HIV data was reported at 47.000 % in 2017. This records an increase from the previous number of 40.000 % for 2016. Laos LA: Antiretroviral Therapy Coverage: % of People Living with HIV data is updated yearly, averaging 13.500 % from Dec 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 47.000 % in 2017 and a record low of 0.000 % in 2002. Laos LA: Antiretroviral Therapy Coverage: % of People Living with HIV data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Laos – Table LA.World Bank.WDI: Health Statistics. Antiretroviral therapy coverage indicates the percentage of all people living with HIV who are receiving antiretroviral therapy.; ; UNAIDS estimates.; Weighted average;

Description: This data file contains household information about the people who usually lived in the household and slept there the previous night as per the time of the interview. Demographic variables were age, sex, marital status, race, language spoken, education status, main source of drinking water, energy for cooking, type of toilet facility. The data set contains 545 variables and 14919 cases. Number of completed household interviews: 10908 Number of refusals from household head or other resident: 1320 Number of unoccupied households and invalid visiting points: 1155 Number of partly completed questionnaires, no one at home or eligible to complete questionnaire, incapacitated and other: 1371 Abstract: The 2012 population-based survey of HIV prevalence was the fourth among the HIV prevalence surveys that have investigated HIV prevalence and behaviour. The survey incorporated new methodologies, technologies and novel laboratory methodologies that enabled direct estimates of HIV incidence and ART exposure. The main objectives of the survey were to determine the prevalence and incidence of HIV infection in South Africa in relation to social and behavioural determinants and to determine the proportion of males in South Africa who are circumcised. The secondary objectives were to determine the proportion of people living with HIV (PLHIV) who are on antiretroviral therapy (ART) in South Africa, to determine the extent to which mother-child pairs include HIV-negative mothers and HIV-positive infants and to describe trends in HIV prevalence, HIV incidence, and risk behaviour in South Africa over the period 2002 to 2012. A multi-stage stratified cluster sampling design was implemented with everyone in the sampled households invited to participate. People of all ages living in South African households and hostels were eligible to participate. Clinical measurements Face-to-face interview

Percentage of HIV and STDs co-infection among persons living with HIV infection (PLWH) ages 13 and older, 2016, Santa Clara County. STD co-infections among PLWH were identified by matching the list of PLWH and the list of STD cases newly reported in Santa Clara County in 2016. STDs include chlamydia, gonorrhea, syphilis(primary, secondary, and early latent). Source: Santa Clara County Public Health Department, enhanced HIV/AIDS reporting system (eHARS), data as of 4/30/2017; CalREDIE, data as of March 15, 2017METADATA:Notes (String): Lists table title, notes and sourcesCategory (String): Lists the category representing the data: Santa Clara County is for total population living with HIV infection; sex: male, female; age group: 13-19, 20-29, 30-39, 40-49, 50-59, 60-64, 65 and older; race/ethnicity: African American, Asian/Pacific Islander, Latino and White (non-Hispanic White only); transmission mode: MSM, IDU, MSM & IDU, heterosexual contactPercentage (Numeric): Percentage of people living with HIV who was diagnosed with chlamydia, gonorrhea, or early syphilis among all people living with HIV in 2016.