This data set includes tables on persons living with HIV/AIDS, newly diagnosed HIV cases and all cause deaths in HIV/AIDS cases by gender, age, race/ethnicity and transmission category. In all tables, cases are reported as of December 31 of the given year, as reported by December 31, 2024, to allow a minimum of 12 months reporting delay. Gender is determined by both current gender and sex at birth variables; transgender values are assigned when current gender is identified as "Transgender" or when a discrepancy is identified between a person's sex at birth and their current gender (e.g., cases where sex at birth is "Male" and current gender is "Female" will become Transgender: Male to Female.) Prior to 2003, Asian and Native Hawaiian/Pacific Islanders were classified as one combined group. In order to present these race/ethnicities separately, living cases recorded under this combined classification were split and redistributed according to their expected proportional population representation estimated from post-2003 data.

Dataset Overview

The dataset provides a comprehensive look at HIV/AIDS adult prevalence rates, the number of people living with HIV, and annual deaths across different countries. It is based on publicly available data sources such as the CIA World Factbook, UNAIDS AIDS Info, and other global health organizations. The dataset primarily focuses on adult HIV prevalence (ages 15–49) and includes estimates from recent years (e.g., 2023–2024).

Data Science Applications

This dataset can be used for: - Epidemiological Analysis: Understanding the regional distribution of HIV/AIDS and identifying high-prevalence areas. - Predictive Modeling: Developing machine learning models to predict HIV prevalence trends or identify risk factors. - Resource Allocation: Informing policymakers about regions requiring urgent intervention or resource allocation. - Health Outcome Monitoring: Tracking progress in combating HIV/AIDS over time. - Social Determinants Research: Analyzing the relationship between socio-economic factors and HIV prevalence.

Column Descriptors

1. Country/Region: The geographical area being analyzed.
2. Adult Prevalence (%): Percentage of adults aged 15–49 living with HIV.
3. Number of People with HIV/AIDS: Absolute count of individuals living with HIV in the region.
4. Annual Deaths from HIV/AIDS: Number of deaths attributed to HIV/AIDS annually.
5. Year of Estimate: The year when the data was collected or estimated.

Ethically Mined Data

The dataset is ethically sourced from publicly available and credible platforms such as the CIA World Factbook, UNAIDS, and WHO. These organizations ensure transparency and ethical standards in data collection, protecting individual privacy while providing aggregate statistics for research purposes.

Acknowledgments

1. Data Source Platforms:
   • CIA World Factbook
   • UNAIDS AIDS Info
   • WHO Global Health Observatory
2. Dataset Visualization Image:
   • Created using DALL-E 3 for illustrative purposes.
3. Research Support:
   • Contributions from platforms like ResearchGate, NIMH, and others for insights into data science applications in HIV research.

This dataset serves as a valuable tool for researchers, policymakers, and public health professionals in addressing the global challenge of HIV/AIDS.

Context

In the time of epidemics, what is the status of HIV AIDS across the world, where does each country stands, is it getting any better. The data set should be helpful to explore much more about above mentioned factors.

Content

The data set contains data on

1. No. of people living with HIV AIDS
2. No. of deaths due to HIV AIDS
3. No. of cases among adults (19-45)
4. Prevention of mother-to-child transmission estimates
5. ART (Anti Retro-viral Therapy) coverage among people living with HIV estimates
6. ART (Anti Retro-viral Therapy) coverage among children estimates

Acknowledgements / Data Source

• WHO : https://www.who.int/hiv/data/en/
• UNESCO : https://data.unicef.org/resources/dataset/hiv-aids-statistical-tables/

Collection methodology

https://github.com/imdevskp/hiv_aids_who_unesco_data_cleaning

Cover Photo

Photo by Anna Shvets from Pexels https://www.pexels.com/photo/red-ribbon-on-white-surface-3900425/

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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

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 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.

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

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.

this graph was created in Tableu and Ourdataworld :

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fc5bb0b21c8b3a126eca89160e1d25d03%2Fgraph1.png?generation=1710708871099084&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Ff81fcfa72e97f08202ba1cb06fe138da%2Fgraph2.png?generation=1710708877558039&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fabbdfd146844a7e8d19e277c2eecb83b%2Fgraph3.png?generation=1710708883608541&alt=media" alt="">

Understanding the Global Distribution of HIV/AIDS Deaths

Introduction:

HIV/AIDS remains one of the most significant public health challenges globally, with its impact varying widely across countries and regions. While the overall share of deaths attributed to HIV/AIDS stands at around 1.5% globally, this statistic belies the stark disparities observed on a country-by-country basis. This essay delves into the global distribution of deaths from HIV/AIDS, examining both the overarching trends and the localized impacts across different regions, particularly focusing on Southern Sub-Saharan Africa.

Understanding Global Trends:

At a global level, HIV/AIDS accounts for approximately 1.5% of all deaths. This figure, though relatively low in comparison to other causes of mortality, represents a significant burden on public health systems and communities worldwide. However, when zooming in on specific regions, such as Europe, the share of deaths attributable to HIV/AIDS drops significantly, often comprising less than 0.1% of total mortality. This pattern suggests varying levels of prevalence and effectiveness of HIV/AIDS prevention and treatment strategies across different parts of the world.

Regional Disparities:

The distribution of HIV/AIDS deaths is not uniform across the globe, with certain regions experiencing disproportionately high burdens. Southern Sub-Saharan Africa emerges as a focal point of the HIV/AIDS epidemic, with a significant portion of deaths attributed to the virus occurring in this region. Factors such as limited access to healthcare, socio-economic disparities, cultural stigmatization, and insufficient education about HIV/AIDS contribute to the heightened prevalence and impact of the disease in this area.

Southern Sub-Saharan Africa: A Hotspot for HIV/AIDS Deaths:

Within Southern Sub-Saharan Africa, countries such as South Africa, Botswana, and Swaziland stand out for their exceptionally high rates of HIV/AIDS-related mortality. In these nations, HIV/AIDS can account for up to a quarter of all deaths, highlighting the acute nature of the epidemic in these regions. The reasons behind this disproportionate burden are multifaceted, encompassing issues ranging from inadequate healthcare infrastructure to socio-cultural barriers inhibiting prevention and treatment efforts.

Challenges and Responses:

Addressing the unequal distribution of HIV/AIDS deaths necessitates a multi-faceted approach that encompasses both prevention and treatment strategies tailored to the specific needs of affected communities. Efforts to expand access to antiretroviral therapy (ART), promote comprehensive sexual education, combat stigma, and strengthen healthcare systems are crucial components of an effective response. Moreover, fostering partnerships between governments, civil society organizations, and international entities is essential for coordinating resources and expertise to tackle the HIV/AIDS epidemic comprehensively.

Lessons Learned and Future Directions:

The global distribution of deaths from HIV/AIDS underscores the importance of context-specific interventions that take into account the unique social, economic, and cultural factors influencing the spread and impact of the disease. While progress has been made in reducing HIV/AIDS-related mortality in some regions, much work remains to be done, particularly in areas where the burden of the epidemic remains disproportionately high. Going forward, sustained investment in research, healthcare infrastructure, and community empowerment initiatives will be vital for achieving meaningful reductions in HIV/AIDS deaths worldwide.

Conclusion:

In conclusion, the global distribution of deaths from HIV/AIDS reveals a complex landscape characterized by both overarching trends and localized disparities. While the overall share of deaths attributable to HIV/AIDS may seem relatively modest on a global scale, the stark contrasts observed across different countries and regions underscore the need for targeted interventions tailored to the specific contexts in which the epidemic is most pronounced. By addressing the underlying social, economic, and healthcare-related factors driving the unequal distribution of HIV/AIDS deaths, the global co...

The mortality estimate methodology is fully described elsewhere and takes into consideration parameters such as ART coverage. For example, HIV associated mortality in Mozambique also reflects injection drug user driven epidemic.ART coverage calculated using 2013 reported people on ART/people estimated to be living with HIV in 2013.** Published guidelines as of December 2014; WHO 2013 Guidelines recommend

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.

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

Effect of suicide rates on life expectancy dataset

Abstract
In 2015, approximately 55 million people died worldwide, of which 8 million committed suicide. In the USA, one of the main causes of death is the aforementioned suicide, therefore, this experiment is dealing with the question of how much suicide rates affects the statistics of average life expectancy.
The experiment takes two datasets, one with the number of suicides and life expectancy in the second one and combine data into one dataset. Subsequently, I try to find any patterns and correlations among the variables and perform statistical test using simple regression to confirm my assumptions.

Data

The experiment uses two datasets - WHO Suicide Statistics[1] and WHO Life Expectancy[2], which were firstly appropriately preprocessed. The final merged dataset to the experiment has 13 variables, where country and year are used as index: Country, Year, Suicides number, Life expectancy, Adult Mortality, which is probability of dying between 15 and 60 years per 1000 population, Infant deaths, which is number of Infant Deaths per 1000 population, Alcohol, which is alcohol, recorded per capita (15+) consumption, Under-five deaths, which is number of under-five deaths per 1000 population, HIV/AIDS, which is deaths per 1 000 live births HIV/AIDS, GDP, which is Gross Domestic Product per capita, Population, Income composition of resources, which is Human Development Index in terms of income composition of resources, and Schooling, which is number of years of schooling.

LICENSE

THE EXPERIMENT USES TWO DATASET - WHO SUICIDE STATISTICS AND WHO LIFE EXPECTANCY, WHICH WERE COLLEECTED FROM WHO AND UNITED NATIONS WEBSITE. THEREFORE, ALL DATASETS ARE UNDER THE LICENSE ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 3.0 IGO (https://creativecommons.org/licenses/by-nc-sa/3.0/igo/).

[1] https://www.kaggle.com/szamil/who-suicide-statistics

[2] https://www.kaggle.com/kumarajarshi/life-expectancy-who

The epidemiological surveillance of HIV in Belgium is based on several data collections carried out by Sciensano. National data are collected from the HIV reference centres (HRCs) and AIDS reference laboratories (ARLs): a) National data collection of all HIV diagnosed patients in Belgium; b) National data collection of all HIV patients in care, through an exhaustive data collection of all viral load measures performed in Belgium and a data collection of demographic, biological, immunological, treatment and death data of patients in care in the HRCs (around 80 % of all patients in care in Belgium); c) A laboratory data collection on viro-immunological follow-up of all new-borns from HIV positive mothers; d) A national data collection of post-exposure prophylaxis episodes. Since the beginning of the HIV epidemic, this surveillance enables the monitoring of the trends in number of people diagnosed with HIV and number of patients in medical follow-up, as well as to identify certain socio-demographic factors associated with the risk of HIV infection or of a pejorative clinical outcome. This information supports health authorities and HIV stakeholders to decide on evidence-based HIV prevention and care strategies and define target groups for tailored interventions. Statbel, the Belgian statistical office collects, produces and disseminates reliable and relevant figures on the Belgian economy, society and territory. The collection is based on administrative data sources and surveys. This project aims to link the HIV surveillance data with selected Statbel information. This will permit to greatly improve the quality of the HIV surveillance data by completing the data already collected by Sciensano with additional socio-economic and socio-demographic information on patients profiles, filling in missing data in the Sciensano database with demographics from Statbel, ascertaining vital status of lost-to-follow-up patients and completing the information on causes of death. Additionally, a linkage with the new-born registry would permit to have more demographic and clinical information on children born from HIV-positive women.

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.

BackgroundWe examined relationships between schistosome infection, HIV transmission or acquisition, and all-cause death.MethodsWe retrospectively tested baseline sera from a heterosexual HIV-discordant couple cohort in Lusaka, Zambia with follow-up from 1994–2012 in a nested case-control design. Schistosome-specific antibody levels were measured by ELISA. Associations between baseline antibody response to schistosome antigens and incident HIV transmission, acquisition, and all-cause death stratified by gender and HIV status were assessed. In a subset of HIV- women and HIV+ men, we performed immunoblots to evaluate associations between Schistosoma haematobium or Schistosoma mansoni infection history and HIV incidence.ResultsOf 2,145 individuals, 59% had positive baseline schistosome-specific antibody responses. In HIV+ women and men, baseline schistosome-specific antibodies were associated with HIV transmission to partners (adjusted hazard ratio [aHR] = 1.8, p<0.005 and aHR = 1.4, p<0.05, respectively) and death in HIV+ women (aHR = 2.2, p<0.001). In 250 HIV- women, presence of S. haematobium-specific antibodies was associated with increased risk of HIV acquisition (aHR = 1.4, p<0.05).ConclusionSchistosome infections were associated with increased transmission of HIV from both sexes, acquisition of HIV in women, and increased progression to death in HIV+ women. Establishing effective prevention and treatment strategies for schistosomiasis, including in urban adults, may reduce HIV incidence and death in HIV+ persons living in endemic areas.

BackgroundMortality from TB continues to be a global public health challenge. TB ranks alongside Human Immunodeficiency Virus (HIV) as the leading infectious causes of death globally. HIV is a major driver of TB related morbidity and mortality while TB is the leading cause of mortality among people living with HIV/AIDS. We sought to determine excess mortality associated with HIV and the effect of antiretroviral therapy on reducing mortality among tuberculosis patients in Kenya.MethodsWe conducted a retrospective analysis of Kenya national tuberculosis program data of patients enrolled from 2013 through 2014. We used direct standardization to obtain standardized mortality ratios for tuberculosis patients compared with the general population. We calculated the population attributable fraction of tuberculosis deaths due to HIV based on the standardized mortality ratio for deaths among TB patients with HIV compared to TB patients without HIV. We used Cox proportional hazards regression for assessing risk factors for mortality.ResultsOf 162,014 patients included in the analysis, 6% died. Mortality was 10.6 (95% CI: 10.4–10.8) times higher among TB patients than the general population; 42% of deaths were attributable to HIV infection. Patients with HIV who were not receiving ART had an over four-fold risk of death compared to patients without HIV (aHR = 4.2, 95% CI 3.9–4.6). In contrast, patients with HIV who were receiving ART had only 2.6 times the risk of death (aHR = 2.6, 95% CI 2.5–2.7).ConclusionHIV was a significant contributor to TB-associated deaths in Kenya. Mortality among HIV-infected individuals was higher among those not on ART than those on ART. Early initiation of ART among HIV infected people (a “test and treat” approach) should further reduce TB-associated deaths.

IntroductionAlthough AIDS-related deaths have had significant economic and social impact following an increased disease burden internationally, few studies have evaluated the cause of AIDS-related deaths among patients with AIDS on combination anti-retroviral therapy (cART) in China. This study examines the causes of death among AIDS-patients in China and uses a methodology to increase data accuracy compared to the previous studies on AIDS-related mortality in China, that have taken the reported cause of death in the National HIV Registry at face-value.MethodsDeath certificates/medical records were examined and a cross-sectional survey was conducted in three provinces to verify the causes of death among AIDS patients who died between January 1, 2010 and June 30, 2011. Chi-square analysis was conducted to examine the categorical variables by causes of death and by ART status. Univariate and multivariate logistic regression were used to evaluate factors associated with AIDS-related death versus non-AIDS related death.ResultsThis study used a sample of 1,109 subjects. The average age at death was 44.5 years. AIDS-related deaths were significantly higher than non-AIDS and injury-related deaths. In the sample, 41.9% (465/1109) were deceased within a year of HIV diagnosis and 52.7% (584/1109) of the deceased AIDS patients were not on cART. For AIDS-related deaths (n = 798), statistically significant factors included CD4 count

The Risks and Benefits of Providing HIV Services during the COVID-19 Pandemic

Introduction

The COVID-19 pandemic has caused widespread disruptions including to health services. In the early response to the pandemic many countries restricted population movements and some health services were suspended or limited. In late 2020 and early 2021 some countries re-imposed restrictions. Health authorities need to balance the potential harms of additional SARS-CoV-2 transmission due to contacts associated with health services against the benefits of those services, including fewer new HIV infections and deaths. This paper examines these trade-offs for select HIV services.

Methods

We used four HIV simulation models (Goals, HIV Synthesis, Optima HIV and EMOD) to estimate the benefits of continuing HIV services in terms of fewer new HIV infections and deaths. We used three COVID-19 transmission models (Covasim, Cooper/Smith and a simple contact model) to estimate the additional deaths due to SARS-CoV-2 transmission among health workers and clients. We examined four HIV services: voluntary medical male circumcision, HIV diagnostic testing, viral load testing and programs to prevent mother-to-child transmission. We compared COVID-19 deaths in 2020 and 2021 with HIV deaths occurring now and over the next 50 years discounted to present value. The models were applied to countries with a range of HIV and COVID-19 epidemics.

Results

Maintaining these HIV services could lead to additional COVID-19 deaths of 0.002 to 0.15 per 10,000 clients. HIV-related deaths averted are estimated to be much larger, 19 - 146 discounted deaths per 10,000 clients.

Discussion

While there is some additional short-term risk of SARS-CoV-2 transmission associated with providing HIV services, the risk of additional COVID-19 deaths is at least 100 times less than the HIV deaths averted by those services. Ministries of Health need to take into account many factors in deciding when and how to offer essential health services during the COVID-19 pandemic. This work shows that the benefits of continuing key HIV services are far larger than the risks of additional SARS-CoV-2 transmission.