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.

The states with the highest rates of HIV diagnoses in 2021 included Georgia, Louisiana, and Florida. However, the states with the highest number of people with HIV were California, Texas, and Florida. In California, there were around 4,399 people diagnosed with HIV. HIV/AIDS diagnoses The number of diagnoses of HIV/AIDS in the United States has continued to decrease in recent years. In 2021, there were an estimated 35,769 HIV diagnoses in the U.S. down from 38,433 diagnoses in the year 2017. In total, since the beginning of the epidemic in 1981 there have been around 1.25 million diagnoses in the United States. Deaths from HIV Similarly, the death rate from HIV has also decreased significantly over the past few decades. In 2019, there were only 1.4 deaths from HIV per 100,000 population, the lowest rate since the epidemic began. However, the death rate varies greatly depending on race or ethnicity, with the death rate from HIV for African Americans reaching 19.1 per 100,000 population in 2020.

The AIDS Public Information Data Set (APIDS) for years 1981-2002 on CDC WONDER online database contains counts of AIDS (Acquired Immune Deficiency Syndrome) cases reported by state and local health departments, by demographics; location (region and selected metropolitan areas); case-definition; month/year and quarter-year of diagnosis, report, and death (if applicable); and HIV exposure group (risk factors for AIDS). Data are produced by the US Department of Health and Human Services (US DHHS), Public Health Service (PHS), Centers for Disease Control and Prevention (CDC), National Center for HIV, STD and TB Prevention (NCHSTP), Division of HIV/AIDS Prevention (DHP).

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.

The total number of people globally living with HIV has increased from 27.2 million people in 2000 to 39.9 million people in 2023. However, the total number of new HIV infections has decreased from 2.8 million in 2000 to 1.3 million in 2023. It has become easier for those infected with HIV to live longer lives. Death rates for HIV-positive people are decreasing, mostly due to antiretroviral drugs that have turned the infection into a chronic disease. Nevertheless, those with HIV are at a higher risk for conditions such as liver disease, heart disease, and cancer. Medication for HIV has become more widespread and has made HIV a more manageable condition. However, medicine is not widely accessible in the developing world and treatment is still lacking. In Eswatini, around 26 percent of all people between 15 and 49 years are living with HIV, while the percentage is around 18 in South Africa. HIV infections are still especially widespread in Eastern and Southern Africa with 20.8 million people living with the condition in 2023. In the same year, there were around 2.3 million people in Latin America living with HIV.

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

As of 2023, South Africa was the country with the highest number of people living with HIV in Africa. At that time, around 7.7 million people in South Africa were HIV positive. In Mozambique, the country with the second-highest number of HIV-positive people in Africa, around 2.4 million people were living with HIV. Which country in Africa has the highest prevalence of HIV? Although South Africa has the highest total number of people living with HIV in Africa, it does not have the highest prevalence of HIV on the continent. Eswatini currently has the highest prevalence of HIV in Africa and worldwide, with almost 26 percent of the population living with HIV. South Africa has the third-highest prevalence, with around 18 percent of the population HIV positive. Eswatini also has the highest rate of new HIV infections per 1,000 population worldwide, followed by Lesotho and South Africa. However, South Africa had the highest total number of new HIV infections in 2023, with around 150,000 people newly infected with HIV that year. Deaths from HIV in Africa Thanks to advances in treatment and awareness, HIV/AIDS no longer contributes to a significant amount of death in many countries. However, the disease is still the fourth leading cause of death in Africa, accounting for around 5.6 percent of all deaths. In 2023, South Africa and Nigeria were the countries with the highest number of AIDS-related deaths worldwide with 50,000 and 45,000 such deaths, respectively. Although not every country in the leading 25 for AIDS-related deaths is found in Africa, African countries account for the majority of countries on the list. Fortunately, HIV treatment has become more accessible in Africa over the years and now up to 95 percent of people living with HIV in Eswatini are receiving antiretroviral therapy (ART). Access to ART does vary from country to country, however, with around 77 percent of people who are HIV positive in South Africa receiving ART, and only 31 percent in the Congo.

Eswatini had the highest AIDS mortality rate in the world, at 2.55 per 1,000 population in 2023. This statistic presents the AIDS mortality rate in select African countries in 2023.

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

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

Data Sources:

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

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

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

In 2023, around 0.5 out of 100 thousand male Canadians died from HIV. In 2000, the death rate for HIV among males stood at approximately three deaths per 100,000. This statistic displays the age-standardized death rates in Canada for HIV from 2000 to 2023, by gender.

HIV/AIDS yearly statistics in Hong Kong 1984 - 2023

Users can access data related to international women’s health as well as data on population and families, education, work, power and decision making, violence against women, poverty, and environment. Background World’s Women Reports are prepared by the Statistics Division of the United Nations Department for Economic and Social Affairs (UNDESA). Reports are produced in five year intervals and began in 1990. A major theme of the reports is comparing women’s situation globally to that of men in a variety of fields. Health data is available related to life expectancy, cause of death, chronic disease, HIV/AIDS, prenatal care, maternal morbidity, reproductive health, contraceptive use, induced abortion, mortality of children under 5, and immunization. User functionality Users can download full text or specific chapter versions of the reports in color and black and white. A limited number of graphs are available for download directly from the website. Topics include obesity and underweight children. Data Notes The report and data tables are available for download in PDF format. The next report is scheduled to be released in 2015. The most recent report was released in 2010.

Tuberculosis (TB) and HIV/AIDS are public health concerns in Papua New Guinea (PNG). This study examines TB and HIV/AIDS mortalities and associated sociodemographic factors in PNG. Method: As part of a longitudinal study, verbal autopsy (VA) interviews were conducted using the WHO 2016 VA Instrument to collect data of 926 deaths occurred in the communities within the catchment areas of the Comprehensive Health and Epidemiological Surveillance System from 2018-2020. InterVA-5 cause of deaths analytic tool was used to assign specific causes of death (COD). Multinomial logistic regression analyses were conducted to identify associated sociodemographic factors, estimate odds ratios (OR), 95% confidential intervals and p-values. Result: TB and HIV/AIDS were the leading CODs from infectious diseases, attributed to 9% and 8% of the total deaths, respectively. Young adults (25-34 years) had the highest proportion of deaths from TB (20%) and the risk of dying from TB among this age group was five times more likely than those aged 75+ years (OR: 5.5 [1.4-21.7]). Urban population were 46% less likely to die from this disease compared rural ones (OR: 0.54 [0.3-1.0]). People from middle household wealth quintile were three times more likely to die from TB than those in the richest quintile (OR: 3.0 [1.3-7.4]). Young adults also had the highest proportion of deaths to HIV/AIDS (18%) and were nearly seven times more likely to die from this disease compared with those aged 75+ years (OR: 6.7 [1.7-25.4]). Males were 48% less likely to die from HIV/AIDS than females (OR: 0.52 [0.3-0.9]). The risk of dying from HIV/AIDS in urban population was 54% less likely than their rural counterparts (OR: 0.46 [0.2-0.9]). Conclusion: TB and HIV/AIDS interventions are needed to target high-risk and vulnerable populations to reduce premature mortality from these diseases in PNG.

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

This table presents a wide variety of historical data in the field of health, lifestyle and health care. Figures on births and mortality, causes of death and the occurrence of certain infectious diseases are available from 1900, other series from later dates. In addition to self-perceived health, the table contains figures on infectious diseases, hospitalisations per diagnosis, life expectancy, lifestyle factors such as smoking, alcohol consumption and obesity, and causes of death. The table also gives information on several aspects of health care, such as the number of practising professionals, the number of available hospital beds, nursing day averages and the expenditures on care. Many subjects are also covered in more detail by data in other tables, although sometimes with a shorter history. Data on notifiable infectious diseases and HIV/AIDS are not included in other tables.

Data available from: 1900

Status of the figures:

2024: The available figures are definite. 2023: Most available figures are definite. Figures are provisional for: - occurrence of infectious diseases; - expenditures on health and welfare; - perinatal and infant mortality. 2022: Most available figures are definite. Figures are provisional for: - occurrence of infectious diseases; - diagnoses at hospital admissions; - number of hospital discharges and length of stay; - number of hospital beds; - health professions; - expenditures on health and welfare. 2021: Most available figures are definite. Figures are provisional for: - occurrence of infectious diseases; - expenditures on health and welfare. 2020 and earlier: Most available figures are definite. Due to 'dynamic' registrations, figures for notifiable infectious diseases, HIV, AIDS remain provisional.

Changes as of 18 december 2024: - Due to a revision of the statistics Health and welfare expenditure 2021, figures for expenditure on health and welfare have been replaced from 2021 onwards. - Revised figures on the volume index of healthcare costs are not yet available, these figures have been deleted from 2021 onwards.

The most recent available figures have been added for: - live born children, deaths; - occurrence of infectious diseases; - number of hospital beds; - expenditures on health and welfare; - perinatal and infant mortality; - healthy life expectancy; - causes of death.

When will new figures be published? July 2025.

The Philippines reported about 17,250 HIV cases, an increase of about 2,300 cases 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 in the Philippines  As the daily 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? In 2023, preliminary figures indicate that ischaemic heart disease led to the deaths of about 124,500 people, making it 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.

BackgroundGlobal and regional projections of mortality and burden of disease by cause for the years 2000, 2010, and 2030 were published by Murray and Lopez in 1996 as part of the Global Burden of Disease project. These projections, which are based on 1990 data, continue to be widely quoted, although they are substantially outdated; in particular, they substantially underestimated the spread of HIV/AIDS. To address the widespread demand for information on likely future trends in global health, and thereby to support international health policy and priority setting, we have prepared new projections of mortality and burden of disease to 2030 starting from World Health Organization estimates of mortality and burden of disease for 2002. This paper describes the methods, assumptions, input data, and results. Methods and FindingsRelatively simple models were used to project future health trends under three scenarios—baseline, optimistic, and pessimistic—based largely on projections of economic and social development, and using the historically observed relationships of these with cause-specific mortality rates. Data inputs have been updated to take account of the greater availability of death registration data and the latest available projections for HIV/AIDS, income, human capital, tobacco smoking, body mass index, and other inputs. In all three scenarios there is a dramatic shift in the distribution of deaths from younger to older ages and from communicable, maternal, perinatal, and nutritional causes to noncommunicable disease causes. The risk of death for children younger than 5 y is projected to fall by nearly 50% in the baseline scenario between 2002 and 2030. The proportion of deaths due to noncommunicable disease is projected to rise from 59% in 2002 to 69% in 2030. Global HIV/AIDS deaths are projected to rise from 2.8 million in 2002 to 6.5 million in 2030 under the baseline scenario, which assumes coverage with antiretroviral drugs reaches 80% by 2012. Under the optimistic scenario, which also assumes increased prevention activity, HIV/AIDS deaths are projected to drop to 3.7 million in 2030. Total tobacco-attributable deaths are projected to rise from 5.4 million in 2005 to 6.4 million in 2015 and 8.3 million in 2030 under our baseline scenario. Tobacco is projected to kill 50% more people in 2015 than HIV/AIDS, and to be responsible for 10% of all deaths globally. The three leading causes of burden of disease in 2030 are projected to include HIV/AIDS, unipolar depressive disorders, and ischaemic heart disease in the baseline and pessimistic scenarios. Road traffic accidents are the fourth leading cause in the baseline scenario, and the third leading cause ahead of ischaemic heart disease in the optimistic scenario. Under the baseline scenario, HIV/AIDS becomes the leading cause of burden of disease in middle- and low-income countries by 2015. ConclusionsThese projections represent a set of three visions of the future for population health, based on certain explicit assumptions. Despite the wide uncertainty ranges around future projections, they enable us to appreciate better the implications for health and health policy of currently observed trends, and the likely impact of fairly certain future trends, such as the ageing of the population, the continued spread of HIV/AIDS in many regions, and the continuation of the epidemiological transition in developing countries. The results depend strongly on the assumption that future mortality trends in poor countries will have a relationship to economic and social development similar to those that have occurred in the higher-income countries.

The human immune virus (HIV) is a viral infection that destroys the human immune system resulting in acquired immunodeficiency syndrome (AIDS). If untreated, it can reduce the cluster of CD4 positive T-cells and increases the HIV viral load, thus causing AIDS. The Zambia HIV prevalence rate is among the highest in the sub-Saharan region. According to WHO, HIV/AIDS is a major cause of death in Zambia, with about a million deaths attributed to HIV/AIDS-related causes. With no HIV vaccine readily available and no permanent cure for HIV/AIDS, the antiretroviral (ARV) drug that slows the spread of the virus remains the only option. The ARV shuts down viral reproduction as well as reduces the immune suppression caused by HIV. Taking a combination of three ARV drugs from different classes suppresses the reproduction of the virus. The administration of ARV has challenges of Transmitted Drug Resistance Mutation strains (TDRMs) in the treatment of HIV naïve patients. In this article, we formulate a technique for determining an optimal ARV combination using Bayesian statistical methods. The proposed technique assist the medical personnel responsible in deciding the optimal ARV combination per patient in the presence of TDRMs test. We developed a transition probability matrix chart for each combination. Using the data from Zambia, we demonstrate the computation process and provide an interpretation of the obtained results. The findings from the analysis indicate that the probability of patients remaining on first baseline combinations namely, 1, 2, 3, 4, 5 and 6 are: 0.96, 0.99, 0.97, 0.91, 0.96, and 0.96 respectively. The probabilities obtained can be used to choose an optimal ARV combination in the presence of Transmitted Drug Resistance Mutation Strains because you can isolate the particular drugs which the patient is resistance.

India IN: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data was reported at 19.800 NA in 2016. This records a decrease from the previous number of 20.000 NA for 2015. India IN: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data is updated yearly, averaging 21.200 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 23.400 NA in 2000 and a record low of 19.800 NA in 2016. India IN: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Objective: To examine if the rankings of state HIV age-standardized death rates (ASDRs) changed if different standard population (SP) was used. Design: A cross-sectional population-based observational study. Setting 36 states in the United States. Participants: People died from 2015 to 2019. Main outcome measures: State HIV ASDR using 4 SPs, namely WHO2000, US2000, US2mor020, and Eur2011–2030. Results: The rankings of 19 states did not change when ASDRs were calculated using US2000 and US2020. Of the 17 states whose rankings changed, the rankings of 9 states calculated using US2000 were higher than those calculated using US2020; in 8 states, the rankings were lower. The states with the greatest changes in rankings between US2000 and US2020 were Kentucky (12th and 9th, respectively) and Massachusetts (8th and 11th, respectively). Conclusions: State ASDRs calculated using the current official SP (US2000) weigh middle-age HIV death rates more heavily than older-age HIV death rates, resulting in lower ASDRs among states with higher older-age HIV death rates. Methods The data were extracted from CDC WONDER.