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.

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

These data were reported to the NYC DOHMH by March 31, 2021 This dataset includes data on new diagnoses of HIV and AIDS in NYC for the calendar years 2016 through 2020. Reported cases and case rates (per 100,000 population) are stratified by United Hospital Fund (UHF) neighborhood, age group, and race/ethnicity. Note: - Cells marked "NA" cannot be calculated because of cell suppression or 0 denominator.

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

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

BO: Prevalence of HIV: Female: % Aged 15-24 data was reported at 0.100 % in 2022. This stayed constant from the previous number of 0.100 % for 2021. BO: Prevalence of HIV: Female: % Aged 15-24 data is updated yearly, averaging 0.100 % from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 0.100 % in 2022 and a record low of 0.100 % in 2022. BO: Prevalence of HIV: Female: % Aged 15-24 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bolivia – Table BO.World Bank.WDI: Social: Health Statistics. Prevalence of HIV, female is the percentage of females who are infected with HIV. Youth rates are as a percentage of the relevant age group.;UNAIDS estimates.;Weighted average;In many developing countries most new infections occur in young adults, with young women especially vulnerable.

Based on 2014 Kenya's HIV/AIDS profile data by County This assesses the HIV/AIDS situation in Kenya's 47 counties with regards to adults and children living with the disease, new infections, gender specific infection rates, homes with orphans and their financial situation.

This shapefile provides HIV statistics by state that can be used in conjunction with the co-morbidities risk profile to provide more nuance on levels of risk by state. Note that values of 0 mean there is no data for that particular state.The source of data for HIV prevalence rates is the Nigeria Institute for Health Metrics and Evaluation (IHME), HIV Prevalence Geospatial Estimates 2000-2017.

United States US: Incidence of HIV: per 1,000 Uninfected Population data was reported at 0.110 Ratio in 2019. This stayed constant from the previous number of 0.110 Ratio for 2018. United States US: Incidence of HIV: per 1,000 Uninfected Population data is updated yearly, averaging 0.120 Ratio from Dec 2010 (Median) to 2019, with 10 observations. The data reached an all-time high of 0.130 Ratio in 2012 and a record low of 0.110 Ratio in 2019. United States US: Incidence of HIV: per 1,000 Uninfected Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Social: Health Statistics. Number of new HIV infections among uninfected populations expressed per 1,000 uninfected population in the year before the period.;UNAIDS estimates.;Weighted average;This is the Sustainable Development Goal indicator 3.3.1 [https://unstats.un.org/sdgs/metadata/].

This map displays the percentage of people ages 15+ with HIV that are female from the 2013 to 2014 dataset. According to the World Bank: "HIV prevalence rates reflect the rate of HIV infection in each country's population. Low national prevalence rates can be misleading, however. They often disguise epidemics that are initially concentrated in certain localities or population groups and threaten to spill over into the wider population. In many developing countries most new infections occur in young adults, with young women especially vulnerable. Data on HIV are from the Joint United Nations Programme on HIV/AIDS (UNAIDS). Changes in procedures and assumptions for estimating the data and better coordination with countries have resulted in improved estimates of HIV and AIDS. The models, which are routinely updated, track the course of HIV epidemics and their impact, making full use of information in HIV prevalence trends from surveillance data as well as survey data. The models take into account reduced infectivity among people receiving antiretroviral therapy (which is having a larger impact on HIV prevalence and allowing HIV-positive people to live longer) and allow for changes in urbanization over time in generalized epidemics. The estimates include plausibility bounds, which reflect the certainty associated with each of the estimates."Source: The World Bank

Data Series: Number of new HIV infections per 1,000 uninfected population, by sex, age and key populations Indicator: III.8 - Number of new HIV infections per 1,000 uninfected population, by sex, age and key populations Source year: 2022 This dataset is part of the Minimum Gender Dataset compiled by the United Nations Statistics Division. Domain: Health and related services

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

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

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

Number of AIDS-related deaths per 100,000 people

The Find Ryan White HIV/AIDS Medical Care Providers tool is a locator that helps people living with HIV/AIDS access medical care and related services. Users can search for Ryan White-funded medical care providers near a specific complete address, city and state, state and county, or ZIP code.

Search results are sorted by distance away and include the Ryan White HIV/AIDS facility name, address, approximate distance from the search point, telephone number, website address, and a link for driving directions.

HRSA's Ryan White program funds an array of grants at the state and local levels in areas where most needed. These grants provide medical and support services to more than a half million people who otherwise would be unable to afford care.

Health Nutrition and Population Statistics database provides key health, nutrition and population statistics gathered from a variety of international and national sources. Themes include global surgery, health financing, HIV/AIDS, immunization, infectious diseases, medical resources and usage, noncommunicable diseases, nutrition, population dynamics, reproductive health, universal health coverage, and water and sanitation.

This research aimed to help two project countries (Malawi and Lesotho) increase access to learning for students living in high HIV prevalence areas who were at risk of grade repetition or school drop-out, through (i) complementing classroom teaching with self-study learner guides to provide more open, distance and flexible delivery of the curriculum and (ii) strengthening community support for learning. The research objectives were: (1) To increase understanding of how open, distance and flexible learning (ODFL) can be used to address the factors that disrupt schooling by conducting research with school teachers and community members; (2) To design and implement an intervention in primary schools (Grade 6) in Malawi and Junior secondary schools (Grade B) in Lesotho over one school year (January to November 2009); (3)To evaluate the effectiveness of the intervention in reducing student absenteeism, drop-out and grade-repetition using an experimental design; (4) To disseminate the new knowledge gained to enable appropriate, evidence informed policy development to better integrate and more open and flexible curriculum delivery into schools and strengthen community support for vulnerable learners. ODFL initiatives, structures and networks that are already in place to implement HIV/AIDS policies were firstly identified through analyses of secondary data. Case studies were developed in contrasting communities severely affected by HIV and AIDS to identify contextual factors that can lead to exclusion from conventional schooling and dropping out. The case studies are complemented by data collected using a range of approaches such as semi-structured interviews, focus group discussions, informal discussions with family members, participatory activities and observation. Based on this formative research, a pilot intervention will then be made through secondary schools to identify and trial a small-scale ODFL intervention package designed to overcome the barriers to conventional schooling identified in the case studies. The intervention will be evaluated qualitatively and also quantitatively using an experimental design. The impact was evaluated in a randomized controlled trial. In each country there were 20 schools in the intervention group and 20 schools in the control group. Data to evaluate the impact of the programme on school attendance, drop-out and grade repetition were collected before and after the intervention. Student achievement was assessed by testing children in Mathematics and English before and after the intervention. The study was conducted in 4 stages: (1) Sampling and randomization of schools; (2) Intervention design (informed by synthesizing existing knowledge, generating new knowledge and inviting critical comment from all stakeholders); (3) Intervention implementation; (4) Intervention evaluation. This study aimed to increase access to education and learning for young people living in high HIV prevalence areas in Malawi and Lesotho, by developing a new, more flexible model of education that uses open, distance and flexible learning (ODFL) to complement and enrich conventional schooling. The findings showed that in Malawi, the programme reduced overall student drop-out by 42% (OR=0.58). This effect was not significantly different among at-risk children targeted by the program and those not targeted in their class suggesting the intervention had spillover effects beyond the intended beneficiaries. There were improvements in mathematics scores for at risk students and a history of grade repetition was a better predictor of future drop-out than orphan-hood. In Lesotho the intervention reduced absenteeism and improved Mathematics and English scores. These findings suggest that the intervention reached the most vulnerable and was effective in increasing access to education and learning. The data collection includes: (I)Quantitative data from the intervention group schools and the control group schools in each of the two project countries to evaluate the impact of the intervention on school attendance, school drop-out and progression to the next grade;the quantitative data set for the Malawi data contains 438 variables for 3275 individuals(40 schools in 2 districts). The quantitative data set for the Lesotho data contains 56 variables for 5528 individuals(34 schools in 2 locations-high altitude and low altitude). Data ware collected from the intervention and the control schools during the pre-intervention baseline survey in October 2008, monthly monitoring forms and the post-intervention follow-up survey in November 2009. Data were collected using the following instruments: (1)pre-intervention pupil questionnaire to gather data on pupil characteristics; (2)pre-and post intervention tests in Mathematics and English;(3) a school checklist to collate data on attendance and progression from school records and monthly SOFIE monitoring forms) with additional questions included for intervention schools to collect data on process indicators during the mid-term and post intervention school visits); (4) pupil tracking records to maintain up-to-date information on pupil educational status. (II)Qualitative data were collected help explain the findings from the quantitative data by providing information on the implementation process and on how the intervention was received. These data were collected through SSIs with intervention class teachers, youth club leaders, school heads and members of the school management committee; FGDs with community members; workshops with ‘at-risk’ pupils to explore their views on schooling and on the intervention; and follow up interviews with workshop participants. (3) Diaries of Teacher's and Club-leader's(Scanned Documents) . The entities under study were in Malawi: primary school students in grade 6 and in Lesotho: junior secondary school students in class B (second year).

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.

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.

This dataset helps to investigate the Spatial Accessibility to HIV Testing, Treatment, and Prevention Services in Illinois and Chicago, USA. The main components are: population data, healthcare data, GTFS feeds, and road network data. The core components are: 1) GTFS which contains GTFS (General Transit Feed Specification) data which is provided by Chicago Transit Authority (CTA) from Google's GTFS feeds. Documentation defines the format and structure of the files that comprise a GTFS dataset: https://developers.google.com/transit/gtfs/reference?csw=1. 2) HealthCare contains shapefiles describing HIV healthcare providers in Chicago and Illinois respectively. The services come from Locator.HIV.gov. 3) PopData contains population data for Chicago and Illinois respectively. Data come from The American Community Survey and AIDSVu. AIDSVu (https://map.aidsvu.org/map) provides data on PLWH in Chicago at the census tract level for the year 2017 and in the State of Illinois at the county level for the year 2016. The American Community Survey (ACS) provided the number of people aged 15 to 64 at the census tract level for the year 2017 and at the county level for the year 2016. The ACS provides annually updated information on demographic and socio economic characteristics of people and housing in the U.S. 4) RoadNetwork contains the road networks for Chicago and Illinois respectively from OpenStreetMap using the Python osmnx package. The abstract for our paper is: Accomplishing the goals outlined in “Ending the HIV (Human Immunodeficiency Virus) Epidemic: A Plan for America Initiative” will require properly estimating and increasing access to HIV testing, treatment, and prevention services. In this research, a computational spatial method for estimating access was applied to measure distance to services from all points of a city or state while considering the size of the population in need for services as well as both driving and public transportation. Specifically, this study employed the enhanced two-step floating catchment area (E2SFCA) method to measure spatial accessibility to HIV testing, treatment (i.e., Ryan White HIV/AIDS program), and prevention (i.e., Pre-Exposure Prophylaxis [PrEP]) services. The method considered the spatial location of MSM (Men Who have Sex with Men), PLWH (People Living with HIV), and the general adult population 15-64 depending on what HIV services the U.S. Centers for Disease Control (CDC) recommends for each group. The study delineated service- and population-specific accessibility maps, demonstrating the method’s utility by analyzing data corresponding to the city of Chicago and the state of Illinois. Findings indicated health disparities in the south and the northwest of Chicago and particular areas in Illinois, as well as unique health disparities for public transportation compared to driving. The methodology details and computer code are shared for use in research and public policy.

This dataset contains surveillance data on diagnoses of HIV for the United States in estimates rates and numbers for Human Immunodeficiency Virus (HIV) infection diagnosis and stage 3 infection Acquired Immunodeficiency Syndrome (AIDS) as collected by the Centers for Disease Control and Prevention (CDC).