This data set includes tables on persons living with HIV/AIDS, newly diagnosed HIV cases and all cause deaths in HIV/AIDS cases by gender, age, race/ethnicity and transmission category. In all tables, cases are reported as of December 31 of the given year, as reported by January 9, 2019, to allow a minimum of 12 months reporting delay. Gender is determined by both current gender and sex at birth variables; transgender values are assigned when current gender is identified as "Transgender" or when a discrepancy is identified between a person's sex at birth and their current gender (e.g., cases where sex at birth is "Male" and current gender is "Female" will become Transgender: Male to Female.) Prior to 2003, Asian and Native Hawaiian/Pacific Islanders were classified as one combined group. In order to present these race/ethnicities separately, living cases recorded under this combined classification were split and redistributed according to their expected proportional population representation estimated from post-2003 data.

This 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 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).

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.

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.

The ultimate goal of HIV treatment is to achieve viral suppression, which means the amount of HIV in the body is very low or undetectable. This is important for people with HIV to stay healthy, have improved quality of life, and live longer. People living with HIV who maintain viral suppression have effectively no risk of passing HIV to others. Texas DSHS is the source of this data. Diagnosed- received a diagnosis of HIV Linked to care-visited an HIV heath care provider within 1 month (30 days) after learning they were HIV positive Received- or were retained in care** received medical care for HIV infection Viral suppression- their HIV “viral load” – the amount of HIV in the blood – was at a very low level.

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/

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.

The dataset “Impact of HIV strategies for MSM” contains data obtained from an agent-based model. The model follows the sexual life of 20,000 men who have sex with men (MSM) in the Netherlands. Via sexual contacts, men may get infected with HIV or N. Gonorrhoeae (NG). The model simulates sexual behaviour, demography, and the course of HIV or NG infection (for those who have been infected). The data from the model are therefore data of “fictitious” (simulated) individuals, not of real individuals. The course of HIV infection was modelled using data from the national database of HIV-positive individuals in the Netherlands (Source: Stichting HIV Monitoring). Parameters relating to sexual behaviour were obtained from data from the Amsterdam Cohort Study and the Network Study among MSM in Amsterdam. The model was calibrated to data on annual HIV diagnoses in 2008-2014 (from Stichting HIV Monitoring) and gonorrhoea positivity in 2009-2014 (data obtained from the National Database of STI Clinics in the Netherlands (SOAP)). Model outcomes include the annual numbers of MSM getting infected with HIV; HIV-positive MSM getting diagnosed, entering care, or starting treatment; MSM developing AIDS; MSM getting infected with NG; MSM treated for gonorrhoea; HIV tests, NG tests, etc. With the model, we calculated these numbers for the years 2018-2027, for the situation with the current testing rates and without PrEP. Subsequently we calculated these numbers with increased HIV/STI testing: a small, a moderate, and a high increase in testing among all MSM or only among MSM in specific subgroups of MSM. Finally, the calculations were repeated accounting for a nationwide PrEP programme for MSM at high risk to acquire HIV.

This data set shows the planned funding allocations for HIV medical and support services in the Austin area from the Ryan White HIV/AIDS Program Part A. The HIV Planning Council, a City of Austin Board/Commission is the responsible body for the allocation of Ryan White HIV/AIDS Program Part A funding. This program provides grant funding from the Health Resources and Services Administration (HRSA) for medical and support services to the Austin Area. Allocation Plans are developed using data including but not limited to: epidemiological overview and demographic information for people living with HIV (PLWH), service utilization data, needs assessment data, and expenditure trends. Allocation Plans are developed based on a maximum amount of funds that can be applied as dictated by HRSA for each grant year. Actual awarded Ryan White Part A amounts may differ from the plan. The HIV Planning Council sets alternative funding scenario plans to adapt the Allocation Plan to the actual amount of Part A funds awarded. The HIV Planning Council can re-allocate awarded funds at any time during the grant year to reflect changes in service needs or the ability to expend funds in each service category. Minority AIDS Initiative (MAI) funding is a subset of Ryan White Part A which funds services for populations disproportionately affected by HIV.

Percentages of MSM newly diagnosed with HIV infection by age and race/ethnicity, 2016, Santa Clara County. Source: Santa Clara County Public Health Department, enhanced HIV/AIDS reporting system (eHARS), data as of 4/30/2017. METADATA:Notes (String): Lists table title, notes and sourcesCategory (String): Lists the category representing the data: Age group: 13-24, 25-29, 30-39, 40-49, 50 and older; race/ethnicity:Asian/Pacific Islander, Black/African American, Latino, White (non-Hispanic White only), Other/Unknown.Percentage (Numeric): Percentage of MSM diagnosed with HIV in a particular category among all MSM diagnoses

Abstract

The 2018 Nigeria AIDS Indicator and Impact Survey (NAIIS) is a cross-sectional survey that will assess the prevalence of key human immunodeficiency virus (HIV)-related health indicators. This survey is a two-stage cluster survey of 88,775 randomly-selected households in Nigeria, sampled from among 3,551 nationally-representative sample clusters. The survey is expected to include approximately 168,029 participants, ages 15-64 years and children, ages 0-14 years, from the selected household. The 2018 NAIIS will characterize HIV incidence, prevalence, viral load suppression, CD4 T-cell distribution, and risk behaviors in a household-based, nationally-representative sample of the population of Nigeria, and will describe uptake of key HIV prevention, care, and treatment services. The 2018 NAIIS will also estimate the prevalence of hepatitis B virus (HBV), hepatitis C virus (HCV) infections, and HBV/HIV and HCV/HIV co-infections.

Geographic coverage

National coverage, the survey covered the Federal Republic and was undertaken in each state and the Federal Capital.

Analysis unit

Household Health Survey

Universe

1. Women and men aged 15-64 years living in residential households and visitors who slept in the household the night before the survey
2. Children aged 0-14 years living in residential households and child visitors who slept in the household the night before the survey

Kind of data

Sample survey data [ssd]

Sampling procedure

This cross-sectional, household-based survey uses a two-stage cluster sampling design (enumeration area followed by households). The target population is people 15-64 and children ages 0-14 years. The overall size and distribution of the sample is determined by analysis of existing estimates of national HIV incidence, sub-national HIV prevalence, and the number of HIV-positive cases needed to obtain estimates of VLS among adults 15-64 years for each of the 36 states and the FCT while not unnecessarily inflating the sample size needed.

From a sampling perspective, the three primary objectives of this proposal are based on competing demands, one focused on national incidence and the other on state-level estimates in a large number of states (37). Since the denominator used for estimating VLS is HIV-positive individuals, the required minimum number of blood draws in a stratum is inversely proportional to the expected HIV prevalence rate in that stratum. This objective requires a disproportionate amount of sample to be allocated to states with the lowest prevalence. A review of state-level prevalence estimates for sources in the last 3 to 5 years shows that state-level estimates are often divergent from one source to the next, making it difficult to ascertain the sample size needed to obtain the roughly 100 PLHIV needed to achieve a 95% confidence interval (CI) of +/- 10 for VLS estimates.

An equal-size approach is proposed with a sample size of 3,700 blood specimens in each state. Three-thousand seven hundred specimens will be sufficiently large to obtain robust estimates of HIV prevalence and VLS among HIV-infected individuals in most states. In states with a HIV prevalence above 2.5%, we can anticipate 95% CI of less than +/-10% and relative standard errors (RSEs) of less than 11% for estimates of VLS. In these states, with HIV prevalence above 2.5%, the anticipated 95% CI around prevalence is +/- 0.7% to a high of 1.1-1.3% in states with prevalence above 6%. In states with prevalence between 1.2 and 2.5% HIV prevalence estimates would remain robust with 95% CI of +/- 0.5-0.6% and RSE of less than 20% while 95% CI around VLS would range between 10-15% (and RSE below 15%). With this proposal only a few states, with HIV prevalence below 1.0%, would have less than robust estimates for VLS and HIV prevalence.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used for the 2018 NAIIS: Household Questionnaire, Adult Questionnaire, and Early Adolescent Questionnaire (10-14 Years).

Cleaning operations

During the household data collection, questionnaire and laboratory data were transmitted between tablets via Bluetooth connection. This facilitated synchronization of household rosters and ensured data collection for each participant followed the correct pathway. All field data collected in CSPro and the Laboratory Data Management System (LDMS) were transmitted to a central server using File Transfer Protocol Secure (FTPS) over a 4G or 3G telecommunication provider at least once a day. Questionnaire data cleaning was conducted using CSPro and SAS 9.4 (SAS Institute Inc., Cary, North Carolina, United States). Laboratory data were cleaned and merged with the final questionnaire database using unique specimen barcodes and study identification numbers.

Response rate

A total of 101,267 households were selected, 89,345 were occupied and 83,909 completed the household interview . • For adults aged 15-64 years, interview response rate was 91.6% for women and 88.2% for men; blood draw response rate was 92.9% for women and 93.6% for men. • For adolescents aged 10-14 years, interview response rate was 86.8% for women and 86.2% for men; blood draw response rate was 91.2% for women and 92.3% for men. • For children aged 0-9 years, blood draw response rate was 68.5% for women and men.

Sampling error estimates

Estimates from sample surveys are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling errors result from mistakes made during data collection, e.g., misinterpretation of an HIV test result and data management errors such as transcription errors during data entry. While NAIIS implemented numerous quality assurance and control measures to minimize non-sampling errors, these were impossible to avoid and difficult to evaluate statistically. In contrast, sampling errors can be evaluated statistically. Sampling errors are a measure of the variability between all possible samples.

The sample of respondents selected for NAIIS was only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples could yield results that differed somewhat from the results of the actual sample selected. Although the degree of variability cannot be known exactly, it can be estimated from the survey results. The standard error, which is the square root of the variance, is the usual measurement of sampling error for a statistic (e.g., proportion, mean, rate, count). In turn, the standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of approximately plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

NAIIS utilized a multi-stage stratified sample design, which required complex calculations to obtain sampling errors. The Taylor linearization method of variance estimation was used for survey estimates that are proportions, e.g., HIV prevalence. The Jackknife repeated replication method was used for variance estimation of more complex statistics such as rates, e.g., annual HIV incidence and counts such as the number of people living with HIV.

The Taylor linearization method treats any percentage or average as a ratio estimate, , where y represents the total sample value for variable y and x represents the total number of cases in the group or subgroup under consideration. The variance of r is computed using the formula given below, with the standard error being the square root of the variance: in which Where represents the stratum, which varies from 1 to H, is the total number of clusters selected in the hth stratum, is the sum of the weighted values of variable y in the ith cluster in the hth stratum, is the sum of the weighted number of cases in the ith cluster in the hth stratum and, f is the overall sampling fraction, which is so small that it is ignored.

In addition to the standard error, the design effect for each estimate is also calculated. The design effect is defined as the ratio of the standard error using the given sample design to the standard error that would result if a simple random sample had been used. A design effect of 1.0 indicates that the sample design is as efficient as a simple random sample, while a value greater than 1.0 indicates the increase in the sampling error due to the use of a more complex and less statistically efficient design. Confidence limits for the estimates, which are calculated as where t(0.975, K) is the 97.5th percentile of a t-distribution with K degrees of freedom, are also computed.

Data appraisal

Remote data quality check was carried out using data editor

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.

Objectives: To review and analyze original studies on HIV prevalence and risk behaviours among men who have sex with men (MSM) in Vietnam. Design: Systematic literature review. Comprehensive identification of material was conducted by systematic electronic searches of selected databases. Inclusion criteria included studies conducted from 2002 onwards, following a systematic review concluding in 2001 conducted by Colby, Nghia Huu, and Doussantousse. Data analysis was undertaken through the application of both the Cochrane Collaboration and ePPI Centre approaches to the synthesis of qualitative and quantitative studies. Setting: Vietnam. Results: Sixteen studies, undertaken during 2005-2011, were identified that met the inclusion criteria. The analysis showed that HIV prevalence among MSM in Vietnam has increased significantly (from 9.4 in 2006 to 20% in 2010 in Hanoi, for instance) and that protective behaviours, such as condom use and HIV testing and counselling, continue at inadequately low levels. Conclusions: Increasing HIV prevalence and the lack of effective protective behaviours such as consistent condom use during anal sex among MSM in Vietnam indicate a potential for a more severe HIV epidemic in the future unless targeted and segmented comprehensive HIV prevention strategies for MSM in Vietnam are designed and programs implemented.

BackgroundConventional epidemiological surveillance of infectious diseases is focused on characterization of incident infections and estimation of the number of prevalent infections. Advances in methods for the analysis of the population-level genetic variation of viruses can potentially provide information about donors, not just recipients, of infection. Genetic sequences from many viruses are increasingly abundant, especially HIV, which is routinely sequenced for surveillance of drug resistance mutations. We conducted a phylodynamic analysis of HIV genetic sequence data and surveillance data from a US population of men who have sex with men (MSM) and estimated incidence and transmission rates by stage of infection.Methods and FindingsWe analyzed 662 HIV-1 subtype B sequences collected between October 14, 2004, and February 24, 2012, from MSM in the Detroit metropolitan area, Michigan. These sequences were cross-referenced with a database of 30,200 patients diagnosed with HIV infection in the state of Michigan, which includes clinical information that is informative about the recency of infection at the time of diagnosis. These data were analyzed using recently developed population genetic methods that have enabled the estimation of transmission rates from the population-level genetic diversity of the virus. We found that genetic data are highly informative about HIV donors in ways that standard surveillance data are not. Genetic data are especially informative about the stage of infection of donors at the point of transmission. We estimate that 44.7% (95% CI, 42.2%–46.4%) of transmissions occur during the first year of infection.ConclusionsIn this study, almost half of transmissions occurred within the first year of HIV infection in MSM. Our conclusions may be sensitive to un-modeled intra-host evolutionary dynamics, un-modeled sexual risk behavior, and uncertainty in the stage of infected hosts at the time of sampling. The intensity of transmission during early infection may have significance for public health interventions based on early treatment of newly diagnosed individuals.Please see later in the article for the Editors' Summary

aNew diagnoses of AIDS in Australia, reported by 31 March 2012. AIDS notifications are accepted as being an incomplete record of AIDS diagnoses in Australia in 2010.bThis dataset is not nationally representative; it includes data from 7 of the 13 provinces and territories. The case definition for AIDS in Canada is based on confirmed HIV diagnosis and presence/diagnosis of an AIDS-defining condition (no criteria for CD4 count are included in Canada’s AIDS case definition). Transmission category information was not available for one of the jurisdictions reported here, which accounts for the small cell sizes under the various transmission categories.cData for the whole country, adjusted for under-reporting and reporting delays. Data reported as of 30 June, 2011.dMSM, men who have sex with men; IDU, injection drug use.

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).

In 2021, 1.9 million people in Nigeria were living with HIV. Women were the most affected group, counting 1.1 thousand individuals. Also, children up to age 14 who were HIV positive equaled 170 thousand.

The primary objectives of the 2007-08 THMIS survey were to provide up-to-date information on the prevalence of HIV infection among Tanzanian adults, and the prevalence of malaria infection and anaemia among children under age five years. The findings will be used to evaluate ongoing programmes and to develop new health strategies. Where appropriate, the findings from the 2007-08 THMIS are compared with those from the 2003-04 Tanzania HIV/AIDS Indicator Survey (THIS). The findings of these two surveys are expected to complement the sentinel surveillance system undertaken by the Ministry of Health and Social Welfare under its National AIDS Control Programme (NACP). The THMIS also provides updated estimates of selected basic demographic and health indicators covered in previous surveys, including the 1991-92 Tanzania Demographic and Health Survey (TDHS), the 1996 TDHS, the 1999 Reproductive and Child Health Survey (RCHS), and the 2004-05 TDHS. More specifically, the objectives of the 2007-08 THMIS were: To measure HIV prevalence among women and men age 15-49; To assess levels and trends in knowledge about HIV/AIDS, attitudes towards people infected with the disease, and patterns of sexual behaviour; To collect information on the proportion of adults who are chronically sick, the extent of orphanhood, levels of and care and support; To gauge the extent to which these indicators vary by characteristics such as age, sex, region, education, marital status, and poverty status; and To measure the presence of malaria parasites and anaemia among children age 6-59 months. The results of the 2007-08 THMIS are intended to provide information to assist policymakers and programme implementers to monitor and evaluate existing programmes and to design new strategies for combating the HIV/AIDS epidemic in Tanzania. The survey data will also be used as inputs in population projections and to calculate indicators developed by the United Nations General Assembly Special Session (UNGASS), the UNAIDS Programme, and the World Health Organization (WHO).

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