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

Description: This data set contains information on adults aged 25 years and older: biographical data, media, communication and norms, knowledge and perceptions of HIV/AIDS, male circumcision, sexual debut, partners and partner characteristics, condoms, vulnerability, HIV testing, alcohol and substance use, general perceptions about government, health and violence in the community. The data set contains 516 variables and 10501 cases. Abstract: South Africa continues to have the largest number of people living with HIV/AIDS in the World. This study intends to understand the determinants that lead South Africans to be vulnerable and susceptible to HIV. This is the third in a series of household surveys conducted by Human Sciences Research Council (HSRC), that allow for tracking of HIV and associated determinants over time using a slightly same methodology used in 2002 and 2005 survey, making it the third national-level repeat survey. The 2002 and 2005 surveys included individuals aged 2+ years living in South Africa while 2008 survey included individuals of all ages living in South Africa, including infants younger than 2 years of age. The interval of three years since 2002 allows for an exploration of shifts over time against a complex of demographic and other variables, as well as allowing for investigation of the new areas. The survey provides the first nationally representative HIV incidence estimates. The study key objectives were to: determine the prevalence of HIV infection in South Africa; examine the incidence of HIV infection in South Africa; assess the relationship between behavioural factors and HIV infection in South Africa; describe trends in HIV prevalence, HIV incidence, and risk behaviour in South Africa over the period 2002-2008; investigate the link between social, values, and cultural determinants and HIV infection in South Africa; assess the type and frequency of exposure to major national behavioural change communication programmes and assess their relationship to HIV prevention, AIDS treatment, care, and support; describe male circumcision practices in South Africa and assess its acceptability as a method of HIV prevention; collect data on the health conditions of South Africans; and contribute to the analysis of the impact of HIV/AIDS on society. In the 13440 valid households or visiting points, 10856 agreed to participate in the survey, 23369 individuals (no more than 4 per household, including infants under 2 years) were eligible to be interviewed, and 20826 individuals completed the interview. Of the 23369 eligible individuals, 15031 agreed to provide a blood specimen for HIV testing and were anonymously linked to the behavioural questionnaires. the household response rate was 80.8%, the individual response rate was 89.1% and the overall response rate for HIV testing was 64.3%.

Description: The data set contains the data of the parents or guardians of children aged 0 to 11 years. Some of the questions included were the child's biographical data, health status and health questions, male circumcision, education of the child on life issues, infant and child feeding practices as well as school attendance and immunisation records. The data set contains 275 variables and 9667 cases. Refer to the user guide for information regarding guidance relating to data analysis. Abstract: South Africa continues to have the largest number of people living with HIV/AIDS in the World. This study intends to understand the determinants that lead South Africans to be vulnerable and susceptible to HIV. This is the fourth in a series of household surveys conducted by Human Sciences Research council (HSRC), that allow for tracking of HIV and associated determinants over time using a slightly same methodology used in 2002 and 2008 survey, making it the fourth national-level repeat survey. The 2002 and 2005 surveys included individuals aged 2+ years living in South Africa while 2008 and 2012 survey included individuals of all ages living in South Africa, including infants less than 2 years of age. The 2008 study included only four people per household, while in 2012 all members of the households participated. The interval of three years since 2002 allows for an exploration of shifts over time against a complex of demographic and other variables, as well as allowing for investigation of the new areas. The surveys provide the nationally representative HIV incidence estimates showing changes over time. The 2012 study key objectives were: to determine the proportion of PLHIV who are on Antiretroviral treatment (ART) in South Africa; to determine the prevalence and incidence of HIV infection in South Africa in relation to social and behavioural determinants; to determine the proportion of males in South Africa who are circumcised; to investigate the link between social values, and cultural determinants and HIV infection in South Africa; to determine the extent to which mother-child pairs include HIV-negative mothers and HIV-positive infants; to describe trends in HIV prevalence, HIV incidence, and risk behaviour in South Africa over the period 2002 to 2012 collect data on the health conditions of South Africans; and contribute to the analysis of the impact of HIV/AIDS on society. In 2012, of the 15000 selected households or visiting points, 11079 agreed to participate in the survey, 42950 individuals (all household members were included) were eligible to be interviewed, and 38431 individuals completed the interview. Of the 38431 eligible individuals, 28997 agreed to provide a blood specimen for HIV testing and were anonymously linked to the behavioural questionnaires. The household response rate was 87.2% , the individual response rate was 89.5% and the overall response rate for HIV testing was 67.5% From the total of 38431 (89.5%) individuals who completed the interview, 2295 (5.3%) refused to be interviewed, 2224(5.2%) were absent from the household and 2224 (5.2%) were classified as missing/other.

Sweden SE: Prevalence of HIV: Male: % Aged 15-24 data was reported at 0.100 % in 2016. This stayed constant from the previous number of 0.100 % for 2015. Sweden SE: Prevalence of HIV: Male: % Aged 15-24 data is updated yearly, averaging 0.100 % from Dec 1990 (Median) to 2016, with 27 observations. The data reached an all-time high of 0.100 % in 2016 and a record low of 0.100 % in 2016. Sweden SE: Prevalence of HIV: Male: % Aged 15-24 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sweden – Table SE.World Bank: Health Statistics. Prevalence of HIV, male is the percentage of males 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 being especially vulnerable.

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.

This data set contains EIIHA populations who received services funded by Ryan White Part A Grant. EIIHA is Early Identification of Individuals with HIV/AIDS (EIIHA) The special populations (EIIHA) with HIV are: Black MSM = Black men and Black transgender women who have sex with men. Latinx MSM = Latinx men and Latinx Transgender women who have sex with men. Black Women - Black women Transgender - Transgender men and women. These populations have the biggest disparities of people living with HIV. Other data is the number of clients and units used in each service category in the Ryan White Part A, a grant that provides services for those with HIV.

Mexico MX: Prevalence of HIV: Male: % Aged 15-24 data was reported at 0.200 % in 2017. This stayed constant from the previous number of 0.200 % for 2016. Mexico MX: Prevalence of HIV: Male: % Aged 15-24 data is updated yearly, averaging 0.200 % from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 0.200 % in 2017 and a record low of 0.100 % in 2009. Mexico MX: Prevalence of HIV: Male: % Aged 15-24 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mexico – Table MX.World Bank.WDI: Health Statistics. Prevalence of HIV, male is the percentage of males 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 being especially vulnerable.

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

Laos LA: Prevalence of HIV: Female: % Aged 15-24 data was reported at 0.100 % in 2017. This stayed constant from the previous number of 0.100 % for 2016. Laos LA: Prevalence of HIV: Female: % Aged 15-24 data is updated yearly, averaging 0.100 % from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 0.200 % in 2009 and a record low of 0.100 % in 2017. Laos LA: 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 Laos – Table LA.World Bank.WDI: 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.

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.

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).

Saudi Arabia SA: Prevalence of HIV: Female: % Aged 15-24 data was reported at 0.100 % in 2016. This stayed constant from the previous number of 0.100 % for 2015. Saudi Arabia SA: Prevalence of HIV: Female: % Aged 15-24 data is updated yearly, averaging 0.100 % from Dec 1990 (Median) to 2016, with 27 observations. The data reached an all-time high of 0.100 % in 2016 and a record low of 0.100 % in 2016. Saudi Arabia SA: 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 Saudi Arabia – Table SA.World Bank: 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.

Venezuela VE: Prevalence of HIV: Female: % Aged 15-24 data was reported at 0.200 % in 2016. This stayed constant from the previous number of 0.200 % for 2015. Venezuela VE: Prevalence of HIV: Female: % Aged 15-24 data is updated yearly, averaging 0.200 % from Dec 1990 (Median) to 2016, with 27 observations. The data reached an all-time high of 0.300 % in 2014 and a record low of 0.100 % in 1996. Venezuela VE: 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 Venezuela – Table VE.World Bank: 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.

Indonesia’s total number of HIV/AIDS cases is still high. Inadequate knowledge about the risk of HIV infection will influence HIV prevention and therapy. This study aimed to map the level of HIV-related knowledge among Indonesians living on six major islands in Indonesia and investigate the relationship between socio-demographic characteristics and HIV/AIDS knowledge. This cross-sectional study used the Bahasa Indonesia version of the HIV Knowledge Questionnaire-18 items (HIV-KQ-18) Instrument. Data collection was done online through the Google form application. A total of 5,364 participants were recruited. The participants from Java had the highest degree of HIV/AIDS knowledge, which was 12.5% higher than participants from Sumatra, Kalimantan, Sulawesi, Papua, and Maluku. Linear regression showed that region, educational level, monthly expenditure, occupation, background in health sciences, and workshop attendance were significantly correlated with HIV knowledge. Participants typically understand that "HIV/AIDS transmission" only happens when sex partners are changed. Additionally, the government still needs improvement in HIV/AIDS education, particularly in the HIV incubation period, HIV transmission from pregnant women to the fetus, and condom use as one method of protection. There are disparities in HIV/AIDS knowledge levels among the major islands of Indonesia. Based on these findings, the government’s health promotion program to increase public awareness of HIV/AIDS must be implemented vigorously. Additionally, in line with our research findings, it is essential to broaden the scope of HIV/AIDS education and promotion materials.

BackgroundThe severe coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has resulted in the most devastating pandemic in modern history. Human immunodeficiency virus (HIV) destroys immune system cells and weakens the body’s ability to resist daily infections and diseases. Furthermore, HIV-infected individuals had double COVID-19 mortality risk and experienced worse COVID-related outcomes. However, the existing research still lacks the understanding of the molecular mechanism underlying crosstalk between COVID-19 and HIV. The aim of our work was to illustrate blood transcriptome crosstalk between COVID-19 and HIV and to provide potential drugs that might be useful for the treatment of HIV-infected COVID-19 patients.MethodsCOVID-19 datasets (GSE171110 and GSE152418) were downloaded from Gene Expression Omnibus (GEO) database, including 54 whole-blood samples and 33 peripheral blood mononuclear cells samples, respectively. HIV dataset (GSE37250) was also obtained from GEO database, containing 537 whole-blood samples. Next, the “Deseq2” package was used to identify differentially expressed genes (DEGs) between COVID-19 datasets (GSE171110 and GSE152418) and the “limma” package was utilized to identify DEGs between HIV dataset (GSE37250). By intersecting these two DEG sets, we generated common DEGs for further analysis, containing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) functional enrichment analysis, protein-protein interaction (PPI) analysis, transcription factor (TF) candidate identification, microRNAs (miRNAs) candidate identification and drug candidate identification.ResultsIn this study, a total of 3213 DEGs were identified from the merged COVID-19 dataset (GSE171110 and GSE152418), and 1718 DEGs were obtained from GSE37250 dataset. Then, we identified 394 common DEGs from the intersection of the DEGs in COVID-19 and HIV datasets. GO and KEGG enrichment analysis indicated that common DEGs were mainly gathered in chromosome-related and cell cycle-related signal pathways. Top ten hub genes (CCNA2, CCNB1, CDC20, TOP2A, AURKB, PLK1, BUB1B, KIF11, DLGAP5, RRM2) were ranked according to their scores, which were screened out using degree algorithm on the basis of common DEGs. Moreover, top ten drug candidates (LUCANTHONE, Dasatinib, etoposide, Enterolactone, troglitazone, testosterone, estradiol, calcitriol, resveratrol, tetradioxin) ranked by their P values were screened out, which maybe be beneficial for the treatment of HIV-infected COVID-19 patients.ConclusionIn this study, we provide potential molecular targets, signaling pathways, small molecular compounds, and promising biomarkers that contribute to worse COVID-19 prognosis in patients with HIV, which might contribute to precise diagnosis and treatment for HIV-infected COVID-19 patients.

The 2006-07 Swaziland Demographic and Health Survey (SDHS) is a nationally representative survey of 4,843 households, 4,987 women age 15-49, and 4,156 men age 15-49. The SDHS also included individual interviews with boys and girls age 12-14 and older adults age 50 and over. The survey of persons age 12-14 and age 50 and over was carried out in every other household selected in the SDHS. Interviews were completed for 459 girls and 411 boys age 12-14, and 661 women and 456 men age 50 and over. The 2006-07 SDHS is the first national survey conducted in Swaziland as part of the Demographic and Health Surveys (DHS) programme. The data are intended to furnish programme managers and policymakers with detailed information on levels and trends in fertility; nuptiality; sexual activity; fertility preferences; awareness and use of family planning methods; breastfeeding practices; nutritional status of mothers and young children; early childhood mortality and maternal mortality; maternal and child health; and awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections. The survey also collected information on malaria prevention and treatment. The 2006-07 SDHS is the first nationwide survey in Swaziland to provide population-based prevalence estimates for anaemia and HIV. Children age 6 months and older as well as adults were tested for anaemia. Children age 2 years and older as well as adults were tested for HIV. The principal objective of the 2006-07 Swaziland Demographic and Health Survey (SDHS) was to provide up-to-date information on fertility, childhood mortality, marriage, fertility preferences, awareness, and use of family planning methods, infant feeding practices, maternal and child health, maternal mortality, HIV/AIDS-related knowledge and behaviour and prevalence of HIV and anaemia. More specifically the 2006-07 SDHS was aimed at achieving the following; Determine key demographic rates, particularly fertility, under-five mortality, and adult mortality rates Investigate the direct and indirect factors which determine the level and trends of fertility Measure the level of contraceptive knowledge and practice of women and men by method Determine immunization coverage and prevalence and treatment of diarrhoea and acute respiratory diseases among children under five Determine infant and young child feeding practices and assess the nutritional status of children 6-59 months, women age 15-49 years, and men aged 15-49 years Estimate prevalence of anaemia Assess knowledge and attitudes of women and men regarding sexually transmitted infections and HIV/AIDS, and evaluate patterns of recent behaviour regarding condom use Identify behaviours that protect or predispose the population to HIV infection Examine social, economic, and cultural determinants of HIV Determine the proportion of households with orphans and vulnerable children (OVCs) Determine the proportion of households with sick people taken care at household level Determine HIV prevalence among males and females age 2 years and older Determine the use of iodized salt in households Describe care and protection of children age 12-14 years, and their knowledge and attitudes about sex and HIV/AIDS. This information is intended to provide data to assist policymakers and programme implementers to monitor and evaluate existing programmes and to design new strategies for demographic, social and health policies in Swaziland. The survey also provides data to monitor the country's achievement towards the Millenium Development Goals. MAIN RESULTS Fertility in Swaziland has been declining rapidly, with the TFR falling from 6.4 births per woman in 1986 to 3.8 births at the time of the SDHS. As expected, fertility is higher in rural areas (4.2 births per woman) than in urban areas (3.0 births per woman). Fertility differentials by education and wealth are substantial. Women with no education have on average 4.9 children compared with 2.4 children for women with tertiary education. Fertility varies widely according to household wealth. Women in the highest wealth quintile have 2.9 children fewer than women in the lowest quintile (2.6 and 5.5 births per woman, respectively). Knowledge of family planning is universal in Swaziland. The most widely known method is the male condom (99 percent for both males and females). Among women, other widely known methods include injectables (96 percent), the pill (95 percent), and the female condom (91 percent). For men, the best known methods besides the male condom are the female condom (94 percent) and the pill and injectables (84 percent each). Children are considered fully vaccinated when they receive one dose of BCG vaccine, three doses each of DPT and polio vaccines, and one dose of measles vaccine. BCG coverage among children age 12-23 months is nearly universal (97 percent); coverage is also high for the first doses of DPT (96 percent) and polio (97 percent). The proportion of children receiving subsequent doses of DPT and polio vaccines drops slightly, with 92 percent of children receiving the third dose of DPT and 87 percent receiving the third dose of polio. Ninety-two percent of children had received a measles vaccination by the time of the SDHS. Overall, 82 percent of children age 12-23 months are fully immunised. In Swaziland, almost all women who had a live birth in the five years preceding the survey received antenatal care from health professionals (97 percent); 9 percent received care from a doctor, and 88 percent received care from a trained nurse or midwife. Only 3 percent of mothers did not receive any antenatal care Overall, 87 percent of children in Swaziland are breastfed for some period of time (ever breastfed). The median duration of any breast-feeding in Swaziland is almost 17 months. However, the median duration of exclusive breast-feeding is much shorter (0.7 months). In interpreting the malaria programme indicators in Swaziland, it is important to recognise that the disease affects an estimated 30 percent of the population where malaria is most prevalent (the Lubombo Plateau, the lowveld, and parts of the middleveld). Malaria is also seasonal, occurring mainly during or after the rainy season (from November to March). A substantial part of the SDHS fieldwork took place outside of this period. Results from the HIV testing component in the 2006-07 SDHS indicate that 26 percent of Swazi adults age 15-49 are infected with HIV. Among women, the HIV rate is 31 percent, compared with 20 percent among men. HIV prevalence peaks at 49 percent for women age 25-29, which is almost five times the rate among women age 15-19 and more than twice the rate observed among women age 45-49. HIV prevalence increases from 2 percent among men in the 15-19 age group to 45 percent in the age group 35-39 and then decreases to 28 percent among men age 45-49. HIV prevalence for women and men age 50 or over is 12 percent and 18 percent, respectively. Among the population age 2-14 years, 4 percent of girls and boys are infected.

This dataset provides supply chain health commodity shipment and pricing data. Specifically, the data set identifies Antiretroviral (ARV) and HIV lab shipments to supported countries. In addition, the data set provides the commodity pricing and associated supply chain expenses necessary to move the commodities to countries for use. The dataset has similar fields to the Global Fund's Price, Quality and Reporting (PQR) data. PEPFAR and the Global Fund represent the two largest procurers of HIV health commodities. This dataset, when analyzed in conjunction with the PQR data, provides a more complete picture of global spending on specific health commodities. The data are particularly valuable for understanding ranges and trends in pricing as well as volumes delivered by country. The US Government believes this data will help stakeholders make better, data-driven decisions. Care should be taken to consider contextual factors when using the database. Conclusions related to costs associated with moving specific line items or products to specific countries and lead times by product/country will not be accurate.

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.

The burden of HIV and related diseases have been areas of great concern pre and post the emergence of COVID-19 in Zimbabwe. Machine learning models have been used to predict the risk of diseases, including HIV accurately. Therefore, this paper aimed to determine common risk factors of HIV positivity in Zimbabwe between the decade 2005 to 2015. The data were from three two staged population five-yearly surveys conducted between 2005 and 2015. The outcome variable was HIV status. The prediction model was fit by adopting 80% of the data for learning/training and 20% for testing/prediction. Resampling was done using the stratified 5-fold cross-validation procedure repeatedly. Feature selection was done using Lasso regression, and the best combination of selected features was determined using Sequential Forward Floating Selection. We compared six algorithms in both sexes based on the F1 score, which is the harmonic mean of precision and recall. The overall HIV prevalence for the combined dataset was 22.5% and 15.3% for females and males, respectively. The best-performing algorithm to identify individuals with a higher likelihood of HIV infection was XGBoost, with a high F1 score of 91.4% for males and 90.1% for females based on the combined surveys. The results from the prediction model identified six common features associated with HIV, with total number of lifetime sexual partners and cohabitation duration being the most influential variables for females and males, respectively. In addition to other risk reduction techniques, machine learning may aid in identifying those who might require Pre-exposure prophylaxis, particularly women who experience intimate partner violence. Furthermore, compared to traditional statistical approaches, machine learning uncovered patterns in predicting HIV infection with comparatively reduced uncertainty and, therefore, crucial for effective decision-making.

Achievement of viral load suppression among people living with HIV is one of the most important goals for effective HIV epidemic response. In Ukraine, people who inject drugs (PWID) experience the largest HIV burden. At the same time, this group disproportionally missed out in HIV treatment services. We performed a secondary data analysis of the national-wide cross-sectional bio-behavioral surveillance survey among PWID to assess the population-level prevalence of detectable HIV viremia and identify key characteristics that explain the outcome. Overall, 11.4% of PWID or 52.6% of HIV-positive PWID had a viral load level that exceeded the 1,000 copies/mL threshold. In the group of HIV-positive PWID, the detectable viremia was attributed to younger age, monthly income greater than minimum wage, lower education level, and non-usage of antiretroviral therapy (ART) and opioid agonistic therapy. Compared with HIV-negative PWID, the HIV-positive group with detectable viremia was more likely to be female, represented the middle age group (35–49 years old), had low education and monthly income levels, used opioid drugs, practiced risky injection behavior, and had previous incarceration history. Implementing the HIV case identification and ART linkage interventions focused on the most vulnerable PWID sub-groups might help closing the gaps in ART service coverage and increasing the proportion of HIV-positive PWID with viral load suppression.