Context :

Dataset contains healthcare statistics and categorical information about patients who have been diagnosed with AIDS. This dataset was initially published in 1996.

Attribute Information :

• time: time to failure or censoring
• trt: treatment indicator (0 = ZDV only; 1 = ZDV + ddI, 2 = ZDV + Zal, 3 = ddI only)
• age: age (yrs) at baseline
• wtkg: weight (kg) at baseline
• hemo: hemophilia (0=no, 1=yes)
• homo: homosexual activity (0=no, 1=yes)
• drugs: history of IV drug use (0=no, 1=yes)
• karnof: Karnofsky score (on a scale of 0-100)
• oprior: Non-ZDV antiretroviral therapy pre-175 (0=no, 1=yes)
• z30: ZDV in the 30 days prior to 175 (0=no, 1=yes)
• preanti: days pre-175 anti-retroviral therapy
• race: race (0=White, 1=non-white)
• gender: gender (0=F, 1=M)
• str2: antiretroviral history (0=naive, 1=experienced)
• strat: antiretroviral history stratification (1='Antiretroviral Naive',2='> 1 but <= 52 weeks of prior antiretroviral therapy',3='> 52 weeks)
• symptom: symptomatic indicator (0=asymp, 1=symp)
• treat: treatment indicator (0=ZDV only, 1=others)
• offtrt: indicator of off-trt before 96+/-5 weeks (0=no,1=yes)
• cd40: CD4 at baseline
• cd420: CD4 at 20+/-5 weeks
• cd80: CD8 at baseline
• cd820: CD8 at 20+/-5 weeks
• infected: is infected with AIDS (0=No, 1=Yes)

Additional Variable Information :

• Personal information (age, weight, race, gender, sexual activity)
• Medical history (hemophilia, history of IV drugs)
• Treatment history (ZDV/non-ZDV treatment history)
• Lab results (CD4/CD8 counts)

Citation :

https://classic.clinicaltrials.gov/ct2/show/NCT00000625

Acknowledgment :

Creators :

1. S. Hammer
2. D. Katzenstein
3. M. Hughes
4. H. Gundacker
5. R. Schooley
6. R. Haubrich
7. W. K.
8. M. Lederman
9. J. Phair
10. M. Niu
11. M. Hirsch
12. T. Merigan

Donor :

https://archive.ics.uci.edu/dataset/890/aids+clinical+trials+group+study+175

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.

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

The Philippines reported about ****** HIV cases, an increase from the previous year. The number of reported HIV cases has gradually increased since 2012, aside from a significant dip in 2020. The state of HIV As the monthly average number of people newly diagnosed with HIV increases, the risk it poses threatens the lives of Filipinos. HIV is a sexually transmitted infection that attacks the body’s immune system, with more males being diagnosed than females. In 2022, the majority of people newly diagnosed with HIV were those between the age of 25 and 34 years, followed by those aged 15 and 24. There is still no cure for HIV and without treatment, it could lead to other severe illnesses such as tuberculosis and cancers such as lymphoma and Kaposi’s sarcoma. However, HIV is now a manageable chronic illness that can be treated with proper medication. What are the leading causes of death in the Philippines? Between January and September 2024, preliminary figures have shown that ischaemic heart disease was the leading cause of death in the Philippines. The prevalence of heart diseases in the nation has been closely attributed to the Filipino diet, which was described as having a high fat, high cholesterol, and high sodium content. In addition, acute respiratory infections and hypertension also registered the highest morbidity rate among leading diseases in the country in 2021.

CEPHIA HIV Recency Assay Data

Global Assay Results and Participant Characteristics

By [source]

About this dataset

The Consortium for the Evaluation and Performance of HIV Incidence Assays (CEPHIA) is continually striving to deepen understanding of HIV epidemiology around the world. By collecting and testing samples from collaborations across the globe they are able to monitor the accuracy and precision of HIV recency assays. This dataset contains assay results plus corresponding participant characteristics, enabling researchers to gain knowledge about both incidence rates as well as long-term dynamics in different cohorts throughout numerous countries.

This data set provides key information such as assay type, specimen type, testing laboratory, participant demographic factors (e.g., sex and country), HIV status at visit time, cohort entry HIV status, elite controller status over time and antiretroviral use history (both current ART treatment & past first treatment episode). Plus viral load test results with related information such as closest measure to visit date offset , sensitivity level , EDDI interval size and number of days since EDDI for enhanced analysis capabilities. All together these variables make this a powerful tool allowing you to probe a myriad of questions ranging from understanding how incidence changes over time by population or country & reducing infection levels in especially vulnerable communities through to exploring potential interactions between other factors such as wealth or gender based disparities in those affected by this virus

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How to use the dataset

This dataset provides a comprehensive look at CEPHIA collaborations across the world to evaluate the accuracy of HIV recency assays. It contains information on assay results and participant characteristics such as HIV status, HIV subtype, country of origin and demographics. The data can be used to gain insight into global trends in HIV incidence and dynamics.

To get started with this dataset, explore the different columns available to you such as assay, cephia_panel, testing_laboratory, etc. These will give an indication of what kind of assay was used, where it was conducted and what samples were tested. Then look at the other columns which provide more detailed information about each participant such as their HIV subtype, HIV status at visit and visit date.

Once you have familiarized yourself with the column titles, start by selecting only those that are relevant for your analysis - there is no need to include all columns if they don't add value your analysis. This will reduce clutter and make analysing your data much easier.

Finally if you have any questions or would like further explanation on any aspect of this dataset please refer to CEPHIA's website or contact them directly for help!

Research Ideas

• Using the HIV subtype and HIV treatment information, researchers can develop and evaluate models that predict treatment effectiveness for different types of HIV.
• Examining the viral load closest to a certain visit date, as well as the viral load type used, allows researchers to better understand the dynamics of viral load within cohorts.
• Analyzing designated-elite controllers during visits can help characterize and track times where a person is intermittently controlling their infection without medication over time allowing investigators to investigate how this occurs in different patient populations with different responses to medications

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: cephia_public_use_dataset_20210604.csv | Column name | Description | |:--------------------------------------------|:--------------------------------------------------------------------------------------------------| | assay | The type of assay used to test the specimen. (String) | | cephia_panel...

BackgroundCOVID-19 experiences on noncommunicable diseases (NCDs) from district-level hospital settings during waves I and II are scarcely documented. The aim of this study is to investigate the NCDs associated with COVID-19 severity and mortality in a district-level hospital with a high HIV/TB burden.MethodsThis was a retrospective observational study that compared COVID-19 waves I and II at Khayelitsha District Hospital in Cape Town, South Africa. COVID-19 adult patients with a confirmed SARS-CoV-2 polymerase chain reaction (PCR) or positive antigen test were included. In order to compare the inter wave period, clinical and laboratory parameters on hospital admission of noncommunicable diseases, the Student t-test or Mann-Whitney U for continuous data and the X2 test or Fishers’ Exact test for categorical data were used. The role of the NCD subpopulation on COVID-19 mortality was determined using latent class analysis (LCA).FindingsAmong 560 patients admitted with COVID-19, patients admitted during wave II were significantly older than those admitted during wave I. The most prevalent comorbidity patterns were hypertension (87%), diabetes mellitus (65%), HIV/AIDS (30%), obesity (19%), Chronic Kidney Disease (CKD) (13%), Congestive Cardiac Failure (CCF) (8.8%), Chronic Obstructive Pulmonary Disease (COPD) (3%), cerebrovascular accidents (CVA)/stroke (3%), with similar prevalence in both waves except HIV status [(23% vs 34% waves II and I, respectively), p = 0.022], obesity [(52% vs 2.5%, waves II and I, respectively), p

CZ: Newly Infected with HIV: Adults (Aged 15+) and Children (Aged 0-14) data was reported at 200.000 Number in 2022. This stayed constant from the previous number of 200.000 Number for 2021. CZ: Newly Infected with HIV: Adults (Aged 15+) and Children (Aged 0-14) data is updated yearly, averaging 200.000 Number from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 200.000 Number in 2022 and a record low of 100.000 Number in 1999. CZ: Newly Infected with HIV: Adults (Aged 15+) and Children (Aged 0-14) data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Czech Republic – Table CZ.World Bank.WDI: Social: Health Statistics. Number of adults (ages 15+) and children (ages 0-14) newly infected with HIV.;UNAIDS estimates.;;This indicator is related to Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/].

Infection with HIV-1 continues to be a serious health threat throughout the world. Chronic exposure to combination anti-retroviral therapy identified anti-retroviral associated long-term toxicities. Hence, there is a need to prevent these co-morbidities. GSK3640254 is a next-generation HIV-1 Maturation Inhibitor (MI) which may be effective for HIV-1 infection. This study will evaluate the antiviral effect, safety, tolerability and pharmacokinetics/ pharmacodynamics of GSK3640254 in HIV-1 infected treatment-naive adults. This study will consists of two parts; Part 1 and Part 2. Part 1 will evaluate two active doses of GSK3640254, 200 milligrams (mg) (Cohort 1) and 10 mg (Cohort 2) along with placebo to match GSK3640254 Mesylate salt. Part 2 will evaluate three active doses of GSK3640254. Dose level 1 of GSK3640254 that can provide at least 30 percent of the maximum effect (Cohort 1), dose level 2 of GSK3640254 that can provide at least 75 percent of the maximum effect (Cohort 2) and dose level 3 of GSK3640254 that can provide at least 90 percent of the maximum effect (Cohort 3). These doses are anticipated to be 5 mg, 40 mg and 100 mg respectively, but could be modified based on data obtained in Part 1. Subjects will also receive placebo to match GSK3640254 Mesylate salt in Part 2 of the study. All doses will be administered after a moderate fat meal. This study will consist of Screening period (up to 14 days), Treatment period (Day 1- Day 10), post-dose Follow-up (Day 11- Day 17) and final Follow-up (Day 18-24). A total of approximately 34 subjects will be enrolled, of which, 14 subjects will be randomized in Part 1 and 20 in Part 2 of the study. Six subjects will be enrolled in each of the active dose cohorts and 2 subjects will be enrolled in each of the placebo cohorts.

Context

• The Western African Ebola virus epidemic (2013–2016) was the most widespread outbreak of Ebola virus disease (EVD) in history
• Causing major loss of life and socioeconomic disruption in the region, mainly in Guinea, Liberia, and Sierra Leone.
• The ** first cases** were recorded in Guinea in December 2013;
• Later, the disease spread to neighboring Liberia and Sierra Leone, with minor outbreaks occurring elsewhere.
• It caused significant mortality, with the case fatality rate reported which was initially considered, while the rate among hospitalized patients was 57–59%
• The final numbers 28,616 people, including 11,310 deaths, for a case-fatality rate of 40%.

Content

Each row contains a report from each region/location for each day Each column represents the number of cases reported from each country/region

Inspiration

To see how the epidemic spread worldwide in such a short time

Acknowledgements / Data Source

https://www.who.int/csr/don/archive/disease/ebola/en/ https://data.humdata.org/dataset/ebola-cases-2014

Collection methodology

https://github.com/imdevskp/ebola_outbreak_dataset

Cover Photo

Photo from CDC website https://www.cdc.gov/vhf/ebola/index.html

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• H1N1 | Swine Flu 2009 Pandemic Dataset - https://www.kaggle.com/imdevskp/h1n1-swine-flu-2009-pandemic-dataset
• SARS 2003 Pandemic - https://www.kaggle.com/imdevskp/sars-outbreak-2003-complete-dataset
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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.

Abstract

The health and demography of the South African population has been undergoing substantial changes as a result of the rapidly progressing HIV epidemic. Researchers at the University of KwaZulu-Natal and the South African Medical Research Council established The Africa Health Research Studies in 1997 funded by a core grant from The Wellcome Trust, UK. Given the urgent need for high quality longitudinal data with which to monitor these changes, and with which to evaluate interventions to mitigate impact, a demographic surveillance system (DSS) was established in a rural South African population facing a rapid and severe HIV epidemic. The DSS, referred to as the Africa Health Research Institute Demographic Information System (ACDIS), started in 2000.

ACDIS was established to ‘describe the demographic, social and health impact of the HIV epidemic in a population going through the health transition’ and to monitor the impact of intervention strategies on the epidemic. South Africa’s political and economic history has resulted in highly mobile urban and rural populations, coupled with complex, fluid households. In order to successfully monitor the epidemic, it was necessary to collect longitudinal demographic data (e.g. mortality, fertility, migration) on the population and to mirror this complex social reality within the design of the demographic information system. To this end, three primary subjects are observed longitudinally in ACDIS: physical structures (e.g. homesteads, clinics and schools), households and individuals. The information about these subjects, and all related information, is stored in a single MSSQL Server database, in a truly longitudinal way—i.e. not as a series of cross-sections.

The surveillance area is located near the market town of Mtubatuba in the Umkanyakude district of KwaZulu-Natal. The area is 438 square kilometers in size and includes a population of approximately 85 000 people who are members of approximately 11 000 households. The population is almost exclusively Zulu-speaking. The area is typical of many rural areas of South Africa in that while predominantly rural, it contains an urban township and informal peri-urban settlements. The area is characterized by large variations in population densities (20–3000 people/km2). In the rural areas, homesteads are scattered rather than grouped. Most households are multi-generational and range with an average size of 7.9 (SD:4.7) members. Despite being a predominantly rural area, the principle source of income for most households is waged employment and state pensions rather than agriculture. In 2006, approximately 77% of households in the surveillance area had access to piped water and toilet facilities.

To fulfil the eligibility criteria for the ACDIS cohort, individuals must be a member of a household within the surveillance area but not necessarily resident within it. Crucially, this means that ACDIS collects information on resident and non-resident members of households and makes a distinction between membership (self-defined on the basis of links to other household members) and residency (residing at a physical structure within the surveillance area at a particular point in time). Individuals can be members of more than one household at any point in time (e.g. polygamously married men whose wives maintain separate households). As of June 2006, there were 85 855 people under surveillance of whom 33% were not resident within the surveillance area. Obtaining information on non-resident members is vital for a number of reasons. Most importantly, understanding patterns of HIV transmission within rural areas requires knowledge about patterns of circulation and about sexual contacts between residents and their non-resident partners. To be consistent with similar datasets from other INDEPTH Member centres, this data set contains data from resident members only.

During data collection, households are visited by fieldworkers and information supplied by a single key informant. All births, deaths and migrations of household members are recorded. If household members have moved internally within the surveillance area, such moves are reconciled and the internal migrant retains the original identfier associated with him/her.

Geographic coverage

Demographic surveillance area situated in the south-east portion of the uMkhanyakude district of KwaZulu-Natal province near the town of Mtubatuba. It is bounded on the west by the Umfolozi-Hluhluwe nature reserve, on the South by the Umfolozi river, on the East by the N2 highway (except form portions where the Kwamsane township strandles the highway) and in the North by the Inyalazi river for portions of the boundary. The area is 438 square kilometers.

Analysis unit

Individual

Universe

Resident household members of households resident within the demographic surveillance area. Inmigrants are defined by intention to become resident, but actual residence episodes of less than 180 days are censored. Outmigrants are defined by intention to become resident elsewhere, but actual periods of non-residence less than 180 days are censored. Children born to resident women are considered resident by default, irrespective of actual place of birth. The dataset contains the events of all individuals ever resident during the study period (1 Jan 2000 to 31 Dec 2015).

Kind of data

Event history data

Frequency of data collection

This dataset contains rounds 1 to 37 of demographic surveillance data covering the period from 1 Jan 2000 to 31 December 2015. Two rounds of data collection took place annually except in 2002 when three surveillance rounds were conducted. From 1 Jan 2015 onwards there are three surveillance rounds per annum.

Sampling procedure

This dataset is not based on a sample but contains information from the complete demographic surveillance area.

Reponse units (households) by year: Year Households 2000 11856
2001 12321
2002 12981
2003 12165
2004 11841
2005 11312
2006 12065
2007 12165
2008 11790
2009 12145
2010 12485
2011 12455
2012 12087 2013 11988 2014 11778 2015 11938

In 2006 the number of response units increased due to the addition of a new village into the demographic surveillance area.

Sampling deviation

None

Mode of data collection

Proxy Respondent [proxy]

Research instrument

Bounded structure registration (BSR) or update (BSU) form: - Used to register characteristics of the BS - Updates characteristics of the BS - Information as at previous round is preprinted

Household registration (HHR) or update (HHU) form: - Used to register characteristics of the HH - Used to update information about the composition of the household - Information preprinted of composition and all registered households as at previous

Household Membership Registration (HMR) or update (HMU): - Used to link individuals to households - Used to update information about the household memberships and member status observations - Information preprinted of member status observations as at previous

Individual registration form (IDR): - Used to uniquely identify each individual - Mainly to ensure members with multiple household memberships are appropriately captured

Migration notification form (MGN): - Used to record change in the BS of residency of individuals or households _ Migrants are tracked and updated in the database

Pregnancy history form (PGH) & pregnancy outcome notification form (PON): - Records details of pregnancies and their outcomes - Only if woman is a new member - Only if woman has never completed WHL or WGH

Death notification form (DTN): - Records all deaths that have recently occurred - Iincludes information about time, place, circumstances and possible cause of death

Cleaning operations

On data entry data consistency and plausibility were checked by 455 data validation rules at database level. If data validaton failure was due to a data collection error, the questionnaire was referred back to the field for revisit and correction. If the error was due to data inconsistencies that could not be directly traced to a data collection error, the record was referred to the data quality team under the supervision of the senior database scientist. This could request further field level investigation by a team of trackers or could correct the inconsistency directly at database level.

No imputations were done on the resulting micro data set, except for:

a. If an out-migration (OMG) event is followed by a homestead entry event (ENT) and the gap between OMG event and ENT event is greater than 180 days, the ENT event was changed to an in-migration event (IMG). b. If an out-migration (OMG) event is followed by a homestead entry event (ENT) and the gap between OMG event and ENT event is less than 180 days, the OMG event was changed to an homestead exit event (EXT) and the ENT event date changed to the day following the original OMG event. c. If a homestead exit event (EXT) is followed by an in-migration event (IMG) and the gap between the EXT event and the IMG event is greater than 180 days, the EXT event was changed to an out-migration event (OMG). d. If a homestead exit event (EXT) is followed by an in-migration event (IMG) and the gap between the EXT event and the IMG event is less than 180 days, the IMG event was changed to an homestead entry event (ENT) with a date equal to the day following the EXT event. e. If the last recorded event for an individual is homestead exit (EXT) and this event is more than 180 days prior to the end of the surveillance period, then the EXT event is changed to an

Abstract

The impact of HIV/AIDS on households in the Free State was assessed by means of a cohort study of households affected by the disease. The survey was conducted in two local communities in the Free State province, one urban (Welkom) and one rural (Qwaqwa), in which the HIV/AIDS epidemic is particularly rife. A survey on the quality of life and household economics was conducted, using the household questionnaire.

Geographic coverage

Due to the sampling design and small sample size, the findings from this household impact study cannot be generalised to households across South Africa, but pertain largely to the experience of poor, African households that utilise public health care services.

Analysis unit

Households

Kind of data

Longitudinal Survey [ls]

Sampling procedure

The household impact of HIV/AIDS was assessed by means of a cohort study of households affected by the disease. The survey was conducted in two local communities in the Free State province, one urban (Welkom) and one rural (Qwaqwa), in which the HIV/AIDS epidemic is particularly rife. Welkom and Qwaqwa are situated in the Lejweleputswa and Thabo Mofutsanyane districts of the Free State province.

Mode of data collection

Face-to-face [f2f]

Research instrument

A household questionnaire on quality of life and household economics was administered. Slight changes were made to the questionnaire during the survey, while certain questions were deleted and others added to the instrument. These changes to the questionnaires are described in the document "SEGA - household AIDS project". Interviews were conducted with one key respondent only, namely the ‘person responsible for the daily organisation of the household, including household finances’. The first four rounds of interviews were completed in May/June and November/December of 2001 and in July/August and November/December of 2002. Rounds five and six of the study were completed in July/August 2003 and May/June 2004 respectively.

Response rate

During the first wave of interviews a total of 404 interviews were conducted. During the second wave of data collection, interviews were conducted with 385 households, which translates into an attrition rate of 4.7% (19 households). During wave III, a total of 354 households were interviewed, with 31 households not being reinterviewed (7.7% of the original sample). In wave IV, 55 new households wererecruited into the study, with particular emphasis on an effort to recruit child-headed households into the survey insofar as the sample to date did not include any such households. During waves IV, V and VI a total of 3, 13 and 9 households respectively could not be re-interviewed.

The payment of a minimal participation fee (R150 per household per survey visit) to those households interviewed in each wave, following the interview and distributed in the form of food parcels, contributed to ensuring sustainability of the sample over the three-year period. The dataset includes data for 331 households interviewed in each of the six rounds of interviews. In almost 90 percent of cases the reasons for attrition are related to migration, given that this study did not intend to follow those households that move outside of the two immediate study areas, i.e. Welkom and Qwaqwa. In the majority of cases, attrition can be ascribed to the failure to establish the current whereabouts of the particular household during follow-up, while in a third of cases it could be established that the household had moved to another country, another province, or another town in the Free State province. Less than ten percent of households had refused to participate in subsequent waves. The reasons for attrition in the original sample illustrate the manner in which migration and the disintegration of households, which are important effects of the epidemic, can act to erode the sample population.

BackgroundThe efficacy of antiretroviral therapy depends on patient adherence to a daily medication regimen, yet many patients fail to adhere at high enough rates to maintain health and reduce the risk of transmitting HIV. Given the explosive global growth of cellular-mobile phone use, text-messaging interventions to promote adherence are especially appropriate. This meta-analysis synthesized available text messaging interventions to promote antiretroviral therapy adherence in people living with HIV.MethodsWe performed Boolean searches of electronic databases, hand searches of recent year conference abstracts and reverse searches. Included studies (1) targeted antiretroviral therapy adherence in a sample of people living with HIV, (2) used a randomized-controlled trial design to examine a text messaging intervention, and (3) reported at least one adherence measurement or clinical outcome.ResultsEight studies, including 9 interventions, met inclusion criteria. Text-messaging interventions yielded significantly higher adherence than control conditions (OR = 1.39; 95% CI = 1.18, 1.64). Sensitivity analyses of intervention characteristics suggested that studies had larger effects when interventions (1) were sent less frequently than daily, (2) supported bidirectional communication, (3) included personalized message content, and (4) were matched to participants’ antiretroviral therapy dosing schedule. Interventions were also associated with improved viral load and/or CD4+ count (k = 3; OR = 1.56; 95% CI = 1.11, 2.20).ConclusionsText-messaging can support antiretroviral therapy adherence. Researchers should consider the adoption of less frequent messaging interventions with content and timing that is individually tailored and designed to evoke a reply from the recipient. Future research is needed in order to determine how best to optimize efficacy.

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.

As of 2021, an average of 350,000 people in Ghana were infected with the human immunodeficiency virus (HIV). This was an increase from the previous year, when a total of 340,000 was registered. Generally, people in the country living with the virus increased in number over the years observed. One of the prevalent modes of infection is sexual intercourse. Moreover, HIV remains one of the leading health threats in Africa.

Objective: To assess the impact of an interprofessional case-based training program to enhance clinical knowledge and confidence among clinicians working in high HIV-burden settings in sub-Saharan Africa (SSA) Setting: Health professions training institutions and their affiliated clinical training sites in 12 high HIV-burden countries in SSA. Participants: Cohort comprising pre-service and in-service learners, from diverse health professions, engaged in HIV service delivery. Intervention: A standardized, interprofessional, case-based curriculum designed to enhance HIV clinical competency, implemented between October 2019 and April 2020. Main outcome measures: The primary outcomes measured were knowledge and clinical confidence related to topics addressed in the curriculum. These outcomes were assessed using a standardized online assessment, completed before and after course completion. A secondary outcome was knowledge retention at least six months post-intervention, measured using the same standardized assessment, six months after training completion. We also sought to determine what lessons could be learned from this training program to inform interprofessional training in other contexts. Results: Data from 3027 learners were collected: together nurses (n=1145, 37.9%) and physicians (n=902, 29.8%) constituted the majority of participants; 58.1% were pre-service learners (n=1755) and 24.1% (n=727) had graduated from training within the prior year. Knowledge scores were significantly higher, post -participation compared to pre-participation, across all content domains, regardless of training level and cadre (all p<0.05). Among 188 learners (6.2%) who retook the test at >6 months, knowledge and self-reported confidence scores were greater compared to pre-course scores (all p<0.05). Conclusion: To our knowledge this is the largest interprofessional, multi-country training program established to improve HIV knowledge and clinical confidence among HCP workers in SSA. The findings are notable given the size and geographical reach and demonstration of sustained confidence and knowledge retention post course completion. The findings highlight the utility of interprofessional approaches to enhance clinical training in SSA. Methods The study was conducted using data from the STRIPE HIV program. The program was launched across 20 health professions training institutions in 14 countries in October 2019. All learners who completed a pre and post-test assessment for an in-person training conducted between October 1, 2019 and March 31, 2020, were included in the study. After April 2020, all training transitioned to online format given widespread restrictions on in-person learning related to the COVID-19 pandemic; these learners were excluded from this analysis. As previously described, training included 17 case-based modules, typically presented over two days, and was designed to foster interprofessional discussion and facilitate learning related to HIV clinical management, quality improvement and interprofessional collaborative practice. Training content included required modules on initiating HIV therapeutics in women of childbearing age (“HIV and Women”), management of opportunistic infections (“HIV-TB”), prevention of mother to child transmission (“PMTCT”) and pediatric HIV (“Paediatric Care”), in which all learners participated regardless of the stage of their career or professional cadre. These modules were all created by the study team which included local HIV practitioners and international and local educational experts. In addition to creation of the learning materials, the study team provided local educators at each partner institution with training resources to implement the course. These local partners were encouraged to ensure that each training course included a diverse mix of professional cadres and, where feasible, a mix of health professionals at different stages of their career (pre-service, post-graduate but within 12 months of graduation, and greater than 12 months post-graduation). The study team also provided training resources to facilitate training of local facilitators. The frequency of training courses offered, the ratio of learners to facilitators, mix of cadres and course timing were all determined by local partner institutions. Given scarcity of training resources, some health professions training institutions had to decline access for eligible candidates; in such circumstances, participation of early career professionals was prioritized over pre-service learners. Cohort: This was a convenience sample, including all learners who participated in the STRIPE HIV training program and had completed both pre- and post-training assessments during the study period. In addition to capturing learner demographic information, the assessment assessed learner (1) clinical and technical knowledge related to the learning objectives outlined in the program and (2) self-reported confidence in skills and abilities covered in the program, including (a) confidence to participate in HIV service delivery, specific to each cadre’s scope of practice, in the domains addressed in the course, (b) confidence to employ quality improvement tools and (c) confidence to practice as part of an interprofessional team. Knowledge was assessed using a series of domain-specific multiple-choice questions; all questions were the same for all participants regardless of training context, participant cadre, training institution, and country. Confidence was assessed on a four-point Likert-type scale, ranging from 1= “I feel uncomfortable with this topic/need supervision from my supervisor” to 4= “I feel very comfortable with this topic/without supervision as though in independent practice.” (Supplemental digital appendix). All learners completed the initial assessment at the time of program enrollment, typically within 24 hours of starting training. They then completed the same assessment immediately after completing the course, typically within 48 hours. For most participants, these pre and post program assessments were accessed on the training program’s website. However, for a small subset that did not have internet or computer access, assessments were completed on paper, and subsequently uploaded into the project database by local research staff. Starting in October 2020, we invited all participants to retake the same assessment at least six months after when they had participated in the program. This repeat assessment was administered electronically via email (Qualtrics, version XM; Provo, Utah; 2013). To increase uptake of this repeat assessment, all individuals who completed it were entered into a lottery to receive a US $50 prize voucher for internet data or airtime. Analysis: We only included data on learners for whom we had both pre-course and post-course assessment data, excluding those participants for whom we did not have both data points. For these eligible learners, we used descriptive statistics to summarize demographic characteristics of program participants, stratifying results by gender, health profession cadre and professional career stage (RStudio Version 1.3.1093). We separately analyzed (1) differences in pre-course and post-course knowledge and self-reported confidence using Wilcoxon signed-rank tests and (2) differences in knowledge and self-reported confidence between cadres and career stage using ANOVA and Tukey’s HSD test. For the subgroup of learners for whom both pre- and post-course assessment results were available, and who had also completed the post-course assessment >6 months after completion of the course, we calculated the change in levels of knowledge and self-reported confidence between the post > 6 months assessment and the pre-course assessment sores using Wilcoxon signed-rank test. We applied Wilcoxon signed-rank tests because distributions of assessment response variables were not normally distributed. All reported P values were two sided.

The HIV-1 viral inhibition assay (VIA) measures CD8 T cell-mediated inhibition of HIV replication in CD4 T cells and is increasingly used for clinical testing of HIV vaccines and immunotherapies. The VIA has multiple sources of variability arising from in vitro HIV infection and co-culture of two T cell populations. Here, we describe multiple modifications to a 7-day VIA protocol, the most impactful being the introduction of independent replicate cultures for both HIV infected-CD4 (HIV-CD4) and HIV-CD4:CD8 T cell cultures. Virus inhibition was quantified using a ratio of weighted averages of p24+ cells in replicate cultures and the corresponding 95% confidence interval. An Excel template is provided to facilitate calculations. Virus inhibition was higher in people living with HIV suppressed on antiretroviral therapy (n=14, mean: 40.0%, median: 43.8%, range: 8.2 to 73.3%; p < 0.0001, two-tailed, exact Mann-Whitney test) compared to HIV-seronegative donors (n = 21, mean: -13.7%, median: -14.4%, range: -49.9 to 20.9%) and was stable over time (n = 6, mean %COV 9.4%, range 0.9 to 17.3%). Cross-sectional data were used to define 8% inhibition as the threshold to confidently detect specific CD8 T cell activity and determine the minimum number of culture replicates and p24+ cells needed to have 90% statistical power to detect this threshold. Last, we note that, in HIV seronegative donors, the addition of CD8 T cells to HIV infected CD4 T cells consistently increased HIV replication, though the level of increase varied markedly between donors. This co-culture effect may contribute to the weak correlations observed between CD8 T cell VIA and other measures of HIV-specific CD8 T cell function.

The “education index” represents the potential of a population to access information and knowledge in a certain area in 2010. Such potential is measured by the capacity of educational system and the diffusion of knowledge about events that impacts local population (as likely climate change will do). The index results from the second cluster of the Principal Component Analysis preformed among 16 potential variables. The analysis identify three dominant variables, namely “adult literacy”, “primary gross enrolment rate” and “prevalence of HIV”, assigning respectively the weights of 0.40, 0.25 and 0.35. Before to perform the analysis all the variables were log transformed (except “primary gross enrolment rate”) to shorten the extreme variation and then were score-standardized (converted to distribution with average of 0 and standard deviation of 1; with inverse method for “prevalence of HIV”) in order to be comparable. The first administrative level data for the “adult literacy” (percentage of population aged 15 years and older who can, with understanding, read and write a short, simple statement on their everyday lives), “primary gross enrolment rate” (total enrollment in primary education, regardless of age, expressed as a percentage of the population of official primary education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition) and “prevalence of HIV” (percentage of people ages 15-49 who are infected with HIV) were derived using survey data collected between 1998 and 2012 from DHS, UNDP National Human Development Reports, UNICEF statistics, and in some cases national survey data. Tabular data were linked by first administrative unit to the first administrative boundaries shapefile (FAO/GAUL) and then converted into raster format (resolution 0.5 arc-minute). Adult literacy rate shows the accumulated achievement of primary education and basic literacy skills of the population crucial for economic, social and political participation and development, especially in today’s knowledge societies. The gross enrolment ratio (GRE) is vital indicators that capture adaptive capacity, as they measure education access and coverage. They show the general level of participation in a given level of education and further indicate the capacity of the education system to enroll students of a particular age group. According to Leichenko et al. (2002), increased overall literacy levels reduce vulnerability by increasing people’s capabilities and access to information, thereby enhancing their ability to cope with adversities. Those without literacy skills may have problems taking advantage of health, educational, political, economic and cultural opportunities. Illiterate people may have difficulty in understanding warnings and access to recovery information. Other researches already combine HIV and education parameters to calculate index of adaptive capacity (Gbetibouo and Ringler 2009). HIV prevalence is used as indicator under the assumption that areas with higher rates of HIV/AIDS are more vulnerable. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.