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ObjectivesIntegrating clinical and laboratory data systems offers opportunities to understand disease aetiology and improve patient outcomes. We developed and piloted a data linkage algorithm to link HIV patient treatment records with routine laboratory service records in an HIV endemic setting in rural South Africa.
ApproachWe used data from the South Africa's National Health Laboratory Service (NHLS) and Three Integrated Electronic Registers (TIER.Net) databases for 17 primary healthcare clinics from uMkhanyakude district, South Africa. The two databases contained data on key demographic variables such as patient first name, surname, date of birth, sex, and health facility, and national ID numbers for a subset of the data. We adapted a probabilistic record linkage algorithm we previously developed for linkage of laboratory results. We trained and validated our algorithm using two approaches: 1) firstly, we constructed a quasi-gold standard based on manual review of potential matches for 1,061 randomly selected patients; 2) secondly, we constructed a quasi-gold standard based on records that contained national ID numbers. We calculated the probabilities that the algorithm could correctly identify a true match (sensitivity) and the probabilities that the match identified by the algorithm was truly a match (positive predictive value) using the two approaches.
ResultsBetween 2015 and 2020, 55,077 patients were recorded in the HIV treatment database and 386,577 laboratory tests were recorded in the laboratory service database. The sensitivity and positive predictive value in the manually reviewed data were estimated to be 94.4% and 81.2% respectively. The sensitivity and positive predictive value in the matched individual identifiers were estimated to be 99.6% and 99.7% respectively.
ConclusionsRecords can be linked successfully, but estimated performance of record linkage depends on the validation set used. Manually reviewed data contain noise and may underestimate performance, while national ID numbers may overestimate performance due to non-random patterns of missingness.

BackgroundHIV cohort data are crucial for understanding the effects of HIV infection and treatment. Linkage between HIV clinical and population records unlocks potential to understand the cause and consequences of social-economic factors on HIV-care seeking behaviors and mortality outcomes. We created HIV data infrastructure by linking HIV treatment records with routinely collected records.
MethodsWe linked HIV cohort data(n=119,434) to Health and Demographic Surveillance(n=256,425), laboratory tests(n=617,314), clinic visits and hospital admissions(n=170,325) from KwaZulu-Natal, an HIV-endemic setting in rural South Africa. We used graph-based probabilistic record linkage algorithm to link the data for HIV patients accessing care at 17 clinics within the Hlabisa sub-district. Africa Health Research Institute (AHRI) has collected health and demographic surveillance data since 2000. AHRI has set up a hospital information system in the district to capture ICD-10 code admissions and discharges.
ResultsFrom the 5% sample of 462,524 linkage clusters, the computed F-score was 0.88, sensitivity was 88% and positive predictive value was 91%. 38.5% of the patient ID clusters contained records linked across multiple databases. 36% of the HIV patient ID clusters contained records linked with laboratory tests. 19.5% of the HIV patient ID clusters contained data on demographic surveillance. 12.5% of the HIV patient ID clusters contained records linked with clinic visits and hospital admissions. 4% of patient IDs contained records across all four databases.
ConclusionWe have successfully created a cross-sectorial HIV cohort through probabilistic record linkage methods. The cohort provides a platform to answer policy-relevant questions.

Stalled fertility declines have been identified in several regions across the developing world, but the current conceptualization of a stalled fertility decline is poorly theorized and does not lend itself to objective measurement. We propose a more rigorous and statistically testable definition of stalled fertility decline that can be applied to time‐series data. We then illustrate the utility of our definition through its application to data from rural South Africa for the period 1990‐2005 collected from a demographic surveillance site. Application of the approach suggests that fertility decline has indeed stalled in rural KwaZulu‐Natal, at about three children per woman. The stall, some 20 percent above the replacement fertility level, does not appear to be associated with a rise in wanted fertility or attenuated access to contraceptive methods. This identification of a stalled fertility decline provides the first evidence of such a stall in southern Africa, the region with the lowest fertility levels in sub‐Saharan Africa.

The International Network for the Demographic Evaluation of Populations and Their Health (INDEPTH) is a global network of research centers that conduct longitudinal health and demographic evaluation of populations in low- and middle-income countries (LMICs) currently in 52 health and demographic surveillance system (HDSS) field sites situated in sub-Saharan Africa (14 countries), Asia (India, Bangladesh, Thailand, Vietnam, and Indonesia), and Oceania (Papua New Guinea). Through this network of HDSS field sites, INDEPTH is capable of producing reliable longitudinal data about the lives of people in the research communities as well as how development policies and programs affect those lives. The aim of the INDEPTH Data Repository is to enable INDEPTH member centers and associated researchers to contribute and share fully documented, high-quality datasets with the scientific community and health policy makers.

AbstractIntroductionWhile it is widely acknowledged that family relationships can influence health outcomes, their impact on the uptake of individual health interventions is unclear. In this study, we quantified how the efficacy of a randomized health intervention is shaped by its pattern of distribution in the family network.MethodsThe "Home‐Based Intervention to Test and Start" (HITS) was a 2×2 factorial community‐randomized controlled trial in Umkhanyakude, KwaZulu‐Natal, South Africa, embedded in the Africa Health Research Institute's population‐based demographic and HIV surveillance platform (ClinicalTrials.gov # NCT03757104).The study investigated the impact of two interventions: a financial micro‐incentive and a male‐targeted HIV‐specific decision support programme. The surveillance area was divided into 45 community clusters. Individuals aged ≥15 years in 16 randomly selected communities were offered a micro‐incentive (R50 [$3] food voucher) for rapid HIV testing (intervention arm). Those living in the remaining 29 communities were offered testing only (control arm). Study data were collected between February and November 2018.Using routinely collected data on parents, conjugal partners, and co‐residents, a socio‐centric family network was constructed among HITS‐eligible individuals. Nodes in this network represent individuals and ties represent family relationships. We estimated the effect of offering the incentive to people with and without family members who also received the offer on the uptake of HIV testing. We fitted a linear probability model with robust standard errors, accounting for clustering at the community level.ResultsOverall, 15,675 people participated in the HITS trial. Among those with no family members who received the offer, the incentive's efficacy was a 6.5 percentage point increase (95% CI: 5.3−7.7). The efficacy was higher among those with at least one family member who received the offer (21.1 percentage point increase (95% CI: 19.9−22.3). The difference in efficacy was statistically significant (21.1–6.5 = 14.6%; 95% CI: 9.3−19.9).ConclusionsMicro‐incentives appear to have synergistic effects when distributed within family networks. These effects support family network‐based approaches for the design of health interventions.

BACKGROUND: South Africa implemented rapid and strict physical distancing regulations to minimize SARS-CoV-2 epidemic spread. Evidence on the impact of such measures on interpersonal contact in rural and lower-income settings is limited. METHODS: We compared population-representative social contact surveys conducted in the same rural KwaZulu-Natal location once in 2019 and twice in mid-2020. Respondents reported characteristics of physical and conversational ('close interaction') contacts over 24 hours. We built age-mixing matrices and estimated the proportional change in the SARS-CoV-2 reproduction number (R (0) ). Respondents also reported counts of others present at locations visited and transport used, from which we evaluated change in potential exposure to airborne infection due to shared indoor space ('shared air'). RESULTS: Respondents in March-December 2019 (n=1704) reported a mean of 7.4 close interaction contacts and 196 shared air person-hours beyond their homes. Respondents in June-July 2020 (n=216), as the epidemic peaked locally, reported 4.1 close interaction contacts and 21 shared air person-hours outside their home, with significant declines in others' homes and public spaces. Adults aged over 50 had fewer close contacts with others over 50, but little change in contact with 15-29 year olds, reflecting ongoing contact within multigenerational households. We estimate potential R (0) fell by 42% (95% plausible range 14-59%) between 2019 and June-July 2020. DISCUSSION: Extra-household social contact fell substantially following imposition of Covid-19 distancing regulations in rural South Africa. Ongoing contact within intergenerational households highlighted the limitation of social distancing measures in protecting older adults. FUNDING: Wellcome Trust, UKRI, DFID, European Union

BACKGROUND: South Africa implemented rapid and strict physical distancing regulations to minimize SARS-CoV-2 epidemic spread. Evidence on the impact of such measures on interpersonal contact in rural and lower-income settings is limited. METHODS: We compared population-representative social contact surveys conducted in the same rural KwaZulu-Natal location once in 2019 and twice in mid-2020. Respondents reported characteristics of physical and conversational ('close interaction') contacts over 24 hours. We built age-mixing matrices and estimated the proportional change in the SARS-CoV-2 reproduction number (R 0 ). Respondents also reported counts of others present at locations visited and transport used, from which we evaluated change in potential exposure to airborne infection due to shared indoor space ('shared air'). RESULTS: Respondents in March-December 2019 (n=1704) reported a mean of 7.4 close interaction contacts and 196 shared air person-hours beyond their homes. Respondents in June-July 2020 (n=216), as the epidemic peaked locally, reported 4.1 close interaction contacts and 21 shared air person-hours outside their home, with significant declines in others' homes and public spaces. Adults aged over 50 had fewer close contacts with others over 50, but little change in contact with 15-29 year olds, reflecting ongoing contact within multigenerational households. We estimate potential R 0 fell by 42% (95% plausible range 14-59%) between 2019 and June-July 2020. DISCUSSION: Extra-household social contact fell substantially following imposition of Covid-19 distancing regulations in rural South Africa. Ongoing contact within intergenerational households highlighted the limitation of social distancing measures in protecting older adults. FUNDING: Wellcome Trust, UKRI, DFID, European Union.

BACKGROUND: In March 2020 South Africa implemented strict non-pharmaceutical interventions (NPIs) to contain Covid-19. Over the subsequent five months NPIs were eased in stages according to national strategy. Covid-19 spread throughout the country heterogeneously, reaching rural areas by July and peaking in July-August. Data on the impact of NPI policies on social and economic wellbeing and access to healthcare is limited. We therefore analysed how rural residents of three South African provinces changed their behaviour during the first epidemic wave. METHODS: The South African Population Research Infrastructure Network (SAPRIN) nodes in Mpumalanga (Agincourt), KwaZulu-Natal (AHRI) and Limpopo (DIMAMO) provinces conducted longitudinal telephone surveys among randomly sampled households from rural and peri-urban surveillance populations every 2-3 weeks. Interviews included questions on: Covid-19 knowledge and behaviours; health and economic impact of NPIs; and mental health. RESULTS: 2262 households completed 10,966 interviews between April and August 2020. By August, self-reported satisfaction with Covid-19 knowledge had risen from 48% to 85% and facemask use to over 95%. As selected NPIs were eased mobility increased, and economic losses and anxiety and depression symptoms fell. When Covid-19 cases spiked at one node in July, movement dropped rapidly, and missed daily medication rates doubled. Economic concerns and mental health symptoms were lower in households receiving a greater number of government-funded old-age pensions. CONCLUSIONS: South Africans reported complying with stringent Covid-19 NPIs despite the threat of substantial social, economic and health repercussions. Government-supported social welfare programmes appeared to buffer interruptions in income and healthcare access during local outbreaks. Epidemic control policies must be balanced against impacts on wellbeing in resource-limited settings and designed with parallel support systems where they threaten income and basic service access.