Suchergebnisse

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.

AbstractIntroductionIn South Africa, the HIV care cascade remains suboptimal. We investigated the impact of small conditional financial incentives (CFIs) and male‐targeted HIV‐specific decision‐support application (EPIC‐HIV) on the HIV care cascade.MethodsIn 2018, in uMkhanyakude district, 45 communities were randomly assigned to one of four arms: (i) CFI for home‐based HIV testing and linkage to care within 6 weeks (R50 [US$3] food voucher each); (ii) EPIC‐HIV which are based on self‐determination theory; (iii) both CFI and EPIC‐HIV; and (iv) standard of care. EPIC‐HIV consisted of two components: EPIC‐HIV 1, provided to men through a tablet before home‐based HIV testing, and EPIC‐HIV 2, offered 1 month later to men who tested positive but had not yet linked to care. Linking HITS trial data to national antiretroviral treatment (ART) programme data and HIV surveillance programme data, we estimated HIV status awareness after the HITS trial implementation, ART status 3 month after the trial and viral load suppression 1 year later. Analysis included all known individuals living with HIV in the study area including those who did not participated in the HITS trial.ResultsAmong the 33,778 residents in the study area, 2763 men and 7266 women were identified as living with HIV by the end of the intervention period and included in the analysis. After the intervention, awareness of HIV‐positive status was higher in the CFI arms compared to non‐CFI arms (men: 793/908 [87.3%] vs. 1574/1855 [84.9%], RR = 1.03 [95% CI: 0.99−1.07]; women: 2259/2421 [93.3%] vs. 4439/4845 [91.6%], RR = 1.02 [95% CI: 1.00−1.04]). Three months after the intervention, no differences were found for linkage to ART between arms. One year after the intervention, only 1829 viral test results were retrieved. Viral suppression was higher but not significant in the EPIC‐HIV intervention arms among men (65/99 [65.7%] vs. 182/308 [59.1%], RR = 1.11 [95% CI: 0.88−1.40]).ConclusionsSmall CFIs can contribute to achieve the first step of the HIV care cascade. However, neither CFIs nor EPIC‐HIV was sufficient to increase the number of people on ART. Additional evidence is needed to confirm the impact of EPIC‐HIV on viral suppression.

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.