Search

BACKGROUND: As the HIV epidemic in sub-Saharan Africa matures, evidence about the age distribution of new HIV infections and how this has changed over the epidemic is needed to guide HIV prevention. We assessed trends in age-specific HIV incidence in six population-based cohort studies in eastern and southern Africa, reporting changes in average age at infection, age distribution of new infections, and birth cohort cumulative incidence. METHODS: We used a Bayesian model to reconstruct age-specific HIV incidence from repeated observations of individuals’ HIV serostatus and survival collected among population HIV cohorts in rural Malawi, South Africa, Tanzania, Uganda, and Zimbabwe. The HIV incidence rate by age, time and sex was modelled using smooth splines functions. Incidence trends were estimated separately by sex and study. Estimated incidence and prevalence results for 2000-2017, standardised to study population distribution, were used to estimate average age at infection and proportion of new infections by age. FINDINGS: Age-specific incidence declined at all ages, though the timing and pattern of decline varied by study. The average age at infection was higher in men (cohort means: 27·8-34·6 years) than women (cohort means: 24·8-29·6 years). Between 2000 and 2017, the average age at infection increased slightly: cohort means 0·5-2·8 years among men and -0·2-2·5 years among women. Across studies, between 38-63% (cohort means) of women’s infections were among 15-24-year-olds and between 30-63% of men’s infections were in 20-29-year-olds. Lifetime risk of HIV declined for successive birth cohorts. INTERPRETATION: HIV incidence declined in all age groups and shifted slightly, but not dramatically, to older ages. Disproportionate new HIV infections occur among 15-24-year-old women and 20-29-year-old men, supporting focused prevention in these groups. But 40-60% of infections were outside these ages, emphasising the importance of providing appropriate HIV prevention to adults of all ages. FUNDING: Bill and Melinda Gates Foundation.

We examined the HIV care cascade in a community-based cohort study in Kisesa, Magu, Tanzania. We analyzed the proportion achieving each stage of the cascade - Seroconversion, Awareness of HIV status, Enrollment in Care and Antiretroviral therapy (ART) initiation - and estimated the median and interquartile range for the time for progression to the next stage. Modified Poisson regression was used to estimate prevalence risk ratios for enrollment in care and initiation of ART. From 2006 to 2017, 175 HIV-seroconverters were identified. 140 (80%) knew their HIV status, of whom 97 (69.3%) were enrolled in HIV care, and 87 (49.7%) had initiated ART. Time from seroconversion to awareness of HIV status was 731.3 [475.5-1345.8] days. Time from awareness to enrollment was 7 [0-64] days, and from enrollment to ART initiation was 19 [3-248] days. There were no demographic differences in enrollment in care or ART initiation. Efforts have been focusing on shortening time from seroconversion to diagnosis, mostly by increasing the number of testing clinics available. We recommend increased systematic testing to reduce time from seroconversion to awareness of status, and by doing so speed up enrollment into care. Interventions that increase enrollment are likely to have the most impact in achieving UNAIDS targets.

HIV care and treatment clinics (CTC) are important for management of HIV morbidity and mortality, and to reduce HIV transmission. Enrollment in HIV care and treatment clinics remains low in many developing countries. We followed up 632 newly diagnosed HIV patients aged 15 years and above from Magu District, Tanzania. Logistic regression was used to assess factors significantly associated with enrollment for CTC services. Kaplan-Meier plots and log-rank tests were used to evaluate differences in timing uptake of services. Among 632 participants, 214 (33.9%) were enrolled in CTC, and of those enrolled 120 (56.6%) took longer than 3 months to enroll. Those living in more rural villages were less likely to be enrolled than in the villages with semi-urban settings (OR 0.36; 95% CI 0.17-0.76). Moreover, those with age group 35-44 years and with age group 45 years and above were 2 times higher odds compared to those with age group 15-24 years, (OR 2.03; 95% CI 1.05-3.91) and (OR 2.69; 95% CI 1.40-5.18) respectively. Enrollment in the CTC in Tanzania is low. To increase uptake of antiretroviral therapy, it is critical to improve linkage between HIV testing and care services, and to rollout these services into the primary health facilities.

Linking a health and demographic surveillance system (HDSS) to data from a health facility that serves the HDSS population generates a research infrastructure for directly observed data on access to and utilization of health facility services. Many HDSS sites, however, are in areas that lack unique national identifiers or suffer from data quality issues, such as incomplete records, spelling errors, and name and residence changes, all of which complicate record linkage approaches when applied retrospectively. We developed Point-of-contact Interactive Record Linkage (PIRL) software that is used to prospectively link health records from a local health facility to an HDSS in rural Tanzania. This prospective approach to record linkage is carried out in the presence of the individual whose records are being linked, which has the advantage that any uncertainty surrounding their identity can be resolved during a brief interaction, whereby extraneous information (e.g., household membership) can be referred to as an additional criterion to adjudicate between multiple potential matches. Our software uses a probabilistic record linkage algorithm based on the Fellegi-Sunter model to search and rank potential matches in the HDSS data source. Key advantages of this software are its ability to perform multiple searches for the same individual and save patient-specific notes that are retrieved during subsequent clinic visits. A search on the HDSS database (n=110,000) takes less than 15 seconds to complete. Excluding time spent obtaining written consent, the median duration of time we spend with each patient is six minutes. In this setting, a purely automated retrospective approach to record linkage would have only correctly identified about half of the true matches and resulted in high linkage errors; therefore highlighting immediate benefit of conducting interactive record linkage using the PIRL software.

BackgroundUNAIDS official estimates of national HIV prevalence are based on trends observed in antenatal clinic surveillance, after adjustment for the reduced fertility of HIV positive women. Uptake of ART may impact on the fertility of HIV positive women, implying a need to re-estimate the adjustment factors used in these calculations. We analyse the effect of antiretroviral therapy (ART) provision on population-level fertility in Southern and East Africa, comparing trends in HIV infected women against the secular trends observed in uninfected women.MethodsWe used fertility data from four community-based demographic and HIV surveillance sites: Kisesa (Tanzania), Masaka and Rakai (Uganda) and uMkhanyakude (South Africa). All births to women aged 15–44 years old were included in the analysis, classified by mother’s age and HIV status at time of birth, and ART availability in the community. Calendar time period of data availability relative to ART Introduction varied across the sites, from 5 years prior to ART roll-out, to 9 years after. Calendar time was classified according to ART availability, grouped into pre ART, ART introduction (available in at least one health facility serving study site) and ART available (available in all designated health facilities serving study site). We used Poisson regression to calculate age adjusted fertility rate ratios over time by HIV status, and investigated the interaction between ART period and HIV status to ascertain whether trends over time were different for HIV positive and negative women.ResultsAge-adjusted fertility rates declined significantly over time for HIV negative women in all four studies. However HIV positives either had no change in fertility (Masaka, Rakai) or experienced a significant increase over the same period (Kisesa, uMkhanyakude). HIV positive fertility was significantly lower than negative in both the pre ART period (age adjusted fertility rate ratio (FRR) range 0.51 95%CI 0.42–0.61 to 0.73 95%CI 0.64–0.83) and when ART was widely available (FRR range 0.57 95%CI 0.52–0.62 to 0.83 95%CI 0.78–0.87), but the difference has narrowed. The interaction terms describing the difference in trends between HIV positives and negatives are generally significant.ConclusionsDifferences in fertility between HIV positive and HIV negative women are narrowing over time as ART becomes more widely available in these communities. Routine adjustment of ANC data for estimating national HIV prevalence will need to allow for the impact of treatment.

The Magu Health and Demographic Surveillance System (Magu HDSS) is part of Kisesa OpenCohort HIV Study located in a rural area of North-Western Tanzania. Since its establishment in 1994, information on pregnancies, births, marriages, migrations and deaths have been monitored and updated between one and three times a year by trained fieldworkers. Other research activities implemented in the cohort include: sero surveys which have been conducted every 2–3 years to collect socioeconomic data, HIV sero status and health knowledge attitude and behaviour in adults aged 15 years or more living in the area; verbal autopsy (VA) interviews conducted to establish cause of death in all deaths encountered in the area; Llnking data collected at health facilities to community-based data; monitoring voluntary counselling and testing (VCT); and assessing uptake of antiretroviral treatment (ART). In addition, within the community, qualitative studies have been conducted to address issues linked to HIV stigma, the perception of ART access and adherence. In 2014, the population was over 35 000 individuals. Magu HDSS has contributed to Tanzanian estimates of fertility and mortality, and is a member of the INDEPTH network. Demographic data for Magu HDSS are available via the INDEPTH Network’s Sharing and Accessing Repository (iSHARE) and applications to access HDSS data for collaborative analysis are encouraged.