Samuel J. Clark

Profile: Agincourt Health and Socio-demographic Surveillance System

The Agincourt health and socio-demographic surveillance system (HDSS), located in rural northeast South Africa close to the Mozambique border, was established in 1992 to support district health systems development led by the post-apartheid ministry of health. The HDSS (90 000 people), based on an annual update of resident status and vital events, now supports multiple investigations into the causes and consequences of complex health, population and social transitions. Observational work includes cohorts focusing on different stages along the life course, evaluation of national policy at population, household and individual levels and examination of household responses to shocks and stresses and the resulting pathways influencing health and well-being. Trials target children and adolescents, including promoting psycho-social well-being, preventing HIV transmission and reducing metabolic disease risk. Efforts to enhance the research platform include using automated measurement techniques to estimate cause of death by verbal autopsy, full ‘reconciliation’ of in- and out-migrations, follow-up of migrants departing the study area, recording of extra-household social connections and linkage of individual HDSS records with those from sub-district clinics. Fostering effective collaborations (including INDEPTH multi-centre work in adult health and ageing and migration and urbanization), ensuring cross-site compatibility of common variables and optimizing public access to HDSS data are priorities.

Implications of mortality transition for primary health care in rural South Africa: a population-based surveillance study.

Summary Background In southern Africa, a substantial health transition is underway, with the heavy burden of chronic infectious illness (HIV/AIDS and tuberculosis) paralleled by the growing threat of non-communicable diseases. We investigated the extent and nature of this health transition and considered the implications for primary health care. Methods Health and sociodemographic surveillance started in the Agincourt subdistrict, rural South Africa, in 1992. In a population of 70 000, deaths (n=6153) were rigorously monitored with a validated verbal autopsy instrument to establish probable cause. We used age-standardised analyses to investigate the dynamics of the mortality transition by comparing the period 2002–05 with 1992–94. Findings Mortality from chronic non-communicable disease ranked highest in adults aged 50 years and older in 1992–94 (41% of deaths [123/298]), whereas acute diarrhoea and malnutrition accounted for 37% of deaths (59/158) in children younger than 5 years. Since then, all-cause mortality increased substantially (risk ratio 1·87 [95% CI 1·73–2·03]; p<0·0001) because of a six-fold rise in deaths from infectious disease affecting most age and sex groups (5·98 [4·85–7·38]; p<0·0001), and a modest increase in deaths from non-communicable disease (1·15 [0·99–1·33]; p=0·066). The change in female risk of death from HIV and tuberculosis (15·06 [8·88–27·76]; p<0·0001) was almost double that of the change in male risk (8·13 [5·55–12·36]; p<0·0001). The burden of disorders requiring chronic care increased disproportionately compared with that requiring acute care (2·63 [2·30–3·01]; p<0·0001 vs 1·31 [1·12–1·55]; p=0·0003). Interpretation Mortality from non-communicable disease remains prominent despite the sustained increase in deaths from chronic infectious disease. The implications for primary health-care systems are substantial, with integrated chronic care based on scaled-up delivery of antiretroviral therapy needed to address this expanding burden. Funding The Wellcome Trust, UK; University of the Witwatersrand, Medical Research Council, and Anglo American and De Beers Chairmans Fund, South Africa; the European Union; Andrew W Mellon Foundation, Henry J Kaiser Family Foundation, and National Institute on Aging, National Institutes of Health, USA.

Research into health, population and social transitions in rural South Africa: Data and methods of the Agincourt Health and Demographic Surveillance System

Rationale for study: Vital registration is generally lacking in infrastructurally weak areas where health and development problems are most pressing. Health and demographic surveillance is a response to the lack of a valid information base that can provide high-quality longitudinal data on population dynamics, health, and social change to inform policy and practice. Design and measurement procedures: Continuous demographic monitoring of an entire geographically defined population involves a multi-round, prospective community study, with annual recording of all vital events (births, deaths, migrations). Status observations and special modules add value to particular research areas. A verbal autopsy is conducted on every death to determine its probable cause. A geographic surveillance system supports spatial analyses, and strengthens field management. Population and sample size considerations: Health and demographic surveillance covers the Agincourt sub-district population, sited in rural north-eastern South Africa, of some 70,000 people (nearly a third are Mozambican immigrants) in 21 villages and 11,700 households. Data enumerated are consistent or more detailed when compared with national sources; strategies to improve incomplete data, such as counts of perinatal deaths, have been introduced with positive effect. Basic characteristics: A major health and demographic transition was documented over a 12-year period with marked changes in population structure, escalating mortality, declining fertility, and high levels of temporary migration increasing particularly amongst women. A dual burden of infectious and non-communicable disease exists against a background of dramatically progressing HIV/AIDS. Potential and research questions: Health and demographic surveillance sites — fundamental to the INDEPTH Network — generate research questions and hypotheses from empirical data, highlight health, social and population priorities, provide cost-effective support for diverse study designs, and track population change and the impact of interventions over time.

Returning home to die: Circular labour migration and mortality in South Africa

Aim: To examine the hypothesis that circular labour migrants who become seriously ill while living away from home return to their rural homes to convalesce and possibly to die. Methods: Drawing on longitudinal data collected by the Agincourt health and demographic surveillance system in rural northeastern South Africa between 1995 and 2004, discrete time event history analysis is used to estimate the likelihood of dying for residents, short-term returning migrants, and long-term returning migrants controlling for sex, age, and historical period. Results: The annual odds of dying for short-term returning migrants are generally 1.1 to 1.9 times (depending on period, sex, and age) higher than those of residents and long-term returning migrants, and these differences are generally highly statistically significant. Further supporting the hypothesis is the fact that the proportion of HIV/TB deaths among short-term returning migrants increases dramatically as time progresses, and short-term returning migrants account for an increasing proportion of all HIV/TB deaths. Conclusions: This evidence strongly suggests that increasing numbers of circular labour migrants of prime working age are becoming ill in the urban areas where they work and coming home to be cared for and eventually to die in the rural areas where their families live. This shifts the burden of caring for them in their terminal illness to their families and the rural healthcare system with significant consequences for the distribution and allocation of health care resources.

Strengthening standardised interpretation of verbal autopsy data: the new InterVA-4 tool.

Background : Verbal autopsy (VA) is the only available approach for determining the cause of many deaths, where routine certification is not in place. Therefore, it is important to use standards and methods for VA that maximise efficiency, consistency and comparability. The World Health Organization (WHO) has led the development of the 2012 WHO VA instrument as a new standard, intended both as a research tool and for routine registration of deaths. Objective : A new public-domain probabilistic model for interpreting VA data, InterVA-4, is described, which builds on previous versions and is aligned with the 2012 WHO VA instrument. Design : The new model has been designed to use the VA input indicators defined in the 2012 WHO VA instrument and to deliver causes of death compatible with the International Classification of Diseases version 10 (ICD-10) categorised into 62 groups as defined in the 2012 WHO VA instrument. In addition, known shortcomings of previous InterVA models have been addressed in this revision, as well as integrating other work on maternal and perinatal deaths. Results : The InterVA-4 model is presented here to facilitate its widespread use and to enable further field evaluation to take place. Results from a demonstration dataset from Agincourt, South Africa, show continuity of interpretation between InterVA-3 and InterVA-4, as well as differences reflecting specific issues addressed in the design and development of InterVA-4. Conclusions : InterVA-4 is made freely available as a new standard model for interpreting VA data into causes of death. It can be used for determining cause of death both in research settings and for routine registration. Further validation opportunities will be explored. These developments in cause of death registration are likely to substantially increase the global coverage of cause-specific mortality data. To access the supplementary material to this article ‘The InterVA-4 User Guide’ please see Supplementary files under Article Tools online.

Prevalence of HIV among those 15 and older in rural South Africa

A greater knowledge of the burden of HIV in rural areas of Southern Africa is needed, especially among older adults. We conducted a cross-sectional biomarker survey in the rural South African Agincourt Health and Socio-demographic Surveillance site in 2010–2011 and estimated HIV prevalence and risk factors. Using an age–sex stratified random sample of ages 15+, a total of 5037 (65.7%) of a possible 7662 individuals were located and 4362 (86.6%) consented to HIV testing. HIV prevalence was high (19.4%) and characterized by a large gender gap (10.6% for men and 23.9% for women). Rates peaked at 45.3% among men and 46.1% among women – both at ages 35–39. Compared with a similar study in the rural KwaZulu-Natal Province, South Africa, peak prevalence occurred at later ages, and HIV prevalence was higher among older adults – with rates above 15% for men and 10% for women through to age 70. High prevalence continues to characterize Southern Africa, and recent evidence confirms that older adults cannot be excluded from policy considerations. The high prevalence among older adults suggests likely HIV infection at older ages. Prevention activities need to expand to older adults to reduce new infections. Treatment will be complicated by increased risk of noncommunicable diseases and by increasing numbers of older people living with HIV.

Probabilistic Projections of the Total Fertility Rate for All Countries

We describe a Bayesian projection model to produce country-specific projections of the total fertility rate (TFR) for all countries. The model decomposes the evolution of TFR into three phases: pre-transition high fertility, the fertility transition, and post-transition low fertility. The model for the fertility decline builds on the United Nations Population Division’s current deterministic projection methodology, which assumes that fertility will eventually fall below replacement level. It models the decline in TFR as the sum of two logistic functions that depend on the current TFR level, and a random term. A Bayesian hierarchical model is used to project future TFR based on both the country’s TFR history and the pattern of all countries. It is estimated from United Nations estimates of past TFR in all countries using a Markov chain Monte Carlo algorithm. The post-transition low fertility phase is modeled using an autoregressive model, in which long-term TFR projections converge toward and oscillate around replacement level. The method is evaluated using out-of-sample projections for the period since 1980 and the period since 1995, and is found to be well calibrated.

Probabilistic projections of HIV prevalence using Bayesian melding

The Joint United Nations Programme on HIV/AIDS (UNAIDS) has developed the Estimation and Projection Package (EPP) for making national estimates and short term projections of HIV prevalence based on observed prevalence trends in antenatal clinics. Understanding uncertainty in its projections and related quantities is important for more informed policy decision making. We propose using Bayesian melding to assess the uncertainty around the EPP predictions. Prevalence data as well as information on the input parameters of the EPP model are used to derive probabilistic HIV prevalence projections - a probability distribution on a set of future prevalence trajectories. We relate antenatal clinic prevalence to population prevalence and account for variability between clinics using a random effects model. Predictive intervals for clinic prevalence are derived for checking the model. We discuss predictions given by the EPP model and the results of the Bayesian melding procedure for Uganda where prevalence peaked at around 28% in 1990; the 95% prediction interval for 2010 ranges from 1% to 7%. (authors)

Mathematical models for HIV transmission dynamics: tools for social and behavioral science research.

HIV researchers have long appreciated the need to understand the social and behavioral determinants of HIV-related risk behavior, but the cumulative impact of individual behaviors on population-level HIV outcomes can be subtle and counterintuitive, and the methods for studying this are rarely part of a traditional social science or epidemiology training program. Mathematical models provide a way to examine the potential effects of the proximate biologic and behavioral determinants of HIV transmission dynamics, alone and in combination. The purpose of this article is to show how mathematical modeling studies have contributed to our understanding of the dynamics and disparities in the global spread of HIV. Our aims are to demonstrate the value that these analytic tools have for social and behavioral sciences in HIV prevention research, to identify gaps in the current literature, and to suggest directions for future research.

Child mobility, maternal status, and household composition in rural South Africa.

This article examines the influence of maternal status, socioeconomic status of the household, and household composition on the mobility of children aged 0–14 in Mpumalanga Province, South Africa, from 1999 to 2008. Using data from the Agincourt Health and Demographic Surveillance System, we found that children whose mothers were temporary migrants, living elsewhere, or dead had higher odds of moving than children whose mothers were coresident. Older children and children living in richer households faced lower odds of mobility. For children whose mothers were coresident, there was no effect of maternal substitutes on child mobility. However, among children whose mothers were temporary migrants or living elsewhere, the presence of prime-aged and elderly females lowered the odds of mobility. For maternal orphans, the presence of elderly women in the household lowered their odds of mobility. The results underscore the importance of examining the conditions under which children move in order to strengthen service delivery targeted at safeguarding children’s well-being.