How did the study come about? 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 Centre for Health and Population Studies in 1997 funded by a large 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. 1 The DSS, referred to as the Africa Centre Demographic Information System (ACDIS), started in 2000. In 2003, population-based HIV testing (also funded by the Wellcome Trust, UK) was started in ACDIS through annual surveys. In this article, we seek to describe the most salient features of ACDIS and the population-based HIV cohort and briefly present some of the most important results to date. What does the study cover? 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. 1 South Africa's political and economic history has resulted in highly mobile urban and rural populations, coupled with complex, fluid households. 2 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 MS-SQL Server database, in a truly longitudinal way—i.e. not as a series of cross-sections. For a comprehensive description of ACDIS and rationale for its design see Hosegood et al. 2 Where is the study area? The surveillance area (Figure 1) selected is located near the market town of Mtubatuba in the Umkanyakude district of KwaZulu-Natal. The area is 438 km2 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. Figure 1 Location of the study area in South Africa Who is in the sample? 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 (Figure 2) 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. 2 Figure 2 Age and sex profile of the surveillance population by residency, 30th June 2006 (n = 56, 791 residents; n = 29, 164 non-residents) Nested within the ACDIS cohort is the population-based HIV cohort. 3 Between 2003 and 2006 (three rounds of data collection), all women aged 15–49 years and men aged 15–54 years resident in the surveillance area were eligible for HIV testing. However, starting in 2007, eligibility was extended to cover all residents aged ≥15 years of age. In addition to the resident sample, a 12.5% stratified sample of non-residents (‘migrants’) is also included in each round of data collection. These non-resident study participants are sampled randomly into equally sized strata by sex and frequency of their presence pattern within the surveillance area (e.g. returns at month end). The total numbers of eligible residents and non-residents during the first three survey rounds are shown (Table 1). Table 1 Eligibility and uptake of the population-based HIV testing Residents Non-residents Eligible Contacted (%) Consented (%) a Eligible Contacted (%) Consented (%) a Round 1 (2003–2004) † 25 901 19 867 (77%) 11 551 (58%) 1952 916 (47%) 551 (60%) Round 2 (2005) 22 357 21 936 (98%) 8909 (41%) 2145 1468 (75%) 605 (41%) Round 3 (2006) 23 338 21 387 (92%) 8136 (38%) 1581 989 (63%) 410 (41%) aCalculated as a percentage of number of participants contacted. †Taken from Welz et al. 2007 3 . What has been measured? ACDIS has two separate cycles of data collection—household and individual. During the household data collection cycle, a set of questionnaires are routinely administered every 6 months (Table 2) to a key informant in each household. These questionnaires record key attributes and events regarding physical structures, households and individuals and their relationship to each other. Additional modules are administered occasionally (Table 3) and provide further descriptive variables for the subjects over time and are intended to extend and enhance the core dataset for specific research purposes. The HIV sero-survey (Table 4) comprises part of the individual data collection cycle (undertaken annually) and requires an interview with the eligible individual in person because of the sensitivity of the questions. Ethical approval for all data collected within the cohorts was obtained from the University of KwaZulu-Natal's Ethics Committee. For a complete list of the most recent questionnaires visit http://www.africacentre.ac.za/Default.aspx?tabid=69 Table 2 Data collected at each routine household visit, 2000 and ongoing Subject Types of information Homestead Latitude, longitude Owner Number of households Household Formation and dissolution Household head Individuals Individual details: inc. date of birth, sex, parents Household membership(s) Household members Update household list: members who join, leave or die Residency status: inc. pattern of return visits Marital and partnership status Relationship to household head Births Pregnancy outcomes: abortions, still and live births. Delivery environment: inc. assistance, place Birthweight, APGAR Deaths Location and care provision at time of death Open description of circumstances Migrations Details of place of origin or destination Type of migration, e.g. household or individual migration Child health On first birthday: vaccination history The forms used to collect this data are available at http://www.africacentre.ac.za/Default.aspx?tabid=69 Refer to forms, BSR, BSU, HHR, HHU, IDR. Table 3 Data collected occasionally during routine household visits, 2000–2007 Topic Types of information Frequency Eligibility criteria Household socio- economic data Household infra-structure: inc. water, sanitation, electricity Economic status: inc. household expenditure, asset ownership Annual 2001, 2003/2004/2005/2006 Household is resident in DSA on date of visit Individual socio-economic data Education Employment Annual 2001, 2003/2004/2005/2006 Individual is a member of a resident household on date of visit Child grants Receipt of government grants for children 2002 All households resident in DSA on date of visit The forms used to collect this data are available at http://www.africacentre.ac.za/Default.aspx?tabid=69 Refer to forms, HSEI,II,III,IV and CGR. Table 4 Data collected at each individual survey visit, 2000-ongoing Topic Types of information Frequency Eligibility criteria HIV status HIV status Reason for refusing test Annual 2003/2004, 2005/2006/2007 2003–2006: women 15–49 years, men 15–54 years 2007 women and men 15 years and older Sexual behaviour Pregnancy history (women only) Contraceptive use Sexual activity Attitudes to condom use Annual 2003/2004, 2005/2006/2007 2000–2003: women 15–49 years only. 2003–2006: women 15–49 years, men 15–54 years 2007 women and men 15 years and older Biomeasures Blood pressure Height and weight 2003/2004 only 2003/2004: women 15–49 years, men 15–54 years The forms used to collect this data are available at http://www.africacentre.ac.za/Default.aspx?tabid=69 Refer to forms, HIV and biomeasures (BMF) and sexual behaviour (WHL, MGH, WGH, MGH-E, WGH-E). How are the data collected? Before embarking on any data collection activity, all research initiatives at the Africa Centre are first discussed with a Community Advisory Board (CAB) for comment and feedback. The CAB consists of approximately 25 members chosen by the community and also provides a forum to discuss the results of specific studies and how best to disseminate these to the community. Since its inception, ACDIS has developed and maintained geographical information systems (GIS) capacity that allows the spatial analysis of any of the variables collected. All homesteads and facilities in the study area have been mapped by fieldworkers using differential global positioning systems (to an accuracy of 99%. For the HIV survey, the contact rates for residents and non-residents improved in the second round compared with Round 1 (Table 1). However, the consent rate to test for HIV decreased from approximately 60% in 2003–2004 to 40% in 2005 and 2006 and raises concerns about selection bias. We are addressing these issues both operationally and analytically. Operationally, the HIV surveillance is implementing a range of activities to increase consent rates, such as rapid testing and home-based delivery of test results. Analytically, Africa Centre researchers are using information about demographic, socio-economic and behavioural characteristics that are available both for those individuals who do consent to an HIV test as well as for those who do not consent, in order to diagnose and adjust for selection effects. 8,9 This information is used to characterize differences across observable variables between consenters and non-consenters, to take into account selection effects when it seems reasonable to assume that missingness is at random [e.g. through multiple imputations (MIs) 10 ] and when random missingness cannot be assumed (e.g. through Heckman-type selection models 11 ). We are able to use the latter approach because the detailed operational information available to researchers includes a number of variables that are relevant and likely valid exclusion restrictions in selection models. 12 What has it found? ACDIS data have been extensively used to provide empirical evidence about the demographic and social impact of the HIV epidemic in a severely affected population. HIV/AIDS has considerably increased mortality rates in the study population and significantly reduced life expectancy at birth. By 2000, the probability of dying between the ages 16 and 60 years was estimated at 58% for women and 75% for men. 13 However, a recent study has suggested that the upward trend in mortality rates is being reversed by the ART programme which has contributed significantly to an increase in life expectancy. 14 Studies using the verbal autopsies show that the leading cause of death is AIDS, followed by non-communicable diseases. In 2000, AIDS caused 73 and 61% of the female and male deaths, respectively among the 15–44 age groups. Among males, deaths from injuries were high. 13 Studies of fertility in the surveillance area show marked declines during the late 1990s (TFR 4.4) and early 2000s but have recently stalled at around three births per woman. 11,15 Unlike in other countries, the stall is correlated neither with levels of education, nor contraceptive use. We find no evidence of a strong substitution of contraceptive methods. The impact of HIV on these changes in the level and pattern of fertility is not yet clear. Early findings suggest that HIV prevalence is subsidiary to the main determinants of fertility (socio-economic, social and demographic). In contrast to overall fertility decline, fertility among adolescents has remained largely stable over the last decade. 16 Studies using ACDIS data to investigate the socio-demographic impacts of HIV have considered a wide range of outcomes including orphanhood, household composition and dissolution, migration, education and grant uptake. In rural South Africa, a large proportion of children live apart from their mothers and fathers due to labour migration, child migration related to care giving and schooling, parental separation and divorce and orphanhood. 17,18 HIV makes this phenomenon more pronounced through its impact on parental survival. Between 2000 and 2005 there was a doubling of orphanhood, which is also related to the high AIDS-related mortality. There was an increase from 9 to 12% (n = 3499), from 3 to 6% (n = 1656) and from 1 to 4% (n = 1031) in paternal, maternal and double orphans, respectively. 19,20 Adverse consequences for children experiencing parental death have been shown in their education achievement, access to welfare support and increased mobility. 21–23 In terms of the impact on households, contrary to a widely anticipated consequence of the epidemic, there is a little evidence from ACDIS that high adult mortality has resulted in a substantial increase in extreme household forms such as child-headed or skipped-generation households. 20,23 Rather, HIV- and AIDS-affected households experience negative consequences in relation to their survival and ability to migrate, 24 economic resources, 21,22 isolation and conflict and ability to respond to subsequent deaths or financial shocks. 24,25 Data from the Household Socio-Economic Surveys have been used to measure the economic well being of households in the DSA. The socio-economic indicators show improvement between 2001 and 2006 (Table 5). The area has experienced a marked increase in the provision of electricity, water and sanitation in the past 5 years. Access to toilet facilities (primarily through access to improved pit latrines) increased from 61 to 77% of households over this period. Households reporting piped water almost doubled—from 43 to 78%, and electricity increased from 50 to 62% of households. Table 5 Household socio-economic (HSE) data collected between 2001 and 2006 HSE1 HSE2 HSE3 HSE4 Collection dates 2001 2003/4 2005 2006 Number of households 10 826 10 806 9736 9140 Government services: Fraction of households with: Electricity 0.5 0.54 0.59 0.62 Piped water 0.43 0.55 0.67 0.78 Toilet 0.61 0.75 0.75 0.77 The population-based HIV survey is the first of its kind in South Africa to investigate the prevalence of HIV in a rural area among residents and non-residents. It shows some of the highest population-based infection rates ever documented worldwide (Figure 3). Prevalence peaked at 51% (95% CI 47–55%) among women aged 25–29 and 44% (95% CI 38–49%) in men aged 30–34. 3 Non-resident men are nearly twice as likely (adjusted OR = 1.8) to be infected in comparison with their resident counterparts; whilst the corresponding ratio for women is 1.5. 3 The disease is far from uniform geographically. Informal settlements located near the National Road have the highest prevalence (>35%); whilst the more inaccessible rural areas are characterized by the lowest prevalence ( 50 years) from 2007 is a further strength and will generate important knowledge on the impact of the HIV epidemic in these neglected age groups. It has become widely acknowledged that processes that go beyond the individual are responsible for the rapid spread of HIV in Africa. 27 Another key strength of the cohorts is the production of detailed comprehensive information at different levels: the community, the household and the individual. Few, if any, sites in Africa have this degree of depth or breadth of information. This allows hierarchical statistical approaches to investigate multi-level determinants of outcomes such as HIV infection. A further strength lies in the opportunity to quickly evaluate the impact of the ART programme on demographic indicators collected in ACDIS. The Africa Centre partners with the Department of Health in the PEPFAR-funded ART programme in the government hospital and 14 peripheral primary health care clinics in the surrounding area. 14 In future, the cohorts will be of vital importance for assessment of interventions for African populations. The ART programme, expanding rapidly in Africa and other developing countries, is the largest public health programme ever contemplated on the continent, but the evidence-base from Africa is severely limited. The cohorts provide a unique platform to monitor and comprehensively evaluate the effects of ART delivery at both a population and individual level. Can I get hold of the data? Where can I find out more? ACDIS data are easily and widely accessible, through a suite of datasets and accompanying documentation posted on the Africa Centre website (http://www.africacentre.ac.za). Dataset documentation includes the definition of variables and the questionnaires which were used for data collection. Use of ACDIS data is however on the basis of a collaborative principle. Collaborators sign a Data Use Agreement to be able to utilize ACDIS data and all analyses are conducted in collaboration with members of the Africa Centre. In addition, each data use request must be accompanied with an Analysis Plan. The Analysis Plan and the Data Use Agreement are submitted to the Director and are discussed and approved internally, through the surveillance scientific meeting.
