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Innovations in health and demographic surveillance systems to establish the causal impacts of HIV policies

Herbst, Kobusa,b; Law, Matthewc; Geldsetzer, Pascald; Tanser, Franka,b,e; Harling, Guyd; Bärnighausen, Tilla,b,d

Author Information
Current Opinion in HIV and AIDS: November 2015 - Volume 10 - Issue 6 - p 483-494
doi: 10.1097/COH.0000000000000203
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Together with HIV treatment cohorts, health and demographic surveillance systems (HDSSs) have made important contributions to our understanding of the HIV epidemic and the impact of interventions against it [1–3]. The article discusses innovations in data collection in HDSS to create novel opportunities to generate data on the HIV treatment cascade and to establish the causal impacts of policies and interventions intended to improve progression through the cascade.

HDSS (dynamic longitudinal population-based cohorts) [4–6] follow entire geographically defined populations through regular household surveys to establish a longitudinal database of individuals and social units in surveillance areas. These population-based and open cohorts allow the monitoring of population mortality and life expectancy over time [7,8], and consequently the impact of interventions such as the introduction of antiretroviral therapy (ART) on mortality and life expectancy [3,9], as well as on a wide range of economic, social and behavioural outcomes. Typically in HDSS, verbal autopsies [10,11,12▪] are used to determine cause-specific mortality including HIV-related mortality [13,14,15▪]. Some HDSS regularly collect HIV serostatus from the survey population [16,17] providing further insight into the epidemiology of HIV, such as the direct measurement of HIV incidence [18], the spatial distribution of HIV risk [19] and treatment uptake among HIV-infected individuals from a population perspective. HDSS data has previously been used to show the full population impacts of ART on life expectancy [3] and HIV transmission [2], as well as the causal effects of ART on employment [20], education, contraception [21] and healthcare seeking [22]. The ability to study causal impacts of ART on outcome variables collected in HDSS is gained through data linkage between the population-based HDSS data and clinical HIV treatment exposure. HDSS data can be linked to two broad categories of clinical cohorts: health systems cohorts of patients in routine care and treatment cohorts specifically designed for research.

Box 1
Box 1:
no caption available

Health system data collection

Of the approximately 12 million people globally receiving ART, more than 8 million live in sub-Saharan Africa (SSA) [23]. However, health services in SSA are often overburdened and high-quality medical records are not typically available. Nevertheless, as the region with the highest HIV burden, it is in SSA where the biggest need exists for accurate data on the treatment cascade. The Africa Centre for Health and Population Studies [24] and other HDSS in SSA have pioneered linkage of routine HIV treatment health systems data to population-based data at the level of the individual. Typically, these initiatives have included investments in improved public sector data collection systems and extraction of data from patient records. Electronic record systems can play an important role in facilitating such data linkage [25–31]. Although potentially limited in content, when widely implemented and of sufficient quality, electronic medical records capturing the care of patients in routine HIV care have the benefit of providing access to much larger patient numbers than can be managed through treatment cohorts that are specifically funded and managed for research purposes. In South Africa, the country with the highest number of individuals on ART, approximately two-thirds of the nationwide close to 4000 public sector ART clinics have fully implemented an electronic patient register [32]. Rolling out electronic record systems more widely across SSA could benefit both clinical care, and our ability to understand how ART is affecting outcomes in routine care and at the population level.

Specific treatment cohorts

In addition to ‘health systems’ treatment cohorts, there are essentially two types of treatment cohort designs commonly adopted in HIV research. First, there is the traditional fixed cohort (such as the Multicenter AIDS Cohort Study or MACS [33]), in which patients are specifically recruited and follow a defined visit structure, such as six or 12 monthly, with standardized assessments made on all patients at each visit. Second, and much more common, are observational cohorts (such as EuroSIDA [34] and the Australian HIV Observational Database or AHOD [35]), in which patients are passively followed, and data is largely collected through routine medical care visits. Fixed cohorts have the advantage of standardized measurements on all patients at a fixed visit structure, making statistical analysis and inferences easier. Observational cohorts, often based on electronic medical records, are much cheaper and easier to maintain, and arguably more accurately reflect true patient care and outcomes. Larger multicohort collaborations have also been successfully established. The Data Collection on Adverse Events of Anti-HIV Drugs study is a multicohort collaboration across Europe, America and Australia [36], which assesses the effect of ART on long-term clinical outcomes, such as cardiovascular disease (CVD), cancer, and liver and kidney failure.

The relationship between ‘health systems’ cohorts and specific treatment cohorts maintained primarily for research purposes is fluid, because specific treatment cohorts are often build on health systems core data. For instance, in the International Epidemiological Database to Evaluate AIDS data from many different sites, including public sector HIV treatment programmes, are pooled and jointly curated [37,38], allowing research across time and different geographical regions.

Observational cohort data have been, and will remain, useful in monitoring how changes in treatment and management guidelines actually result in changes in treatment of HIV-positive patients. For example, treatment guidelines have rapidly changed over the last few years from starting ART at CD4 less than 200 cells/μl, to CD4 less than 350 cells/μl, to CD4 less than 500 cells/μl, and more recently in some countries to no CD4 threshold in key populations. The International Epidemiological Database to Evaluate AIDS network was able to assess how CD4 cell count at initiation of ART has actually changed in response to these changes in guidelines, showing that although there have been some trends to earlier ART, treatment initiation still mostly occurs relatively late – at median CD4 cell counts of less than 150 cells/μl in low- and middle-income countries [39].

In this paper, we focus on innovations in data collection and data linkage in those HDSS cohorts that have been linked to HIV treatment data available in the routine health system and in specific treatment cohorts. A number of approaches to establish causal relationships have recently been adapted for use with this type of longitudinal data and are increasingly applied to answer causal questions regarding the HIV treatment cascade [40]. These approaches include fixed-effects analyses [41], marginal structural models [42], instrumental variable analyses and regression discontinuity analysis [43–45]. These approaches are discussed in another article in this issue (Bor et al., pp. 495–501). The objective of this article is to introduce novel data opportunities to provide better exposure and outcome measures for causal evaluation of the impacts of HIV treatment as well as health policies and interventions to improve progression through the HIV treatment cascade.


To establish causal relationships related to the HIV treatment cascade, the following are needed: data on cascade exposures and outcomes, the ability to link these exposures and outcome data within relevant units of observation, and data structures and approaches sufficient for causal inference. Here, we focus on innovations in building the data infrastructure to enable causal analyses of the HIV treatment cascade using health systems and routine treatment cohort data; Bor et al.'s article in this issue focuses on novel analytical approaches to establish causality in quasiexperimental studies of interventions to improve the HIV treatment cascade.


Additional biomeasures

To establish the causal impacts of HIV treatment, policies and interventions to improve the HIV treatment cascade at the population level, a range of biomarkers of disease will be useful [46]. For instance, as a result of the ART scaleup in SSA, a new population is emerging: older adults in SSA who have lived with HIV for more than a decade and have received ART for many years [47]. After HIV, CVD and diabetes are already the most common cause of death and premature mortality in many countries in SSA, such as South Africa [48,49], and it is expected that survival of HIV-positive populations into old age because of ART will reveal cardiometabolic disease burdens previously ‘hidden’ by high HIV mortality [47,50]. However, the patterns and extent of the expected epidemiological transition from HIV/tuberculosis to CVD and diabetes because of the ART scaleup in SSA remains largely unknown. Adding detailed biomarker data to existing HDSS on markers of CVD and risk (e.g. lipid profiles, creatinine and markers of long-term blood sugar such as glycated haemoglobin A1c) will create opportunities to establish causal impact of ART on cardiometabolic disease in relevant populations. The range of biological measures available through noninvasive (e.g. hair samples) or minimally invasive (e.g. dried blood spots) techniques are increasing. Table 1 provides a nonexhaustive list of biomeasures currently available on dried blood spots. Biomarkers particularly relevant to population-based HIV research that can be measured using dried blood spots include HIV viral load and the serum concentration of antiretroviral drugs, but also measures of cardiovascular risk (e.g. triglycerides) and diabetes-related measures (e.g. glycated haemoglobin A1c), which may be affected by ART. For many important indicators, however, venous blood samples will have to be collected at the population level. Currently, few HDSS routinely collect venous blood, but such data collection is theoretically possible and is likely to be increasingly employed. One approach is to introduce venous blood data collection during the standard HDSS household visits and then, given consent, to send a specialized phlebotomy team to a household for venous blood-letting.

Table 1
Table 1:
A nonexhaustive list of biomarkers that can be measured using dried blood spots
Table 1
Table 1:
(Continued) A nonexhaustive list of biomarkers that can be measured using dried blood spots
Table 1
Table 1:
(Continued) A nonexhaustive list of biomarkers that can be measured using dried blood spots

Behavioural data collection

Measuring health behaviours – including behaviours relevant to the HIV treatment cascade such as medication adherence – is complicated by the difficulty of validating self-reported behavioural data and the potential for misreporting because of social desirability [148], especially if continued ART provision is believed to be linked to self-reports of behaviours [149]. Biases are likely to be exacerbated by verbal responses, since such answers are then known to the interviewer and others within earshot. Self-interview methods [notably computer-assisted self-interviews (CASIs)] have been shown to increase reporting of socially undesirable behaviours, particularly sexual behaviour [150,151]. Self-report of HIV diagnosis, engagement in care and ART receipt may thus also be improved by CASI methods, as has been previously proposed [152]. The addition of pictorial representations of medications may further improve validity [153]. Substantive use of CASI methods in HDSS work has historically been limited by concerns regarding reading and computer literacy. However, increasing availability of audio-CASI methods – where respondents use headphones to listen to questions and response options – and rising market penetration of mobile and now smartphones, is making the use of CASI increasingly acceptable and practical.

Community exposure and spatial data

There is increasing recognition of the need to develop explanations of outcomes that incorporate individual and community-level factors and move away from an individual-centred approach to understanding causal relationships [154–156]. Many HDSS sites now routinely collect spatial data as part of the ongoing surveillance activities. The spatial data can be used to create community-level exposure variables to use in causal analyses. For example, at our HDSS site in rural KwaZulu-Natal, we have shown that after controlling for multiple variables associated with uptake of ART, an individual living 4.78 km from a clinic was 50% less likely to be on ART relative to someone living next to a clinic [157]. We have recently demonstrated the causal impact of community coverage of ART in reducing an individual's risk of HIV acquisition. Holding other key HIV risk factors constant, individual HIV acquisition risk declined significantly with increasing ART coverage in the surrounding local community. For example, an HIV-uninfected individual living in a community with high ART coverage (30–40% of all HIV-infected individuals on ART) was 38% less likely to acquire HIV than someone living in a community where ART coverage was low (<10% of all HIV-infected individuals on ART) [2]. Adding geographical location data to existing HDSS datasets already including the location of people's homes will improve the assessment of access and exposure to public services (such as primary care clinics, HIV testing and counselling facilities, government grant distribution infrastructure and schools) that can affect progression through the HIV treatment cascade. In using distance to a particular facility, however, it is important to keep in mind that the standard approach to protect individuals’ privacy when working with geolocation data – that is, adding a random spatial error to true location coordinates or ‘scrambling’ – will lead to systematic overestimation of the distance between people's homes and other places, such as facilities where services can be accessed [158▪].

Network exposure and contact data

Accurate measurement of each step of the treatment cascade is crucial to predicting future population health, and thus requires resources. Standard models of resource use implicitly assume that nontesting, nonuse of care and nonadherence are random within the population. However, in practice, individuals tend to act similarly to their contacts (e.g. friends, family, work colleagues) [159]. This homophily has important implications for epidemic control. Typically, homophily implies that greater intervention efforts are required than would be the case, were behaviour randomly distributed through the population. This requirement arises because interventions evenly spread throughout the population can either entirely miss some high-risk subgroups or have insufficient impact to control transmission from members of these subgroups. In both cases, the high-risk subgroups will continue to generate new infections. Behaviour patterns can be elicited in surveys either by asking respondents to report on their contacts’ behaviour (egocentric networks) or by gathering contacts’ identifiers and thus building an overall picture (sociocentric networks) [160]. Careful modelling that takes account of network structures can then be used to estimate how the HIV epidemic is likely to progress [161,162], and how it is likely to respond to interventions [163–165], in light of these contact patterns.

Tracking migration events and use of mobile phone data

In another article in this issue, we review recent work done on the HIV treatment cascade in migrants and mobile populations [166]. Realizing the full treatment and preventive benefits of the Joint United Nations Programme on HIV/AIDS ( UNAIDS) 90–90–90 strategy will require reaching all vulnerable subpopulations of which migrants are a particularly important group. One area that HDSS sites could contribute significantly is the follow-up of HIV-infected patients who have disengaged from the healthcare system. Mobile individuals are at a significantly higher risk of being lost to follow-up (LTFU) within ART programs [167–170]. However, typically only a proportion of those declared to be LTFU actually disengage from care [171]. The ability to track people as they move from one area to another area is essential to assure their continued HIV care, and to generate valid estimates of each step of the treatment cascade as well as objective LTFU rates [171]. Standard HDSS sites typically do not measure outcomes on individuals who are no longer resident in their respective study areas. Some HDSS sites such as the Agincourt and the Africa Centre HDSS in South Africa continue to collect information on household members who are no longer predominantly resident in the study area but who may continue to return intermittently [172–174]. Although such information can yield valuable information, it does not go far enough. It has recently been estimated that the worldwide penetration rate of cellular phones will soon be 97% with more than 7 billion subscriptions [175]. HDSS sites could harness mobile phone technology to track individuals in time and space, collect information via electronic questionnaires and facilitate the interaction with healthcare providers. Rather than assuming a single neighbourhood influence, mobile phone technology offers the opportunity to measure the dynamic context surrounding an individual – that is the combination of physical locations the individual occupies in their existence that places him or her at additional risk of adverse health outcomes. The use of mobile health technology to improve HIV treatment outcomes is comprehensively reviewed in this issue [166] and elsewhere [176].


The linkage of routine administrative records [177], including medical records, to surveillance populations offers an important opportunity to study the impact of public health intervention on the HIV treatment cascade. Effective record linkage is greatly assisted by broadly used unique individual identifiers. Most developing countries lack population registration systems that facilitate the availability of such identifiers, and thus linking datasets requires probabilistic linkage techniques [178▪]. HDSSs are in a strong position to collaborate with local authorities to improve the availability of government identity documents in the surveillance population [179], or to issue identity cards to facilitate identification. Linkage to surveillance populations need not be restricted to health service records: linkage to other administrative records could offer additional information or verify self-reported information that would increase our understanding of reasons for failures in the treatment cascade or to evaluate interventions to improve HIV care.

Health service administrative data

Extracts from routine administrative systems such as human resources, financial and logistics systems can be used to determine the impact of personnel movement and staffing levels in ART programmes on health system outcomes. Access to financial and logistics data will allow for more detailed and ongoing activity costing at service delivery level to better quantify the costs associated with specific interventions.

Education records

Linkage to school records will provide more detailed information on educational attainment and school absenteeism to broaden our understanding of the impact of interventions and determinants of intervention success.

Welfare service records

In countries where individual social support services (e.g. state-sponsored old-age pensions, health insurance, and child support and disability grants) exist, linkage will allow us to study how these programmes mitigate the impact of HIV-related mortality or morbidity, and affect access to and retention in HIV care.

Other data sources

There are a wide variety of other data sources that could usefully be linked to the population-based data, but where data access barriers or identity disclosure risks currently limit the potential of these data sources. For example, access to mobile phone call metadata could improve our understanding of the role of human mobility in the observation of small scale geographic variability in HIV acquisition risk or on the retention in care of patients.


HDSSs are excellent scientific infrastructures for establishing population impacts of health interventions, in particular those that affect large proportions of the population, such as HIV treatment in high HIV prevalence settings. Innovations in data collection and data linkage in these surveillance systems can substantially enhance the scientific opportunities to establish the impacts of HIV treatment on outcomes, and the effects of health policies and intervention on the HIV treatment cascade. In particular, recent innovations in data collection can be harnessed to expand and improve the assessment of biomarkers, behavioural data, community exposures and spatial data and social network and contact data. In particular, biomarkers of high relevance to population-based HIV research, such as HIV viral load and markers for cardiovascular risk, can now be reliably measured using the minimally invasive dried blood spots. Longitudinal and geographically linked data using geographic information systems, monitoring of migration and mobile phone tracking allow individuals to be accurately located in time and place. These data can provide novel and rich data analytical opportunities for the study of HIV treatment impacts and interventions to improve the treatment cascade, when they are nested within the overall population-based cohort data infrastructure that HDSSs provide. Additionally, through data linkage, routine medical records and education and welfare service records can be used to provide novel data on exposures and outcomes that are relevant for studies of the treatment cascade, such as the effects of education on cascade progression.

To gain the research opportunities on ART impacts and cascade progression that can become available through innovations in HDSS data collection and data linkages, researchers will need to build scientific infrastructure and political relationships. Particularly important components of the scientific infrastructure include data management specialists, computing environments and laboratory capacity. Building close relationships with both policymakers and programme managers is crucial not only to gain access to routine programme and administrative data, but also to understand how policies intervene in the data generation processes. These investments in research capacity and relationships with policymakers aiming to enhance HDSS data are likely to generate large returns, increasing the evidence that is needed to ensure that the ART scaleup can be sustained over the coming decades and continues to improve population health outcomes.



Financial support and sponsorship

K.H. salary is supported through Wellcome Trusts grants 097318 and 097410. P.G. is partially supported through a Clinton Health Access Initiative grant CHSWAZHTAP10. F.T. and T.B. are partially supported through grant R01 HD058482–01 from the National Institute of Child Health and Development, National Institutes of Health (NIH). F.T. was partially supported by a South African MRC Flagship grant (MRC-RFA-UFSP-01–2013/UKZN HIVEPI).

Conflicts of interest

The authors did not declare any conflict of interest. M.L.'s institution received funding from Boehringer Ingelheim, Gilead Sciences, Merck Sharp & Dohme, Bristol-Myers Squibb, Janssen-Cilag, ViiV HealthCare. M.L. received DSMB sitting fees from Sirtes Pty Ltd.


Papers of particular interest, published within the annual period of review, have been highlighted as:

  • ▪ of special interest
  • ▪▪ of outstanding interest


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      biomeasures; data collection; data linkage; health and demographic surveillance; treatment cohorts

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