Share this article on:

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

doi: 10.1097/COH.0000000000000203

Purpose of review: Health and demographic surveillance systems (HDSS), in conjunction with HIV treatment cohorts, have made important contributions to our understanding of the impact of HIV treatment and treatment-related interventions in sub-Saharan Africa. The purpose of this review is to describe and discuss innovations in data collection and data linkage that will create new opportunities to establish the impacts of HIV treatment, as well as policies affecting the treatment cascade, on population health and economic and social outcomes.

Recent findings: Novel approaches to routine collection of biomarkers, behavioural data, spatial data, social network information, migration events and mobile phone records can significantly strengthen the potential of HDSS to generate exposure and outcome data for causal analysis of HIV treatment impact and policies affecting the HIV treatment cascade. Additionally, by linking HDSS data to health service administration, education and welfare service records, researchers can substantially broaden opportunities to establish how HIV treatment affects health and economic outcomes when delivered through public sector health systems and at scale.

Summary: As the HIV treatment scaleup in sub-Saharan Africa enters its second decade, it is becoming increasingly important to understand the long-term causal impacts of large-scale HIV treatment and related policies on broader population health outcomes, such as noncommunicable diseases, as well as on economic and social outcomes, such as family welfare and children's educational attainment. By collecting novel data and linking existing data to public sector records, HDSS can create near-unique opportunities to contribute to this research agenda.

aWellcome Trust Africa Centre for Health and Population Studies, UKZN, Somkhele, South Africa

bINDEPTH Network, Accra, Ghana

cThe Kirby Institute, UNSW Australia, Sydney, New South Wales, Australia

dDepartment of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA

eSchool of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa

Correspondence to Kobus Herbst, Wellcome Trust Africa Centre for Health and Population Studies, UKZN, Somkhele, PO Box 198, Mtubatuba, 3935, South Africa. Tel: +27355507503; fax: +27355507565; e-mail:

This is an open access article distributed under the creative commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Back to Top | Article Outline


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.

Back to Top | Article Outline

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.

Back to Top | Article Outline

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.

Back to Top | Article Outline


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.

Back to Top | Article Outline


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.

Back to Top | Article Outline

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.

Back to Top | Article Outline

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▪].

Back to Top | Article Outline

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.

Back to Top | Article Outline

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].

Back to Top | Article Outline


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.

Back to Top | Article Outline

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.

Back to Top | Article Outline

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.

Back to Top | Article Outline

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.

Back to Top | Article Outline

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.

Back to Top | Article Outline


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.

Back to Top | Article Outline



Back to Top | Article Outline

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).

Back to Top | Article Outline

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.

Back to Top | Article Outline


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

▪ of special interest

▪▪ of outstanding interest

Back to Top | Article Outline


1. Sankoh O, Arthur SA, Nyide B, Weston M. The history and impact of HIV&AIDS. A decade of INDEPTH research. HIV AIDS Rev 2014; 13:78–84.
2. Tanser F, Bärnighausen T, Grapsa E, et al. High coverage of ART associated with decline in risk of HIV acquisition in rural KwaZulu-Natal, South Africa. Science 2013; 339:966–971.
3. Bor J, Herbst AJ, Newell ML, Bärnighausen T. Increases in adult life expectancy in rural South Africa: valuing the scale-up of HIV treatment. Science 2013; 339:961–965.
4. Sankoh O, Byass P. The INDEPTH Network: filling vital gaps in global epidemiology. Int J Epidemiol 2012; 41:579–588.
5. Tollman SM, Zwi AB. Health system reform and the role of field sites based upon demographic and health surveillance. Bull World Health Organ 2000; 78:125–134.
6. Baiden F, Hodgson A, Binka FN. Demographic Surveillance Sites and emerging challenges in international health. Bull World Health Organ 2006; 84:163.
7. Sankoh OA, Ngom P, Clark SJ. Jamison DT, Feachem RG, Makgoba MW, et al. Levels and patterns of mortality at INDEPTH demographic surveillance systems. Disease and mortality in sub-Saharan Africa 2nd ed.Washington, DC: World Bank; 2006. 75–86.
8. Sankoh O, Sharrow D, Herbst K, et al. The INDEPTH standard population for low- and middle-income countries. Glob Health Action 2014; 7:23286.
9. Jahn A, Floyd S, Crampin AC, et al. Population-level effect of HIV on adult mortality and early evidence of reversal after introduction of antiretroviral therapy in Malawi. Lancet 2008; 371:1603–1611.
10. Kamali A, Wagner HU, Nakiyingi J, et al. Verbal autopsy as a tool for diagnosing HIV-related adult deaths in rural Uganda. Int J Epidemiol 1996; 25:679–684.
11. Sankoh O, Byass P. Cause-specific mortality at INDEPTH Health and Demographic Surveillance System Sites in Africa and Asia: concluding synthesis. Glob Health Action 2014; 7:25590.
12▪. Byass P, Herbst K, Fottrell E, et al. Comparing verbal autopsy cause of death findings as determined by physician coding and probabilistic modelling: a public health analysis of 54 000 deaths in Africa and Asia. J Glob Health 2015; 5:010402.

The study showed a high degree of concordance of cause of death determination in verbal autopsies by physicians coding vs. as determined by InterVA, an automated, probabilistic mode to assign cause of death.

13. Herbst AJ, Mafojane T, Newell ML. Verbal autopsy-based cause-specific mortality trends in rural KwaZulu-Natal, South Africa, 2000–2009. Popul Health Metr 2011; 9:47.
14. Byass P, Calvert C, Miiro-Nakiyingi J, et al. InterVA-4 as a public health tool for measuring HIV/AIDS mortality: a validation study from five African countries. Glob Health Action 2013; 6:22448.
15▪. Streatfield PK, Khan WA, Bhuiya A, et al. HIV/AIDS-related mortality in Africa and Asia: evidence from INDEPTH health and demographic surveillance system sites. Glob Health Action 2014; 7:25370.

The study presents standardised HIV/AIDS-related mortality rates from health and demographic surveillance sites across Africa and Asia.

16. Maher D, Biraro S, Hosegood V, et al. Translating global health research aims into action: the example of the ALPHA network. Trop Med Int Health 2010; 15:321–328.
17. Reniers G, Slaymaker E, Nakiyingi-Miiro J, et al. Mortality trends in the era of antiretroviral therapy: evidence from the Network for Analysing Longitudinal Population based HIV/AIDS data on Africa (ALPHA). AIDS 2014; 28 (Suppl 4):S533–S542.
18. Bärnighausen T, Tanser F, Gqwede Z, et al. High HIV incidence in a community with high HIV prevalence in rural South Africa: findings from a prospective population-based study. AIDS 2008; 22:139–144.
19. Tanser F, Bärnighausen T, Cooke GS, Newell ML. Localized spatial clustering of HIV infections in a widely disseminated rural South African epidemic. Int J Epidemiol 2009; 38:1008–1016.
20. Bor J, Tanser F, Newell ML, Bärnighausen T. In a study of a population cohort in South Africa, HIV patients on antiretrovirals had nearly full recovery of employment. Health Aff (Millwood) 2012; 31:1459–1469.
21. Raifman J, Chetty T, Tanser F, et al. Preventing unintended pregnancy and HIV transmission: effects of the HIV treatment cascade on contraceptive use and choice in rural KwaZulu-Natal. J Acquir Immune Defic Syndr 2014; 67 (Suppl 4):S218–S227.
22. Hontelez J, Tanser F, de-Vlas S, et al. Effects of antiretroviral treatment on healthcare utilization in rural South Africa. In: Conference on Retroviruses and Opportunistic Infections (CROI). Seattle, 23–26 February 2015.
23. UNAIDS. The GAP report. Geneva: UNAIDS; 2014.
24. Cooke GS, Tanser FC, Bärnighausen TW, Newell ML. Population uptake of antiretroviral treatment through primary care in rural South Africa. BMC Public Health 2010; 10:585doi: 10.1186/1471-2458-10-585.
25. Siika AM, Rotich JK, Simiyu CJ, et al. An electronic medical record system for ambulatory care of HIV-infected patients in Kenya. Int J Med Inform 2005; 74:345–355.
26. Tierney WM, Rotich JK, Hannan TJ, et al. The AMPATH medical record system: creating, implementing, and sustaining an electronic medical record system to support HIV/AIDS care in western Kenya. Stud Health Technol Inform 2007; 129:372–376.
27. Williams F, Boren SA. The role of the electronic medical record (EMR) in care delivery development in developing countries: a systematic review. Inform Prime Care 2008; 16:139–145.
28. Braitstein P, Einterz RM, Sidle JE, et al. ‘Talkin’about a revolution’: how electronic health records can facilitate the scale-up of HIV care and treatment and catalyze primary care in resource-constrained settings. J Acquir Immune Defic Syndr 2009; 52:S54–S57.
29. World Health Organization. Three interlinked patient monitoring systems for HIV care/ART, MCH/PMTCT (including malaria prevention during pregnancy), and TB/HIV: standardized minimum data set and illustrative tools. Geneva: World Health Organization; 2009.
30. Douglas GP, Gadabu OJ, Joukes S, et al. Using touchscreen electronic medical record systems to support and monitor national scale-up of antiretroviral therapy in Malawi. PLoS Med 2010; 7:e1000319.
31. Osler M, Hilderbrand K, Hennessey C, et al. A three-tier framework for monitoring antiretroviral therapy in high HIV burden settings. J Int AIDS Soc 2014; 17: 17(1).
33. Kaslow RA, Ostrow DG, Detels R, et al. The Multicenter AIDS Cohort Study: rationale, organization, and selected characteristics of the participants. Am J Epidemiol 1987; 126:310–318.
34. Lundgren JD, Phillips AN, Vella S, et al. Regional differences in use of antiretroviral agents and primary prophylaxis in 3122 European HIV-infected patients. EuroSIDA Study Group. J Acquir Immune Defic Syndr Hum Retrovirol 1997; 16:153–160.
35. The Australian HIV Observational Database. Time trends in antiretroviral treatment use in Australia, 1997-2000. Venereology 2001; 14:162–168.
36. Friis-Møller N, Weber R, Reiss P, et al. Cardiovascular disease risk factors in HIV patients: association with antiretroviral therapy. Results from the DAD study. AIDS 2003; 17:1179–1193.
37. Egger M, Ekouevi DK, Williams C, et al. Cohort Profile: the international epidemiological databases to evaluate AIDS (IeDEA) in sub-Saharan Africa. Int J Epidemiol 2012; 41:1256–1264.
38. Chi BH, Yiannoutsos CT, Westfall AO, et al. Universal definition of loss to follow-up in HIV treatment programs: a statistical analysis of 111 facilities in Africa, Asia, and Latin America. PLoS Med 2011; 8:e1001111.
39. Avila D, Althoff KN, Mugglin C, et al. IeDEA Collaborations A.R.T.C. Immunodeficiency at the start of combination antiretroviral therapy in low-, middle-, and high-income countries. J Acquir Immune Defic Syndr 2014; 65:e8–e16.
40. Rockers PC, Røttingen JA, Shemilt I, et al. Inclusion of quasi-experimental studies in systematic reviews of health systems research. Health Policy 2015; 119:511–521.
41. Gunasekara FI, Richardson K, Carter K, Blakely T. Fixed effects analysis of repeated measures data. Int J Epidemiol 2014; 43:264–269.
42. Hernán MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology 2000; 11:561–570.
43. Moscoe E, Bor J, Bärnighausen T. Regression discontinuity designs are underutilized in medicine, epidemiology, and public health: a review of current and best practice. J Clin Epidemiol 2015; 68:122–133.
44. Bor J, Moscoe E, Bärnighausen T. Three approaches to causal inference in regression discontinuity designs. Epidemiology 2015; 26:e28–e30.discussion e.
45. Bor J, Moscoe E, Mutevedzi P, et al. Regression discontinuity designs in epidemiology: causal inference without randomized trials. Epidemiology 2014; 25:729–737.
46. Petersen M, Yiannoutsos CT, Justice A, Egger M. Observational research on NCDs in HIV-positive populations: conceptual and methodological considerations. J Acquir Immune Defic Syndr 2014; 67 (Suppl 1):S8–S16.
47. Hontelez JA, de Vlas SJ, Baltussen R, et al. The impact of antiretroviral treatment on the age composition of the HIV epidemic in sub-Saharan Africa. AIDS 2012; 26 (Suppl 1):S19–S30.
48. Institute for Health Metrics and Evaluation (IHME). GBD profile: South Africa. Seattle: IHME; 2013.
49. Statistics South Africa (StatsSA). Mortality and causes of death in South Africa, 2013: findings from death notification. Pretoria: StatsSA; 2014.
50. Bärnighausen T, Welz T, Hosegood V, et al. Hiding in the shadows of the HIV epidemic: obesity and hypertension in a rural population with very high HIV prevalence in South Africa. J Hum Hypertens 2008; 22:236–239.
51. Uttayamakul S, Likanonsakul S, Sunthornkachit R, et al. Usage of dried blood spots for molecular diagnosis and monitoring HIV-1 infection. J Virol Methods 2005; 128:128–134.
52. De Crignis E, Re MC, Cimatti L, et al. HIV-1 and HCV detection in dried blood spots by SYBR Green multiplex real-time RT-PCR. J Virol Methods 2010; 165:51–56.
53. Yourno J, Conroy J. A novel polymerase chain reaction method for detection of human immunodeficiency virus in dried blood spots on filter paper. J Clin Microbiol 1992; 30:2887–2892.
54. Barin F, Plantier JC, Brand D, et al. Human immunodeficiency virus serotyping on dried serum spots as a screening tool for the surveillance of the AIDS epidemic. J Med Virol 2006; 78 (Suppl 1):S13–S18.
55. Tuaillon E, Mondain AM, Meroueh F, et al. Dried blood spot for hepatitis C virus serology and molecular testing. Hepatology 2010; 51:752–758.
56. Judd A, Parry J, Hickman M, et al. Evaluation of a modified commercial assay in detecting antibody to hepatitis C virus in oral fluids and dried blood spots. J Med Virol 2003; 71:49–55.
57. Parker SP, Khan HI, Cubitt WD. Detection of antibodies to hepatitis C virus in dried blood spot samples from mothers and their offspring in Lahore, Pakistan. J Clin Microbiol 1999; 37:2061–2063.
58. Waterboer T, Dondog B, Michael KM, et al. Dried blood spot samples for seroepidemiology of infections with human papillomaviruses, Helicobacter pylori, hepatitis C virus, and JC virus. Cancer Epidemiol Biomarkers Prev 2012; 21:287–293.
59. Jardi R, Rodriguez-Frias F, Buti M, et al. Usefulness of dried blood samples for quantification and molecular characterization of HBV-DNA. Hepatology 2004; 40:133–139.
60. Villar LM, de Oliveira JC, Cruz HM, et al. Assessment of dried blood spot samples as a simple method for detection of hepatitis B virus markers. J Med Virol 2011; 83:1522–1529.
61. Lehmann S, Delaby C, Vialaret J, et al. Current and future use of ‘dried blood spot’ analyses in clinical chemistry. Clin Chem Lab Med 2013; 51:1897–1909.
62. Tappin DM, Greer K, Cameron S, et al. Maternal antibody to hepatitis B core antigen detected in dried neonatal blood spot samples. Epidemiol Infect 1998; 121:387–390.
63. de Almeida LM, Azevedo RS, Guimaraes AA, et al. Detection of antibodies against hepatitis A virus in eluates of blood spotted on filter-paper: a pilot study in Rio de Janeiro, Brazil. Trans R Soc Trop Med Hyg 1999; 93:401–404.
64. Gil A, GonzálezF A, Dal-RéF R, et al. Detection of antibodies against hepatitis A in blood spots dried on filter paper. Is this a reliable method for epidemiological studies? Epidemiol Infect 1997; 118:189–191.
65. Helfand RF, Keyserling HL, Williams I, et al. Comparative detection of measles and rubella IgM and IgG derived from filter paper blood and serum samples. J Med Virol 2001; 65:751–757.
66. Hardelid P, Williams D, Dezateux C, et al. Agreement of rubella IgG antibody measured in serum and dried blood spots using two commercial enzyme-linked immunosorbent assays. J Med Virol 2008; 80:360–364.
67. Balmaseda A, Saborio S, Tellez Y, et al. Evaluation of immunological markers in serum, filter-paper blood spots, and saliva for dengue diagnosis and epidemiological studies. J Clin Virol 2008; 43:287–291.
68. Fachiroh J, Prasetyanti PR, Paramita DK, et al. Dried-blood sampling for Epstein-Barr virus immunoglobulin G (IgG) and IgA serology in nasopharyngeal carcinoma screening. J Clin Microbiol 2008; 46:1374–1380.
69. Strenger V, Pfurtscheller K, Wendelin G, et al. Differentiating inherited human herpesvirus type 6 genome from primary human herpesvirus type 6 infection by means of dried blood spot from the newborn screening card. J Pediatr 2011; 159:859–861.[Eub2011/08/16].
70. Lewensohn-Fuchs I, Osterwall P, Forsgren M, Malm G. Detection of herpes simplex virus DNA in dried blood spots making a retrospective diagnosis possible. J Clin Virol 2003; 26:39–48.
71. Göhring K, Dietz K, Hartleif S, et al. Influence of different extraction methods and PCR techniques on the sensitivity of HCMV-DNA detection in dried blood spot (DBS) filter cards. J Clin Virol 2010; 48:278–281.
72. Scanga L, Chaing S, Powell C, et al. Diagnosis of human congenital cytomegalovirus infection by amplification of viral DNA from dried blood spots on perinatal cards. J Mol Diagn 2006; 8:240–245.
73. de la Fuente L, Toro C, Soriano V, et al. HTLV infection among young injection and noninjection heroin users in Spain: prevalence and correlates. J Clin Virol 2006; 35:244–249.
74. Stevens R, Pass K, Fuller S, et al. Blood spot screening and confirmatory tests for syphilis antibody. J Clin Microbiol 1992; 30:2353–2358.
75. Backhouse JL. Dried blood spot technique for detecting Treponema infection. Trans R Soc Trop Med Hyg 1998; 92:469.
76. Hong HA, Ke NT, Nhon TN, et al. Validation of the combined toxin-binding inhibition test for determination of neutralizing antibodies against tetanus and diphtheria toxins in a vaccine field study in Viet Nam. Bull World Health Organ 1996; 74:275–282.
77. Takkouche B, Iglesias J, Alonso-Fernandez JR, et al. Detection of Brucella antibodies in eluted dried blood: a validation study. Immunol Lett 1995; 45:107–108.
78. Fenollar F, Raoult D. Diagnosis of rickettsial diseases using samples dried on blotting paper. Clin Diagn Lab Immunol 1999; 6:483–488.
79. Thanasekaraan V, Wiseman MS, Rayner RJ, et al. Pseudomonas aeruginosa antibodies in blood spots from patients with cystic fibrosis. Arch Dis Child 1989; 64:1599–1603.
80. Corran PH, Cook J, Lynch C, et al. Dried blood spots as a source of antimalarial antibodies for epidemiological studies. Malar J 2008; 7:195.
81. Mason PR, Fiori PL, Cappuccinelli P, et al. Seroepidemiology of Trichomonas vaginalis in rural women in Zimbabwe and patterns of association with HIV infection. Epidemiol Infect 2005; 133:315–323.
82. Zicker F, Smith PG, Luquetti AO, Oliveira OS. Mass screening for Trypanosoma cruzi infections using the immunofluorescence, ELISA and haemagglutination tests on serum samples and on blood eluates from filter-paper. Bull World Health Organ 1990; 68:465–471.
83. Peralta RH, Macedo HW, Vaz AJ, et al. Detection of anticysticercus antibodies by ELISA using whole blood collected on filter paper. Trans R Soc Trop Med Hyg 2001; 95:35–36.
84. Sørensen T, Spenter J, Jaliashvili I, et al. Automated time-resolved immunofluorometric assay for Toxoplasma gondii-specific IgM and IgA antibodies: study of more than 130 000 filter-paper blood-spot samples from newborns. Clin Chem 2002; 48:1981–1986.
85. Wang XL, Dudman NP, Blades BL, Wilcken DE. Changes in the immunoreactivity of Apo A-I during storage. Clin Chim Acta 1989; 179:285–293.
86. Quraishi R, Lakshmy R, Prabhakaran D, et al. Use of filter paper stored dried blood for measurement of triglycerides. Lipids Health Dis 2006; 5:20.
87. Brindle E, Fujita M, Shofer J, O’Connor KA. Serum, plasma, and dried blood spot high-sensitivity C-reactive protein enzyme immunoassay for population research. J Immunol Methods 2010; 362:112–120.
88. Hu P, Herningtyas EH, Kale V, et al. External quality control for dried blood spot-based C-reactive protein assay: experience from the indonesia family life survey and the longitudinal aging study in India. Biodemography Soc Biol 2015; 61:111–120.
89. Crimmins E, Kim JK, McCreath H, et al. Validation of blood-based assays using dried blood spots for use in large population studies. Biodemography Soc Biol 2014; 60:38–48.
90. D’Avolio A, Simiele M, Siccardi M, et al. HPLC-MS method for the quantification of nine anti-HIV drugs from dry plasma spot on glass filter and their long term stability in different conditions. J Pharm Biomed Anal 2010; 52:774–780.
91. Koal T, Burhenne H, Römling R, et al. Quantification of antiretroviral drugs in dried blood spot samples by means of liquid chromatography/tandem mass spectrometry. Rapid Commun Mass Spectrom 2005; 19:2995–3001.
92. Blessborn D, Romsing S, Bergqvist Y, Lindegardh N. Assay for screening for six antimalarial drugs and one metabolite using dried blood spot sampling, sequential extraction and ion-trap detection. Bioanalysis 2010; 2:1839–1847.
93. Lindkvist J, Malm M, Bergqvist Y. Straightforward and rapid determination of sulfadoxine and sulfamethoxazole in capillary blood on sampling paper with liquid chromatography and UV detection. Trans R Soc Trop Med Hyg 2009; 103:371–376.
94. Henderson LO, Powell MK, Hannon WH, et al. Radioimmunoassay screening of dried blood spot materials for benzoylecgonine. J Anal Toxicol 1993; 17:42–47.
95. Li PK, Lee JT, Conboy KA, Ellis EF. Fluorescence polarization immunoassay for theophylline modified for use with dried blood spots on filter paper. Clin Chem 1986; 32:552–555.
96. Hoffman DL. Purification and large-scale preparation of antithrombin III. Am J Med 1989; 87 (3B):23S–26S.
97. Lakshmy R, Gupta R. Measurement of glycated hemoglobin A1c from dried blood by turbidimetric immunoassay. J Diabetes Sci Technol 2009; 3:1203–1206.
98. Hu P, Edenfield M, Potter A, et al. Validation and modification of dried blood spot-based glycosylated hemoglobin assay for the longitudinal aging study in India. Am J Hum Biol 2015; 27:579–581.
99. Dowlati B, Dunhardt PA, Smith MM, et al. Quantification of insulin in dried blood spots. J Lab Clin Med 1998; 131:370–374.
100. Burrin JM, Price CP. Performance of three enzymic methods for filter paper glucose determination. Ann Clin Biochem 1984; 21 (Pt 5):411–416.
101. Zimmermann MB, Moretti D, Chaouki N, Torresani T. Development of a dried whole-blood spot thyroglobulin assay and its evaluation as an indicator of thyroid status in goitrous children receiving iodized salt. Am J Clin Nutr 2003; 77:1453–1458.
102. Dussault JH, Morissette J, Letarte J, et al. Thyroxine-binding globulin capacity and concentration evaluated from blood spots on filter-paper in a screening program for neonatal hypothyroidism. Clin Chem 1980; 26:463–465.
103. Hofman LF, Foley TP, Henry JJ, Naylor EW. The use of filter paper-dried blood spots for thyroid-antibody screening in adults. J Lab Clin Med 2004; 144:307–312.
104. Chace DH, Singleton S, Diperna J, et al. Rapid metabolic and newborn screening of thyroxine (T4) from dried blood spots by MS/MS. Clin Chim Acta 2009; 403:178–183.
105. Pacchiarotti A, Bartalena L, Falcone M, et al. Free thyroxine and free triiodothyronine measurement in dried blood spots on filter paper by column adsorption chromatography followed by radioimmunoassay. Horm Metab Res 1988; 20:293–297.
106. Worthman CM, Stallings JF. Measurement of gonadotropins in dried blood spots. Clin Chem 1994; 40:448–453.
107. Mitchell ML, Hermos RJ, Moses AC. Radioimmunoassay of somatomedin-C in filter paper discs containing dried blood. Clin Chem 1987; 33:536–538.
108. Diamandi A, Khosravi MJ, Mistry J, et al. Filter paper blood spot assay of human insulin-like growth factor I (IGF-I) and IGF-binding protein-3 and preliminary application in the evaluation of growth hormone status. J Clin Endocrinol Metab 1998; 83:2296–2301.
109. Xu YY, Pettersson K, Blomberg K, et al. Simultaneous quadruple-label fluorometric immunoassay of thyroid-stimulating hormone, 17 alpha-hydroxyprogesterone, immunoreactive trypsin, and creatine kinase MM isoenzyme in dried blood spots. Clin Chem 1992; 38:2038–2043.
110. Costa X, Jardi R, Rodriguez F, et al. Simple method for alpha1-antitrypsin deficiency screening by use of dried blood spot specimens. Eur Respir J 2000; 15:1111–1115.
111. Macri JN, Anderson RW, Krantz DA, et al. Prenatal maternal dried blood screening with alpha-fetoprotein and free beta-human chorionic gonadotropin for open neural tube defect and Down syndrome. Am J Obstet Gynecol 1996; 174:566–572.
112. Yamaguchi A, Fukushi M, Arai O, et al. A simple method for quantification of biotinidase activity in dried blood spot and its application to screening of biotinidase deficiency. Tohoku J Exp Med 1987; 152:339–346.
113. deWilde A, Sadilkova K, Sadilek M, et al. Tryptic peptide analysis of ceruloplasmin in dried blood spots using liquid chromatography-tandem mass spectrometry: application to newborn screening. Clin Chem 2008; 54:1961–1968.
114. Cowans NJ, Stamatopoulou A, Liitti P, et al. The stability of free-beta human chorionic gonadotrophin and pregnancy-associated plasma protein-A in first trimester dried blood spots. Prenat Diagn 2011; 31:293–298.
115. Daniel YA, Turner C, Haynes RM, et al. Quantification of hemoglobin A2 by tandem mass spectrometry. Clin Chem 2007; 53:1448–1454.
116. Jacomelli G, Micheli V, Peruzzi L, et al. Simple nonradiochemical HPLC-linked method for screening for purine metabolism disorders using dried blood spot. Clin Chim Acta 2002; 324:135–139.
117. Wang D, Wood T, Sadilek M, et al. Tandem mass spectrometry for the direct assay of enzymes in dried blood spots: application to newborn screening for mucopolysaccharidosis II (Hunter disease). Clin Chem 2007; 53:137–140.
118. Chamoles NA, Niizawa G, Blanco M, et al. Glycogen storage disease type II: enzymatic screening in dried blood spots on filter paper. Clin Chim Acta 2004; 347:97–102.
119. Chamoles NA, Blanco MB, Gaggioli D, Casentini C. Hurler-like phenotype: enzymatic diagnosis in dried blood spots on filter paper. Clin Chem 2001; 47:2098–2102.
120. Chamoles NA, Blanco M, Gaggioli D. Diagnosis of alpha-L-iduronidase deficiency in dried blood spots on filter paper: the possibility of newborn diagnosis. Clin Chem 2001; 47:780–781.
121. ten Brink HJ, van den Heuvel CM, Christensen E, et al. Diagnosis of peroxisomal disorders by analysis of phytanic and pristanic acids in stored blood spots collected at neonatal screening. Clin Chem 1993; 39:1904–1906.
122. Vladutiu GD, Glueck CJ, Schultz MT, et al. beta-Lipoprotein quantitation in cord blood spotted on filter paper: a screening test. Clin Chem 1980; 26:1285–1290.
123. Laberge C, Grenier A, Valet JP, Morissette J. Fumarylacetoacetase measurement as a mass-screening procedure for hereditary tyrosinemia type I. Am J Hum Genet 1990; 47:325–328.
124. Chamoles NA, Blanco MB, Iorcansky S, et al. Retrospective diagnosis of GM1 gangliosidosis by use of a newborn-screening card. Clin Chem 2001; 47:2068.
125. Kirby LT, Applegarth DA, Davidson AG, et al. Use of a dried blood spot in immunoreactive-trypsin assay for detection of cystic fibrosis in infants. Clin Chem 1981; 27:678–688.
126. Fujimoto A, Okano Y, Miyagi T, et al. Quantitative Beutler test for newborn mass screening of galactosemia using a fluorometric microplate reader. Clin Chem 2000; 46:806–810.
127. Guthrie R, Susi A. A simple phenylalanine method for detecting phenylketonuria in large populations of newborn infants. Pediatrics 1963; 32:338–343.
128. Accinni R, Campolo J, Parolini M, et al. Newborn screening of homocystinuria: quantitative analysis of total homocyst(e)ine on dried blood spot by liquid chromatography with fluorimetric detection. J Chromatogr B Analyt Technol Biomed Life Sci 2003; 785:219–226.
129. Lacey JM, Minutti CZ, Magera MJ, et al. Improved specificity of newborn screening for congenital adrenal hyperplasia by second-tier steroid profiling using tandem mass spectrometry. Clin Chem 2004; 50:621–625.
130. Schulze A, Schmidt C, Kohlmuller D, et al. Accurate measurement of free carnitine in dried blood spots by isotope-dilution electrospray tandem mass spectrometry without butylation. Clin Chim Acta 2003; 335:137–145.
131. Carducci C, Santagata S, Leuzzi V, et al. Quantitative determination of guanidinoacetate and creatine in dried blood spot by flow injection analysis-electrospray tandem mass spectrometry. Clin Chim Acta 2006; 364:180–187.
132. Conroy JM, Trivedi G, Sovd T, Caggana M. The allele frequency of mutations in four genes that confer enhanced susceptibility to venous thromboembolism in an unselected group of New York State newborns. Thromb Res 2000; 99:317–324.
133. Bobillo Lobato J, Sánchez Peral BA, Durán Parejo P, Jiménez Jiménez LM. Detection of c. -32T > G (IVS1-13T > G) mutation of Pompe disease by real-time PCR in dried blood spot specimen. Clin Chim Acta 2013; 418:107–108.
134. Chien YH, Lee NC, Chiang SC, et al. Fabry disease: incidence of the common later-onset α-galactosidase A IVS4+919G→A mutation in Taiwanese newborns: superiority of DNA-based to enzyme-based newborn screening for common mutations. Mol Med 2012; 18:780–784.
135. Abdallah MW, Larsen N, Grove J, et al. Neonatal chemokine levels and risk of autism spectrum disorders: findings from a Danish historic birth cohort follow-up study. Cytokine 2013; 61:370–376.
136. Cordovado SK, Hendrix M, Greene CN, et al. CFTR mutation analysis and haplotype associations in CF patients. Mol Genet Metab 2012; 105:249–254.
137. Harahap NI, Harahap IS, Kaszynski RH, et al. Spinal muscular atrophy patient detection and carrier screening using dried blood spots on filter paper. Genet Test Mol Biomarkers 2012; 16:123–129.
138. Coffee B, Keith K, Albizua I, et al. Incidence of fragile X syndrome by newborn screening for methylated FMR1 DNA. Am J Hum Genet 2009; 85:503–514.
139. O’Broin SD, Gunter EW. Screening of folate status with use of dried blood spots on filter paper. Am J Clin Nutr 1999; 70:359–367.
140. McDade TW, Shell-Duncan B. Whole blood collected on filter paper provides a minimally invasive method for assessing human transferrin receptor level. J Nutr 2002; 132:3760–3763.
141. Fallah E, Peighambardoust SH. Validation of the use of dried blood spot (DBS) method to assess vitamin A status. Health Promot Perspect 2012; 2:180–189.
142. Craft NE, Bulux J, Valdez C, et al. Retinol concentrations in capillary dried blood spots from healthy volunteers: method validation. Am J Clin Nutr 2000; 72:450–454.
143. Fisher RS, Chan DW, Bare M, Lesser RP. Capillary prolactin measurement for diagnosis of seizures. Ann Neurol 1991; 29:187–190.
144. Skogstrand K, Ekelund CK, Thorsen P, et al. Effects of blood sample handling procedures on measurable inflammatory markers in plasma, serum and dried blood spot samples. J Immunol Methods 2008; 336:78–84.
145. Tanner S, McDade TW. Enzyme immunoassay for total immunoglobulin E in dried blood spots. Am J Hum Biol 2007; 19:440–442.
146. Zytkovicz TH, Fitzgerald EF, Marsden D, et al. Tandem mass spectrometric analysis for amino, organic, and fatty acid disorders in newborn dried blood spots: a two-year summary from the New England Newborn Screening Program. Clin Chem 2001; 47:1945–1955.
147. Burse VW, DeGuzman MR, Korver MP, et al. Preliminary investigation of the use of dried-blood spots for the assessment of in utero exposure to environmental pollutants. Biochem Mol Med 1997; 61:236–239.
148. Chaiyachati K, Hirschhorn LR, Tanser F, et al. Validating five questions of antiretroviral nonadherence in a public-sector treatment program in rural South Africa. AIDS Patient Care STDs 2011; 25:163–170.
149. van der Straten A, Stadler J, Montgomery E, et al. Women's experiences with oral and vaginal pre-exposure prophylaxis: the VOICE-C qualitative study in Johannesburg, South Africa. PLoS One 2014; 9:e89118.
150. Phillips AE, Gomez GB, Boily MC, Garnett GP. A systematic review and meta-analysis of quantitative interviewing tools to investigate self-reported HIV and STI associated behaviours in low- and middle-income countries. Int J Epidemiol 2010; 39:1541–1555.
151. Langhaug LF, Sherr L, Cowan FM. How to improve the validity of sexual behaviour reporting: systematic review of questionnaire delivery modes in developing countries. Trop Med Int Health 2010; 15:362–381.
152. Bangsberg DR, Bronstone A, Chesney MA, Hecht FM. Computer-assisted self-interviewing (CASI) to improve provider assessment of adherence in routine clinical practice. J Acquir Immune Defic Syndr 2002; 31 (Suppl. 3):S107–S111.
153. Tolley EE, Harrison PF, Goetghebeur E, et al. Adherence and its measurement in phase 2/3 microbicide trials. AIDS Behav 2010; 14:1124–1136.
154. Diez Roux AV. The study of group-level factors in epidemiology: rethinking variables, study designs, and analytical approaches. Epidemiol Rev 2004; 26:104–111.
155. Susser M. The logic in ecological: II. The logic of design. Am J Public Health 1994; 84:830–835.
156. Schwartz S. The fallacy of the ecological fallacy: the potential misuse of a concept and the consequences. Am J Public Health 1994; 84:819–824.
157. Cooke GS, Tanser FC, Bärnighausen TW, Newell ML. Population uptake of antiretroviral treatment through primary care in rural South Africa. BMC public health 2010; 10:585.
158▪. Elkies N, Fink G, Bärnighausen T. ‘Scrambling’ geo-referenced data to protect privacy induces bias in distance estimation. Population and Environment 2015; 37:93–98.doi: 10.1007/s11111-014-0225-0.

The study showed for the first time (and proved mathematically) that ‘scrambling’ of geolocation data, a common practice among researchers and survey agencies to protect privacy, leads to a systematic overestimation of the distance between two points.

159. McPherson M, Smith-Lovin L, Cook JM. Birds of a feather: homophily in social networks. Annu Rev Sociol 2001; 27:415–444.
160. Berkman LF, Krishna A. Social network epidemiology. In: Social Epidemiology (eds Lisa F. Berkman, Ichiro Kawachi, Maria Glymour). Oxford University Press. Oxford 2014. 234–289.
161. Goodreau SM, Cassels S, Kasprzyk D, et al. Concurrent partnerships, acute infection and HIV epidemic dynamics among young adults in Zimbabwe. AIDS Behav 2012; 16:312–322.
162. Aral SO, Hughes JP, Stoner BP, et al. Sexual mixing patterns in the spread of gonococcal and chlamydial infections. Am J Public Health 1999; 89:825–833.
163. Ward H. Prevention strategies for sexually transmitted infections: importance of sexual network structure and epidemic phase. Sex Transm Infect 2007; 83 (Suppl 1):i43–i49.
164. Hontelez JA, Nagelkerke N, Bärnighausen T, et al. The potential impact of RV144-like vaccines in rural South Africa: a study using the STDSIM microsimulation model. Vaccine 2011; 29:6100–6106.
165. Salathé M, Jones JH. Dynamics and control of diseases in networks with community structure. PLoS Comput Biol 2010; 6:e1000736.
166. Tanser F, Bärnighausen T, Vandormael A, Dobra A. The HIV treatment cascade in migrants and mobile populations. Curr Opin HIV AIDS 2015.
167. Lima V, Fernandes K, Rachlis B, et al. Migration adversely affects antiretroviral adherence in a population-based cohort of HIV/AIDS patients. Soc Sci Med 2009; 68:1044–1049.
168. Bygrave H, Kranzer K, Hilderbrand K, et al. Trends in loss to follow-up among migrant workers on antiretroviral therapy in a community cohort in Lesotho. PLoS One 2010; 5:e13198.
169. Abgrall S, Fugon L, Lélé N, et al. Visiting one's native country: the risks of nonadherence in HIV-infected sub-Saharan migrants—ANRS VIHVO Study. J Int Assoc Provid AIDS Care 2013; 12:407–413.
170. Mutevedzi PC, Lessells RJ, Newell M-L. Disengagement from care in a decentralised primary healthcare antiretroviral treatment programme: cohort study in rural South Africa. Trop Med Int Health 2013; 18:934–941.
171. Buskin SE, Kent JB, Dombrowski JC, Golden MR. Migration distorts surveillance estimates of engagement in care: results of public health investigations of persons who appear to be out of HIV care. Sex Transm Dis 2014; 41:35–40.
172. Tanser F, Hosegood V, Bärnighausen T, et al. Cohort profile: Africa Centre Demographic Information System (ACDIS) and population-based HIV survey. Int J Epidemiol 2008; 37:956–962.
173. Collinson MA, White MJ, Bocquier P, et al. Migration and the epidemiological transition: insights from the Agincourt sub-district of northeast South Africa. Glob Health Action 2014; 7:23514.
174. Kahn K, Collinson MA, Gómez-Olivé FX, et al. Profile: Agincourt health and socio-demographic surveillance system. Int J Epidemiol 2012; 41:988–1001.
175. International Telecommunication Union, ITU. International Telecommunication Union facts and figures. 2015.
176. Catalani C, Philbrick W, Fraser H, Mechael P. mHealth for HIV treatment & prevention: a systematic review of the literature. Open AIDS J 2013; 7:17–41.
177. Green E, Ritchie F, Webber D, et al. Enabling data linkage to maximise the value of public health research data. London, UK: The Wellcome Trust; 2015.
178▪. Kabudula CW, Clark B D, Gómez-Olivé FX, et al. The promise of record linkage for assessing the uptake of health services in resource constrained settings: a pilot study from South Africa. BMC Med Res Methodol 2014; 14:71.

The pilot study investigated the feasibility of linking data from a health and demographic surveillance site in South Africa to clinical data from a local healthcare facility. The authors demonstrated that fully automated probabilistic record linkage using identifiers routinely collected in South African healthcare facilities is feasible, and achieves a high degree of accurate matching of data (using fingerprint matching as the gold standard comparator).

179. Joubert J, Bradshaw D, Kabudula C, et al. Record-linkage comparison of verbal autopsy and routine civil registration death certification in rural north-east South Africa: 2006-09. Int J Epidemiol 2014; 43:1945–1958.

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

Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.