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The double burden of malnutrition in a rural health and demographic surveillance system site in South Africa: a study of primary schoolchildren and their mothers
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Abstract
Background
In South Africa, the occurrence of the double burden of malnutrition is on the rise at a household level predisposing children and their mothers to negative health outcomes. However, few studies have been conducted at a household level. Therefore, we studied a double burden of malnutrition using child-mother pairs in a rural setting.
Methods
A cross-sectional quantitative survey was conducted among 508 child-mother pairs selected from primary schools using a multistage sampling in a rural Dikgale Health and Demographic Site in Limpopo Province, South Africa. Anthropometric measurements of children and mothers, and socio-demographic data were collected. WHO AnthroPlus was used to generate body-mass-index z-scores of children and the BMI was used to indicate overweight and obesity among the mothers. Mann Whitney test was used to compare the means of variables between sexes and age groups, while the prevalence of thinness and overweight/obesity were compared using a chi-square. Multivariate logistic regression with a stepwise backward elimination procedure, controlling for confounding, was used to determine the association between the thinness and overweight/obesity and the covariates.
Results
Twenty five percent (25%) of the children were thin, 4% were overweight and 1% obese, while mothers were overweight (27.4%) and 42.3% obesity (42.3%) were observed among the mothers. The odds of being thin were higher in boys than in girls (AOR = 1.53, 95%CI: 1.01–2.35). Overweight/obese mothers were more likely to have thin children (AOR = 1.48, 95% CI: 1.01–2.18) and less likely to have overweight/obese children (AOR = 0.18, 95%CI: 0.07–0.46).
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Most cited references60
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Global nutrition transition and the pandemic of obesity in developing countries.
Barry M. Popkin, Linda S Adair, Shu Ng (2012)
Decades ago, discussion of an impending global pandemic of obesity was thought of as heresy. But in the 1970s, diets began to shift towards increased reliance upon processed foods, increased away-from-home food intake, and increased use of edible oils and sugar-sweetened beverages. Reductions in physical activity and increases in sedentary behavior began to be seen as well. The negative effects of these changes began to be recognized in the early 1990s, primarily in low- and middle-income populations, but they did not become clearly acknowledged until diabetes, hypertension, and obesity began to dominate the globe. Now, rapid increases in the rates of obesity and overweight are widely documented, from urban and rural areas in the poorest countries of sub-Saharan Africa and South Asia to populations in countries with higher income levels. Concurrent rapid shifts in diet and activity are well documented as well. An array of large-scale programmatic and policy measures are being explored in a few countries; however, few countries are engaged in serious efforts to prevent the serious dietary challenges being faced. © 2012 International Life Sciences Institute.
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Fecal Contamination of Drinking-Water in Low- and Middle-Income Countries: A Systematic Review and Meta-Analysis
Robert Bain, Ryan Cronk, Jim Wright … (2014)
Introduction The importance of water to human health and wellbeing is encapsulated in the Human Right to Water and Sanitation, which entitles everyone to “sufficient, safe, acceptable physically accessible and affordable water for personal and domestic uses” [1], as reaffirmed by the United Nations General Assembly and Human Rights Council in 2010 [2]. Millennium Development Goals (MDGs) Target 7c aims “to halve the proportion of the population without sustainable access to safe drinking-water …” [3], a step towards universal access. “Use of an improved source” was adopted as an indicator for monitoring access to safe drinking-water globally (Table 1) and relies on national censuses and nationally representative household surveys as the primary sources of data. 10.1371/journal.pmed.1001644.t001 Table 1 Types of improved source and the estimated proportion of the global population using these as their primary source of drinking-water. Source Categorya Description Global Population Using Water Source in 2010b (%) Urban Rural Total Household or yard connection Piped water into dwelling, also called a household connection, is defined as a water service pipe connected with in-house plumbing to one or more taps. Piped water to yard/plot, also called a yard connection, is defined as a piped water connection to a tap placed in the yard or plot outside the dwelling. 80 29 54 Standpipe Public tap or standpipe is a public water point from which people can collect water. A standpipe is also known as a public fountain or public tap. Public standpipes can have one or more taps and are typically made of brickwork, masonry, or concrete. 6 8 7 Borehole Tubewell or borehole is a deep hole that has been driven, bored, or drilled, with the purpose of reaching groundwater supplies. Boreholes/tubewells are constructed with casing, or pipes, which prevent the small diameter hole from caving in and protects the water source from infiltration by runoff water. 8 30 18 Protected dug well Protected dug well is a dug well that is protected from runoff water by a well lining or casing that is raised above ground level and a platform that diverts spilled water away from the well. A protected dug well is also covered, so that bird droppings and animals cannot fall into the well. 2c 10c 6c Protected spring The spring is typically protected from runoff, bird droppings, and animals by a “spring box,” which is constructed of brick, masonry, or concrete and is built around the spring so that water flows directly out of the box into a pipe or cistern, without being exposed to outside pollution. 100, “high risk” or “very high risk” [24],[25]. However FIB are imperfect and their level does not necessarily equate to risk [26]; since quality varies both temporally and spatially, occasional sampling may not accurately reflect actual exposure. A complementary approach in safety assessment is the identification of hazards and preventative risk management measures through “sanitary inspection” of a water source and its surroundings [24],[27]. The improved source indicator is in effect a very simplified form of sanitary inspection. Like FIB, sanitary inspections have long been a tool in assessing drinking-water safety. In 1904, Prescott and Winslow stated, “[t]he first attempt of the expert called in to pronounce upon the character of a potable water should be to make a thorough sanitary inspection…” [28]. Standardized forms can be used to assess sanitary risk and derive a summary measure, the sanitary risk score. These forms typically include questions about the integrity of protective elements, such as fencing or well covers, and the proximity of hazards such as latrines; forms are available for different types of water source. Like water quality, some sanitary risk factors may vary spatially and temporally. The approach can be combined with microbiological analysis, either to yield a risk cross-tabulation [24],[25] or as a part of a more detailed Water Safety Plan [29]. In January 2012, WHO and UNICEF established working groups to develop targets and indicators for enhanced global monitoring of drinking-water, sanitation, and hygiene post-2015. The water working group proposed to continue using the improved water source classification as part of a revised set of indicators for assessing progressive improvements in service [30]. This review was commissioned to assist the group in evaluating the evidence linking improved source types and health-related indicators of water quality. The following specific questions were considered in order to determine the potential and limits of classification by source type in assessing safety in future global reporting: (i) Is water from improved sources less likely to exceed health-based guidelines for microbial water quality than water from unimproved sources? (ii) To what extent does microbial contamination vary between source types, between countries, and between rural and urban areas? (iii) Are some types of water source associated with higher risk scores as assessed by sanitary inspection? Methods We conducted a systematic review of studies of fecal contamination of drinking-water in LMICs in adherence with PRISMA guidelines (Text S1) [31]. The protocol for the review is described in Protocol S1. Search Strategy Studies were identified from both peer-reviewed and grey literature. To identify peer-reviewed literature, the topic “water quality” was combined with terms to restrict the search to drinking-water and either a measure of microbial water quality (e.g., “coli”) or sanitary risk (e.g., “sanitary inspection”). We further restricted the search to LMICs using a list of country names based on the MDG regions [32]. Online databases were searched including PubMed, Web of Science, and the Global Health Library. Grey literature was sourced from a variety of sites including those used in previous drinking-water–related reviews [33]–[35]. Translated search terms (Chinese, French, Portuguese, and Spanish) were used to identify additional studies. An email requesting submissions of relevant studies was distributed to water sector professional networks. We searched bibliographies of included studies and contacted authors where full texts could not be obtained through other means. Searches were conducted between 7th January and 1st August 2013. Eligibility and Selection Studies were included in the review provided they: reported on water quality, at either the point of collection or consumption, from sources used for drinking that would not be classified as surface waters by the JMP; contained extractable data on TTC or E. coli with sample volumes not less than 10 ml; were published between January 1990 (the baseline year for MDG targets) and August 2013; included results from at least ten separate water samples from different water sources of a given type or, in the case of piped systems, individual taps, and in the case of packaged waters, brands; reported data from LMICs as defined by the MDG regions [32] (thereby excluding 55 high-income countries, comprising 18.1% of the global population in 2010 [36]); were published in languages spoken by at least one author (Chinese, English, French, Portuguese, or Spanish); and included sufficient detail about the water sources and associated results with a water source with sufficient detail to be categorized (refer to Figure 1 for details). Other indicators such as coliphage and direct pathogen detection are not as widely used and are not included in this review [37]. We did not include studies that only assessed surface waters as these are generally considered unfit for drinking. We included bottled water and sachet water that do not form part of the JMP improved source classification (which is concerned with the household's primary source of water for drinking, cooking, and personal hygiene [38]) but are nonetheless important sources of drinking-water in many countries. 10.1371/journal.pmed.1001644.g001 Figure 1 Matching drinking-water source types to the classification used by the Joint Monitoring Programme. Independent primary screening of English language titles and abstracts for studies was conducted by two authors (RB and RC). If any reviewer selected a study, we referred to the full text. Data from eligible studies were extracted into a standardized spreadsheet and 10% of the English language texts were subjected to independent quality control by a second author (RB and RC). Screening and extraction of data in other languages was conducted by one author (RB or HY). Data Extraction and Matching Where possible we extracted or calculated the following information for each type of water source in the studies: (i) total number of samples and proportion containing E. coli or TTC; (ii) proportion of samples within microbial risk categories ( 100 E. coli or TTC per 100 ml); (iii) geometric mean, mean, or median levels of E. coli or TTC; and (iv) risk categories according to the sanitary inspection (“low,” “medium,” “high,” and “very high” risk) as reported in the studies. For intervention studies (other than the provision of an improved source, for example the protection of unprotected springs), estimates could be based on either the baseline or control group; when both were available we used whichever had the largest sample size. For studies reporting both E. coli and TTC, we used only the E. coli results. Where repeated measures were taken at the same source and data permitted we extracted the lowest compliance level (e.g., wet season data) with WHO Guideline values as well as the overall proportion of samples containing FIB. We identified countries as “low,” “lower middle,” “upper middle,” and “high” income using the 2013 World Bank classification [39]. We recorded whether studies took place during or shortly after emergencies or natural disasters and if they were in non-household settings such as schools and health facilities. We identified additional study characteristics expected to influence water quality, including the setting (urban/rural), season (wet/dry or period of sampling), and study design [34]. Each type of water source in a given study was classified as improved or unimproved and matched to a specific water source type following the classification used in household surveys including the Demographic and Health Surveys [38]. We recorded whether samples had been taken directly from the water source or after storage, for example in the home. Where the appropriate match could not be determined, our approach differed depending on the type of source. We grouped groundwater sources from studies that did not distinguish between protected and unprotected (unclassified dug well, unclassified spring) and we created groups for studies of other sources such as bottled and sachet water. Further information about the matching is available in Figure 1. Study Quality and Bias Studies were rated for quality on the basis of the criteria summarized in Table 2. A quality score between 0 and 13 for each study was determined on the basis of the number of affirmative responses. We also categorized studies on the basis of anticipated susceptibility to bias in estimating the compliance to health-based guidelines and the extent of microbial contamination; our categories were: case-control or cohort, intervention, diagnostic study, cross-sectional survey, and longitudinal survey. Any study of at least 6 months duration and more than two samples at each water point was categorized as longitudinal. We identified studies where authors indicated whether selection was intended to be representative or selection had been randomized. 10.1371/journal.pmed.1001644.t002 Table 2 Quality criteria used to assess studies of microbial water quality. Criterion Question Selection described Do the authors describe how the water samples were chosen, including how either the types of water source or their users were selected? Selection representative Did the authors detail an approach designed to provide representative picture water quality in a given area? Selection randomized Was sampling randomized over a given study area or population? Region described Does the study report the geographic region within the country where it was conducted? Season reported Were the seasons or months of sampling reported? Quality control Were quality control procedures specified or referred to? Method described Are well-defined and appropriate methods of microbial analysis described or referenced? Point of sampling Was the point at which water was sampled well defined? (For example whether the water was collected from within a household storage container or directly from a water source) Handling described Are sample handling procedures described, including sample collection, transport method, and duration? Handling minimum criteria Does sample handling and processing meet the following criteria: transport on ice or between 2–8°C, analysis within 6 hours of collection, and specified incubation temperature? Accredited laboratory Was the microbial analysis conducted in an accredited laboratory setting? Trained technician Do the authors state whether trained technicians conducted the water quality assessments or the analyses were undertaken by laboratory technicians? External review Was the study subject to peer review or external review prior to publication? Analysis Because of the extent of heterogeneity between studies, we chose to plot cumulative density functions (CDFs) of the proportion of samples with detected (>1 per 100 ml) and high (>100 per 100 ml) FIB in each study to compare water source types between studies. This approach has been used in a systematic review of prevalence of schizophrenia [40]. CDFs are used to qualitatively assess the proportion of studies reporting frequent and high levels of microbial contamination. Measures of central tendency from studies were not included in the meta-analysis because of limited reporting of measures of dispersion, inadequate explanation of the handling of censored data, and the difficulty in reconciling diverse reported measures of central tendency (e.g., geometric versus arithmetic mean) [41]. Random effects meta-regression was used to investigate risk factors and settings where fecal contamination is most common and other possible explanations for the observed heterogeneity between studies [42]. A logit transformation is recommended for the analysis of proportions [43] and was applied to both the proportion of samples with detectable (>1 per 100 ml) and high (>100 per 100 ml) levels of FIB. The metareg function in Stata was used after a continuity correction of ±0.5 where the proportion of samples positive was zero or one [44], and we estimated the within study variance for each proportion as the reciprocal of the binomial variance [45]. Subgroup analysis included variables defined a priori (including water source type, rural versus urban, and income-level) and defined a posteriori (for example if piped water had been treated prior to distribution). We separately evaluated piped and other improved sources for those variables reaching significance at the 5% level in bivariate analysis for all source types. Studies that included both improved and unimproved sources were then combined using meta-analysis with the odds ratio (OR) as the effect measure. We calculated a pooled estimate of the protective effect of an improved source and corresponding confidence intervals using the metan function in Stata. We then assessed the influence of small study bias by the funnel plot method and performed an Egger's test using a normal likelihood approximation. The extent of heterogeneity in protective effect was determined using Higgins I2 and corresponding confidence intervals were calculated [42]. Calculations were performed in Stata 13SE. Results Search Results As shown in Figure 2, in total, 6,586 reports were identified through database searches. A further 1,274 reports were identified from grey literature and correspondence with experts. Most studies were excluded because they did not test water that was clearly used for drinking, did not associate results with a water source type, or did not include enough different water sources or in the case of packaged water, brands. Studies often did not provide an adequate description of the water sources to allow them to be matched to the JMP source categories; this limitation was particularly the case for ground water sources. For example, several studies reported results for “hand pumps” (a description of the technology above ground) but did not provide details about well construction. Although these may often be boreholes, hand pump conversions are also applied to dug wells. Other studies simply described water sources as “wells” or “springs.” Some studies provided details that are not captured in the JMP classification, such as whether water from a piped supply had been treated. Full texts could not be obtained for 99 potentially relevant reports, many of which were conference presentations and most of which were identified from bibliographies. The remaining 310 reports [6],[24],[46]–[353] were incorporated in our review and provide information on 96,737 water samples. The total number of studies is higher (319) due to a small number of multi-country reports. On average each study provides information on 1.7 water source types, resulting in a database with 555 datasets (Dataset S1). 10.1371/journal.pmed.1001644.g002 Figure 2 Flowchart for a review of safety of sources of drinking-water. Study Characteristics Characteristics of included studies are summarized in Table 3. The review is dominated by cross-sectional studies (n = 241, 75%) with fewer longitudinal surveys (n = 39, 12%). Authors report selecting sources or households at random in a minority of studies (n = 68, 21%); most of these studies selected sources randomly within a region or community rather than at national level. The main exceptions were the Rapid Assessment of Drinking-Water Quality (RADWQ) studies commissioned by WHO and UNICEF, of which five have been published [64],[65],[164],[281],[322] and a repeated cross-sectional study in Peru for which only the total coliform results have previously been reported but for which we were able to secure E. coli data [227]. 10.1371/journal.pmed.1001644.t003 Table 3 Characteristics of included studies. Characteristic Studies Datasets Samples Number (%) Number (%) Number (%) Setting Urban 146 (46) 227 (41) 30,038 (31) Rural 130 (41) 243 (44) 34,850 (36) Both urban and rural 41 (13) 83 (15) 31,767 (33) Unclassified setting 2 (1) 2 (0) 82 (0) Emergencies 13 (4) 26 (5) 2,897 (3) Non-household 17 (5) 21 (4) 2,121 (2) Point of sampling Stored water 50 (15) 74 (13) 19,965 (21) Directly from source 293 (92) 481 (87) 76,772 (79) Water supply Improved 209 (65) 273 (49) 56, 268 (58) Piped 118 (37) 119 (21) 32,348 (33) Borehole 83 (26) 83 (15) 11,452 (12) Protected dug well 36 (11) 36 (6) 8,697 (9) Protected spring 11 (3) 11 (2) 978 (1) Rainwater 25 (8) 25 (5) 2,793 (3) Unimproved 62 (19) 71 (13) 5,594 (6) Unprotected dug well 49 (15) 49 (9) 4,577 (5) Unprotected spring 16 (5) 16 (3) 810 (1) Tanker truck 6 (2) 6 (1) 207 (0) Unclassified 167 (53) 213 (38) 35,087 (36) Sachet 15 (5) 15 (3) 1,305 (1) Bottled 35 (11) 35 (6) 2,339 (2) Dug well 49 (15) 49 (9) 4,577 (5) Spring 16 (5) 16 (3) 810 (1) Design Randomized 68 (21) 131 (24) 31,210 (32) Representative 74 (23) 148 (27) 37,614 (39) Cohort or case control 5 (2) 15 (3) 4,114 (4) Intervention 22 (7) 47 (8) 9,799 (10) Cross-sectional survey 241 (75) 404 (73) 48,559 (50) Longitudinal survey 39 (12) 66 (12) 32,302 (33) Diagnostic 12 (4) 23 (4) 1,963 (2) Parameter E. coli 152 (48) 270 (49) 32,298 (33) TTC only 167 (52) 285 (51) 64,439 (67) Language English 276 (86) 502 (90) 81,349 (84) Spanish 6 (2) 8 (1) 3,024 (3) Portuguese 24 (8) 29 (5) 9,146 (9) French 4 (1) 5 (1) 187 (0) Chinese 9 (3) 11 (2) 3,031 (3) Reporting Presence/absence of FIB 287 (90) 499 (90) 90,056 (93) Microbial risk classification 90 (28) 165 (30) 23,953 (25) Mean FIB 80 (25) 136 (25) 15,530 (16) Geometric mean FIB 34 (11) 68 (12) 11,797 (12) Range of FIB 74 (23) 108 (19) 9,407 (10) Standard deviation of FIB 21 (7) 38 (7) 4,417 (5) Sanitary risk 44 (14) 82 (15) 15,808 (16) WHO sanitary risk 12 (4) 31 (6) 9,160 (9) Sanitary risk classification 17 (5) 44 (8) 10,667 (11) Sample Size a Small (n = 10–30) NA 192 (35) 3,711 (4) Medium (n = 31–100) NA 187 (34) 11,615 (12) Large (n = 101–6,021) NA 176 (32) 81,411 (84) Quality b Low (1–5) 113 (36) 199 (36) 27,892 (29) Medium (6–7) 94 (29) 142 (26) 16,980 (17) High (8–13) 112 (35) 214 (39) 51,865 (54) Total 319 (100) 555 (100) 96,737 (100) a Terciles by datasets. b Terciles by study. NA, not applicable. Study quality varied greatly spanning from a quality score of 1 to 12 and with an interquartile range of 5 to 8 (Figure S1). Whereas most studies described the analytical method used to detect E. coli or TTC (80%), how water sources were selected (67%), and the setting in which the study took place (86%), fewer specified quality control procedures (15%), met the basic sample handling criteria (25%), used trained technicians to conduct the water quality tests (15%), or arranged testing in an accredited laboratory (12%) (Figure S2). Most studies were from sub-Saharan Africa, southern Asia, or Latin America and the Caribbean (Figure 3). The majority of included studies investigated water quality at the source. Studies reporting on the quality of water stored in households by provenance were less common (n = 49), and few of these compare quality of stored water with that of the associated source (n = 26). Several studies took place during or after emergencies [97],[201] and natural hazards, including cyclones [235], floods [78],[208], droughts [341], and tsunamis [130],[147],[331]. Non-household settings such as schools and health facilities were addressed in a small number of studies (n = 17). Few studies separately report water quality information from slum or peri-urban settings (n = 7). 10.1371/journal.pmed.1001644.g003 Figure 3 Map of study locations. Qualitative Synthesis In Figures S3 and S4 levels of microbial contamination are shown using the FIB level classification ( 100 FIB per 100 ml), grouped by type of improved water source. These results are broadly in agreement with a comparison using measures of central tendency (Figure S5) and show great variability in the likelihood and extent of contamination between studies and source types. Large studies with random sampling demonstrate marked differences in water quality between countries; for example less than 0.01% of samples from utility piped supplies in Jordan [281] were found to contain TTC compared with 9% to 23% of utility piped supplies in the other four RADWQ countries [64],[65],[164],[322]. Only one national randomized study differentiated between rural and urban areas; the proportion of samples from piped supplies containing E. coli was found to be substantially higher in rural (61%, n = 101) than urban (37%, n = 1470) areas in Peru [227]. In comparison to microbial testing, sanitary inspections are less widely practiced or data are rarely published. Sanitary inspection procedures vary considerably and are usually adapted to the local context; of the 44 studies reporting sanitary inspections only 12 used standardized WHO forms. In Figure S6 the sanitary risk levels as reported in nine studies are compared with the proportion of samples containing FIB and suggest that there is no strong association between these two measures. Between Studies Analysis: CDF and Meta-regression The number of studies reporting high proportions of samples contaminated or high levels of FIB is lower for improved sources as can be seen in Figure S7. Yet, in 38% of 191 studies reporting the quality of improved sources, at least a quarter of samples exceeded recommended levels of FIB. Figure S8 shows CDFs by source type with similar patterns to those from the FIB level classification. Results of the meta-regression are shown in Table 4. We find that country income-level is a significant determinant of water quality and the odds of contamination are 2.37 times (95% CI 1.52–3.72 [p = 0.001]; Table 4) higher in low-income countries compared with wealthier countries. However this result is not significant when separately considering piped and other improved sources (Tables S1 and S2). 10.1371/journal.pmed.1001644.t004 Table 4 Between studies meta-regression. Variables Proportion of Samples >1 FIB per 100 ml Proportion of Samples >100 FIB per 100 ml Obs. OR [95% CI] p-Value Obs. OR [95% CI] p-Value Source type Improved vs. unimproved 291 0.14 [0.08–0.25] 9 out of 13; Figure S1) with description of quality control procedures, meeting handling criteria, and statement of season(s) of sampling most frequently omitted quality factors. Many studies, particularly of groundwaters, were excluded as we could not match water source types or determine whether they were “improved.” At the review level, we may not have identified all studies that meet the inclusion criteria. To capture additional studies would have required the screening of tens of thousands of records, as we were unable to identify more specific search terms. Two sources of water quality information that could be used in future studies and monitoring: regulatory surveillance and utility quality control data are likely to be extensive and not well represented as they may not be published and publicly available. Publicly available data from these sources rarely matched our inclusion criteria, usually because of failure to report sample sizes or associate water quality with source type. We identified few studies in languages other than English despite conducting searches in four other languages, and several regions are underrepresented (Figure 3) including Caucasus and Central Asia and Oceania for which studies may be available in other languages. Since few studies separately report water quality in slums, we combined studies of slum and peri-urban populations with those taking place in formal urban areas and we were therefore unable to investigate intra-urban disparities [7]. There may be a small number of errors in the database; in the 10% of English language studies independent extraction 1 per 100 ml) and high (>100 per 100 ml) E. coli or TTC, by improved and unimproved source. (EPS) Click here for additional data file. Figure S8 Cumulative density function of the proportion of samples containing fecal indicator bacteria in each study for improved (left) and unimproved (right) sources by type. (EPS) Click here for additional data file. Figure S9 Funnel plot for the odds ratio comparing the safety of improved and unimproved sources in a given study. (EPS) Click here for additional data file. Table S1 Between studies meta-regression for piped supplies. (DOCX) Click here for additional data file. Table S2 Between studies meta-regression for other improved sources. (DOCX) Click here for additional data file. Table S3 Variation in microbial safety during the year, findings of included studies for selected source types. (DOCX) Click here for additional data file. Alternative Language Abstract S1 Mandarin Chinese translation of the abstract by Hong Yang. (DOCX) Click here for additional data file. Dataset S1 Database of included water quality studies. (XLSX) Click here for additional data file. Protocol S1 Systematic review protocol. (DOCX) Click here for additional data file. Text S1 PRISMA checklist of items to include when reporting a systematic review or meta-analysis. (DOC) Click here for additional data file.
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The INDEPTH Network: filling vital gaps in global epidemiology
Osman Sankoh, Peter Byass (2012)
What is the INDEPTH Network? The International Network for the Demographic Evaluation of Populations and their Health (INDEPTH) Network is an umbrella organization for a group of independent health research centres operating health and demographic surveillance system (HDSS) sites in low- and middle-income countries (LMICs). Founded in 1998, it brought together a number of existing HDSS sites, and since then has encouraged newer HDSS sites to join. 1 The purpose of this Editorial is to set the scene for a series of profiles from INDEPTH HDSS member sites, the first examples of which are published in this edition of IJE. 2–5 All these profiles will follow a set pattern, to facilitate a systematic understanding of the multiplicity of HDSS sites involved in the Network and the various ways in which they are operated by their parent institutions. This Editorial therefore, follows the same general pattern as the individual profiles, but seeks to explore the epidemiological basis on which the HDSSs operate in general, and the role of the Network, rather than dealing with site-specific issues. At the central level, the INDEPTH Network operates from its base in Accra, Ghana, as an international NGO and is also registered as a not-for-profit entity in the USA. The emphasis on the Network’s position as a Southern-led and -based organization was an important founding tenet, and this is very welcome in a world where vestiges of colonialism still occasionally surface in relation to health data and policy. Day-to-day operations are led by the Executive Director (O.S.), and governance and oversight are provided by an international Board of Trustees and a Scientific Advisory Committee (chaired by P.B.). Why was the INDEPTH Network set up and what does it cover now? The raison d’être behind the emergence of the Network was the apparently intractable lack of reliable population-based data on health across many LMICs in Africa, Asia and Oceania. Recognizing that there are no quick fixes in terms of achieving universal individual registration of populations in LMICs, 6 the Network represents a medium-term attempt to break the link between material and data poverty. 7 Epidemiology in many LMICs suffers from a dual lack of reliable population data and human capacity to make use of them. The immediate consequence is that health policy making often lacks its essential evidence base, with the possible effect of failing to use scarce resources effectively in some of the world’s poorest countries. There are considerable global disparities in terms of epidemiological research output per population. Figure 1 shows the countries of the world shaded by a crude measure of this, namely the number of PubMed hits for a search on (‘epidemiology’ and ) per 1000 population. Much of Africa and Asia falls under the level of 0.05 per 1000, corresponding to rates which represent less than one-twentieth of some of the world’s leading countries in terms of epidemiological output. Superimposed on the map in Figure 1 are the current 43 HDSS sites run by 36 member centres of the INDEPTH Network. Although the locations of these sites are somewhat serendipitous, rather than being strategically planned, it is evident that there is considerable coverage across the areas of the world that lack substantial epidemiological output. Thus, it is clear that the INDEPTH Network, through these 43 sites in 20 countries, collectively following a population of 3.2 million people, does indeed offer possibilities for filling some of the global gaps in epidemiology. Figure 1 Countries of the world classified by PubMed citations for (‘epidemiology’ and ) per 1000 population, also showing the location of 43 HDSS site members of the INDEPTH Network (white dots) Where are the INDEPTH HDSSs? From the outset, the INDEPTH Network has operated by accepting as members already functioning independent health research centres that run HDSSs. Therefore, the Network has little influence over the locations or geographical distribution of member HDSS sites. However, since the concept of an HDSS would be somewhat irrelevant in countries with universal population registration, in practice there is self-selection of site locations in places where the lack of other reliable population-based data justifies the considerable effort involved in launching an HDSS. As is evident from Figure 1, this means that HDSS sites are located across Africa, Asia and Oceania, but by no means randomly. Several countries contain multiple HDSS sites, whereas many epidemiologically poor countries contain none. What populations are covered by the HDSSs and how are they followed up? HDSSs set out to collect epidemiological data (risks, exposures and outcomes) within a defined population on a longitudinal basis. In terms of Pearce’s classification scheme for epidemiological study designs, 8 this places HDSSs as representing ‘the most comprehensive approach since they use all of the available information on the source population over the risk period’. Unlike many epidemiological study designs, in which study participants are somehow selected to represent particular population subgroups, HDSSs generally set out to cover a real-life population and see what happens epidemiologically over a period of years and even decades. Issues of representativity and sampling are nevertheless critical considerations for all HDSSs, and need to be considered at the outset, when often little is known about potential target populations. Many HDSSs have started from intentions of covering an area that is at least subjectively thought to be typical of wider areas, maybe up to national levels. A chicken-and-egg situation arises, however, in that the motivation for having an HDSS is driven by a recognized lack of population-based health data, so that at the outset, very little may be known about candidate areas and maybe even less about the wider situation. There are no simple solutions to this conundrum. Even after identifying a target area for an HDSS, there are a number of possible design considerations. A range of different sampling strategies can be used within the target area, that have both epidemiological and practical implications. 9 In practical terms, one important consideration is whether the final population is defined as being within a contiguous area or in a collection of small areas (e.g. discrete villages) within a wider area. This has important logistic implications in terms of organizing and maintaining on-going surveillance, as well as affecting the definition of migration events (see below). The independent INDEPTH HDSSs naturally include a mixture of approaches to initially identifying target areas, within-area sampling and population contiguity. The overall size of the population within an HDSS is a further important factor, as is the case in any epidemiological study. However, an HDSS is not a classic sample survey, and so determining the size of the target population is not straightforward. Size is of course driven by considerations of the rarest event(s) of interest, which for most HDSSs are mortality-related outcomes. If specific causes of mortality are of particular concern, then the overall population size needs to be based on numbers relating to the nth ranked cause of interest. 10 Current INDEPTH member HDSS sites range in population size from tens of thousands up to around a quarter of a million. In most HDSSs the overall numbers are driven by mortality outcomes, with the result that surveillance of particular more common outcomes (such as morbidity and social measures) may in some situations be more effectively undertaken using a sample drawn from within the overall HDSS population. During the life of the INDEPTH Network, the technological and methodological possibilities for obtaining and using geographical data have advanced considerably, to the point where recording the latitude and longitude of every residential unit, and other salient features, in an HDSS using global positioning system (GPS) technology have become commonplace. Once an HDSS population is defined, an initial detailed census is usually undertaken to capture details of all residents and the social units in which they live. This usually involves assigning unique identifiers to all the residents and social units encountered in the census, using a numbering system that has sufficient capacity for expansion to reflect the addition of future residents and social units. It is not simple to arrive at generic definitions of social units across cultures and traditions, and individual HDSSs have to handle these issues in ways that make sense for their own context, both for physical structures (housing) and groups of inhabitants (families). INDEPTH has tried to standardize definitions as far as possible by publishing a resource kit for HDSS design on its website. This initial census then forms the basis of a database system that is updated on a regular basis to reflect the dynamic cohort of people living within the HDSS, as conceptualized in Figure 2. An important consideration is to determine the modality of the regular update rounds. Since HDSSs operate by definition in populations that are not otherwise enumerated, and generally have weak infrastructures, the norm is that local staff have to be recruited to undertake regular update visits to all the social units in the defined area. This forms a major component of the ongoing effort of running an HDSS, and consequently issues such as the frequency of update rounds need to be considered very carefully. Different INDEPTH HDSSs use various update frequencies, from one to four annual rounds. Certain types of events, e.g. neonatal mortality, are likely to be particularly sensitive to recall bias, which in turn is related to update frequency. Thus, it tends to be the case that more frequent updates are needed in high mortality or high migration settings, whereas in societies that are more stable, or at later stages of demographic transition, less frequent updates may prove adequate. Figure 2 Conceptual structure of the dynamic cohort model used by INDEPTH Health and Demographic Surveillance System (HDSS) sites What is being measured and how are the HDSS databases constructed? Having set up an HDSS, the next challenge is to track the progress of the dynamic cohort shown in Figure 2 by regularly updating a series of core parameters, detailed below. Naturally, the operation of an HDSS is not confined only to these core activities, and most HDSSs will have specific agendas defining what other parameters they may need to handle, e.g. in relation to the epidemiology of specific diseases, the execution of clinical trials, monitoring the effectiveness of health systems and other important issues that can be built onto the basic HDSS platform. Social units Keeping track of social units is a challenging issue, since it involves both physical structures (that can be newly built, in existence or be demolished) and the family groups associated with physical structures (that can migrate in or out as complete groups, or particular individuals can migrate to join or leave a group). In some cultures the physical structures may be large and complex compounds, perhaps housing up to 100 people and possibly containing subunits based on a polygamous social structure. At the other end of the spectrum, nuclear families may occupy small, discrete dwellings. Many HDSSs also aim to gather data on socio-economic status, often reflected by a basket of parameters including details of the physical structure, as well as owning traditional and modern assets. Births Capturing details of new births is a critical function of any HDSS, since births form a major part of new entrants to the cohort and are critical to any analyses of fertility. In some settings, traditional behaviours around childbirth (e.g. going to stay at the maternal grandmother’s residence for the birth and neonatal period) may make births more difficult to record accurately. There is a particular difficulty around detecting early neonatal deaths, and separating these reliably from intra-partum stillbirths, and this becomes more difficult with less frequent update rounds. Migrations Tracking details of migration patterns is one of the most complex areas in HDSSs, fundamentally comprising people moving into the surveillance area, within the area and out of the area. Many of these complexities are reflected in INDEPTH’s monograph on migration. 11 Every type of migration needs to be defined by rules (involving duration, intent, destination, etc.) which are appropriate to the population concerned. Some communities experience regular patterns of seasonal migration, related to employment or agricultural production. The possibility of multiple moves per individual over a period of time must be incorporated, and a further challenge can be the reliable re-identification of an individual on in-migration as being the same person who previously moved out. The design of an HDSS site in terms of the contiguity of the surveyed population is also important, since local moves in a non-contiguous population may be classified as in- and out-migrations, whereas similar moves in a contiguous area would amount to within-site migrations. Deaths Deaths, documented by age and sex, are a critical outcome measure for every HDSS and, in addition to reporting basic mortality rates, are an essential component in formulating life tables and other demographic measures for HDSS populations. As noted above, one of the most difficult issues involves reliably identifying early neonatal deaths. Causes of death Identifying the causes of death is a much more difficult issue in populations where most deaths do not occur in health facilities. The only realistic approach to attributing the cause of death is by carrying out verbal autopsy (VA) interviews with relatives or caretakers of deceased individuals, and then using those data to arrive at a likely cause of death. The INDEPTH Network was closely associated with developing a WHO standard instrument for VA interviews. 12 In many HDSSs, interpretation of the VA data was done by giving the VA data to local physicians, often more than one per case, in order to arrive at a consensus cause. However, this is an expensive and time consuming process that is gradually being superseded for most purposes by the application of computer-based probabilistic models. 13 INDEPTH is currently part of a new round of VA tool development in conjunction with WHO, which aims to simplify and shorten the VA process, as well as moving the scope of VA beyond research settings into non-enumerated populations. Databases Maintaining a database that reflects all the details of the population in a dynamic cohort is one of the most demanding tasks for most HDSSs, and a range of different approaches are used. The longitudinal nature of the HDSS data demands the use of relational database management systems (RDBMS) to handle the considerable volume of data involved over long periods of time. The basic principles of implementing an RDBMS for an HDSS have not changed fundamentally since the 1980s, when one of the longest-standing INDEPTH member HDSS sites made the transition to an RDBMS system. 14 However, appropriate hardware and software resources have progressed through several generations of development in the meantime, and that is reflected in the current range of implementations across the INDEPTH Network. These include implementations built on proprietary RDBMS systems such as Microsoft FoxPro™, Microsoft Access™ and Structured Query Language (SQL), as well as generic systems made available for the use of HDSS sites, such as the Household Registration System from the Population Council, 15 subsequently re-engineered as the paperless SQL-based ‘Open-HDS’. As commercial hardware and software specifications move on (e.g. Microsoft’s decision to cease supporting FoxPro™), long-term HDSS operations are sometimes forced to migrate their database operations onto new platforms, which is not a trivial matter for long-term databases linked to live surveillance. Ethical issues Running an HDSS over a long period raises a range of ethical issues that are different in some respects from those pertaining to many epidemiological studies. In the first place, the core HDSS data on vital events that are routinely collected in an HDSS population tend to be considered as research data, and subject to research ethics approval and informed consent, even though in countries that implement universal vital registration, it is regarded as a civic duty or even a legal obligation to provide such data. But, however population data are viewed, there are essential standards of confidentiality and anonymity that must be safeguarded. In HDSS data, there are three particularly critical types of data in this respect. Individual identities (whether by name or some other identifier) have to be protected at all stages of the process—from field interviewers observing adequate standards of confidentiality through database systems (and their backups) being held securely, to not revealing identifiers in any data sharing or outputs. Closely coupled with this, since HDSSs now commonly collect the GPS locations of households, it is important to also regard these data as confidential, since in principle they can be used to identify and locate households, and thereby their residents. Anonymizing GPS data is a much more difficult issue than simply removing names from a database. 16 Third, HDSS databases typically accumulate a large volume of personal, often medical, data (such as HIV status) that are sensitive and must be kept confidential. Key findings and publications Outputs from the INDEPTH Network mentioned here comprise those that are based on data from more than one HDSS site, or which make external comparisons. The individual HDSS site profile papers will provide further details of site-specific outputs. The INDEPTH Network website (www.indepth-network.org) provides information about the Network, its organization and current activities. One of the clear strengths of a network such as INDEPTH is its potential to collate data from member HDSS sites into outputs that enable systematic comparisons to be made. The first major INDEPTH output was a monograph published in 2002 that outlined basic HDSS concepts and gave details of 22 HDSS site members at that time. 17 Two further monographs relating to health equity in small areas 18 and migration 11 followed in 2005 and 2009, respectively. In a different format, using a supplement in an open-access journal, three sets of multi-site papers were published in 2009–10. The first related to cross-site findings on non-communicable disease risk factors from a group of INDEPTH member HDSS sites in Asia. 19– 27 The second related to mortality clustering across a range of INDEPTH member HDSS sites 28 – 36 and the third to results from eight INDEPTH member HDSS sites, which participated in the WHO–SAGE programme on ageing. 37– 46 The latter Supplement represented an innovation for the INDEPTH Network with the combined dataset used for the analyses also being published online together with the papers. Publications based on these public-domain data are now emerging. 47 A number of other papers have considered particular issues at the Network level. 48– 53 In addition, there have been some outputs that have involved inter-site collaborations but not included wide representation across the Network. 54– 59 In some cases, multiple INDEPTH members are also members of other research networks such as the RTS,S Clinical Trials Partnership 60 and the Alpha Network. 61 Several other studies have made comparisons between HDSS data from single INDEPTH HDSS sites and other sources. 62– 65 Future analysis plans As well as the substantial and continuing volume of outputs from individual HDSS sites, the INDEPTH Network will continue to produce multi-site outputs in particular topic areas. Current priorities include comparative assessments of fertility and cause-specific mortality patterns, as well as retrospective analyses of HDSS data against correspondingly timed weather data, which offer insights into the possible future population effects of changes in climatic conditions. Strengths and weaknesses HDSS sites represent an inherently strong epidemiological design, giving considerably greater analytical scope than can be achieved from e.g. cross-sectional approaches. However, the resources required to run an HDSS effectively are very considerable, particularly since the greatest gaps in health data are generally found in more logistically challenged environments. Not least this makes it very difficult for many HDSS sites to recruit and retain highly competent personnel, particularly those with experience in database management and epidemiological analysis, with the result that HDSS sites sometimes find it difficult to maximize their outputs. A recurrent issue that arises in considering HDSS data is how the site populations are, or are not, representative of the wider surrounding populations. Although this does not pose any technical issues in terms of analysing data within an HDSS site, it is of concern when it comes to interpreting HDSS data into wider epidemiological and policy arenas. There are no simple solutions to this issue, since HDSSs are always located in places where little is known about the surrounding population. It is possible to make comparisons with other data sources, such as national censuses and cluster sample surveys, 62– 65 but these sources come with their own disadvantages such as greater recall bias, and hence it is very difficult to attribute causes to observed differences. An empirical investigation into this issue used Swedish national data from 1925, a time when Sweden shared many characteristics with contemporary LMICs. 66 This showed that the majority of individual counties could have been taken as adequately representative of the national population, and the less representative counties were self-evidently so (including the capital city and the most remote regions). Although this does not offer any absolute evidence about the representativity of INDEPTH member HDSS sites, it suggests that it is not reasonable to assume by default that HDSS populations are unrepresentative. The diversity observed across the INDEPTH member HDSS sites is a further source of both strength and weakness. As discussed earlier, there has never been any master plan for establishing HDSS sites in particular locations, and there are also significant (but often locally appropriate) detailed methodological differences between HDSS sites. This brings strength in terms of having highly functional and locally supported HDSS sites in many locations, something that might not have happened so effectively in trying to locate HDSS sites more systematically. However, it also brings some weaknesses when it comes to making comparisons across HDSS sites and between the countries that they represent. In contrast, the much stricter uniformity enforced across the Demographic and Household Survey (DHS) series of cross-sectional surveys makes comparisons simpler, 67 but that stems from a completely different organizational paradigm. Nevertheless, the common core activities of all INDEPTH member DHSS sites, in following vital events longitudinally in a defined population, mean that the pooled INDEPTH data represent a major unified source of data on otherwise undocumented populations. An interesting development in some situations, e.g. in China, 68 is the concept of a distributed national network of HDSS-type surveillance, which perhaps represents a further intermediate step for the future. This has the advantage of being more widely representative, but at the same time bringing the advantages of a longitudinal approach. This may become a more common model as countries move towards universal individual registration. Data sharing and collaboration Data sharing issues have become increasingly important for all health researchers in recent years, and also continue to generate much debate. 69 There is also a continuing dialogue between researchers and funders on these issues. 70 The INDEPTH Network is firmly committed to the principles and practice of sharing data, as expressed in the INDEPTH Data Access and Sharing Policy document, available as Supplementary data at IJE online. The issues involved in sharing HDSS data are complex. By the nature of the dynamic cohort, there is never any point in time when data collection is ‘complete’, and talking about sharing data at pre-determined intervals after completion is therefore not entirely helpful. Ways to work around these conceptual difficulties therefore have to be found, involving declaring particular periods of data from an HDSS as being ready for sharing at appropriate times. INDEPTH has already launched the iSHARE portal for making data from HDSS member sites publicly available (www.idepth-ishare.org) to bona fide users, not unlike the arrangements for access to DHS data sets. In the existing version of iSHARE, data files from the participating HDSS sites are arranged in separate event files (births, deaths, migrations), but plans are underway to standardize iSHARE data into a common event-based data format. The common event attributes involved are shown in Table 1, and the range of different possible events are listed in Table 2. This structure will allow all participating sites to present HDSS core data in a straightforward and standardized format, which will facilitate a wide range of possible analytical approaches. Table 1 Common event attributes for the INDEPTH data specification Attribute Variable name Description Record number RecNr A sequential number uniquely identifying each record in the data file Centre identifier CentreId An identifier issued by INDEPTH to each member centre of the format CCCSS, where CCC is a sequential centre identifier and SS is a sequential identifier of the site within the centre in the case of multiple site centres Individual identifier IndividualId A number uniquely identifying all the records belonging to a specific individual in the data file. For data anonymization purposes, this number should not be the same as the identifier used by a contributing centre to identify the individual, but the contributing centre should retain a mapping from this identifier to their identifier Country identifier CountryId ISO 3166-1 numeric code of the country in which the surveillance site is situated Location identifier LocationId Unique identifier associated with a residential unit within the site and is the location where the individual was or became resident when the event occurred. For data anonymization purposes, this identifier should not be the same as the identifier used internally by the contributing centre, but the contributing centre should retain a mapping of this identifier to their internal location identifier Date of birth DoB The date of birth of the individual Event EventCode A code identifying the type of event that has occurred (Table 2) Event date EventDate The date on which the event occurred Observation date ObservationDate Date on which the event was observed (recorded), also known as surveillance visit date Event count EventCount The total number of events associated with this individual in this data set Event number EventNr A number increasing from 1 to EventCount for each event record in order of event occurrence Table 2 Event types for the INDEPTH data specification Event Code Definition Attributes Attribute description Birth BTH The birth of an individual to a resident female MotherId DeliveryEventId The IndividualId of the mother The RecNr of the delivery event associated with this birth Enumeration ENU Starting event for all individuals present at the baseline census of the surveillance area. It is the date on which the individual was first observed to be present in the surveillance area during the baseline census In-migration IMG The event of migrating into the surveillance area Origin Classification scheme to be developed Out-migration OMG The event of migrating out of the surveillance area Destination Classification scheme to be developed Location exit EXT The event of leaving a residential location within the surveillance area to take up residence in another residential location within the surveillance area Destination The LocationId of the location within the surveillance area to which the individual relocated Location entry ENT The event of taking up residence in a residential location within the surveillance area following a location exit event. Note that location exit and entry are actually two parts of the same action of changing residential location and as such happen on the same event date Origin The LocationId of the residential location from which the individual moved Death DTH The death of the individual under surveillance. The date of death is the event date Cause1 Cause2 Cause3 Likelihood1 Likelihood2 Likelihood3 Up to three causes of death coded using the WHO list of verbal autopsy death causes. Likelihood values associated with each possible cause of death Delivery DLV The event of a pregnancy end after 28 weeks of gestation, which may or may not result in the birth of one or more individuals (represented in this dataset by a BTH event linked to this delivery event) LBCnt SBCnt Parity Live birth count Stillbirth count The number of live births to these women prior to this delivery Observation end OBE An event inserted when a data set is right censored at an arbitrary date and this individual remained under surveillance beyond this date. The right censor date is the date of this event Last observation OBL An event indicating the last point in time on which this individual was observed to be present and under surveillance. Event date equals observation date in this instance. Normally there should be no individuals with this event as their last event if the right censoring date is prior to the start of the last complete census round Observation OBS Used to record characteristics of individuals under surveillance valid at the time of the observation. Could be used to record aspects such as educational attainment, employment status or anthropometry measures. Specific examples of this event are not part of the minimum core individual dataset, but are specified to allow for site or working group needs Conclusion Our aim here is to describe the essential nature of the INDEPTH Network as a background to detailed profiles of constituent member HDSS sites. Although all those sites have important differences, the huge volume of detailed individual data generated across Africa, Asia and Oceania by the Network constitutes a unique resource of great value to demographers, epidemiologists and health planners. Supplementary Data Supplementary Data are available at IJE online. Funding Osman Sankoh is funded by core support grants to INDEPTH from the Hewlett Foundation, Gates Foundation, Sida/GLOBFORSK and Wellcome Trust. Supplementary Material Supplementary Data
Author and article information
Contributors
Perpetua Modjadji: Perpetua.modjadji@smu.ac.za
,
Perpetuamodjadji@gmail.com
Journal
Journal ID (nlm-ta): BMC Public Health
Journal ID (iso-abbrev): BMC Public Health
Title:
BMC Public Health
Publisher:
BioMed Central
(London
)
ISSN
(Electronic):
1471-2458
Publication date
(Electronic):
9
August
2019
Publication date PMC-release: 9
August
2019
Publication date Collection: 2019
Volume: 19
Electronic Location Identifier: 1087
Affiliations
Article
Publisher ID: 7412DOI: 10.1186/s12889-019-7412-y
PMC ID: 6689169
PubMed ID: 31399048
SO-VID: 96f57967-b490-41ce-98b4-aaa42534117c
Copyright © © The Author(s). 2019
License:
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
History
Date
received
: 17
May
2019
Date
accepted
: 31
July
2019
Funding
Funded by: Sefako Makgatho Health Sciences University
Award ID: D105 MODJADJI RDG
Categories
Subject:
Research Article
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issue-copyright-statement © The Author(s) 2019
ScienceOpen disciplines: Public health