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Exploring water, sanitation, and hygiene coverage targets for reaching and sustaining trachoma elimination: G-computation analysis [1]

['Kristin M. Sullivan', 'Department Of Epidemiology', 'University Of North Carolina At Chapel Hill', 'Chapel Hill', 'North Carolina', 'United States Of America', 'Emma M. Harding-Esch', 'Clinical Research Department', 'London School Of Hygiene', 'Tropical Medicine']

Date: 2023-02

The ethics committee at the University of North Carolina at Chapel Hill determined that this study did not constitute human subjects research (18–2360). It was approved by the ethics committee at the London School of Hygiene & Tropical Medicine (16275). GTMP/TD examiners sought informed consent before surveying. Data were collected, transmitted, and stored in a manner intended to protect participant anonymity and confidentiality.

Three survey types are conducted for estimating TF 1-9 . Baseline surveys are conducted in suspected-endemic EUs to establish baseline prevalence. After completing the recommended 1–5 years of AFE interventions, impact surveys are conducted to determine if the EU has reached a TF 1-9 <5%. If that target is not met, interventions continue. If the target is met, the EU discontinues MDA (F and E continue). A surveillance survey is conducted two years later to determine if TF 1-9 remains <5%.

WaSH survey questions are asked at the household level and are typically answered by self-nominated heads of households. WaSH questions, developed in line with WHO/UNICEF Joint Monitoring Program indicators, ask (1) the source and distance to water used for drinking during the dry season, (2) the source type and distance to water used for washing faces during the dry season, (3) the latrine types used and defecation practices among adults, and (4) the handwashing facilities available.

In each selected household, consenting residents are examined by certified trachoma graders after consent is obtained [ 8 ]. Graders evaluate each eye for TF based on the WHO trachoma simplified grading scheme [ 5 , 9 ]. TF is indicated by the presence of ≥5 follicles, each ≥0.5 mm in diameter, in the central part of the upper tarsal conjunctiva. People with active trachoma are offered antibiotic treatment.

Surveys are conducted at the EU level using two-stage cluster sampling, with sampling at the village and household levels [ 7 ]. Sampling is designed such that, to the extent possible, all EU residents have equal selection probability. All household members aged ≥1 year are eligible to participate.

Primary data were collected in population-based surveys supported by the Global Trachoma Mapping Project (GTMP; December 2012–January 2016; 29 countries) and Tropical Data (TD; initiated February 2016; 46 countries to date). These surveys are conducted where trachoma is or was suspected to be endemic. Comprehensive methodological details have been published elsewhere [ 3 , 7 ], however key details are provided below.

Present analysis

Setting. We invited all countries that had surveyed with GTMP/TD support through 2019 to share data. EUs that had been surveyed at least twice in the baseline-impact, impact-impact, or impact-surveillance survey sequence were eligible. Most EU boundaries did not change over time. If they did, we included an EU if its entire area was contained within a single prior EU boundary.

Design. Using data from the most recent survey in the EU, we estimated generalized intervention prevalence differences for two hypothetical WaSH exposures achieving serially increasing coverage levels. The generalized intervention prevalence difference contrasts the observed prevalence with the prevalence observed in a population in which, counter to the fact, there was increased exposure coverage due to a hypothetical yet realistic intervention [10]. In this study, the term "intervention" describes any hypothetical approaches (e.g., technological, behavioral, etc.) that would achieve the desired WaSH coverage in an EU. To correspond with programmatic activities, the EU was our chosen unit of analysis, rather than smaller geographic units such as clusters. Our hypothetical interventions were operationalized by identifying EUs with coverage below a target and then predicting overall prevalence, had those EUs been brought up to the coverage target. This approach implies that, as the intervention level increases, increasingly more EUs are intervened upon, so it is naturally dependent on the distribution of exposures in the population and respects the idea that the positive impacts of a realistic intervention would have natural bounds determined by how much the population is exposed prior to the intervention (unlike standard regression estimates which typically contrast “everyone exposed” vs. “no-one exposed”).

EU categorization. We categorized eligible EUs by their presumed programmatic goal over the survey period: "reaching elimination target" EUs had a prior baseline or impact survey and were seeking to reach TF 1-9 <5% at the subsequent impact survey; "maintaining elimination target" EUs had TF 1-9 <5% at a prior impact survey and were seeking to demonstrate that TF 1-9 had remained <5% at the subsequent surveillance survey. This distinction stratified the sample into EUs in which MDA was and was not recommended over the observation period. If ≥3 surveys were conducted in an EU and the most recent survey was a surveillance survey (e.g., baseline-impact-surveillance), the surveillance survey data were used in the maintaining elimination target EUs analysis, and the impact survey data were used in the reaching elimination target EUs analysis.

Population. The study population comprised 1–9-year-olds examined during the most recent survey in each EU (Fig 1, blue boxes). PPT PowerPoint slide

PNG larger image

TIFF original image Download: Fig 1. EU and participant inclusion by elimination group. Observations from the most recent surveys (denoted with a t) were the basis for the study populations, while observations from the prior surveys (denoted with a t-1) were used to calculate EU-level measures used for confounding adjustments. 69 EUs (n = 253,017 individuals) had a survey sequence of baseline-impact-surveillance and were therefore included in both the reaching elimination target EUs and in the adjustment EUs for the maintaining elimination target EUs. https://doi.org/10.1371/journal.pntd.0011103.g001

Outcome, exposures, covariates. The model outcome was participant-level presence or absence of TF in either eye. We characterized WaSH-related exposures in two household-level and two EU-level ways. The first household-level exposure was the collection time for face-washing water used during the dry season (the period during which washing water may be least available). For each household, we defined "nearby" face-washing water as water requiring <30 minutes roundtrip collection time and "not nearby" as ≥30 minutes roundtrip collection time. This facilitated comparison with prior literature and aligned with WHO/UNICEF Joint Monitoring Programme drinking water program indicators (where washing water indicators are not used) [11]. We did not differentiate between improved and unimproved water sources, as this distinction is less crucial for trachoma where water quantity is believed to be more important. The second household-level exposure categorized defecation sites of adult householders. We assigned "latrine use" to households in which adults usually defecated in latrines (private, shared, or public) or other closed/potentially closed sites (e.g., chamber pots, buckets, or surface water) and "no latrine use" to other households (defecation outside in open, exposed environments). We used the label "latrine use" because only 0.3% of households reported non-latrine closed facility use. We did not distinguish between improved and unimproved latrines because M. sorbens preferentially oviposits on human feces left exposed on the soil in open environments [12–14]. We characterized exposures at the EU level as (1) the proportion of households that reported nearby face-washing water use ("nearby face-washing water coverage") and (2) the proportion of households that reported usual latrine use by adults ("latrine coverage"). Coverages were estimated from all surveyed households in the EU, including those with and without resident children (Fig 1, green boxes). We constructed a directed acyclic graph (S1 Fig) based on relationships established through previous studies to determine potential confounders [15]. We included covariates at EU, household, and individual levels. Environmental covariates were considered but were not part of the minimally sufficient adjustment set. Since surveyed households and individuals differed for each of the paired EU surveys, data from the prior survey in the EU was only used to adjust for EU-level confounding. EU-level confounders were: TF 1-9 at the prior survey (Fig 1, orange boxes), nearby face-washing water coverage at the prior survey, latrine coverage at the prior survey, time between end of the prior survey and start of the most recent survey, and country. The only household-level confounder was population density (people/km2) surrounding the household. This was extracted from external georeferenced raster data for the survey year using unconstrained estimates from 2015–2019 (worldpop.org). Child’s age was included as a covariate.

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[1] Url: https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0011103

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