Children are exposed to environmental contaminants by placing contaminated hands or objects in their mouths. We quantified hand- and object-mouthing frequencies of Bangladeshi children and determined if they differ from those of U.S. children to evaluate the appropriateness of applying U.S. exposure models in other socio-cultural contexts. We conducted a five-hour structured observation of the mouthing behaviors of 148 rural Bangladeshi children aged 3-18 months. We modeled mouthing frequencies using 2-parameter Weibull distributions to compare the modeled medians with those of U.S. children. In Bangladesh the median frequency of hand-mouthing was 37.3 contacts/h for children 3-6 months old, 34.4 contacts/h for children 6-12 months old, and 29.7 contacts/h for children 12-18 months old. The median frequency of object-mouthing was 23.1 contacts/h for children 3-6 months old, 29.6 contacts/h for children 6-12 months old, and 15.2 contacts/h for children 12-18 months old. At all ages both hand- and object-mouthing frequencies were higher than those of U.S. children. Mouthing frequencies were not associated with child location (indoor/outdoor). Using hand- and object-mouthing exposure models from U.S. and other high-income countries might not accurately estimate children's exposure to environmental contaminants via mouthing in low- and middle-income countries.

Accurately assessing the microbiological safety of water sources is essential to reduce waterborne fecal exposures and track progress toward global targets of safe water access. Sanitary inspections are a recommended tool to assess water safety. We collected 1,684 water samples from 902 shallow tubewells in rural Bangladesh and conducted sanitary surveys to assess whether sanitary risk scores could predict water quality, as measured by Escherichia coli. We detected E. coli in 41% of tubewells, mostly at low concentrations. Based on sanitary scores, 31% of wells were low risk, 45% medium risk, and 25% high or very high risk. Older wells had higher risk scores. Escherichia coli levels were higher in wells where the platform was cracked or broken (Δlog₁₀ = 0.09, 0.00-0.18) or undercut by erosion (Δlog₁₀ = 0.13, 0.01-0.24). However, the positive predictive value of these risk factors for E. coli presence was low (< 50%). Latrine presence within 10 m was not associated with water quality during the wet season but was associated with less frequent E. coli detection during the dry season (relative risk = 0.72, 0.59-0.88). Sanitary scores were not associated with E. coli presence or concentration. These findings indicate that observed characteristics of a tubewell, as measured by sanitary inspections in their current form, do not sufficiently characterize microbiological water quality, as measured by E. coli. Assessments of local groundwater and geological conditions and improved water quality indicators may reveal more clear relationships. Our findings also suggest that the dominant contamination route for shallow groundwater sources is short-circuiting at the wellhead rather than subsurface transport.

The asset index is often used as a measure of socioeconomic status in empirical research as an explanatory variable or to control confounding. Principal component analysis (PCA) is frequently used to create the asset index. We conducted a simulation study to explore how accurately the principal component based asset index reflects the study subjects' actual poverty level, when the actual poverty level is generated by a simple factor analytic model. In the simulation study using the PC-based asset index, only 1% to 4% of subjects preserved their real position in a quintile scale of assets; between 44% to 82% of subjects were misclassified into the wrong asset quintile. If the PC-based asset index explained less than 30% of the total variance in the component variables, then we consistently observed more than 50% misclassification across quintiles of the index. The frequency of misclassification suggests that the PC-based asset index may not provide a valid measure of poverty level and should be used cautiously as a measure of socioeconomic status.

Objectives

Escherichia coli is the standard water quality indicator for diarrhoea risk. Yet, the association between E. coli and diarrhoea is inconsistent across studies without a systematic assessment of methodological differences behind this variation. Most studies measure water quality cross-sectionally with diarrhoea, risking exposure misclassification and reverse causation. Studies use different recall windows for self-reported diarrhoea; longer periods increase potential outcome misclassification through misrecall. Control of confounding is inconsistent across studies. Additionally, diarrhoea measured in unblinded intervention trials can present courtesy bias. We utilised measurements from a randomised trial of water interventions in Bangladesh to assess how these factors affect the E. coli-diarrhoea association.

Methods

We compared cross-sectional versus prospective measurements of water quality and diarrhoea, 2-versus 7-day symptom recall periods, estimates with and without controlling for confounding and using measurements from control versus intervention arms of the trial.

Results

In the control arm, 2-day diarrhoea prevalence, measured prospectively 1 month after water quality, significantly increased with log₁₀ E. coli (PR = 1.50, 1.02-2.20). This association weakened when we used 7-day recall (PR = 1.18, 0.88-1.57), cross-sectional measurements of E. coli and diarrhoea (PR = 1.11, 0.79-1.56) or did not control for confounding (PR = 1.20, 0.88-1.62). Including data from intervention arms led to less interpretable associations, potentially due to courtesy bias, effect modification and/or reverse causation.

Conclusions

By systematically addressing potential sources of bias, our analysis demonstrates a clear relationship between E. coli in drinking water and diarrhoea, suggesting that the continued use of E. coli as an indicator of waterborne diarrhoea risk is justified.

Microbiological water quality is usually assessed by the identification of Escherichia coli (E. coli), a fecal indicator. The hydrogen sulfide (H₂S) test is an inexpensive, easy-to-use, and portable alternative field-based water quality test. Our study evaluated the H₂S test's effectiveness as a water quality indicator for diarrhea risk. Field workers collected stored drinking water samples for H₂S analysis and detection of E. coli by membrane filtration and measured caregiver-reported diarrhea among children < 5 years in the same households 1 month later. We assessed the association between the H₂S test (incubated for 24 hours and 48 hours) and diarrhea prevalence, with 2-day and 7-day symptom recall periods (N = 1,348). We determined the sensitivity, specificity, and positive and negative predictive value (PPV, NPV) of the H₂S test compared with E. coli (N = 525). Controlling for potentially confounding covariates, H₂S-positive water (at 24 or 48 hours) was not associated with 2-day diarrhea prevalence (24-hour prevalence ratio [PR] = 1.03, 95% confidence interval [CI]: 0.63-1.69; 48-hour PR = 0.89, 95% CI: 0.58-1.38) or 7-day diarrhea prevalence (24-hour PR = 1.17, 95% CI: 0.76-1.78; 48-hour PR = 1.21, 95% CI: 0.81-1.80). The sensitivity, PPV, and NPV of the H₂S test was significantly higher when the H₂S test was incubated for 48 versus 24 hours whereas specificity showed the opposite trend. H₂S test sensitivity, PPV, and NPV increased with increasing E. coli levels, consistent with previous evidence that the H₂S test is a useful water quality tool in high-contamination settings. However, our results suggest that the H₂S test is not an effective indicator for waterborne diarrhea.

Childhood obesity has been associated with consumption of energy-dense foods such as caloric beverages and fast foods. Many low- and middle-income countries like Bangladesh are now experiencing a rising problem of noncommunicable diseases along with the long-standing problem of stunting and undernutrition. WASH Benefits Bangladesh was a large community-based cluster randomized controlled trial conducted in rural Bangladesh. Study clusters were randomized into seven arms: single nutrition (N); water (W); sanitation (S); hygiene (H); combined water, sanitation, and hygiene (WSH); WSH and nutrition (N + WSH); and a double sized control (C). Nutrition intervention messages included four promotional components: maternal nutrition, breastfeeding, complementary feeding, and lipid-based nutrient supplements. The World Health Organization infant food frequency questionnaire (24-hr recall and 7-day recall) was administered at Year 1 and Year 2 of intervention. The likelihood of any snack food consumption was significantly lower (odds ratio 0.37: 95% confidence interval [0.28, 0.49]) in the nutrition intervention arms compared to the control arm in Year 2 follow-up. In addition, in the water intervention arm, fewer children (about 50% less) consumed soft drinks, but not the other sugar-sweetened beverages, compared with control in Year 2. There were no other differences between groups. Simple messages about balanced diet and feeding family foods were effective in lowering commercially produced snack food consumption of the young children in low-income rural communities of Bangladesh. Provision of safe water apparently encouraged mothers to reduce offering unhealthy beverages to the young children.

Many public health interventions provide benefits that extend beyond their direct recipients and impact people in close physical or social proximity who did not directly receive the intervention themselves. A classic example of this phenomenon is the herd protection provided by many vaccines. If these 'spillover effects' (i.e. 'herd effects') are present in the same direction as the effects on the intended recipients, studies that only estimate direct effects on recipients will likely underestimate the full public health benefits of the intervention. Causal inference assumptions for spillover parameters have been articulated in the vaccine literature, but many studies measuring spillovers of other types of public health interventions have not drawn upon that literature. In conjunction with a systematic review we conducted of spillovers of public health interventions delivered in low- and middle-income countries, we classified the most widely used spillover parameters reported in the empirical literature into a standard notation. General classes of spillover parameters include: cluster-level spillovers; spillovers conditional on treatment or outcome density, distance or the number of treated social network links; and vaccine efficacy parameters related to spillovers. We draw on high quality empirical examples to illustrate each of these parameters. We describe study designs to estimate spillovers and assumptions required to make causal inferences about spillovers. We aim to advance and encourage methods for spillover estimation and reporting by standardizing spillover parameter nomenclature and articulating the causal inference assumptions required to estimate spillovers.