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Water, sanitation, and handwashing interventions may confer spillover effects on intervention recipients' neighbors by interrupting pathogen transmission. We measured geographically local spillovers in the Water Quality, Sanitation, and Handwashing (WASH) Benefits Study, a cluster-randomized trial in rural Bangladesh, by comparing outcomes among neighbors of intervention versus those of control participants. Geographically defined clusters were randomly allocated to a compound-level intervention (i.e., chlorinated drinking water, upgraded sanitation, and handwashing promotion) or control arm. From January 2015 to August 2015, in 180 clusters, we enrolled 1,799 neighboring children who were age matched to trial participants who would have been eligible for the study had they been conceived slightly earlier or later. After 28 months of intervention, we quantified fecal indicator bacteria in toy rinse and drinking water samples and measured soil-transmitted helminth infections and caregiver-reported diarrhea and respiratory illness. Neighbors' characteristics were balanced across arms. Detectable Escherichia coli prevalence in tubewell samples was lower for intervention participants' neighbors than control participants' (prevalence ratio = 0.83; 95% confidence interval: 0.73, 0.95). Fecal indicator bacteria prevalence did not differ between arms for other environmental samples. Prevalence was similar in neighbors of intervention participants versus those of control participants for soil-transmitted helminth infection, diarrhea, and respiratory illness. A compound-level water, sanitation, and handwashing intervention reduced neighbors' tubewell water contamination but did not affect neighboring children's health.

Rainstorms increase levels of fecal indicator bacteria in urban coastal waters, but it is unknown whether exposure to seawater after rainstorms increases rates of acute illness. Our objective was to provide the first estimates of rates of acute illness after seawater exposure during both dry- and wet-weather periods and to determine the relationship between levels of indicator bacteria and illness among surfers, a population with a high potential for exposure after rain. We enrolled 654 surfers in San Diego, California, and followed them longitudinally during the 2013-2014 and 2014-2015 winters (33,377 days of observation, 10,081 surf sessions). We measured daily surf activities and illness symptoms (gastrointestinal illness, sinus infections, ear infections, infected wounds). Compared with no exposure, exposure to seawater during dry weather increased incidence rates of all outcomes (e.g., for earache or infection, adjusted incidence rate ratio (IRR) = 1.86, 95% confidence interval (CI): 1.27, 2.71; for infected wounds, IRR = 3.04, 95% CI: 1.54, 5.98); exposure during wet weather further increased rates (e.g., for earache or infection, IRR = 3.28, 95% CI: 1.95, 5.51; for infected wounds, IRR = 4.96, 95% CI: 2.18, 11.29). Fecal indicator bacteria measured in seawater (Enterococcus species, fecal coliforms, total coliforms) were strongly associated with incident illness only during wet weather. Urban coastal seawater exposure increases the incidence rates of many acute illnesses among surfers, with higher incidence rates after rainstorms.

Complier average causal effects (CACE) estimate the impact of an intervention among treatment compliers in randomized trials. Methods used to estimate CACE have been outlined for parallel-arm trials (e.g., using an instrumental variables (IV) estimator) but not for other randomized study designs. Here, we propose a method for estimating CACE in randomized stepped wedge trials, where experimental units cross over from control conditions to intervention conditions in a randomized sequence. We illustrate the approach with a cluster-randomized drinking water trial conducted in rural Mexico from 2009 to 2011. Additionally, we evaluated the plausibility of assumptions required to estimate CACE using the IV approach, which are testable in stepped wedge trials but not in parallel-arm trials. We observed small increases in the magnitude of CACE risk differences compared with intention-to-treat estimates for drinking water contamination (risk difference (RD) = -22% (95% confidence interval (CI): -33, -11) vs. RD = -19% (95% CI: -26, -12)) and diarrhea (RD = -0.8% (95% CI: -2.1, 0.4) vs. RD = -0.1% (95% CI: -1.1, 0.9)). Assumptions required for IV analysis were probably violated. Stepped wedge trials allow investigators to estimate CACE with an approach that avoids the stronger assumptions required for CACE estimation in parallel-arm trials. Inclusion of CACE estimates in stepped wedge trials with imperfect compliance could enhance reporting and interpretation of the results of such trials.

Many community-based studies of acute child illness rely on cases reported by caregivers. In prior investigations, researchers noted a reporting bias when longer illness recall periods were used. The use of recall periods longer than 2-3 days has been discouraged to minimize this reporting bias. In the present study, we sought to determine the optimal recall period for illness measurement when accounting for both bias and variance. Using data from 12,191 children less than 24 months of age collected in 2008-2009 from Himachal Pradesh in India, Madhya Pradesh in India, Indonesia, Peru, and Senegal, we calculated bias, variance, and mean squared error for estimates of the prevalence ratio between groups defined by anemia, stunting, and underweight status to identify optimal recall periods for caregiver-reported diarrhea, cough, and fever. There was little bias in the prevalence ratio when a 7-day recall period was used (

Understanding whether influenza vaccine promotion strategies produce community-wide indirect effects is important for establishing vaccine coverage targets and optimizing vaccine delivery. Empirical epidemiologic studies and mathematical models have been used to estimate indirect effects of vaccines but rarely for the same estimand in the same dataset. Using these approaches together could be a powerful tool for triangulation in infectious disease epidemiology because each approach is subject to distinct sources of bias. We triangulated evidence about indirect effects from a school-located influenza vaccination program using two approaches: a difference-in-difference (DID) analysis, and an age-structured, deterministic, compartmental model. The estimated indirect effect was substantially lower in the mathematical model than in the DID analysis (2.1% (95% Bayesian credible intervals 0.4 - 4.4%) vs. 22.3% (95% CI 7.6% - 37.1%)). To explore reasons for differing estimates, we used sensitivity analyses and probabilistic bias analyses. When we constrained model parameters such that projections matched the DID analysis, results only aligned with the DID analysis with substantially lower pre-existing immunity among school-age children and older adults. Conversely, DID estimates corrected for potential bias only aligned with mathematical model estimates under differential outcome misclassification. We discuss how triangulation using empirical and mathematical modelling approaches could strengthen future studies., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)