Associations between High Temperature, Heavy Rainfall, and Diarrhea among Young Children in Rural Tamil Nadu, India: A Prospective Cohort Study.

BACKGROUND: The effects of weather on diarrhea could influence the health impacts of climate change. Children have the highest diarrhea incidence, especially in India, where many lack safe water and sanitation. OBJECTIVES: In a prospective cohort of 1,284 children under 5 y of age from 900 households across 25 villages in rural Tamil Nadu, India, we examined whether high temperature and heavy rainfall was associated with increased all-cause diarrhea and water contamination. METHODS: Seven-day prevalence of diarrhea was assessed monthly for up to 12 visits from January 2008 to April 2009, and hydrogen sulfide ([Formula: see text]) presence in drinking water, a fecal contamination indicator, was tested in a subset of households. We estimated associations between temperature and rainfall exposures and diarrhea and [Formula: see text] using binomial regressions, adjusting for potential confounders, random effects for village, and autoregressive-1 error terms for study week. RESULTS: There were 259 cases of diarrhea. The prevalence of diarrhea during the 7 d before visits was 2.95 times higher (95% CI: 1.99, 4.39) when mean temperature in the week before the 7-d recall was in the hottest versus the coolest quartile of weekly mean temperature during 1 December 2007 to 15 April 2009. Diarrhea prevalence was 1.50 times higher when the 3 weeks before the diarrhea recall period included [Formula: see text] (vs. 0 d) with rainfall of [Formula: see text] (95% CI: 1.12, 2.02), and 2.60 times higher (95% CI: 1.55, 4.36) for heavy rain weeks following a 60-d dry period. The [Formula: see text] prevalence in household water was not associated with heavy rain prior to sample collection. CONCLUSIONS: The results suggest that, in rural Tamil Nadu, heavy rainfall may wash pathogens that accumulate during dry weather into child contact. Higher temperatures were positively associated with diarrhea 1-3 weeks later. Our findings suggest that diarrhea morbidity could worsen under climate change without interventions to reduce enteric pathogen transmission through multiple pathways. https://doi.org/10.1289/EHP3711.

Reactivity in rapidly collected hygiene and toilet spot check measurements: a cautionary note for longitudinal studies.

Discreet collection of spot check observations to measure household hygiene conditions is a common measurement technique in epidemiologic studies of hygiene in low-income countries. The objective of this study was to determine whether the collection of spot check observations in longitudinal studies could itself induce reactivity (i.e., change participant behavior). We analyzed data from a 12-month prospective cohort study in rural Tamil Nadu, India that was conducted in the absence of any hygiene or toilet promotion activities. Our data included hygiene and toilet spot checks from 10,427 household visits. We found substantial evidence of participant reactivity to spot check observations of hygiene practices that were easy to modify on short notice. For example, soap observed at the household's primary handwashing location increased from 49% at enrollment to 81% by the fourth visit and remained at or above 77% for the remainder of the study.

H2S as an indicator of water supply vulnerability and health risk in low-resource settings: a prospective cohort study.

In this large-scale longitudinal study conducted in rural Southern India, we compared a presence/absence hydrogen sulfide (H2S) test with quantitative assays for total coliforms and Escherichia coli as measures of water quality, health risk, and water supply vulnerability to microbial contamination. None of the three indicators showed a significant association with child diarrhea. The presence of H2S in a water sample was associated with higher levels of total coliform species that may have included E. coli but that were not restricted to E. coli. In addition, we observed a strong relationship between the percent positive H2S test results and total coliform levels among water source samples (R(2) = 0.87). The consistent relationships between H2S and total coliform levels indicate that presence/absence of H2S tests provide a cost-effective option for assessing both the vulnerability of water supplies to microbial contamination and the results of water quality management and risk mitigation efforts.

Causal inference methods to study nonrandomized, preexisting development interventions.

Empirical measurement of interventions to address significant global health and development problems is necessary to ensure that resources are applied appropriately. Such intervention programs are often deployed at the group or community level. The gold standard design to measure the effectiveness of community-level interventions is the community-randomized trial, but the conditions of these trials often make it difficult to assess their external validity and sustainability. The sheer number of community interventions, relative to randomized studies, speaks to a need for rigorous observational methods to measure their impact. In this article, we use the potential outcomes model for causal inference to motivate a matched cohort design to study the impact and sustainability of nonrandomized, preexisting interventions. We illustrate the method using a sanitation mobilization, water supply, and hygiene intervention in rural India. In a matched sample of 25 villages, we enrolled 1,284 children <5 y old and measured outcomes over 12 mo. Although we found a 33 percentage point difference in new toilet construction [95% confidence interval (CI) = 28%, 39%], we found no impacts on height-for-age Z scores (adjusted difference = 0.01, 95% CI = -0.15, 0.19) or diarrhea (adjusted longitudinal prevalence difference = 0.003, 95% CI = -0.001, 0.008) among children <5 y old. This study demonstrates that matched cohort designs can estimate impacts from nonrandomized, preexisting interventions that are used widely in development efforts. Interpreting the impacts as causal, however, requires stronger assumptions than prospective, randomized studies.

Causal inference methods to study nonrandomized, preexisting development interventions

Empirical measurement of interventions to address significant global health and development problems is necessary to ensure that resources are applied appropriately. Such intervention programs are often deployed at the group or community level. The gold standard design to measure the effectiveness of community-level interventions is the community-randomized trial, but the conditions of these trials often make it difficult to assess their external validity and sustainability. The sheer number of community interventions, relative to randomized studies, speaks to a need for rigorous observational methods to measure their impact. In this article, we use the potential outcomes model for causal inference to motivate a matched cohort design to study the impact and sustainability of nonrandomized, preexisting interventions. We illustrate the method using a sanitation mobilization, water supply, and hygiene intervention in rural India. In a matched sample of 25 villages, we enrolled 1,284 children <5 y old and measured outcomes over 12 mo. Although we found a 33 percentage point difference in new toilet construction [95% confidence interval (CI) = 28%, 39%], we found no impacts on height-for-age Z scores (adjusted difference = 0.01, 95% CI = −0.15, 0.19) or diarrhea (adjusted longitudinal prevalence difference = 0.003, 95% CI = −0.001, 0.008) among children <5 y old. This study demonstrates that matched cohort designs can estimate impacts from nonrandomized, preexisting interventions that are used widely in development efforts. Interpreting the impacts as causal, however, requires stronger assumptions than prospective, randomized studies.