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. 2013 Sep 16;8(9):e73784.
doi: 10.1371/journal.pone.0073784. eCollection 2013.

Open Defecation and Childhood Stunting in India: An Ecological Analysis of New Data From 112 Districts

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Free PMC article

Open Defecation and Childhood Stunting in India: An Ecological Analysis of New Data From 112 Districts

Dean Spears et al. PLoS One. .
Free PMC article

Erratum in

  • PLoS One. 2013;8(9). doi:10.1371/annotation/9ffcb740-f394-41af-bbbc-800c7cc25ea8

Abstract

Poor sanitation remains a major public health concern linked to several important health outcomes; emerging evidence indicates a link to childhood stunting. In India over half of the population defecates in the open; the prevalence of stunting remains very high. Recently published data on levels of stunting in 112 districts of India provide an opportunity to explore the relationship between levels of open defecation and stunting within this population. We conducted an ecological regression analysis to assess the association between the prevalence of open defecation and stunting after adjustment for potential confounding factors. Data from the 2011 HUNGaMA survey was used for the outcome of interest, stunting; data from the 2011 Indian Census for the same districts was used for the exposure of interest, open defecation. After adjustment for various potential confounding factors--including socio-economic status, maternal education and calorie availability--a 10 percent increase in open defecation was associated with a 0.7 percentage point increase in both stunting and severe stunting. Differences in open defecation can statistically account for 35 to 55 percent of the average difference in stunting between districts identified as low-performing and high-performing in the HUNGaMA data. In addition, using a Monte Carlo simulation, we explored the effect on statistical power of the common practice of dichotomizing continuous height data into binary stunting indicators. Our simulation showed that dichotomization of height sacrifices statistical power, suggesting that our estimate of the association between open defecation and stunting may be a lower bound. Whilst our analysis is ecological and therefore vulnerable to residual confounding, these findings use the most recently collected large-scale data from India to add to a growing body of suggestive evidence for an effect of poor sanitation on human growth. New intervention studies, currently underway, may shed more light on this important issue.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Open defecation predicts stunting, bivariate linear regression.
Note: n = 112 Indian districts; R2 = 34.8%. The size of the circles is proportionate to the population of the districts they represent. The grey shaded area is the 95% confidence set for the regression line.
Figure 2
Figure 2. Female literacy predicts stunting, bivariate linear regression.
Note: n = 112 Indian districts; R2 = 48.5%. The size of the circles is proportionate to the population of the districts they represent. The grey shaded area is the 95% confidence set for the regression line.
Figure 3
Figure 3. District average calorie consumption does not predict stunting, bivariate linear regression.
Note: n = 112 Indian districts; R2 = 0.7%. NSS = National Sample Survey. The size of the circles is proportionate to the population of the districts they represent. The grey shaded area is the 95% confidence set for the regression line.
Figure 4
Figure 4. Dichotomization reduces statistical power: R 2, simulations using NFHS-3.
Note: Observations are 1,000 Monte Carlo samples of 20,000 children under 5 drawn from India’s 2005 National Family and Health Survey. PSU = survey primary sampling unit (local area). The legend reports regression dependent variables.
Figure 5
Figure 5. Dichotomization reduces statistical power: t-statistics, simulations using NFHS-3.
Note: Observations are 1,000 Monte Carlo samples of 20,000 children under 5 drawn from India’s 2005 National Family and Health Survey. PSU = survey primary sampling unit (local area). The legend reports regression dependent variables.

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Grant support

There are no current external funding sources specifically for this study. Oliver Cumming's time was in part funded by UK aid from the Department for International Development (DfID), as part of the SHARE research programme. Dean Spears's time was in part funded by the Bill and Melinda Gates Foundation, as part of r.i.c.e.’s sanitation policy research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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