Handwashing, sanitation and family planning practices are the strongest underlying determinants of child stunting in rural indigenous communities of Jharkhand and Odisha, Eastern India: a cross-sectional study : Child stunting in Jharkhand and Odisha
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Abstract
Abstract
The World Health Organisation has called for global action to reduce child stunting
by 40% by 2025. One third of the world's stunted children live in India, and children
belonging to rural indigenous communities are the worst affected. We sought to identify
the strongest determinants of stunting among indigenous children in rural Jharkhand
and Odisha, India, to highlight key areas for intervention.
We analysed data from 1227 children aged 6–23.99 months and their mothers, collected
in 2010 from 18 clusters of villages with a high proportion of people from indigenous
groups in three districts. We measured height and weight of mothers and children,
and captured data on various basic, underlying and immediate determinants of undernutrition.
We used Generalised Estimating Equations to identify individual determinants associated
with children's height‐for‐age z‐score (HAZ; p < 0.10); we included these in a multivariable
model to identify the strongest HAZ determinants using backwards stepwise methods.
In the adjusted model, the strongest protective factors for linear growth included
cooking outdoors rather than indoors (HAZ +0.66), birth spacing ≥24 months (HAZ +0.40),
and handwashing with a cleansing agent (HAZ +0.32). The strongest risk factors were
later birth order (HAZ −0.38) and repeated diarrhoeal infection (HAZ −0.23).
Our results suggest multiple risk factors for linear growth faltering in indigenous
communities in Jharkhand and Odisha. Interventions that could improve children's growth
include reducing exposure to indoor air pollution, increasing access to family planning,
reducing diarrhoeal infections, improving handwashing practices, increasing access
to income and strengthening health and sanitation infrastructure.
Most studies have some missing data. Jonathan Sterne and colleagues describe the appropriate use and reporting of the multiple imputation approach to dealing with them