Risk factors for chronic undernutrition among children in India: Estimating relative importance, population attributable risk and fractions
Introduction
The prevalence of childhood stunting and underweight in India remains persistently high (IIPS, 1995, IIPS, 2007). In 1992–93, amongst children aged 0–47 months, 57% and 49%, were stunted and underweight, respectively, declining to 47% and 42% in 2005–2006 (Authors calculation from the 1992-93 and 2005-06 NFHS datasets, 2015), and to 39% and 29% in 2013–14 (Ministry of Women and Child Development (2015)), the most recent year for which national data are available. In 2011, India accounted for 38% of the global burden of stunting (nearly 62 million children) (UNICEF, 2013) underscoring the importance of reducing undernutrition in India. Evidence also shows that steady increases in per capita income (or economic growth) that India experienced between 1992 and 2006 did not translate into reductions in undernutrition (Subramanyam et al., 2011). In parallel, there appears to be momentum to make direct investments in programs and interventions aimed at nutrition (e.g., breastfeeding, complementary feeding, micronutrient nutrition, and therapeutic and supplementary feeding), health (e.g., prevention and management of infectious diseases) and water, sanitation, and hygiene (WASH) programmes (Bhutta et al., 2013, Dangour et al., 2013, Ruel et al., 2013). Even though conceptual models on understanding child undernutrition emphasize a multifactorial framework (Bhutta et al., 2008), interventions and factors are often considered in isolation. Further, there appears to be disproportionate focus on what can be termed ‘nutrition-specific’ interventions targeted at addressing the immediate causes of undernutrition (Ruel et al., 2013), and considerably less emphasis has been placed on more social and structural factors including poverty reduction, improvements to socioeconomic status and maternal education, and intergenerational factors, all of which have been seen to be strongly associated with child nutritional outcomes (Özaltin et al., 2010, Subramanian et al., 2009, Subramanyam et al., 2011).
Using the most recent, comprehensive, and nationally representative data, that provides objective measures of child undernutrition, based on measured anthropometry (Corsi et al., 2011b), as well as a range of known “upstream” and “downstream” risk factors, we assess the relative and joint contribution of 15 factors in predicting the risk of childhood stunting/underweight in India, and provide estimates of population attributable risks and fractions associated with each risk factor and combinations of risk factors among all children and among stunted and underweight children.
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Data
Data for the analysis come from the 2005–2006 Indian National Family Health Survey (NFHS) (International Institute for Population Sciences, 2007), which remains the most recent nationally representative data on child anthropometry. The survey is based on a nationally representative sample of households across 29 states in India and the sampling frame covered approximately 99% of the population. Within households, the target sample was all married and unmarried women aged 15–49 years, men aged
Results
The prevalence of stunting and underweight in the study sample were 51.1% (95% confidence interval [CI]: 50.1–52.1) and 44.9% (95% CI: 43.9–45.9), respectively. Levels of risk factors were common in the sample including low household wealth (28.6%), no formal education (50.0%), short maternal stature (<145 cm, 12.1%), and poor dietary diversity (31.4%). 17.8% of the children had access to improved drinking water, and 40.9% of children were fully vaccinated (Table 1). Among mothers, 41.1% were
Discussion
This study has three salient findings. First, factors such as maternal height, BMI, education, and household wealth were highly related to child nutrition, and explained between 60 and 80% of the burden of undernutrition among stunted/underweight children. Second, among the remaining risk factors dietary diversity appears to be the major factor. Other factors including sanitation, household air quality, and vaccination showed relatively less contribution to explaining variation in child
Conflict of interest
Authors declare no conflict of interest.
Contributions
SVS and DJC conceptualized and designed the study. DJC conducted the analyses with assistance from IM and wrote the first draft of the manuscript. SVS contributed to the writing and provided overall supervision. All authors participated in writing and revising the final manuscript.
Ethics
This study was based entirely on a publicly available dataset without access to personal identifiers.
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