Rethinking policy perspectives on childhood stunting: time to formulate a structural and multifactorial strategy

Abstract

Stunting and chronic undernutrition among children in South Asia remain a major unresolved global health issue. There are compelling intrinsic and moral reasons to ensure that children attain their optimal growth potential facilitated via promotion of healthy living conditions. Investments in efforts to ensure that children's growth is not faltered also have substantial instrumental benefits in terms of cognitive and economic development. Using the case of India, we critique three prevailing approaches to reducing undernutrition among children: an over-reliance on macroeconomic growth as a potent policy instrument, a disproportionate focus on interpreting undernutrition as a demand-side problem and an over-reliance on unintegrated single-factorial (one at a time) approaches to policy and research. Using existing evidence, we develop a case for support-led policy approach with a focus on integrated and structural factors to addressing the problem of undernutrition among children in India. Key messages Eliminating child undernutrition is important from an intrinsic perspective and offers considerable instrumental benefits to individual and society. Evidence suggests that an exclusive reliance on a growth-mediated strategy to eliminate stunting needs to be reconsidered, suggesting the need for a substantial support-led strategy. Interpreting and addressing undernutrition as a demand-side problem with proximal single-factorial interventions is futile. There is an urgent need to develop interventions that address the broader structural and upstream causes of child undernutrition.

Keywords: India; childhood; cognition; economic growth; multifactorial; social determinants; stunting; support-led strategy; undernutrition; upstream interventions.

Figure 1

Correlation between mean height‐for‐age z‐scores (HAZ) and prevalence (expressed as proportion) of children who were stunted in 1992–1993 and 2005–2006. Source: Authors' calculations using data from National Family Health Survey (NFHS)‐1 and NFHS‐3. State abbreviations: AS, Assam; BR, Bihar; GA, Goa; GJ, Gujarat; HR, Haryana; JK, Jammu and Kashmir; KA, Karnataka; KL, Kerala; MH, Maharashtra; MN, Manipur; ML, Meghalaya; MZ, Mizoram; NL, Nagaland; OR, Odisha; PB, Punjab; RJ, Rajasthan; UP, Uttar Pradesh; DL, New Delhi; AR, Arunachal Pradesh.

Figure 2

Association between stunting prevalence and child mortality rate (CMR)/infant mortality rate (IMR) among (a) Indian states and (b) 56 low‐ and middle‐income countries

Figure 3

Prevalence of childhood stunting (overall, poorest and richest household wealth quintiles in 1992‐93 (< 48 months) and 2013–2014 (< 60 months). Source: Authors' calculations using data from National Family Health Survey (NFHS)‐1 and Rapid Survey on Children (RSOC) 2013–2014.

Figure 4

Association of (a) state‐level economic growth and state‐level prevalence of stunting in India, and (b) change in state‐level economic growth and change in state‐level prevalence of stunting for children from the poorest and richest quintile of household wealth as well as all children, between 1992–1993 and 2005‐2006. (*) Change in stunting prevalence = (prevalence in 2005–2006 − prevalence in 1992–1993) / 13. Change in net per capita domestic product = (state product in 2005–2006 − state product in 1992–1993) / 12. Data for stunting are from National Family Health Survey (NFHS)‐1 and NFHS‐3. Data for per capita net state domestic product (PCNSDP) are from Subramanyam et al. (2011).

Figure 5

Association between change in state‐level economic growth and change in state‐level Child Nutrition Score (CNS). Source: Authors' calculations using data from National Family Health Survey (NFHS)‐1 and NFHS‐3. (*) The dashed line was estimated after excluding GA. State abbreviations: AS, Assam; BR, Bihar; GA, Goa; GJ, Gujarat; HR, Haryana; JK, Jammu and Kashmir; KA, Karnataka; KL, Kerala; MH, Maharashtra; MN, Manipur; ML, Meghalaya; MZ, Mizoram; NL, Nagaland; OR, Odisha; PB, Punjab; RJ, Rajasthan; UP, Uttar Pradesh; DL, New Delhi; AR, Arunachal Pradesh. Child Nutrition Score (CNS) – We followed Aguayo et al. (2014) to estimate a CNS using five risk factors constructed using NFHS‐1 and NFHS‐3. The risk factors included were as follows: (1) early initiation of breastfeeding; (2) exclusive breastfeeding under 6 months; (3) timely introduction of complementary foods; (4) full vaccination; and (5) access to improved sanitary facilities. Although Aguayo et al. (2014) recommend the use of 10 proven essential interventions in the construction of CNS, we only used the first four indicators that we were able to estimate consistently from the available information in NFHS‐1 and NFHS‐3 and used access to improved sanitary facilities as a proxy for safe disposal of stools.