Adult consequences of growth failure in early childhood

Abstract

Background: Growth failure is associated with adverse consequences, but studies need to control adequately for confounding.

Objective: We related height-for-age z scores (HAZs) and stunting at age 24 mo to adult human capital, marriage, fertility, health, and economic outcomes.

Design: In 2002-2004, we collected data from 1338 Guatemalan adults (aged 25-42 y) who were studied as children in 1969-1977. We used instrumental variable regression to correct for estimation bias and adjusted for potentially confounding factors.

Results: A 1-SD increase in HAZ was associated with more schooling (0.78 grades) and higher test scores for reading and nonverbal cognitive skills (0.28 and 0.25 SDs, respectively), characteristics of marriage partners (1.39 y older, 1.02 grade more schooling, and 1.01 cm taller) and, for women, a higher age at first birth (0.77 y) and fewer number of pregnancies and children (0.63 and 0.43, respectively). A 1-SD increase in HAZ was associated with increased household per capita expenditure (21%) and a lower probability of living in poverty (10 percentage points). Conversely, being stunted at 2 y was associated with less schooling, a lower test performance, a lower household per capita expenditure, and an increased probability of living in poverty. For women, stunting was associated with a lower age at first birth and higher number of pregnancies and children. There was little relation between either HAZ or stunting and adult health.

Conclusion: Growth failure in early life has profound adverse consequences over the life course on human, social, and economic capital.

Figures

FIGURE 1.

Instrumental variable analysis of consequences of stunting. A simple linear model for instrumental variable estimation for the effect of S (stunting) on Y (an adult outcome) has 2 equations. Equation 1 determines S, and Equation 2 determines the effect of S on Y:where W is a vector of observed determinants that affect both S and Y directly, Z is a vector of instrumental variables, and U and V represent all remaining unobserved determinants that directly affect S and Y, respectively. If U is correlated with V, the ordinary least-squares estimate of β2 from Equation 2 on its own will be biased because S would be correlated with V. In addition to the true effect of S on Y, the estimate of β2 would include the correlated effect of the unobserved determinants, or confounders, in V. The correlation between U and V would be present, eg, if unobserved parental preferences or community services that affected S in early life also directly affected Y in later life. In instrumental variable estimates, S in Equation 2 is replaced by the predicted value of S from Equation 1. Under the following 2 assumptions, the instrumental variable estimate of β2 will be asymptotically unbiased: 1) Z is sufficiently strongly associated with S, and 2) Z affects Y only through S (ie, Z is independent of V). The first assumption is verifiable in the data. Tests for the second assumption are available when there are multiple instruments in Z (called overidentification tests).