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Methods
Results
Tables 2 and 3 report summary statistics of cognitive achievement and growth measures and covariates. Mean height-for-age was at least one standard deviation below that of the reference child at all ages, and there are changes in mean HAZ as children age.
Tables 4 and 5 present estimates of direct, indirect, and total associations between HAZ at each age and cognitive achievement scores at age 8y. The results presented in Table 4 suggest that the total association between HAZ 1y and PPVT at age 8y (PPVT 8y) is positive and significant in Ethiopia, India, and Peru, whereas it is positive though insignificant in Vietnam. A comparison of the direct and indirect associations of HAZ 1y with PPVT 8y indicate that the share of the total association between HAZ 1y and PPVT 8y that is mediated though HAZ in subsequent periods is 40% in Ethiopia and 75% in India. In Peru and Vietnam, direct associations are negative whereas indirect associations are positive and larger in absolute value than the direct associations, suggesting that the positive total association between HAZ 1 y with PPVT 8y is fully mediated through HAZ at later ages in these two countries.
Path analysis results for MATH presented in Table 5 suggest positive and significant associations between HAZ at all ages and MATH scores at age 8 years in all countries. Indirect associations account for around 50% of the total association between HAZ 1 y and MATH 8 y in Ethiopia and Vietnam and around 60% in India, whereas direct and indirect associations are of opposite sign in Peru with the latter dominating the former. In the case of HAZ 5 y in Ethiopia, India, and Peru around 50% of the total association is mediated through HAZ 8 y, whereas in Vietnam the total association is almost completely indirect. Overall, in almost all cases, the magnitudes of the associations between HAZ at different ages and cognitive achievement measures at age 8 years suggest that growth in earlier periods of life is not more strongly associated with cognitive achievement at age 8 years than growth in later periods.
Table 6 presents estimates of direct, indirect, and total associations of growth trajectories and dietary LJI308 cost at age 5 and 8 y and primary school starting age. Table 6 does not report associations between early HAZ and later HAZ that were estimated in the case of dietary diversity at age 8 y and school starting age because these are the same as those reported in Tables 4 and 5. Results suggest that HAZ 1 y is positively and significantly associated with dietary diversity at age 5 y in India and Peru. This implies that the positive association between HAZ 1 y and HAZ 5 y can be partly explained by the fact that HAZ 1 y predicts dietary diversity between age 1 and 5 y that in turn may be a predictor of HAZ 5 y. The positive and significant association between HAZ 1 y and dietary diversity persists through age 8 y in Peru but the same is not the case in India, where only HAZ 5 y has a weakly significant and positive association with dietary diversity at age 8 y. This suggests that the link between HAZ 5 y and HAZ 8 y cannot be explained by the link between HAZ 5 y and dietary diversity between age 5 and 8 y. Moreover, results suggest a negative association between HAZ at each age and school starting age but the association is not significant in all cases. In particular, although the association between HAZ 1 y and school starting age is significant in all countries, HAZ 5 y is significantly associated with school starting age in Ethiopia and Peru, and HAZ 8 y only in Peru.
Discussion
We show that the conditional body size model estimates total effects that, combined with the result of Tu et al. (2013) that the lifecourse plot estimates direct effects, implies that the two approaches are expected to produce systematically different results that have distinct interpretations. This supports that differences in the approach used to model growth trajectories could provide a partial explanation of the differences in the results across studies. Moreover, this result is in contrast to previous empirical and methodological studies that treat lifecourse plot and conditional models as alternatives to testing a given relationship and suggest that the latter should be preferred on statistical grounds. In particular, this result implies that the two approaches are not substitutes but complements and thus combining them may yield better insights. We also show that the combination of the two approaches provides a way to explore direct and indirect pathways linking growth trajectories and cognitive development, as it is equivalent to path analysis of a general model. This is in line with Tu et al. (2013) who argue that “it is likely that a combination of approaches will be required to unravel the complexity in lifecourse research”.