Comparative models of biological and social pathways to predict child growth through age 2 years from birth cohorts in Brazil, India, the Philippines, and South Africa

Background: Early growth faltering accounts for one-third of child deaths, and adversely impacts the health and human capital of surviving children. Social as well as biological factors contribute to growth faltering, but their relative strength and interrelations in different contexts have not been fully described. Objective: The aim of this study was to use structural equation modelling to explore social and biological multidetermination of child height at age 2 y in longitudinal data from 4 birth cohort studies in low- and middle-income countries. Methods: We analyzed data from 13,824 participants in birth cohort studies in Brazil, India, the Philippines, and South Africa. We used exploratory structural equation models, with height-for-age at 24 mo as the outcome to derive factors, and path analysis to estimate relations among a wide set of social and biological variables common to the 4 sites. Results: The prevalence of stunting at 24 mo ranged from 14.0% in Brazil to 67.7% in the Philippines. Maternal height and birthweight were strongly predictive of height-for-age at 24 mo in all 4 sites (all P values <0.001). Three social-environmental factors, which we characterized as "child circumstances," "family socioeconomic status," and "community facilities," were identified in all sites. Each social-environmental factor was also strongly predictive of height-for-age at 24 mo (all P values <0.001), with some relations partly mediated through birthweight. The biological pathways accounted for 59% of the total explained variance and the social-environmental pathways accounted for 41%. The resulting path coefficients were broadly similar across the 4 sites. Conclusions: Early child growth faltering is determined by both biological and social factors. Maternal height, itself a marker of intergenerational deprivation, strongly influences child height at 2 y, including indirect effects through birthweight and social factors. However, concurrent social factors, many of which are modifiable, directly and indirectly contribute to child growth. This study highlights opportunities for interventions that address both biological and social determinants over the long and short term. COHORTS (the Consortium of Health Orientated Research in Transitioning Societies) is a collaboration among 5 large and long-running birth cohort studies in LMICs (22). The 5 birth cohorts are: the 1982 Pelotas (Brazil) Birth Cohort (23); the Institute of Nutrition of Central America and Panama Nutrition Trial Cohort (INTC; Guatemala) (24); the New Delhi (India) Study (25); the Cebu Longitudinal Health and Nutrition Survey (CLHNS; Cebu, Philippines) (26); and the Birth to Twenty Plus (Bt20+; South Africa) cohort (27). Data from 4 of the 5 cohorts, Brazil, India, the Philippines, and South Africa, were included in this analysis. The data from Guatemala were excluded because this study was a randomized community trial, designed to minimize social variation relevant to the outcome of a nutrition intervention on children's growth. The processes for identifying Guatemalan villages hence controlled for the social variables we were examining. The cohorts vary by country, epoch, and SES, providing different contexts for the examination of determinants of child growth. The Brazilian study enrolled 5914 children from all socioeconomic groups born in Pelotas’ maternity hospital in 1982, which covered >99% of all births in the city. The Indian cohort enrolled 8181 babies born to married, mostly middle-class, women in a defined area of New Delhi between 1969 and 1972. The Philippine cohort enrolled pregnant women from all socioeconomic groups living in 33 randomly selected, mostly urban (75%) neighborhoods in Cebu between 1983 and 1984 (3080 infants). The South African cohort enrolled mostly poor black pregnant women living in a defined urban area of Johannesburg in 1990 (3273 infants). All the studies were reviewed and approved by an appropriate ethics committee or institutional review board. Birthweight was measured in grams in hospitals and clinics at delivery in Brazil and South Africa, in hospitals or at home by birth attendants in the Philippines, and in the community within 72 h of birth in India. Maternal height was measured by a stadiometer and recorded to the nearest 0.1 cm following standard procedures at cohort enrolment in Brazil and the Philippines, and at birth or in early childhood in India and South Africa. Height-for-age was measured at around 24 mo of age, with some variability among sites. In all cohorts, measurements were converted to height-for-age z scores with reference to WHO standards and with the use of children’s exact age at measurement (28). The specific social factors used in this analysis were selected based on their commonality across the 4 birth cohort sites and their prior identification as determinants of height in childhood (29). They include maternal and paternal schooling, maternal age at the birth of the child, marital status, wealth (an index calculated from a list of pertinent assets), annual income (per capita), social class (paternal occupation), household crowding (ratio of people per room), sex, birth order, child dependency (ratio of children aged <18 y to adults), and health utilization, sanitation, and access to safe water. The variables were defined in the same way across cohorts except that wealth, social class, and health service utilization, sanitation, and access to water were coded into site-specific ordinal scales of 3 or 5 categories. Birth order, the dependency ratio, and the crowding ratio were reverse-coded to represent more optimal conditions as higher scores. We compiled a common dataset from data provided by the 4 birth cohort studies. Participants from the 4 sites were included in the analysis if they had child height at 24 mo. Differences between means from the pooled data between cases included and excluded were small according to Cohen's d, except for paternal schooling, social class, annual income, and birth weight, which were small to middling (Supplemental Table 1). Child birthweight (10) and maternal height (11) were investigated as biological determinants of height-for-age at 24 mo. The social variables pertain to the first 2 y of a child's life. Exploratory structural equation modeling (ESEM) (30) was used to review the loading of the social variables onto 2, 3, and 4 factors in relation to specified paths to the outcome. ESEM combines exploratory and confirmatory factor analytic strategies, which is helpful when prior theory is limited. Modelling decisions were based on a biologically driven conceptual framework (Figure 1) in which maternal height was considered to be exogenous to the child's birth circumstances (including family socioeconomic circumstances and community facilities) since maternal height primarily reflects the biological and social conditions when the mother herself was born and raised as a young child (3). Model depicting direct and indirect paths from social and biological determinants to child height. Letters correspond to paths in Table 3. HAZ, height-for-age z score; SES, socioeconomic circumstances. The resulting measurement models were tested for invariance across the 4 sites by means of multigroup confirmatory factor analysis, with the use of a composite sample weighted equally by site. The factor scores from the measurement models were included with the biological determinants (maternal height and birthweight), together with the outcome (height-for-age z score at 24 mo) in a hypothesized path model (Figure 1) that tested the main research question about the relation among predictors and their relative strength in transmission of growth failure (11). From the final model, we computed the total direct and indirect effects from predictors to outcome. Models were validated by a split-sample approach. Initial modelling was done with the use of a sample of two-thirds of the data, randomly sampled from each site, and the final model was checked against the remaining hold-out sample. As shown in SupplementalTables 1and2, when the measurement and path models were run with the one-third “hold-out” sample, model fit criteria were all good [root mean square error of approximation (RMSEA) ≤ 0.05, Comparative Fit Index (CFI) ≥ 0.95, and Tucker-Lewis Index (TLI) ≥ 0.95], except for RMSEA for the path model of Brazil, which was fair (0.07) (31). Even the smallest sample, that from South Africa, was >20 times larger than the number of parameters being estimated, reducing the chances of substantial model overfit to data (31). Missing data were accommodated by full information maximum likelihood, which returns unbiased parameter estimates when data are missing at random, conditional on the variables included in the model (32, 33). Fit statistics are reported for RMSEA, CFI, and TLI. All analyses were conducted with SPSS (version 21, IBM Corporation) and Mplus (version 7.1, Muthén & Muthén).