Investigating tangible and mental resources as predictors of perceived household food insecurity during pregnancy among women in a South African birth cohort study

Abstract

RationaleFood insecurity during pregnancy is concerning given the increased nutritional needs of the mother for proper fetal development. However, research is lacking within the South African context to investigate the association of economic and psychosocial factors and food insecurity among pregnant women, using comprehensive, conceptually driven models. ObjectiveThis study applies the Network-Individual-Resource (NIR) Model to investigate individual, intimate dyadic, and family level predictors of perceived household food insecurity for pregnant women. Methods826 pregnant women enrolled in the Drakenstein Child Health Study (DCHS), a birth cohort in two communities in a peri-urban area of South Africa. Hierarchical logistic regressions were used to investigate the impact of household/family, intimate dyads, and individual tangible and mental resources on perceived household food insecurity during the critical period of pregnancy. Perceived household food insecurity was assessed through an adapted version of the USDA Household Food Security Scale – Short Form. ResultsAmong 826 pregnant women in South Africa, individual-level tangible resources (e.g. income, social assistance, HIV status) and mental resources (e. g. depression, childhood trauma) predicted perceived household food insecurity and these predictors differed by community. Intimate dyadic and family level resources did not predict household food insecurity. ConclusionsOur findings of the economic and psychosocial predictors of perceived household food insecurity among pregnant women in South Africa, mirror findings in general populations. This study provides support for the extension of the NIR model to perceived household food insecurity, particularly regarding individual-level mental and tangible resources, as well as the impact of community-level factors. Future research should investigate the extent to which resource sharing occurs within networks.