The South African National Income Dynamics Study (SA-NIDS) is a nationally representative panel survey of households in South Africa (18). The survey which began in 2008 is conducted by the Southern Africa Labour and Development Research Unit based at the University of Cape Town, Cape Town, South Africa. To date, data from the first five waves of NIDS are available with Wave 5 concluding in 2016. The study utilizes a stratified two-stage cluster sample design to randomly select 400 of Statistics South Africa's 3000 primary sampling units (PSUs) for inclusion in the surveys (18).
Ethics Approval
The SA-NIDS was approved by the Ethics Committee of the Commerce Faculty, University of Cape Town, and the de-identified datasets are publicly available. Ethics approval for this secondary analysis was obtained from the University of the Witwatersrand Research Ethics Committee (protocol number M1909101).
The components of the secondary analysis on which this paper is based are described below:
The current paper utilized data from wave 1, 2 and 3 of NIDS. The authors obtained household level food security data and individual maternal characteristics from wave 1 household and adult questionnaires. The data from women who became pregnant between wave 1 and 2 of the study were matched with the data of their children from wave 3. Information on children was obtained from the responses to the child questionnaires which were answered on behalf of children by their mother.
Inclusion and exclusion criteria
We excluded younger adolescents (age<15) and older mothers (age> 44) because mothers on both ends of the age spectrum are more likely to experience obstetric complications and LBW and may differ systematically from the population of interest (19). Data on children were limited to children born between January 2008 and December 2011, the commencement of data collection in wave 1 and the conclusion of data collection in wave 2. Children were aged between one and five years at the time of data collection with a mean age of 38 months.
Measures
Food Insecurity
The primary exposure during the antenatal period was the household food insecurity score, an adapted composite measure of food insecurity that is described in detail elsewhere (21). This measure covers three domains (anxiety, food quality and physical consequences) and includes six indicators (the subjective indicators of adult and child hunger and household food sufficiency and the objective indicators of dietary diversity, proportion of household expenditure on food and maternal underweight BMI). Adult and child hunger was defined as households were an adult or child never or seldom went hungry versus households were an adult or child sometimes, often or always went hungry in the past year. Household food adequacy was defined as households who reported that food consumption was more than adequate or just adequate for household needs versus less than adequate.
A household dietary diversity score (HDDS) based on 30-day recall was calculated using the Food and Agriculture Organization (FAO) guidelines. The HDDS is comprised of 32 individual food types and 12 different food groups. There is no standardized cutoff to describe low dietary diversity, but one recommendation is to use the mean HDDS score as a cutoff. The mean dietary diversity of the sample was 9.06 which indicates a fairly high number of households with diverse diets. Household scores are generated on a whole number basis and the authors therefore used a cutoff of 9 for low dietary diversity. For household food expenditure, a cutoff of expenditure above 0.6 was used to define a household as food insecure, as recommended by the FAO. While this method of measurement from household expenditure surveys (HESs) is less precise than that of food consumption surveys, it is reasonably accurate, and yields roughly the same estimates of food energy deficiency for population groups (11). A cutoff of BMI<18.5 was used to classify women as underweight per the WHO growth standards (12).
Each indicator is assigned a value of 0 or 1, with the final score being a minimum of 0 (food secure) and a maximum of 6 (severely food insecure). Given the low percentage of underweight women, households were classified as food insecure if they scored more than one in the food quality and food anxiety domains (i.e. 2 out of 3 total domains). The food insecurity score was examined as a continuous and binary (domain insecure) variable.
Covariates
In addition to the primary exposure of the household food security score, we included variables associated with LBW and stunting in the literature in our models. These included both distal and proximate characteristics such as maternal age, height, BMI, smoking status, depression status measured using the CES-D scale (13), employment, years of education and self-reported health (8,23,24). At the household level we included household size, individual food security domains, per capita food expenditure below the Stats SA Poverty level of R274 and geotype (urban, rural or traditional areas) as well as province of residence (12,25). Child characteristics included gender, child’s age in months and social grant status (14).
Primary Outcomes
The primary outcome is a dichotomous measure of child LBW (£2500g). The secondary outcome is childhood stunting (height for age ≤2SD) and severe stunting (height for age ≤3SD) in the first five years of life for children born to women between wave 1 and 2 of the NIDS study. Children’s Z scores were calculated using the WHO child growth standards (12). Maternal anthropometry was collected in wave 1 of the study, while child birthweight and anthropometry were recorded in wave 3. Birthweight information was obtained from the Road to Health Card (RTHC), a child health information booklet provided by the Department of Health when women give birth (15).
Statistical Analyses
There are several steps to the analysis:
First, initial unadjusted bivariate analyses to explore covariates associated with LBW, stunting and severe stunting were conducted. Second, birthweight and height for age scores were analysed both continuously and as binary variables. Third, forward stepwise logistic regression was employed to identify potential explanatory variables associated with LBW, stunting and severe stunting. We conducted logistic regression for the final LBW model and multivariable logistic regression for the final stunting model. The latter model incorporated stunting and severe stunting. Variables included were identified from the bivariate analyses and exposures with a strength of association of p<=0.05 were then selected. These variables were added to the model one at a time, with those showing the strongest association added first. Those that were not significant in the stepwise model (p>0.05) were removed. Last, post-stratification weights were adjusted to the age- sex-race distribution produced by Statistics South Africa based on population estimates. Analyses were based on a dataset with imputation values generated by the NIDS data team (18). All analyses were conducted using Stata 15 (Stata Corporation, College Station, TX).
A series of logistic regression models were run to compute odds ratios (ORs) with 95% confidence intervals (CI) with LBW as the primary outcome. This model aimed to determine the strength of the associations between food insecurity and other sociodemographic risk factors and LBW. Separate regression models were run to explore associations with stunting and severe stunting in children aged 0 – 60 months. Significant variables were combined in a final multiple logistic regression model that included both stunting and severe stunting and non-significant variables excluded.