Statistical analysis
malaria, anemia
MALARIA, REGRESSION, ANEMIA, REGRESSIONS
To check for differences in anemia status by each of the independent variables, we first conducted descriptive analyses using chi-square tests and ran univariate logistic regressions. Based on the results of these univariate tests, we constructed a base adjusted model in which only socio-demographic and health variables that had been significantly associated with anemia in the univariate models at the 5% alpha level were included. In addition, we include age and malaria in these models despite statistical significance levels, as these factors were judged to be biologically important predictors of anemia, and potential confounders of the relationship between anemia and the other factors. The goal of these models was to understand the associations between each factor and anemia, while controlling for confounding of the other factors. We used this base adjusted model to determine adjusted relationships between each of the independent variables and anemia status by adding in each of the remaining independent variables one-by-one. We use logistic regression to obtain odds ratios for the relationship between the factors and each category of anemia (i.e. none vs. any, none vs. moderate/severe), and we use linear regression to model the relationship between the factors and continuous hemoglobin in g/dL. Analyses were done using Stata 16.1(Blackwell et al., 2009) (StataCorp, 2017. 