Data analysis
REGRESSION
The data were coded and entered into epidata V.3.1 to minimize design skipping patterns and logical errors. A cleaned copy of data was transported from epidata to SPSS version 26 for data analysis. Descriptive statistics were used to summarize the baseline socio-demographic characteristics of the women by group status. A chi-square test was performed to compare the baseline characteristics of the intervention and control groups. Principal component analysis was used to construct the wealth index. Then the wealth index was classified into wealth quintiles. The procedure of the wealth index is described elsewhere [Comparisons of hemoglobin level between and within the control and intervention groups were done using independent samples and paired sample t-tests, respectively. In all analysis, A two-sided A per-protocol analysis was performed in this study. Pregnant women who attended three counseling session and gave end line data were included in the final analysis. A linear mixed-effects model was used to determine the effect of the intervention on changes in the hemoglobin level of pregnant women over time. This model enables to accommodate the correlation of observations due to the repeated measures (pre- and post- intervention) and the clustering of individuals within the 6 randomly selected clusters.Before fitting the model, the normality assumption of the outcome variable, hemoglobin level, was tested using Shapiro-Wilk’s test, and the test revealed that the assumption was satisfied (p > 0.05). We used the Akaike information criterion (AIC) to help us choose the best statistical model. We selected the model that had the smallest AIC. During fitting the model, participants and clusters were analyzed as random effects.The model also enables to control the effects of potential confounding factors (food security, wealth index, education, family size, age).Variables in the bivariate linear mixed regression model with 