logitπ‘Œπ‘–π‘”π‘‘ = 𝛽 0 + πœƒπ‘‘ + 𝛽1𝐺+ 𝛽2𝑑 + 𝛽3𝐺.𝑑 + π‘ˆπ‘–π‘”π‘‘ + πœ€π‘–π‘”π‘‘β€¦β€¦ (1)
Where Y represents the pregnancy outcome indicator.𝛽 0 is the intercept. πœƒπ‘‘ captures the period of time-invariant fixed effects. 𝐺 is an area indicator for treatment (𝐺 =1) or comparison (𝐺 = 0) districts.t is an indicator variable for baseline (=0) or endline (=1), the𝛽 s are the regression coefficients to be estimated,𝛽3 captures the average treatment effect of GEHIP intervention on pregnancy outcome; Uigt captures individual-level factors that predict adverse pregnancy outcome. Predictor variables include mother’s age, marital status, educational status, household wealth index, religion, ethnicity and parity.πœ€π‘–π‘”π‘‘ is the error term.
To assess if GEHIP’s intervention had an impact in the reduction of inequalities in adverse pregnancy outcomes, two variables; hosuehold wealth index and maternal eductaional attainment were used as equity stratifiers in line with the literature .
logistic regression models with interaction terms are used to examine the equity effect of GEHIP’s community-based health program on adverse pregnancy outcomes stratified by household wealth index and maternal education. Equation (2) shows the specification of the logistic model for estimating the effect of wealth status: