Multiple Logistic Regression Model for Determinants of Injectable Contraceptive Uptake Among Women of Reproductive Age in Kenya

Objective: The recent increase in the uptake of injectable contraceptives has occurred at the expense of the other modern contraceptive methods but the knowledge gap still exists on modeling dynamics and determinants associated with the use of the injectable. This study sought to model for injectable contraceptive usage to bridge the knowledge gap on the use of injectable contraceptives among women of childbearing age in Kenya. Materials and methods: Analytical cross-sectional study design was adopted. Secondary data for women collected during the (Performance Monitoring for Action) PMA2020 survey was used. PMA2020 survey used multistage stratified sampling with urban-rural representation. To establish the factors associated with the uptake of injectable contraceptives, a multiple logistic regression model was fitted using Stata version 13 and R version 3.5.3 statistical software. Hosmer-Lemeshow Test statistic was used to evaluate the goodness of model fit in predicting injectable contraceptive usage. Results: Multivariable analysis showed that women with post-primary/vocational levels of education were 54% less likely to use an injectable contraceptive compared to those who had no education at all. Hosmer-Lemeshow (HL) goodness of fit test statistic indicated that the model was a good fit for prediction. Education, marital status, wealth quintile, place of residence and number of births were significant predictors of the injectable contraceptive uptake among women of reproductive age in Kenya. Conclusion: The findings of this study will inform the design of targeted interventions aimed at addressing the increasing demand for injectable devices among women of reproductive age in Kenya.