Peer-reviewed article
The Effect of Multiple Imputations by Chained Equations on the Factors Associated with Immunization Coverage in India
Sachit Ganapathy, Binukumar Bhaskarapillai, Shailendra Dandge ·
Summary
Written by AI, not yet AICS's view ·Key points
Limitations
Abstract
Background: National Family Health Survey-4 (NFHS-4) revealed a significant improvement in the percentage of complete immunization attained in India. Even though determinants of immunization coverage in India are addressed by some studies, the impact of missing data in such large-scale surveys has not been accounted earlier. The present study aimed to identify the potential factors associated with immunization coverage in India using the complete case analysis (CCA) and multiple imputation by chained equations (MICE) analysis. Materials and methods: We created a dichotomous immunization variable based on the status of all the vaccines given to the child. All relevant variables were summarized using appropriate descriptive statistics along with the proportion of missingness. Further, MICE procedure was performed to impute the missing values after assessing the missing data mechanism. Multiple logistic regression after accounting for the sampling weights were used to report the estimates of odds-ratio (OR) and 95% confidence intervals (CI) for both CCA and MICE analysis and compared. Results: The percentage of children under five years of age who had total immunization was 69%. Further, we observed that female sex and rural habitation had higher odds of getting immunized in both CCA and MICE. Moreover, wealth index, number of antenatal visits, checkup after delivery and place of birth played an important role in the immunization coverage. Conclusion: MICE provided more precise risk estimates on potential factors associated with vaccination coverage compared to CCA, even if the major findings did not alter due to large sample size. Key words: Immunization, Health surveys, missing data, Logistic regression, complete case analysis, MICE.