Multilevel survival analysis of factors associated with under-five mortality in Manipur

Abstract

Background: Child mortality is a major public health issue. The studies on under-five mortality that ignore the hierarchical facts mislead the interpretation of the results due to observations in the same cluster sharing common cluster-level random effects. Objectives: The present study uses a multilevel model to analyze under-five mortality and identify the significant factors for under-five mortality in Manipur. Methods: National Family Health Survey-5 (2019–21) data are used in the present study. A multilevel mixed-effect Weibull parameter survival model was fitted to determine the factors affecting under-five mortality. We construct three-level data, individual levels are nested within primary sampling units (PSUs), and PSUs are nested within districts. Results: Out of the 3225 under-five children, 85 (2.64%) died. The three-level mixed-effects Weibull parametric survival model with PSUs nested within the districts, the likelihood-ratio test with Chi-square value = 10.98 and P = 0.004 < 0.05 indicated that the model with random-intercept effects model with PSUs nested within the districts fits the data better than the fixed effect model. The four covariates, namely the number of birth in the last 5 years, age of mother at first birth, use of contraceptive, and size of child at birth, were found as the risk factor for under-five mortality at a 5% level of significance. Conclusions: In the random-intercept effect model, the two estimated variances of the random-intercept effects for district and PSU levels are 0.27 and 0.31, respectively. The values indicate variations (unobserved heterogeneities) in the risk of death of the under-five children between districts and PSUs levels.

Keywords

• multilevel data structure
• multilevel mixed effect model
• under-five mortality
• weibull parametric model