Multilevel Survival Modelling of Neonatal Mortality under Some Prognostic Factors in Uttar Pradesh

Multilevel Survival Modelling of Neonatal Mortality

Abstract

Introduction: Neonatal Mortality, a critical indicator of country’s socio-economic and healthcare status, remains a significant global concern. While India has made significant progress in reducing child mortality over the past decades, further improvements are needed in reducing newborn mortality. The traditional survival models like cox proportional hazard model assume independence among individual, whereas multilevel survival models integrate hierarchical structures, provide more accurate insights into mortality trends and determinants. This study identifies significant prognostic determinants of neonatal mortality in Uttar Pradesh, India, using data from NFHS-V (2019-2021) and explore regional and community-level effects with the multilevel parametric models.

Methods: The multilevel mixed-effects Weibull and exponential parametric survival models were applied, incorporating both individual and community-level factors. Primary Sampling Units (PSUs) and districts were treated as hierarchical levels. Akaike Information Criteria (AIC) was used to determine the best fitted model.  

Result:  This study highlighted the complex, multi-level determinants of neonatal mortality in Uttar Pradesh. The multilevel Weibull mixed-effects model provided a better fit compared to individual-level and Exponential models. The results determine the significant impact of social inequalities, the crucial role of maternal education, place of delivery and the importance of newborn birth weight and size in determining neonatal outcomes.

Conclusion: The study recommends that efforts to reduce neonatal mortality should address not only individual-level risk factors but also community-level disparities in healthcare access and quality.