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Revisiting Factors Influencing Under-Five Mortality in India: The Application of a Generalised Additive Cox Proportional Hazards Model

Author

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  • Maroof Ahmad Khan

    (Department of Biostatistics, All India Institute of Medical Sciences, New Delhi 110029, India)

  • Sumit Kumar Das

    (Department of Biostatistics, All India Institute of Medical Sciences, New Delhi 110029, India)

Abstract

Background: Despite the implementation of various preventive measures, India continues to experience an alarmingly high under-five mortality rate (U5MR). The most recent nationwide data on U5MRs has provided an opportunity to re-examine the associated factors of U5MRs using advanced techniques. This study attempted to identify the associated determinants of U5MRs via the generalised additive Cox proportional hazards method. Methods: This study analysed the fifth round of unit-level data for 213,612 children from the National Family Health Survey (NFHS-5) to identify the risk factors associated with U5MRs, employing a generalised additive Cox proportional hazards regression analysis. Results: The children who had a length of pregnancy of less than 9 months had a 2.621 (95% CI: 2.494, 2.755) times greater hazard of U5MRs than the children who had a gestational period of 9 months or more. The non-linear association with U5MRs was highest in the mother’s age, followed by the mother’s haemoglobin, the mother’s education, and household wealth score. The relationships between the mother’s age and the mother’s haemoglobin level with the U5MR were found to be U-shaped. Conclusions: This study highlights the importance of addressing maternal and socioeconomic factors while improving access to healthcare services in order to reduce U5MRs in India. Furthermore, the findings underscore the necessity for more sophisticated approaches to healthcare delivery that consider the non-linear relationships between predictor variables and U5MRs.

Suggested Citation

  • Maroof Ahmad Khan & Sumit Kumar Das, 2024. "Revisiting Factors Influencing Under-Five Mortality in India: The Application of a Generalised Additive Cox Proportional Hazards Model," IJERPH, MDPI, vol. 21(10), pages 1-13, September.
    • Handle: RePEc:gam:jijerp:v:21:y:2024:i:10:p:1303-:d:1488727
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    References listed on IDEAS

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    1. Marx, Brian D. & Eilers, Paul H. C., 1998. "Direct generalized additive modeling with penalized likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 28(2), pages 193-209, August.
    2. Monica Alexander & Leontine Alkema, 2018. "Global estimation of neonatal mortality using a Bayesian hierarchical splines regression model," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 38(15), pages 335-372.
    3. Helen Andriani & Septiara Putri & Reynaldi Ikhsan Kosasih & Hsien-Wen Kuo, 2019. "Parental Smoking and Under-Five Child Mortality in Southeast Asia: Evidence from Demographic and Health Surveys," IJERPH, MDPI, vol. 16(23), pages 1-12, November.
    4. Simon N. Wood & Natalya Pya & Benjamin Säfken, 2016. "Smoothing Parameter and Model Selection for General Smooth Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1548-1563, October.
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