Multilevel Modelling of Under-Five Time to Death, and Risk Factors

Keywords: Ethiopia Demographic and Health Survey (EDHS), Under-five Children, Under-five mortality, Mixed-effects Model, logistic regression analysis, Cox regression

Abstract

Under-five mortality is a leading indicator of child health and overall development of a country. Sub-Saharan Africa remains the region with the highest under-5 mortality rate in the world, with 1 child in 13 dying before his or her fifth birthday. Half of all under-five deaths in 2019 occurred in just five countries: Nigeria, India, Pakistan, the Democratic Republic of the Congo, and Ethiopia. In Ethiopia, as the 2016 Ethiopia Demographic and Health Survey (EDHS) report showed that the under-five mortality declined from 166 deaths per 1,000 live births in 2000 to 67 deaths per 1,000 live births in 2019 mini EDHS report (60% decreasing rate). However, there are regional disparities problems on under-five children mortality in Ethiopia. Thus, the major purpose of this study was to model the multilevel effects of U5 child time to death, and to determine the risk factors for child’s death based on the last full report (2016 EDHS). The data were analysed using descriptive statistics, stratified Cox proportional hazards regression and multilevel parametric survival models. In the study, 635 (6.1%) U5 deaths have observed from 10,331 children. And, the overall probability of survival was 0.93. Results obtained by fitting both stratified Cox proportional hazards regression and lognormal parametric fixed-effect models: sex of child, type of birth, birth order, size (weight) of child at birth, months of breastfeeding, number of U5 and five children, family size, wealth index, frequency of listening radio, place of delivery place of residence, and geographical region were found to be significant factors for U5 children death or estimated mean survival time. Furthermore a high risk death of U5 children was found to be associated with male children, twined children, ≤ 6 months breastfeeding children, few number of children in the home, children from small family size, children average weight below, children from poor families, private health sectors delivered children, children from mothers didn’t not listen radio, children from rural areas, children from Afar, Somali and Harari regional states. In the lognormal parametric random effects model, 1.7 and 0.9 estimated variations were observed among regional and household cluster levels on U5 children mean survival times. The researchers recommended that governments, and other concerned bodies should give special supports for mothers whose children are at high risk of death.

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References

1. Hug, L., Sharrow, D., Zhong, K., & You, D. (2018). Levels & Trends in Child Mortality. Report 2018. www.unicef.org. Retrieved from https://www.unicef.org/media/47626/file/UN-IGME-Child-Mortality-Report-2018.pdf
2. Children: improving survival and well-being. (2020). www.who.int. Retrieved from https://www.who.int/news-room/fact-sheets/detail/children-reducing-mortality#:~:text=Malnourished
3. Work of the Statistical Commission pertaining to the 2030 Agenda for Sustainable Development. (2017). Resolution adopted by the General Assembly on 6 July 2017. undocs.org. Retrieved from https://undocs.org/A/RES/71/313
4. Ethiopia Demographic and Health Survey 2016. (2017). Central Statistical Agency, ICF. Retrieved from https://dhsprogram.com/pubs/pdf/FR328/FR328.pdf
5. Zike, D. T., Fenta, H. M., Workie, D. L., & Swain, P. K. (2018). Determinants of Under-Five Mortality in Ethiopia: an Application of Cox Proportional Hazard and Frailty Models. Turkiye Klinikleri Journal of Biostatistics, 10, 2, 123–136. doi: 10.5336/biostatic.2018-60550
6. Dejene, T., & Girma, E. (2013). Social determinants of under-five mortality in Ethiopia: Event history analysis using evidence from Ethiopian Demographic and Health Survey (EDHS). Health, 5, 879–884. doi: 10.4236/health.2013.55115
7. Bedane, A., Asena, T., Shamenna, A., & Abshoko, A. (2016). Variations in Under-five Child Mortality among Regional States of Ethiopia: A Multi-level Modelling Approach. Current Journal of Applied Science and Technology, 15, 2, 1–16. doi: 10.9734/bjast/2016/24448
8. Kandala, N. B., Ji, C., Stallard, N., Stranges, S., & Cappuccio, F. P. (2007) Spatial analysis of risk factors for childhood morbidity in Nigeria. The American Journal of Tropical Medicine and Hygiene, 77, 4, 770–778. doi: 10.4269/ajtmh.2007.77.770
9. Antai, D. (2011). Regional inequalities in under-5 mortality in Nigeria: a population-based analysis of individual- and community-level determinants. Population Health Metrics, 9, 1, 6, , doi: 10.1186/1478-7954-9-6
10. Pickett, K. E., & Pearl, M. (2001). Multilevel analyses of neighbourhood socioeconomic context and health outcomes: A critical review. Journal of Epidemiology and Community Health, 55, 2, 111–122. doi: 10.1136/jech.55.2.111
11. Stephenson, R., Baschieri, A., Clements, S., Hennink, M. & Madise, N. (2006). Contextual influences on the use of health facilities for childbirth in Africa. American Journal of Public Health, 96, 1, 84–93. doi: 10.2105/AJPH.2004.057422
12. Siddiqi, A. Irwin, L. G. & Hertzman, C. (2007). Total Environment Assessment Model for Early Child Development. Evidence Report. www.who.int. Retrieved from https://www.who.int/social_determinants/resources/ecd_kn_evidence_report_2007.pdf
13. Ferede, T. (2013). Multilevel Modelling of Modern Contraceptive Use among Rural and Urban Population of Ethiopia. American Journal of Mathematics and Statistics, 3, 1, 1–16. doi: 10.5923/j.ajms.20130301.01
14. Kumar, P., & File, G. (2011). Infant and child mortality in Ethiopia: A statistical analysis approach. Ethiopian Journal of Education and Sciences, 5, 2, 51–57. doi: 10.4314/ejesc.v5i2.65373
15. Worku, Z. (2009). Factors That Affect Under-Five Mortality among South African Children: Analysis of the South African Demographic and Health Survey Data Set. Proceedings of the World Congress on Engineering and Computer Science 2009 (WCECS 2009), October 20–22, 2009. Vol. II. San Francisco, USA. Retrieved from http://www.iaeng.org/publication/WCECS2009/WCECS2009_pp794-796.pdf
16. Fox, J. (2008). Cox Proportional-Hazards Regression for Survival Data The Cox Proportional-Hazards Model. Appendix to An R and S-PLUS Companion to Applied Regression, June, 15, 1–18. Retrieved from https://socialsciences.mcmaster.ca/jfox/Books/Companion-1E/appendix-cox-regression.pdf
17. Austin, P. C. (2017). A tutorial on multilevel survival analysis: Methods, models and applications. International Statistical Review, 85, 2, 185–203. doi: 10.1111/insr.12214
18. Mekonnen, D. (2011). Infant and Child Mortality in Ethiopia. The role of Socioeconomic, Demographic and Biological factors In the previous five years period of 2000 and 2005. Master thesis. Lund University. Retrieved from https://lup.lub.lu.se/luur/download?func=downloadFile&recordOId=2060678&fileOId=2060717
19. Kembo, J., & Van Ginneken, J. K. (2009). Determinants of infant and child mortality in Zimbabwe: Results of multivariate hazard analysis, Demographic Research, 21, 367–384. doi: 10.4054/DemRes.2009.21.13
20. Gebretsadik, S., & Gabreyohannes, E. (2016). Determinants of Under-Five Mortality in High Mortality Regions of Ethiopia: An Analysis of the 2011 Ethiopia Demographic and Health Survey Data. International Journal of Population Research, Vol. 2016, Article ID 1602761, 1–7. doi: 10.1155/2016/1602761
21. Lindgren, A. (2016). Infant Mortality Factors of Bangladesh Supervisor. Master thesis. Lund University. Retrieved from https://lup.lub.lu.se/luur/download?func=downloadFile&recordOId=8878528&fileOId=8878583
22. Ekholuenetale, M., Wegbom, A. I., Tudeme, G., & Onikan, A. (2020). Household factors associated with infant and under-five mortality in sub-Saharan Africa countries. International Journal of Child Care and Education Policy, 14, 1, 1–15. doi: 10.1186/s40723-020-00075-1
23. Olawuwo, S., Forcheh, N., & Setlhare, S. (2018). Individual, Household and Community-Level Effects of Infant and Child Mortality in Nigeria: A Logistic Regression Approach. Global Journal of Health Science, 10, 10, 136–151. doi: 10.5539/gjhs.v10n10p136
24. Adeolu, M. O., Akpa, O. M., Adeolu, A. T., & Aladeniyi, I. O. (2016). Environmental and Socioeconomic Determinants of Child Mortality: Evidence from the 2013 Nigerian Demographic Health Survey. American Journal of Public Health Research, 4, 4, 134–141. doi: 10.12691/ajphr-4-4-3
25. Bello, R. A., & Joseph, A. I. (2014). Determinants of Child Mortality in Oyo State, Nigeria. African Research Review. An International Multidisciplinary Journal, Ethiopia, Vol. 8 (1), Serial No. 32, 252–272. doi: 10.4314/afrrev.v8i1.17
26. Wogi, A. A., Wakweya, S. T., & Tesfay, Y. Y. (2018). Determinants of Time-to-Under-Five Mortality in Ethiopia. International Journal of Biomedical and Clinical Engineering, 7, 1, 1–24. doi: 10.4018/ijbce.2018010101
27. Woldeamanuel, B. T. (2019). Socioeconomic, Demographic, and Environmental Determinants of Under-5 Mortality in Ethiopia : Evidence from Ethiopian Demographic and Health Survey, 2016. Child Development Research, Vol. 2019, Article ID 1073782, 15 p. Retrieved from https://doi.org/10.1155/2019/1073782
28. Tai, N., Su, H. T. H., & Swe, Th. (2019). Impact of Use of Health Care on Under-5 Child Mortality among States and Regions. Analysis of the 2015–16 Myanmar Demographic and Health Survey. DHS Working Papers, No. 147. Retrieved from https://dhsprogram.com/pubs/pdf/WP147/WP147.pdf
29. Boco, A. G. (2010). Individual and Community-level Effects on Child Mortality: An Analysis of 28 Demographic and Health Surveys in Sub-Saharan Africa. DHS Working Papers, No. 73. Retrieved from http://www.measuredhs.com/pubs/pdf/WP73/WP73.pdf
30. Adedini, S. A., Odimegwu, C., Imasiku, E. N. S., Ononokpono, D. N., & Ibisomi, L. (2015). Regional variations in infant and child mortality in Nigeria: A multilevel analysis. Journal of Biosocial Science, 47, 2, 165–187. doi: 10.1017/S0021932013000734
31. Yu, F., Yan, Z., Pu, R., Tang, S., Ghose, B., & Huang, R. (2018). Do mothers with lower socioeconomic status contribute to the rate of all-cause child mortality in Kazakhstan? BioMed Research International, Vol. 2018, Article ID 3629109. doi: 10.1155/2018/3629109
32. Gayawan, E., Adarabioyo, M. I., Okewole, D. M., Fashoto, S. G., & Ukaegbu, J. C. (2016). Geographical variations in infant and child mortality in West Africa: A geo-additive discrete-time survival modelling. Genus, 72, 1. doi: 10.1186/s41118-016-0009-8
33. Wegbom, A. I., Essi, I. D., & Kiri, V. A. (2019). Survival Analysis of Under-five Mortality and Its Associated Determinants in Nigeria: Evidence from a Survey Data. International Journal of Statistics and Applications, 9, 2, 59–66. doi: 10.5923/j.statistics.20190902.03
34. Kazembe, L., Clarke, A., & Kandala, N. B. (2012). Childhood mortality in sub-Saharan Africa: Cross-sectional insight into small-scale geographical inequalities from Census data. BMJ Open, 2, 5. doi: 10.1136/bmjopen-2012-001421
35. Adebowale, S. A., Morakinyo, O. M., & Ana, G. R. (2017). Housing materials as predictors of under-five mortality in Nigeria: Evidence from 2013 demographic and health survey. BMC Pediatrics, 17, 1, 1–13. doi: 10.1186/s12887-016-0742-3
36. Mugarura, A. (2011). Multilevel analysis of factors associated with childmortality in Uganda. Master’s of Statistics thesis. Makerere University, Kampala, Uganda. Retrieved from http://makir.mak.ac.ug/bitstream/handle/10570/2725/Mugarura-CoBAMS-Master.pdf?sequence=1&isAllowed=y

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Published
2021-03-01
How to Cite
Argawu, A. S. (2021). Multilevel Modelling of Under-Five Time to Death, and Risk Factors. Statistics of Ukraine, 91(1), 34-46. Retrieved from https://su-journal.com.ua/index.php/journal/article/view/322