Geo-Additive Discrete-Time Survival Modelling of Geographical Variations in Infant and Child Mortality in Jigawa North East Region
DOI:
https://doi.org/10.56919/usci.2433.021Keywords:
Geo-additive model, mortality, survival, infant, variationAbstract
Study’s Excerpt/Novelty
- This study investigates the regional disparities in infant and child mortality within Jigawa North East by employing geo-additive regression to show the spatial distribution and contributing risk factors.
- The research identifies specific geographical 'hotspots' and the underlying causes of under-five mortality, which offers crucial insights that can guide targeted policy interventions.
- The study's focus on a previously under-researched area enhances the understanding of localized health challenges, directly contributing to the attainment of SDG 3 targets in the region.
Full Abstract
This research examined the lingering regional variations in infant and child mortality in the Jigawa North East area. The study investigated how different categories of risk factors contributed to the spatial disparities observed in the area. A census on population and housing was carried out employing research assistants who collected information from one household to another across the entire region. Geo-additive regression was employed in this study to analyze the factors linked with infant and child mortality within the Jigawa North East region. The findings indicate that, in the Jigawa North East region, Birniwa and Kafin-Hausa have the highest rate of infant and under-five mortality. This can be ascribed to the widespread occurrence of childhood illnesses, overall healthcare practices, persistent poverty levels, and severe malnutrition stemming from food insecurity in areas. The current study has helped to identify geographical ‘hotspots’ as well as the key factors driving under-5 deaths in Jigawa North East Region. Therefore, policymakers need to pay attention to the findings from this study, as it can help in designing better strategies that will enable the attainment of the SDG 3 targets of reducing under-five mortality to at least 25 per 1000 live births.
References
Adebayo, S. B., & Fahrmeir, L. (2005). Analysing child mortality in Nigeria with geoadditive discrete-time survival models. Statistics in Medicine, 24(5), 709–728. https://doi.org/10.1002/sim.1842
Adebayo, S. B., Gayawan, E., Ujuju, C., & Ankomah, A. (2013). Modelling geographical variations and determinants of use of modern family planning methods among women of reproductive age in Nigeria. Journal of Biosocial Science, 45(1), 57–77. https://doi.org/10.1017/S0021932012000326
Akinyemi, J. O., Adebowale, A. S., Bamgboye, E. A., & Ayeni, O. (2015a). Child survival dynamics in Nigeria: Is the 2006 child health policy target met? Nigerian Journal of Health Sciences, 15(1), 18. https://doi.org/10.4103/1596-4078.171378.
Akinyemi, J. O., Bamgboye, E. A., & Ayeni, O. (2015b). Trends in neonatal mortality in Nigeria and effects of bio-demographic and maternal characteristics. BMC Pediatrics, 15, 36. https://doi.org/10.1186/s12887-015-0349-0.
Alkema, L., Chao, F., You, D., Pedersen, J., & Sawyer, C. C. (2014). National, regional, and global sex ratios of infant, child, and under-5 mortality and identifcation of countries with outlying ratios: A systematic assessment. The Lancet Global Health, 2(9), 521–530. https://doi.org/10.1016/S2214-109X(14)70280-3.
Amusa, L. & Yahaya, W. (2019). Stepwise Geo-additive Modelling of the Ideal Family Size in Nigeria. Turkiye Klinikleri Journal of Biostatistics, 11(2), 123-132. https://doi.org/10.5336/biostatic.2019-66016
Ayele, D. G., Zewotir, T. T., & Mwambi, H. G. (2015). Structured additive regression models with spatial correlation to estimate under-fve mortality risk factors in Ethiopia Biostatistics and methods. BMC Public Health, 15(1), 1–11. https://doi.org/10.1186/s12889-015-1602-z.
Balk, D., Pullum, T., Storeygard, A., Greenwell, F., & Neuman, M. (2004). A spatial analysis of childhood mortality in West Africa. Population, Space and Place. Nutrition, 10, 175–216. https://doi.org/10.1002/psp.328
Belitz, C., Brezger, A., Knieb, T., et al. (2012). BayesX-software for Bayesian inference in structured additive regression models. Retrieved from http://www.stat.uni-muenchen.de/~bayesx/bayesx.html
Brezger, A. L. (2006). Generalized structured additive regression based on Bayesian P-spline. Computational Statistics and Data Analysis, 50, 947–991. https://doi.org/10.1016/j.csda.2004.10.011
Chao, F., You, D., Pedersen, J., Hug, L., & Alkema, L. (2018). National and regional under-5 mortality rate by economic status for low-income and middle-income countries: a systematic assessment. The Lancet Global Health, 6(5), e535–e547. https://doi.org/10.1016/S2214-109X(18)30059-7.
Ezeh, O. K., Agho, K. E., Dibley, M. J., Hall, J. J., & Page, A. N. (2015). Risk factors for postneonatal, infant, child and under-5 mortality in Nigeria: A pooled cross-sectional analysis. British Medical Journal Open, 5(3), e006779. https://doi.org/10.1136/bmjopen-2014-006779.
Fagbamigbe, A. F., Kandala, N. B., & Uthman, O. A. (2020a). Decomposing the educational inequalities in the factors associated with severe acute malnutrition among under-five children in low-income and middle-income countries. BMC Public Health, 20(555), 1–14. https://doi.org/10.1186/s12889-020-08635-3.
Fagbamigbe, A. F., Kandala, N. B., & Uthman, O. A. (2020b). Severe acute malnutrition among under-five children in low- and middle-income countries: A hierarchical analysis of associated risk factors. Nutrition, 75–76(2020), 110768. https://doi.org/10.1016/j.nut.2020.110768.
Folasade, B. I. (2000). Environmental factors, situation of women and child mortality in southwestern Nigeria. Social Science and Medicine, 51, 1473–1489. https://doi.org/10.1016/S0277-9536(00)00047-2
Forste, R. (1994). The effects of breastfeeding and child mortality in Bolivia. Population Studies, 48, 397–511. https://doi.org/10.1080/0032472031000147996
Fuse, K. (2010). Variations in attitudinal gender preferences for children across 50 less-developed countries. Demographic Research, 23(36), 1031–1048. https://doi.org/10.4054/DemRes.2010.23.36
Gayawan, E. & Adebayo, S. B. (2015). Spatial analysis of women employment status in Nigeria. CBN Journal of Applied Statistics, 6(2), 1-17.
Gayawan, E. (2014). Spatial analysis of choice of place of delivery in Nigeria. Sexual & Reproductive Healthcare, 5, 59–67. https://doi.org/10.1016/j.srhc.2014.01.004
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(5), 1–20. https://doi.org/10.1186/s41118-016-0009-8
Gelman, A., & Little, T. (1997). Postratification into many categories using hierarchical logistic regression. Survey Methodology, 23(2), 127–135.
Ghilagaber, G., Diddy, A., & Kandala, N. B. (2014). Modeling spatial effects on childhood mortality via geo-additive Bayesian discrete-time survival model: A case study from Nigeria. In N. B. Kandala & G. Ghilagaber (Eds.), Advance techniques for modelling maternal and child health in Africa (Vol. 34, pp. 29–48). New York: Springer Dordrecht Heidelberg. https://doi.org/10.1007/978-94-007-6778-2_3
Hobcraft, J. N. (1993). Women’s education, child welfare and child survival: a review of evidence. Health Transition Review, 3(3), 159–175.
Kandala, N. B., Nnanatu, C. C., & Atilola, G. (2019). A spatial analysis of the prevalence of female genital mutilation/cutting among 0–14-year-old girls in Kenya. International Journal of Environmental Research and Public Health, 16(21), 4155. https://doi.org/10.3390/ijerph16214155.
Kandala, N.-B., Nwakeze, N., & Ngianga, S. K. I. (2009). Spatial distribution of female genital mutilation in Nigeria. American Journal of Tropical Medicine and Hygiene, 81(5), 784–792. https://doi.org/10.4269/ajtmh.2009.09-0129
Kneib, T., & Fahrmeir, L. (2006). Structured additive regression for multicategorical space-time data: A mixed model approach. Biometrics, 63, 109–118. https://doi.org/10.1111/j.1541-0420.2005.00392.x
Little, R. J. A. (1993). Post-stratification: A modeler’s perspective. Journal of the American Statistical Association, 88(423), 1001-1019. https://doi.org/10.1080/01621459.1993.10476368
Little, R. J. A. (2012). Calibrated Bayes, an alternative inferential paradigm for official statistics. Journal of Official Statistics, 28(3), 309–334.
Magadi, M. A., Agwanda, O. A., & Obare, F. O. (2007). A comparative analysis of the use of maternal health services between teenagers and older mothers in sub-Saharan Africa: evidence from Demographic and Health Surveys (DHS). Social Science and Medicine, 64, 1311–1325. https://doi.org/10.1016/j.socscimed.2006.11.004
Malec, D., Davis, W. W., & Cao, X. (1999). Model-based small area estimates of overweight prevalence using sample selection adjustment. Statistics in medicine, 18, 3189–3200. https://doi.org/10.1002/(SICI)1097-0258(19991215)18:23<3189::AID-SIM309>3.0.CO;2-C
Manda, S. (1999). Birth intervals, breastfeeding and determinants of childhood mortality in Malawi. Social Science and Medicine, 48(3), 301–312. https://doi.org/10.1016/S0277-9536(98)00359-1
MDG Report. (2012). Assessing progress in Africa towards the millennium development goals. Adis Ababa: African Union Commission, United Nations Economic Commission for Africa, African Development Bank and United Nations Development Programme.
Molina, I., Nandram, B., & Rao, J. N. K. (2014). Small area estimation of general parameters with application to poverty indicators: A hierarchical Bayes approach. Annals of Applied Statistics, 8(2), 852–885. https://doi.org/10.1214/13-AOAS702
Morakinyo, O. M., & Fagbamigbe, A. F. (2017). Neonatal, infant and under-fve mortalities in Nigeria: An examination of trends and drivers (2003–2013). PLoS ONE, 12(8), e0182990. https://doi.org/10.1371/journal.pone.0182990.
Mutunga, J. C. (2004). Environmental determinants of child mortality in urban Kenya. Paper presented at the Discussion in an informal workshop held at Abdus Salam ICTP, Trieste, Italy.
National Population Commission (NPC) [Nigeria] & ICF International. (2009). Nigeria Demographic and Health Survey, 2008. DHS Measure Macro, New York and Nigeria Population Commission, Abuja, Nigeria.
National Population Commission (NPC) [Nigeria], & ICF International. (2014). Nigeria Demographic Health Survey, 2013. Abuja.
National Population Commission (NPC) [Nigeria], & ICF International. (2019). Nigeria Demographic and Health Survey 2018. Abuja, Nigeria, And Rockville, Maryland, USA.
National Population Commission (NPC). (2006).
Rao, J. N. K., & Molina, I. (2015). Small area estimation (2nd edn., Vol. 1). Hoboken, New Jersey: Wiley. Retrieved from https ://www.wiley .com/en-gb/Small +Area+Estim ation %2C+2nd+Edition-p-97811 18735 787. https://doi.org/10.1002/9781118735855
Si, Y., Pillai, N. S., & Gelman, A. (2015). Bayesian nonparametric weighted sampling inference. Bayesian Analysis, 10(3), 605–625. https://doi.org/10.1214/14-BA924
Spiegelhalter, D., Best, N., Carlin, B., & Van der Line, A. (2002). Bayesian measures of models complexity and fit. Journal of the Royal Statistical Society: Series B, 64, 1–34. https://doi.org/10.1111/1467-9868.00353
Sugasawa, S. (2020). Small area estimation of general parameters: Bayesian transformed spatial prediction approach. Japanese Journal of Statistics and Data Science, 3(1), 167–181. https://doi.org/10.1007/s42081-019-00067-7
UN Inter-agency Group for Child Mortality. (2014). Level and trends in child mortality. New York.
UNICEF. (2020). Child-survival: Under-fve mortality. Retrieved from April 23, 2020. https://data.unicef.org/topic/child-survival/under-fve-mortality/.
United Nations Population. (2020). United Nations World Population Prospects 2019. Retrieved from May 9, 2020. https://doi.org/10.18356/b564c742-en
United Nations. (2015). Sustainable development goals (SDG). Washington, DC. Retrieved from. http://www.un.org/sustainabledevelopment/sustainable-development-goals/.
Uthman, A. O., Uthman, M. B., & Yahaya, I. (2008). A population-based study of effect of multiple birth on infant mortality in Nigeria. BMC Pregnancy Childbirth, 8, 41. https://doi.org/10.1186/1471-2393-8-41
Yaya, S., Bishwajit, G., Okonofua, F., & Uthman, O. A. (2018). Under fve mortality patterns and associated maternal risk factors in sub-Saharan Africa: A multi-country analysis. PLoS ONE. https://doi.org/10.1371/journal.pone.0205977.
Yaya, S., Uthman, O. A., Okonofua, F., & Bishwajit, G. (2019). Decomposing the rural-urban gap in the factors of under-fve mortality in sub-Saharan Africa? Evidence from 35 countries. BMC Public Health, 19(1), 1–10. https://doi.org/10.1186/s12889-019-6940-9.
Zhang, X., Holt, J. B., Lu, H., Wheaton, A. G., Ford, E. S., Greenlund, K. J., & Croft, J. B. (2014). Multilevel regression and poststratification for small-area estimation of population health outcomes. American Journal of Epidemiology, 179(8), 1025–1033. https://doi.org/10.1093/aje/kwu018
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Isah Muhammad, Yahaya Zakari, Mannir Abdu
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.