Background predictors of time to death in infancy: evidence from a survival analysis of the 2018 Nigeria DHS data
Abstract Background Nigeria’s child health profile is quite concerning with an infant mortality rate of 67 deaths per 1000 live births and a significant slowing down in progress towards improving child health outcomes. Nigeria’s 2018 Demographic and Health Survey (DHS) suggests several bio-demograph...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-01-01
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| Series: | BMC Public Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12889-021-12424-x |
| _version_ | 1818979395988094976 |
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| author | Michael Kunnuji Idongesit Eshiet Bright Opoku Ahinkorah Temitope Omogbemi Sanni Yaya |
| author_facet | Michael Kunnuji Idongesit Eshiet Bright Opoku Ahinkorah Temitope Omogbemi Sanni Yaya |
| author_sort | Michael Kunnuji |
| collection | DOAJ |
| description | Abstract Background Nigeria’s child health profile is quite concerning with an infant mortality rate of 67 deaths per 1000 live births and a significant slowing down in progress towards improving child health outcomes. Nigeria’s 2018 Demographic and Health Survey (DHS) suggests several bio-demographic risk factors for child death, including mother’s poor education, poverty, sex of child, age of mother, and location (rural vs urban) but studies are yet to explore the predictive power of these variables on infant survival in Nigeria. Methods The study extracted data for all births in the last 12 months preceding the 2018 Nigeria DHS and used the Cox proportional hazard model to predict infant survival in Nigeria. Failure in this analysis is death with two possible outcomes – dead/alive – while the survival time variable is age at death. We censored infants who were alive at the time of the study on the day of the interview. Covariates in the analysis were: age of mother, education of mother, wealth quintile, sex of child, location, region, place of delivery, and age of pregnancy. Results The study found that a higher education of a mother compared to no education (β = .429; p-value < 0.05); belonging to a household in the richer wealth quintile (β = .618; p-value < 0.05) or the highest quintile (β = .553; p-value < 0.05), compared to the lowest wealth quintile; and living in North West (β = 1.418; p-value < 0.05) or South East zone (β = 1.711; p-value < 0.05), significantly predict infant survival. Conclusion Addressing Nigeria’s infant survival problem requires interventions that give attention to the key drivers – education, socio-economic status, and socio-cultural contextual issues. We therefore recommend full implementation of the universal basic education policy, and child health education programs targeted at mothers as long- and short-term solutions to the problem of poor child health outcomes in Nigeria. We also argue in favor of better use of evidence in policy and program development in Nigeria. |
| first_indexed | 2024-12-20T16:58:52Z |
| format | Article |
| id | doaj.art-4d43aa9647e9417683a9295bd2264927 |
| institution | Directory Open Access Journal |
| issn | 1471-2458 |
| language | English |
| last_indexed | 2024-12-20T16:58:52Z |
| publishDate | 2022-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Public Health |
| spelling | doaj.art-4d43aa9647e9417683a9295bd22649272022-12-21T19:32:38ZengBMCBMC Public Health1471-24582022-01-012211810.1186/s12889-021-12424-xBackground predictors of time to death in infancy: evidence from a survival analysis of the 2018 Nigeria DHS dataMichael Kunnuji0Idongesit Eshiet1Bright Opoku Ahinkorah2Temitope Omogbemi3Sanni Yaya4Department of Sociology, University of LagosDepartment of Sociology, University of LagosSchool of Public Health, University of Technology SydneyDepartment of Sociology, University of LagosSchool of International Development and Global Studies, Faculty of Social Sciences, University of OttawaAbstract Background Nigeria’s child health profile is quite concerning with an infant mortality rate of 67 deaths per 1000 live births and a significant slowing down in progress towards improving child health outcomes. Nigeria’s 2018 Demographic and Health Survey (DHS) suggests several bio-demographic risk factors for child death, including mother’s poor education, poverty, sex of child, age of mother, and location (rural vs urban) but studies are yet to explore the predictive power of these variables on infant survival in Nigeria. Methods The study extracted data for all births in the last 12 months preceding the 2018 Nigeria DHS and used the Cox proportional hazard model to predict infant survival in Nigeria. Failure in this analysis is death with two possible outcomes – dead/alive – while the survival time variable is age at death. We censored infants who were alive at the time of the study on the day of the interview. Covariates in the analysis were: age of mother, education of mother, wealth quintile, sex of child, location, region, place of delivery, and age of pregnancy. Results The study found that a higher education of a mother compared to no education (β = .429; p-value < 0.05); belonging to a household in the richer wealth quintile (β = .618; p-value < 0.05) or the highest quintile (β = .553; p-value < 0.05), compared to the lowest wealth quintile; and living in North West (β = 1.418; p-value < 0.05) or South East zone (β = 1.711; p-value < 0.05), significantly predict infant survival. Conclusion Addressing Nigeria’s infant survival problem requires interventions that give attention to the key drivers – education, socio-economic status, and socio-cultural contextual issues. We therefore recommend full implementation of the universal basic education policy, and child health education programs targeted at mothers as long- and short-term solutions to the problem of poor child health outcomes in Nigeria. We also argue in favor of better use of evidence in policy and program development in Nigeria.https://doi.org/10.1186/s12889-021-12424-xTime to death in infancySurvival analysisNigeria: DHSGlobal Health |
| spellingShingle | Michael Kunnuji Idongesit Eshiet Bright Opoku Ahinkorah Temitope Omogbemi Sanni Yaya Background predictors of time to death in infancy: evidence from a survival analysis of the 2018 Nigeria DHS data BMC Public Health Time to death in infancy Survival analysis Nigeria: DHS Global Health |
| title | Background predictors of time to death in infancy: evidence from a survival analysis of the 2018 Nigeria DHS data |
| title_full | Background predictors of time to death in infancy: evidence from a survival analysis of the 2018 Nigeria DHS data |
| title_fullStr | Background predictors of time to death in infancy: evidence from a survival analysis of the 2018 Nigeria DHS data |
| title_full_unstemmed | Background predictors of time to death in infancy: evidence from a survival analysis of the 2018 Nigeria DHS data |
| title_short | Background predictors of time to death in infancy: evidence from a survival analysis of the 2018 Nigeria DHS data |
| title_sort | background predictors of time to death in infancy evidence from a survival analysis of the 2018 nigeria dhs data |
| topic | Time to death in infancy Survival analysis Nigeria: DHS Global Health |
| url | https://doi.org/10.1186/s12889-021-12424-x |
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