Social determinants and child survival in Nigeria in the era of Sustainable Development Goals: Progress, challenges, and opportunities
Adeyinka, Daniel A
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Introduction: Like in many low- and middle-income settings, childhood mortality remains a big challenge in Nigeria—being the second largest contributor to under-five mortality globally, after India. Currently, there is little local evidence to guide policymakers in Nigeria to tailor appropriate social interventions to make the Sustainable Development Goal (SDG) targets of child survival (SDG-3), gender equality (SDG-5), and social inclusiveness (SDG-10) achievable by 2030. In addition, lack of methodological rigor and theoretical foundations of child survival research in Nigeria limit their use for proper planning of child health services. Aims: The basis of this thesis is to understand the complex issues relating to child survival and recommend new approaches to guide policymakers on interventions that will improve child survival in Nigeria. The overarching goal of this thesis is to address the methodological and theoretical shortcomings identified in the previous studies conducted in Nigeria. Using robust interdisciplinary analytic techniques, this thesis assessed the following specific objectives. Objective 1: (a) Compare predictive abilities of the most used conventional statistical time-series methods—ARIMA and Holt-Winters exponential smoothing models, with artificial intelligence technique such as group method of data handling (GMDH)-type artificial neural network (ANN), and (b) estimate the age- and sex-specific mortality trends in child-related SDG indicators (i.e., neonatal and under-five mortality rates) over the 1960s-2017 period, and estimate the expected annual reduction rates needed to achieve the SDG-3 targets by projecting rates from 2018 to 2030. Objective 2: (a) Identify the social determinants of age-specific childhood (0-59 months) mortalities, which are disaggregated into neonatal mortality (0-27 days), post-neonatal mortality (1-11 months) and child mortality (12-59 months), and (b) estimate the within- and between-community variations of mortality among under-five children in Nigeria. Objective 3: Identify the critical pathways through which social factors (at maternal, household, community levels) determine neonatal, infant, and under-five mortalities in Nigeria. Objective 4: (a) Determine patterns and determinants of geographical clustering of neonatal mortality at the state and regional levels in Nigeria, (b) assess gender inequity for neonatal mortality between urban and rural communities across the regions in Nigeria, and (c) measure gaps in SDG-3 target for neonatal mortality at the state and regional levels in Nigeria. Methods: This thesis is a quantitative study which used two secondary datasets—aggregated historical childhood mortality data from 1960s to 2017 (objective 1), and the latest (2016/2017) Nigeria Multiple Indicator Cluster Survey (MICS) for 36 states and Federal Capital Territory (FCT) in Nigeria (objectives 2-4). To minimize recall bias, analysis was limited to a weighted nationally representative sample of 30,960 live births delivered within five years before the survey. The selection of relevant social determinants of child survival was primarily informed by Mosley-Chen framework. The candidate variables were layered across child, maternal, household, and community-levels. The analytic approaches include artificial intelligence technique (i.e., group method of data handling (GMDH)-type artificial neural network, and multilayer perceptron (MLP) neural network), autoregressive integrated moving average (ARIMA), Holt-Winters exponential smoothing models, spatial cluster analysis, hierarchical path analysis with time-to-event outcome, and multilevel multinomial regression. Results: Progress towards achieving SDG targets – Nigeria is not likely to achieve SDG targets for child survival and, within, gender equity by 2030 at the current annual reduction rates (ARR) under-five mortality rate (U5MR): 1.2%, and neonatal mortality rate (NMR): 2.0%. If the current trend continues, U5MR will begin to increase by 2028. Also, at the end of SDG-era, female deaths will be higher than male deaths (80.9 vs. 62.6 deaths per 1000 live births). To make child-related SDG targets achievable by 2030, Nigeria needs to reduce annual U5MR by 9 times and annual NMR by 4 times the current rate of decrease. Social determinants of childhood mortality – At each stage of early childhood development, there are different factors relating to survival outcomes. Surprisingly, attendance of skilled health providers during delivery was associated with an increased neonatal mortality risk, although its effect disappeared during post-neonatal and toddler/pre-school stages. The observed association requires cautious interpretation because of unavailability of variables on quality of care in MICS dataset to assess how skilled birth delivery impacts child survival in Nigeria. However, there is a possibility of under-reporting under-five mortalities at the community level. Also, it could indicate a functioning referral system that sends the high-risk deliveries to health facilities to a greater extent. There is a large variation (39%) of under-five mortalities across the Nigerian communities, which is accounted for by maternal-level factors (i.e., maternal education, contraceptive use, maternal wealth, parity, death of previous children and quality of perinatal care). Pathways to childhood mortality – Region and area of residence (urban/rural), infrastructural development, maternal education, contraceptive use, marital status, and maternal age at birth were found to operate indirectly on neonatal, infant and under-five survival. Female children, singleton, children whose mothers delivered at least two years apart and aged 20-34 years survived much longer. Specifically, women from Northern areas of Nigeria were less likely to reside in urban cities and towns than those in the Southern areas. This, in turn, limited their access to social infrastructure and acted as a barrier to maternal education. Without adequate education, women were less likely to use contraceptive methods. Women with no history of contraceptive use were more likely to have childbirths closer together (less than two-year gap), which in turn, negatively impacted child survival. Regional inequities in childhood mortality – There was significant state-level clustering of NMR in Nigeria. The states with higher neonatal mortality rates were majorly clustered in the North-West and North-Central regions, and states with lower neonatal mortality rates were clustered in the South-South and South-East regions. Gender inequity was worse in the rural areas of Northern Nigeria, while it was worse in the urban areas of Southern Nigeria. NMR was disproportionately higher among females in urban areas (except North-West and South-West regions). Conversely, male neonates had higher mortality risks in the rural areas for all the regions. Conclusions: This thesis provides more refined age- and sex-specific mortality estimates for Nigeria. At the current rates, Nigeria will not meet SDG targets for child survival. In addition, this thesis identifies the critical intervention pathways to child survival in Nigeria during the SDG-era. The new estimates may be used to improve the design and accelerate the implementation of child health programmes to attain the SDG targets. Also, it is important for stakeholders to implement more impactful policies that promote maternal education and improve living conditions of women (especially in the rural areas). To address gender inequities, gender-sensitive policies, and community mobilization against gender-based discrimination towards girl-child should be implemented. Further research is required to assess the quality of skilled birth attendants in Nigeria.
DegreeDoctor of Philosophy (Ph.D.)
DepartmentCommunity Health and Epidemiology
ProgramCommunity and Population Health Science
CommitteeLeis, Anne; Pahwa, Punam; Petruka, Pammla; Elon, Isaac
Copyright DateSeptember 2020
Sustainable Development Goals
social determinants of health
artificial neural network
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