A predictive model and socioeconomic and demographic determinants of under-five mortality in Sierra Leone

Tagoe, Eunice Twumwaa; Agbadi, Pascal; Nakua, Emmanuel Kweku; Duodu, Precious Adade; Nutor, Jerry John; Aheto, Justice Moses K.

doi:10.1016/j.heliyon.2020.e03508

Cited by 29 publications

(38 citation statements)

References 26 publications

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“…Poverty, region, and poor education are the major drivers of infant mortality in Nigeria in agreement with several other studies [5,8,15,19]. Poverty is a factor because parents who cannot afford quality care are likely to delay use of facility-based care in the hope that their infants' health will improve with the use of traditional medicine or care with drugs purchased from Patent Medicine Vendors [29].…”

Section: Discussionsupporting

confidence: 83%

“…Proximate predictors of child deaths include sociodemographic factors [5][6][7][8], biomedical factors such as birth interval, weight and gestation period, sex of child and diseases [9][10][11][12][13][14][15][16] and health-seeking behavior and barriers to care [17,18]. Research shows that education, income, and access to healthcare resources (including safe drinking water and sanitation) enhance child survival globally [5-8, 10, 15, 19, 20], and countries having higher inequality in access to education, income and healthcare resources also have higher mortality rates [5,8].…”

Section: Introductionmentioning

confidence: 99%

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Background predictors of time to death in infancy: evidence from a survival analysis of the 2018 Nigeria DHS data

et al. 2022

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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.

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Section: Discussionsupporting

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Background predictors of time to death in infancy: evidence from a survival analysis of the 2018 Nigeria DHS data

et al. 2022

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“…Children who were perceived as small-or very-small-sized after birth by their mothers had a higher likelihood of U5M than those perceived as average-or larger-sized. This result contradicts a study performed by Yaya et al [10]; however, few other similar studies showed a significant relationship with U5M [25,[55][56][57][58]. Even if our outcome on perceived children's size was significant, the rationale mothers applied in estimating their children's size after birth remains unclear.…”

Section: Discussioncontrasting

confidence: 99%

Under-5 Mortality and Its Associated Factors in Northern Nigeria: Evidence from 22,455 Singleton Live Births (2013–2018)

Ezeh

Ogbo

Odumegwu³

et al. 2021

IJERPH

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The northern geopolitical zones (NGZs) continue to report the highest under-5 mortality rates (U5MRs) among Nigeria’s six geopolitical zones. This study was designed to identify factors related to under-5 mortality (U5M) in the NGZs. The NGZ populations extracted from the 2018 Nigeria Demographic and Health Survey were explored to assess the factors associated with U5M using logistic regression, generalised linear latent, and mixed models. Between 2013 and 2018, the northwest geopolitical zone reported the highest U5MR (179 deaths per 1000 live births; 95% confidence interval [CI]: 163–194). The adjusted model showed that geopolitical zone, poor household, paternal occupation, perceived children’s body size at birth, caesarean delivery, and mothers and fathers’ education were highly associated with increased odds of U5M. Other significant factors that influenced U5M included children of fourth or higher birth order with shorter interval ≤ 2 years (adjusted odds ratio [aOR] = 1.68; CI: 1.42–1.90) and mothers who did not use contraceptives (aOR = 1.41, CI: 1.13–1.70). Interventions are needed and should primarily spotlight children residing in low-socioeconomic households. Educating mothers on the benefits of contraceptive use, child spacing, timely and safe caesarean delivery and adequate care for small-sized babies may also reduce U5M in Nigeria, particularly in the NGZs.

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“…The selection of predictor variables that influence neonatal mortality was based on the conceptual framework for the study of child survival in developing countries and predictor variables used in previous studies [14][15][16] and available in the 2014 GDHS datasets. The explanatory variables selected for the analysis included sex of child, birth type, birth interval, health insurance status of mother, size of child, maternal age, ever terminated pregnancy, mode of delivery, maternal marital status, parity, household size, maternal religion, wealth index, and prenatal care assistance.…”

Section: Independent Variablesmentioning

confidence: 99%

Predictive model and determinants of odds of neonates dying within 28 days of life in Ghana

Takramah

Aheto

2021

Health Science Reports

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Background One of the priorities and important current problem in public health research globally is modeling of neonatal mortality and its risk factors in using the appropriate statistical methods. It is believed that multiple risk factors interplay to increase the risk of neonatal mortality. To understand the risk factors of neonatal mortality in Ghana, the current study carefully evaluated and compared the predictive accuracy and performance of two classification models. Methods This study reviewed the birth history data collected on 5884 children born in the 5 years preceding the 2014 Ghana Demographic and Health Survey (GDHS). The 2014 GDHS is a cross‐sectional nationally representative household sample survey. The relevant variables were selected using leaps‐and‐bounds method, and the area under curves were compared to evaluate the predictive accuracy of unweighted penalized and weighted single‐level multivariable logistic regression models for predicting neonatal mortality using the 2014 GDHS data. Results The study found neonatal mortality prevalence of 2.8%. A sample of 4514 children born in the 5 years preceding the 2014 GDHS was included in the inferential analysis. The results of the current study show that for the unweighted penalized single‐level multivariable logistic model, there is an increased risk of neonatal death among babies born to mothers who received prenatal care from non‐skilled worker [OR: 3.79 (95% CI: 2.52, 5.72)], multiple births [OR: 3.10 (95% CI: 1.89, 15.27)], babies delivered through caesarian section [OR: 2.24 (95% CI: 1.30, 3.85)], and household with 1 to 4 members [OR: 5.74 (95% CI: 3.16, 10.43)], respectively. The predictive accuracy of the unweighted penalized and weighted single‐level multivariable logistic regression models was 82% and 80%, respectively. Conclusion The study advocates that prudent and holistic interventions should be institutionalized and implemented to address the risk factors identified in order to reduce neonatal death and, by large, improve child and maternal health outcomes to achieve the SDG target 3.2.

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A predictive model and socioeconomic and demographic determinants of under-five mortality in Sierra Leone

Cited by 29 publications

References 26 publications

Background predictors of time to death in infancy: evidence from a survival analysis of the 2018 Nigeria DHS data

Background predictors of time to death in infancy: evidence from a survival analysis of the 2018 Nigeria DHS data

Under-5 Mortality and Its Associated Factors in Northern Nigeria: Evidence from 22,455 Singleton Live Births (2013–2018)

Predictive model and determinants of odds of neonates dying within 28 days of life in Ghana

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