https://orcid.org/0000-0002-7314-2523
Figures
Abstract
Background
Under-five mortality (U5M) is one of the most important and sensitive indicators of the health status of the community. Despite there having been a substantial reduction in U5M since 1990, its rate is still high in Sub-Saharan African countries. Thus, this study aimed to assess time to under-five mortality and its predictors in rural Ethiopia.
Methods
This study utilized a secondary analysis of the 2016 Ethiopia Demographic and Health Survey (EDHS). A total of 9,807 weighted under-five children selected at different stages were included in the analysis. The Kaplan-Meier and Cox’s-gamma shared frailty models were used to estimate survival time and to identify predictors of under-five mortality, respectively. An adjusted Hazard Ratio (AHR) along with a 95% Confidence Interval (CI) was used to measure the effect size and direction of the association.
Results
The study indicated that 6.69% (95% CI: 6.13, 7.30) of children died before celebrating their fifth birthday in rural Ethiopia. Of all the deaths, the median time to death was 27 months. After controlling the effect of cluster and other confounding factors, female sex (AHR = 0.62, 95% CI: 0.52, 0.75), ever born greater than five children (AHR = 1.40, 95% CI: 1.07, 1.83), very large size at birth (AHR = 1.33, 95% CI: 1.03 1.71), very small size at birth (AHR = 1.41, 95% CI: 1.10, 1.82), twin pregnancy (AHR = 3.5, 95% CI: 2.47, 4.88), not ever breastfeeding (AHR = 11.29, 95% CI: 9.03, 14.12), unimproved latrine (AHR = 3.44, 95% CI: 1.91, 6.17), covered by health insurance (AHR = 0.29, 95% CI: 0.12, 0.70) were predictors of under-five mortality.
Conclusions
Still under-five mortality was high in rural Ethiopia as compared to the global under-five mortality rate. In the final model, sex of a child, the total number of children ever born, children’s size at birth, type of pregnancy, breastfeeding, type of toilet, and being covered by health insurance were significant predictors of under-five mortality. Further emphasis should be given to twin and not breastfeeding children, as well as households’ better encouraging membership of community health insurance and utilization of improved latrines.
Citation: Yalew M, Arefaynie M, Bitew G, Amsalu ET, Kefale B, Muche A, et al. (2022) Time to under-five mortality and its predictors in rural Ethiopia: Cox-gamma shared frailty model. PLoS ONE 17(4): e0266595. https://doi.org/10.1371/journal.pone.0266595
Editor: Kannan Navaneetham, University of Botswana, BOTSWANA
Received: January 29, 2021; Accepted: March 24, 2022; Published: April 6, 2022
Copyright: © 2022 Yalew et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: This study used the 2016 Ethiopia Demographic and Health Survey (EDHS) dataset(s) (search criteria in Methods section). Even if it is open after legal registration, it is restricted to share the dataset to anyone else without consent of DHS. In order to gain access to the data, interested researchers must register at the following website: https://dhsprogram.com/data/new-user-registration.cfm. Once registered, researchers may log in to access the data at the following website: https://www.dhsprogram.com/data/dataset_admin/login_main.cfm.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
List of abbreviations: AHR, Adjusted Hazard Ratio; CHR, Crude Hazard Ratio; CSA, Central Statistical Agency; EDHS, Ethiopia Demographic and Health Survey; U5M, Under-five Mortality
Introduction
Under-five mortality (U5M) is one of the most important sensitive indicators of the health status of the community. The global estimate showed 5.6 million children died before reaching their fifth birthday each year [1, 2]. Even though there has been a substantial reduction in child mortality in the past three decades, the progress has not been effective in Sub-Saharan Africa and South Asia [3]. According to a systematic analysis for the Global Burden of Disease Study from 1970–2016, the global under-five mortality rate (U5M) was found to be 4.10%, which needs major public health interventions [4]. Still, many developing countries are suffering from a high number of U5M [5]. Mainly, Sub-Saharan African countries and Southern Asian countries share over 80% of under-five deaths [6]. Children in those countries were 10 times more likely to die before 5 years than children in developed countries [7]. Particularly in Ethiopia, the 2016 EDHS report showed that U5M was 6.7% [8].
Under-five, mortality was significantly associated with maternal mental distress [9]. In addition, the rate of hospitalization was very high among mothers who had lost their child compared with those who had not [10]. Under-five mortality was influenced by numerous factors: child factors (age, sex, and size at birth) [11–15], household factors (wealth, family size, water source, and latrine type) [16–19], and community-level factors (residence, community-level of education, and wealth) [20, 21].
At least 80% of the Ethiopian population were rural residents, and life expectancy and age were relatively short among rural residents [22, 23]. Keeping this in mind, Ethiopia aimed to reduce the U5MR to 25 or less per 1000 live births by 2030 [24]. This aim will not be achieved unless the risk factors are extensively investigated and intervened upon. Although different studies have been conducted in Ethiopia regarding under-five mortality, all of them targeted both urban and rural populations, despite significant variations being documented [12, 25]. Furthermore, the data were correlated, even though previous studies on this data had ignored it [12, 26, 27]. Though some of the studies tried to consider the nature of the data, under-five mortality is time to the event of interest, and the analysis didn’t consider the effect of time [28, 29]. Modeling such types of data is better to account for both hierarchical and censoring data [11, 14, 17, 25, 28, 30–32]. Therefore, this study aimed to assess time to under-five mortality and its predictors in rural Ethiopia using survival analysis, accounting for the correlated nature of the data.
Methods
Study setting, population and data source
A community-based cross-sectional survey was conducted in Ethiopia. The survey was conducted from January 18 to June 27, 2016, by the Central Statistical Agency (CSA) in coordination with the Federal Minister of Health (FMoH) and the Ethiopia Public Health Institute (EPHI). The 2016 EDHS data were accessed from the DHS program after contacting them through formal registration. The source and study population were all under-five children in rural Ethiopia and all under-five children selected clusters of rural Ethiopia, respectively. The data set was limited to rural under-five children whose ages at death for the deceased and current age of a child for the living were recorded.
Sample size determination and sampling procedure
A total of 9,807 weighted under-five children from EDHS 2016 dataset were included from nine geographical regions and two administrative cities of Ethiopia. The 2016 EDHS sample was stratified and selected in two stages. In the first stage, stratification was conducted by region, and then each region was stratified as urban and rural, yielding 21 sampling strata. A total of 645 EAs (enumeration areas) (202 in urban areas and 443 in rural areas) were selected with probability proportional to EA size in each sampling stratum. A household listing operation was carried out in all of the selected EAs.
In the second stage, a fixed number of 28 households per cluster were selected with an equal probability systematic selection from the newly created household listing. Data coding and recoding was done to reach the exact number of under-five children in the 2016 EDHS. In addition, the timing of death for the deceased and age of the child for non-deceased was available with separate codes for all times with the respective type of respondent. Finally, by combing the above scenarios using the appropriate STATA command new variable was generated containing the total sample size which was 9,807 under-five children in the 2016 EDHS (Fig 1).
Variable measurement
The dependent variable (time to death) was the age of a child in months when he/she died after live birth (beginning time). Similarly, events (uncensored) refer to children who died either before or at the time of the EDHS data collection period. Whereas "censored" refers to an event that didn’t happen (children alive) until the last EDHS data collection date [8]. Community-level variables were computed by aggregating individual-level variables in each cluster.
Data quality control and analysis
A pre-test was conducted and necessary modifications were performed before the actual data collection period. The data collection instrument was transformed into the local language of the participants [8]. An initial exploratory data analysis was conducted to check for outliers, missing, and consistency after sample weighting. Stata/SE version 14.0 was used to analyze the data. A Log-rank test was used to assess statistical differences between categorical predictor variables in the outcome of interest over time. The Kaplan-Meier method was also used to estimate the time to under-five mortality. The Likelihood ratio chi-square and Wald chi-square tests were used to assess the significant contribution of clusters (frailty) on time to child mortality and for the overall model significance tests respectively. The Cox-gamma shared frailty model was used to identify predictors of under-five mortality. Shared gamma frailty analysis was done since the data was cluster level grouping, which was made between-group heterogeneity measured as theta chi-square test and was significant (p<0.001). So, this between-group heterogeneity creates unexploded variations. The hazard or risk of death was modeled using a mixed-effect model as follows [33]:
Where: λO is the baseline hazard, x is the vector of covariates, v takes account the non-linear or non-proportional effects, w is the random effect defined at the cluster level and u is equal to is called the shared frailty.
Gamma distribution for the frailty, z, whose density is
And the mean value of the frailty is 1, and the variance of the frailty is θ, which may be used as a measure of association. Large values of θ reflect greater heterogeneity between subgroups and a stronger association among members of a subgroup [34].
In the analysis, the first uni-variable cox-gamma regression model was fitted, and variables with a p-value less than 0.2 were selected for multivariable cox-regression. Then, the analysis was done in four steps: Model 0 (null model or empty model) without any explanatory variables, model1 (only individual-level variables); model 2 (only cluster or community-level variables) and model 3 (both individual and community-level variables). The measure of association (fixed effect) was estimated by using Adjusted Hazard Ratio (AHR) and random effect with θ along with their 95% Confidence Interval (CI). In the final model, the level of statistical significance was set at a P-value of less than 0.05. Model adequacy was checked by using Cox-Snell residuals, which is a Nelson Aalen cumulative hazard function against the Cox-Snell residuals plot; a linear pattern making a straight line through the origin indicates the best fit model [35]. Some variability in the right-hand tail around the 45° line is due to a smaller effective sample due to prior early-age deaths and censoring. Log-likelihood ratio deviance test was also applied.
Ethical consideration.
A written approval letter was obtained to use the EDHS dataset from the DHS program for the current study. The IRB also approved the dataset for public use without personal, household, or sample community identification. So, the privacy of the participants was kept anonymously. Moreover, per DHS program discipline, this data was used for the current study only. However, the data underlying the study can be accessed after legal registration at www.measuredhs.com and writing a convincing letter to the project for the DHS program.
Results
Household and parental characteristics
Three hundred fifty-nine (3.67%) of the children were from teenage mothers. Similarly, more than two-thirds (70.95%) of mothers of under-five children were not formally educated. More than half (57.40%) of children’s mothers were not working at the time of the survey. Regarding household wealth, 2572 (26.23%) of the mothers lived in the poorest wealth quintile. One thousand two hundred sixteen (12.40%) of the households were headed by women. Only, 324 (3.31%) and 527 (5.38%) of the households were covered by health insurance (Log-rang test: 8.88, p-value 0.003) and used improved latrines (Log-rank test: 4.37, p-value = 0.04) respectively. Seven thousand two hundred eighty-four (74.27%) of mothers had 3 or above ever-born children (Log-rank test: 8.28, p-value = 0.02). Regarding the preceding birth interval in months, only 3602 (36.76%) of mothers had more than 35 months of spacing (Log-rank test: 70.36, p-value = 0.001) (Table 1).
Characteristics of the children
The total number of under-five children included in the analysis were 9,807. From those, 5092 (51.92%) were female (Log-rank test: 14.26, P-value<0.001) and 251 (2.56%) were twins (Log-rank test: 125.59, P-value<0.001). Regarding breastfeeding status, 488 (4.97%) children were not breastfeeding at all (Log-rank test: 736.27, P-value<0.001). Only 2060 (21.00%) of children under the age of five were born in health facilities (Log-rank test: 4.63, P-value = 0.032). Concerning the size of a child at birth, 1684 (17.17%) of children were very large at birth and 4043 (41.23%) were average size at birth (Log-rank test: 18.05, P-value = 0.001) (Table 2).
Time to under-five mortality (Kaplan-Meier estimates of failure function)
In this study, the participants were assessed for 272,291 person-months retrospectively. The analysis indicated that 3.31% (95% CI: 2.97, 3.68) and 5.13% (95% CI: 4.70, 5.60) of children died before 28 days and before celebrating their first birthday, respectively, in rural Ethiopia (Table 3). Similarly, as it is illustrated in the figure below, the result of the Kaplan-Meier estimate showed that 6.69% of children died before celebrating their fifth birthday in rural Ethiopia (95% CI: 6.13, 7.30) (Fig 2).
The incidence of under-five mortality was 2.70 per 100 (95% CI: 2.35, 3.12) person-months; of all deaths, the median time to death was 27 months (IQR: 12, 43 months).
Predictors of under-five mortality in rural Ethiopia
After keeping the effect of cluster and other confounding variables, the Cox-gamma shared frailty model indicated that sex of a child, total number of children ever born, children’s size at birth, type of pregnancy, breastfeeding, type of latrine, and health insurance coverage were significant independent predictors of under-five mortality.
Keeping the effect of cluster and other variables constant, the mortality rate of female children was reduced by 38% before celebrating their fifth birthday as compared to males (AHR = 0.62, 95% CI: 0.52, 0.75). Similarly, those children who resided in households covered by health insurance were 71% at a lower risk of death than their counterparts (AHR = 0.29, 95% CI: 0.12, 0.70).
Keeping the effect of cluster and other variables constant, the mortality hazard of children living in a household with an unimproved latrine was increased by 3.44 times when compared to having an improved latrine (AHR = 3.44, 95% CI: 1.91, 6.17).
Again, the hazard of child mortality for children who had never been breastfed and had a twin pregnancy was increased by 11 times (95% CI: 9.03, 14.12) and 3.5 times (95% CI: 2.47, 4.88) as compared to children who had been breastfed and had no twin pregnancy, respectively.
Keeping the effect of cluster similar and other variables constant, those children who had extreme sized at birth (very large or very small) also had a greater hazard of death than average size babies. Those children who had very large and very small sizes at birth were 1.33 (95% CI: 1.03, 1.71) and 1.41 (95% CI: 1.10, 1.82) times more likely to die than average.
Lastly, the risk of under-five mortality was 1.4 times higher among children whose mothers had born more than five children (AHR = 1.40, 95% CI: 1.07, 1.83) (Table 4).
Model fitness and adequacy test.
The Cox-Snell residuals plot for fitting the Cox model indicated that the final model was the best fit with the data than the other preceding models (Fig 3) and (Table 5).
Discussion
The analysis indicated that 3.31%, 5.13%, and 6.69% of children died before 28 days, before celebrating their first and fifth birthdays, respectively, in rural Ethiopia. After keeping the effect of cluster and other confounders, the result indicated that sex of a child, the total number of children ever born, children size at birth, type of pregnancy, breastfeeding, type of toilet, and health insurance coverage were significant predictors of under-five mortality.
The finding of neonatal mortality was similar to a study conducted in Jimma, Ethiopia (3.55%) [36]. It is also similar to a study conducted in Ethiopia (3.30%) [37]. The finding was lower than a study conducted in Gondar (14.30%) [38], Tigray (6.30%) [39], and Somali (5.70%) [40]. It is also lower than a study conducted in Nigeria (4.10%) [41] and Pakistan (4.73%) [42]. The discrepancy could be caused by time differences. Moreover, it may be due to the difference in sociocultural contexts, especially concerning the economic status of the population and cultural mal-practices during and after delivery as well as postpartum.
The percentage of infant mortality was similar to a study conducted in rural Ethiopia (4.70%) [43]. But it is lower than a study conducted in Southwest Ethiopia (9.70%) [44] and Ethiopia (11.00%) [45]. It is also lower than a study conducted in Amhara, Ethiopia (8.80%) [46] and Ethiopia (5.90%) [47]. Again, this is lower than a study conducted in Sidama Zone (9.6.%) [48]. But it was higher as compared to a study conducted in Nepal (3.21%) [49]. This difference may be due to the fact that as time goes different advancements in disease management as well as increased access to different health facilities in the current study and not in previous studies of Ethiopia. The finding of under-five mortality was in line with studies conducted in Ethiopia (6.70% to 6.72%) [27, 50]. The finding was also similar to a study conducted in Tanzania (7.04%) [51]. But, the rate of under-five mortality was higher as compared to a study conducted in Ethiopia (6.00%) [12]. The finding was also higher than a study conducted in Ghana (4.91%) [15] and Bhutan (3.70%) [52]. The possible reason for this discrepancy might be differences in socio-cultural and other contextual factors.
Whereas it was lower than studies conducted in Ethiopia (7.44% to 10.00%) [11, 53]. The finding was also lower than a study conducted in South Sudan (10.10%) [54] and Bengal (9.69%) [17]. The discrepancy might be due to time variation as well as differences in infrastructure, and the health service strategies may not be similar with Bengal.
Those female children were 38% less likely to die before celebrating their fifth birthday as compared to males. The finding was in agreement with studies conducted in Ethiopia [11, 12, 16, 20, 27, 50, 55, 56] and our non-parametric Log-rank test also revealed a significant difference between females and males (Table 2). The result was also similar to studies conducted in Malawi [57], Ghana [15], and Nigeria [14, 58]. It is also supported by a study conducted in Sierra Leone [13] and Tanzania [51]. The possible reason for this association may be biological differences like hormones circulating in the bloodstream and natural protection mechanisms for different diseases. So, it implies that male new borne need special attention in the adaptation of the new environment, and their sickness required vigorous management due to its deadly outcome.
Similarly, the risk of death in children who resided in a household covered by health insurance was 71% less than their counterparts. This was supported by our descriptive result that the Log-Rank test showed a significant difference in under-five mortality between those households covered by health insurance and those not covered (Table 1). This may be due to the fact that most rural people may fear the cost of health care and may have poor health-seeking behavior for childhood illness. This implies the requirement of intensive efforts to enhance health insurance coverage in the country to reduce child mortality.
The hazard ratio of children who lived in a household with an unimproved latrine was increased by 3.44 fold as compared to the improved. The finding was in agreement with a study conducted in Bengal [17]. It was also supported in a study conducted in Bhutan [52] and Nepal [59]. The association could be due to the fact that unimproved latrines were a source of infections such as intestinal parasites and other diarrheal diseases. Diarrheal diseases are the third leading cause of child mortality in the country [60]. So, enhancing improved latrine and its utilization must be one focus of child mortality programs in the country.
The hazard of children mortality that had never been breastfed was increased by 11 times more than their counterparts. The finding was supported by a study conducted in Ethiopia [16, 19, 29]. It was also in agreement with a study conducted in Indonesia [61]. This might be due to mal-absorption of foods in the first 6 months, which may lead them to infection. Moreover, they lack natural antibodies (i.e., antibodies passed through breast milk) for infection prevention. This implies that in the countries like Ethiopia where infectious diseases are the leading cause of child mortality [60], breastfeeding is an option-less intervention in child mortality. The hazard ratio (risk of death) of multiple pregnancies was increased by 3.5 times as compared to a single. The finding was in agreement with studies conducted in Ethiopia [12, 18, 19, 27, 29, 55, 56]. It was also supported by a study conducted in Ghana [15]. It was again supported by a study conducted in Rwanda [62]. It is obvious that most multiple births are premature and which is the second cause of child mortality next to sepsis [60]. The possible reason for this association may be that different medical assistance during labour and postpartum may increase the risk of death as most of the Ethiopian health facilities are not fully equipped with all the necessary materials [63]. Moreover, it may be due to differences in physiological and pathological processes like twin-twin transfusion syndrome. So that mothers and health professionals need to give particular attention to multiple births to prevent child mortality of morbidity.
Children born in extreme sizes (very large and very small) had a higher risk of dying than the general population. The result was supported by a study conducted in Ethiopia [18, 27, 29, 50]. The result of this study was congruent with studies conducted in Malawi [57], Tanzania [64], and Nigeria [14, 58]. It is also supported by a study conducted in Sierra Leone [13]. This might be due to the fact that very small births were exposed to frequent infections. In contrast, very large babies were subjected to different birth traumas during delivery.
The risk of under-five mortality was higher among children whose mothers had born more than five children. The result was in agreement with a study conducted in Sierra Leone [13]. The finding was also supported by a study conducted in Nigeria [14]. The possible reasons may be that mothers may have less attention to children and the children may not get enough food and safe health care. Therefore, policymakers better focus on the prevention of grand parity through determining the number of children per woman to reduce child mortality.
Despite the Ethiopian government have been working to reduce neonatal, infant, and under-five mortality as a primary concern, in reality, significant numbers of neonates, infants, and under-five children died before 28 days, before celebrating their first and fifth birthdays, respectively, in rural Ethiopia. So, different concerned bodies (policymakers, planners, health care providers) need to be focused on the above mentioned statistical factors (sex of a child, total number of children ever born, children’s size at birth, type of pregnancy, breastfeeding, type of toilet, health insurance coverage) to achieve Sustainable Developmental Goals.
Strength and limitation of the study
The study has the following strengths: utilizes nationwide data that can easily detect small differences (effect size), considering the effect of clustering (the data recorded in the same cluster were correlated), and time to under-five mortality is time to the event of interest. Therefore, the model (cox-gamma shared frailty model) accounts for both the hierarchical nature of the data and time to the event of interest including censoring. However, it is not without limitations. The cross-sectional nature of the study restricts to build a causal and effect relationship. Moreover, as the analysis was based on secondary data, certain variables that may be potential factors for under-five mortality were missed.
Conclusion
Still now, neonatal, infant, and under-five mortality were very high in rural Ethiopia. In the final adjusted model, being ever born from a mother who had greater than five children, very small or very large children’s size at birth, twin pregnancy, not having improved latrine, and not breastfeeding were positively associated with under-five mortality. However, being female sex, and covered by health insurance were negatively associated with under-five mortality. Further emphasis should be given to twin and non-breastfeeding children as well as households better to encourage membership of community health insurance and utilization of improved latrines.
Acknowledgments
We would like to express our deepest gratitude to DHS program for helping to access the dataset for conducting the research.
References
- 1.
Lopez AD. Levels & Trends in Child Mortality: Report 2014, Estimates Developed by the UN Inter-agency Group for Child Mortality Estimation: United Nations Inter-agency Group for Child Mortality Estimation (UN IGME); 2014.
- 2.
UNICEF U. Levels and trends in child mortality. New York: UNICEF. 2015.
- 3. You D, Hug L, Ejdemyr S, Idele P, Hogan D, Mathers C, et al. Global, regional, and national levels and trends in under-5 mortality between 1990 and 2015, with scenario-based projections to 2030: a systematic analysis by the UN Inter-agency Group for Child Mortality Estimation. The Lancet. 2015;386(10010):2275–86. https://doi.org/10.1016/S0140-6736(15)00120-8.
- 4. Wang H, Abajobir AA, Abate KH, Abbafati C, Abbas KM, Abd-Allah F, et al. Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970–2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet. 2017;390(10100):1084–150.
- 5. Honwana FE, Melesse SF. Socio-economic and demographic determinants of under-five mortality in Ethiopia, 2011. The Open Public Health Journal. 2017;10(1).
- 6. Adebowale SA, Morakinyo OM, Ana GR. Housing materials as predictors of under-five mortality in Nigeria: evidence from 2013 demographic and health survey. BMC pediatrics. 2017;17(1):30. pmid:28103828
- 7. Anteneh ZA, Andargie K, Tarekegn M. Prevalence and determinants of acute diarrhea among children younger than five years old in Jabithennan District, Northwest Ethiopia, 2014. BMC public health. 2017;17(1):1–8. pmid:28049454
- 8. Banda I, Michelo C, Hazemba A. Factors associated with late antenatal care attendance in selected rural and urban communities of the copperbelt province of Zambia. Medical Journal of Zambia. 2012;39(3):29–36.
- 9. Shifa GT, Ahmed AA, Yalew AW. The relationship between under-five child death and maternal mental distress in Gamo Gofa Zone, Southern Ethiopia: a community based comparative cross-sectional study. BMC women’s health. 2018;18(1):44. pmid:29482605
- 10. Li J, Laursen TM, Precht DH, Olsen J, Mortensen PB. Hospitalization for mental illness among parents after the death of a child. New England Journal of Medicine. 2005;352(12):1190–6. pmid:15788495
- 11. Ayele DG, Zewotir TT, Mwambi H. Survival analysis of under-five mortality using Cox and frailty models in Ethiopia. Journal of Health, Population and Nutrition. 2017;36(1):25. pmid:28578680
- 12. Berelie Y, Yismaw L, Tesfa E, Alene M. Risk factors for under-five mortality in Ethiopia: Evidence from the 2016 Ethiopian Demographic and Health Survey. South African Journal of Child Health. 2019;13(3):137–40. https://hdl.handle.net/10520/EJC-18ab300f14.
- 13. Tagoe ET, Agbadi P, Nakua EK, Duodu PA, Nutor JJ, Aheto JMK. A predictive model and socioeconomic and demographic determinants of under-five mortality in Sierra Leone. Heliyon. 2020;6(3):e03508. pmid:32181389
- 14. Wegbom AI, Essi ID, Kiri VA. Survival analysis of under-five mortality and its associated determinants in Nigeria: Evidence from a survey data. International Journal of Statistics and Applications. 2019;9(2):59–66.
- 15. Aheto JMK. Predictive model and determinants of under-five child mortality: evidence from the 2014 Ghana demographic and health survey. BMC public health. 2019;19(1):64. pmid:30642313
- 16. Bedada D. Determinant of under-five child mortality in Ethiopia. American Journal of Theoretical and Applied Statistics. 2017;6(4):198–204.
- 17. Mallick A, Roy SK. Survival of under-five children and its socio-demographic correlates among the Oraon tea plantation inhabitants of Dooars, Northern West Bengal. Demography India. 2020;49(1):26–35.
- 18. Woldeamanuel BT. Socioeconomic, demographic, and environmental determinants of under-5 mortality in Ethiopia: evidence from Ethiopian demographic and health survey, 2016. Child Development Research. 2019;2019. https://doi.org/10.1155/2019/1073782.
- 19. Gebretsadik S, Gabreyohannes E. 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. 2016;2016. https://doi.org/10.1155/2016/1602761.
- 20. Debebe B. Levels, Trends and Determinants of Under-Five Mortality in Amhara Region, Ethiopia, Evidence from Demographic and Health Survey (2000–2011). Sciences. 2016;3(2):96–112.
- 21. Adjiwanou V, Engdaw AW. Household environmental health hazards’ effect on under-five mortality in sub-Saharan Africa: What can we learn from the Demographic and Health Survey? Global public health. 2017;12(6):780–94. pmid:28114853
- 22. Fantahun M, Berhane Y, Högberg U, Wall S, Byass P. Ageing of a rural Ethiopian population: who are the survivors? Public Health. 2009;123(4):326–30. pmid:19254801
- 23. Dorosh P, Schmidt E. The rural-urban transformation in Ethiopia. Citeseer, 2010.
- 24.
World Health Organization (WHO) from MDGs, millennium development goals to SDGs, sustainable development goals. 2015.
- 25. Beatriz ED, Molnar BE, Griffith JL, Salhi C. Urban-rural disparity and urban population growth: A multilevel analysis of under-5 mortality in 30 sub-Saharan African countries. Health & place. 2018;52:196–204. pmid:29957397
- 26. Zewudie AT, Gelagay AA, Enyew EF. Determinants of Under-Five Child Mortality in Ethiopia: Analysis Using Ethiopian Demographic Health Survey, 2016. International Journal of Pediatrics. 2020;2020. pmid:33029153
- 27. Fikru C, Getnet M, Shaweno T. Proximate determinants of under-five mortality in Ethiopia: using 2016 nationwide survey data. Pediatric health, medicine and therapeutics. 2019;10:169. pmid:31908566
- 28. Fenta SM, Fenta HM. Risk factors of child mortality in Ethiopia: Application of multilevel two-part model. PloS one. 2020;15(8):e0237640. pmid:32804942
- 29. Bedane AS, Asena TF, Shamenna AT, Abshoko AD. Variations in Under-five Child Mortality among Regional States of Ethiopia: A Multi-level Modelling Approach. Current Journal of Applied Science and Technology. 2016:1–16.
- 30.
Warrohmah ANI, Berliana SM, Nursalam N, Efendi F, Haryanto J, Has EMM, et al., editors. Analysis of the survival of children under five in Indonesia and associated factors. IOP Conference Series: Earth and Environmental Science; 2018: IOP Publishing.
- 31. Efendi F, Berliana SM, Nursalam N, Ulfiana E. Analysis of the Survival of Children under Five in Indonesia and Associated Factors. 2019.
- 32. Niragire F, Wangombe A. Use of the shared frailty model to identify the determinants of child mortality in Rwanda. Rwanda Journal. 2011;20:90–107.
- 33. Govindarajulu US, Lin H, Lunetta KL, D’Agostino R Sr. Frailty models: applications to biomedical and genetic studies. Statistics in medicine. 2011;30(22):2754–64. pmid:21786277
- 34. Klein JP. Semiparametric estimation of random effects using the Cox model based on the EM algorithm. Biometrics. 1992:795–806. https://www.jstor.org/stable/2532345. pmid:1420842
- 35.
Cleves M, Gould W, Gould WW, Gutierrez R, Marchenko Y. An introduction to survival analysis using Stata. Stata press; 2008 May 15.
- 36. Debelew GT, Afework MF, Yalew AW. Determinants and causes of neonatal mortality in Jimma zone, southwest Ethiopia: a multilevel analysis of prospective follow up study. Plos one. 2014;9(9):e107184. pmid:25232842
- 37. Basha GW, Woya AA, Tekile AK. Determinants of neonatal mortality in Ethiopia: an analysis of the 2016 Ethiopia Demographic and Health Survey. African Health Sciences. 2020;20(2):715–23. pmid:33163036
- 38. Demisse AG, Alemu F, Gizaw MA, Tigabu Z. Patterns of admission and factors associated with neonatal mortality among neonates admitted to the neonatal intensive care unit of University of Gondar Hospital, Northwest Ethiopia. Pediatric health, medicine and therapeutics. 2017;8:57. pmid:29388628
- 39. Mengesha HG, Sahle BW. Cause of neonatal deaths in Northern Ethiopia: a prospective cohort study. BMC public health. 2017;17(1):62. pmid:28077109
- 40. Elmi Farah A, Abbas AH, Tahir Ahmed A. Trends of admission and predictors of neonatal mortality: A hospital based retrospective cohort study in Somali region of Ethiopia. PloS one. 2018;13(9):e0203314. pmid:30216343
- 41. Akinyemi JO, Bamgboye EA, Ayeni O. Trends in neonatal mortality in Nigeria and effects of bio-demographic and maternal characteristics. BMC pediatrics. 2015;15(1):36. pmid:25886566
- 42. Jehan I, Harris H, Salat S, Zeb A, Mobeen N, Pasha O, et al. Neonatal mortality, risk factors and causes: a prospective population-based cohort study in urban Pakistan. Bulletin of the world Health Organization. 2009;87:130–8. pmid:19274365
- 43. Weldearegawi B, Melaku YA, Abera SF, Ashebir Y, Haile F, Mulugeta A, et al. Infant mortality and causes of infant deaths in rural Ethiopia: a population-based cohort of 3684 births. BMC public health. 2015;15(1):770. pmid:26260495
- 44. Asefa M, Drewett R, Tessema F. A birth cohort study in South-West Ethiopia to identify factors associated with infant mortality that are amenable for intervention. Ethiopian Journal of Health Development. 2000;14(2):161–8.
- 45. Muluye S, Wencheko E. Determinants of infant mortality in Ethiopia: A study based on the 2005 EDHS data. Ethiopian Journal of Health Development. 2012;26(2):72–7.
- 46. Biks GA, Berhane Y, Worku A, Gete YK. Exclusive breast feeding is the strongest predictor of infant survival in Northwest Ethiopia: A longitudinal study. Journal of Health, Population and Nutrition. 2015;34(1):9. pmid:26825334
- 47.
Negera A, Abelti G, Bogale T, Gebreselassie T, Pearson R. An analysis of the trends, differentials and key proximate determinants of infant and under-five mortality in Ethiopia. ICF International: Calverton, Maryland USA. 2013.
- 48. Regassa N. Infant mortality in the rural sidama zone, southern Ethiopia: Examining the contribution of key pregnancy and postnatal health care services. Journal of Nursing, Social Studies, Public Health and Rehabilitation. 2012;1(2):51–61.
- 49. Rhee V, Mullany LC, Khatry SK, Katz J, LeClerq SC, Darmstadt GL, et al. Maternal and birth attendant hand washing and neonatal mortality in southern Nepal. Archives of pediatrics & adolescent medicine. 2008;162(7):603–8. pmid:18606930
- 50. Gebreyohannis B. Regional Disparities in Under-Five Mortality in Ethiopia: A Cross-Sectional Analysis of Nationally Representative Sample 2016. http://urn.fi/URN:NBN:fi:tuni-202002192193.
- 51. Kanté AM, Nathan R, Jackson EF, Levira F, Helleringer S, Masanja H, et al. Trends in socioeconomic disparities in a rapid under-five mortality transition: a longitudinal study in the United Republic of Tanzania. Bulletin of the World Health Organization. 2016;94(4):258. pmid:27034519
- 52. Dendup T, Zhao Y, Dema D. Factors associated with under-five mortality in Bhutan: an analysis of the Bhutan National Health Survey 2012. BMC public health. 2018;18(1):1375. pmid:30558601
- 53. Deribew A, Tessema GA, Deribe K, Melaku YA, Lakew Y, Amare AT, et al. Trends, causes, and risk factors of mortality among children under 5 in Ethiopia, 1990–2013: findings from the Global Burden of Disease Study 2013. Population health metrics. 2016;14(1):42. https://doi.org/10.1186/s12963-016-0112-2.
- 54. Mugo NS, Agho KE, Zwi AB, Damundu EY, Dibley MJ. Determinants of neonatal, infant and under-five mortality in a war-affected country: analysis of the 2010 Household Health Survey in South Sudan. BMJ global health. 2018;3(1).
- 55. Ayele DG, Zewotir TT. Childhood mortality spatial distribution in Ethiopia. Journal of Applied Statistics. 2016;43(15):2813–28. https://doi.org/10.1080/02664763.2016.1144727
- 56. ZIKE DT, Fenta HM, Workie DL, Swain PK. Determinants of Under-Five Mortality in Ethiopia: An Application of Cox Proportional Hazard and Frailty Models. Turkiye Klinikleri Journal of Biostatistics. 2018;10(2).
- 57. Ntenda PAM, Chuang K-Y, Tiruneh FN, Chuang Y-C. Factors associated with infant mortality in Malawi. Journal of Experimental & Clinical Medicine. 2014;6(4):125–31. https://doi.org/10.1016/j.jecm.2014.06.005.
- 58. Ezeh OK, Agho KE, Dibley MJ, Hall JJ, Page AN. Risk factors for postneonatal, infant, child and under-5 mortality in Nigeria: a pooled cross-sectional analysis. BMJ open. 2015;5(3):e006779. pmid:25818271
- 59. Ghimire PR, Agho KE, Renzaho AM, Nisha MK, Dibley M, Raynes-Greenow C. Factors associated with perinatal mortality in Nepal: evidence from Nepal demographic and health survey 2001–2016. BMC pregnancy and childbirth. 2019;19(1):88. pmid:30866847
- 60. Abraha H.E., Belachew A.B., Ebrahim M.M. et al. Magnitude, trend, and causes of under-five mortality from Kilite-Awlaelo health demographic surveillance database, northern Ethiopia, 2009–2017. BMC Public Health 20, 1465 (2020). pmid:32993625
- 61.
Warrohmah ANI, Berliana SM, Nursalam N, Efendi F, Haryanto J, Misbahatul E, et al., editors. Analysis of the Survival of Children Under Five in Indonesia and Associated Factors. 3rd International Conference on Tropical and Coastal Region Eco Development 2017; 2018.
- 62. Gupta N, Hirschhorn LR, Rwabukwisi FC, Drobac P, Sayinzoga F, Mugeni C, et al. Causes of death and predictors of childhood mortality in Rwanda: a matched case-control study using verbal social autopsy. BMC public health. 2018;18(1):1378. pmid:30558586
- 63. Monden CW, Smits J. Mortality among twins and singletons in sub-Saharan Africa between 1995 and 2014: a pooled analysis of data from 90 Demographic and Health Surveys in 30 countries. The Lancet Global Health. 2017;5(7):e673–e9. pmid:28578941
- 64. Ogbo FA, Ezeh OK, Awosemo AO, Ifegwu IK, Tan L, Jessa E, et al. Determinants of trends in neonatal, post-neonatal, infant, child and under-five mortalities in Tanzania from 2004 to 2016. BMC public health. 2019;19(1):1243. pmid:31500599