https://orcid.org/0000-0001-9734-9054
Figures
Abstract
Background
The death of children under-five years is one of the critical issues in public health and improving child survival continues to be a matter of urgent concern. In this paper, we assessed the proximate and socio-economics determinants of child mortality in Guinea.
Methods
Using the 2018 Guinea Demographic and Health Survey (GDHS), we extracted demographic and mortality data of 4,400 children under-five years. Both descriptive and multivariable logistic regression analyses were conducted.
Results
Under-five mortality was 111 deaths per 1,000 live births in Guinea. The likelihood of death was higher among children born to mothers who belong to other religions compared to Christians (aOR = 2.86, 95% CI: 1.10–7.41), smaller than average children compared to larger than average children (aOR = 1.97, 95% CI: 1.28–3.04) and those whose mothers had no postnatal check-up visits after delivery (aOR = 1.72, 95% CI: 1.13–2.63). Conversely, the odds of death in children with 2–3 birth rank & >2 years of birth interval compared to ≥4 birth rank and ≤2 years of birth interval were low (aOR = 0.53, 95% CI: 0.34–0.83).
Conclusion
We found that household/individual-level socioeconomic and proximate factors predict under-five mortality in Guinea. With just about a decade left to the 2030 deadline of the Sustainable Development Goals (SDGs), concerted efforts across all key stakeholders, including government and development partners, need to be geared towards implementing interventions that target these predictors.
Citation: Ahinkorah BO, Budu E, Seidu A-A, Agbaglo E, Adu C, Osei D, et al. (2022) Socio-economic and proximate determinants of under-five mortality in Guinea. PLoS ONE 17(5): e0267700. https://doi.org/10.1371/journal.pone.0267700
Editor: Florian Fischer, Charite Universitatsmedizin Berlin, GERMANY
Received: January 14, 2021; Accepted: April 13, 2022; Published: May 5, 2022
Copyright: © 2022 Ahinkorah 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: The dataset if freely available for download at https://dhsprogram.com/data/dataset/Guinea_Standard-DHS_2018.cfm?flag=0.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: aOR, Adjusted Odds Ratio; CI, Confidence Interval; GDHS, Guinea Demographic and Health Survey; VIF, Variance Inflation Factor; PNC, postnatal check-up; SDG, Sustainable Development Goal; LMICs, Low-and Middle-Income Countries
Introduction
Globally, the death of children under-five years is one of the critical issues in public health and improving child survival continues to be a matter of urgent concern [1]. A renewed commitment to realize any such improvements is enshrined in the Sustainable Development Goal (SDG) 3, which has as one of its targets to ensure a reduction of under-five mortality to 25 deaths per 1000 live births globally by 2030 [2]. However, under-five mortality remains alarmingly high in many low- and middle-income countries (LMICs) [3]. In 2018 alone, an estimated 5.3 million children under-five years died globally, with sub-Saharan Africa (SSA) contributing more than half of the death burden (2.7 million) [4, 5]. At an average of 78 deaths of children under-five per 1,000 live births, one in every 13 children dies before their fifth birthday in the sub-Saharan African region. This prevalence is 16 times higher than the risk of children born in high-income countries [6]. Though significant progress has been made in Guinea, a 2019 UNICEF report found Guinea to be one of the countries with high under-five mortality rates, with the current rate at 101 deaths per 1,000 live births [6]. Intrapartum related complications, preterm birth complications, pneumonia, diarrhea, and malaria often contribute to the majority of such deaths [3, 6].
Efforts to reduce under-five mortality in any strategic manner require a clear understanding of its predictors. Several studies have been published concerning the predictors of under-five mortality in the sub-Saharan African region as a whole [7–10] and specific countries such as Ethiopia [11], Ghana [12], Sierra Leone [13], and Nigeria [14]. These studies have to varying degrees of significance, identified maternal age, birth weight, maternal education, wealth index, educational status and birth order, sex of the child, and geographical location as predictors of under-five mortality. This suggests that the findings of these studies conducted in other sub-Saharan African countries may not be particularly applicable in the context of Guinea. Moreover, each country in SSA may have a different socio-cultural context [12] and children in each of these countries may be exposed to different risks that can result in death. It is against this background that the present study investigated the proximate and socio-economic determinants of under-five mortality in Guinea. We believe that country-specific findings from this study could contribute to identifying critical priority interventions to address under-five mortality in Guinea.
Methods
Data source
The 2018 Guinea Demographic and Health Survey provided the data for this study (GDHS). Specifically, the children’s file, which contains data of children born five years prior to the survey, was used. GDHS is part of several surveys obtainable from the MEASURE DHS Program, which contains information on a number of issues on population, health, and nutrition, including under-five mortality. Data for 4,400 children under the age of five, who served as the study’s unit of analysis, were gathered by interviewing women who had given birth within the previous five years. Due to the inclusion of variables that asked for information about the partners of women with children under-five years, the sample size of 4400 included only children born to mothers who were either married or cohabiting at the time of the study. The GDHS utilized a multi-stage, stratified sampling design to select all eligible women for interviews from households, which were considered sampling units [15]. The dataset if freely available for download at https://dhsprogram.com/data/dataset/Guinea_Standard-DHS_2018.cfm?flag=0
Conceptual framework
Based on the variables available in the 2018 GDHS datasets and earlier research [9–14], the study’s conceptual framework was developed from Mosley and Chen’s conceptual framework for the study of child survival in developing countries (Fig 1) [16].
Source: Mosley and Chen [16].
Study variables
The outcome variable was under-five mortality, which is the death of a child under five years (0–59 months) of life. To derive this variable, responses on whether the child under-five is alive or dead was recoded into binary outcome (0 = No and 1 = Yes). The variable was estimated based on complete maternal birth histories that include the date of every live birth (singleton and multiple births), survival status, current age for living children and age at death of children [9–14]. The explanatory variables considered in this study included two community-level socioeconomic variables (place of residence and region), seven household and individual level socioeconomic variables (wealth index, maternal ethnicity, maternal religion, maternal and paternal highest level of education, and maternal and paternal occupation) and nine proximate determinants variables (sex of child, birth size, birth rank and birth interval, age of mother at childbirth, antenatal care visit, use of tobacco, place of delivery, delivery assistance, and postnatal check-up (PNC) visits.
Data analysis
The first step of the analysis involved the use of frequency tabulations to describe the proportions of all the explanatory variables, followed by a distribution of under-five mortality per the explanatory variables considered in this study, with their respective confidence intervals (CIs). We used the STATA command “syncmrates”, which calculates child mortality rates using the synthetic cohort probability method employed in Demographic and Health Surveys (DHS) to calculate the under-five mortality rates. The under-five mortality rates were obtained using 7,885 children born in the last five years. However, for subsequent analyses, the unit was children born to married and cohabiting women (4,400) due to the inclusion of variables related to partners of mothers of children under-five. This was followed by the use of Pearson’s chi-square to test the association between the explanatory variables and under-five mortality. Statistical significance was pegged at p<0.20. A multicollinearity test was performed on all statistically significant variables prior to the multivariable hierarchical logistic regression analysis to check if there was evidence of multicollinearity between them. The results showed no evidence of multicollinearity except family size and mother’s age at childbirth. These were therefore excluded from the study. The Multicollinearity test was done using the variance inflation factor. Whether significant or not, all the explanatory variables in the chi-square test were included in a multivariable logistic regression analysis. The multivariable logistic regression analysis was carried out in three stages.
The first step of the multivariable hierarchical logistic regression analysis included community-level socioeconomic indicators and under-five mortality (see Model I). In the second step, household and individual-level socioeconomic characteristics were included in the first model to investigate their association with under-five mortality (see Model II). In the last step, the proximate determinants were added to Model II to analyze their relationship with under-five mortality. Pseudo R2 was used to measure the logistic models’ goodness-of-fit. Stata SE version 14.2 was used for data cleansing, management, and analysis (StataCorp, College Station, Texas, USA). We utilized sample weight (v005/1,000,000) to compensate for over and under-sampling and the svy command to account for the complicated survey design and generalizability of the findings.
Ethical considerations
The actual conduct of the 2018 GDHS received ethical approval from the ICF’s ethical committee. All survey participants provided informed consent and permission to use the data anonymously, which the enumerators obtained [15]. We acquired permission from the DHS program to utilize the 2018 GDHS data in our analysis. There was no need for additional ethical approval because our study only comprised secondary data analysis of publically available data that did not contain any personal information linked to the actual survey participants.
Results
This study includes a total of 4,400 children below five years. Table 1 indicates results on the characteristics and the distribution of under-five mortality. The majority of the children were from the Kankan region (18.9%). A greater proportion of the children were from rural areas (74.7%), middle poorest quintile households (25.4%), Peulh ethnic group (37.8%) and Muslims (88.1%). Close to 52% of the children were males, 36.7% were average in size, and 39.9% were born with a ≥4 birth rank & >2 years of birth interval.
Under-five mortality was 111 deaths per 1,000 live births in the country. Deaths of children under five was 136 per 1,000 live births in the Faranah region compared to 37 deaths per 1,000 live births in the Conakry region. Similarly, under-five mortality was higher in the rural areas (131 deaths per 1,000 live births) compared to the urban areas (60 deaths per 1,000 live births). Children from poorer households experienced a higher under-five mortality rate (143 deaths per 1,000 live births) compared to those from richest households (41 deaths per 1,000 live births). Children who were very small at birth had a higher under-five mortality rate (207 deaths per 1,000 live births) than those with larger than average size at birth (102 deaths per 1,000 live births). Under-five mortality was high among children with 2–3 birth rank & ≤2 years of birth interval (120 deaths per 1,000 live births) compared to those with ≥4 birth rank & ≤2 years of birth interval (94 deaths per 1,000 live births). Regarding the sex of the child, under-five mortality was high among males (115 per 1,000 live births) compared to females (110 per 1,000 live births).
Table 2 shows the results of the multivariable logistic regression analysis. In model III, all the community, household/individual-level socioeconomic and proximate factors were included in the analysis. In this model, religion, size of the child at birth, birth rank and birth interval, and postnatal check-ups had significant associations with under-five mortality. The likelihood of death was higher among children born to mothers who belong to other religions compared to Christians (aOR = 2.86, 95% CI: 1.10–7.41), smaller than average children compared to larger than average children (aOR = 1.97, 95% CI: 1.28–3.04) and those whose mothers had no postnatal check-up visits after delivery compared to those whose mothers had postnatal check-up visits less than 24 hours after delivery (aOR = 1.72, 95% CI: 1.13–2.63). Conversely, there was lower odds of death in children with 2–3 birth rank & >2 years of birth interval compared to ≥4 birth rank and ≤2 years of birth interval (aOR = 0.53, 95% CI: 0.34–0.83).
Discussion
In this study, we investigated the determinants of under-five mortality in Guinea using data from the most recent DHS survey conducted in 2018. Our study revealed that region of residence, religion, birth order, birth interval, and postnatal check-up visits are strong predictors of under-five mortality in Guinea. These factors are discussed further in the subsequent paragraphs.
Our study also revealed a strong relationship between religion and under-five mortality in Guinea. Specifically, children born to Christians recorded a lower likelihood of under-five mortality compared to their counterparts whose mothers were practitioners of other religions (Hinduism, Buddhism, Atheism, Judaism, Taoism, Confucianism, Sikhism). A lower likelihood of under-five mortalities among Christians has been reported in Mozambique [17], Ghana [18], and Brazil [19]. With nearly 85 percent of Guinea’s population being Muslim, Islam is the demographically, socially, and culturally dominant religion. This study’s conclusion could be explained by prior research findings on the cultural practice of keeping newborns hidden from the public due to fear of harm after birth [20]. To reduce under-five mortality among religious groups in Guinea, religious leaders’ role in promoting maternal and child healthcare utilization must be increased. This study implies that the impact of religion on under-five mortality should be examined further to understand the underlying factors better.
Children born to poor women were more likely to die compared to those born to rich women. According to research, wealthier women are more likely to use maternal and childcare. [21]. Most poor women live in rural areas where 84% of babies in urban areas receive a postnatal examination, compared to only 43% of babies in rural areas. A similar disparity exists with regard to access to complete immunization and postnatal care [22]. Poor women are also characterized by a lack of essential services (such as potable water) and proper sanitation practices, the lack of which causes diarrhea and dehydration in children in rural Guinea, resulting in the death of many children in rural Guinea [23]. Similar observations have been made in Nigeria [24, 25], Kenya [26], Turkey [27], and Ethiopia [25]. Possible explanations include lower socio-economic status and households’ inability to cover childbirth costs [28].
Furthermore, birth rank and birth interval and birth size were also associated with under-five mortality among children in Guinea. In this regard, our study revealed that the odds of death in children with 2–3 birth rank & >2 years of birth interval compared to ≥4 birth rank and ≤2 years of birth interval. This finding agrees with those of some previous studies in Ethiopia [29], Ghana [30, 31], and Uganda [32]. Yaya et al. also made a similar observation in Chad, the Democratic Republic of Congo, Mali, Niger and Zimbabwe [33]. Children with smaller birth sizes were more likely to die before age five than those with very large sizes at birth. Akin to that, a previous study in India identified small birth size as a strong determinant for child mortality [34].
Children of parents who had no postnatal check-up visits also had higher risks of dying before their fifth birthday than those who had postnatal check-ups. This finding resonates with the findings of other studies [10, 35–37]. The social institutions of society, such as health facilities where women obtain PNC services, may expose mothers to helpful health information such as the benefits of using PNC services for their newborns, influencing their care for their babies. A plausible reason given in the literature is that access to healthcare services, including postnatal care, reduces the likelihood of children dying before their fifth birthday. In practice, reproductive healthcare services are provided in both publicly and privately owned health clinics and hospitals in Guinea. However, a study by Ettarh and Kimani did not find any significant association between postnatal check-up visits and under-five mortality [26].
Strengths and limitations
The main strength of this study lies in its use of a nationally representative data. With this, the findings can be generalized to all children in Guinea. Notwithstanding this, the findings need to be interpreted in relation to some limitations. In the first place, in terms of causal relation between the variables measured, this study is handicapped due to the cross-sectional nature of the study design. Again, the data for the study is also subject to recall bias, as the parents may not be able to give accurate information because of the retrospective nature of reporting child deaths. Some important factors such as family size and age of mother at first birth were not included due to collinearity issues.
Policy implications
The study’s findings serve as a starting point for further debate. Our findings have several policy implications. While there is a need to address the different variables across Guinea, our study findings imply that special attention should be paid to some specific groups who appear to be the most susceptible among the vulnerable. For starters, children in the country’s poorer districts, such as Farannah, will benefit from steps to reduce inequality. Furthermore, behavioral change communication about the importance of postnatal checks for babies, as well as family planning and spacing, must be implemented while ensuring that those efforts reach moms and dads of large families with a low birth rank and interval, as well as non-Christians. Furthermore, campaigning on the importance of postnatal check-ups after birth can be performed both during mothers’ interaction with the health system, such as during ante-natal care clinics and more generally to the broader community, as part of outreaches.
Conclusion
We found that socioeconomic and proximal determinants are associated with under-five mortality. As a result, it is vital that interventions aimed at reducing under-five mortality take these factors into account. Future studies should focus on understanding the processes through which these factors contribute to childhood mortality. However, with less than a decade until the SDGs’ 2030 deadline, all major players, including the government and development partners, must work together to assist Guinea meet its under-five mortality reduction targets.
Acknowledgments
We are grateful to the MEASURE DHS project for giving us free access to the original data.
References
- 1. Black RE, Levin C, Walker N, Chou D, Liu L, Temmerman M, et al. Reproductive, maternal, newborn, and child health: key messages from Disease Control Priorities 3rd Edition. Lancet. 2016;388(10061):2811–24.
- 2. United Nations. Sustainable Development Goals: 17 goals to transform our world. Sustainable Development Goals. 2016 [cited 2020 Mar 29]. Available from: http://www.un.org/sustainabledevelopment/sustainable-development-goals/
- 3. Liu L, Oza S, Hogan D, Chu Y, Perin J, Zhu J, et al. Global, regional, and national causes of under-5 mortality in 2000–15: an updated systematic analysis with implications for the Sustainable Development Goals. Lancet. 2016;388(10063):3027–35.
- 4. 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. Lancet. 2015;386(10010):2275–86.
- 5. WHO. Number of deaths (thousands): Data by WHO region [Internet]. Global Health Observatory data repository. 2019 [cited 2020 May 4]. Available from: https://apps.who.int/gho/data/view.main.CM1300N?lang=en
- 6. UNICEF, WHO, World Bank, UN-DESA Population Division. Levels and trends in child mortality report 2019: Estimates developed by the UN Inter-agency Group for Child Mortality Estimation [Internet]. 2019. Available from: https://www.unicef.org/media/60561/file/UN-IGME-child-mortality-report-2020.pdf
- 7. Issaka AI, Agho KE, Renzaho AMN. The Impact of Internal Migration on under-Five Mortality in 27 Sub-Saharan African Countries. PLoS One. 2016;11(10):e0163179.
- 8. Kipp AM, Blevins M, Haley CA, Mwinga K, Habimana P, Shepherd BE, et al. Factors associated with declining under-five mortality rates from 2000 to 2013: an ecological analysis of 46 African countries. BMJ Open. 2016;6(1):e007675.
- 9. Van Malderen C, Amouzou A, Barros AJD, Masquelier B, Van Oyen H, Speybroeck N. Socioeconomic factors contributing to under-five mortality in sub-Saharan Africa: a decomposition analysis. BMC Public Health. 2019;19(1):760.
- 10. Yaya S, Bishwajit G, Okonofua F, Uthman OA. Under five mortality patterns and associated maternal risk factors in sub-Saharan Africa: A multi-country analysis. PLoS One. 2018;13(10):e0205977.
- 11. Fikru C, Getinet M, Shaweno T. Proximate Determinants of Under-Five Mortality in Ethiopia: Using 2016 Nationwide Survey Data. Pediatr Heal Med Ther. 2019;Volume 10:169–76.
- 12. 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.
- 13. Liwin LK, Houle B. The effects of household and community context on mortality among children under five in Sierra Leone: Evidence from the 2013 Demographic and Health Survey. Demogr Res. 2019;40:279–306.
- 14. Morakinyo OM, Fagbamigbe AF. Neonatal, infant and under-five mortalities in Nigeria: An examination of trends and drivers (2003–2013). PLoS One. 2017;12(8).
- 15.
INS, ICF. Enquête Démographique et de Santé en Guinée 2018 [Internet]. Conakry, Guinea, and Rockville, Maryland, USA; 2019. Available from: https://dhsprogram.com/pubs/pdf/FR353/FR353.pdf
- 16. Mosley WH, Chen LC. An Analytical Framework for the Study of Child Survival in Developing Countries. Popul Dev Rev. 1984;10:25.
- 17. Cau BM, Sevoyan A, Agadjanian V. Religious affiliation and under-five mortality in Mozambique. J Biosoc Sci. 2013;45(3):415–29.
- 18. Obeng Gyimah S. What has faith got to do with it? Religion and cilld survival in Ghana. J Biosoc Sci. 2007;39(6):923–37.
- 19. Wood CH, Williams P, Chijiwa K. Protestantism and Child Mortality in Northeast Brazil, 2000. J Sci Study Relig. 2007;46(3):405–16.
- 20. Dankwah E., Feng C., Kirychuck S. et al. Assessing the contextual effect of community in the utilization of postnatal care services in Ghana. BMC Health Serv Res 21, 40 (2021). https://doi.org/10.1186/s12913-020-06028-1
- 21. Ahmed S, Creanga AA, Gillespie DG, Tsui AO. Economic status, education and empowerment: implications for maternal health service utilization in developing countries. PLoS One. 2010;5(6):e11190.
- 22.
UNICEF. Maternal and Newborn Health Disparities country profiles: Guinea. Resources. 2018 [cited 2020 May 5]. Available from: https://data.unicef.org/resources/maternal-newborn-health-disparities-country-profiles/
- 23.
World Bank. Guinea—Systematic Country Diagnostic: overcoming growth stagnation to reduce poverty [Internet]. Washington, D.C.; 2018. Report No.: 123649-GN. Available from: http://documents.worldbank.org/curated/en/830641522072107327/pdf/Guinea-SCD-final-03222018.pdf
- 24.
Abu IN, Madu IA, Ajaero CK. The Prevalence and Determinants of Under-Five Mortality in Benue State, Nigeria. SAGE Open. 2015;5(4):215824401561193.
- 25. Adedini SA. Neighbourhood characteristics and under-five mortality in Nigeria. African Popul Stud. 2014;27(2):273.
- 26. Ettarh RR, Kimani J. Determinants of under-five mortality in rural and urban Kenya. Rural Remote Health. 2012;12:1812.
- 27. Yalçin SS, Tezel B, Köse MR, Tugay D, Mollahaliloğlu S, Erkoç Y. Changes and determinants in under-five mortality rate in Turkey since 1988. Cent Eur J Public Health. 2013;21(2):80–7. pmid:24053063
- 28. Al-Kibria GM, Ghosh S, Hossen S, Barsha RAA, Sharmeen A, Uddin SMI. Factors affecting deliveries attended by skilled birth attendants in Bangladesh. Matern Health Neonatol Perinatol. 2017;3:7.
- 29. 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. Int J Popul Res. 2016;2016:1–7.
- 30. Dwomoh D, Amuasi S, Agyabeng K, Incoom G, Alhassan Y, Yawson AE. Understanding the determinants of infant and under-five mortality rates: a multivariate decomposition analysis of Demographic and Health Surveys in Ghana, 2003, 2008 and 2014. BMJ Glob Heal. 2019;4(4):e001658.
- 31. Nakamura H, Ikeda N, Stickley A, Mori R, Shibuya K. Achieving MDG 4 in sub-Saharan Africa: what has contributed to the accelerated child mortality decline in Ghana? PLoS One. 2011;6(3):e17774.
- 32. Nasejje JB, Mwambi HG, Achia TNO. Understanding the determinants of under-five child mortality in Uganda including the estimation of unobserved household and community effects using both frequentist and Bayesian survival analysis approaches. BMC Public Health. 2015;15(1):1003.
- 33. Yaya S, Uthman OA, Okonofua F, Bishwajit G. Decomposing the rural-urban gap in the factors of under-five mortality in sub-Saharan Africa? Evidence from 35 countries. BMC Public Health. 2019;19(1):616.
- 34. Patel KK, Gouda J. Infant mortality in northern and southern regions of India: differentials and determinants. Social Science Spectrum. 2018;3(2):81–92.
- 35. Kazembe L, Clarke A, Kandala N-B. Childhood mortality in sub-Saharan Africa: cross-sectional insight into small-scale geographical inequalities from Census data. BMJ Open. 2012;2(5):e001421.
- 36. Yaya S, Ahinkorah BO, Ameyaw EK, Seidu AA, Darteh EK, Adjei NK. Proximate and socio-economic determinants of under-five mortality in Benin, 2017/2018. BMJ global health. 2020 Aug 1;5(8):e002761.
- 37. Y G S B. Survival Analysis of Under-Five Mortality of Children and its Associated Risk Factors in Ethiopia. J Biosens Bioelectron. 2016;7(3):1–10.