Skip to main content
Download PDF

Socio-economic and geographical inequalities in infant mortality rates in Sierra Leone, 2008–2019

BMC Public Health volume 25, Article number: 1697 (2025) Cite this article

Abstract

Background

The death of a child before their first birthday remains a significant global challenge, particularly in sub-Saharan Africa. This study examined the socio-economic and geographical inequalities in infant mortality in Sierra Leone between 2008 and 2019, utilising data from the Sierra Leone demographic and health surveys.

Methods

Three Sierra Leone demographic health survey rounds (2008, 2013, 2019) were analysed. Simple [difference and ratio] and complex [population attributable risk and fraction] measures of inequality in the infant mortality rates were calculated using the World Health Organization's health equity assessment toolkit software.

Results

The national infant mortality rate dropped from 111.1/1,000 live births (LBs) in 2008 to 77.4/1,000 LBs in 2019. Inequality for age decreased from 20.1 (2008) to 14.7/1,000 LBs (2019); economic inequality from 54.9 (2008) to 30.4/1,000 LBs (2019); and inequality due to maternal education fell from 28.9 (2008) to 9.7/1,000 LBs (2019). However, inequality by urban/rural residence increased from 7.4 (2008) to 13.8/1,000 LBs (2019). The population attributable risk revealed that addressing place of residence inequality would reduce the infant mortality rate -5.4 /1,000 LBs, -5.3/1,000 LBs, and -9.1 /1,000 LBs points in 2008, 2013 and 2019 respectively. Inequality associated with the child's sex decreased from -12.8/1,000 LBs in 2008 to -17.0 in 2019. The population attributable fraction and risk were zero in all survey years, indicating that female and male children had statistically equivalent mortality rates. Provincial inequality increased (2008: 26.9/1,000 LBs; 2019: 47.0/1,000 LBs). The population attributable risk suggests if provincial inequality were eliminated the infant mortality rate would have been -15.7/1,000 LBs, -19.0 /1,000 LBs, and -23.5/1,000 LBs lower in 2008, 2013 and 2019 respectively.

Conclusion

Low socio-economic status, limited maternal education, adolescent motherhood, and residence in rural areas and Northwestern province were associated with higher infant mortality rate. Tailored interventions that target vulnerable populations, like adolescent mothers, families living in poverty, and Northwestern province, are essential to improving child health outcomes in Sierra Leone.

Peer Review reports

Introduction

The Infant mortality rate (IMR), which is the probability of babies dying between birth and their first birthday, expressed per 1,000 live births [1,2,3], is a key indicator of a population's socioeconomic development. High rates are usually indicative of unmet health and social needs [4, 5]. Despite improvements in most global regions in achieving set targets [6] many children in vulnerable situations remain susceptible to death before their first birthday from preventable causes such as diarrhea, malaria, pneumonia, as well as perinatal conditions such as preterm birth complications, birth asphyxia, congenital anomalies, and neonatal infections. Most of these deaths occur in Sub-Saharan Africa [1, 3, 6,7,8,9] requiring urgent attention to achieve Sustainable Development Goal (SDG) 3.2, which aims to reduce under-5 and neonatal mortality [6, 10]. While SDG 3.2 does not specifically target infant mortality, around two-thirds of under-5 deaths occur in the first year of life.

The global IMR in 2021 was 38 deaths/1000 LBs compared to 74/1000 LBs in Sub-Saharan Africa [1, 2]. In Sierra Leone, the IMR was 75/1000 LBs in 2019, with 1 in 13 children dying before their first birthday. The under-five mortality rate was 122/1000 LBs that year, with 9 out of 13 (69%) under-five deaths being infant deaths, thus the focus on this population.

While there has been a steady decline in under-five mortality in Sierra Leone, from 156 deaths/1000 LBs (2013) to 122 deaths/1000 in 2019 [9], but is the highest under-five mortality rate in sub-Sahara Africa [1]. These death rates far exceed the 2030 target for Sustainable Development (SDG 3.2) which aims to reduce under-five mortality to less than 25 deaths/1000 LBs and neonatal mortality to less than 12 deaths/1000 LBs [10].

In April 2010, in a bid to reduce the IMR and accelerate progress towards MDG 4 and 5, the Government of Sierra Leone launched the Free Healthcare Initiative (FHCI) in collaboration with non-governmental organizations and development partners [11,12,13,14]. This initiative provides free care to pregnant and lactating mothers, and children under five years at all public health facilities. Prior to the FHCI, user fees had been cited as a major barrier to accessing healthcare [11, 13].

Despite the FHCI, there has been uneven health service utilization among children under five, with disparities across regions, place of residence and wealth levels [15, 16]. Indirect costs contribute to inequality in healthcare utilization [16,17,18,19]. This study examines socio-economic and geographical inequalities in infant mortality rates in Sierra Leone from 2008 to 2019.

Methods

Study setting and data source

Data from the 2008, 2013, and 2019 Sierra Leone Demographic Health Survey (SLDHS) were utilised. These comprehensive, national population-based surveys, designed to identify patterns in demographic and health indicators, and social issues, target households across Sierra Leone, collecting data from men and women of reproductive age, typically women aged 15–49 and men aged 15–59 [9]. The SLDHS employed a cross-sectional design, selecting participants through a stratified multi-stage cluster sampling procedure [9]. They are carefully designed to comply with the standards and guidelines established in Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) [20]. This study focused on women who had given birth to a live infant in the one year prior to the respective SLDHS periods. The data were accessed through the World Health Organization (WHO) Health Equity Assessment Toolkit (HEAT) online platform [21].

Outcome variable and Inequality measures

The study's outcome is the infant mortality rate. To evaluate inequalities in infant mortality, six variables were examined: maternal age (15–19 and 20–49), economic status measured as wealth quintile (1, 2, 3, 4, 5), maternal education (none, primary, secondary/higher education), place of residence (rural, urban), child sex (female, male), and sub-national region (East, North, South, West).

Statistical analysis

The analysis used the web-based iteration of the HEAT developed by the WHO [21]. The HEAT software is designed to assess health inequalities within and among countries, focusing on health and socio-economic indicators, including child, maternal, and reproductive health [22, 23].

This study analyzed four inequality indicators:

  1. 1.

    Difference (D): An absolute measure of the disparity in mortality rates among different groups. D is calculated as the difference in mortality rates between the highest and lowest risk groups. A value of zero indicates no inequality, while higher values signify greater inequality in mortality rates.

  2. 2.

    Ratio (R): A relative measure comparing mortality rates of two groups. A value of 1 indicates no inequality, and values greater than 1 indicate increasing inequality. The ratio was determined by dividing the mortality rate of the higher-risk group by that of the lower-risk group. An R equal to 1 denotes no inequality, while values exceeding 1 indicate increasing levels of inequality.

  3. 3.

    Population-Attributable Fraction (PAF): This measure reflects the proportion of a health outcome that can be attributed to a specific risk factor or condition.

  4. 4.

    Population-Attributable Risk (PAR): This measure quantifies the risk of a health outcome associated with a particular exposure or characteristic.

    Both the PAF and PAR were calculated to assess the impact of socio-economic factors on infant mortality. Positive values for the PAF and PAR indicate favorable conditions, while negative values denote unfavorable conditions. An absence of further development is indicated by values of PAF and PAR approaching zero, suggesting that all subgroups have reached parity with the reference subgroup.

Results

Table 1 shows a decline in the IMR in Sierra Leone between 2008 and 2019, from 111.1/1,000 live births (LBs) in 2008 to 77.4/1,000 LBs in 2019, with most of the improvement between 2013 and 2019. There are significant differences in the IMR across the variables of interest. The poorest quintile (quintile 1) consistently had the highest rates, the richest (quintile 5) the lowest. Likewise, for education, those without education had the highest IMR. Despite inequalities, all groups showed lower IMR over time, indicating progress in reducing infant mortality across Sierra Leone. The Northwestern province had the highest IMR in 2019 at 101.0/1,000 LBs and the Northern province the lowest at 53.9/1,000 LBs. With no data available for the Northwestern province before 2019, initial rates were highest in the Eastern and Northern provinces and lower in the Western and Southern province. The Northern province experienced the most significant decline.

Table 1 Trends in the prevalence of infant mortality rates (deaths per 1000 live births) by different inequality dimensions in Sierra Leone, 2008–2019
Full size table

Age of the mother

Table 2 shows inequality indices for IMR in Sierra Leone from 2008 to 2019. There was a decrease in inequality for maternal age between children of older and younger mothers, from 20.1/1,000 LBs in 2008 to 14.7/1,000 LBs in 2019. The PAF and PAR for age suggests that if the age disparity in IMR were eliminated, the IMR could be reduced by −3.5 and −3.8/1,000 LBs in 2008, −3.4 and −3.7/1,000 LBs in 2013, and −3.5 and −2.7/1,000 LBs in 2019.

Table 2 Inequality measures of estimates of factors associated with infant mortality rates (deaths per 1000 live births) in Sierra Leone, 2008–2019
Full size table

Maternal economic status

The economic inequality in the IMR between children of the richest (Q5) and poorest (Q1) mothers declined from 54.9/1,000 LBs in 2008 to 30.4/1,000 LBs in 2019. The PAR for maternal economic status suggests that eliminating the socio-economic disparity between children of the richest and poorest mothers, the IMR would have been −18.1/1,000 LBs in 2008, −18.9/1,000 LBs in 2013, and −17.1/1,000 LBs in 2019 lower.

Maternal education status

Inequality in IMR between the least and most educated participants decreased from 28.9/1,000 LBs (2008) to 9.7/1,000 LBs (20,190. The PAF and PAR for maternal education suggests that closing this disparity would reduce the IMR by −23.3/1,000 LBs and −25.9/1,000 LBs in 2008, −6.7/1,000 LBs and −7.4/1,000 LBs in 2013, and -10.5/1,000 LBs and −8.1/1,000 LBs in 2019 respectively.

Place of residence

Inequality between children born to mothers in urban versus rural areas increased from 7.4/1,000 LBs in 2008 to 13.8/1,000 LBs in 2019. The PAR for place of residence suggests that addressing disparity between urban and rural children would lessen the IMR by −5.4/1,000 LBs in 2008, −5.3/1,000 LBs in 2013, and −9.1/1,000 LBs in 2019.

Child’s sex

The inequality between male and female children decreased from −12.8/1,000 LBs in 2008 to −17.0/1,000 LBs in 2019. The PAF and PAR however had zero values in all survey years, indicating that no further improvement would be expected, implying that the risk to male children was statistically equivalent to that of female children.

Provincial inequalities

There were significant inequalities in the IMR in Sierra Leone among the regions which worsened over time. The D, increased from 26.9/1,000 LBs in 2008 to 31.4/1,000 LBs in 2013, and sharply rose to 47.0/1,000 LBs in 2019, indicating more pronounced provincial inequalities in infant mortality rates over time. It should however be noted that data were missing for the two earlier observation periods for the Northwestern, the province with the highest rate in 2019. The PAF, which estimates the proportion of infant mortality that could be reduced if inequalities were eliminated, worsened from −14.2/1,000 LBs in 2008 to −30.3/1,000 LBs in 2019. This suggests that provincial inequalities are responsible for a growing share of the infant mortality rates documented in 2019.

The PAR, which reflects the absolute reduction in infant mortality rates that could occur if inequalities were eliminated, also worsened over time, increasing from −15.7/1,000 live births in 2008 to −23.5/1,000 live births in 2019. Furthermore, the R, comparing the likelihood of infant mortality between the worst- and best-performing provinces, rose from 1.2/1,000 live births in 2008 to 1.8/1,000 live births in 2019. Thus by 2019, infants in provinces with the highest mortality rates were 80% more likely to die compared to those in provinces with the lowest rates.

Discussion

This study explored the association of socio-economic and geographic inequalities with infant mortality in Sierra Leone from 2008 to 2019. While the infant mortality rate declined from 111.1/1,000 LBs in 2008 to 77.4/1,000 LBs in 2019, little or no change occurred between 2008 and 2013. Most of the improvement occurring between 2013 and 2019, possibly due to the FHCI, implemented in 2010 [12], which has contributed to their increased healthcare utilization, positively impacting the IMR [13]. Despite these gains, Sierra Leone's IMR remains substantially higher than the under-5 mortality rate target of 25 per 1,000 live births by 2030 [24]. Continued efforts are needed to align with these international benchmarks. Comparatively, other countries in the region, such as Liberia, 63 deaths per 1,000 LBs and Nigeria, 67 deaths per 1,000 LBs, reported substantial reductions, albeit similar, IMR [25, 26].

Improvements in the IMR in Sierra Leone may be attributed to improved nutrition, increased access to healthcare services, vaccination coverage and enhanced maternal literacy which increased from 35% in 2008 t0 52% in 2019 [9, 12]. Investments in healthcare infrastructure, enhanced education and health literacy, alongside community-based development programs, can enable Sierra Leone to make substantial progress toward reducing IMR and improving health outcomes for infants, ultimately contributing to the broader goals of sustainable development. Socio-economic factors such as adolescent pregnancy and poverty increase mortality risk, consistent with the experience of other LMICs [27,28,29,30]. These disparities are further exacerbated by cultural norms, and social stigma [31,32,33]. Mothers with no formal education lack awareness about essential health practices and the value of accessing preventive healthcare services [34,35,36,37,38,39,40,41,42]. Addressing these challenges requires comprehensive strategies that enhance access to reproductive health education, affordable healthcare, and nutritional support while promoting educational opportunities and economic empowerment for women [12, 34, 38,39,40,41, 43,44,45]. By integrating maternal health services with educational and economic initiatives, we can improve health outcomes for mothers and their children, ultimately reducing infant mortality rates [34, 45].

The urban–rural and provincial IMR divide in Sierra Leone aligns with studies in Gambia [46] and Nigeria [47]. Geographic disparities in healthcare infrastructure, essential services and socioeconomic conditions are exacerbated by transportation barriers and inadequate road networks [48]. Such limitations contribute to higher levels of poverty, lower educational attainment, and limited access to resources, which collectively impact health outcomes [49, 50]. Additionally, selected traditional practices that are more prevalent in rural communities can negatively influence maternal health-seeking behaviors [51]. Provincial inequalities underscored the uneven progress in reducing infant mortality across the country. Provinces, such as the Northwestern, have lagged behind the national progress, exacerbating health inequities. The significant lowering of IMR in the Northern compared to the Northwestern can be attributed to targeted investments in the Northern in healthcare infrastructure enabling improved access to skilled birth attendants, antenatal care, immunization, and postnatal services, which are critical for reducing infant mortality. Additionally, the North may have seen improvements in socio-economic conditions, such as education and income levels, and community-level health promotion initiatives. In contrast, provinces like the Northwestern continue to have higher IMR, due to persistent limitations in healthcare infrastructure, inadequate distribution of skilled healthcare providers, and poor transportation networks as well as higher levels of poverty and lower levels of maternal education. These factors, combined with gaps in the implementation and reach of health programs, could explain why the Northwestern has not experienced the same level of improvement as the Northern. To effectively address these inequalities and reduce IMR in regions with the highest mortality rates, targeted interventions must be implemented to address the unique challenges faced by these areas [52]. Addressing socio-economic determinants, such as maternal education and reducing poverty, will also be essential for achieving more equitable progress in reducing IMR across all provinces. In addition, to effectively reduce inequaliities associated with place of residence, targeted interventions to improve healthcare access in rural communities should include enhancing transportation options for mothers, and training of rural community health workers to provide essential education on maternal and child health [52].

Although the study does not directly measure access to health services, the observed provincial inequalities in infant mortality rates suggest significant variations in access to healthcare across regions. The rising disparity index and worsening PAF indicate that some regions are likely to have poorer access to critical services such as antenatal care, skilled birth attendants, or post-natal care, which are vital for reducing infant mortality. The increase in relative risk from 1.2/1,000 live births in 2008 to 1.8/1,000 live births in 2019 further highlights the widening gap between provinces with the best and worst health outcomes, likely to reflect unequal distribution of health resources and services.

Provincial inequalities may also be compounded by other dimensions, such as economic status, education, and place of residence, which influence access to health services. Residents of rural or economically disadvantaged provinces may be less likely to utilize the available services given the limited health infrastructure and fewer healthcare providers. The worsening PAF and PAR over time suggest these inequalities have deepened, with underserved provinces bearing a disproportionate burden of infant mortality.

IMR variations by sex of the infant, with males consistently exhibiting higher mortality rates, aligns with global trends of higher male risk of mortality during infancy [27, 28]. To reduce any excess risk associated with gender, targeted interventions need to address underlying cultural and social factors that contribute to disparities. Community awareness campaigns and healthcare provider training regarding specific health needs of male and female infants will ensure equitable access to healthcare resources for all infants [53].

Strength and limitations

Using demographic health surveys (DHS) data to investigate risk factors for infant mortality in Sierra Leone offers several strengths. The data are population-based and nationally representative and provide a comprehensive view of the health status and behaviors of the population. The large sample size enhances statistical power to detect significant effects. The standardized methodology enables comparability across regions and time periods. Additionally, the diverse data collected on demographic characteristics, health behaviors, and health outcomes allow for exploration of various potential risk factors for infant mortality.

The limitations of using DHS data to investigate infant mortality risk factors in Sierra Leone include the cross-sectional nature of DHS surveys which limits the capacity to establish causal relationships between risk factors and infant death. Further, the lack of detailed information on certain risk or contextual factors may limit the depth of analysis that can be undertaken.

Conclusion

This study examined the association of socio-economic and geographic factors with infant mortality in Sierra Leone. The findings suggest that low socio-economic status, lack of maternal education, adolescent childbearing, rural residence, and living in the Northwestern province in the country were associated with higher IMR. Efforts to reduce the IMR were enhanced by the introduction of the FHCI which improved equitable access to healthcare services. Other initiatives will be required to promote female education, address poverty, and strengthen health systems across different provinces of the country. Tailored interventions that target vulnerable populations, such as adolescent mothers, families living in poverty, and rural residents, are essential to improve child health outcomes in Sierra Leone.

Data availability

The dataset used can be accessed at https://whoequity.shinyapps.io/heat/

Abbreviations

D:

Difference

HEAT:

Health Equity Assessment Toolkit

PAF:

Population Attributable Fraction

PAR:

Population Attributable Risk

R:

Ratio

SDG:

Sustainable Development Goal

SLDHS:

Sierra Leone Demographic and Health Survey

STROBE:

Strengthening the Reporting of Observational Studies in Epidemiology

WHO:

World Health Organization

References

  1. The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME). Levels and Trends in Child Mortality: Report 2019. New York: UNICEF; 2022 https://childmortality.org/wp-content/uploads/2023/01/UN-IGME-Child-Mortality-Report-2022.pdf. Accessed 5 July 2024.

  2. State of inequality: reproductive, maternal, newborn and child health. 2015 http://www.who.int/gho/health_equity/report_2015/.

  3. World Health Organization. World Health Statistics; 2015. Accessed 14 July 2024. https://www.who.int/.

  4. Dube L, Taha M, Asefa H. Determinants of infant mortality in community of Gilgel Gibe Field Research Center, Southwest Ethiopia: a matched case control study. BMC Public Health. 2013;13:401.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Azmach NN, Abrar T. Analysis of the levels and trends in infant and under-five mortality in Ethiopia. Int J Novel Res Life Sci. 2018;4:18.

    Google Scholar 

  6. United Nations: The Millennium Development Goals Report 2010 New York: United Nations; 2015. Accessed 7th July 2024.

  7. Woldeamanuel BT, Aga MA. 2021. Count models analysis of factors associated with under-five mortality in Ethiopia. Global Pediatric Health 8:2333794X21989538.

  8. Statistics Sierra Leone and ICF International. 2014. Sierra Leone Demographic and Health Survey 2013. Freetown, Sierra Leone and Rockville, MD: Statistics Sierra Leone and ICF International.

  9. Statistics Sierra Leone—StatsSL, ICF. Sierra Leone Demographic and Health Survey 2019. Freetown/ Sierra Leone: StatsSL/ICF, 2020.

  10. UN. Transforming our world: the 2030 Agenda for Sustainable Development. United Nations General Assembly; October 2015.

  11. Donnelly J. How did Sierra Leone provide free health care? Lancet. 2011;377:1393–6.

    Article  PubMed  Google Scholar 

  12. GoSL. Free healthcare services for pregnant and lactating women and young children in Sierra Leone. Freetown: Government of Sierra Leone; 2009.

  13. MoHS. 2010 health sector performance report. Sierra Leone: Ministry of Health and Sanitation; 2012.

  14. Bertone MP, Samai M, Edem-Hotah J, et al. A window of opportunity for reform in post-conflict settings? The case of human resources for health policies in Sierra Leone, 2002–2012. Confl Heal. 2014;8:11.

    Article  Google Scholar 

  15. Edoka I, Ensor T, McPake B, et al. Free health care for under-fives, expectant and recent mothers? Evaluating the impact of Sierra Leone’s free health care initiative. Health Econ Rev. 2016;6:19. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13561-016-0096-4.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Jalloh MB, Bah AJ, James PB, Sevalie S, Hann K, Shmueli A. Impact of the free healthcare initiative on wealth-related inequity in the utilization of maternal & child health services in Sierra Leone. BMC Health Serv Res. 2019;19:352.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Witter S, Brikci N, Harris T, Williams R, Keen S, Mujica A, Jones A, MurrayZmijewski A, Bale B, Leigh B: The Sierra Leone free health care initiative (FHCI): process and effectiveness review. 2016. https://eresearch.qmu.ac.uk/bitstream/handle/20.500.12289/4358/eResearch%204358.pdf?sequence=1. Accessed 17 July 2020.

  18. Koroma MM, Kamara SS, Bangura EA, et al. The quality of free antenatal and delivery services in Northern Sierra Leone. Health Res Policy Sys. 2017;15(Suppl 1):49. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12961-017-0218-4.

    Article  Google Scholar 

  19. Koroma MM, Kabba JA, Wanda J, Yu J, Zhou F, Liang Z, Tarawally AB, Chigoneka K, Dai YC. Under-five mortality in Sierra Leone and possible associated factors: evidence from the 2019 Demographic and Health Survey. Health Policy Plan. 2022Nov 14;37(10):1210–20. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/heapol/czac070. PMID: 36052949.

    Article  PubMed  Google Scholar 

  20. Von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for reporting observational studies. Int J Surg. 2014Dec;12(12):1495–9.

    Article  Google Scholar 

  21. World Health Organization. Health Equity Assessment Toolkit Plus (HEAT Plus): Software for exploring and comparing health inequalities in countries. Upload database edition. Geneva: World Health Organization; 2024.

    Google Scholar 

  22. World Health Organization. Handbook on health inequality monitoring: with a special focus on low-and-middle-income countries. Geneva: World Health Organization; 2013.

    Google Scholar 

  23. Hosseinpoor AR, Nambiar D, Schlotheuber A, Reidpath D, Ross Z. Health Equity Assessment Toolkit (HEAT): software for exploring and comparing health inequalities in countries. BMC Med Res Methodol. 2016Dec;16(1):1–10.

    Article  Google Scholar 

  24. Liberia Institute of Statistics and Geo-Information Services, Ministry of Health of Liberia, The DHS program ICF. Liberia demographic and health survey. 2021:137–41.

  25. Patel KK, Prasad JB, Biradar RA. Trends in and determinants of neonatal and infant mortality in Nigeria based on Demographic and Health Survey data. J Biosoc Sci. 2021Nov;53(6):924–34.

    Article  PubMed  Google Scholar 

  26. Raina N, Khanna R, Gupta S, Jayathilaka CA, Mehta R, Behera S. Progress in achieving SDG targets for mortality reduction among mothers, newborns, and children in the WHO South-East Asia Region. The Lancet Regional Health-Southeast Asia. 2023 Oct 29.

  27. Tesema GA, Seifu BL, Tessema ZT, Worku MG, Teshale AB. Incidence of infant mortality and its predictors in East Africa using Gompertz gamma shared frailty model. Archives of Public Health. 2022Aug 23;80(1):195.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Shobiye DM, Omotola A, Zhao Y, Zhang J, Ekawati FM, Shobiye HO. Infant mortality and risk factors in Nigeria in 2013–2017: A population-level study. EClinicalMedicine. 2022;1:51.

    Google Scholar 

  29. Gruebner O, Lautenbach S, Khan MM, Kipruto S, Epprecht M, Galea S. Place of residence moderates the risk of Infant Death in Kenya: Evidence from the most Recent Census 2009. PLoS ONE. 2015Oct 9;10(10):e0139545.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Sartorius BK, Sartorius K. Global infant mortality trends and attributable determinants–an ecological study using data from 192 countries for the period 1990–2011. Popul Health Metrics. 2014;12:1–5.

    Article  Google Scholar 

  31. França AS, Pirkle CM, Sentell T, Velez MP, Domingues MR, Bassani DG, Câmara SM. Evaluating health literacy among adolescent and young adult pregnant women from a low-income area of northeast brazil. Int J Environ Res Public Health. 2020;17(23):8806.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Denney L, Gordon R, Ibrahim A. Teenage Pregnancy after Ebola in Sierra Leone. London: Overseas Development Institute; 2015.

    Google Scholar 

  33. Kumar M, Huang KY, Othieno C, Wamalwa D, Madeghe B, Osok J, Kahonge SN, Nato J, McKay MM. Adolescent pregnancy and challenges in Kenyan context: perspectives from multiple community stakeholders. Global Social Welfare. 2018;5:11–27.

    Article  PubMed  Google Scholar 

  34. Chandra-Mouli V, Camacho AV, Michaud PA. WHO guidelines on preventing early pregnancy and poor reproductive outcomes among adolescents in developing countries. J Adolesc Health. 2013;52(5):517–22.

    Article  PubMed  Google Scholar 

  35. Ighodaro EO. Socio-economic determinants of infant mortality rate in Nigeria: Evidence from autoregressive distributed lag technique. African Journal of Health Economics. 2021;10:15–34.

    Google Scholar 

  36. Rahman MM, Alam K, Khanam R. Socio-economic factors affecting high infant and child mortality rates in selected African countries: does globalisation play any role? Glob Health. 2022;18(1):69.

    Article  Google Scholar 

  37. Ekholuenetale M, Wegbom AI, Tudeme G, Onikan A. Household factors associated with infant and under-five mortality in sub-Saharan Africa countries. International Journal of Child Care and Education Policy. 2020;14:1–5.

    Article  Google Scholar 

  38. Larson CP. Poverty during pregnancy: Its effects on child health outcomes. Paediatr Child Health. 2007;12(8):673–7.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Lartey A. Maternal and child nutrition in Sub-Saharan Africa: challenges and interventions. Proceedings of the Nutrition Society. 2008;67(1):105–8.

    Article  PubMed  Google Scholar 

  40. Muthuswamy VV, Nithya N. Health Literacy’s Influence on Maternal Care Satisfaction: SES and Pregnancy Knowledge. Journal of Natural Science, Biology and Medicine. 2024;15(2):254.

    Google Scholar 

  41. Amin R, Shah NM, Becker S. Socioeconomic factors differentiating maternal and child health-seeking behavior in rural Bangladesh: A cross-sectional analysis. International journal for equity in health. 2010;9:1–1.

    Article  Google Scholar 

  42. Jeong J, McCoy DC, Fink G. Pathways between paternal and maternal education, caregivers’ support for learning, and early child development in 44 low-and middle-income countries. Early Childhood Research Quarterly. 2017;1(41):136–48.

    Article  Google Scholar 

  43. Jain G, Bisen V. Female literacy & its relevance with maternal and infant mortality rates. Int J Manag. 2012May;3(2):65–79.

    Google Scholar 

  44. Ahmed S, Creanga AA, Gillespie DG, Tsui AO. Economic status, education and empowerment: implications for maternal health service utilization in developing countries. PLoS ONE. 2010Jun 23;5(6):e11190.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Amwonya D, Kigosa N, Kizza J. Female education and maternal health care utilization: evidence from Uganda. Reprod Health. 2022;19(1):142.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Jarde A, Mohammed NI, Gomez P, Saine PC, D'Alessandro U, Roca A. Risk factors of infant mortality in rural The Gambia: a retrospective cohort study. BMJ paediatrics open. 2021;5(1).

  47. Nwanze LD, Siuliman A, Ibrahim N. Factors associated with infant mortality in Nigeria: A scoping review. PLoS ONE. 2023;18(11): e0294434.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Essendi H, Johnson FA, Madise N, Matthews Z, Falkingham J, Bahaj AS, James P, Blunden L. Infrastructural challenges to better health in maternity facilities in rural Kenya: community and healthworker perceptions. Reprod Health. 2015;12:1–1.

    Article  Google Scholar 

  49. Miller CE, Vasan RS. The southern rural health and mortality penalty: a review of regional health inequities in the United States. Soc Sci Med. 2021;1(268): 113443.

    Article  Google Scholar 

  50. Woo H, Kim JS. Regional Disparities in the Infant Mortality Rate in Korea Between 2001 and 2021. Journal of Korean Medical Science. 2023;38(44).

  51. Kifle D, Azale T, Gelaw YA, Melsew YA. Maternal health care service seeking behaviors and associated factors among women in rural Haramaya District, Eastern Ethiopia: a triangulated community-based cross-sectional study. Reprod Health. 2017;14:1–1.

    Article  Google Scholar 

  52. Caviglia M, Dell’Aringa M, Putoto G, Buson R, Pini S, Youkee D, Jambai A, Vandy MJ, Rosi P, Hubloue I, Della CF. Improving access to healthcare in Sierra Leone: the role of the newly developed national emergency medical service. Int J Environ Res Public Health. 2021;18(18):9546.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Bango M, Ghosh S. Reducing infant and child mortality: assessing the social inclusiveness of child health care policies and programmes in three states of India. BMC Public Health. 2023;23(1):1149.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We are grateful to MEASURE DHS and the World Health Organization for making the dataset and the HEAT software accessible.

Funding

This study received no funding.

Author information

Authors and Affiliations

  1. Department of Biological Sciences, School of Basic Sciences, Njala University, PMB, Freetown, Sierra Leone

    Augustus Osborne & Camilla Bangura

  2. Institute for Development (IfD), Western Area, Freetown, Sierra Leone

    Halimatu Kamara

  3. Boston College School of Social Work, 140 Commonwealth Ave, Chestnut Hill, MA, 02467, USA

    Abdulai Jawo Bah

Authors
  1. Augustus Osborne
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Halimatu Kamara
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Camilla Bangura
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Abdulai Jawo Bah
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

AO, HK, CB, and AJB conceived the study, performed the data analysis, and wrote the initial draft of the manuscript. All the authors reviewed and approved the final version of the manuscript.

Corresponding author

Correspondence to Augustus Osborne.

Ethics declarations

Ethics approval and consent to participate

This study did not seek ethical clearance since the WHO HEAT software and dataset are freely available in the public domain.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Osborne, A., Kamara, H., Bangura, C. et al. Socio-economic and geographical inequalities in infant mortality rates in Sierra Leone, 2008–2019. BMC Public Health 25, 1697 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12889-025-22952-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12889-025-22952-5

Keywords

BMC Public Health

ISSN: 1471-2458

Contact us