Determinants of Under-Five Mortality in Mozambique: Insights From the Demographic and Health Survey 2022/23

Abstract

Although there has been a substantial drop in the global under-five mortality rate over time, Sub-Saharan African countries, such as Mozambique, are still experiencing a significant burden. Therefore, this study evaluated the determinants of under-five mortality in Mozambique. This study used weighted data from 9668 live births in Mozambique’s Demographic and Health Survey. A mixed-effect logistic regression model identified determinants of under-five mortality, with statistical significance based on adjusted odds ratios and 95% confidence intervals. An under-five mortality rate of 48 per 1000 live births was significantly associated with being female, twin, small at birth, high birth order, and having a short birth interval. While under-five mortality is declining nationally, Mozambique still faces high child deaths. Key determinants include the child’s sex, twin births, higher birth order, small birth size, and short birth intervals. Targeted interventions addressing these factors are needed to meet Sustainable. Development Goal targets for child survival.

Keywords

under-five mortality determinants prevalence Mozambique

Introduction

The under-five mortality rate is the likelihood of a newborn dying before reaching the exact age of 5 years, calculated per 1000 live births.¹ Globally under-five mortality rate declined by 61%, from 94 deaths per 1000 live births in 1990 to 37 in 2023.¹ Notwithstanding the significant advancements made, child survival still requires immediate attention. In 2023 alone, approximately 13 100 under-five deaths took place daily, an unacceptably high count of mostly preventable deaths.¹

Sub-Saharan Africa (SSA) continues to report the highest global under-five mortality rate (U5MR), with an estimated 61 deaths per 1000 live births.² In 2023, this translated to a probability of 1 in 15 children in the region dying before reaching their fifth birthday. This risk is approximately 14 times greater than that for children born in high-income countries. Furthermore, the region’s current U5MR lags nearly 2 decades behind the global average, which had already attained a rate of 1 in 15 by 2004.¹ Large disparities in child survival rates also exist among countries, with a child born in the country with the highest mortality rate facing a risk of death before the age of 5 that is approximately 80 times greater than a child born in the country with the lowest mortality rate, and all 4 nations with mortality rates exceeding 100 deaths per 1000 live births are located in Sub-Saharan Africa.¹

Despite a drastic decline since 1990, Mozambique’s under-five mortality rate (U5M) remains among the highest globally,³ with an estimated 62 deaths per 1000 live births in 2023,¹ the country still faces significant challenges in this area.³ The main causes of these deaths include complications related to pregnancy, pneumonia, diarrhea, neonatal sepsis, and malaria.⁴ Moreover, factors such as maternal age, place of residence, education level, income index, sex of the child, birth order, birth weight, place of birth, attendance during childbirth, and the number of prenatal visits are also associated with the deaths of children under 5 years in this country.⁵

Between 2016 and 2030, roughly 95 million children are at risk of dying before reaching the age of 5 if the 2015 mortality rate persists in each country unchanged. If every country maintains its current pace of reducing mortality rates, seen between 2000 and 2015, around 68.8 million children might die during this time frame.⁶ To meet the SDG targets, which involve fewer than 25 deaths per 1000 live births by 2030 and a total of 56 million deaths, most SSA countries, including Mozambique, must substantially quicken their advancement.⁷ Determining and estimating mortality rates among children under the age of 5 and their contributing factors is a crucial method that enables local authorities and strategists to swiftly implement actions. This study sought to evaluate the under-five mortality and its determinants in Mozambique, using a model based on the 2022/23 Demographic and Health Survey data.

Methods

Data Source

The analysis of secondary data was performed using data from the Mozambique Demographic and Health Survey, which took place between July 2022 and March 2023. A nationally representative, population-based survey employed standardized questionnaires and a multi-stage stratified cluster sampling design. The design comprised stratification by province and urban/rural area, the selection of primary sampling units with equal probability, and the selection of enumeration areas (EAs) in proportion to their size. Demographic and health data were collected from women between 15 and 49 years of age and children under the age of 5 in the selected households. The dataset can be accessed online via the link supplied by the database: https://dhsprogram.com/data/dataset_admin/index.cfm. This study included a total weighted sample of 9668 women who had a live birth within the 5 years prior to the survey. For women who had 2 or more live births in the preceding 5 years, the last birth was considered.

Study Variables

The outcome variable was the death of children under 5 years old, which was coded as a dichotomous variable, with “0” indicating the child is alive and “1” indicating the child has died. The independent variables included respondent’s current age, placenta previa, mother’s level of education, household water source, presence of a toilet facility, child’s sex, media exposure, marital status, maternal occupation, parity, birth order, birth type, gestational age reported at the ANC visit, and the interval preceding the current birth.

Data Management and Analysis

The data was extracted, recoded, and analyzed using Stata 17 software. Before performing any statistical analysis, the data were adjusted using the sampling weight, primary sampling unit, and strata in order to restore their representativeness and obtain a reliable estimate along with its standard error. The study employed descriptive statistics, including frequencies and percentages. The MDHS data possess a hierarchical structure, which contradicts the independent assumptions of a standard logistic regression model. Consequently, a mixed-effects logistic regression model was employed to evaluate the determinants. Assessment of multicollinearity between independent variables was conducted using the variance inflation factor (VIF) and the mean VIF for each variable was found to be less than 5. The Intra-class Correlation Coefficient (ICC) and Median Odds Ratio (MOR) were examined to determine if clustering existed, and deviance was employed for model evaluation. Both bivariable and multivariable mixed effect logistic regressions were conducted. In the bivariable analysis, variables with a P-value less than .2 were selected for multivariable analysis, and variables with a P-value of less than .05 in the multivariable analysis were identified as determinant factors of under-five mortality.

Ethical Considerations

Ethical approval and permission letter were requested online from the DHS program at www.dhsprogram.com and received permission letter with the reference number of (AuthLetter_219107) to access the data for this study, and the DHS program was granted permission through email. The data used in this study was freely available and did not contain any personal information. The research is done based on secondary data from Mozambique demography health survey (MDHS). Issues of informed consent, confidentiality, anonymity, and privacy of the study sample were already done ethically by the MDHS authority and we did not manipulate and use the data for other issues. There was no patient or public involvement in this study. Therefore, there is no direct contact with the study participants and consent information’s is only the source of DHS dataset that we obtained by permission letter.

Informed Consent Statement

This statement does not replace the original informed consent obtained the Demographic and Health Surveys (DHS) Program for the original data collection. That original consent form is available www.dhsprogram.com/data/dataset_admin/login_main.cfm.

Results

Sociodemographic Characteristics of Study Participants

A total weighted sample of 9668 was included in this study. Nearly half (49.03%) of the mothers had primary education. Approximately, 72% of study participants were from rural areas and 48.38% were from poor household wealth index and 68.91% of respondents did not have occupation. 41% of the respondent’s ages were young between 15 and 24 years old and majority (82.56% of mothers were married. On the other hand, more than half of mothers have 2-3 parity and average size of child at birth. About 61.02% of respondents were from households with safe drinking water supplies and the majority (64.99%) of participants was from households with no toilet facility. Concerning media exposure, 68.46% of respondents have no media exposure. Most (86.78%) of mothers have ANC visit during their last pregnancy. Regarding birth order, 76.43% of mothers had a birth order less than 4 and 50% of mothers gave birth with a preceding birth interval above 3 years. Moreover, 51.51% of children were females and the great majorities (96.43%) were singletons (Table 1).

Table 1.

Descriptive Characteristics of the Respondents in Mozambique, 2022/23 (N = 9668).

Variables	Weighted frequency (%)
Maternal age
15-24	3956 (40.91%)
25-34	3795 (39.25%)
35-49	1917 (19.83%)
Residence
Urban	2753 (28.48%)
Rural	6915 (71.52%)
Maternal education
No education	2920 (30.20%)
Primary	4741 (49.03%)
Secondary and higher	2007 (20.76%)
Wealth index
Poor	4678 (48.38%)
Middle	1896 (19.61%)
Rich	3094 (32.01%)
Source of drinking water
Unprotected	3537 (38.98%)
Protected	5536 (61.02%)
Type of toilet
Unprotected	3385 (35.01%)
Protected	6283 (64.99%)
Sex of child
Male	4688 (48.49%)
Female	4980 (51.51%)
Media exposure
No	6618 (68.46%)
Yes	3050 (31.54%)
Marital status
Married	7982 (82.56%)
Single	484 (5.00%)
Widowed/divorced/separated	1202 (12.44%)
Maternal occupation
Yes	2998 (31.09%)
No	6645 (68.91%)
Parity
1st	3639 (40.11)
2-3	4962 (54.69)
4 and above	472 (5.20 )
Birth order
≤4	7389 (76.43%)
>4	2279 (23.57%)
Type of birth
Single	9367 (96.88%)
Multiple	301 (3.12%)
Size of child at birth
Average	3363 (56.22%)
Below average	1370 (22.91%)
Above average	1249 (20.87%)
Antenatal visit
No	719 (13.22%)
Yes	4721 (86.78%)
Preceding birth interval
≤2 years	1028 (14.07%)
2-3 years	2618 (35.83%)
Above 3 years	3662 (50.11%)

Prevalence of Under-Five Mortality in Mozambique

Four hundred sixty-five children (AOR = 4.81: 95%CI; 4.40%, 5.25%) died before 5 years of age from total live birth with a mortality rate of 48 per 1000 live births. Among mothers with no ANC visit during their last pregnancy, there is higher under-five mortality (47 deaths per 1000 live births) relative to mothers with ANC visits (36 deaths per 1000 live birth). In multiple births, the rate of under-five mortality was higher (40 deaths per 1000 live births) than in singletons (37 deaths per 1000 live births).

The under-five mortality rate mothers with level of education were decreased from 43 deaths to 35 deaths per 1000 live births between no formal education versus secondary and higher education. The under-five mortality rate is also differed with wealth status, falling from 50 deaths to 42 deaths per 1000 live births in poor versus rich households. Similarly, in rural and urban areas, the under-five mortality rate was 50 and 42 deaths per 1000 live births, respectively. In households with untreated water sources, the under-five mortality rate was higher (53 deaths per 1000 live births) than in those with a safe water source (44 deaths per 1000). Among households with no standard toilet facility, under-five mortality was higher (99 deaths per 1000 live births) compared to those with a standard toilet facility (47 deaths per 1000 live births). Male children were higher U5M than females with rate of deaths (54 and 42 deaths per 1000 live births respectively. Among respondent’s with no media exposures, U5M was higher (81 deaths per 1000 live births) compared with those have media exposures. On the other hand, mothers did not have occupations were higher U5M (52 deaths per 1000 live births) than mothers have work (39 deaths per 1000 live births; Table 2).

Table 2.

prevalence of Under-Five Mortality in Mozambique.

Variables	Total	No. of children died	Under-five mortality rate per 1000
Maternal age
15-24	3956	218	55
25-34	3795	153	40
35-49	1917	94	49
Residence
Urban	2753	115	42
Rural	6915	349	50
Maternal education
No education	2920	127	43
Primary	4741	268	57
Secondary and higher	2007	70	35
Wealth index
Poor	4678	235	50
Middle	1896	99	52
Rich	3094	131	42
Source of drinking water
Unprotected	4402	232	53
Protected	5266	233	44
Type of toilet
Unprotected	3385	167	49
Protected	6283	298	47
Sex of child
Male	4688	254	54
Female	4980	210	42
Media exposure
No	6618	336	51
Yes	3050	129	42
Marital status
Married	7982	396	50
Single	484	18	37
Widowed/divorced/separated	1202	50	42
Maternal occupation
Yes	2998	117	39
No	6645	348	52
Parity
1-3	1636	85	52
4-6	3959	169	43
>6	4074	211	52
Birth interval
≤4	7389	340	46
>4	2279	125	55
Type of birth
Single	9367	344	37
Multiple	301	120	40
Size of child at birth
Average	3363	113	34
Below average	1370	127	93
Above average	1249	37	30
Antenatal visit
No	719	34	47
Yes	4721	172	36
Preceding birth interval
≤2 years	1028	89	87
2-3 years	2618	96	37
Above 3 years	3662	125	34

Determinants of Under-Five Mortality in Mozambique

Model Comparison/Random Effect Analysis

The deviance and log-likelihood Ratio (LLR) test were evaluated, and a mixed effect logistic regression model was selected due to its minimal deviance value. The ICC value was 0.20, indicating that approximately 20% of the variability in under-five mortality was associated with the differences between communities or clusters. The median odds ratio of 1.62 suggests that when randomly selecting a child from a high-risk group, the likelihood of under-five mortality is 1.62 times greater than that of a child from a low-risk group. Consequently, we selected a mixed-effect logistic regression model in preference to the basic model (Table 3).

Table 3.

Model Comparison and Fitness for Evaluate the Determinants of Under-Five Mortality.

Comparison model	The standard logistic regression model	Mixed effect logistic regression model
LLR	−686.51	−675.68
Deviance	1373.02	1351.36
Intra class correlation (ICC) and median odds ratio (MOR) of the mixed effect model of Final model
ICC	0.20
MOR	1.62

Fixed Effects Analysis

Risk factors of U5M in Mozambique were analyzed using a mixed-effect logistic regression model. Following the testing of each variable in turn, the bivariable analyses revealed that maternal age, residence, maternal education, source of drinking water, sex of child, media exposure, maternal occupation, parity, birth order, type of birth, size of child at birth, preceding birth interval were all significantly were significant determinants of U5M. Variables with a P-value < .2 in the bivariable analysis were eligible for the multivariable analysis.

In the multivariable analysis, sex of child, birth type, parity, size of child at birth, and preceding birth interval were significant determinants of U5M. The odds of U5M was decreased by 96% (AOR = 0.04: 95%CI; 0.01, 0.06) and 95% (AOR = 0.05: 95%CI; 0.03, 0.08) mothers with parity of greater than 6 compared with those mothers having children of 3 and less and 4 up to 6 in the family, respectively. Mothers with preceding birth intervals of 2 to 3 years and above 3 years had 66% (AOR = 0.44: 95%CI; 0.27, 0.70) and 49% (AOR = 0.51: 95% CI; 0.32, 0.79) lower odds of U5M as compared to mothers with preceding birth interval of fewer than 2 years. Being multiple births had 4.16 (AOR = 4.16: 95%CI; 2.33, 7.43) times higher odds of U5M compared to a single birth. Moreover, there was a 30% (AOR = 0.70: 95%CI; 0.50, 0.97) lower odds of U5M among children’s of female sex compared to their counterparts. Lastly, the odds of U5M were 4.12 (AOR = 4.12: 95%CI; 2.79, 6.09) times higher than those children with small size at birth compared with the average size at birth (Table 4).

Table 4.

Bivariable and Multivariable Mixed Effect Analysis for Assessing the Determinants of Under-Five Mortality in Mozambique, 2022/23.

Variables	COR (95% CI)	AOR (95% CI)
Maternal age
15-24	1	1
25-34	0.70 (0.56, 0.87)*	0.90 (0.58, 1.40)
35-49	0.88 (0.68, 1.13)	1.13 (0.62, 2.06)
Residence
Urban	1	1
Rural	1.24 (0.9, 1.59)	1.14 (0.67, 1.94)
Maternal education
No education	1.28 (0.94, 1.75)	1.17 (0.75, 2.52)
Primary	1.64 (1.24, 2.17)*	1.00 (0.51, 1.97)
Secondary and higher	1	1
Source of drinking water
Protected	1	1
Unprotected	1.17 (0.95, 1.44)	0.99 (0.68, 1.48)
Sex of child
Male	1	1
Female	0.76 (0.63, 0.92 )*	0.70 (0.50, 0.97) *
Media exposure
Yes	1	1
No	1.25 (1.01, 1.56)*	1.12 (0.76, 1.66)
Maternal occupation
Yes	1	1
No	1.35 (1.07, 1.68)*	1.18 (0.79, 1.76)
Parity
1-3	0.17 (0.14, 0.22)*	0.04 (0.01, 0.06)*
4-6	0.24 (0.13, 0.45)*	0.05 (0.03, 0.08)*
>6	1	1
Birth order
≤4	1	1
>4	1.21 (0.97, 1.50)	1.23 (0.78, 1.95)
Type of birth
Single	1	1
Multiple	3.62 (2.51, 5.21)*	4.16 (2.33, 7.43)*
Size of child at birth
Average	1	1
Below average	3.14 (2.37, 4.18)*	4.12 (2.79, 6.09)*
Above average	0.90 (0.61, 1.34)	1.48 (0.91, 2.43)
Preceding birth interval
≤2 years	1	1
2-3 years	0.41 (0.30, 0.55)*	0.44 (0.27, 0.70)*
Above 3 years	0.38 (0.28, 0.51)*	0.51 (0.32, 0.79)*

Significant at P < .05 levels.

Discussion

Under-five mortality remains a critical public health challenge in Mozambique, where it is a leading cause of death among children. Reducing Mozambique’s under-five mortality rate is essential for progress toward national health targets and the achievement of the Sustainable Development Goals (SDGs).^7,8 In this study, the death rate was 48 per 1000 live births, meaning that 4.81% (95% CI; 4.40, 5.25%) of children under 5 had died before turning 5.

The finding of this study was lower than study in in Ethiopia,^2,9,10 in Bangladesh,¹¹ and higher than study in Australia,¹² in china.¹³ The discrepancy could be explained by differences in the study’s settings and sample size. Additionally, the higher prevalence or discrepancy observed in this study may be the result of the participants’ low socioeconomic standing in Mozambique.¹⁴ Furthermore, the discrepancy between this study’s findings and those from other countries could be attributed to differences in study settings and sample size. More fundamentally, the higher prevalence observed in Mozambique likely stems from the interplay of participants’ low socioeconomic standing and significant weaknesses in the local health system.¹⁴ Compared to settings like Australia or China, Mozambique’s health system may face challenges in accessibility, quality of care, or health workforce density, which directly impacts health outcomes independent of socioeconomic status.¹⁴

The multivariable mixed-effects analysis showed that sex of child, type of birth, parity, size of child at birth and preceding birth interval had significant determinants of under-five mortality.

This study revealed that female children had a lower risk of mortality compared to male children. These findings are consistent with previous research conducted in Ethiopia,^15
-17 Sierra Leone,¹⁸ Nigeria,¹⁹ sub-Saharan Africa,² and Tanzania.²⁰ Due to their biological makeup, boys are more susceptible to disease and premature death,²¹ with a heightened susceptibility to intrauterine growth restriction, preterm birth, and morbidities such as respiratory and gastrointestinal infections due to elevated testosterone levels that may suppress the immune system.^22,23 Additionally, the generally higher birth weight of boys compared to girls may contribute to complications during delivery and subsequent stages.²⁴

The odds of under-five mortality among multiple births were higher as compared to singleton births and this is consistent with different studies conducted in Ethiopia,⁹ Guinea-Bissau,²⁵ USA,²⁶ and Ghana.²² This might be because multiple births can lead to growth retardation and prematurity, which are the main risk factors for under-five mortality.⁹ In addition, the most likely explanation for this finding is that multiple pregnancies and births result in adverse pregnancy outcomes and the health of child.²⁷ There is also the possibility that parents of multiples have higher levels of stress, anxiety, and depression in the first year of life compared to parents of singletons.²⁸

In the present study, the probability of under-five mortality increases with total children ever born or parity increases. This is consistent with a study carried out in Ethiopia,⁹ Nigeria,²⁹ Rwanda,³⁰ and Bangladesh.³¹ This might be because mothers with higher parity might be busy caring for many children in the family and may give limited attention to the health of their last child. Also, an increase in the total number of children ever born could result in lack of care, low birth weight, premature births and heavy drain on the limited household resources as children have to compete for the little resources available for their survival.³²

This study also showed that children with small size at birth had a high risk of under-five mortality. Children with small size had 4.12 times higher risk of deaths as compared to average size. The finding was supported by earlier studies.^2,24,33 Low birth weight infants are at greater risk of mortality due to their heightened vulnerability to preterm birth and other complications.³⁴

This study found that a shorter preceding birth interval (<2 years) is a risk factor for under-five mortality. Children born after 2-3 years and greater or equal to 3 years of the preceding birth interval had a lower risk of mortality compared to children born from less than 2 years.^2,17 This finding is consistent with the study conducted in Bangladesh,³¹ Ethiopia,³⁵ Pakistan,³⁶ and sub Saharan Africa.^2,33 This might be shorter birth interval may affect maternal health and reduction of nutrition during pregnancy that leads the child would prone to malnutrition during intrauterine life, low birth weight, premature birth.^37,38 Additionally, the recovery time for the uterus will short, and lactation will deplete maternal nutrient that it will further increase the risk of child death.³⁹

Strength and Limitations

This study’s strength is that it was carried out using nationally representative data, which could improve the generalizability of the estimated death rate for children under 5 in Mozambique. The conclusion is also reached by the use of multilevel logistic regression analysis, which may be able to identify factors other than those at the individual level, and a cross-nationally representative population-based study. However, because the study relied on data from a cross-sectional survey, it was limited in that it did not address the cause-and-effect conclusion.

Conclusion and Recommendation

Despite a declining national under-five mortality rate, Mozambique continues to experience an unacceptably high number of deaths among children under five. This study identifies key determinants of this mortality: the child’s sex, twin birth status, higher birth order, small size at birth, and short preceding birth intervals. To accelerate progress toward achieving the Sustainable Development Goal (SDG) targets for child survival, Mozambican health authorities and policymakers must design and implement interventions targeted at these specific risk factors. Male children, twins, children of higher birth order, those born small size at birth, and those born after short interpregnancy intervals.

Footnotes

Acknowledgements

The authors acknowledge the DHS program office to take ethical consideration of this study.

ORCID iD

Engidaw Fentahun Enyew

Ethical Considerations

Ethical approval and permission letter were requested online from the DHS program at and received permission letter to access the data for this study, and the DHS program was granted permission through email. The data used in this study was freely available and did not contain any personal information. The research is done based on secondary data from Mozambique demography health survey (MDHS). Issues of informed consent, confidentiality, anonymity, and privacy of the study sample were already done ethically by the MDHS authority and we did not manipulate and use the data for other issues. There was no patient or public involvement in this study. Therefore, there is no direct contact with the study participants and consent information’s is only the source of DHS dataset that we obtained by permission letter.

Consent to Participate

This study uses anonymized data from the 2022/23 Mozambique National Survey. The data were collected under a separate informed consent process by the Demographic and Health Surveys (DHS) Program, and all ethical approvals were obtained from The IRB-approved procedures for DHS public-use datasets. The data we are using have been de-identified, meaning that individual participants cannot be identified. This statement does not replace the original informed consent obtained the Demographic and Health Surveys (DHS) Program for the original data collection. That original consent form is available .

Author Contributions

EFE: Conceptualization, data curation, formal analysis, investigation, methodology, resources, software, validation, visualization, Writing—original draft, Writing, MG: Data curation, investigation, methodology, review & editing. HTE: Data curation, investigation, methodology, resources, validation, visualization, writing—review & editing. TAM: Conceptualization, data curation, formal analysis, investigation, methodology, resources, software, validation, visualization, Writing—original draft, Writing, TA: Data curation, investigation, methodology, review & editing. GD: Data curation, investigation, methodology, resources, validation, visualization, writing—review & editing. TKY: Data curation, investigation, methodology, resources, validation, visualization, writing—review & editing. ABG: Conceptualization, data curation, formal analysis, investigation, methodology, resources, software, validation, visualization, Writing—original draft, Writing, MTG: Data curation, investigation, methodology, review & editing. HLE: Data curation, investigation, methodology, resources, validation, visualization, writing—review & editing. Both the authors have read and approved the final manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data for this study were obtained from the DHS Program as well as obtained the extract data from the principal investigator by requesting.*

Declarations

We hereby declare that this study represents our work which has been done after registration and reception for the accessing of data at and confirms that all methods were carried out following relevant guidelines and regulations. We have attempted to identify all the risks related to this research that may arise in conducting this research, obtained the relevant ethical and/or safety approval (where applicable), and acknowledged my obligations and the rights of the participants by the demography and health survey program authorities. The research is conducted following the declaration of Helsinki.

References

UNICEF. Levels and trends in child mortality: United Nations Inter-Agency Group for Child Mortality Estimation (UN IGME), Report 2024. March 24, 2025. Accessed April 10, 2025. https://data.unicef.org/resources/levels-and-trends-in-child-mortality-2024/

Birhanie

Tessema

Endalew

Tamirat

KS.

Under-five mortality and its associated factors in sub-Saharan Africa: a multilevel analysis of recent demographic and health surveys data based on Bayesian approach. BMC Pediatr. 2025;25(1):103.

Källander

Counihan

Cerveau

Mbofana

Barriers on the pathway to survival for children dying from treatable illnesses in Inhambane province, Mozambique. J Glob Health. 2019;9(1):010809.

Neal

Channon

Chintsanya

The impact of young maternal age at birth on neonatal mortality: evidence from 45 low and middle income countries. PLoS One. 2018;13(5):e0195731.

Grønvik

Fossgard Sandøy

Complications associated with adolescent childbearing in Sub-Saharan Africa: a systematic literature review and meta-analysis. PLoS One. 2018;13(9):e0204327.

You

Hug

Ejdemyr

, 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):2275-2286.

Sharrow

Hug

You

, et al. Global, regional, and national trends in under-5 mortality between 1990 and 2019 with scenario-based projections until 2030: a systematic analysis by the UN Inter-agency Group for Child Mortality Estimation. Lancet Glob Health. 2022;10(2):e195-e206.

Macicame

Kante

Wilson

, et al. Countrywide mortality surveillance for action in Mozambique: results from a national sample-based vital statistics system for mortality and cause of death. Am J Trop Med Hyg. 2023;108(5_Suppl):5-16.

Worku

Teshale

Tesema

GA.

Determinants of under-five mortality in the high mortality regions of Ethiopia: mixed-effect logistic regression analysis. Arch Public Health. 2021;79(1):55.

10.

Yemane

GD.

The factors associated with under-five mortality in Ethiopia. Ann Med Surg. 2022;79:104063.

11.

Rahman

MH.

Explore the factors related to the death of offspring under age five and appraise the hazard of child mortality using machine learning techniques in Bangladesh. BMC Public Health. 2025;25(1):360.

12.

Kaforau

Tessema

Jancey

Bugoro

Pereira

Prevalence and risk factors associated with under-five mortality in the Solomon Islands: an investigation from the 2015 Solomon Islands demographic and health survey data. The Lancet regional health. Western Pacific. 2023;33:100691.

13.

Liang

Macinko

Yue

Meng

The impact of the health care workforce on under-five mortality in rural China. Hum Resour Health. 2019;17(1):21.

14.

Daca

CSL

Sebastian

Arnaldo

Schumann

Namatovu

. Socioeconomic and geographical inequalities in health care coverage in Mozambique: a repeated cross-sectional study of the 2015 and 2018 national surveys. BMC Public Health. 2023;23(1):1007.

15.

Yemane

Tareke

Zakaria

Takele

Jemal

SS.

Time to death and its determinants of under-five children in rural Ethiopia by using shared frailty. Sci Rep. 2024;14(1):5647.

16.

Zewudie

Gelagay

Enyew

EF.

Determinants of under-five child mortality in Ethiopia: analysis using Ethiopian demographic health survey, 2016. Int J Pediatr. 2020;2020:1-9.

17.

Fikru

Getnet

Shaweno

Proximate determinants of under-five mortality in Ethiopia: using 2016 nationwide survey data. Pediatric Health Med Ther. 2019;10:169-176.

18.

Tagoe

Agbadi

Nakua

Duodu

Nutor

Aheto

JMK

. A predictive model and socioeconomic and demographic determinants of under-five mortality in Sierra Leone. Heliyon. 2020;6(3):e03508.

19.

Okoli

Hajizadeh

Rahman

Khanam

Geographic and socioeconomic inequalities in the survival of children under-five in Nigeria. Sci Rep. 2022;12(1):8389.

20.

Kanté

Nathan

Jackson

, et al. Trends in socioeconomic disparities in a rapid under-five mortality transition: a longitudinal study in the United Republic of Tanzania. Bull World Health Organ. 2016;94(4):258-266a.

21.

Almossawi

O’Brien

Parslow

Nadel

Palla

A study of sex difference in infant mortality in UK pediatric intensive care admissions over an 11-year period. Sci Rep. 2021;11(1):21838.

22.

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.

23.

Enyew

Getnet

Gebiru

Dessie

Individual and community level determinants of neonatal mortality in sub Saharan Africa: findings from recent demographic and health survey data. Ital J Pediatr. 2025;51(1):144.

24.

Yaya

Bishwajit

Okonofua

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.

25.

Byberg

Østergaard

Rodrigues

, et al. Analysis of risk factors for infant mortality in the 1992-3 and 2002-3 birth cohorts in rural Guinea-Bissau. PLoS One. 2017;12(5):e0177984.

26.

Zhao

Zou

Lei

Zhang

Gender differences in infant mortality and neonatal morbidity in mixed-gender twins. Sci Rep. 2017;7(1):8736.

27.

Santana

Surita

Cecatti

JG.

Multiple pregnancy: epidemiology and association with maternal and perinatal morbidity. RBGO Gynecol Obstet. 2018;40(9):554-562.

28.

Egsgaard

Bliddal

Lund

Vigod

Munk-Olsen

Risk and timing of postpartum depression in parents of twins compared to parents of singletons. Acta Psychiatr Scand. 2025;151(2):163-172.

29.

Kayode

Adekanmbi

Uthman

OA.

Risk factors and a predictive model for under-five mortality in Nigeria: evidence from Nigeria demographic and health survey. BMC Pregnancy Childbirth. 2012;12(1):10.

30.

Amoroso

Nisingizwe

Rouleau

, et al. Next wave of interventions to reduce under-five mortality in Rwanda: a cross-sectional analysis of demographic and health survey data. BMC Pediatr. 2018;18(1):27.

31.

Abir

Agho

Page

Milton

Dibley

MJ.

Risk factors for under-5 mortality: evidence from Bangladesh Demographic and Health Survey, 2004-2011. BMJ Open. 2015;5(8):e006722.

32.

Tesfa

Tiruneh

Azanaw

, et al. Time to death and its determinants among under-five children in Sub-Saharan Africa using the recent (2010-2018) demographic and health survey data: country-based shared frailty analyses. BMC Pediatr. 2021;21(1):515.

33.

Yaya

Uthman

Okonofua

Bishwajit

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.

Belachew

Tewabe

Neonatal sepsis and its association with birth weight and gestational age among admitted neonates in Ethiopia: systematic review and meta-analysis. BMC Pediatr. 2020;20(1):55.

35.

Fenta

HM.

Risk factors of child mortality in Ethiopia: application of multilevel two-part model. PLoS One. 2020;15(8):e0237640.

36.

Naz

Patel

Dilanchiev

Are socioeconomic status and type of residence critical risk factors of under-five mortality in Pakistan? Evidence from nationally representative survey. Clin Epidemiol Glob Health. 2021;10:100670.

37.

Rutstein

SO.

Effects of preceding birth intervals on neonatal, infant and under-five years mortality and nutritional status in developing countries: evidence from the demographic and health surveys. Int J Gynecol Obstet. 2005;89:S7-S24.

38.

Geremew

Gelaye

Melesse

Akalu

Baraki

AG.

Factors affecting under-five mortality in Ethiopia: a multilevel negative binomial model. Pediatric Health Med Ther. 2020;11:525-534.

39.

DaVanzo

Hale

Razzaque

Rahman

Effects of interpregnancy interval and outcome of the preceding pregnancy on pregnancy outcomes in Matlab, Bangladesh. BJOG. 2007;114(9):1079-1087.