INTRODUCTION

Globally, the under-five mortality rate (U5MR) has declined by 61%, from 94 deaths per 1000 live births in 1990 to 37 in 2023. Nevertheless, reducing preventable child deaths remains a central global priority under Sustainable Development Goal (SDG) 3.2, which targets a reduction to ≤25 deaths per 1000 live births by 2030 [1,2]. Despite substantial progress, improvements have been uneven. Children in sub-Saharan Africa continue to face nearly 14 times the mortality risk of those in high-income countries, and neonatal causes now account for approximately 40% of under-five deaths [1]. Indonesia reflects this global pattern of overall progress accompanied by persistent inequity. Although the national U5MR declined from over 80 in 1990 to 20 in 2023 [3], marked regional disparities remain. Some districts have already achieved the SDG target, whereas others report mortality rates up to 3 times higher [4].

The geographic distribution of child survival in Indonesia reveals a pronounced divide. Rural and remote districts, particularly in eastern provinces such as Papua, Maluku, and East Nusa Tenggara, continue to report substantially higher mortality rates than urban municipalities in Java and Bali [5,6]. These interregional gaps suggest that structural and contextual determinants extending beyond individual or household factors play a central role. Despite expanded coverage under the National Health Insurance Scheme (Jaminan Kesehatan Nasional, JKN), persistent poverty, limited maternal education, inequitable health spending, and the uneven distribution of health infrastructure remain key contributors [7]. Addressing these structural determinants is therefore essential to achieving equitable child survival across Indonesia’s 514 districts and municipalities.

Previous studies have demonstrated that interconnected economic, social, and health-system factors shape mortality among children under 5 years of age. Higher gross regional domestic product (GRDP) per capita and maternal education are consistently associated with lower U5MR, whereas poverty, short birth intervals, and younger maternal age are associated with increased mortality risk [8–10]. Gender inequality, limited access to health services, unsafe water, and low vaccination coverage further exacerbate mortality, particularly in resource-constrained settings [11–13]. Spatial analyses have documented geographic clustering of deaths, underscoring the need for region-specific, multisectoral interventions [14].

In Indonesia, most prior research has relied on individual- or household-level survey data (e.g., the Indonesia Demographic and Health Survey [IDHS]) and has focused primarily on maternal, infant, and household determinants of child mortality [15–17]. However, such studies are limited in geographic representativeness and do not capture broader district-level structural conditions, including public resource allocation, fertility patterns, and spatial spillover effects between neighboring regions. Consequently, empirical evidence linking district-level socioeconomic structures, fertility patterns, and spatial dependence to U5MR remains limited. This study addresses these gaps by analyzing ecological data from 514 districts and municipalities using ordinary least squares (OLS) and spatial regression models. Rather than re-examining individual-level risk factors, we examined how poverty, education, fertility, and administrative context interact across districts and municipalities to influence child survival. By incorporating spatial dependence into district-level socioeconomic analysis, this study provides evidence to inform equitable health policies and accelerate progress toward SDG 3.2.

METHODS

Study Design and Data Sources

An ecological cross-sectional study was conducted using district-level and municipality-level data (n=514) obtained from official publications of Statistics Indonesia (Badan Pusat Statistik [BPS]). The primary outcome was the U5MR, expressed as deaths per 1000 live births. Explanatory variables included: (1) poverty rate (percentage of the population living below the national poverty line); (2) mean years of schooling; (3) GRDP per capita (million Indonesian rupiah [IDR]); and (4) total fertility rate (TFR), defined as the average number of children per woman. All variables were derived from the 2020 Population Census Long Form (SP2020), except GRDP per capita, which was obtained from the 2020 Gross Regional Domestic Product by District/Municipality reports [18] to ensure temporal alignment.

Unit of Analysis

The unit of analysis was the district or municipality, the country’s second-level administrative division. These units differ structurally and socio-demographically: municipalities are predominantly urban, with higher population density and infrastructure coverage, whereas districts are generally rural and face greater geographic and service-access barriers. Administrative type (district vs. municipality) was coded as a binary variable (0=district; 1=municipality) and included as a potential effect modifier in multivariable models.

Variables

The primary outcome was U5MR, defined as deaths among children aged 0–4 years per 1000 live births. District-level and municipality-level U5MR values were indirectly estimated by Statistics Indonesia using the Trussell method based on reported numbers of children ever born and surviving in the Sensus Penduduk SP2020 [4]. This approach follows United Nations guidelines for indirect child mortality estimation [19].

Explanatory variables were selected based on theoretical and empirical relevance and the availability of comparable district-level data. Poverty rate reflects material deprivation that may constrain access to adequate nutrition, safe water, sanitation, and healthcare, factors consistently associated with increased child mortality [10,20]. Mean years of schooling reflect educational attainment and may influence health literacy, caregiving practices, and healthcare utilization [8,9]. GRDP per capita serves as proxy for local economic capacity supporting infrastructure and health-system investment [21]. TFR reflects reproductive patterns and access to family planning; higher fertility levels are often associated with increased mortality risk through shorter birth intervals and constrained household resources [11].

Statistical Analysis

All descriptive, bivariate, and multivariable analyses were conducted for the full sample and stratified by administrative status (district vs. municipality). Descriptive statistics (mean, standard deviation, median, interquartile range [P25–P75], and range) summarized variation across districts. Bivariate associations were examined using simple linear regression, with unadjusted β coefficients reported alongside 95% confidence intervals (CIs).

Multivariable analyses were conducted using OLS regression with heteroscedasticity-robust standard errors. Model 1 included poverty rate, mean years of schooling, and GRDP per capita. Model 2 added TFR to assess its independent demographic contribution and potential mediating role. Model 3 incorporated municipality status and an interaction term between poverty and municipality status to assess effect modification.

Model fit was evaluated using the Akaike information criterion (AIC) and Bayesian information criterion (BIC). Spatial autocorrelation in OLS residuals was assessed using Moran’s I. Although initial diagnostics did not demonstrate strong residual spatial dependence, a spatial autoregressive (SAR) model was estimated to assess robustness and capture potential spatial spillover effects. The SAR model included a spatially lagged dependent variable.

All analyses were conducted using Stata version 16 (StataCorp., College Station, TX, USA). Spatial visualization was performed using Quantum GIS Desktop 3.40.13 ( https://qgis.org/).

Ethics Statement

This study relied exclusively on publicly available aggregated data and did not involve identifiable individual-level information or human intervention. Therefore, ethics approval was not required. The study followed the principles of the Declaration of Helsinki for research involving secondary data.

RESULTS

Descriptive Statistics

Table 1 summarizes under-five mortality and key socioeconomic determinants for all 514 Indonesian districts and municipalities in 2020. A consistent district-municipality disparity was evident across all indicators. Districts had a substantially higher mean U5MR (26.1 deaths per 1000 live births) than municipalities (16.8 deaths per 1000 live births), with a wider interquartile range, indicating greater heterogeneity across predominantly rural districts.

Socioeconomic conditions followed a clear gradient unfavorable to districts. Municipalities had markedly higher economic output (GRDP per capita) and educational attainment (mean years of schooling), reflecting the concentration of resources and human capital in urban areas. In contrast, districts faced a dual burden of higher poverty rates and lower educational attainment. The mean poverty rate in districts (13.1%) was nearly twice that in municipalities (7.0%), and municipalities had approximately 3 more years of schooling. Fertility patterns mirrored this structural divide, with higher TFR in districts (2.44) than in municipalities (2.13).

Figure 1 shows the provincial distribution of U5MR in Indonesia in 2020. The map reveals clear spatial disparities, with provinces in eastern Indonesia, particularly Papua and East Nusa Tenggara, exhibiting substantially higher mortality rates than western provinces such as Java and Bali. This geographic clustering indicates persistent regional inequities in child survival and likely reflects broader socioeconomic development gaps and differences in healthcare accessibility across the archipelago.

Bivariate Analysis

Figure 2A-D presents scatterplots with fitted regression lines showing associations between U5MR (deaths per 1000 live births) and 4 district-level predictors. (A) U5MR decreased with increasing mean years of schooling, indicating a strong protective association of education. (B) GRDP per capita showed a weaker inverse association with U5MR. (C) Poverty rate was positively associated with U5MR, indicating that mortality was concentrated in poorer districts. (D) TFR showed the steepest positive gradient, underscoring its central role in mortality disparities. Together, these patterns highlight the structural determinants of child survival in Indonesia, whereby low education, poverty, and high fertility jointly contribute to elevated U5MR (Figure 2).

Bivariate analyses indicated that all key socioeconomic and demographic indicators were significantly associated with U5MR. Districts with higher poverty rates had higher U5MR (β=1.08; 95% CI, 0.96 to 1.20), whereas higher mean years of schooling were associated with lower U5MR (β=−3.26; 95% CI, −3.91 to −2.61). TFR showed a strong positive association with U5MR (β=20.84; 95% CI, 18.56 to 23.13), whereas GRDP per capita showed a modest but significant inverse association (β=−0.04; 95% CI, −0.06 to −0.02). Municipalities had lower U5MR than districts (β=−9.27; 95% CI, −10.75 to −7.79), consistent with persistent disparities between districts and municipalities. These findings support inclusion of all variables in multivariable models to adjust for confounding and to evaluate spatial dependence (Table 2).

Multivariable Models

Table 2 shows that in model 1, poverty and education remained significant predictors of U5MR, with poverty positively and education negatively associated with under-five mortality. After fertility was added in model 2, TFR emerged as the strongest predictor of U5MR (β=13.21; 95% CI, 10.92 to 15.49). The coefficient for poverty was attenuated but remained statistically significant, consistent with partial mediation through fertility.

Model 3 added administrative type (district vs. municipality) and an interaction term between poverty rate and municipality status. The positive municipality coefficient reflects the baseline difference in predicted U5MR at the lowest poverty level, whereas the negative interaction term (β=−0.30, p<0.10) was consistent with a steeper poverty-U5MR association in districts than in municipalities. This pattern suggests that increases in poverty were more strongly associated with higher U5MR in predominantly rural districts, highlighting differential vulnerability by administrative context. Across models, educational attainment consistently showed a protective association with lower U5MR.

Figure 3 illustrates the marginal association between poverty prevalence and U5MR by administrative type. OLS models with robust standard errors showed a positive association between poverty and under-five mortality, with a steeper gradient in districts than in municipalities. This pattern suggests that poverty was more strongly associated with child mortality in rural district settings, reinforcing persistent inequalities across subnational administrative units.

Spatial Model

Although Moran’s I for OLS residuals did not indicate statistically significant spatial autocorrelation, a SAR model was estimated to evaluate robustness and capture potential spillover effects. The SAR model identified significant spatial dependence (ρ=0.24; 95% CI, 0.14 to 0.34), indicating that higher mortality in 1 district was associated with higher mortality in neighboring districts. After accounting for the spatial lag, the association with schooling weakened but remained statistically significant (β=−0.70; 95% CI, −1.23 to −0.16), and the poverty association showed a similar pattern (β=0.57; 95% CI, 0.46 to 0.67). TFR remained a strong predictor (Table 2).

The SAR model fit was better than the OLS model (AIC= 3365.34; BIC=3403.52) and eliminated residual spatial autocorrelation (Moran’s I=0.005, p=0.213), supporting model adequacy. Because no residual spatial dependence remained, further local spatial diagnostics (e.g., local indicators of spatial association) were not pursued beyond exploratory mapping. Spatial impact decomposition (Supplemental Material 1) indicated that both poverty and fertility had significant direct within-district effects and indirect spillover effects across neighboring areas.

These findings suggest that U5MR is shaped not only by local socioeconomic conditions but also by spatially clustered disadvantage. Elevated mortality in eastern Indonesia may reflect persistent structural inequities, including limited access to healthcare infrastructure, constrained fiscal capacity, and reduced service-delivery capability, despite decentralization reforms.

DISCUSSION

Structural Determinants of Child Mortality

This study indicates that fertility, poverty, and low maternal education are key correlates of under-five mortality in Indonesia. The strong association between higher TFR and higher U5MR is consistent with evidence from other high-fertility settings, where limited access to family planning and lower educational attainment are associated with poorer child-survival outcomes [21,22]. In Indonesia, fertility decline has been uneven and remains elevated in poorer rural regions, including East Nusa Tenggara [23]. This pattern likely reflects interrelated structural barriers, including constrained access to contraception, economic disadvantage, and persistent cultural norms [24,25]. Maternal education showed a protective association, consistent with evidence that additional schooling is associated with improved health knowledge, healthcare utilization, and care-seeking practices, and with lower mortality risk by 10–16% [26,27]. In Indonesia, improvements in women education have coincided with sustained mortality decline and narrowing gender gaps [20,28]. Together, these findings support investment in family planning and girls’ education as complementary strategies to improve child survival.

Spatial Spillovers and Geographic Inequalities

Our findings suggest that child mortality is shaped not only by local district conditions but also by spillovers from neighboring areas. The SAR model identified significant spatial dependence (ρ=0.24), indicating clustering in under-five mortality across contiguous districts. This pattern corresponds to an east-west divide, with elevated mortality clustering in eastern provinces, particularly Papua and East Nusa Tenggara, and lower mortality in western provinces such as Java and Bali [5,6]. The steeper poverty-mortality gradient in districts than in municipalities (interaction β=−0.30, p<0.10) further indicates that place-based disadvantage may amplify vulnerability to poverty in predominantly rural districts [29]. These spatially clustered disadvantages may arise from overlapping structural deficits, including weaker health infrastructure, limited road connectivity, uneven fiscal capacity, and fragmented referral systems that extend beyond single administrative units [30]. Accordingly, geographic interdependence may reinforce mortality disparities, a dimension that conventional non-spatial regression models may not capture adequately.

Policy Implications

These findings support equity-oriented, spatially informed policy approaches that address both within-district deprivation and cross-district spillovers. First, evidence of spatial clustering suggests that interventions should be coordinated across district boundaries within high-mortality clusters (e.g., Papua and Nusa Tenggara), rather than implemented solely as district-specific programs. Second, the stronger poverty-mortality association in rural districts supports integrating child health components into rural poverty-alleviation programs. The JKN and Special Health Allocation Fund (DAK Kesehatan) could adopt mortality-based performance criteria to prioritize funding for high-risk rural and eastern regions. Third, the persistent associations with fertility and schooling underscore the need to strengthen reproductive health services and accelerate investment in girls’ secondary education, particularly in high-fertility, high-mortality districts.

Limitations and Future Research

This ecological, cross-sectional study cannot support causal inference and may be subject to ecological fallacy. Measurement error is also possible because U5MR was indirectly estimated, although the Trussell method is a well-established demographic approach [22]. In addition, unmeasured district-level factors (e.g., quality of local governance and cultural practices) may partly explain the observed associations. Future research should use longitudinal or multilevel designs to assess within-district trends and examine the effects of decentralization policies on child survival. Qualitative research exploring mechanisms underlying spatial spillovers could further clarify how geographic proximity shapes mortality outcomes.

In conclusion, the U5MR in Indonesia reflects persistent socioeconomic and spatial disparities in health outcomes. Fertility, maternal education, and regional inequality remain central determinants, particularly in the eastern provinces. Using subnational and spatial analyses, this study shows that child mortality is shaped not only by local conditions but also by spillover effects from neighboring regions. While national progress has been substantial, achieving SDG 3.2 requires equity-oriented strategies that integrate reproductive health, women education, and geographically targeted investments. Future policies must prioritize disadvantaged districts and address structural and spatial barriers to ensure that reductions in child mortality are both inclusive and sustainable.

Data Availability

Supplemental Materials

Notes

Conflict of Interest

The authors have no conflicts of interest associated with the material presented in this paper.

Funding

None.

Acknowledgements

The author thanks Statistics Indonesia (Badan Pusat Statistik, BPS) for providing open-access district-level demographic and economic data used in this study.

Author Contributions

All work was done by Hermawan A.

Figure 1

Provincial distribution of under-five mortality rate (U5MR) per 1000 live births, Indonesia, 2020.

Figure 2

Bivariate associations between under-five mortality rate (U5MR) and key socioeconomic and demographic factors, Indonesia, 2020. (A) Mean years of schooling; (B) GRDP per capita; (C) Poverty rate; and (D) TFR. Solid lines represent fitted linear regression estimates. GRDP, gross regional domestic product; IDR, Indonesian rupiah; TFR, total fertility rate.

Figure 3

Marginal effect of poverty rate on under-five mortality rate (U5MR), by districts and municipalities, Indonesia, 2020. Ordinary least squares robust regression estimates; 95% confidence interval omitted for clarity.

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References

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