Abstract
Background
This article examines the association between population density, maternal mortality, and under-5 mortality in countries throughout the world, as well as the mediating impacts of the Universal Health Coverage Service Coverage Index (UHC-SCI).
Methods
The World Health Organization’s website provided data on maternal mortality and the Universal Health Coverage Service Coverage Index for the years 2000–2020. The World Bank database included information on population density and under-5 mortality rates for nations between 2000 and 2020. Panel regressions were used to examine the association between population density and maternal and under-5 mortality in each nation, as well as the mediating influence of the Universal Health Coverage Service Coverage Index, while accounting for economic, environmental, and medical factors. Finally, data is divided into regressions based on World Bank member countries’ income levels to examine heterogeneity.
Results
The study included 175 countries and found a significant negative correlation between population density, maternal mortality, and under-5 mortality (B = -1.015, -1.146, P < 0.05). The Universal Health Coverage Service Coverage Index mediated this relationship (B = -1.044, -1.141, P < 0.05).
Conclusions
Increasing population density in countries around the world has helped to reduce maternal and child mortality. As population density has increased, so has the level of the Universal Health Coverage Service Coverage Index, which has proven effective in lowering maternal and under-5 mortality. Governments should plan interventions to build basic health facilities and allocate resources to health services based on population density, level of economic development, and the current state of their health systems, with the goal of stabilizing the rate of change in maternal and under-5 mortality and, eventually, achieving the Sustainable Development Goals.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-025-23217-x.
Keywords: Density of population, Maternal mortality rate, Child mortality rate, Universal Health Coverage Service Coverage Index, Sustainable development goals
Background
The World Health Organization (WHO) has long sought to improve the health of maternal and children under five. The Sustainable Development Goals (SDGs) include it [1]. Data reveal that the worldwide maternal mortality ratio (MMR) fell by roughly 34% between 2000 and 2020; however, about 287,000 women will still die in 2020 during or after pregnancy and childbirth. There has also been a remarkable 59% drop in under-5 mortality (U5MR) worldwide since 1990, but development has halted dramatically since 2015 [2]. This demonstrates that there has been some progress in maternal and child health worldwide, but there are still many issues. As a result, the decrease in MMR and U5MR has been a hot topic in contemporary research.
High population densities, frequently associated with resource limits, environmental pollution, and increased pressure on public services, have been shown to increase the risk of disease and hurt the physical health of populations [3–6]. Pregnant women and children under five are important WHO priority groups, and their health is a major social issue that has a direct impact on achieving certain targets in the Sustainable Development Goals by 2030 [7]. Relevant studies have shown that economic development, medical conditions, and environmental pollution are important factors affecting MMR and U5MR. However, no research has been conducted on the impact of population density on MMR and U5MR. The impact of complicated issues, including healthcare, economic situations, and environmental pollution, is therefore taken into account globally in this paper, which also thoroughly examines the association between population density and MMR and U5MR in different nations.
WHO has designed the Universal Health Coverage Service Coverage Index (UHC-SCI) as a core SDG 3.8.1 to promote the health and well-being of the global population and improve overall population health [1]. This goal aims to ensure that everyone can access the health services they need without falling into financial hardship due to payment. Research indicates that increasing population density can promote the aggregation of medical resources, optimize service supply efficiency, and thereby enhance the level of health service coverage [8]. Higher levels of health service coverage can significantly improve health conditions through early screening, timely intervention, and continuous care [9–12]. As a result, this study hypothesizes that differences in population density among nations may affect maternal and under-5 child health by the level of UHC-SCI.
Based on this hypothesis, this study analyzes the impact of population density on the health of pregnant women and children under five and the mediating role of UHC-SCI while controlling for complex factors such as economic conditions, healthcare, and environmental pollution. Last but not least, this paper will look into how changes in population density affect the health of maternal and children under five in nations with different income levels. This will help us create health policies that address MMR and U5MR in each nation, meet the SDGs, and advance global health improvement.
Methods
Study design
The association between population density and MMR and U5MR across nations worldwide for the years 2000–2020 is examined in this work using a population-based retrospective investigation. The mediating effect of the UHC-SCI in this relationship is also examined, as previously mentioned. A total of 192 United Nations member states had their population and health statistics collected; 17 nations with missing sample data were eliminated, leaving 175 countries remaining.
Index selection and data source
Maternal mortality and under-5 mortality rate: dependent variables
Maternal mortality and the under-5 mortality rate are dependent variables. The mortality indicator is used in this paper to measure maternal and child health because it is a crucial metric for the WHO to evaluate health status, which not only indicates the degree of medical care development in a nation or region but also gauges that nation or region’s socioeconomic development. The World Bank database provided the U5MR data for this investigation [13], whereas the WHO database provided the pertinent MMR data [2].
Density of population: independent variable
The population density is the independent variable. Land area is defined as the entire extent of a nation’s territory, excluding interior rivers, claimed continental shelf, and exclusive economic zones. The indicator is computed as mid-year population/land area in square kilometers. This information comes from the World Bank database.
Universal Health Coverage Service Coverage Index: mediator variable
The Universal Health Coverage Service Coverage Index is the mediator variable. This index will serve as a typical measure of the extent of Universal Health Coverage Service Coverage in this study. The indicator is divided into four categories: reproductive, maternal, newborn, and child health (RMNCH); infectious diseases (Infectious); noncommunicable diseases (NCDs); and service capacity and access (Capacity) [14]. It refers to the average coverage of essential services based on tracking interventions among the general population and the most vulnerable populations. The indicator is composed of 14 composite indicators integrated by geometric weighting, and it is reported on a unitless scale from 0 to 100. A higher index value means that the nation is doing better in terms of universal healthcare service coverage. This indicator is derived from WHO SDG 3.8.1, which was released every two years after 2015 and every five years before 2015. This work employs two techniques, regression filling and linear interpolation, respectively, to fill in the missing values in other data [15–16].
Linear interpolation is suitable for scenarios where data points are evenly distributed and exhibit a linear trend. The known data points (2000, 2005, 2010, 2015, 2017, 2019) meet these conditions, so this study first used linear relationships to estimate some missing data. The regression-based imputation rule is a more complex interpolation method, suitable for handling multidimensional data or nonlinear trend variations. Considering that data from certain years may exhibit nonlinear changes or other complex patterns, this study also applies this method to further enhance the accuracy of data imputation.
Compared to other imputation methods, linear interpolation is the officially recommended method by UHC-SCI, which is more suitable for the characteristics of the data in this study. Regression-based imputation, on the other hand, can utilize multiple regression models to quantify the relationships between variables, improve the imputation’s accuracy, and compensate for the limitations of linear interpolation in handling complex data. It can also use significance tests to confirm the imputation’s reliability and provide a more reasonable and scientific prediction for missing values. The combined application of the two methods has the advantages of adhering to research standards, being easy to operate, and being computationally efficient. It is the optimal choice for the characteristics and research needs of UHC-SCI data, providing reliable support for subsequent analysis.
Covariates
Due to the complexity of the factors influencing global MMR and U5MR, this paper synthesizes the findings of pertinent research and concludes that maternal and child mortality are influenced by the economy, healthcare, and environment. As a result, it chooses representative indicators as control variables in these three areas (Table 1), with data taken from the World Bank’s official website.
Table 1.
Variable definitions and sources
| Indicator | Definition | Unit | Source |
|---|---|---|---|
| GDP per capita | GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. | current U.S. dollars | World Bank |
| Current health expenditure per capita | Current expenditures on health per capita in current US dollars. Estimates of current health expenditures include healthcare goods and services consumed during each year. | current U.S. dollars | World Bank |
| PM2.5 air pollution, mean annual exposure | Population-weighted exposure to ambient PM2.5 pollution is defined as the average level of exposure of a nation’s population to concentrations of suspended particles measuring less than 2.5 microns in aerodynamic diameter, which are capable of penetrating deep into the respiratory tract and causing severe health damage. Exposure is calculated by weighting mean annual concentrations of PM2.5 by population in both urban and rural areas. | micrograms per cubic meter | World Bank |
GDP per capita was chosen for this study as a representative measure of economic development. According to research, people who live in nations with more developed economies typically earn more money, which encourages them to make investments in their health and so enhance it [17]. Using maternal and child mortality as an example, pertinent researchers have demonstrated that economic factors are a substantial determinant of mortality and that MMR and U5MR vary greatly between nations or regions [18].
For this study, current per capita health spending was chosen as a representative measure of medical conditions. Maternal and children can receive prompt and efficient diagnosis and treatment from well-equipped medical facilities and a well-established health system, which lowers waiting times and the chance that the disease will worsen as a result of insufficient funding. Medical problems play a significant role in lowering maternal and child mortality, according to research by scholars like Solange Mianda [19].
As a representative measure of environmental pollution, PM2.5 air pollution (mean annual exposure) was chosen for this investigation. According to macro studies, human health may suffer as a result of environmental degradation [20]. The possible effect of airborne pollution on maternal and infant mortality has been demonstrated by several studies [21–22]. Controlling environmental risk factors is therefore essential to lowering maternal and infant mortality.
Data analysis
The study begins with a panel regression of the data using the statistical program Stata 17.0 to examine the relationship between the independent and dependent variables, as well as the function of the mediating variables, under the assumption of constant control variables. Second, to check for multicollinearity among the model’s independent variables, run a VIF test on the data. Scholar Stoltzfus’s research suggests that multicollinearity is absent if VIF is less than 10 [23]. To guarantee the results’ reliability, robustness tests were performed on the data, followed by a heterogeneity analysis using the grouped regression approach.
Results
Descriptive statistics
Trend analysis of population density
This survey includes 175 nations, with 51 in Africa (29.1%), 41 in Europe (23.4%), 42 in Asia (24.0%), 21 in North America (12.0%), 11 in South America (6.2%), and 9 in Oceania (5.1%). Asia has a varied data distribution, with the median population density at its greatest value, according to an analysis of the distribution of population density data and changes in population density per continent for the years 2000–2020. In contrast to South America, where the median population density is at its lowest, the data distribution is very stable. According to trends, Fig. 1 illustrates how population density is gradually rising throughout all continents.
Fig. 1.
Distribution of and trends in population density by continent, 2000–2020
Trend analysis of maternal mortality and under-5 mortality
From 2000 to 2020, the distribution and trends in MMR and U5MR were examined by continent. The findings indicate that data distribution varies across Africa, with the median MMR and U5MR at their greatest levels. The data distribution is very consistent when compared to Europe, with the median MMR and U5MR at their lowest values, as shown in Fig. 2. In terms of trends, the MMR is declining across all continents, but it may have been influenced by the Corona Virus Disease 2019, which caused a modest spike in Europe, North America, and South America in 2020. At the same time, the MMR in Africa remains high, although it has decreased dramatically since 2000. The U5MR values followed the same decreasing trend across all continents, indicating that the continents have made significant progress in their ongoing child health efforts, as illustrated in Fig. 3.
Fig. 2.
Distribution of maternal mortality and under-5 mortality rate by continent, 2000–2020
Fig. 3.
Trends in maternal mortality and under-5 mortality rate by continent, 2000–2020
Trend analysis of the relationship between population density and maternal mortality and under-5 mortality
We further group the 175 nations by continent and income level to examine the association between population density and MMR and U5MR, as shown in Figs. 4 and 5. The findings are displayed in Figs. 4 and 5. According to the research, the relationships between population density and MMR and U5MR for nations on different continents and income levels vary significantly. The majority of Asia and European nations exhibit lower MMR and U5MR and higher population densities. African nations, on the other hand, typically have high MMR and U5MR but low population densities. Low MMR and U5MR typically accompany low population densities in North America, South America, and Oceania. While high-income and upper-middle-income regions of a country have low population densities and low MMR and U5MR, lower-middle-income and low-income countries typically have low population densities and high MMR and U5MR. However, some countries exhibit high population density alongside low MMR and U5MR.
Fig. 4.
Relationship between population density and maternal mortality and under-5 mortality rates (by continental grouping)
Fig. 5.
Relationship between population density and maternal mortality and under-5 mortality rates (by income level)
Panel regression model results
First, due to the spatial differences in panel data, this paper will conduct a variance analysis of MMR and U5MR data by country and year. The results indicate that the differences in mortality rates primarily stem from country differences. Therefore, an individual fixed effects model will be applied to perform panel regression on the data, effectively controlling for time-invariant confounding factors at the country level to address potential endogeneity issues. Second, the VIF test is performed to evaluate the data, and the results show that the expansion factors have mean values less than 10, indicating that the model is free of multicollinearity. This paper avoids multicollinearity among the independent variables. To determine the optimal panel model, the variables in the study were subjected to individual effects, Hausman, and analysis of covariance tests, respectively. Table 2 presents the specific test results.
Table 2.
Panel model category test results
| Maternal mortality | Under-5 mortality | |||
|---|---|---|---|---|
| statistics | P-value | statistics | P-value | |
| F-test | 504.00 | < 0.001 | 302.64 | < 0.001 |
| LM-test | 30487.36 | < 0.001 | 27677.35 | < 0.001 |
| Hausman-test | 153.05 | < 0.001 | 423.97 | < 0.001 |
Both MMR and U5MR were fitted as fixed-effects models based on the findings of the correlation screening of the independent variables in the preceding section. Table 3 displays the estimated results. Increased population density could successfully lower MMR and U5MR and improve the health of this population, according to the fixed-effects model, which also revealed a negative correlation between population density and both MMR and U5MR (B = -1.015, -1.146, P < 0.05).
Table 3.
Fixed effect model density of population regression coefficient table
| Maternal mortality | Under-5 mortality | |||
|---|---|---|---|---|
| B | P-value | B | P-value | |
| Density of population | -1.015 | < 0.001 | -1.146 | < 0.001 |
| GDP per capita | -7.97e-06 | < 0.001 | -2.79e-06 | < 0.001 |
| Current health expenditure per capita | -0.000 | 0.010 | -0.000 | < 0.001 |
| PM2.5 air pollution, mean annual exposure | 0.007 | < 0.001 | 0.007 | < 0.001 |
| cons | 8.273 | < 0.001 | 7.786 | < 0.001 |
| sigma_u | 1.822 | 1.621 | ||
| sigma_e | 0.238 | 0.188 | ||
| rho | 0.983 | 0.987 | ||
| Within R² | 0.316 | 0.461 | ||
| Obs | 3629 | 3629 | ||
| N | 175 | 175 | ||
To confirm that increasing population density significantly lowers MMR and U5MR, this study also creates scatter plots illustrating the relationship between population density and MMR and U5MR, as seen in Fig. 6.
Fig. 6.
Scatterplot of the relationship between population density and maternal mortality and under-5 mortality rate
Intermediate effect test
First, this study uses the stepwise regression coefficient method to explore whether the level of universal health service coverage mediates the relationship between population density and maternal mortality, as well as under-5 child mortality. Models 1, 2, and 3 together form the mediation effect equation group. In Tables 4 and 5, Model 1 is a regression of population density with MMR and U5MR as explanatory variables. The results reveal that increasing population density has a favorable effect on reducing MMR and U5MR (B = -0.836, -0.829, P < 0.05). In Model 2, UHC-SCI is used as the dependent variable to regress against population density, and the results in the table all indicate that increasing population density is beneficial for improving UHC-SCI levels (B = 0.472, P < 0.05). In Model 3, the mediating effects of UHC-SCI on reducing MMR and U5MR when increasing population density were verified separately. The results showed that the regression coefficients were both negative (B = -1.044, -1.141, P < 0.05), indicating that the mediating effects of UHC-SCI are significantly present.
Table 4.
Intermediate effect analysis (1)
| Variables | Model 1 | Model 2 | Model 3 |
|---|---|---|---|
| Maternal mortality | UHC Service Coverage Index | Maternal mortality | |
| Density of population | -0.836*** | 0.472*** | -0.045 |
| (-26.99) | (34.97) | (-1.43) | |
| UHC Service Coverage Index | -1.044*** | ||
| (-36.85) | |||
| GDP per capita | -0.000*** | 0.000*** | -0.000*** |
| (-7.46) | (5.00) | (-7.85) | |
| Current health expenditure per capita | -0.000** | 0.000 | -0.000** |
| (-2.52) | (0.88) | (-2.15) | |
| PM2.5 air pollution, mean annual exposure | 0.008*** | -0.005*** | 0.004*** |
| (7.57) | (-9.63) | (4.47) | |
| Constant | 7.512*** | 2.111*** | 8.454*** |
| (46.64) | (33.09) | (65.32) | |
| Observations | 3,629 | 3,629 | 3,629 |
| N | 175 | 175 | 175 |
Note: z-statistics in parentheses
*** p < 0.01, ** p < 0.05, * p < 0.1
Table 5.
Intermediate effect analysis (2)
| Variables | Model 1 | Model 2 | Model 3 |
|---|---|---|---|
| Under-5 mortality | UHC Service Coverage Index | Under-5 mortality | |
| Density of population | -0.829*** | 0.472*** | -0.063*** |
| (-34.71) | (34.97) | (-3.24) | |
| UHC Service Coverage Index | -1.141*** | ||
| (-61.84) | |||
| GDP per capita | -0.000*** | 0.000*** | -0.000*** |
| (-5.61) | (5.00) | (-5.53) | |
| Current health expenditure per capita | -0.000*** | 0.000 | -0.000*** |
| (-6.72) | (0.88) | (-8.11) | |
| PM2.5 air pollution, mean annual exposure | 0.009*** | -0.005*** | 0.004*** |
| (10.67) | (-9.63) | (6.88) | |
| Constant | 6.447*** | 2.111*** | 7.890*** |
| (54.72) | (33.09) | (95.57) | |
| Observations | 3,629 | 3,629 | 3,629 |
| N | 175 | 175 | 175 |
Note: z-statistics in parentheses
*** p < 0.01, ** p < 0.05, * p < 0.1
Second, to address the weak statistical validity of the regression coefficient test, this work uses the Sobel-Goodman test and the Bootstrap test to determine the mediating influence of UHC-SCI on the link between population density, MMR, and UM5R. The Sobel-Goodman test, which is based on normal distribution theory, uses a standardized test statistic to assess the significance of the mediating effect.|Z| > 1.96 and P < 0.05 indicate a substantial mediating effect. The bootstrap test, on the other hand, entails repeated sampling with sample replacement, 1000 bootstrap iterations, and the creation of a non-parametric confidence interval for the mediation effect with a 95% confidence level. If the confidence interval does not include 0, it indicates that the mediation effect is significant (Tables 6 and 7). As a result, when population density increases, so does the level of UHC-SCI, which helps to reduce MMR and U5MR.
Table 6.
Sobel test
| Maternal mortality | Under-5 mortality | |||||||
|---|---|---|---|---|---|---|---|---|
| B | S.E | Z | P-value | B | S.E | Z | P-value | |
| Indirect effect | -0.0412 | 0.0079 | -5.2420 | < 0.001 | -0.0352 | 0.0056 | -6.2707 | < 0.001 |
| Direct effect | -2.7787 | 0.0454 | -61.2026 | < 0.001 | -1.8629 | 0.0257 | -72.5488 | < 0.001 |
| Total effect | -2.8199 | 0.0453 | -62.1865 | < 0.001 | -1.8982 | 0.0259 | -73.3579 | < 0.001 |
Note: When |Z| > 1.96 and P < 0.05, the mediating effect can be considered significant
Table 7.
Bootstrap text
| Maternal mortality | Under-5 mortality | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| B | S.E | Z | P-value | LLCI | ULCI | B | S.E | Z | P-value | LLCI | ULCI | |
| Indirect effect | -0.0412 | 0.0070 | -5.85 | <0.001 | -0.0550 | -0.0274 | -0.0352 | 0.0048 | -7.26 | <0.001 | -0.0447 | -0.0257 |
| Direct effect | -2.7787 | 0.0440 | -63.11 | <0.001 | -2.8650 | -2.6924 | -1.8629 | 0.0254 | -73.10 | <0.001 | -1.9129 | -1.8130 |
Note: LL = low limit, UL = upper limit, CI = confidence interval, The bootstrap 95% confidence interval does not include 0, indicating a significant effect
Robustness test
To test for endogeneity, this study first employs the lagged independent variable. The bidirectional causality dilemma is a typical endogeneity problem. Regressing the lagged independent variable strategy minimizes the potential bidirectional causation problem by significantly reducing the influence of the current period. The findings are in line with the fixed-effects regression results and are shown in Table 8’s column (1). This work uses a single lag to treat population density. At the 1% significance level, the population density and MMR and U5MR regression coefficients are − 0.987 and − 1.081, respectively. This suggests that increasing population density is associated with a decrease in MMR and U5MR.
Table 8.
Robustness test
| Maternal mortality | Under-5 mortality | |||
|---|---|---|---|---|
| (1) | (2) | (1) | (2) | |
| Density of population | -0.987*** | -0.982*** | -1.081*** | -1.102*** |
| (0.0339) | (0.0329) | (0.0272) | (0.0271) | |
| GDP per capita | -8.25e-06*** | -1.17e-05*** | -6.41e-06*** | -5.83e-06*** |
| (1.32e-06) | (1.56e-06) | (1.28e-06) | (1.29e-06) | |
| Current health expenditure per capita | -3.56e-05*** | -1.66e-05 | -6.80e-05*** | -6.94e-05*** |
| (1.36e-05) | (1.57e-05) | (1.31e-05) | (1.29e-05) | |
| PM2.5 air pollution, mean annual exposure | 0.00571*** | 0.00632*** | 0.00707*** | 0.00812*** |
| (0.00104) | (0.00103) | (0.000837) | (0.000845) | |
| Constant | 8.164*** | 8.169*** | 7.532*** | 7.609*** |
| (0.145) | (0.140) | (0.116) | (0.116) | |
| Observations | 3,458 | 3,629 | 3,458 | 3,629 |
| N | 175 | 175 | 175 | 175 |
| R-squared | 0.306 | 0.325 | 0.442 | 0.451 |
Note: Standard errors in parentheses
*** p < 0.01, ** p < 0.05, * p < 0.1
Second, this study reduced the range of treatments by adjusting the sample size. According to the results of the fixed effects regression, population density is still substantially inversely correlated with MMR and U5MR, suggesting that the results are reliable. To mitigate the effect of discrete values on the robustness of the study’s conclusions, all continuous variable data undergo 1% quantile up and down shrinkage, which eliminates observations that fall outside of the continuous variables’ one% and ninety-nine percentiles. Column (2) of Table 8 displays the outcomes after changing the shrinking therapy.
Heterogeneity analysis
Considering the World Bank’s classification of nations by income level, the study divides 175 countries into four income groups: high, upper middle, lower middle, and low. 55 countries have high incomes, 48 have upper-middle incomes, 49 have lower-middle incomes, and 23 have low incomes. Assuming that the control variables remain constant, group regression analyses of the model are used in this study to evaluate the relationship between the dependent and independent variables at different income levels. The results show that the empirical analyses in this work are reliable and valid, and they generally agree with past panel regression findings indicating population density is negatively related to MMR and U5MR across nations of all economic levels. Increasing population density in lower-middle-income countries and low-income countries was found to be significantly more effective in reducing MMR and U5MR than in high-income and upper-middle-income countries, as shown in Tables 9 and 10.
Table 9.
Maternal mortality heterogeneous outcome
| Variables | Maternal mortality | |||
|---|---|---|---|---|
| High income | Upper middle income | Lower middle income | Low income | |
| Density of population | -0.447*** | -0.333*** | -1.018*** | -0.967*** |
| (0.0675) | (0.0987) | (0.0631) | (0.0514) | |
| GDP per capita | -1.01e-05*** | -6.31e-05*** | -0.000147*** | -1.83e-05 |
| (1.54e-06) | (4.57e-06) | (1.76e-05) | (3.26e-05) | |
| Current health expenditure per capita | 2.04e-05 | -0.000190*** | 0.000624** | -0.00225*** |
| (1.62e-05) | (2.87e-05) | (0.000276) | (0.000635) | |
| PM2.5 air pollution, mean annual exposure | 0.0309*** | 0.00515*** | -0.00102 | -0.00133 |
| (0.00336) | (0.00195) | (0.00127) | (0.00167) | |
| Constant | 3.955*** | 5.523*** | 9.869*** | 10.13*** |
| (0.304) | (0.393) | (0.263) | (0.203) | |
| Observations | 1,155 | 999 | 1,016 | 459 |
| N | 55 | 48 | 49 | 23 |
| R-squared | 0.290 | 0.415 | 0.529 | 0.668 |
Note: Standard errors in parentheses
*** p < 0.01, ** p < 0.05, * p < 0.1
Table 10.
Under-5 mortality heterogeneous outcome
| Variables | Under-5 mortality | |||
|---|---|---|---|---|
| High income | Upper middle income | Lower middle income | Low income | |
| Density of population | -0.0967** | -1.017*** | -1.343*** | -1.213*** |
| (0.0426) | (0.0777) | (0.0482) | (0.0437) | |
| GDP per capita | -5.25e-06*** | -5.00e-05*** | -7.09e-05*** | -2.69e-05 |
| (9.69e-07) | (3.60e-06) | (1.35e-05) | (2.76e-05) | |
| Current health expenditure per capita | -5.43e-05*** | -0.000124*** | -0.000476** | -0.00204*** |
| (1.02e-05) | (2.26e-05) | (0.000211) | (0.000539) | |
| PM2.5 air pollution, mean annual exposure | 0.0343*** | 0.00545*** | 0.000583 | -0.00116 |
| (0.00212) | (0.00154) | (0.000970) | (0.00142) | |
| Constant | 1.881*** | 7.281*** | 9.869*** | 9.402*** |
| (0.192) | (0.309) | (0.201) | (0.172) | |
| Observations | 1,155 | 999 | 1,016 | 459 |
| N | 55 | 48 | 49 | 23 |
| R-squared | 0.489 | 0.544 | 0.710 | 0.804 |
Note: Standard errors in parentheses
*** p < 0.01, ** p < 0.05, * p < 0.1
Discussion
This study seeks to evaluate the relationship between population density and the health of pregnant women and children under the age of five, as well as whether UHC-SCI plays an intermediary role in this association. According to the study’s findings, population density and both MMR and U5MR were negatively correlated, with UHC-SCI serving as a substantial mediating element in each relationship.
This study found that higher population density significantly decreased MMR and U5MR. Investment in healthcare infrastructure, medical professionals, and transportation networks may explain this phenomenon. First, several scholars established that as population density rises, so does the need for medical services, compelling the government to increase its investment in medical infrastructure [24–26]. This includes hospitals, clinics, and maternal and child health centers, resulting in a more densely packed network of medical services [27–28], allowing for more efficient resource sharing and collaboration. It makes it easier for pregnant women to access services such as antenatal check-ups and pregnancy health counseling, which reduces the risks of pregnancy and lowers the MMR. Additionally, when a rare disease requires referral to a higher level of treatment for children under five, health constraints due to geographic accessibility difficulties can be avoided [29–30]. However, increased investment in medical infrastructure may result in uneven resource distribution. Even if the overall number of medical facilities increases, some areas within densely populated regions may still not fully benefit.
Secondly, in terms of medical professionals, Daniel and other scholars have found that high population density provides people with more employment opportunities and career development space and is more likely to attract high-level medical professionals [31–33]. Specialist teams can give consultation on challenging pediatric disorders and complex maternal diseases, increasing diagnosis accuracy and treatment success rates and lowering mortality rates. However, after attracting high-level medical personnel, they may encounter issues such as collaboration and friction within medical teams, as well as brain drain, which can have an impact on the effectiveness of diagnosis and treatment. Third, some researchers have stated that places with high population density often have more established transportation networks [34]. Good transportation conditions help ensure that pregnant women and children under the age of five receive timely medical care when they become ill, lowering their chances of dying. But during peak hours, traffic congestion can be quite severe, affecting the timeliness of emergency medical transport.
Furthermore, it may be said that UHC-SCI mediates the relationship between MMR and U5MR and population density. MMR and U5MR can be efficiently decreased in the event of an increase in population density by increasing the degree of universal health coverage. In line with the findings of this study, related researchers have demonstrated that a higher population density has a positive impact on the degree of service coverage in favor of public health services [4, 8]. The coverage effect of health services is more noticeable when health promotion, prevention, treatment, rehabilitation, and palliative care services are offered to high population densities.
On the one hand, this might be because high population densities allow healthcare providers to more precisely identify population concentrations and facilitate the planning of frequent maternal health seminars on diet, exercise, and mental health during pregnancy, as well as prenatal checkups. They also make it easier to set up vaccination stations to boost the vaccination rate of children under five and prevent related infectious diseases, which in turn lowers the mortality rate of these children. On the other hand, a high population density will increase social interactions, foster greater cohesion, and facilitate the faster and wider dissemination of health information [35–36]. Health knowledge can be quickly spread throughout the population through a variety of channels, including the media, school education, and community activities. This helps to increase the coverage of health and hygiene services for the entire population, improve the knowledge literacy of pregnant women, and lower the risk of death for children under five.
The present study finds that the effect of increased population density on reducing maternal mortality and under-5 mortality is more pronounced in low-income and lower-middle-income countries than in high-income and upper-middle-income countries. Differences in the level of economic development and healthcare resources in different income countries may explain this phenomenon. In terms of economic development, lower-middle-income and low-income countries’ relatively low levels of economic development may encourage local economic growth as labor resources are concentrated and population density rises [37]. This is favorable for attracting more financial investment in the area of maternal and under-5 child health, which will more effectively lower their mortality rates. In contrast, high-income and upper-middle-income countries have higher levels of socio-economic development [38–39], more adequate economic support for the provision of maternal and under-5 health services, and relatively stable social assistance systems and economic investment in health. As a result, increases in population density have had a relatively limited effect on reducing MMR and U5MR.
The healthcare resource base in lower-middle-income and low-income countries is weak [40–41], and because of their low population densities, it is challenging for the limited resources to cover a large geographic area, with certain regions lacking basic medical equipment and specialized healthcare personnel. When population density increases, it is easier to achieve scale effects in the supply of healthcare resources. Concurrently, the government and social forces can focus more on allocating funds for hospital construction and medical staff training, which will facilitate access to healthcare for expectant maternal and children under five and have a greater impact on lowering death rates. High-income and upper-middle-income nations, on the other hand, have highly developed infrastructures, abundant healthcare resources, and high levels of healthcare conditions [42–44]. For instance, in several high-income nations, rural areas have built primary health care systems [45], and, despite lower population densities, access to healthcare is greater for mothers and children under five. Consequently, the effect of increased population density on reducing maternal and under-five mortality is relatively small.
As a result, it is advised that governments create health policies based on population density to maximize healthcare effectiveness while lowering MMR and U5MR. MMR and U5MR reductions should be prioritized in public health policy, especially in low- and middle-income nations. To further reduce MMR and U5MR, governments should continue to increase universal health coverage, service provision, risk assessment, early intervention, and long-term management of maternal and child health. The study also advises that future research should focus on the optimal population density criteria for countries looking to enhance maternal and under-5 child health.
Limitations
The factors affecting MMR and U5MR are very complex in terms of data collection, and this study does not fully account for policy, legal, socio-cultural, and other factors in addition to economic, environmental, and medical conditions. There may be other influencing factors that have been left out of this paper. Second, due to disparities in economic development levels and medical conditions between countries, some low-income countries and areas with poor healthcare may lack essential data or have significant gaps. This paper examines the present database, which may undervalue particular linkages in geographically isolated places, resulting in selection bias and data quality difficulties.
Furthermore, there can be variations in the methods and reporting of maternal and under-5 mortality data among nations, and measurement mistakes, including underreporting and misreporting, could compromise the validity of the study’s findings. This study was unable to investigate the particular elements of the UHC-SCI that had the greatest impact on its mediating function. The most recent data in some of the final databases may be some time away from the present and may not reflect the current situation.
Conclusions
Population density and MMR and U5MR at the global level were revealed to be statistically significantly correlated in this study, along with the mediating function of UHC-SCI. UHC-SCI continued to rise as population density rose, but MMR and U5MR both displayed a declining tendency. This finding provides favorable evidence for countries to further increase population densities and determine suitable density thresholds, which can help guide policy and decision-makers to better understand and address the challenges of MMR and U5MR.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
Thank you Professor Kang for your guidance on this article.
Author contributions
ZK took overall responsibility for the study design. XJ, JX, FC, XZ were responsible for data analysis and manuscript writing. DW, WY, LC applied and obtained the research data. FX, LW help with data interpretation and manuscript writing. BA, GT made the charts and participated in the manuscript revision. All authors critically reviewed and revised the manuscript, and approved the final manuscript.
Funding
This study was funded by the National Natural Science Foundation of China (72074064, 71573068). The funding body had no influence on study design, data collection, data analysis, data interpretation or writing the manuscript.
Data availability
The data is available in a public open access repository. The data used in the study is publicly available; These publicly available datasets may have been described in the manuscript.
Declarations
Ethics approval and consent to participate
We confirm that all methods were carried out according to relevant guidelines and regulations. We are not involved in experiments on humans and/or the use of human tissue samples. Ethics approval for the study protocol was obtained from the Ethics Committee of Harbin Medical University. Informed consent was obtained from all participants through online responses before the start of the survey. The Ethics Committee of Harbin Medical University approved the procedure for obtaining informed consent.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Xinyan Jiang and Jinpeng Xu contributed equally to this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data is available in a public open access repository. The data used in the study is publicly available; These publicly available datasets may have been described in the manuscript.