Macroeconomic Determinants of Maternal and Neonatal Mortality: A Global Panel Analysis

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Despite recognition of macroeconomic influences on health outcomes, longitudinal cross-country evidence remains limited. Methods: This study analyzed panel data from 152 countries between 1991 and 2023 using World Health Organization and World Bank datasets. Dependent variables were MMR (per 100 000 live births) and NMR (per 1 000 live births). Independent variables included gross domestic product (GDP) per capita, inflation rate (IR), and unemployment rate (UR). Descriptive statistics, correlation analyses, the Levin–Lin–Chu unit root test, the Breusch–Pagan LM test, and the Hausman specification test guided model selection. Fixed and random effects regressions with robust standard errors were applied. Results: GDP per capita was inversely associated with both MMR (β = − 0.445, p < 0.001) and NMR (β = − 0.423, p < 0.001). Inflation showed a positive association with MMR (β = 0.035, p = 0.016) and NMR (β = 0.034, p = 0.004), while unemployment had no global effect. Regional heterogeneity was observed: in Africa, unemployment increased both MMR and NMR; in Europe, inflation correlated positively with mortality; and in Asia, the GDP–MMR relationship was strongest. Conclusion: Macroeconomic stability is crucial for improving maternal and neonatal survival. Sustained growth, inflation control, and labor market interventions—particularly in low-income regions—are essential for reducing mortality. Integrating macroeconomic and health policies could mitigate structural inequities and enhance global health outcomes. Clinical trial number: Not applicable. Maternal mortality neonatal mortality global health socioeconomic factors gross domestic product Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Global health indicators are essential measures of societal development and population well-being. Maternal and neonatal health is closely linked not only to increased life expectancy but also to broader developmental outcomes, including education and economic growth ( 1 , 2 ). The maternal mortality ratio (MMR)—defined as deaths occurring during pregnancy or within 42 days of termination from causes related to pregnancy—is a key indicator of health system performance. Globally, an estimated 800 women die every day from largely preventable causes, with more than 95% of these deaths occurring in low-income and middle-income countries ( 3 , 4 ). In 2020, the MMR in Africa was 531 per 100 000 live births, reaching as high as 1 223 in South Sudan ( 3 ). Similarly, the neonatal mortality rate (NMR)—defined as the number of deaths within the first 28 days of life per 1 000 live births—is a key indicator of health system performance. In 2022, an estimated 2.3 million newborns died, representing 47% of all under-5 child deaths ( 3 ). In sub-Saharan Africa, the NMR was 27 per 1 000 live births, 11 times higher than in low-mortality regions. These disparities are strongly associated with socioeconomic conditions, health system capacity, and inequities in access to quality neonatal care across countries ( 5 , 6 ). Macroeconomic indicators such as income level, unemployment, and public health expenditure are major determinants of health system capacity and access to care ( 7 , 8 ). Economic growth and public health investment have been associated with reductions in mortality ( 9 ), whereas inadequate resource allocation and lack of insurance coverage can increase maternal and neonatal deaths ( 10 , 11 ). In contexts with low social capital, restricted access to essential health services further exacerbates inequalities ( 12 ). Long-term, multi-country studies examining maternal and neonatal mortality in relation to macroeconomic conditions remain scarce. This study analyses the effects of inflation, unemployment, and GDP per capita on maternal and neonatal mortality, using panel data from 152 countries between 1991 and 2023. A cross-continental comparative approach was adopted to assess the role of socioeconomic inequalities in shaping outcomes. A limitation of the study is the exclusion of exchange rate fluctuations and external debt, which affect health financing and stability but lack consistent data across all settings. Future research could address these gaps with region-specific data sources. METHODS Study design This study is a comparative analysis based on country-level secondary data, applying panel linear regression methods. It does not involve experimental intervention but relies on macro-level observational data derived from publicly available statistical sources. Study population and sample This study included all countries that regularly reported maternal and neonatal mortality data between 1991 and 2023. Although the analysis was undertaken in 2025, 2023 was the latest year with WHO data available. A total of 152 countries (Appendix 1) with complete and comparable health and macroeconomic data were included. Inclusion and exclusion criteria Countries with complete annual data on maternal and neonatal mortality and key macroeconomic indicators (inflation, unemployment, GDP per capita) from 1991 to 2023, with statistics available in both the World Bank and WHO databases, and with at least 33 consecutive years of data were included. Countries with missing, inconsistent, or fragmented data limited to specific years were excluded to ensure construction of a balanced panel dataset. Study period The study was conducted in August 2025. No field research involving primary data collection was undertaken. The study utilised data obtained from publicly accessible, open-access international databases, and all analyses were performed using institutional computing facilities. Data sources Health data were obtained from the World Health Organization (WHO) database, and macroeconomic indicators were retrieved from the World Bank’s World Development Indicators (WDI). All data were publicly available from internationally recognized sources. Statistical analysis The data were downloaded in Microsoft Excel-compatible formats from open-access, internationally recognized databases. All datasets were pre-processed and analyzed using panel data methods in Stata version 15.0. Prior to analysis, all continuous variables—maternal mortality ratio (MMR), neonatal mortality rate (NMR), gross domestic product per capita (GDP), inflation rate (IR), and unemployment rate (UR)—were transformed into their natural logarithmic forms to reduce skewness, stabilize variance, and allow elasticity-based interpretation of coefficients. Correlation analysis was first conducted to explore associations between variables. Stationarity was tested using the Levin–Lin–Chu (LLC) unit root test, and the presence of panel effects was assessed with the Breusch–Pagan Lagrange Multiplier (LM) test. Both fixed-effects and random-effects panel regression models were estimated, and the Hausman specification test was used to select the appropriate estimator. Fixed-effects results were reported when systematic differences between estimators were detected; otherwise, random-effects estimates were presented. The general functional form of the models was: ln(Y it ) = β₀ + β₁ln(GDP it ) + β₂ln(IR it ) + β₃ln(UR it ) + µ i + λₜ + ε it where Y it​ denotes either MMR or NMR for country i in year t; GDP it is GDP per capita; IR it ​ is the inflation rate; UR it is the unemployment rate; µ i represents country-specific effects; λt accounts for year-specific effects; and ε it is the error term. All regressions included year fixed effects, and for random-effects specifications, common year dummies were added. Robust standard errors clustered at the country level were applied to correct for heteroskedasticity and within-country serial correlation ( 13 , 14 ). Model fit was evaluated using within R² for fixed-effects models and overall R² for random-effects models. Variables Dependent and independent variables The maternal mortality ratio (MMR) was defined as the number of maternal deaths per 100 000 live births, and the neonatal mortality rate (NMR) as the number of neonatal deaths per 1000 live births. Independent variables included gross domestic product (GDP) per capita (measured in current US dollars), the annual inflation rate (percentage change in consumer prices), and the unemployment rate (percentage of the labor force unemployed). Hypotheses and model specification It was hypothesized that maternal and neonatal mortality would be significantly associated with GDP per capita, inflation, and unemployment rates. Two panel linear regression models were constructed—one for MMR and one for NMR—each estimated under both fixed-effects and random-effects specifications, with the final model choice determined by the Hausman test. Results Descriptive results From 1991 to 2023, 5,016 country–year observations from 152 countries were analysed. The mean maternal mortality ratio (MMR) was 199.3 per 100,000 live births (SD 298.5), and the mean neonatal mortality rate (NMR) was 16.2 per 1,000 live births (SD 14.1). The mean GDP per capita was US$11,764.6 (SD 17,907.3), with mean annual inflation of 12.8% (SD 63.1) and mean unemployment of 7.6% (SD 5.6). Regional disparities were evident: Africa recorded the highest average MMR (505.1) and NMR (30.4) with the lowest GDP per capita (US$1,840.6), while Europe reported the lowest MMR (13.8) and NMR (4.8) and the highest GDP per capita (US$24,608.5). Inflation variability was greatest in the Americas, and unemployment was highest in Africa and lowest in Oceania (Table 1). Table 1 Descriptive Statistics of Variables Region Variable Obs Mean SD Min Max All Countries MMR 5,016 199.25 298.52 1 5,721 NMR 5,016 16.18 14.05 0 65 GDP 5,016 11,764.63$ 17,907.32 22.95$ 133,711.80$ IR 5,016 12.83 63.09 -16.86 2,075.89 UR 5,016 7.64 5.63 .1 35.36 Africa MMR 1,452 505.09 379.00 35 5,721 NMR 1,452 30.41 11.48 5 65 GDP 1,452 1,840.58$ 2,406.21 96.32$ 18,210.56$ IR 1,452 11.70 28.53 -16.86 515 UR 1,452 9.32 7.50 .316 35.36 Americas MMR 891 96.73 99.63 7 831 NMR 891 12.17 7.08 2 39 GDP 891 9,386.18$ 11,729.67 258.06$ 82,769.41$ IR 891 15.19 103.15 -7.11 2,075.89 UR 891 8.06 4.30 1.58 25.22 Asia MMR 1,089 123.87 160.77 2 889 NMR 1,089 15.80 13.89 0 64 GDP 1,089 11,663.10$ 16,738.01 22.95$ 108,470.40$ IR 1,089 7.89 22.77 -16.12 448.50 UR 1,089 5.01 4.04 .1 21.40 Europe MMR 1,320 13.82 13.91 1 104 NMR 1,320 4.79 5.44 0 36 GDP 1,320 24,608.45$ 23,622.17 60.23$ 133,711.80$ IR 1,320 17.81 82.91 -10.63 1,877.37 UR 1,320 8.31 4.51 .6 30.01 Oceania MMR 264 101.28 86.93 2 353 NMR 264 9.90 7.56 2 32 GDP 264 10,573.79$ 16,350.62 506.33$ 68,190.70$ IR 264 4.25 3.69 -2.90 17.32 UR 264 4.44 2.33 .694 10.88 Note: GDP per capita is reported in current US dollars; MMR = maternal mortality ratio, NMR = neonatal mortality rate, IR = inflation rate, UR = unemployment rate. (Table 1: Near here) Maternal mortality declined in all regions but at different levels (Fig. 1). In Africa, MMR exceeded 700 per 100,000 in the early 1990s and fell below 300 by 2023. Asia declined from more than 200 in 1991 to below 100 after 2010. The Americas, Oceania, and Europe maintained far lower levels, with Europe consistently below 20. A spike in the Americas around 2020–21 may reflect data anomalies or pandemic effects. Neonatal mortality showed similar patterns (Fig. 2): Africa declined from about 40 to 22 per 1,000, Asia from 25 to under 10, the Americas and Oceania converged at 8–9, and Europe dropped from under 9 to 2. (Fig. 1,2: Near here) Macroeconomic indicators also diverged across regions. GDP per capita rose in all regions but with marked gaps (Fig. 3): Europe increased from about US$13,000 to nearly US$40,000, Oceania and the Americas converged at US$15,000–18,000, Asia grew steadily to about US$18,000, while Africa remained lowest, rising only from about US$2,000 to US$3,000. Inflation was highly volatile in the early 1990s, peaking above 70–110% in Africa, the Americas, and Europe before stabilising after 2000, with a mild global rise in the early 2020s (Fig. 4). Unemployment remained relatively stable but with clear regional differences (Fig. 5), highest in Africa (9–10%) and lowest in Oceania (3–5%), with peaks in Europe and the Americas in the early 1990s, early 2010s, and 2020. (Fig. 3,4,5: near here) Across all countries, MMR and NMR were strongly and positively correlated (r = 0.9163). Both mortality indicators were strongly and negatively correlated with GDP per capita, suggesting that higher national income levels are associated with lower maternal and neonatal mortality. GDP per capita showed a moderate negative correlation with inflation rates in most regions, and mixed, generally weaker correlations with unemployment rates. The strength and direction of associations varied across continents, with the strongest negative GDP–MMR relationship observed in Oceania (r = − 0.9411) and the weakest in Europe (r = − 0.6353). Model specification tests results LLC unit root tests confirmed that all variables were stationary (Table 2). The Breusch–Pagan LM test indicated significant panel effects (Table 3). Hausman tests favored fixed effects for most models, with random effects preferred for neonatal mortality in the Americas, Asia, and Europe (Table 4). Robust standard errors were applied to correct for heteroskedasticity and within-country correlation. Table 2 Levin–Lin–Chu (LLC) panel unit root test results Variable All Countries t* (p) Africa t* (p) Americas t* (p) Asia t* (p) Europe t* (p) Oceania t* (p) MMR -19.4039 (0.0000) -8.4244 (0.0000) -7.2345 (0.0000) -15.6932 (0.0000) -7.9437 (0.0000) -2.5610 (0.0052) NMR -15.6419 (0.0000) -5.4121 (0.0000) -8.4125 (0.0000) -11.0930 (0.0000) -9.1846 (0.0000) -1.9637 (0.0248) GDP -4.8298 (0.0000) -2.6555 (0.0040) -1.8661 (0.0310) -2.8605 (0.0021) -3.9209 (0.0000) -2.0583 (0.0198) IR -14.1515 (0.0000) -12.9872 (0.0000) -5.9699 (0.0000) -6.8140 (0.0000) -7.7833 (0.0000) -3.3493 (0.0004) UR -6.2090 (0.0000) -2.0010 (0.0227) -3.9935 (0.0000) -2.9568 (0.0016) -3.2059 (0.0007) -2.0923 (0.0182) All variables were found to be stationary at level (p < 0.05) according to the LLC test, indicating that they do not require differencing for the subsequent panel regression analyses. Table 3 Breusch–Pagan LM test results Region Dependent Variable Chi²(1) p-value All Countries MMR 48709.12 0.0000 NMR 45096.41 0.0000 Africa MMR 12185.55 0.0000 NMR 11243.80 0.0000 Americas MMR 4082.54 0.0000 NMR 321.17 0.0000 Asia MMR 3392.18 0.0000 NMR 1727.13 0.0000 Europe MMR 2611.58 0.0000 NMR 4642.13 0.0000 Oceania MMR 380.46 0.0000 NMR 1107.56 0.0000 For all models, the p-values were below 0.05, leading to the rejection of the null hypothesis and indicating the presence of panel effects. Therefore, the use of a panel data model is preferred over the classical OLS model. Whether the random effects or fixed effects model should be applied was determined in the next step using the Hausman test (see Section 3.5). Table 4 Hausman model specification test results Region Dependent Variable Chi-square (χ²) p-value Preferred Model All Countries MMR 342.20 0.0000 Fixed Effects NMR 68.50 0.0000 Fixed Effects Africa MMR 16.82 0.0008 Fixed Effects NMR 8.93 0.0302 Fixed Effects Americas MMR 119.31 0.0000 Fixed Effects NMR 1.85 0.6048 Random Effects Asia MMR 38.82 0.0000 Fixed Effects NMR 7.65 0.0539 Random Effects Europe MMR 9.58 0.0225 Fixed Effects NMR 7.15 0.0672 Random Effects Oceania MMR 92.63 0.0000 Fixed Effects NMR 22.32 0.0001 Fixed Effects Note: p-values < 0.05 indicate rejection of the null hypothesis, favouring the fixed effects model; otherwise, the random effects model is preferred. Table 5 Correlation matrices between maternal and neonatal mortality and macroeconomic variables, by continent All Countries MMR NMR GDP IR UR MMR 1 NMR 0.9163 1 GDP -0.8507 -0.8716 1 IR 0.2754 0.3275 -0.3847 1 UR -0.1054 -0.0648 0.1117 0.0831 1 Africa MMR NMR GDP IR UR MMR 1 NMR 0.8467 1 GDP -0.7102 -0.6797 1 IR 0.0909 0.0745 -0.1678 1 UR -0.4734 -0.4176 0.5548 0.0994 1 Americas MMR NMR GDP IR UR MMR 1 NMR 0.8361 1 GDP -0.7668 -0.7187 1 IR 0.3215 0.3449 -0.3536 1 UR -0.0223 0.0934 0.1005 -0.0717 1 Asia MMR NMR GDP IR UR MMR 1 NMR 0.8898 1 GDP -0.8827 -0.8776 1 IR 0.4531 0.4654 -0.5311 1 UR 0.0188 0.0130 -0.0947 0.1494 1 Europe MMR NMR GDP IR UR MMR 1 NMR 0.7261 1 GDP -0.6353 -0.8088 1 IR 0.4991 0.5489 -0.5975 1 UR 0.0369 0.1233 -0.2708 -0.0105 1 Oceania MMR NMR GDP IR UR MMR 1 NMR 0.8803 1 GDP -0.9411 -0.8728 1 IR 0.3097 0.2979 -0.3248 1 UR -0.4597 -0.3140 0.4085 -0.2831 1 (Table 2,3,4: Near here) Africa had the highest rates (around 9–10%), declining slightly after 2015. The Americas and Europe fluctuated between 7–10%, with peaks in the early 1990s, early 2010s, and a spike in 2020 likely linked to COVID-19. Asia maintained moderate levels (4–6%), while Oceania remained lowest (3–5%). Overall, unemployment showed more stability than inflation, though short-term shocks were evident. Panel regression results In the global sample, GDP per capita was inversely associated with both MMR (β=–0.445, p < 0.001) and NMR (β=–0.423, p < 0.001), while inflation was positively associated with MMR (β = 0.035, p = 0.016) and NMR (β = 0.034, p = 0.004). Unemployment was not significant. R² values were 0.449 for MMR and 0.540 for NMR (Table 6). Table 6 Panel regression estimates for maternal and neonatal mortality by region Region / Dependent variable Variable β (95% CI) p-value R² All countries – MMR GDP –0.445 (–0.513 to − 0.377) < 0.001 0.449 IR 0.035 (0.007 to 0.064) 0.016 UR 0.018 (–0.064 to 0.100) 0.666 All countries – NMR GDP –0.423 (–0.489 to − 0.357) < 0.001 0.540 IR 0.034 (0.011 to 0.058) 0.004 UR –0.006 (–0.077 to 0.065) 0.866 Africa – MMR GDP –0.418 (–0.536 to − 0.301) < 0.001 0.375 IR 0.007 (–0.027 to 0.040) 0.698 UR 0.173 (0.011 to 0.335) 0.036 Africa – NMR GDP –0.268 (–0.350 to − 0.186) < 0.001 0.429 IR 0.009 (–0.005 to 0.023) 0.207 UR 0.105 (0.010 to 0.200) 0.031 Americas – MMR GDP –0.335 (–0.455 to − 0.215) < 0.001 0.395 IR 0.031 (–0.002 to 0.065) 0.065 UR 0.048 (–0.052 to 0.147) 0.337 Americas – NMR GDP –0.368 (–0.462 to − 0.273) < 0.001 0.542 IR 0.028 (–0.017 to 0.073) 0.220 UR 0.068 (–0.082 to 0.217) 0.375 Asia – MMR GDP –0.490 (–0.624 to − 0.355) < 0.001 0.392 IR –0.005 (–0.080 to 0.070) 0.890 UR –0.082 (–0.236 to 0.073) 0.289 Asia – NMR GDP –0.456 (–0.571 to − 0.342) < 0.001 0.774 IR –0.016 (–0.060 to 0.027) 0.462 UR –0.050 (–0.172 to 0.072) 0.424 Europe – MMR GDP –0.441 (–0.587 to − 0.296) < 0.001 0.512 IR 0.098 (0.035 to 0.161) 0.003 UR 0.074 (–0.052 to 0.199) 0.242 Europe – NMR GDP –0.536 (–0.685 to − 0.387) < 0.001 0.664 IR 0.082 (0.015 to 0.149) 0.016 UR –0.055 (–0.177 to 0.066) 0.371 Oceania – MMR GDP –0.278 (–0.414 to − 0.143) 0.002 0.481 IR –0.024 (–0.076 to 0.029) 0.317 UR 0.099 (–0.121 to 0.318) 0.322 Oceania – NMR GDP –0.320 (–0.457 to − 0.182) < 0.001 0.551 IR –0.015 (–0.041 to 0.012) 0.238 UR 0.062 (–0.050 to 0.174) 0.234 Note: FE models include country fixed effects and year dummies (two-way FE). RE models include common year dummies. Standard errors are clustered at the country level. Year-dummy coefficients are not shown in the table. R² is reported as within R² for FE and overall R² for RE. When stratified by continent, GDP per capita consistently predicted lower mortality but with varying strength: the strongest GDP–MMR association was in Asia (β=–0.490), and the strongest GDP–NMR association in Europe (β=–0.536). Inflation was harmful in Europe (MMR β = 0.098, NMR β = 0.082) and marginally significant for MMR in the Americas (p = 0.065). Unemployment was significant only in Africa, where higher rates were associated with increased MMR (β = 0.173, p = 0.036) and NMR (β = 0.105, p = 0.031). Overall, higher GDP per capita was consistently linked to lower maternal and neonatal mortality, inflation increased risks in some contexts, and unemployment eff ects were region-specific. (Table 6: Near here) DISCUSSION In this 33-year, 152-country panel (1991–2023), macroeconomic conditions were closely related to maternal and neonatal survival. Globally, higher GDP per capita was consistently associated with lower maternal (MMR) and neonatal (NMR) mortality, with elasticities indicating that a 1% income increase corresponded to ≈ 0.45% and ≈ 0.42% reductions in MMR and NMR, respectively. By contrast, inflation was positively associated with both outcomes, whereas unemployment showed no global association. Taken together, the results underscore that macroeconomic stability—income growth with contained prices—remains integral to ending preventable maternal and neonatal deaths ( 15 , 16 , 17 ). Interpretation and relation to prior evidence The uniform GDP–mortality gradient across continents aligns with extensive evidence that rising income expands fiscal space and household purchasing power, improving coverage and quality of RMNCH services ( 15 , 16 , 18 ). WHO trend reports similarly document better service availability and utilisation in higher-income settings ( 16 , 17 , 19 ). The inflation findings cohere with research showing that price shocks raise the costs of transport, medicines, and consumables, erode real health budgets—especially where procurement is import-dependent—and reduce timely access to care ( 20 – 22 ). Macro-fiscal stress has also been linked to compressed public outlays and deterioration in service quality that can disproportionately affect maternal and newborn health ( 22 , 23 ). Evidence from programmatic and modelling studies indicates that such constraints are especially consequential for poor and rural populations ( 24 , 25 ). The absence of a global unemployment effect—alongside a clear positive association with both MMR and NMR in Africa—matches literature emphasising that labour-market shocks translate into health risks where social protection is limited, informality is high, and out-of-pocket financing prevails ( 6 , 10 – 12 , 26 ). Conversely, in settings with generous welfare systems, the health impact of unemployment may be muted ( 1 , 27 ). Mixed neonatal findings around unemployment in other contexts also accord with prior macro-panel and country analyses ( 4 , 5 , 8 ). Regional heterogeneity Continental models sharpen policy-relevant nuance. In Africa, GDP per capita was protective while unemployment was positively related to both MMR and NMR, consistent with finance and access barriers in systems with high out-of-pocket spending and limited insurance ( 11 , 12 , 26 ). Although inflation was not statistically significant in our African regression, prior evidence links price instability to poorer access to antenatal/postnatal care and essential commodities, suggesting a plausible pathway even when estimates attenuate after adjustment ( 22 , 24 , 25 ). In the Americas, GDP per capita was protective, and inflation’s association with MMR was marginal. Such attenuation likely reflects heterogeneity—from universal coverage to fragmented systems with high out-of-pocket burdens—across which economic shocks have uneven effects ( 28 , 29 , 30 ). Episodes of crisis and fiscal contraction have reduced health spending and exacerbated inequalities in parts of Latin America, whereas reforms in some countries have strengthened resilience ( 31 – 33 ). In Asia, the strongest GDP–MMR elasticity indicates substantial marginal returns to growth for maternal outcomes amid rapid health-system expansion ( 15 ). Non-significant unemployment and inflation may reflect countervailing demographic and service-capacity dynamics over time, including cyclical fertility and load on obstetric services ( 34 , 35 ). In Europe, inflation was positively associated with both MMR and NMR despite high baseline resources, echoing crisis-era evidence that macro-fiscal stress and austerity impaired access, quality, and timeliness of perinatal care ( 18 , 20 , 21 , 36 ). Variation within the region—stronger social insurance and lower inequality in the North/West versus structural constraints in parts of the East and Balkans—helps explain sensitivity to macroeconomic shocks ( 37 ). In Oceania, GDP per capita was protective while inflation and unemployment were not significant, which may reflect a mix of high-income systems and small island economies with limited samples and diverse financing structures; nonetheless, greater economic capacity has been linked to better neonatal inputs in the region ( 3 , 38 ). Plausible mechanisms Three macroeconomic pathways are evident. First, income and fiscal capacity: higher GDP per capita enables investment in skilled birth attendance, emergency obstetric care, and neonatal intensive care ( 15 , 16 ). Second, price instability: inflation erodes household purchasing power and health-system inputs, reducing utilisation and quality ( 21 , 22 ). Third, labour-market distress: in the absence of strong safety nets, unemployment heightens vulnerability and delays care-seeking ( 10 – 12 ). Demographic dynamics may also play a role; for example, fertility postponement during downturns can transiently reduce exposure to obstetric risk, complicating unemployment–mortality associations ( 34 ). Policy implications Three priorities emerge. First, during inflationary episodes, protect RMNCH inputs and household access through ring-fenced budgets, indexed procurement of essential commodities, and targeted transport, cash, or voucher support to sustain ANC/PNC, skilled delivery, emergency obstetric care, and neonatal intensive care ( 21 – 23 ). Second, sustain pro-poor growth and channel fiscal gains into frontline capacity—strengthening primary care, referral networks, emergency obstetrics, and neonatal critical care—particularly in Africa and Asia where elasticities indicate high returns ( 15 – 17 ). Third, where unemployment predicts mortality (notably in Africa), link employment and social-protection programmes with fee waivers, insurance enrolment, and transport support for pregnant women and newborns ( 11 , 12 , 26 ). CONCLUSION Macroeconomic stability—rising incomes with contained inflation—is consequential for maternal and neonatal survival. Future research should link macro shocks to subnational and microdata on coverage and quality, evaluate resilience policies (eg, inflation indexation of RMNCH inputs, transport subsidies), and leverage quasi-experimental variation to clarify mechanisms across diverse financing and labour-market settings ( 5 , 18 , 28 ). LIMITATIONS This study has several strengths, including its wide geographic and temporal coverage; use of log transformations enabling elasticity interpretation; pre-analysis stationarity checks; formal FE/RE model selection; and robust inference with country-clustered errors. However, certain limitations are inherent to ecological macro-panels. The analysis relies on secondary, modelled series for maternal and neonatal mortality and on administrative macroeconomic indicators; cross-country reporting differences, periodic methodological revisions, and under-registration may introduce measurement error and affect comparability, particularly in low-income settings. GDP per capita was measured in current US dollars, which is suitable for elasticity-based modelling but may limit comparability relative to constant-price or PPP-adjusted metrics. Important macro-financial channels—such as exchange-rate volatility, external debt dynamics, and food-price inflation—were excluded due to the lack of comparable global series across countries and years, precluding assessment of these mechanisms and potentially obscuring their role in mediating or amplifying the inflation–mortality relationship ( 17 , 39 ). Although two-way FE/RE models with country-clustered robust errors reduce bias from time-invariant confounders and heteroskedasticity/autocorrelation, they cannot eliminate residual time-varying confounding, simultaneity, or reverse causality (eg, deteriorating health feeding back into macro performance). In large macro-panels, cross-sectional dependence may also persist due to common shocks (eg, commodity cycles, pandemics). Inclusion criteria requiring complete series may induce selection bias by under-representing fragile states with weaker statistical systems. Finally, as national-level aggregates can mask subnational disparities in health access and outcomes, ecological inference should be interpreted with caution. RECOMMENDATIONS Policy implications Three priorities emerged. First, during inflationary periods, RMNCH budgets should be protected, procurement indexed to inflation, and transport subsidies and cash/voucher support maintained to safeguard ANC, PNC, skilled birth attendance, and neonatal care. Second, pro-poor growth should be translated into frontline capacity through investment in primary care, referral systems, and emergency obstetrics and neonatal services. Third, in contexts of labor-market distress, social protection and employment programs should be linked to fee waivers, insurance enrolment, and transport support. Monitoring should include inflation-sensitive indicators such as stock-outs, transport costs, and user fees. Research implications Future studies should use subnational data to reveal disparities, expand macroeconomic indicators (eg, constant-price GDP, food inflation, debt, health financing), and apply quasi-experimental or advanced panel methods to prove causality. Strengthening CRVS and price-tracking systems will reduce measurement errors and support real-time policy. Qualitative and mixed-methods approaches can further explain behavioral and institutional pathways affecting care and survival. Abbreviations MMR Maternal mortality ratio NMR Neonatal mortality rate GDP Gross domestic product IR Inflation rate UR Unemployment rate WHO World Health Organization LM Lagrange multiplier LLC Levin–Lin–Chu US$ United States dollar WDI World Development Indicators SD Standard deviation RMNCH Reproductive, Maternal, Newborn and Child Health ANC Antenatal Care PNC Postnatal Care PPP Purchasing Power Parity CRVS Civil Registration and Vital Statistic Declarations Ethical Approval and Consent to Participate This study used publicly available and de-identified secondary data from international databases; therefore, ethical approval was not required. Consent for publication Not applicable. Availability of data and materials Data and materials are available. Competing Interests None Funding The funding body had no role in the design, data collection, analysis, interpretation, or writing of this manuscript. Authors’ Contributions Esra Özer: Conceptualization, Methodology, Writing Original Draft Preparation, Writing – Review & Editing, Supervision Gamze Acavut: Conceptualization, Methodology, Writing Original Draft Preparation, Writing – Review & Editing, Supervision Aykan Coşkun: Conceptualization, Methodology, Data Curation, Formal Analysis, Investigation, Project administration, Software, Validation, Visualization, Writing Original Draft Preparation, Writing – Review & Editing, Supervision Şerife İrem Döner: Writing Original Draft Preparation, Writing – Review & Editing, Supervision Acknowledgments The authors gratefully acknowledge the institutions and organizations that provided the data used in this analysis, particularly the World Bank and the World Health Organization. Consent to Publish declaration: Not applicable. Consent to Participate declaration: Not applicable Data Availability The datasets analyzed during the current study are publicly available from the World Bank (https://data.worldbank.org), World Health Organization (https://www.who.int/data), and United Nations (https://data.un.org) databases. Processed data and analysis code can be obtained from the corresponding author upon reasonable request. References OECD. Health at a glance 2019: OECD indicators. Paris: OECD Publishing; 2019. 10.1787/4dd50c09-en . Taşcı H. Socio-economic determinants of infant mortality rates in Turkey: the role of physician density, urbanization, gender equality and economic growth [in Turkish]. MTU J Soc Sci Humanit. 2025;5:83–96. World Health Organization Western Pacific Regional Office. State of health in the Pacific: maternal and newborn care report. Manila: WHO; 2023. Girum T, Wasie A. Correlates of maternal mortality in developing countries: an ecological study in 82 countries. 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09:14:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":684505,"visible":true,"origin":"","legend":"\u003cp\u003eGDP per Capita by Continent (USD, 1991–2023) (World Bank data)\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8451078/v1/a46bafd0b94386c235c528ce.png"},{"id":100664109,"identity":"b564f2a5-cf7b-4b8e-a34d-11c73ec8d1d2","added_by":"auto","created_at":"2026-01-20 09:14:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":558810,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual Inflation Rates by Continent (World Bank data, 1991–2023) (Figure 4 shows the annual inflation rates across continents from 1991 to 2023, illustrating substantial volatility in the early 1990s and relative stabilization in subsequent decades.)\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8451078/v1/9230f6dcff2b8abbe40c6d17.png"},{"id":100664078,"identity":"afdbff9c-c70b-4f9a-8c8c-680c7a73db4b","added_by":"auto","created_at":"2026-01-20 09:13:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":680774,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual Unemployment Rates by Continent (World Bank data, 1991–2023) (Figure 5 illustrates annual unemployment rate trends across continents from 1991 to 2023, showing relatively stable patterns with region-specific fluctuations over time.)\u003c/p\u003e","description":"","filename":"Figure51.png","url":"https://assets-eu.researchsquare.com/files/rs-8451078/v1/bbeab1d773f101bd62cbe165.png"},{"id":108437751,"identity":"bc2eb945-9d81-4b91-b44d-82a07f73ab32","added_by":"auto","created_at":"2026-05-04 16:03:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4229637,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8451078/v1/f3c28ad9-34aa-442f-831c-85c5e165d768.pdf"},{"id":100664100,"identity":"e10ae016-b9ec-47bc-b401-519b9464ee60","added_by":"auto","created_at":"2026-01-20 09:14:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19405,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8451078/v1/d3f2abbe7aeb2b9b0f3fb8cb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Macroeconomic Determinants of Maternal and Neonatal Mortality: A Global Panel Analysis","fulltext":[{"header":"Background","content":"\u003cp\u003eGlobal health indicators are essential measures of societal development and population well-being. Maternal and neonatal health is closely linked not only to increased life expectancy but also to broader developmental outcomes, including education and economic growth (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The maternal mortality ratio (MMR)\u0026mdash;defined as deaths occurring during pregnancy or within 42 days of termination from causes related to pregnancy\u0026mdash;is a key indicator of health system performance. Globally, an estimated 800 women die every day from largely preventable causes, with more than 95% of these deaths occurring in low-income and middle-income countries (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In 2020, the MMR in Africa was 531 per 100 000 live births, reaching as high as 1 223 in South Sudan (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly, the neonatal mortality rate (NMR)\u0026mdash;defined as the number of deaths within the first 28 days of life per 1 000 live births\u0026mdash;is a key indicator of health system performance. In 2022, an estimated 2.3\u0026nbsp;million newborns died, representing 47% of all under-5 child deaths (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In sub-Saharan Africa, the NMR was 27 per 1 000 live births, 11 times higher than in low-mortality regions. These disparities are strongly associated with socioeconomic conditions, health system capacity, and inequities in access to quality neonatal care across countries (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMacroeconomic indicators such as income level, unemployment, and public health expenditure are major determinants of health system capacity and access to care (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Economic growth and public health investment have been associated with reductions in mortality (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), whereas inadequate resource allocation and lack of insurance coverage can increase maternal and neonatal deaths (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). In contexts with low social capital, restricted access to essential health services further exacerbates inequalities (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLong-term, multi-country studies examining maternal and neonatal mortality in relation to macroeconomic conditions remain scarce. This study analyses the effects of inflation, unemployment, and GDP per capita on maternal and neonatal mortality, using panel data from 152 countries between 1991 and 2023. A cross-continental comparative approach was adopted to assess the role of socioeconomic inequalities in shaping outcomes. A limitation of the study is the exclusion of exchange rate fluctuations and external debt, which affect health financing and stability but lack consistent data across all settings. Future research could address these gaps with region-specific data sources.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis study is a comparative analysis based on country-level secondary data, applying panel linear regression methods. It does not involve experimental intervention but relies on macro-level observational data derived from publicly available statistical sources.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population and sample\u003c/h3\u003e\n\u003cp\u003eThis study included all countries that regularly reported maternal and neonatal mortality data between 1991 and 2023. Although the analysis was undertaken in 2025, 2023 was the latest year with WHO data available. A total of 152 countries (Appendix 1) with complete and comparable health and macroeconomic data were included.\u003c/p\u003e\n\u003ch3\u003eInclusion and exclusion criteria\u003c/h3\u003e\n\u003cp\u003eCountries with complete annual data on maternal and neonatal mortality and key macroeconomic indicators (inflation, unemployment, GDP per capita) from 1991 to 2023, with statistics available in both the World Bank and WHO databases, and with at least 33 consecutive years of data were included. Countries with missing, inconsistent, or fragmented data limited to specific years were excluded to ensure construction of a balanced panel dataset.\u003c/p\u003e\n\u003ch3\u003eStudy period\u003c/h3\u003e\n\u003cp\u003eThe study was conducted in August 2025. No field research involving primary data collection was undertaken. The study utilised data obtained from publicly accessible, open-access international databases, and all analyses were performed using institutional computing facilities.\u003c/p\u003e\n\u003ch3\u003eData sources\u003c/h3\u003e\n\u003cp\u003eHealth data were obtained from the World Health Organization (WHO) database, and macroeconomic indicators were retrieved from the World Bank\u0026rsquo;s World Development Indicators (WDI). All data were publicly available from internationally recognized sources.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe data were downloaded in Microsoft Excel-compatible formats from open-access, internationally recognized databases. All datasets were pre-processed and analyzed using panel data methods in Stata version 15.0. Prior to analysis, all continuous variables\u0026mdash;maternal mortality ratio (MMR), neonatal mortality rate (NMR), gross domestic product per capita (GDP), inflation rate (IR), and unemployment rate (UR)\u0026mdash;were transformed into their natural logarithmic forms to reduce skewness, stabilize variance, and allow elasticity-based interpretation of coefficients.\u003c/p\u003e \u003cp\u003eCorrelation analysis was first conducted to explore associations between variables. Stationarity was tested using the Levin\u0026ndash;Lin\u0026ndash;Chu (LLC) unit root test, and the presence of panel effects was assessed with the Breusch\u0026ndash;Pagan Lagrange Multiplier (LM) test. Both fixed-effects and random-effects panel regression models were estimated, and the Hausman specification test was used to select the appropriate estimator. Fixed-effects results were reported when systematic differences between estimators were detected; otherwise, random-effects estimates were presented.\u003c/p\u003e \u003cp\u003eThe general functional form of the models was:\u003c/p\u003e \u003cp\u003eln(Y\u003csub\u003eit\u003c/sub\u003e) = β₀ + β₁ln(GDP\u003csub\u003eit\u003c/sub\u003e) + β₂ln(IR\u003csub\u003eit\u003c/sub\u003e) + β₃ln(UR\u003csub\u003eit\u003c/sub\u003e) + \u0026micro;\u003csub\u003ei\u003c/sub\u003e + λₜ + ε\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003cp\u003ewhere Y\u003csub\u003eit​\u003c/sub\u003e denotes either MMR or NMR for country i in year t; GDP\u003csub\u003eit\u003c/sub\u003e is GDP per capita; IR\u003csub\u003eit\u003c/sub\u003e​ is the inflation rate; UR\u003csub\u003eit\u003c/sub\u003e is the unemployment rate; \u0026micro;\u003csub\u003ei\u003c/sub\u003e represents country-specific effects; λt accounts for year-specific effects; and ε\u003csub\u003eit\u003c/sub\u003e is the error term. All regressions included year fixed effects, and for random-effects specifications, common year dummies were added. Robust standard errors clustered at the country level were applied to correct for heteroskedasticity and within-country serial correlation (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Model fit was evaluated using within R\u0026sup2; for fixed-effects models and overall R\u0026sup2; for random-effects models.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariables\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDependent and independent variables\u003c/h2\u003e \u003cp\u003eThe maternal mortality ratio (MMR) was defined as the number of maternal deaths per 100 000 live births, and the neonatal mortality rate (NMR) as the number of neonatal deaths per 1000 live births. Independent variables included gross domestic product (GDP) per capita (measured in current US dollars), the annual inflation rate (percentage change in consumer prices), and the unemployment rate (percentage of the labor force unemployed).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHypotheses and model specification\u003c/h2\u003e \u003cp\u003eIt was hypothesized that maternal and neonatal mortality would be significantly associated with GDP per capita, inflation, and unemployment rates. Two panel linear regression models were constructed\u0026mdash;one for MMR and one for NMR\u0026mdash;each estimated under both fixed-effects and random-effects specifications, with the final model choice determined by the Hausman test.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eDescriptive results\u003c/h2\u003e\n \u003cp\u003eFrom 1991 to 2023, 5,016 country\u0026ndash;year observations from 152 countries were analysed. The mean maternal mortality ratio (MMR) was 199.3 per 100,000 live births (SD 298.5), and the mean neonatal mortality rate (NMR) was 16.2 per 1,000 live births (SD 14.1). The mean GDP per capita was US$11,764.6 (SD 17,907.3), with mean annual inflation of 12.8% (SD 63.1) and mean unemployment of 7.6% (SD 5.6). Regional disparities were evident: Africa recorded the highest average MMR (505.1) and NMR (30.4) with the lowest GDP per capita (US$1,840.6), while Europe reported the lowest MMR (13.8) and NMR (4.8) and the highest GDP per capita (US$24,608.5). Inflation variability was greatest in the Americas, and unemployment was highest in Africa and lowest in Oceania (Table 1).\u003c/p\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDescriptive Statistics of Variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eObs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll Countries\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e199.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e298.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11,764.63$\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17,907.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.95$\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133,711.80$\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-16.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,075.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAfrica\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e505.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e379.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,840.58$\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,406.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.32$\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18,210.56$\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-16.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmericas\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e831\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,386.18$\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11,729.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e258.06$\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82,769.41$\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,075.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e123.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e160.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e889\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11,663.10$\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16,738.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.95$\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108,470.40$\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-16.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e448.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEurope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24,608.45$\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23,622.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.23$\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133,711.80$\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,877.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOceania\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e353\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10,573.79$\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16,350.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e506.33$\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68,190.70$\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e GDP per capita is reported in current US dollars; MMR = maternal mortality ratio, NMR = neonatal mortality rate, IR = inflation rate, UR = unemployment rate.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003e(Table\u0026nbsp;1: Near here)\u003c/h2\u003e\n \u003cp\u003eMaternal mortality declined in all regions but at different levels (Fig.\u0026nbsp;1). In Africa, MMR exceeded 700 per 100,000 in the early 1990s and fell below 300 by 2023. Asia declined from more than 200 in 1991 to below 100 after 2010. The Americas, Oceania, and Europe maintained far lower levels, with Europe consistently below 20. A spike in the Americas around 2020\u0026ndash;21 may reflect data anomalies or pandemic effects. Neonatal mortality showed similar patterns (Fig.\u0026nbsp;2): Africa declined from about 40 to 22 per 1,000, Asia from 25 to under 10, the Americas and Oceania converged at 8\u0026ndash;9, and Europe dropped from under 9 to 2.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003e(Fig.\u0026nbsp;1,2: Near here)\u003c/h2\u003e\n \u003cp\u003eMacroeconomic indicators also diverged across regions. GDP per capita rose in all regions but with marked gaps (Fig.\u0026nbsp;3): Europe increased from about US$13,000 to nearly US$40,000, Oceania and the Americas converged at US$15,000\u0026ndash;18,000, Asia grew steadily to about US$18,000, while Africa remained lowest, rising only from about US$2,000 to US$3,000. Inflation was highly volatile in the early 1990s, peaking above 70\u0026ndash;110% in Africa, the Americas, and Europe before stabilising after 2000, with a mild global rise in the early 2020s (Fig.\u0026nbsp;4). Unemployment remained relatively stable but with clear regional differences (Fig.\u0026nbsp;5), highest in Africa (9\u0026ndash;10%) and lowest in Oceania (3\u0026ndash;5%), with peaks in Europe and the Americas in the early 1990s, early 2010s, and 2020.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003e(Fig.\u0026nbsp;3,4,5: near here)\u003c/h2\u003e\n \u003cp\u003eAcross all countries, MMR and NMR were strongly and positively correlated (r\u0026thinsp;=\u0026thinsp;0.9163). Both mortality indicators were strongly and negatively correlated with GDP per capita, suggesting that higher national income levels are associated with lower maternal and neonatal mortality. GDP per capita showed a moderate negative correlation with inflation rates in most regions, and mixed, generally weaker correlations with unemployment rates. The strength and direction of associations varied across continents, with the strongest negative GDP\u0026ndash;MMR relationship observed in Oceania (r = \u0026minus;\u0026thinsp;0.9411) and the weakest in Europe (r = \u0026minus;\u0026thinsp;0.6353).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003eModel specification tests results\u003c/h2\u003e\n \u003cp\u003eLLC unit root tests confirmed that all variables were stationary (Table 2). The Breusch\u0026ndash;Pagan LM test indicated significant panel effects (Table 3). Hausman tests favored fixed effects for most models, with random effects preferred for neonatal mortality in the Americas, Asia, and Europe (Table 4). Robust standard errors were applied to correct for heteroskedasticity and within-country correlation.\u003c/p\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eLevin\u0026ndash;Lin\u0026ndash;Chu (LLC) panel unit root test results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAll Countries t* (p)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAfrica t* (p)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAmericas t* (p)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAsia t* (p)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEurope t* (p)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOceania t* (p)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-19.4039 (0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.4244\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.2345\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-15.6932 (0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.9437\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.5610\u003c/p\u003e\n \u003cp\u003e(0.0052)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-15.6419 (0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.4121\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.4125\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-11.0930 (0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.1846\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.9637\u003c/p\u003e\n \u003cp\u003e(0.0248)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGDP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.8298 (0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.6555\u003c/p\u003e\n \u003cp\u003e(0.0040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.8661\u003c/p\u003e\n \u003cp\u003e(0.0310)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.8605 (0.0021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.9209 (0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.0583 (0.0198)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-14.1515 (0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.9872 (0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.9699\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.8140 (0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.7833 (0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.3493 (0.0004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.2090 (0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.0010\u003c/p\u003e\n \u003cp\u003e(0.0227)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.9935\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.9568 (0.0016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.2059\u003c/p\u003e\n \u003cp\u003e(0.0007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.0923 (0.0182)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eAll variables were found to be stationary at level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) according to the LLC test, indicating that they do not require differencing for the subsequent panel regression analyses.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBreusch\u0026ndash;Pagan LM test results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDependent Variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChi\u0026sup2;(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll Countries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48709.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45096.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAfrica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12185.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11243.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmericas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4082.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e321.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3392.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1727.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2611.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4642.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e380.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1107.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eFor all models, the p-values were below 0.05, leading to the rejection of the null hypothesis and indicating the presence of panel effects. Therefore, the use of a panel data model is preferred over the classical OLS model. Whether the random effects or fixed effects model should be applied was determined in the next step using the Hausman test (see Section 3.5).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eHausman model specification test results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDependent Variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChi-square (\u0026chi;\u0026sup2;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePreferred Model\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll Countries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e342.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFixed Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFixed Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAfrica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFixed Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFixed Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmericas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFixed Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRandom Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFixed Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRandom Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFixed Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRandom Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFixed Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFixed Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicate rejection of the null hypothesis, favouring the fixed effects model; otherwise, the random effects model is preferred.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCorrelation matrices between maternal and neonatal mortality and macroeconomic variables, by continent\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAll Countries\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMMR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNMR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGDP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.8507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.8716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAfrica\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGDP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n 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align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGDP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.7668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n 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\u003cp\u003e\u003cstrong\u003eNMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGDP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.8827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.8776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEurope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGDP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGDP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.6353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.8088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOceania\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGDP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGDP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.9411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.8728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003e(Table\u0026nbsp;2,3,4: Near here)\u003c/h2\u003e\n \u003cp\u003eAfrica had the highest rates (around 9\u0026ndash;10%), declining slightly after 2015. The Americas and Europe fluctuated between 7\u0026ndash;10%, with peaks in the early 1990s, early 2010s, and a spike in 2020 likely linked to COVID-19. Asia maintained moderate levels (4\u0026ndash;6%), while Oceania remained lowest (3\u0026ndash;5%). Overall, unemployment showed more stability than inflation, though short-term shocks were evident.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\"\u003e\n \u003ch2\u003ePanel regression results\u003c/h2\u003e\n \u003cp\u003eIn the global sample, GDP per capita was inversely associated with both MMR (\u0026beta;=\u0026ndash;0.445, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and NMR (\u0026beta;=\u0026ndash;0.423, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while inflation was positively associated with MMR (\u0026beta;\u0026thinsp;=\u0026thinsp;0.035, p\u0026thinsp;=\u0026thinsp;0.016) and NMR (\u0026beta;\u0026thinsp;=\u0026thinsp;0.034, p\u0026thinsp;=\u0026thinsp;0.004). Unemployment was not significant. R\u0026sup2; values were 0.449 for MMR and 0.540 for NMR (Table 6).\u003c/p\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 6\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ePanel regression estimates for maternal and neonatal mortality by region\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRegion / Dependent variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u0026sup2;\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll countries \u0026ndash; MMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.445 (\u0026ndash;0.513 to \u0026minus;\u0026thinsp;0.377)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.449\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035 (0.007 to 0.064)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018 (\u0026ndash;0.064 to 0.100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll countries \u0026ndash; NMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.423 (\u0026ndash;0.489 to \u0026minus;\u0026thinsp;0.357)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034 (0.011 to 0.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.006 (\u0026ndash;0.077 to 0.065)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAfrica \u0026ndash; MMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.418 (\u0026ndash;0.536 to \u0026minus;\u0026thinsp;0.301)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007 (\u0026ndash;0.027 to 0.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.173 (0.011 to 0.335)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAfrica \u0026ndash; NMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.268 (\u0026ndash;0.350 to \u0026minus;\u0026thinsp;0.186)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009 (\u0026ndash;0.005 to 0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.105 (0.010 to 0.200)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmericas \u0026ndash; MMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.335 (\u0026ndash;0.455 to \u0026minus;\u0026thinsp;0.215)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031 (\u0026ndash;0.002 to 0.065)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048 (\u0026ndash;0.052 to 0.147)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmericas \u0026ndash; NMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.368 (\u0026ndash;0.462 to \u0026minus;\u0026thinsp;0.273)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028 (\u0026ndash;0.017 to 0.073)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.068 (\u0026ndash;0.082 to 0.217)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsia \u0026ndash; MMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.490 (\u0026ndash;0.624 to \u0026minus;\u0026thinsp;0.355)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.005 (\u0026ndash;0.080 to 0.070)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.082 (\u0026ndash;0.236 to 0.073)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsia \u0026ndash; NMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.456 (\u0026ndash;0.571 to \u0026minus;\u0026thinsp;0.342)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.016 (\u0026ndash;0.060 to 0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.050 (\u0026ndash;0.172 to 0.072)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEurope \u0026ndash; MMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.441 (\u0026ndash;0.587 to \u0026minus;\u0026thinsp;0.296)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.098 (0.035 to 0.161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.074 (\u0026ndash;0.052 to 0.199)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEurope \u0026ndash; NMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.536 (\u0026ndash;0.685 to \u0026minus;\u0026thinsp;0.387)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.082 (0.015 to 0.149)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.055 (\u0026ndash;0.177 to 0.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOceania \u0026ndash; MMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.278 (\u0026ndash;0.414 to \u0026minus;\u0026thinsp;0.143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.024 (\u0026ndash;0.076 to 0.029)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.099 (\u0026ndash;0.121 to 0.318)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOceania \u0026ndash; NMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.320 (\u0026ndash;0.457 to \u0026minus;\u0026thinsp;0.182)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;0.015 (\u0026ndash;0.041 to 0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.062 (\u0026ndash;0.050 to 0.174)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: FE models include country fixed effects and year dummies (two-way FE). RE models include common year dummies. Standard errors are clustered at the country level. Year-dummy coefficients are not shown in the table. R\u0026sup2; is reported as within R\u0026sup2; for FE and overall R\u0026sup2; for RE.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eWhen stratified by continent, GDP per capita consistently predicted lower mortality but with varying strength: the strongest GDP\u0026ndash;MMR association was in Asia (\u0026beta;=\u0026ndash;0.490), and the strongest GDP\u0026ndash;NMR association in Europe (\u0026beta;=\u0026ndash;0.536). Inflation was harmful in Europe (MMR \u0026beta;\u0026thinsp;=\u0026thinsp;0.098, NMR \u0026beta;\u0026thinsp;=\u0026thinsp;0.082) and marginally significant for MMR in the Americas (p\u0026thinsp;=\u0026thinsp;0.065). Unemployment was significant only in Africa, where higher rates were associated with increased MMR (\u0026beta;\u0026thinsp;=\u0026thinsp;0.173, p\u0026thinsp;=\u0026thinsp;0.036) and NMR (\u0026beta;\u0026thinsp;=\u0026thinsp;0.105, p\u0026thinsp;=\u0026thinsp;0.031).\u003c/p\u003e\n \u003cp\u003eOverall, higher GDP per capita was consistently linked to lower maternal and neonatal mortality, inflation increased risks in some contexts, and unemployment eff\u003c/p\u003e\n \u003cp\u003eects were region-specific.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\"\u003e\n \u003ch2\u003e(Table\u0026nbsp;6: Near here)\u003c/h2\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this 33-year, 152-country panel (1991\u0026ndash;2023), macroeconomic conditions were closely related to maternal and neonatal survival. Globally, higher GDP per capita was consistently associated with lower maternal (MMR) and neonatal (NMR) mortality, with elasticities indicating that a 1% income increase corresponded to \u0026asymp;\u0026thinsp;0.45% and \u0026asymp;\u0026thinsp;0.42% reductions in MMR and NMR, respectively. By contrast, inflation was positively associated with both outcomes, whereas unemployment showed no global association. Taken together, the results underscore that macroeconomic stability\u0026mdash;income growth with contained prices\u0026mdash;remains integral to ending preventable maternal and neonatal deaths (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eInterpretation and relation to prior evidence\u003c/h2\u003e \u003cp\u003eThe uniform GDP\u0026ndash;mortality gradient across continents aligns with extensive evidence that rising income expands fiscal space and household purchasing power, improving coverage and quality of RMNCH services (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). WHO trend reports similarly document better service availability and utilisation in higher-income settings (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The inflation findings cohere with research showing that price shocks raise the costs of transport, medicines, and consumables, erode real health budgets\u0026mdash;especially where procurement is import-dependent\u0026mdash;and reduce timely access to care (\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Macro-fiscal stress has also been linked to compressed public outlays and deterioration in service quality that can disproportionately affect maternal and newborn health (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Evidence from programmatic and modelling studies indicates that such constraints are especially consequential for poor and rural populations (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe absence of a global unemployment effect\u0026mdash;alongside a clear positive association with both MMR and NMR in Africa\u0026mdash;matches literature emphasising that labour-market shocks translate into health risks where social protection is limited, informality is high, and out-of-pocket financing prevails (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Conversely, in settings with generous welfare systems, the health impact of unemployment may be muted (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Mixed neonatal findings around unemployment in other contexts also accord with prior macro-panel and country analyses (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eRegional heterogeneity\u003c/h2\u003e \u003cp\u003eContinental models sharpen policy-relevant nuance. In Africa, GDP per capita was protective while unemployment was positively related to both MMR and NMR, consistent with finance and access barriers in systems with high out-of-pocket spending and limited insurance (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Although inflation was not statistically significant in our African regression, prior evidence links price instability to poorer access to antenatal/postnatal care and essential commodities, suggesting a plausible pathway even when estimates attenuate after adjustment (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the Americas, GDP per capita was protective, and inflation\u0026rsquo;s association with MMR was marginal. Such attenuation likely reflects heterogeneity\u0026mdash;from universal coverage to fragmented systems with high out-of-pocket burdens\u0026mdash;across which economic shocks have uneven effects (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Episodes of crisis and fiscal contraction have reduced health spending and exacerbated inequalities in parts of Latin America, whereas reforms in some countries have strengthened resilience (\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Asia, the strongest GDP\u0026ndash;MMR elasticity indicates substantial marginal returns to growth for maternal outcomes amid rapid health-system expansion (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Non-significant unemployment and inflation may reflect countervailing demographic and service-capacity dynamics over time, including cyclical fertility and load on obstetric services (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Europe, inflation was positively associated with both MMR and NMR despite high baseline resources, echoing crisis-era evidence that macro-fiscal stress and austerity impaired access, quality, and timeliness of perinatal care (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Variation within the region\u0026mdash;stronger social insurance and lower inequality in the North/West versus structural constraints in parts of the East and Balkans\u0026mdash;helps explain sensitivity to macroeconomic shocks (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Oceania, GDP per capita was protective while inflation and unemployment were not significant, which may reflect a mix of high-income systems and small island economies with limited samples and diverse financing structures; nonetheless, greater economic capacity has been linked to better neonatal inputs in the region (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003ePlausible mechanisms\u003c/h2\u003e \u003cp\u003eThree macroeconomic pathways are evident. First, income and fiscal capacity: higher GDP per capita enables investment in skilled birth attendance, emergency obstetric care, and neonatal intensive care (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Second, price instability: inflation erodes household purchasing power and health-system inputs, reducing utilisation and quality (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Third, labour-market distress: in the absence of strong safety nets, unemployment heightens vulnerability and delays care-seeking (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Demographic dynamics may also play a role; for example, fertility postponement during downturns can transiently reduce exposure to obstetric risk, complicating unemployment\u0026ndash;mortality associations (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003ePolicy implications\u003c/h2\u003e \u003cp\u003eThree priorities emerge. First, during inflationary episodes, protect RMNCH inputs and household access through ring-fenced budgets, indexed procurement of essential commodities, and targeted transport, cash, or voucher support to sustain ANC/PNC, skilled delivery, emergency obstetric care, and neonatal intensive care (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Second, sustain pro-poor growth and channel fiscal gains into frontline capacity\u0026mdash;strengthening primary care, referral networks, emergency obstetrics, and neonatal critical care\u0026mdash;particularly in Africa and Asia where elasticities indicate high returns (\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Third, where unemployment predicts mortality (notably in Africa), link employment and social-protection programmes with fee waivers, insurance enrolment, and transport support for pregnant women and newborns (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eMacroeconomic stability—rising incomes with contained inflation—is consequential for maternal and neonatal survival. Future research should link macro shocks to subnational and microdata on coverage and quality, evaluate resilience policies (eg, inflation indexation of RMNCH inputs, transport subsidies), and leverage quasi-experimental variation to clarify mechanisms across diverse financing and labour-market settings (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e "},{"header":"LIMITATIONS","content":"\u003cp\u003eThis study has several strengths, including its wide geographic and temporal coverage; use of log transformations enabling elasticity interpretation; pre-analysis stationarity checks; formal FE/RE model selection; and robust inference with country-clustered errors. However, certain limitations are inherent to ecological macro-panels. The analysis relies on secondary, modelled series for maternal and neonatal mortality and on administrative macroeconomic indicators; cross-country reporting differences, periodic methodological revisions, and under-registration may introduce measurement error and affect comparability, particularly in low-income settings. GDP per capita was measured in current US dollars, which is suitable for elasticity-based modelling but may limit comparability relative to constant-price or PPP-adjusted metrics. Important macro-financial channels—such as exchange-rate volatility, external debt dynamics, and food-price inflation—were excluded due to the lack of comparable global series across countries and years, precluding assessment of these mechanisms and potentially obscuring their role in mediating or amplifying the inflation–mortality relationship (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Although two-way FE/RE models with country-clustered robust errors reduce bias from time-invariant confounders and heteroskedasticity/autocorrelation, they cannot eliminate residual time-varying confounding, simultaneity, or reverse causality (eg, deteriorating health feeding back into macro performance). In large macro-panels, cross-sectional dependence may also persist due to common shocks (eg, commodity cycles, pandemics). Inclusion criteria requiring complete series may induce selection bias by under-representing fragile states with weaker statistical systems. Finally, as national-level aggregates can mask subnational disparities in health access and outcomes, ecological inference should be interpreted with caution.\u003c/p\u003e"},{"header":"RECOMMENDATIONS","content":"\u003ch2\u003ePolicy implications\u003c/h2\u003e\u003cp\u003eThree priorities emerged. First, during inflationary periods, RMNCH budgets should be protected, procurement indexed to inflation, and transport subsidies and cash/voucher support maintained to safeguard ANC, PNC, skilled birth attendance, and neonatal care. Second, pro-poor growth should be translated into frontline capacity through investment in primary care, referral systems, and emergency obstetrics and neonatal services. Third, in contexts of labor-market distress, social protection and employment programs should be linked to fee waivers, insurance enrolment, and transport support. Monitoring should include inflation-sensitive indicators such as stock-outs, transport costs, and user fees.\u003c/p\u003e\n\u003ch3\u003eResearch implications\u003c/h3\u003e\n\u003cp\u003eFuture studies should use subnational data to reveal disparities, expand macroeconomic indicators (eg, constant-price GDP, food inflation, debt, health financing), and apply quasi-experimental or advanced panel methods to prove causality. Strengthening CRVS and price-tracking systems will reduce measurement errors and support real-time policy. Qualitative and mixed-methods approaches can further explain behavioral and institutional pathways affecting care and survival.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMMR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMaternal mortality ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNMR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeonatal mortality rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGDP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGross domestic product\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInflation rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eUR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnemployment rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWHO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLagrange multiplier\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLLC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLevin\u0026ndash;Lin\u0026ndash;Chu\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eUS$\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnited States dollar\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWDI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Development Indicators\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRMNCH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReproductive, Maternal, Newborn and Child Health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eANC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAntenatal Care\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePNC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePostnatal Care\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePPP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePurchasing Power Parity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCRVS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCivil Registration and Vital Statistic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used publicly available and de-identified secondary data from international databases; therefore, ethical approval was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData and materials are available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funding body had no role in the design, data collection, analysis, interpretation, or writing of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEsra \u0026Ouml;zer: Conceptualization, Methodology, Writing Original Draft Preparation, Writing \u0026ndash; Review \u0026amp; Editing, Supervision\u003c/p\u003e\n\u003cp\u003eGamze Acavut: Conceptualization, Methodology, Writing Original Draft Preparation, Writing \u0026ndash; Review \u0026amp; Editing, Supervision\u003c/p\u003e\n\u003cp\u003eAykan Coşkun: Conceptualization, Methodology, Data Curation, Formal Analysis, Investigation, Project administration, Software, Validation, Visualization, Writing Original Draft Preparation, Writing \u0026ndash; Review \u0026amp; Editing, Supervision\u003c/p\u003e\n\u003cp\u003eŞerife İrem D\u0026ouml;ner: Writing Original Draft Preparation, Writing \u0026ndash; Review \u0026amp; Editing, Supervision\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the institutions and organizations that provided the data used in this analysis, particularly the World Bank and the World Health Organization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are publicly available from the World Bank (https://data.worldbank.org), World Health Organization (https://www.who.int/data), and United Nations (https://data.un.org) databases. Processed data and analysis code can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOECD. Health at a glance 2019: OECD indicators. Paris: OECD Publishing; 2019. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1787/4dd50c09-en\u003c/span\u003e\u003cspan address=\"10.1787/4dd50c09-en\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaşcı H. Socio-economic determinants of infant mortality rates in Turkey: the role of physician density, urbanization, gender equality and economic growth [in Turkish]. MTU J Soc Sci Humanit. 2025;5:83\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization Western Pacific Regional Office. State of health in the Pacific: maternal and newborn care report. 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Washington (DC): World Bank; 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1596/978-1-4648-2193-6\u003c/span\u003e\u003cspan address=\"10.1596/978-1-4648-2193-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Maternal mortality, neonatal mortality, global health, socioeconomic factors, gross domestic product","lastPublishedDoi":"10.21203/rs.3.rs-8451078/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8451078/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMaternal mortality ratio (MMR) and neonatal mortality rate (NMR) are vital indicators of population health and health system performance. Despite recognition of macroeconomic influences on health outcomes, longitudinal cross-country evidence remains limited.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study analyzed panel data from 152 countries between 1991 and 2023 using World Health Organization and World Bank datasets. Dependent variables were MMR (per 100 000 live births) and NMR (per 1 000 live births). Independent variables included gross domestic product (GDP) per capita, inflation rate (IR), and unemployment rate (UR). Descriptive statistics, correlation analyses, the Levin–Lin–Chu unit root test, the Breusch–Pagan LM test, and the Hausman specification test guided model selection. Fixed and random effects regressions with robust standard errors were applied.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGDP per capita was inversely associated with both MMR (β = − 0.445, p \u0026lt; 0.001) and NMR (β = − 0.423, p \u0026lt; 0.001). Inflation showed a positive association with MMR (β = 0.035, p = 0.016) and NMR (β = 0.034, p = 0.004), while unemployment had no global effect. Regional heterogeneity was observed: in Africa, unemployment increased both MMR and NMR; in Europe, inflation correlated positively with mortality; and in Asia, the GDP–MMR relationship was strongest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMacroeconomic stability is crucial for improving maternal and neonatal survival. Sustained growth, inflation control, and labor market interventions—particularly in low-income regions—are essential for reducing mortality. Integrating macroeconomic and health policies could mitigate structural inequities and enhance global health outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number: \u003c/strong\u003eNot applicable.\u003c/p\u003e","manuscriptTitle":"Macroeconomic Determinants of Maternal and Neonatal Mortality: A Global Panel Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-20 08:27:40","doi":"10.21203/rs.3.rs-8451078/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-06T06:11:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-05T10:51:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129654052210520303581155635058660087490","date":"2026-02-04T19:03:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"107827524297011457295141587476087232527","date":"2026-02-04T12:24:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217687514698790930311244079639849604420","date":"2026-01-22T16:18:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-19T15:49:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224254560403457800383642856145342557984","date":"2026-01-15T23:08:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3253441994600932017298247653210021933","date":"2026-01-15T04:23:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-15T01:59:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-06T17:01:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-05T11:33:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-05T11:29:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-12-25T21:58:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7140d921-ff57-471e-b50d-d24429300ab6","owner":[],"postedDate":"January 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T16:02:11+00:00","versionOfRecord":{"articleIdentity":"rs-8451078","link":"https://doi.org/10.1186/s12889-026-27425-x","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2026-05-01 15:58:25","publishedOnDateReadable":"May 1st, 2026"},"versionCreatedAt":"2026-01-20 08:27:40","video":"","vorDoi":"10.1186/s12889-026-27425-x","vorDoiUrl":"https://doi.org/10.1186/s12889-026-27425-x","workflowStages":[]},"version":"v1","identity":"rs-8451078","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8451078","identity":"rs-8451078","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}