Abstract
Background
Maternal hypertensive Disorders (MHD), including gestational hypertension, preeclampsia, and eclampsia, remain a major contributor to maternal and neonatal morbidity and mortality worldwide. While global healthcare advancements have improved maternal survival, the incidence of MHD continues to rise, particularly in low- and middle-income countries (LMICs).
Objective
This study aimed to comprehensively assess the global and regional trends in the burden of MHD among women aged 15–49 years from 1990 to 2021 using data from the Global Burden of Disease (GBD) 2021 study. We examined disparities across countries and Socio-Demographic Index (SDI) regions, analyzed the influence of demographic and epidemiological factors through decomposition analysis, and projected future trends in MHD burden through 2050 using a Bayesian age-period-cohort (BAPC) model.
Methods
We extracted incidence, mortality, and disability-adjusted life years (DALYs) of women aged 15–49 years from the GBD 2021 database across 204 countries. Temporal trends were analyzed using Joinpoint regression and the estimated annual percentage change (EAPC). Socioeconomic disparities were evaluated using the SDI, and future projections were conducted using a BAPC model with sensitivity analysis.
Results
The global MHD mortality (AAPC: -2.16%; 95% CI: -2.1 to -2.21) and incidence (AAPC: -0.5%, 95% CI: -0.45% to -0.56%) rate has declined. Decomposition analysis revealed that population growth and epidemiological changes were the main drivers of the increasing incidence, while aging had a less pronounced effect. Health inequality analysis demonstrated a clear negative correlation between MHD burden and socioeconomic development, highlighting the disproportionate impact on disadvantaged populations.
Conclusion
The global burden of MHD exhibits significant regional disparities, with an declined trend in incidence. To address this persistent challenge, targeted interventions focusing on strengthening healthcare systems, improving access to quality obstetric care, and addressing socioeconomic inequities are urgently needed.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12884-025-07926-0.
Keywords: Maternal hypertension, Preeclampsia, Global burden of disease, Maternal health, Health disparities, Low- and middle-income countries
Introduction
Maternal hypertensive disorders (MHD), also known as hypertensive disorders of pregnancy, include a range of conditions encompassing gestational hypertension, preeclampsia, eclampsia, and chronic hypertension and remain a leading cause of maternal and neonatal mortality and morbidity worldwide [1].
Despite improvements in obstetric care, disparities in MHD burden persist globally [2, 3]. The increasing prevalence of metabolic risk factors—such as obesity, diabetes, and advanced maternal age—exacerbates the MHD burden, particularly in low- and middle-income countries (LMICs) [4, 5]. Although some middle- and high-income nations have experienced slowed BMI growth rates since 2000, the global obesity prevalence continues to climb due to accelerating trends in developing regions [6]. Compounding this issue, global diabetes prevalence was estimated at 9.3% (463 million people) in 2019, with projections indicating a rise to 10.2% (578 million) by 2030 and 10.9% (700 million) by 2045 [7]. Concurrently, the proportion of advanced maternal age pregnancies has shown significant growth, accounting for approximately 5% of all births in LMICs during 2010–2020, while demonstrating even higher and steadily increasing rates in high-income countries [8].
Recent studies highlight that while maternal mortality due to MHD has declined in high-income regions, incidence remains high due to improved diagnostic capabilities and shifting reproductive trends [9]. While previous Global Burden of Disease (GBD) analyses have reported on maternal health, including hypertensive disorders of pregnancy, few studies have provided a dedicated, up-to-date synthesis of MHD-specific incidence, mortality, and disability-adjusted life years (DALYs) trends across both global and regional levels through 2021. This study addresses this gap by conducting a focused analysis using the most recent GBD 2021 data to examine temporal trends and regional disparities in MHD burden over the past three decades. Recent systematic reviews provide important updates on regional prevalence: in China, a pooled prevalence of MHD was estimated at 7.30% (95% CI: 6.60–8.00) across 58 studies [10]; in Ethiopia, the pooled prevalence of MHD and preeclampsia was 6.82% (95% CI: 5.90–7.74) based on 34 studies [11]; in sub-Saharan Africa, a systematic review covering 19 countries reported a prevalence of approximately 8% between 2000 and 2018 [12]; and in India, the overall pooled prevalence of MHD was estimated at 11% (95% CI: 5–17%) from 18 studies involving 92,220 pregnant women [13]. These findings emphasize the persistent global burden and regional heterogeneity of MHD.
GBD provides a large data set to measure the global impact of MHD among women of childbearing age (WCBA). By analyzing data on incidence, mortality, and DALYs, the GBD study offers valuable insights into the trends and disparities in MHD burden across different regions and socioeconomic strata [14–17]. This study aims to provide an up-to-date analysis of the global burden of MHD from 1990 to 2021, using the GBD 2021 database, with a particular focus on examining the epidemiological trends and regional disparities across different countries. Through rigorous analysis of the GBD data, this study seeks to shed light on the evolving landscape of MHD, highlighting areas of progress and persistent challenges, particularly in LMICs. The findings of this study may also contribute to a deeper understanding of the evolving challenges posed by MHD and to inform the development of targeted interventions to improve maternal and neonatal health outcomes worldwide.
Methods
Data sources
Data of this study were obtained from the Global Burden of Disease (GBD) 2021 database, a comprehensive source of epidemiological data on more than 370 diseases and injuries across 204 countries and territories, hosted by the Institute for Health Metrics and Evaluation (IHME, https://vizhub.healthdata.org/gbd-results/). The GBD database includes data on incidence, mortality, and DALYs, providing an invaluable resource for understanding the global burden of diseases like MHD[18]. The data collection, processing, and analysis methods within the GBD 2021 have been thoroughly documented in previous publications [19, 20]. MHD cases were defined using the International Classification of Diseases (ICD), specifically ICD-9 (codes 642-642.9) and ICD-10 (codes O10-O16.9), to capture all forms of hypertensive disorders during pregnancy, including chronic hypertension, gestational hypertension, and preeclampsia [20, 21]. Our analysis targeted women aged 15–49 years, aligning with internationally accepted definitions for reproductive-aged populations in public health studies. The data were stratified by regional, national, and socioeconomic variables, with a focus on countries categorized by the Socio-Demographic Index (SDI), which combines factors such as education levels, per capita income, and fertility rates. SDI was divided into five categories: low, low-middle, middle, high-middle, and high [22].
ASR Estimation and the estimated annual percentage change (EAPC) model
To facilitate comparisons across populations with varying age structures, the study calculates age-standardized rates (ASRs) for MHD incidence, mortality, and DALYs. To compute the ASR per 100,000 individuals with MHD in the 15–49 age group, we used the following formula [23]:
 |
where 
:the age-specific rate in the age group; 
: the number of people in the corresponding 
age group among the standard population; A: the number of age groups. Age standardization aims to remove the influence of the population age composition and ensure comparability of the study indicators, which are based on GBD 2021 Standard Demographic Structure Data.
Pearson’s correlation analysis was used to evaluate the relationship between ASR and SDI. The EAPC was applied to elucidate the trends in the global and regional burden of MHD. EAPC was estimated using a linear regression model, providing the percentage change per year during a specified period. The sign of EAPC indicates whether the disease burden is increasing or decreasing [22]. EAPC is calculated as follows:
 |
 |
x represents the year, y is the natural logarithm of ASRα is the intercept, β is the slope, and ε is the random error term.
Joinpoint regression model
To assess the temporal trends of MHD from 1990 to 2021, we employed a Joinpoint regression analysis (Version 5.4.0) with ASRs calculated above. This model uses piecewise regression based on a log-linear approach to identify inflection points in the trends. The grid search method (GSM) was applied to calculate all potential joinpoints, and the one with the smallest mean square error (MSE) was selected as the best joinpoint. The model allows a maximum of 5 connections and a minimum of 0 connections. A Monte Carlo permutation test was used to determine the optimal number of joinpoints. The annual percentage change (APC) and average annual percentage change (AAPC), along with the corresponding 95% confidence intervals (CI), were used to quantify the temporal changes in MHD burden [24]. Statistical significance set at P < 0.05.
Cross-Country inequality analysis
To quantify inequalities in the burden of MHD across countries and regions, we calculated the slope index of inequality (SII) and concentration index (CI). The SII was estimated by performing a weighted least squares regression, using population size as weights, on the ASR and the relative SDI ranking of 204 countries and territories. In order to ensure optimal control for bias and heterogeneity, a robust regression model (rlm) was employed in the health inequality analysis, as opposed to the utilisation of an ordinary linear regression model (lm) [24]. The SII represents the absolute inequality in health outcomes between the highest and lowest socioeconomic groups [25]. The CI is a measure of relative inequality, indicating the concentration of health outcomes within disadvantaged or advantaged groups. The CI was calculated through numerical integration of the Lorenz curve, which compares the cumulative proportion of MHD ASR with the cumulative population distribution ordered by SDI. A greater deviation from the 45° line indicates a more unequal distribution of disease burden. The larger the absolute value of the SII, the higher the level of inequality [26].
Decomposition analysis and bayesian Age-Period-Cohort (BAPC) model
Decomposition analysis (details in Supplementary methods) was performed to quantify the contributions of age structure, population growth, and epidemiological changes to the overall burden of MHD. The decomposition approach breaks down the total change in disease burden into these factors, allowing for the assessment of their contributions [24]. Understanding these trends helps to identify whether they contribute to the increase or decrease of MHD burden, providing valuable insights for public health policy development.
Additionally, we utilized the Bayesian age-period-cohort (BAPC) model (details in Supplementary methods) to predict the MHD burden globally from 2022 to 2050. The BAPC model employs the integrated nested LaPlace approximation (INLA) to approximate marginal posterior distributions, effectively overcoming the issues of mixing and convergence associated with Markov Chain Monte Carlo (MCMC) methods [27]. The BAPC model has been widely used for analyzing chronic disease trends and forecasting future disease burden [28]. This modeling approach presumes the continuation of established age-period-cohort effect patterns during the forecasting timeframe, unless significant external disturbances occur [29]. All data analyses and visualizations were performed using R (version 4.3.2), with statistical significance set at P < 0.05.
Results
Incidence, mortality, and dalys of MHD in females
In 2021, the global burden of MHD comprised 17,997,261 incident cases (95% uncertainty interval [UI]: 13,301,344.81-23,382,988.71), 37,579 deaths (95% UI: 31,003.76-45,724.37), and 2,438,170 DALYs (95% UI: 2,030,621.37-2,934,268.93) (Table 1, Supplementary Table S1 and S2). Notably, age-standardized rates showed consistent declines from 1990 to 2021: incidence decreased from 1,115.99 to 930.68 per 100,000 females (estimated annual percentage change [EAPC]: −0.5%, 95% confidence interval [CI]: −0.56% to −0.45%); mortality declined from 3.85 to 1.94 per 100,000 (EAPC: −2.16%, −2.21% to −2.1%); and DALYs fell from 245.5 to 126.62 per 100,000 (EAPC: −2.1%, −2.16% to −2.04%). Importantly, Low SDI regions bore disproportionate burdens, accounting for 6,729,350 cases (37.4% global total), 18,308 deaths (48.7%), and 1,177,942 DALYs (48.3%).
Table 1.
Incidence of MHD in women between 1990 and 2021 at the global and regional levels
| Rate per 100 000 (95% UI) location |
1990 | 2021 | 1990–2021 | ||
|---|---|---|---|---|---|
| Incidence Cases | Incidence Rate | Incidence Cases | Incidence Rate | EAPC(95%CI) | |
| Global | 15610773.27 (11094808.29-21226360.53) | 1115.99 (795.28-1512.95) | 17997261.34 (13301344.81-23382988.71) | 930.68 (687.19-1210.17) | −0.5 (−0.56 - −0.45) |
| High SDI | 1230572.68 (824119.81-1788042.13) | 543.05 (362.93-790.26) | 1233638.81 (906874.43-1645579.78) | 505.13 (371.3-674.35) | −0.48 (−0.66 - −0.31) |
| High-middle SDI | 1486499.13 (961273.49-2182861.03) | 506.38 (327.81-742.96) | 1247096.29 (879022.13-1725282.79) | 418.02 (293.42-579.74) | 0.05 (−0.31–0.41) |
| Middle SDI | 4040249.51 (2793054.87-5636041.37) | 849.38 (590.09-1180.11) | 3,687,932 (2699112.45-4906907.05) | 605.48 (442.83-805.87) | −0.72 (−0.87 - −0.57) |
| Low-middle SDI | 4717868.39 (3358831.3-6359941.88) | 1658.28 (1183.47-2231.3) | 5087965.66 (3747325.9-6635953.97) | 981.36 (723.65-1278.68) | −1.83 (−1.99 - −1.68) |
| Low SDI | 4125375.21 (3043738.06-5314074.2) | 3608.64 (2685-4599.67) | 6729349.72 (5003790.23-8605471.87) | 2420.68 (1814.72-3067.25) | −1.28 (−1.4 - −1.16) |
| Andean Latin America | 59979.16 (47934.85–76079) | 612.64 (489.33-777.87) | 101259.29 (89906.83-116715.01) | 574.99 (510.74-662.41) | −0.16 (−0.36–0.04) |
| Australasia | 35013.46 (25725.43-45943.95) | 647.27 (474.88-850.12) | 33807.84 (23023.71-49177.69) | 453.26 (308.63-659.44) | −1.03 (−1.23 - −0.84) |
| Caribbean | 93054.62 (61042.82-137549.09) | 939.16 (617.64-1384.47) | 82868.07 (55999.61-120129.16) | 692.29 (467.77-1003.61) | −0.95 (−1.02 - −0.87) |
| Central Asia | 70752.7 (46601.85-104660.43) | 396.62 (260.73-585.89) | 91588.64 (61257.96-133330.8) | 364.07 (243.81-529.42) | 0.41 (0.11–0.7) |
| Central Europe | 112548.87 (69657.02-173024.1) | 388.48 (240.16-597.66) | 80758.7 (56444.54-113488.45) | 342.33 (238.64-483.01) | −0.29 (−0.64–0.06) |
| Central Latin America | 676340.37 (493076.83-894749.1) | 1514.2 (1109.64-1995.45) | 614886.88 (484831.3-765516.42) | 907.72 (715.83–1130) | −0.98 (−1.32 - −0.64) |
| Central Sub-Saharan Africa | 562063.35 (404415.46-732786.12) | 4442.35 (3232.16-5727.84) | 876365.7 (619457.62-1161482.65) | 2666.47 (1897.78-3508.44) | −1.51 (−1.63 - −1.38) |
| East Asia | 1277083.32 (811650.16-1915093.86) | 345.89 (220.87-517.95) | 708235.63 (464573.26-1043517.84) | 219.37 (143.56-322.98) | −0.57 (−1.7–0.58) |
| Eastern Europe | 429725.42 (266315.05-635568.53) | 818.03 (504.18-1212.37) | 372232.88 (246758.9-523399.17) | 844.12 (554.29-1192.23) | 1.12 (0.65–1.59) |
| Eastern Sub-Saharan Africa | 1968337.99 (1440876.18-2513013.77) | 4462.07 (3309.78-5613.34) | 3076719.95 (2302013.42-3871266.58) | 2834.04 (2141.58-3526.03) | −1.39 (−1.52 - −1.26) |
| High-income Asia Pacific | 178,977 (124589.23-250759.44) | 417.2 (290.16-585.16) | 115153.55 (88993.85-147836.05) | 311.53 (239.99-401.82) | −1.52 (−1.99 - −1.05) |
| High-income North America | 579342.9 (377570.21-854885.68) | 787.92 (511.71-1165.52) | 625611.18 (479921.29-794148.87) | 746.25 (572.12-947.58) | −0.47 (−0.7 - −0.23) |
| North Africa and Middle East | 1087885.88 (708629.39-1590537.28) | 1384.24 (907.31-2010.95) | 1202844.43 (789444.7-1733459.77) | 746.1 (489.5-1075.95) | −1.49 (−1.65 - −1.32) |
| Oceania | 17867.19 (11579-26276.18) | 1141.05 (742.52-1673.89) | 33423.92 (22007.87-49021.49) | 948.01 (625.11-1388.45) | −0.71 (−0.76 - −0.67) |
| South Asia | 4011475.28 (2841308.93-5534028.82) | 1491.85 (1056.21-2058.23) | 3474487.67 (2472204.31-4745335) | 683.9 (486.51-933.95) | −2.8 (−3.17 - −2.44) |
| Southeast Asia | 1192306.3 (781535.75-1719845.27) | 961.5 (632.86-1380.26) | 1141454.06 (782372.62-1598494.35) | 623.98 (427.2-874.58) | −1.32 (−1.39 - −1.26) |
| Southern Latin America | 107352.1 (67434.37-161007.95) | 857.52 (538.99-1285.32) | 134370.05 (100240.01-180375.38) | 773.88 (576.01-1041.49) | −0.1 (−0.2–0.01) |
| Southern Sub-Saharan Africa | 341560.63 (243446.67-439955.41) | 2423.37 (1741.33-3096.71) | 366,001 (267305.04-461383.25) | 1639.92 (1195.63-2069.93) | −1.08 (−1.14 - −1.02) |
| Tropical Latin America | 407168.74 (272916.89-600120.95) | 954.62 (641.72-1401.99) | 370258.09 (275338.52-493711.73) | 628.43 (467.17-838.71) | −1.22 (−1.49 - −0.95) |
| Western Europe | 371969.4 (247342.94-541680.45) | 385.26 (256.05-561.46) | 386953.9 (271883.65-541945.84) | 421.59 (295.46–592.2) | 0.41 (0.27–0.54) |
| Western Sub-Saharan Africa | 2029968.59 (1506827.54-2554655.4) | 4540.72 (3410.67-5639.66) | 4107979.91 (3104215.01-5090823.09) | 3378.94 (2577.75-4145.9) | −0.84 (−1.02 - −0.66) |
MHD Maternal Hypertensive Disorders, EAPC Estimated Annual Percentage Change, SDI Socio-demographic Index, UI Uncertainty Interval, CI Confidence Interval
Geographic analysis revealed striking disparities: Western Sub-Saharan Africa showed the highest age-standardized incidence rate (3,378.94/100,000) versus East Asia’s lowest (219.37/100,000) (Fig. 1A; Table 1). Similarly, Central Sub-Saharan Africa exhibited peak age-standardized mortality (8.27/100,000) and DALYs rates (507.98/100,000), contrasting with High-income Asia Pacific’s minimal rates (mortality: 0.02/100,000; DALYs: 3.83/100,000) (Fig. 1B and C, Supplementary Table S1 and S2).
Fig. 1.
The burdens of MHD among 204 countries and territories in 2021. A Age-standardized incidence rates (ASIR) of MHD per 100,000 population. B Age-standardized mortality rates (ASMR) of MHD per 100,000 population. C Age-standardized Disability-Adjusted Life Years (DALYs) rates (ASDR) per 100,000 population. The choropleth maps illustrate the geographic distribution of MHD burden in 2021. Higher rates are represented by darker shades, emphasizing regional disparities. DALYs reflect the total disease burden, combining years of life lost due to premature death and years lived with disability. MHD, Maternal Hypertensive Disorders
At the national level, India reported the highest number of MHD cases in 2021, with a total of 2,292,023.27 cases (95% UI: 1,652,926.66-3,108,244.74). South Sudan exhibited the highest age-standardized incidence rate, at 4,705.92 per 100,000 women. Kazakhstan had the greatest increase in MHD incidence, with an annual percentage change of 2.24% (95% CI: 1.75-2.74%). Regarding mortality, India recorded the highest number of female deaths attributable to MHD, totaling 5,566.31 deaths (95% UI: 4,099.54-7,411.96). South Sudan again showed the highest age-standardized mortality rate, at 13.8 per 100,000 women. Guam experienced the highest increase in mortality rate, with an EAPC of 2.65% (95% CI: 2.02-3.29%). In terms of DALYs, India bore the heaviest burden, accumulating 350,522.31 DALYs (95% UI: 260,152.81–460,820). Chad had the highest age-standardized DALYs rate, at 887.36 per 100,000 women. Zimbabwe witnessed the largest increase in DALYs rate, with an EAPC of 1.85% (95% CI: 1.0-2.71%). For more detailed information on the national-level disease burden of MHD, please refer to Supplementary Table S4.
Temporal trends in incidence, mortality, and DALYs burden of MHD across global and different SDI regions
Joinpoint regression analysis revealed that between 1990 and 2021, the global age-standardized incidence rate for female MHD showed an overall downward trend, albeit with a relatively small AAPC of −0.6% (95% CI: −0.67 to −0.52). Notably, two periods saw significant declines: 1990–1994 (AAPC=−1.76%, 95% CI: −2.01 to −1.52) and 2005–2009 (AAPC=−1.72%, 95% CI: −2.11 to −1.34). All regions across different SDI levels exhibited declining trends in incidence rates, with the most pronounced decrease observed in Low-middle SDI regions (AAPC=−1.72%, 95% CI: −1.92 to −1.52), and the least decline in High SDI regions (AAPC=−0.24%, 95% CI: −0.42 to −0.06), as shown in Fig. 2A.
Fig. 2.
Changes in Age-standardized Incidence, Mortality, and DALYs Rates per 100,000 Population for MHD Globally and Across Five SDI Regions from 1990 to 2021. A Age-standardized incidence rate per 100,000 population. B Age-standardized mortality rate per 100,000 population. C Age-standardized DALYs rate per 100,000 population. Data are presented globally and stratified by five SDI regions: low, low-middle, middle, high-middle, and high SDI. Trends over time are depicted with APC and AAPC estimates, with statistically significant changes indicated by *P < 0.05. A, Incident rates. B, Death rates. C, DALYs rates. DALYs, Disability-Adjusted Life-Years; MHD, Maternal Hypertensive Disorders
For age-standardized mortality rates, the global trend was more pronounced, with an AAPC of −2.18% (95% CI: −2.3 to −2.05). Significant reductions were particularly evident during 1990–1992 (AAPC=−3.31%, 95% CI: −4.58 to −2.02) and 2017–2021 (AAPC=−2.74%, 95% CI: −3.14 to −2.33). All regions experienced decreasing trends in mortality rates, with the largest decrease in High-middle SDI regions (AAPC=−5.5, 95% CI: −5.97 to −5.03) and the smallest in Low SDI regions (AAPC=−2.67, 95% CI: −2.76 to −2.59), as depicted in Fig. 2B. Similarly, the age-standardized DALYs rate for female MHD also demonstrated a general downward trend globally and across all SDI levels, as detailed in Fig. 2C.
Correlation between MHD burden and SDI
Figure 3 illustrates the negative correlation between MHD’s age-standardized incidence rate (ASIR) and SDI at both regional (R = −0.66, p < 0.001) and national (R = −0.78, p < 0.001) levels. Globally, the observed ASIR exceeded the expected ASIR. From 1990 to 2021, the actual ASIR in Western Sub-Saharan Africa, Central Sub-Saharan Africa, Southern Sub-Saharan Africa, Central Latin America, and High-income North America was higher than what would be expected based on their SDI (Fig. 3A). Similar negative correlations were observed for MHD’s age-standardized deaths rate (ASDR) (regional R = −0.80, p < 0.001; national R = −0.77, p < 0.001) and age-standardized DALYs (AS-DALYs) rate (regional R = −0.81, p < 0.001; national R = −0.78, p < 0.001) with SDI at both regional and national levels (Supplementary Figures S1, S2).
Fig. 3.
Correlation between MHD Incidence and SDI at Regional (A) and National (B) Levels. A Temporal trends of MHD incidence from 1990 to 2021, stratified by SDI regions (low, low-middle, middle, high-middle, and high SDI). The black line represents the expected incidence rate based on SDI trends across all regions. B Cross-sectional analysis of MHD incidence in 204 countries and territories in 2021, categorized by SDI. The black regression line illustrates the expected relationship between SDI and MHD incidence at the national level. MHD, Maternal Hypertensive Disorders; SDI, Socio-demographic Index
Decomposition analysis of MHD burden
Through decomposition analysis of MHD’s incidence, deaths, and DALYs, this study evaluated the impact of aging, population growth, and epidemiological changes on MHD epidemiology from 1990 to 2021 (Fig. 4 and Table 2). Results indicated that, except for High-middle SDI and Middle SDI regions, the incidence numbers increased globally and across other SDI levels. Globally, 267.02% of the IR change could be attributed to population growth, followed by epidemiological changes (−130.35%) and aging (−36.67%). In Low SDI regions, these determinants had the greatest influence on incidence, with contributions of aging (−1.64%), population growth (191.87%), and epidemiological changes (−90.23%). These findings suggest that in Low SDI regions, population growth (191.87%) serves as the primary driver of incidence numbers increase, likely reflecting case number surges caused by high fertility rates and expanding youth populations. Meanwhile, the negative contribution of epidemiological changes (−90.23%) indicates that improved prevention measures or diagnostic criteria may have partially counterbalanced the impact of population growth. For mortality, except for Low SDI regions, there was a declining trend globally and across other SDI levels. Aging had the least impact on disease burden in Low-middle SDI regions (2.5%), and population growth and epidemiological changes had the least impact in High-middle SDI regions (−6.83% and 101.45%, respectively). In real-world terms, the minimal 2.5% impact of aging on mortality in Low-middle SDI regions reflects their relatively young population structures that have not yet undergone significant demographic aging. Conversely, the substantial positive contribution of epidemiological changes (101.45%) in High-middle SDI regions likely stems from advancements in medical technology and the implementation of early screening policies. In real-world terms, the minimal 2.5% impact of aging on mortality in Low-middle SDI regions reflects their relatively young population structures that have not yet undergone significant demographic aging. Conversely, the substantial positive contribution of epidemiological changes (101.45%) in High-middle SDI regions likely stems from advancements in medical technology and the implementation of early screening policies.
Fig. 4.
Decomposition of population-level determinants influencing Maternal Hypertension Disorders (MHD) burden globally and across SDI regions from 1990 to 2021. The contributions of three key components—aging, population growth, and epidemiological changes—are analyzed to determine their respective impacts on MHD burden. Black dots indicate the total net change attributed to all three factors combined. Positive values signify an increase in disease burden, while negative values represent a reduction. A incidence; (B) mortality; (C) DALYs. MHD, Maternal Hypertensive Disorders; DALYs, disability-adjusted life years; SDI, socio-demographic index
Table 2.
Age-standardized Incidence, deaths and dalys rates of MHD with decomposition analysis, categorized by global and SDI regions
| Location | Measure | Overll.difference | Aging | Population | Epidemiological.change |
|---|---|---|---|---|---|
| Global | Incidence | 2386488.07 |
−875229.48 (−36.67%) |
6372427.77 (267.02%) |
−3110710.23 (−130.35%) |
| High SDI | Incidence | 3066.13 |
−62203.72 (−2028.74%) |
85781.96 (2797.73%) |
−20512.11 (−668.99%) |
| High-middle SDI | Incidence | −239402.84 |
−161862.05 (67.61%) |
126552.02 (−52.86%) |
−204092.81 (85.25%) |
| Middle SDI | Incidence | −352317.51 |
−297740.83 (84.51%) |
1275228.78 (−361.95%) |
−1329805.45 (377.45%) |
| Low-middle SDI | Incidence | 370097.26 |
−95534.64 (−25.81%) |
3183214.16 (860.1%) |
−2717582.27 (−734.29%) |
| Low SDI | Incidence | 2603974.51 |
−42716.44 (−1.64%) |
4996351.09 (191.87%) |
−2349660.14 (−90.23%) |
| Global | Deaths | −15591.6 |
−1788.96 (11.47%) |
17961.18 (−115.2%) |
−31763.82 (203.72%) |
| High SDI | Deaths | −201.47 |
−9.09 (4.51%) |
16.57 (−8.22%) |
−208.95 (103.71%) |
| High-middle SDI | Deaths | −1646.03 |
−88.59 (5.38%) |
112.48 (−6.83%) |
−1669.92 (101.45%) |
| Middle SDI | Deaths | −5503.08 |
−318.55 (5.79%) |
2642.32 (−48.02%) |
−7826.84 (142.23%) |
| Low-middle SDI | Deaths | −8942.94 |
−223.55 (2.5%) |
12895.54 (−144.2%) |
−21614.94 (241.7%) |
| Low SDI | Deaths | 703.1 |
−68.58 (−9.75%) |
18233.46 (2593.31%) |
−17461.79 (−2483.56%) |
| Global | DALYs | −998,259 |
−173474.54 (17.38%) |
1160152.44 (−116.22%) |
−1984936.89 (198.84%) |
| High SDI | DALYs | −12655.82 |
−1345.28 (10.63%) |
1874.76 (−14.81%) |
−13185.31 (104.18%) |
| High-middle SDI | DALYs | −103572.3 |
−9358.09 (9.04%) |
8154.1 (−7.87%) |
−102368.31 (98.84%) |
| Middle SDI | DALYs | −355633.8 |
−37564.87 (10.56%) |
173287.73 (−48.73%) |
−491356.66 (138.16%) |
| Low-middle SDI | DALYs | −586141.47 |
−32623.23 (5.57%) |
822967.43 (−140.4%) |
−1376485.67 (234.84%) |
| Low SDI | DALYs | 59852.58 |
−7194.5 (−12.02%) |
1162202.84 (1941.78%) |
−1095155.77 (−1829.76%) |
MHD Maternal Hypertensive Disorders, DALYs Disability-Adjusted Life-Years, SDI Socio-demographic Index
Regarding MHD’s DALYs, except for Low SDI regions, there was a declining trend globally and across other SDI levels.
Health inequality analysis of MHD burden
The study revealed significant absolute and relative inequalities in the burden of MHD incidence, deaths, and DALYs rates globally. Poorer low SDI regions disproportionately bore a higher disease burden. According to the slope index of inequality, the gap in ASIR between the highest and lowest SDI countries and regions in 1990 was − 3,974.36 (95% CI: −4,342.09 to −3,606.63), which narrowed to −2,059.95 (95% CI: −2,331.22 to −1,788.68) by 2021, without a significant change in CI (Fig. 5; Table 3). The slope index of inequality for ASDR decreased from − 14.92 (95% CI: −16.00 to −13.84) in 1990 to −5.01 (95% CI: −5.55 to −4.47) in 2021, with no significant change in CI (Figure S3, Table 3). Similarly, the slope index of inequality for AS-DALYs rate decreased from − 925.60 (95% CI: −989.94 to −861.27) in 1990 to −321.57 (95% CI: −356.52 to −286.61) in 2021, with no significant change in CI (Figure S4, Table 3). These findings indicate that while absolute inequalities have decreased, relative inequalities persist, making global health inequality regarding MHD a long-standing issue warranting attention.
Fig. 5.
Global Regression and Concentration Curves for Inequality in MHD Incidence, 1990 and 2021. (A) The Slope Index of Inequality (SII) curve illustrates the relationship between the SDI and age-standardized incidence rates of MHD across countries and territories. Each point represents a country or territory, with marker size proportional to population scale. The regression lines for 1990 (blue) and 2021 (red) indicate changes in the inequality gradient over time. (B) The Concentration Index (CI) curve quantifies relative inequality by integrating the area under the Lorenz curve, which aligns the distribution of MHD incidence rates with the population distribution according to SDI. A higher deviation from the line of equality reflects greater inequality in disease burden. MHD, Maternal Hypertensive Disorders; SII, Slope Index of Inequality; CI, Concentration Index; SDI, Socio-demographic Index
Table 3.
Age-standardized incidence, deaths and dalys rates of MHD, along with slope index of inequality and concentration index analysis, categorized by global, and SDI regions
| Region | Measure | Year | Slope index of inequality | SII_lower | SII_upper | Concentration_index | CI_lower | CI_upper |
|---|---|---|---|---|---|---|---|---|
| Global | Incidence | 1990.00 | −3974.36 | −4342.09 | −3606.63 | −0.33 | −0.39 | −0.27 |
| Global | Incidence | 2021.00 | −2059.95 | −2331.22 | −1788.68 | −0.35 | −0.42 | −0.29 |
| Global | Deaths | 1990.00 | −14.92 | −16.00 | −13.84 | −0.58 | −0.66 | −0.50 |
| Global | Deaths | 2021.00 | −5.01 | −5.55 | −4.47 | −0.60 | −0.68 | −0.52 |
| Global | DALYs | 1990.00 | −925.60 | −989.94 | −861.27 | −0.57 | −0.64 | −0.49 |
| Global | DALYs | 2021.00 | −321.57 | −356.52 | −286.61 | −0.58 | −0.66 | −0.50 |
| High SDI | Incidence | 1990.00 | −332.74 | −623.49 | −41.99 | 0.02 | −0.07 | 0.11 |
| High SDI | Incidence | 2021.00 | −269.19 | −468.64 | −69.73 | −0.04 | −0.13 | 0.04 |
| High SDI | Deaths | 1990.00 | −0.11 | −0.19 | −0.04 | −0.32 | −0.65 | 0.02 |
| High SDI | Deaths | 2021.00 | 0.00 | 0.00 | 0.01 | −0.13 | −0.28 | 0.02 |
| High SDI | DALYs | 1990.00 | −10.79 | −17.87 | −3.72 | −0.18 | −0.40 | 0.03 |
| High SDI | DALYs | 2021.00 | −3.48 | −6.32 | −0.63 | −0.08 | −0.18 | 0.01 |
| High-middle SDI | Incidence | 1990.00 | −674.14 | −1313.66 | −34.63 | 0.14 | 0.09 | 0.19 |
| High-middle SDI | Incidence | 2021.00 | −24.46 | −361.50 | 312.58 | 0.20 | 0.12 | 0.27 |
| High-middle SDI | Deaths | 1990.00 | −1.75 | −2.79 | −0.72 | 0.04 | −0.12 | 0.20 |
| High-middle SDI | Deaths | 2021.00 | −0.08 | −0.32 | 0.17 | 0.01 | −0.12 | 0.14 |
| High-middle SDI | DALYs | 1990.00 | −112.34 | −178.75 | −45.94 | 0.05 | −0.09 | 0.19 |
| High-middle SDI | DALYs | 2021.00 | −4.46 | −22.36 | 13.43 | 0.10 | 0.01 | 0.19 |
| Middle SDI | Incidence | 1990.00 | −845.19 | −1214.35 | −476.03 | 0.01 | −0.08 | 0.09 |
| Middle SDI | Incidence | 2021.00 | −317.96 | −591.23 | −44.68 | 0.05 | −0.03 | 0.13 |
| Middle SDI | Deaths | 1990.00 | −1.31 | −2.76 | 0.13 | −0.12 | −0.27 | 0.02 |
| Middle SDI | Deaths | 2021.00 | −0.62 | −1.24 | 0.00 | −0.09 | −0.26 | 0.07 |
| Middle SDI | DALYs | 1990.00 | −89.16 | −178.42 | 0.09 | −0.12 | −0.26 | 0.02 |
| Middle SDI | DALYs | 2021.00 | −41.49 | −79.59 | −3.39 | −0.08 | −0.23 | 0.07 |
| Low-middle SDI | Incidence | 1990.00 | −953.55 | −2068.64 | 161.54 | −0.10 | −0.18 | −0.01 |
| Low-middle SDI | Incidence | 2021.00 | −607.89 | −1356.13 | 140.35 | −0.25 | −0.37 | −0.14 |
| Low-middle SDI | Deaths | 1990.00 | −8.12 | −10.94 | −5.31 | −0.22 | −0.29 | −0.15 |
| Low-middle SDI | Deaths | 2021.00 | −1.99 | −3.53 | −0.45 | −0.33 | −0.45 | −0.20 |
| Low-middle SDI | DALYs | 1990.00 | −508.03 | −691.62 | −324.43 | −0.22 | −0.29 | −0.16 |
| Low-middle SDI | DALYs | 2021.00 | −122.01 | −219.04 | −24.98 | −0.33 | −0.45 | −0.20 |
| Low SDI | Incidence | 1990.00 | −1179.20 | −1871.85 | −486.54 | −0.03 | −0.08 | 0.02 |
| Low SDI | Incidence | 2021.00 | −2351.15 | −3392.64 | −1309.66 | −0.11 | −0.16 | −0.05 |
| Low SDI | Deaths | 1990.00 | −3.08 | −10.22 | 4.07 | −0.02 | −0.10 | 0.05 |
| Low SDI | Deaths | 2021.00 | −2.98 | −6.70 | 0.74 | −0.03 | −0.11 | 0.05 |
| Low SDI | DALYs | 1990.00 | −200.77 | −617.89 | 216.34 | −0.03 | −0.09 | 0.04 |
| Low SDI | DALYs | 2021.00 | −207.52 | −431.39 | 16.35 | −0.04 | −0.12 | 0.03 |
MHD Maternal Hypertensive Disorders, DALYs Disability-Adjusted Life-Years, SDI Socio-demographic Index, SII Slope Index of Inequality, CI Concentration Index
BAPC model prediction of MHD burden
Using the BAPC model, we forecasted the MHD burden from 2021 to 2050 (Fig. 6). Results suggest a continued decline in the global MHD burden. By 2050, the number of new MHD cases across all ages is projected to decrease to 12,107,748.56 (95% CI: 17,477.93 to 28,043,993.40), with deaths reduced to 21,889.35 (95% CI: 0 to 51,894.60), and DALYs reduced to 1,397,425.86 (95% CI: 0 to 3,292,715.34). Correspondingly, age-standardized rates are expected to continue declining. Detailed predictions for each disease burden indicator are provided in Supplementary Table S5.
Fig. 6.
Global trends and future projections of Maternal Hypertension Disorders (MHD) burden from 1990 to 2050. A, B Number of MHD cases and corresponding ASIR per 100,000 population. C, D Number of MHD-related deaths and corresponding ASDR per 100,000 population. E, F Number of DALYs due to MHD and corresponding AS-DALYs rate per 100,000 population. Solid lines represent observed values from 1990 to 2021, while dashed lines depict projected trends from 2022 to 2050, as estimated by the BAPC model. The shaded region indicates the 95% prediction interval, providing uncertainty estimates for future burden. MHD, Maternal Hypertensive Disorders; BAPC, Bayesian Age-Period-Cohort model
Discussion
Overview of findings
This study provides a comprehensive analysis of the global burden of MHD from 1990 to 2021, emphasizing trends in declining incidence, mortality, and DALYs. Our findings reveal a consistent decline in the age-standardized incidence, mortality, and DALYs associated with MHD from 1990 to 2021, with the most substantial decrease in incidence observed in low-middle SDI regions. These reductions likely reflect the positive impact of improved maternal care, better antenatal surveillance, and increased global health investments. However, low-SDI regions, particularly Sub-Saharan Africa, continue to bear a disproportionate burden, with Central Sub-Saharan Africa reporting the highest ASDR at 8.27 per 100,000 females in 2021. These findings align with those of Jiang et al., who documented similar regional trends and identified Sub-Saharan Africa as a hotspot for MHD-related mortality due to limited healthcare access and inadequate resource allocation [15].
The negative correlation between MHD burden and socioeconomic development, as measured by SDI, underscores the critical role of social determinants of health such as education, income, and access to healthcare services. Middle-income countries, despite economic growth, face rising MHD burdens due to increasing rates of obesity, advanced maternal age, and environmental risks like air pollution [30]. For instance, exposure to PM2.5 has been associated with heightened odds of MHD, illustrating the multifaceted nature of risk factors and the need for comprehensive maternal health policies [9].
Implications of regional disparities
Our analysis revealed that although the global age-standardized incidence, mortality, and DALYs rates of MHD have declined from 1990 to 2021, low-SDI regions continue to bear a disproportionate burden, accounting for 37.4% of incident cases, 48.7% of deaths, and 48.3% of DALYs in 2021. Striking regional disparities were observed, with Western and Central Sub-Saharan Africa exhibiting the highest age-standardized rates, while high-income Asia Pacific reported the lowest. At the national level, countries such as India, South Sudan, and Chad demonstrated the greatest absolute and relative MHD burdens, reflecting stark inequities in maternal health systems. Joinpoint regression analysis confirmed downward trends in MHD burden globally and across all SDI categories; however, the pace of decline varied. The most substantial reductions occurred in high-middle SDI regions, while low-SDI regions showed only modest progress. One possible explanation for this discrepancy is the concurrent decline in fertility rates observed in higher-SDI regions. Notably, while our results are based on age-standardized mortality rates, they are calculated relative to the female population aged 15–49 years rather than the number of pregnancies or births. This may introduce bias, as regions with lower fertility may show greater reductions in AAPC due to a smaller population at risk, rather than genuine improvements in maternal health outcomes. Thus, interpreting trends in MHD mortality requires consideration of fertility dynamics and the underlying exposure population.
Health inequality analysis further demonstrated that although absolute disparities in MHD burden have narrowed over the past three decades, relative inequalities remain persistent, underscoring the entrenched structural inequities in global maternal healthcare. Decomposition analysis highlighted population growth as the dominant driver of increasing incidence rates in low-SDI regions, while the negative contribution of epidemiological changes suggests that existing prevention and diagnostic improvements were insufficient to offset the effects of demographic expansion. These findings call for targeted investments in maternal health systems, including scaling up antenatal care coverage, strengthening community-based health services, and promoting maternal health education. Brazil’s public health reforms, which have improved outcomes among marginalized populations, underscore the importance of equitable health systems. Meanwhile, persistent racial and socioeconomic disparities in the United States illustrate the need to address systemic barriers to maternal care [31, 32].
To address these persistent inequalities, a comprehensive global approach is essential. This includes improving antenatal care infrastructure, investing in health information systems to reduce misclassification and underreporting, and standardizing diagnostic criteria across regions. Advancing research on the biological, environmental, and behavioral drivers of MHDs, such as the role of gut microbiota will also be vital [33]. Furthermore, enhancing global collaboration, promoting technology-driven solutions [34], and aligning maternal health policies with social determinants are critical steps toward equitable outcomes.
Limitations and study weaknesses
While this study provides valuable insights into the global trends and regional disparities in MHD burden, several limitations must be acknowledged. First, the accuracy of GBD estimates is highly dependent on the quality of primary data, which varies considerably across countries. In LMICs, where vital registration systems are often incomplete or unreliable, underreporting and systematic misclassification of maternal deaths and MHD diagnoses are common. For example, maternal deaths and diagnoses of hypertensive disorders may not be adequately captured in regions with poor healthcare infrastructure. Second, there is a lack of standardized diagnostic criteria for MHD across regions. This clinical heterogeneity, such as differences in the defining of preeclampsia or gestational hypertension, can lead to misclassification bias, compromising the comparability of estimates between countries and SDI groups. These classification errors may systematically distorts incidence, mortality, and DALYs estimates and undermines cross-country comparability. This study’s reliance on ecological data from the GBD study introduces the risk of ecological fallacy, where associations at the country level do not necessarily reflect individual-level patterns. While the correlation between SDI and MHD burden is statistically significant, it may not capture the individual-level determinants of MHD, such as genetic predispositions, access to care, and behavioral factors. Future studies that incorporate individual-level data could provide deeper insights into the causal mechanisms of MHD. Another limitation is that our mortality estimates were standardized by the total female population aged 15–49 years, rather than by the number of pregnancies or live births. As fertility rates have declined in many regions over the past three decades, especially in high-SDI countries, this may result in an overestimation of mortality reduction trends, since the true population at risk (i.e., pregnant women) has decreased more than the general reproductive-age population. The absence of pregnancy-specific denominators in the GBD dataset constrains our ability to fully capture the changing risk pool. Future research should consider fertility-adjusted rates where pregnancy or birth data are available to improve accuracy and comparability. Furthermore, the BAPC prediction model has several inherent limitations. First, predictive bias increases and precision declines when forecasting long-term trends (e.g., beyond 15 years) [35]. Second, the model heavily relies on trend continuity; consequently, any future trend reversals (e.g., sudden incidence changes due to prevention interventions or diagnostic criteria modifications) would significantly compromise prediction accuracy [36]. These limitations suggest that while the BAPC model holds substantial value in epidemiological research, its results require cautious interpretation and application.
Conclusion
In conclusion, this study underscores the dynamic nature of the global MHD burden, highlighting both progress in reducing mortality and persistent challenges. These findings emphasize the urgent need for a multifaceted approach that strengthens healthcare systems, promotes research into the underlying causes of MHDs, and addresses the social determinants of health. Moving forward, a global commitment to prioritizing maternal health is crucial. A sustained commitment to policy refinement and investment in maternal health programs will be essential to mitigating the global impact of hypertensive disorders in pregnancy.
Supplementary Information
Acknowledgements
We would like to express our sincere gratitude to the Institute for Health Metrics and Evaluation (IHME) for providing access to the Global Burden of Disease (GBD) 2021 database, which was fundamental to conducting this study. We also acknowledge the efforts of the researchers and organizations involved in the GBD study for their contributions in gathering and curating this valuable global health data.
Abbreviations
- MHD
Maternal hypertensive disorders
- GBD
Global burden of disease
- DALYs
Disability-adjusted life years
- SDI
Socio-demographic index
- EAPC
Estimated annual percentage change
- AAPC
Average annual percentage change
- ASR
Age-standardized rate
- SII
Slope index of inequality
- CI
Concentration index
- BAPC
Bayesian age-period-cohort
- INLA
Integrated nested LaPlace approximation
- R
Pearson’s correlation coefficient
- ASIR
Age-standardized incidence rate
- ASMR
Age-standardized mortality rate
- ASDALYs
Age-standardized DALYs
- IHME
Institute for health metrics and evaluation
- IRB
Institutional review board
- BMI
Body mass index
- PM2.5
Particulate matter 2.5
Author contributions
Conceptualization, J.Y.W. (Jiayu Wang), J.X.W. (Jingxuan Wang) and T.L.Y (Tailang Yin); methodology and formal analysis, J.X.W. (Jingxuan Wang) and Y.G. (Yu Guan); writing—original draft preparation J.Y.W. (Jiayu Wang), J.X.W. (Jingxuan Wang) and Y.G. (Yu Guan); writing—review and editing, J.Y.W. (Jiayu Wang), J.L. (Jia Liang) and L.H.D (Lianghui Diao); Supervising: L.H.D (Lianghui Diao), T.L.Y (Tailang Yin) and D.D.T. (Dongdong Tang). All authors have read and agreed to the published version of the manuscript.
Funding
This study was supported by the grants from National Key Research and Development Program of China (2023YFC2705700), National Natural Science Foundation of China (82371684, 82271672), Guangdong Basic and Applied Basic Research Foundation (2023A1515011675, 2024A1515012355). The authors have declared no conflict of interest.
Data availability
The original data presented in this study are openly available in the GBD 2021 results (https://vizhub.healthdata.org/gbd-results/).
Declarations
Ethics approval and consent to participate
This study utilized publicly available, de-identified data from the Global Burden of Disease (GBD) 2021 database, which does not require ethical approval from an institutional review board (IRB). The data were collected in accordance with ethical standards set by the Institute for Health Metrics and Evaluation (IHME), and informed consent was obtained from the original participants involved in the GBD study.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Jiayu Wang, Jingxuan Wang and Yu Guan contributed equally to this work.
Contributor Information
Dongdong Tang, Email: tangdongdong@ahmu.edu.cn.
Tailang Yin, Email: reproductive@whu.edu.cn.
Lianghui Diao, Email: diaolianghui@gmail.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The original data presented in this study are openly available in the GBD 2021 results (https://vizhub.healthdata.org/gbd-results/).