Global burden and inequality of maternal and neonatal disorders: based on data from the 2019 Global Burden of Disease study

Summary

Background

Maternal and neonatal disorders account for substantial health loss across the lifespan from early childhood. These problems may be related to health inequality.

Aim

To provide evidence for improvement in health policies regarding maternal and neonatal disorder inequity.

Design

This was a population-based cross-sectional study based on 2019 Global Burden of Disease data.

Methods

Annual cases and age-standardized rates (ASRs) of incidence, prevalence, death, and disability-adjusted life-years (DALYs) in maternal and neonatal disorders between 1990 and 2019 were collected from the 2019 Global Burden of Disease study. Concentration curves and concentration indices were used to summarize the degree of socioeconomic-related inequality.

Results

For maternal disorders, the global ASRs of incidence, prevalence, death and DALYs were 2889.4 (95% uncertainty interval (UI), 2562.9–3251.9), 502.9 (95% UI 418.7–598.0), 5.0 (95% UI 4.4–5.8) and 324.9 (95% UI 284.0–369.1) per 100 000 women in 2019, respectively. The ASRs of maternal disorders were all obviously reduced and remained pro-poor from 1990 to 2019. In neonatal disorders, the global ASRs of incidence, prevalence, death and DALYs were 363.3 (95% UI 334.6–396.8), 1239.8 (95% UI 1142.1–1356.7), 29.1 (95% UI 24.8–34.5) and 2828.3 (95% UI 2441.6–3329.6) per 100 000 people in 2019, respectively. The global ASRs of incidence, death and DALYs in neonatal disorders have remained pro-poor. However, the socioeconomic-related fairness in the ASR of neonatal disorder prevalence is being levelled.

Conclusions

The global burden of maternal and neonatal disorders has remained high, and socioeconomic-related inequality (pro-poor) tended not to change between 1990 and 2019.

Background

Maternal and child health is the primary task of public health affairs in all countries worldwide.¹ One important reason is that maternal and child health may significantly affect the physical, emotional, psychological and socioeconomic health of women and their families.² The mortality and morbidity of maternal and neonatal disorders affect a large proportion of women and children. According to the latest data released by the World Health Organization (WHO), the maternal mortality ratio remained at approximately 211 maternal deaths per every 100 000 live births, which resulted in approximately 295 000 deaths in year 2017.³ Meanwhile, every year an estimated 4 million of the 130 million babies born die in the neonatal period.^4,5 Most of these deaths occurred in low- and middle-income countries.^6,7 Most notably, maternal and neonatal disorders affect a large proportion of women and children in developed countries.^8,9 Additionally, according to estimates from the WHO, approximately 15% of all pregnant women experience acute severe obstetric complications each year, including obstetric haemorrhage, maternal sepsis and other maternal infections, maternal hypertensive disorders, obstructed labour and uterine rupture and abortion and miscarriage.^8,10 These maternal disorders affect the health of foetuses, newborns and women themselves.^10,11 Similarly, complications of neonatal preterm birth, intrapartum conditions, neonatal sepsis and other neonatal infections could increase the risk of neonatal death, especially neonatal sepsis and acute respiratory infections.⁷

Providing high-quality care during the perinatal period requires more developed health systems, including education and skilled medical personnel, special equipment, referral processes and other support structures.¹² For maternal and newborn care service providers, not all hospitals in a region can provide these comprehensive services. The high mortality rate in perinatal women and newborns is often caused by insufficient healthcare during pregnancy and childbirth. Additionally, high neonatal mortality is often associated with maternal health. Inequality and inability to access basic maternal healthcare services have been identified as the main causes of global mortality.^5,13

In the past few decades, health inequalities have always been a hot topic.¹⁴ Health inequality between and within countries is often judged to be inequitable because it could be greatly reduced or even eliminated through robust health plans. Since the 1990s, measuring and striving to reduce inequities in health care has been an important aspect of the global health agenda.¹²,14–16 An increasing number of studies are focusing on the issue of health inequality among pregnant women, newborns and children in low- and middle-income countries.¹⁴ Progress has been made in some areas, such as the use of insecticidal mosquito nets for malaria prevention, exclusive breastfeeding and immunization.¹² However, in many countries, inequality in maternal and child mortality has become serious and presents a worsening trend.¹⁴ By 2015, the global maternal mortality ratio declined by 44%, failing to achieve the ambitious target of 75%, and the maternal mortality ratio was still almost 20 times higher in low-income countries than in high-income countries.³ Meanwhile, the global neonatal mortality rate decreased by 48% in the same period to 19.1 per 1000 live births, and approximately 2.6 million newborns died in 2015, with the majority of deaths occurring in developing countries. The pace of decline is falling far short of the Sustainable Development Goals.^7,17 Worryingly, the 10 countries with the highest maternal mortality rates, including Afghanistan, Nigeria, Sierra Leone, Chad, Central African Republic, Guinea-Bissau, Liberia, South Sudan, Somalia and Mauritania, have stagnated or slowed their annual mortality rate decline. Moreover, most maternal and neonatal deaths are caused by ‘preventable conditions’, such as maternal infections, noncommunicable diseases and obstetric complications.⁷ The source of maternal and child health inequality may be associated with income inequality. This conclusion is based on the evidence that since the late 1970s, income inequality in many countries has been steadily increasing. The intensification of income inequality affects the health, human capital and income distribution of contemporary and future generations.¹⁸ In summary, maternal and neonatal health inequalities are a global issue that has far-reaching and serious consequences. Improving maternal and children’s health and reducing health inequality have become challenges that the global community must face together. It is time to consider the problem on a global scale and pick up the pace to meet the global target of maternal and newborn mortality by 2030. It is necessary to identify areas with poor accessibility and review inequalities in maternal and newborn health to effectively plan health services.

Because of the lack of reliable data on the mortality and morbidity of maternal and neonatal disorders, especially in low- and middle-income countries, reducing the mortality and morbidity of maternal and neonatal disorders and improving inequality have been hampered. At present, there are several sporadic works to estimate the global and national mortality and morbidity of specific diseases, while the work to estimate the mortality and morbidity from a series of comprehensive causes is more limited.⁹,19–26 The latest assessment by the Maternal and Child Epidemiology Estimation (MCEE) team reported estimates for 15 groups of causes of child deaths in 194 countries between 2000 and 2015.²⁷ The Global Health Estimates (GHE) programme of the WHO recently released estimates of 176 causes of death in 183 countries from 2000 to 2015.²⁸

However, it is unclear whether the health equity of maternal and neonatal disorders has changed in different years, regions and sexes. It is notable that the 2019 Global Burden of Disease (GBD) study provided the possibility of integrating newly available datasets and standardizing methods.²⁹ GBD 2019 reported the burden of maternal and neonatal disorders categorized as one group,²⁹ including 10 maternal disorders (maternal haemorrhage, sepsis and other maternal infections, hypertensive disorders, obstructed labour and uterine rupture, abortion and miscarriage, ectopic pregnancy, indirect maternal deaths, late maternal deaths, maternal deaths aggravated by human immunodeficiency virus (HIV)/acquired immune deficiency syndrome (AIDS), and other maternal disorders) and five neonatal disorders (neonatal preterm birth, encephalopathy due to birth asphyxia and trauma, sepsis and other neonatal infections, haemolytic disease and other neonatal jaundice, and other neonatal disorders). The comparative advantage of the GBD study data is that they provide the only comprehensive annual assessment of a detailed set of potential causes by age, sex, location and year, enhancing the opportunity for comparison across time and between locations.

In this context, we aimed to evaluate the global burden and health equity of maternal and neonatal disorders. Our findings will enable national and regional policy-makers to develop strategies to manage the current and future burden of maternal and neonatal disorders and have an active effect on improving equity in health status, health services utilization and provision.

Methods

This study is a population-based cross-sectional study based on the 2019 GBD data. The GBD study is supervised by the Institute of Health Measurement and Evaluation (IHME).^1,30 GBD research data come from censuses, household registration, air pollution monitoring, satellite imaging and other sources. As a continuous improvement project, the GBD research data include annual disease burden from 1990 to 2019. The official website of the GBD provides detailed instructions on applying the GBD database (https://www.healthdata.org/gbd/2019). Global Health Data Exchange (GHDx) was an online data source query tool used to obtain data sources for the disease burden (https://vizhub.healthdata.org/gbd-results/). Various indicators, such as ‘GBD Estimate’, ‘Cause’, ‘Location’ and ‘Year’, can be selected in the query tool. In this study, we obtained data on the annual number of cases for the incidence, prevalence, death and disability-adjusted life-years (DALYs) of maternal and neonatal disorders at all-age levels and age-standardized rates (ASRs) including the 95% uncertainty interval (UI) from 1990 to 2019. The GBD world population age standard was used as the age-standardized population. A detailed introduction to the GBD age-standardized population is provided in a previous article.²⁹ The UI provided by the GBD databases represents the confidence interval (CI). This study is part of the GBD 2019. The cause-of-death ensemble model and spatiotemporal Gaussian process regression were used to calculate the cause-specific death rates and cause fractions. To ensure consistency between incidence, prevalence and cause-specific mortality of most causes, the Bayesian meta-regression modelling tool DisMod-MR 2.1 was used. DALYs are a comprehensive measure of health losses caused by both fatal and nonfatal outcomes. DALYs are equal to the sum of estimated years lived with disability and years of life lost.²⁹ For administrative and data analysis purposes, the world has been divided into 21 GBD regions based on epidemiological similarity and geographic proximity. The Socio-demographic Index (SDI) is a composite indicator of socioeconomic per capita.²⁹ The SDI was developed by the Human Development Index method,³¹ which is an indicator of the social and economic conditions that could affect health outcomes in each region. In the 2019 GBD, calculation of the SDI combines three major indicators: lag-distributed income per capita, average education level for individuals aged 15 years and older and the total birth rate in women younger than 25 years.²⁹ The SDI levels range from 0 (low SDI) to 100 (high SDI): with quintiles used to describe the SDI levels, including low, low-middle, middle, high-middle and high SDI. A lower SDI level means a lower socioeconomic development level.²⁹

The causes of maternal and neonatal disorders were based on the categories of causes listed in the 2019 GBD study.²⁹ Based on the International Classification of Diseases-10th Revision (ICD-10), each disease had an operational definition³² (Table S1, Supplementary Material, p 3).

We ended up with 333 960 records used to analyse the burden and health equity of maternal disorders and 427 680 records used to analyse the burden and health equity of neonatal disorders. We analysed the incidence, prevalence, deaths and DALYs of maternal and neonatal disorders worldwide by 21 GBD regions, SDI, sex, age and categories of causes. The estimated annual percentage changes (EAPCs) were calculated to evaluate the changing trends of ASR. EAPC was calculated as previously described.³³ In brief, EAPC = 100 × (exp(β) − 1), y = α + βx + ε, where x is year, y=In (ASR). ASR was in an increasing trend if the lower boundary of the EAPC’s 95% CI was >0, or, conversely, ASR was in a decreasing trend if the upper boundary of the EAPC’s 95% CI was <0. In other cases, ASR was stable over time. Concentration curves and concentration indeces³⁴ were used to summarize the degree of socioeconomic-related inequality in the burden of maternal and neonatal disorders. A concentration curve plots the cumulative percentage of ASR in incidence, prevalence, death and DALYs (y-axis) against the cumulative percentage of population ranked by SDI, from the poorest to the richest (x-axis). If the concentration curve is below the line of equality (the 45° line), it indicates that the maternal and neonatal disorder-induced disease burden is higher among high-SDI countries; otherwise, the disease burden is higher among low-SDI countries. We computed the concentration index based on the concentration curve.

Concentration index = 0 means that the health outcome has complete equal distribution, and the concentration curve is consistent with the equality line. A concentration index >0 demonstrates the concentration of adverse health outcomes (disease burden) among richer (high-SDI) countries, and the concentration curve is placed below the equality line, and vice versa. All statistics were performed using STATA (Version Stata/MP 14.0), R programme (Version 4.1.3) and RStudio 2022.02.1 Build 461. A two-tailed P-value of <0.05 was considered to indicate statistical significance. The statistical code used for GBD estimation in our study is publicly available online (see Supplementary Materials). Missing data were reported in the footnotes. Imputation methods were not used for missing data. The analysis code in RStudio and STATA is openly available at the following website: https://pan.baidu.com/s/1VgJ8AwSPl1HprX6wxVOxMw?pwd=4321.

Results

Maternal disorders

The number and ASR of incidence, prevalence, deaths and DALYs for maternal disorders in 2019, and changing trends from 1990 to 2019 are presented in Table 1, Figure 1 and Table S2 (Supplementary Material, p 4). The global numbers and ASRs of deaths and DALYs had similar trends, clearly decreasing from 1990 to 2019 (Figure 1). The global DALY count of maternal disorders decreased from 192.24 × 10⁵ (95% UI 174.78–213.29) in 1990 to 126.76 × 10⁵ (110.89–143.92) in 2019. Meanwhile, the global age-standardized DALY rate of maternal disorders decreased from 695.1 (95% UI 631.9–771.0) per 100 000 women to 324.9 (95% UI 284.0–369.1) per 100 000 women, and the global age-standardized DALY rate decreased with an EAPC of −2.67 (95% CI −2.75 to −2.59) from 1990 to 2019 (Table 1). The number of deaths attributed to maternal disorders was 196.47 × 10³ (95% UI 171.20–225.8) in 2019 globally, and the global age-standardized death rate (ASDR) of maternal disorders was 5.0 (95% UI 4.4–5.8) per 100 000 women in 2019. The global ASDR decreased with an EAPC of −2.80 (95% CI −2.89 to −2.71) from 1990 to 2019 (Table S2, Supplementary Material, p 4). The new cases and numbers of patients with maternal disorders remained relatively stable, but the ASR of incidence and prevalence showed a decreasing tendency from 1990 to 2019 (Figure 1). In 2019, there were 1119.94 × 10⁵ (95% UI 992.80.2–1261.12) newly diagnosed maternal disorders worldwide, compared with 1227.19 × 10⁵ (95% UI 1072.29–1389.62) in 1990. The global age-standardized incidence rate (ASIR) of maternal disorders decreased from 4259.3 (95% UI 3733.4–4819.3) per 100 000 women in 1990 to 2889.4 (95% UI 2562.9–3251.9) per 100 000 women in 2019, and the global ASIR decreased with an EAPC of −1.20 (95% CI −1.26 to −1.15) from 1990 to 2019 (Table 1). In addition, the accumulated number of maternal disorders decreased from 208.78 × 10⁵ (95% UI 171.94–251.77) in 1990 to 196.49 × 10⁵ (95% UI 163.30–233.23) in 2019 globally, the global age-standardized prevalence rate (ASPR) of maternal disorders decreased from 742.8 (95% UI 616.0–888.8) per 100 000 women in 1990 to 502.9 (95% UI 418.7–598.0) per 100 000 women in 2019, and the global ASPR decreased with an EAPC of −1.20 (95% CI −1.27 to −1.12) from 1990 to 2019 (Table S2, Supplementary Material, p 4).

In 2019, a high number of new cases of maternal disorders were recorded in India (18 454 929), whereas a few new cases were recorded in Niue (16). Niger had the largest annual rise in the number of new cases of maternal disorders from 1990 to 2019 (increased 201.20%), and Japan had the largest annual fall in the number of new cases of maternal disorders from 1990 to 2019 (decreased 66.29%). Niger recorded the highest (9210.6/100 000 women) ASIR in 2019, whereas Singapore had the lowest ASIR (435.6/100 000 women) among the 204 countries or territories. Spain had the largest annual rise in the ASIR of maternal disorders from 1990 to 2019 (EAPC = 1.23, 95% CI 0.92–1.55), and Japan had the largest annual fall in the ASIR of maternal disorders from 1990 to 2019 (EAPC = −4.73, 95% CI −4.09 to −5.36). A high DALY count of maternal disorders was recorded in India (2 841 256), whereas a few DALY counts were recorded in Niue (1). Belize had the largest annual rise in the DALY count of maternal disorders from 1990 to 2019 (increased 148.36%), and China had the largest annual fall in the DALY count of maternal disorders from 1990 to 2019 (decreased 88.65%). Chad recorded the highest ASR of DALYs (2806.5/100 000 women) in 2019, whereas Cyprus and Singapore had the lowest ASR of DALYs (6.6/100 000 women) among the 204 countries or territories. Georgia had the largest annual rise in ASR-DALYs of maternal disorders from 1990 to 2019 (EAPC = 3.98, 95% CI 5.27–2.70), and the Maldives had the largest annual fall in ASR-DALYs of maternal disorders from 1990 to 2019 (EAPC = −8.54, 95% CI −9.24 to −7.83); see Figure 2 and Supplementary Material (the file of the burden of maternal disorders at the national level, Supplementary Material, p 21).

The ASRs of incidence, prevalence, death and DALYs in maternal disorders were all highest in low SDI regions and low-middle SDI regions, followed by middle SDI regions, high-middle SDI regions and high SDI regions. The most significant reduction occurred in SDI under 0.5 (Figure S1, Supplementary Material, p 13).

The ASRs of incidence and prevalence in maternal disorders were highest in those aged 20–29 years, and the most significant reduction occurred in those aged 20–29 years. The ASRs of death and DALYs in maternal disorders were highest in the age group of 20–39 years, and the most significant reduction occurred in the age group of 20–39 years (Figure S2, Supplementary Material, p 14).

From 1990 to 2019, there were changes in the sequence of the categories of maternal disorders. In 2019, maternal haemorrhage was the leading cause of DALYs in maternal disorders, followed by other maternal disorders, hypertensive disorders, indirect maternal deaths, abortion and miscarriage, sepsis and other maternal infections, obstructed labour and uterine rupture, late maternal deaths, ectopic pregnancy, and deaths aggravated by HIV/AIDS (Figure S3, Supplementary Material, p 15). Approximately 24% of DALYs due to maternal disorders were caused by maternal haemorrhage (Table 1). When continent-level (21 GBD regions) age-standardized DALYs were compared with the global average for the categories causes of maternal disorders, Central Sub-Saharan Africa, Eastern Sub-Saharan Africa and Western Sub-Saharan Africa were the regions with significantly higher values for all causes. High-income Asia Pacific, Central Asia, East Asia, Australasia, Europe, Central Latin America, Southern Latin America, Tropical Latin America and High-income North America were the regions with significantly lower values for all causes (Figure S4, Supplementary Material, p 16).

The concentration curves and concentration indices for the ASIR, ASPR, ASDR and ASR-DALYs of maternal disorders from 1990 to 2019 are presented in Figure 3 and Table 2. All the concentration curves were above the line of equality and statistically significant (Figure 3), suggesting that the ASIR, ASPR, ASDR and ASR-DALYs of maternal disorders were more concentrated among the lower SDIs between 1990 and 2019. There was no significant difference (P = 9885, 0.9935, 0.9788 and 0.9856, respectively) in the ASRs of incidence, prevalence, death and DALYs for different years (Table 2), which meant that there were no significant changes in SDI-related inequality happened over 30 years.

The regression equation between the concentration indices for ASIR, ASPR, ASDR and ASR-DALYs of maternal disorders and their influencing factors was deduced (Table S3, Supplementary Material, p 7). The regression analysis showed that influencing factors, including location, age, categories of causes and SDI, were statistically correlated with ASIR and ASR-DALYs. Meanwhile, location, year, age, categories of causes and SDI were statistically correlated with ASPR and ASDR. Analysis of the contribution rate on the effect factors of the concentration indices showed that location, age, categories of causes and SDI were the major contributing factors to the concentration indices of ASIR, ASPR, ASDR and ASR-DALYs in maternal disorders.

Neonatal disorders

The global numbers and ASRs of deaths and DALYs had similar trends, clearly decreasing from 1990 to 2019 (Figure 4). The global DALY count of neonatal disorders decreased from 2744.20 × 10⁵ (95% UI 2562.71–2943.03) in 1990 to 1858.86 × 10⁵ (95% UI 1605.01–2185.26) in 2019. Meanwhile, the global age-standardized DALY rate of neonatal disorders decreased from 4198.5 (95% UI 3918.7–4501.3) per 100 000 people in 1990 to 2828.3 (95% UI 2441.6–3329.6) per 100 000 people in 2019, and the global age-standardized DALY rate decreased with an EAPC of −1.30 (95% CI −1.42 to −1.18) from 1990 to 2019 (Table 3). The number of deaths attributed to neonatal disorders was 18.82 × 10⁵ (95% UI 16.06–22.37) in 2019 globally, and the global ASDR of neonatal disorders was 29.1 (95% UI 24.8–34.5) per 100 000 people in 2019. The global ASDR decreased with an EAPC of −1.51 (95% CI −1.66 to −1.36) from 1990 to 2019 (Table S4, Supplementary Material, p 8). The new cases and ASR of incidence remained relatively stable (Figure 3). In 2019, there were 235.32 × 10⁵ (95% UI 216.72–257.02) newly diagnosed neonatal disorders worldwide, compared with 235.93 × 10⁵ (95% UI 219.253–255.71) in 1990. The global ASIR of neonatal disorders was 358.8 (95% UI 333.4–389.0) per 100 000 live birth infants in 1990 and 363.3 (95% UI 334.6–396.8) per 100 000 live birth infants in 2019. The global ASIR remained stable, with an EAPC of 0.0 (95% CI −0.06 to 0.06) from 1990 to 2019 (Table 3). However, the accumulated number and the ASR of prevalence showed an increasing tendency from 1990 to 2019 (Figure 4). The accumulated number of neonatal disorders increased from 474.30 × 10⁵ (95% UI 435.06–515.05) in 1990 to 925.19 × 10⁵ (95% UI 850.67–1012.86) in 2019 globally, the global ASPR of neonatal disorders increased from 826.0 × 10⁵ (95% UI 756.7–898.7) in 1990 to 1239.8 (95% UI 1142.1–1356.7) per 100 000 people in 2019, and the global ASPR increased with an EAPC of 1.53 (95% CI 1.44–1.63) from 1990 to 2019 (Table S4, Supplementary Material, p 8).

In 2019, a high number of new cases of neonatal disorders were recorded in India (4 113 756), whereas a few new cases were recorded in Niue (3). Qatar had the largest annual increase in the number of new cases of neonatal disorders from 1990 to 2019 (increased 178.04%), and Albania had the largest annual decrease in the number of new cases of neonatal disorders from 1990 to 2019 (decreased 63.42%). Yemen recorded the highest (642.4/100 000 live birth infants) ASIR in 2019, whereas Sweden had the lowest ASIR (107.3/100 000 live birth infants) among the 204 countries or territories. Greece had the largest annual rise in the ASIR of neonatal disorders from 1990 to 2019 (EAPC = 3.35, 95% CI 3.13–3.56), and Serbia had the largest annual fall in the ASIR of neonatal disorders from 1990 to 2019 (EAPC = −2.34, 95% CI −2.10 to −2.58). A high DALY count of neonatal disorders was recorded in India (43 186 986), whereas a few DALY counts were recorded in Tokelau (10). Somalia had the largest annual rise in the DALY count of neonatal disorders from 1990 to 2019 (increased 109.94%), and the Cook Islands had the largest annual fall in the DALY count of neonatal disorders from 1990 to 2019 (decreased 87.75%). Pakistan recorded the highest ASR of DALYs (7251.5/100 000 people) in 2019, whereas Japan had the lowest ASR of DALYs (267.8/100 000 people) among the 204 countries or territories. Dominica had the largest annual rise in ASR-DALYs of neonatal disorders from 1990 to 2019 (EAPC = 1.25, 95% CI 1.03–1.49), and Saudi Arabia had the largest annual fall in ASR-DALYs of neonatal disorders from 1990 to 2019 (EAPC = −6.19, 95% CI −6.42 to −5.96); see Figure 5 and Supplementary Material (the file of the burden of neonatal disorders at the national level, Supplementary Material, p 21).

Regarding the SDI groups, the ASRs of death and DALYs due to neonatal disorders were both highest in low SDI regions and tended to decrease with time. The ASIR was relatively stable, and the ASPR showed an increasing tendency with time. The ASRs of incidence, death and DALYs were all highest in low SDI regions, followed by low-middle SDI regions, middle SDI regions, high-middle SDI regions and high SDI regions. The most significant reduction occurred in SDI under 0.4. However, the highest point of ASPR was at an SDI of approximately 0.6 (Figure S5, Supplementary Material, p17).

The sex disparities in ASRs of incidence, prevalence, death and DALYs for neonatal disorders were all unobvious over the past 30 years (Figure 4). Regarding the age groups, the ASRs of incidence, prevalence, death and DALYs for neonatal disorders were all highest in the youngest age groups and decreased with age. Most ASRs of incidence and death for neonatal disorders occurred younger than 27 days. In addition, most ASRs of prevalence and DALYs for neonatal disorders occurred younger than 1 year. The ASR of DALYs due to neonatal disorders occurred younger than 1 year (Figure S6, Supplementary Material, p 18).

From 1990 to 2019, there were no changes in the sequence of the categories of neonatal disorders. In 2019, neonatal preterm birth was the leading cause of DALYs in neonatal disorders, followed by encephalopathy due to birth asphyxia and trauma, other neonatal disorders, sepsis and other neonatal infections, and haemolytic disease and other neonatal jaundice (Figure S7, Supplementary Material, p 19). Approximately 37% of DALYs count due to neonatal disorders were caused by neonatal preterm birth (Table 3). When continent-level (21 GBD regions) age-standardized DALYs were compared with the global average for the categories of causes of neonatal disorders, Africa and South Asia were the regions with significantly higher values for most of the categories of causes of neonatal disorders. High-income Asia Pacific, Central Asia, East Asia, Europe, Australasia, Southern Latin America and High-income North America were the regions with significantly lower values for all causes (Figure S8, Supplementary Material, p 20).

The concentration curves and concentration indices for the ASIR, ASPR, ASDR and ASR-DALYs of neonatal disorders from 1990 to 2019 are presented in Figure 6 and Table 4. Apart from ASPR, the concentration curves of ASIR, ASDR and ASR-DALYs were above the line of equality and statistically significant, suggesting that the ASIR, ASDR and ASR-DALYs of neonatal disorders were more concentrated among the lower SDI regions between 1990 and 2019 (Figure 6). The concentration curves of ASPR approached the equality line (Figure 6), signifying that the ASPR of neonatal disorders was almost equivalent in SDI-related fairness. There was no significant difference (P = 0.9996, 0.6752, 0.4015 and 0.5945, respectively) in the ASRs of incidence, prevalence, death and DALYs among different years (Table 4), which meant that there were no significant changes in SDI-related inequality over 30 years.

The regression equation between the concentration indices for ASIR, ASPR, ASDR and ASR-DALYs of neonatal disorders and their influencing factors was deduced (Table S5, Supplementary Material, p 11). The regression analysis showed that influencing factors, including location, year, sex, age, categories of causes and SDI, were statistically correlated with ASIR, ASPR, ASDR and ASR-DALYs. Analysis of the contribution rate on the effect factors of the concentration indices showed that age, categories of causes and SDI were the major contributing factors to the concentration indices of ASIR, ASPR, ASDR and ASR-DALYs in neonatal disorders.

Discussion

Our study included a comprehensive analysis of the global burden and inequality of maternal and neonatal disorders based on GBD data from 1990 to 2019. The results showed that on a global scale, the number and rates of deaths and DALYs due to maternal and neonatal disorders largely decreased from 1990 to 2019, while the number of incidents of maternal and neonatal disorders remained stable, and the number and rate of prevalence of neonatal disorders generally increased. Approximately 24% of maternal disorders were caused by maternal haemorrhage, and approximately 37% of neonatal disorders were caused by neonatal preterm birth. Location, year, age, categories of causes and SDI were important factors affecting the burden of maternity-related disorders. Among neonatal disorders, location, year, sex, age, categories of causes and SDI were the main factors. Socioeconomic-related inequality (remaining pro-poor) tended not to change between 1990 and 2019. However, the global burden of maternal and neonatal disorders is still too high, and there is an urgent need to develop effective interventions in the perinatal period and reduce the extent of health inequalities in maternal, newborn and child health.

Figures from several different sources indicate that global maternal and neonatal mortality has rapidly decreased in recent decades, but the morbidity and mortality of maternal and neonatal disorders remain too high.³ The number of maternal and neonatal deaths has decreased more slowly than the rate proposed in the Millennium Development Goals (1990–2015) and the newly established Sustainable Development Goals (2016–2030).⁷ From the UN Inter-agency Group for Child Mortality Estimation (UN IGME) database (consisting of WHO, UNICEF, UNFPA, World Bank Group and UNPD), the global maternal mortality ratio fell from 385 deaths per 100 000 live births in 1990 to 216 in 2015 (fell by approximately 44%)¹⁹; however, the reduction in maternal deaths is far from adequate (the Millennium Development Goal embraced cutting the maternal mortality ratio by 75% between 1990 and 2015). Furthermore, according to the UN Maternal Mortality Estimation Inter-Agency Group database (consisting of UNICEF, WHO, the World Bank Group and the UN Population Division), the global neonatal mortality rate decreased from 36.6 deaths per 1000 live births in 1990 to 17.5 in 2019 (a decrease of approximately 52%).³⁵ The reduction in neonatal mortality rate also needs to occur at an accelerated pace: the Sustainable Development Goals call for all countries to reach a neonatal mortality rate lower than 12 deaths per 1000 live births by 2030.35 Our study has produced results essentially in agreement with these figures.

The potentially life-threatening maternal and neonatal disorders did not obviously change. Based on the systematic reviews by Gon et al., postpartum haemorrhage accounts for the maximal proportion of cases of direct maternal morbidities (estimated 6–11%).³⁶ Meanwhile, the study by Moller et al., showed that there was no significant improvement in the incidence of preterm birth from 2010 to 2014. In 2010, the Global Action Report on Pre-Term Birth was released, and the global premature delivery rate was 11.1% of live births; there were 15 million premature births and 1 million deaths from related complications. The premature birth rate in the poorest countries (12–13%) is much higher than that in high-income countries (7–9%). In 2014, there were 14.8 million premature births, with a global premature birth rate of 10.6%.⁷ The above results are reinforced in our study.

Over the past several decades, inequities and inequalities in maternal, newborn and child health have always been hot topics of social concern. By reviewing the practice of the Millennium Development Goals, recent evidence suggests that the population coverage of key interventions provided by health services has increased faster among the poorest quintiles than among the wealthiest quintiles. However, the coverage rate in the poorest quintiles is still significantly lower than that in the wealthier quintiles, and the 74 countries with the highest burden of maternal, newborn, and child mortality account for 97% of the world’s child and maternal deaths.¹⁴ The maternal mortality ratio in the high-income regions was 12 deaths per 100 000 live births, while in sub-Saharan Africa the maternal mortality ratio was 546 in 2015.¹⁹ Impoverished regions worldwide face a greater burden of maternal and neonatal diseases. In addition, from the maternal, neonatal and child health (MNCH) programmes, where the fairness effect was rigorously scrutinized, MNCH programmes have shown that the overall trend of child mortality rates is decreasing, and the inequality in child mortality rates is expanding.¹⁴ However, our research shows that socioeconomic-related inequality (remaining pro-poor) in the burdens of maternal and neonatal disorders tended not to change between 1990 and 2019. The probable reason for the differences (pro-poor inequality worsened or remained unchanged) might be that there are many factors affecting inequality, and socioeconomic status is only one factor.

Having sufficient information on factors affecting the burden and its inequalities could help to prevent and decrease the risk of maternal and neonatal disorders. This study examined the global burden of maternal and neonatal disorders and socioeconomic-related inequality. Here we present evidence that, in maternal disorders, the age distribution showed that maternal disorders affected were mainly women aged between 20 and 40 years old. The regional distribution showed that maternal disorders occurred mainly in low SDI regions. The changing trends of ASRs of maternal disorders showed that the ASIRs, ASPRs, ASDRs and ASR-DALYs of maternal disorders were all obviously reduced, particularly in regions with SDI under 0.4. In neonatal disorders, there were no obvious differences in sex distribution in neonatal disorders. The age distribution showed that the neonatal disorders affected were mainly infants under one year of age. The regional distribution showed that neonatal disorders occurred mainly in low SDI regions, typically in Africa. The changing trends of ASRs of neonatal disorders showed that the ASDR and ASR-DALYs of neonatal disorders were obviously reduced. However, the ASIR remained stable, and the ASPR had an increasing trend over time. The global ASIR, ASDR and ASR of DALYs in neonatal disorders remained pro-poor, and socioeconomic-related inequality tended not to change between 1990 and 2019. However, socioeconomic-related fairness in the ASPR of neonatal disorders is being levelled. These findings will enhance the recognition and care of maternal and neonatal disorders and guide future research. Our results will improve the latest incidence, prevalence, death and DALY estimates and identify the important contributing factors to the burden and health equity of maternal and neonatal disorders. Our research provides targeted and important evidence for public health policy and interventions. Our results could be used by governments at the continental and national levels to identify major health issues and assign priority to health requirements. In addition, our research contributes to evaluating the effectiveness of various continental and national public health policies and interventions.

Previous research has shown that there is limited clinical or epidemiological evidence in maternal and neonatal disorders.¹⁰ Several previous studies have found that maternal and neonatal disorders are difficult to screen and diagnose in poorer areas of the world.³⁷ There exists an imbalance in the locations of studies on maternal and neonatal disorders. However, ethnic differences and pelvic structure might be a possible explanation for the regional differences in variables of maternal and neonatal disorders.³⁷ Having sufficient information on factors affecting the burden of disease and its inequalities could help to prevent and decrease the risk of maternal and neonatal disorders. Using SDI could help to identify areas where potential continental and national policies, laws and regulations helped improve population health worldwide. However, improving health equity has always been a top priority for the government. Targeted strategies need to be used to reduce inequalities and reduce the main risk factors.

The evidence-based solutions suggest that many protective factors could help to improve fairness in maternal and neonatal health status and health services provision and utilization, including socioeconomic determinants, national policies, improvement of maternal and child health care systems, enhancing health-related social rights of women and children (such as education, employment, security, welfare, childbearing and supportive surroundings), healthcare insurance reform and poverty reduction plans, improving the quality of perinatal care, skilled midwives and appropriate management of obstetric complications, and increasing the vaccination rate of influenza vaccine for pregnant women.⁷ An example of success in the field of health equity is China, where the generic security of health rights of women and children, and the lessons from China can help low- or lower-middle-income countries improve health fairness.³⁸

The present study has some limitations. First, international comparisons of the burden of disease should be carried out cautiously, because there is a significant difference in the quality of data on incidence, prevalence, death and DALYs among countries. Second, it was difficult to quantify and remove all variation due to measurement errors in our estimating epidemiological indicators.³⁹ Third, our study is conducted at the global and national levels. The findings have limited quantitative applicability to a specific country. To provide evidence in areas within a country, it is necessary to conduct a study at the micro-level within that country. Fourth, the absence of data from low-income countries, and the lack of studies reporting population-representative estimates for incidence, prevalence, death and DALYs meant that generalized hypotheses had to be made to build a comprehensive model for the distribution of maternal and neonatal disorders. To move from estimations and assumptions towards certainties, future studies should set up special databases. In particular, data on maternal and neonatal disorders from routine surveillance, sentinel surveillance and special surveys in developing countries are urgently needed.

Conclusions

Although the global burden of maternal and neonatal disorders has been reduced, it remains severe. Socioeconomic-related inequality (pro-poor) tended not to change between 1990 and 2019.

Supplementary material

Supplementary material is available at QJMED online.

Acknowledgements

The authors would like to thank the GBD dataset for sharing the valuable data.

Funding

The study was supported by Innovation team project of Clinical Medical college & Affiliated hospital of Chengdu University (Award Number: CDFYCX202202, CDFYCX202204, CDFYCX202205, CDFYCX202208), project of Clinical Medical college & Affiliated hospital of Chengdu University (Award Number: Y202232 and T202234, Recipient: Zheng Shi), the Project of Chengdu Municipal Health Commission (Award Number: 2023073, Recipient: Rong Peng), Sichuan Provincial Science and Technology Foundation (Award Number: 22NZZH0031, Recipient: Zheng Shi; Award Number: 2023JDKP0037, Recipient: Huiqing Wang), program for Excellent Talents in Clinical Medical college & Affiliated hospital of Chengdu University (Recipient: Rong Peng). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conflict of interest

All authors declare that there is no conflict of interest.

Author contributions

Rong Peng (Data curation (equal), Funding acquisition (equal), Methodology (equal), Supervision (equal), Writing—original draft (equal), Writing—review & editing (equal)), Yu Tong (Data curation (equal), Writing—original draft (equal), Writing—review & editing (equal)), Minglong Yang (Formal analysis (equal), Writing—original draft (equal), Writing—review & editing (equal)), Ji Wang (Formal analysis (equal), Writing—original draft (equal), Writing—review & editing (equal)), Lijun Yang (Data curation (equal), Methodology (equal), Writing—original draft (equal), Writing—review & editing (equal)), Junchen Zhu (Writing—original draft (equal), Writing—review & editing (equal)), Yu Liu (Writing—original draft (equal), Writing—review & editing (equal)), Huiqing Wang (Funding acquisition (equal), Writing—original draft (equal), Writing—review & editing (equal)), Zheng Shi (Funding acquisition (equal), Visualization (equal), Writing—original draft (equal), Writing—review & editing (equal)), and Ya Liu (Supervision (equal), Writing—original draft (equal), Writing—review & editing (equal)).

Data availability

The GBD dataset is openly available at https://vizhub.healthdata.org/gbd-results/. The data that support the findings of this study are openly available at https://pan.baidu.com/s/1VlFt_flOxpSYvrpuGW7XiA?pwd=1234.

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References