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Country- and state-wise maternal mortality shows the highest disparity among health statistics. The erstwhile National Rural Health Mission. (NRHM) in India aimed reduction in maternal mortality ratio. (MMR) to <100 per lakh live births. Accordingly, many new initiatives were planned and started. This analysis was carried out using data from the Sample Registration System. The data from 1997 to 1998 are available which dates 8 years prior to the launching of NRHM. Hence, comparison period was considered as 8 years of implementation of NRHM. The overall decline in MMR prior to NRHM was 36% and after NRHM implementation 30%. The difference is not significant. The best states and lowest states had changed, but the disparity also has remained almost at the ratio of 1:5. The pace of decline has not increased after NRHM. As well disparity ratio has not reduced indicating the differentially better treatment to the vulnerable states was not adequate.

A number of development partners, including the World Bank, have been actively supporting the health sector in Afghanistan since 2003-04 (1382 AC). Collectively, they invested more than {dollar}820 million between 2003 (1382 AC) and 2008-09 (1387 AC) and played key roles in supporting the government in reshaping the country's health sector. This support continues, with all partners starting new projects aimed at further strengthening the sector and building on the successes that have been achieved. The book is organized as follows. Chapters one-four tell a coherent story about the achievements of the sector between 2002 and 2008 (1381-87AC), the financial resources used to achieve the results, and the contribution the private sector has made to the achievements. Chapters five-eight) look forward. They identify the challenges the sector is facing in meeting human resource needs, expanding the coverage of the basic package of health services (BPHS), and increasing the institutional capacity of the Ministry of Public Health (MoPH). Chapter eight summarizes the lessons learned and provides options for moving forward.

Background Kenya abolished delivery fees in all public health facilities through a presidential directive effective on June 1, 2013 with an aim of promoting health facility delivery service utilization and reducing pregnancy-related mortality in the country. This paper aims to provide a brief overview of this policy’s effect on health facility delivery service utilization and maternal mortality ratio and neonatal mortality rate in Kenyan public health facilities. Methods A time series analysis was conducted on health facility delivery services utilization, maternal and neonatal mortality 2 years before and after the policy intervention in 77 health facilities across 14 counties in Kenya. Results A statistically significant increase in the number of facility-based deliveries was identified with no significant changes in the ratio of maternal mortality and the rate of neonatal mortality. Conclusion The findings suggest that cost is a deterrent to health facility delivery service utilization in Kenya and thus free delivery services are an important strategy to promote utilization of health facility delivery services; however, there is a need to simultaneously address other factors that contribute to pregnancy-related and neonatal deaths.

The long-term trends in maternal and child health (MCH) in China and the national-level factors that may be associated with these changes have been poorly explored. This study aimed to assess trends in MCH indicators nationally and separately in urban and rural areas and the impact of public policies over a 30‒year period. An ecological study was conducted using data on neonatal mortality rate (NMR), infant mortality rate (IMR), under-five mortality rate (U5MR), and maternal mortality ratio (MMR) nationally and separately in urban and rural areas in China from 1991 to 2020. Joinpoint regression models were used to estimate the annual percentage changes (APC), average annual percentage changes (AAPC) with 95% confidence intervals (CIs), and mortality differences between urban and rural areas. From 1991 to 2020, maternal and child mortalities in China gradually declined (national AAPC [95% CI]: NMRs − 7.7% [− 8.6%, − 6.8%], IMRs − 7.5% [− 8.4%, − 6.6%], U5MRs − 7.5% [− 8.5%, − 6.5%], MMRs − 5.0% [− 5.7%, − 4.4%]). However, the rate of decline nationally in child mortality slowed after 2005, and in maternal mortality after 2013. For all indicators, the decline in mortality was greater in rural areas than in urban areas. The AAPCs in rate differences between rural and urban areas were − 8.5% for NMRs, − 8.6% for IMRs, − 7.7% for U5MRs, and − 9.6% for MMRs. The AAPCs in rate ratios (rural vs. urban) were − 1.2 for NMRs, − 2.1 for IMRs, − 1.7 for U5MRs, and − 1.9 for MMRs. After 2010, urban‒rural disparity in MMR did not diminish and in NMR, IMR, and U5MR, it gradually narrowed but persisted. MCH indicators have declined at the national level as well as separately in urban and rural areas but may have reached a plateau. Urban‒rural disparities in MCH indicators have narrowed but still exist. Regular analyses of temporal trends in MCH are necessary to assess the effectiveness of measures for timely adjustments.

Maternal mortality ratio (MMR) estimates have been studied over time for understanding its variation across the country. However, it is never sufficient without accounting for presence of variability across in terms of space, time, maternal and system level factors. The study endeavours to estimate and quantify the effect of exposures encompassing all maternal health indicators and system level indicators along with space–time effects influencing MMR in India. Using the most recent level of possible -factors of MMR, maternal health indicators from the National Family Health Survey (NFHS: 2019–21) and system level indicators from government reports a heatmap compared the relative performance of all 19 SRS states. Facet plots with a regression line was utilised for studying patterns of MMR for different states in one frame. Using Bayesian Spatio-temporal random effects, evidence for different MMR patterns and quantification of spatial risks among individual states was produced using estimates of MMR from SRS reports (2014–2020). India has witnessed a decline in MMR, and for the majority of the states, this drop is linear. Few states exhibit cyclical trend such as increasing trends for Haryana and West Bengal which was evident from the two analytical models i.e., facet plots and Bayesian spatio- temporal model. Period of major transition in MMR levels which was common to all states is identified as 2009–2013. Bihar and Assam have estimated posterior probabilities for spatial risk that are relatively greater than other SRS states and are classified as hot spots. More than the individual level factors, health system factors account for a greater reduction in MMR. For more robust findings district level reliable estimates are required. As evident from our study the two most strong health system influencers for reducing MMR in India are Institutional delivery and Skilled birth attendance.

Background Maternal morbidity and mortality remain critical health concerns globally. As a result, reducing the maternal mortality ratio (MMR) is part of goal 3 in the global sustainable development goals (SDGs), and previously, it was an important indicator in the Millennium Development Goals (MDGs). Therefore, identifying high-risk groups during pregnancy is crucial for decision-makers and medical practitioners to mitigate mortality and morbidity. However, the availability of accurate predictive models for maternal mortality and maternal health risks is challenging. Compared with traditional predictive models, machine learning algorithms have emerged as promising predictive modelling methods providing accurate predictive models. Methods This work aims to explore the potential of machine learning (ML) algorithms in maternal risk level prediction using a nationwide maternal mortality dataset from Oman for the first time. A total of 402 maternal deaths from 1991 to 2023 in Oman were included in this study. We utilised principal component analysis (PCA) in the ML algorithms and compared them to the results of model performance without PCA. We employed and compared ten ML algorithms, including decision tree (DT), random forest (RF), K—Nearest Neighbors (KNN), Naïve Bayes (NB), Extreme Gradient Boosting (xgboost), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Logistic Regression (LR), Support Vector Machine (SVM) and Artificial Neural Network (ANN). Different metrics, including, accuracy, sensitivity, precision, and the F1- score, were utilised to assess Model performance. Results The results indicated that the RF model outperformed the other methods in predicting the risk level (low or high) with an accuracy of 75.2%, precision of 85.7% and F1- score of 73% after PCA was applied. Conclusions We applied several machine learning models to predict maternal risk levels for the first time using real data from Oman. RF outperformed the other algorithms in this classification problem. A reliable estimate of maternal risk level would facilitate intervention plans for medical practitioners to reduce maternal death.

Background Maternal sepsis and other maternal infections (MSMI) have considerable impacts on women’s and neonatal health, but data on the global burden and trends of MSMI are limited. Comprehensive knowledge of the burden and trend patterns of MSMI is important to allocate resources, facilitate the establishment of tailored prevention strategies and implement effective clinical treatment measures. Methods Based on data from the Global Burden of Disease database, we analysed the global burden of MSMI by the incidence, death, disability-adjusted life year (DALY) and maternal mortality ratio (MMR) in the last 30 years. Then, the trends of MSMI were assessed by the estimated annual percentage change (EAPC) of MMR as well as the age-standardized rate (ASR) of incidence, death and DALY. Moreover, we determined the effect of sociodemographic index (SDI) on MSMI epidemiological parameters. Results Although incident cases almost stabilized from 1990 to 2015, the ASR of incidence, death, DALY and MMR steadily decreased globally from 1990 to 2019. The burden of MSMI was the highest in the low SDI region with the fastest downward trends. MSMI is still one of the most important causes of maternal death in the developed world. Substantial diversity of disease burden and trends occurred in different regions and individual countries, most of which had reduced burden and downward trends. The MMR and ASR were negatively correlated with corresponding SDI value in 2019 in 204 countries/territories and 21 regions. Conclusion These findings highlight significant improvement in MSMI care in the past three decades, particularly in the low and low-middle SDI regions. However, the increased burden and upward trends of MSMI in a few countries and regions are raising concern, which poses a serious challenge to maternal health. More tailored prevention measures and additional resources for maternal health are urgently needed to resolve this problem.

Background Unacceptably high levels of preventable maternal deaths persist across sub-Saharan Africa. Due to limited research on maternal mortality in Sudan, a thorough examination is crucial to develop effective reduction strategies. This study aims to analyze maternal mortality trends at national and subnational levels in Sudan from 2009 to 2019. Methods In this retrospective-comparative study, the researchers reviewed mortality data covering 2009 to 2019 from the reports issued by the national maternal death surveillance and response. The maternal mortality ratios for the national and state levels were adjusted based on the population of women of reproductive age. The trends were assessed for statistical significance using the Mann–Kendall test, implemented in Python (version 3.12). The cut-off p -value for significance was taken as < 0.05. Results The national maternal mortality ratio declined significantly by nearly 60% from 2009 to 2019 (S = -53, p <  0.001). The states of Kassala (S = -51, p <  0.001), Gadarif (S = -43, p <  0.001), Gezira (S = -41, p =  0.002), White Nile (S = -41, p =  0.002), Blue Nile (S = -39, p =  0.003), Red Sea (S = -39, p =  0.003), Khartoum (S = -39, p =  0.003), Northern State (S = -27, p =  0.043), River Nile (S = -27, p =  0.043), and Sinnar (S = -27, p =  0.043) showed significant declining trends. Blue Nile state recorded the highest average maternal mortality ratio in the study period (339.76), while Southern Darfur (66.46) and River Nile (89.59) recorded the lowest ratios. Major causes of maternal death include Obstetric hemorrhage (45.5%), hypertensive disorders (16%), and sepsis (12.6%). Important characteristics of pregnancy-related death include condition at admission, gestational age, antenatal care, mode of delivery, and areas of delay. Conclusions The national maternal mortality ratio significantly declined between 2009 and 2019, with wide regional disparities. Direct causes of maternal death remain a critical challenge. Effective strategies or frameworks focused on reducing maternal mortality ratios in Sudan are strongly solicited.

Background Maternal mortality ratio is an important indicator to evaluate the health status in developing countries. Previous studies on maternal mortality ratio in China were limited to certain areas or short periods of time, and there was a lack of research on correlations with public health funding. This study aimed to assess the trends in the maternal mortality ratio, the causes of maternal death, and the correlations between maternal mortality ratio and total health financing composition in China from 1990 to 2019. Methods Data in this longitudinal study were collected from the China Health Statistics Yearbooks (1991–2020) and China Statistical Yearbook 2020. Linear regression analysis was used to assess the trends in the maternal mortality ratio in China. Pearson correlation analysis was used to assess the correlations between national maternal mortality ratio and total health financing composition. Results The yearly trends of the national, rural and urban maternal mortality ratio were − 2.290 ( p  < 0.01), − 3.167 ( p  < 0.01), and − 0.901 ( p  < 0.01), respectively. The gap in maternal mortality ratio between urban and rural areas has narrowed. Obstetric hemorrhage was the leading cause of maternal death. The mortalities ratios for the main causes of maternal death all decreased in China from 1990 to 2019. The hospital delivery rate in China increased, with almost all pregnant women giving birth in hospitals in 2019. Government health expenditure as a proportion of total health expenditure was negatively correlated with the maternal mortality ratio ( r  = − 0.667, p  < 0.01), and out-of-pocket health expenditure as a proportion of total health expenditure was positively correlated with the maternal mortality ratio ( r  = 0.516, p  < 0.01). Conclusion China has made remarkable progress in improving maternal survival, especially in rural areas. The maternal mortality ratio in China showed a downward trend over time. To further reduce the maternal mortality ratio, China should take effective measures to prevent obstetric hemorrhage, increase the quality of obstetric care, improve the efficiency and fairness of the government health funding, reduce income inequality, and strengthen the medical security system.

Background Reducing maternal mortality remains a global priority. In 2000, the United Nations Member States pledged to work towards a series of Millennium Development Goals (MDGs), in which the fifth target was to reduce maternal mortality ratio by 75% from 1990 to 2015. The Chinese government introduced Basic Public Health Service project in 2009 to the further improvement of maternal health services and reduction in maternal mortality. China had achieved the goal of MDG5 1 year ahead of the schedule in 2014, but the effects of the project on reducing maternal mortality were rarely evaluated with robust methods. Methods We conducted a longitudinal study on maternal mortality ratio by extracting mortality data from the National Maternal Mortality Surveillance System (1991–2016) and maternal health services measures from the China health statistic yearbook (2001–2016). We utilized the segmented linear regression model to assess changes and trends of maternal mortality ratio and maternal health services before and after the introduction of Basic Public Health Service project. Pearson correlation analysis was conducted to measure the strength of association between the maternal mortality ratio and maternal health services. Results The yearly trend change of national maternal mortality ratio was − 1.76 ( p  < 0.01) after the introduction of Basic Public Health Service project in 2009, while the yearly trend change of maternal health record establish rate, prenatal examination rate, postpartum visit rate was 0.77 ( p  < 0.01), 0.61 ( p  < 0.01) and 0.83 ( p  < 0.01) separately. The negative correlations were also found between national maternal mortality ratio and prenatal examination rate ( r  = − 0.95, p  < 0.01), maternal health record establish rate ( r  = − 0.93, p  < 0.01) and postpartum visit rate ( r  = − 0.92, p  < 0.01). Conclusions The Basic Public Health Service project was found to be associated with the improvements in the maternal health services and reduction in maternal mortality. The design and implementation of the project may serve as a positive example for other developing countries. Continued monitoring and assessment of project effects should be stressed.