Effectiveness of a continuum of care in maternal health services on the reduction of maternal and neonatal mortality: Systematic review and meta-analysis

Assessing the joint effects of education, economic status, empowerment, and employment (4Es) disparities on the co-coverage of maternal, newborn and child health care services in sub-Saharan Africa: an application of the intersectionality approach

Background

Although education, employment, economic status, and empowerment (4Es) are known to individually influence inequalities in maternal, newborn, and child health (MNCH), their combined effects have not been thoroughly studied in sub-Saharan Africa (SSA). We applied an intersectional approach to understand the joint effect of the 4Es on MNCH co-coverage in different settings in SSA.

Methods

We used 25 Demographic and Health Survey data sets and employed a multilevel analysis of individual heterogeneity and discriminatory accuracy to assess the intersectional effects of the 4Es on MNCH service co-coverage and inequalities within and across countries. The variance partition coefficient and proportional change in variance (PCV) statistics were applied to quantify total intersectional effects.

Results

Among 103 388 women with children aged 12-59 months, 4.6% of the variance in co-coverage of ≥6 MNCH interventions (co-coverage ≥6) occurred at the intersectional strata level. Most of this variance (90.3%) was due to the additive effects of the 4Es, with education (PCV partial = 80.7%) the primary contributor, followed by economic status (PCV partial = 9.8%). The lowest co-coverage was observed among women with no education, unemployment, low economic status, and low empowerment. Inequalities were more pronounced in countries with lower universal health coverage (UHC) indices, where co-coverage ranged from 17.5% (95% confidence interval (CI) = 14.6-21.1) to 67.0% (95% CI = 62.9-70.8), compared with 42.8% (95% CI = 38.0-47.8) to 68.5% (95% CI = 64.7-71.8) in countries with higher UHC indices. Evidence of multiplicative effects was also observed. Services with a high disparity included skilled birth attendance, antenatal care, and access to improved water sources. Country-specific analysis revealed that 11 countries showed very low heterogeneity (<5%) in the co-coverage of ≥6 interventions.

Conclusions

This is the first study to explore how the 4Es jointly affect MNCH co-coverage in SSA. The results reveal that these 4Es are connected and affect MNCH co-coverage, particularly in key services, including skilled birth attendance, antenatal care, and access to improved water sources. The most privileged groups had significant protective effects, whereas those with fewer societal privileges showed minor effects. Learning from countries with low disparities in service co-coverage can help reduce the gaps in other countries.

Looking into opportunities for maternity continuum of care improvement within the primary health care system in Northwest Ethiopia: primary health care-oriented research

BACKGROUND

The maternity continuum of care (MCC) is an integral component of universal health coverage and a crucial strategy for reducing maternal and neonatal mortality. Despite its importance, MCC coverage remains low in low- and middle-income countries (LMICs), including Ethiopia, which bear the highest burden of maternal and neonatal mortality. This underscores the need for proactive interventions. In this perspective, the primary health care (PHC) approach holds significant potential for enhancing MCC. Exploring demand- and/or supply-side opportunities for improving MCC within the PHC system could help inform policy, practice, and further research. However, there is limited evidence on these aspects in Ethiopia. Therefore, this study aims to explore demand- and/or supply-side opportunities for MCC enhancement within the PHC system in northwest Ethiopia.

METHODS

We conducted a qualitative study using an interpretive description approach within the PHC system in northwest Ethiopia from March 3 to November 27, 2022. Data collection included four key informant interviews, three focus group discussions with 29 participants, and 28 in-depth interviews, all selected through maximum variation sampling. Interviews were audio-recorded, transcribed verbatim, translated into English, and analyzed using reflexive thematic analysis in ATLAS.ti version 7.1.4 software.

RESULTS

Three themes and 11 sub-themes have emerged. These include: (1) PHC structural enhancement with its categories: onsite mentorship, decentralized ambulance service platform, structural networks, and expansion of the scope of frontline PHC facilities; (2) PHC input enhancement with its categories: expansion of the maternity service workforce and technological advancements; and (3) PHC maternity service delivery enhancement with its categories: community engagement, approaches for reducing wait times, tailored maternity service provision in conflict-affected communities, flexible schedules for maternity service, and integrated midwifery model of care.

CONCLUSIONS

This study finds that there are several structural, input, and service delivery opportunities for enhancing the MCC within the PHC system in northwest Ethiopia. These opportunities indicate potential pathways to strengthen PHC resilience for MCC. Widespread utilization of these opportunities could significantly improve the existing MCC within the PHC system. We also recommend further research to assess the impact of these opportunities on MCC.

Challenges of maternity continuum of care within the primary health care in northwest Ethiopia: interpretive description using a socio-ecological model

Background

The maternity continuum of care plays a vital role in improving maternal and neonatal outcomes. However, its uptake remains low in Ethiopia, highlighting the need to identify challenges within the primary health care system to inform practice. Hence, this study aimed to explore the challenges of the maternity continuum of care within the primary health care system in northwest Ethiopia.

Methods

An interpretive description approach was employed from March 3, 2022, to November 27, 2022, within the primary health care system in northwest Ethiopia. Maximum variation sampling was utilized, comprising 28 in-depth interviews, three focus group discussions with 29 participants, and four key informant interviews. The reflexive thematic analysis method was applied, and the results were mapped onto the constructs of the socio-ecological model.

Results

The analysis identified four main themes: low maternity healthcare-seeking behavior (intrapersonal level), lack of peer and family support (interpersonal level), cultural influences on maternity care and low community responsiveness (community level), and inadequate health system readiness and response (health facility/system level). Some of the sub-themes include low health literacy and self-efficacy and misconceptions regarding maternity care at the intrapersonal level; peer and family pressure against seeking maternity care, low autonomy, and intimate partner violence at the interpersonal level; cultural influences on pregnancy disclosure and postnatal care and low social accountability at the community level; and delays in accessing ambulance services, long waiting times for maternity care, shortages of essential healthcare supplies, poor coordination of care, inadequate monitoring and evaluation, disrespectful maternity care, and dissatisfaction among healthcare workers at the health facility/system level.

Conclusion

Intrapersonal, interpersonal, community, and health facility- and system-level challenges have influenced the maternity continuum of care within the primary health care in northwest Ethiopia. Since these challenges are interdependent, considering a holistic approach within primary health care could lead to an improved maternity continuum of care.

Impact of scaling up health intervention coverage on reducing maternal mortality in 26 low- and middle-income countries: A modelling study

BACKGROUND

Prioritising actions is urgently needed to address the stagnation of the global maternal mortality ratio (MMR). As most maternal deaths occur in low- and middle-income countries (LMICs), we aimed to assess the impact of scaling up health intervention coverage on reducing MMR under four scenarios for 26 LMICs.

METHODS

We conducted a modelling study to estimate the MMR and additional maternal lives saved by intervention by 2030 for 26 LMICs using the Lives Saved Tool (LiST). We used four scenarios to assess the impact of scaling up health intervention coverage by no scale-up (no change), modest scale-up (increased by 2% per year), substantial scale-up (increased by 5% per year), and universal coverage (coverage reached 95% by 2030). We divided the selected 26 countries into three groups according to their MMR levels in 2020.

RESULTS

Among 26 LMICs, six (23.1%) countries showed an increase in MMRs and 13 (50.0%) stalled on the reduction of MMR from 2015 to 2020. Under a substantial scale-up of coverage or scaling up to universal coverage, the average MMR in 2030 of 26 LMICs would be 62.8 or 52.8, reaching the Sustainable Development Goal (SDG) 3.1. Caesarean delivery, uterotonics for postpartum haemorrhage, and assisted vaginal delivery had a more important role in this reduction compared to other interventions.

CONCLUSIONS

Scaling up the coverage of health interventions is critical for reducing MMRs. If a substantial scale-up or scaling up to universal coverage of continuous maternity interventions from preconception to postpartum period can be achieved, LMICs in Southeast Asia and Western Pacific regions could reach the SDG 3.1 on time.

Artificial intelligence assisted common maternal fetal planes prediction from ultrasound images based on information fusion of customized convolutional neural networks

Ultrasound imaging is frequently employed to aid with fetal development. It benefits from being real-time, inexpensive, non-intrusive, and simple. Artificial intelligence is becoming increasingly significant in medical imaging and can assist in resolving many problems related to the classification of fetal organs. Processing fetal ultrasound (US) images increasingly uses deep learning (DL) techniques. This paper aims to assess the development of existing DL classification systems for use in a real maternal-fetal healthcare setting. This experimental process has employed two publicly available datasets, such as FPSU23 Dataset and Fetal Imaging. Two novel deep learning architectures have been designed in the proposed architecture based on 3-residual and 4-residual blocks with different convolutional filter sizes. The hyperparameters of the proposed architectures were initialized through Bayesian Optimization. Following the training process, deep features were extracted from the average pooling layers of both models. In a subsequent step, the features from both models were optimized using an improved version of the Generalized Normal Distribution Optimizer (GNDO). Finally, neural networks are used to classify the fused optimized features of both models, which were first combined using a new fusion technique. The best classification scores, 98.5 and 88.6% accuracy, were obtained after multiple steps of analysis. Additionally, a comparison with existing state-of-the-art methods revealed a notable improvement in the suggested architecture's accuracy.

Predictors of antenatal care services utilization by pregnant women in Afghanistan: Evidence from the Afghanistan Health Survey 2018

BACKGROUND

Poor utilization of maternal healthcare services remains a public health concern in most low and middle-income countries (LMICs), including Afghanistan. Late, inadequate, or no utilization of antenatal care (ANC) services pose a great concern.

OBJECTIVES

This study assessed the predictors of ANC service utilization among Afghan pregnant women, using secondary data from the Afghanistan Health Survey 2018 (AHS2018).

METHODS

In this study, we used data from 10,855 ever-married women, aged 13-49 years, who gave birth in the two years prior to the survey or those women who were currently pregnant. The outcome variable was defined as a binary variable on ANC utilization (≥1 ANC visit equals 1, and zero otherwise). We fitted a binary logistic regression model and examined the associations between ANC utilization and explanatory variables, providing findings based on univariate and multivariate analysis. STATA version 17 was employed for the data analysis.

RESULTS

Overall, 63.2%, 22.0%, and 3.1% of women had at least one ANC visit, ≥4 ANC visits, and ≥8 ANC visits during their last pregnancy, respectively. Higher odds of ANC utilization were observed in women who could read and write (AOR = 1.55, 95%CI: 1.36-1.77), whose husbands could read and write (AOR = 1.42, 95%CI: 1.29-1.56), who knew 1 sign (AOR = 1.93, 95%CI: 1.74-2.14), 2 signs (AOR = 2.43, 95%CI: 2.17-2.71) and ≥ 3 signs (AOR = 1.55, 95%CI: 1.36-1.77) of complicated pregnancy, and those with almost daily access to radio (AOR = 1.19, 95%CI: 1.08-1.327) and TV (AOR = 1.92, 95%CI: 1.73-2.13). We also found that women with one (AOR = 0.64, 95%CI: 0.49-0.84) and more than one (AOR = 0.60, 95%CI: 0.47-0.76) parity status, those for whom in-laws and others decided for their birthplace [(AOR = 0.85, 95%CI: 0.74-0.97) and (AOR = 0.63, 95%CI: 0.55-0.72), respectively], and those that resided in rural areas (AOR = 0.89, 95%CI: 0.79-1.00) had reduced odds of ANC utilization.

CONCLUSION

ANC service utilization is unacceptably low by pregnant women in Afghanistan. The predictors of ANC utilization identified by the study should be considered in the design of future interventions to enhance antenatal care utilization in Afghanistan.

Population modifiable risk factors associated with neonatal mortality in 35 sub-Saharan Africa countries: analysis of data from demographic and health surveys

Background

Sub-Saharan Africa (SSA) has the highest burden of neonatal mortality in the world. Identifying the most critical modifiable risk factors is imperative for reducing neonatal mortality rates. This study is the first to calculate population-attributable fractions (PAFs) for modifiable risk factors of neonatal mortality in SSA.

Methods

We analysed the most recent Demographic and Health Surveys data sets from 35 SSA countries conducted between 2010 and 2022. Generalized linear latent and mixed models were used to estimate odds ratios (ORs) along with 95% confidence intervals (CIs). PAFs adjusted for communality were calculated using ORs and prevalence estimates for key modifiable risk factors. Subregional analyses were conducted to examine variations in modifiable risk factors for neonatal mortality across Central, Eastern, Southern, and Western SSA regions.

Findings

In this study, we included 255,891 live births in the five years before the survey. The highest PAFs of neonatal mortality among singleton children were attributed to delayed initiation of breastfeeding (>1 h after birth: PAF = 23.88%; 95% CI: 15.91, 24.86), uncleaned cooking fuel (PAF = 5.27%; 95% CI: 1.41, 8.73), mother's lacking formal education (PAF = 4.34%; 95% CI: 1.15, 6.31), mother's lacking tetanus vaccination (PAF = 3.54%; 95% CI: 1.55, 4.92), and infrequent antenatal care (ANC) visits (PAF = 2.45; 95% CI: 0.76, 3.63). Together, these five modifiable risk factors were associated with 39.49% (95% CI: 21.13, 48.44) of neonatal deaths among singleton children in SSA. Our subregional analyses revealed some variations in modifiable risk factors for neonatal mortality. Notably, delayed initiation of breastfeeding consistently contributed to the highest PAFs of neonatal mortality across all four regions of SSA: Central, Eastern, Southern, and Western SSA.

Interpretation

The PAF estimates in the present study indicate that a considerable proportion of neonatal deaths in SSA are preventable. We identified five modifiable risk factors that accounted for approximately 40% of neonatal deaths in SSA. The findings have policy implications.

Funding

None.

Prevalence and determinants of adequate postnatal care in Ethiopia: evidence from 2019 Ethiopia mini demographic and health survey

BACKGROUND

The postpartum period is critical for both the mother's and newborn child's health and survival. Rising morbidity and mortality are usually the consequence of absence of adequate, suitable, or timely care during that time period. There is lack of information on the adequacy of postnatal care in Ethiopia and this study was aimed to investigate adequacy of postnatal care and its determinants in the study area.

METHODS

In this study, we used a cross-sectional dataset from the 2019 Ethiopia Mini Demographic and Health Survey. A multistage stratified clustered design applied and survey weights were used to take into account the complicated sample design. A multilevel mixed effects logistic regression was fitted on 3772 women who were nested within 305 clusters. The fixed effect models were fitted and expressed as adjusted odds ratios with 95% confidence intervals, while intra-class correlation coefficients, median odds ratio, and proportional change in variance explained measures of variation. As model fitness criteria, the deviance information criterion and the Akaike information criterion were used.

RESULTS

This study found that only 563(16.14%, 95% CI: 16.05-16.24) women had adequate post natal care. Age of between 25-35 years old (AOR = 1.55, 95%CI = 1.04-2.31), secondary level of education (AOR = 2.23, 95%CI = 1.43-3.45), Having parity of between two and four had (AOR = 0.62, 95%CI = 0.42 0.93), having ANC follow up four and above (AOR = 1.74, 95%CI = 1.31-2.33), being residents of Oromia region (AOR = 0.10, 95CI = 0.02- 0.43) were strong predictors of adequate postnatal care.

CONCLUSION

The study found that prevalence of adequate PNC in Ethiopia was significantly low. To increase postnatal care adequacy, it was recommended to reinforce existing policies and strategies such as increasing number of antenatal care follow up, and scheduling mothers based on the national postnatal care follow-up protocol.

Trends, Projections, and Regional Disparities of Maternal Mortality in Africa (1990-2030): An ARIMA Forecasting Approach

With the United Nations Sustainable Development Goals (SDG) (2015-2030) focused on the reduction in maternal mortality, monitoring and forecasting maternal mortality rates (MMRs) in regions like Africa is crucial for health strategy planning by policymakers, international organizations, and NGOs. We collected maternal mortality rates per 100,000 births from the World Bank database between 1990 and 2015. Joinpoint regression was applied to assess trends, and the autoregressive integrated moving average (ARIMA) model was used on 1990-2015 data to forecast the MMRs for the next 15 years. We also used the Holt method and the machine-learning Prophet Forecasting Model. The study found a decline in MMRs in Africa with an average annual percentage change (APC) of -2.6% (95% CI -2.7; -2.5). North Africa reported the lowest MMR, while East Africa experienced the sharpest decline. The region-specific ARIMA models predict that the maternal mortality rate (MMR) in 2030 will vary across regions, ranging from 161 deaths per 100,000 births in North Africa to 302 deaths per 100,000 births in Central Africa, averaging 182 per 100,000 births for the continent. Despite the observed decreasing trend in maternal mortality rate (MMR), the MMR in Africa remains relatively high. The results indicate that MMR in Africa will continue to decrease by 2030. However, no region of Africa will likely reach the SDG target.