Mapping the quality of prenatal and postnatal care and demographic differences on child mortality in 26 low to middle-income countries

Global, Regional, and National Burden of Child Growth Failure, 1990-2021: A Systematic Analysis for the Global Burden of Disease Study 2021

Background/Objectives: Child growth failure is a manifestation of chronic malnutrition expressed in stunting, wasting, and underweight in children. This study aimed to analyze global trends in child growth failure disease burden and mortality across children of all age groups on a global, regional, and national level. Methods: This cross-sectional study utilized data from the 1990 and 2021 Global Burden of Disease (GBD) study. Growth failure Disability-adjusted life years (DALYs), years lived with a disability (YLDs), and mortality in children under 20 years of age were analyzed. Average annual percentage change (AAPC) was calculated to determine and identify improvements in growth failure disease burden and mortality in the past 30 years. Results: Greatest reduction in growth failure DALYs (AAPC = -0.96, 95% CI = -0.97 to -0.95), YLDs (AAPC = -0.73, 95% CI = -0.77 to -0.66) and mortality rate (AAPC = -0.96, 95% CI = -0.97 to -0.95) in children under 5 years of age was observed in high-middle SDI countries. In contrast, improvements in the number of growth failure DALYs (AAPC = -0.64, 95% CI = -0.76 to -0.53), YLDs (AAPC = -0.21, 95% CI = -0.25 to -0.13) and mortalities (-0.57, 95% CI = -0.59 to -0.52) are less pronounced in regions with low SDI scores. Improvements in disease burden and mortality are reduced in older age groups, with the lowest reduction observed in children 15-19 years old. Conclusions: Barriers hindering the delivery of nutritional supplements and access to quality healthcare in regions with low SDI scores need to be overcome to address the disproportionately high numbers of growth failure DALYs, YLDs, and mortalities in regions with low SDI.

Trend and Burden of Vitamin A Deficiency in 1990-2021 and Projection to 2050: A Systematic Analysis for the Global Burden of Disease Study 2021

Background/Objectives: In this study, we aim to provide an update on the global, regional, and national trends in VAD-associated mortality and morbidity for children under 20 years of age, across different age groups and sociodemographic backgrounds, to identify populations at risk that require further attention. Methods: Data from the Global Disease of Burden study were analysed to determine the temporal trends in VAD mortalities and VAD disease burden through disability-adjusted life years (DALYs) and Years Lived with Disability (YLD). Data on children under 20 years of age from 1990 to 2021 from 204 countries and territories were included for analysis. The Average Annual Percentage Change (AAPC) was used to show a temporal trend over a 30-year period. Results: Global VAD-associated mortality has decreased significantly, with an AAPC of -0.91 (95% CI= -0.95 to -0.85). No significant improvements in VAD morbidities were identified across Sub-Saharan African regions. In Central Sub-Saharan Africa, the number of VAD-associated disabilities increased from 70,032.12 to 73,534.15. Significant heterogeneity in changes in VAD morbidities were also identified across different countries. The highest age-standardized rate (ASR) of VAD YLD was 282.36 in Somalia, while countries with high sociodemographic indices had an ASR of 0. Conclusions: Significant global improvements in VAD mortalities indicate the efficacy of wide-scale high-dose vitamin A supplementation for children under 5 years of age. However, the lack of improvements in VAD morbidities in low-SDI countries highlights the need to continue crucial high-dose vitamin A supplementation and to implement additional vitamin A supplementation programs.

Population attributable fractions for modifiable risk factors of neonatal, infant, and under-five mortality in 48 low- and middle-income countries

Background

Identifying the modifiable risk factors for childhood mortality using population-attributable fractions (PAFs) estimates can inform public health planning and resource allocation in low- and middle-income countries (LMICs). We estimated PAFs for key population-level modifiable risk factors of neonatal, infant, and under-five mortality in LMICs.

Methods

We used the most recent Demographic and Health Survey data sets (2010-22) from 48 LMICs, encompassing 35 sub-Saharan African countries and 13 countries from South and Southeast Asia (n = 506 989). We used generalised linear latent mixed models to compute odds ratios (ORs), and we calculated the PAFs adjusted for commonality using ORs and prevalence estimates for key modifiable risk factors.

Results

The highest PAFs of neonatal mortality were attributed to delayed initiation of breastfeeding (>1 hour of birth) (PAF = 23.9; 95% confidence interval (CI) = 23.1, 24.8), uncleaned cooking fuel (PAF = 6.2; 95% CI = 6.4, 7.8), infrequent antenatal care (ANC) visits (PAF = 4.3; 95% CI = 3.3, 5.9), maternal lack of formal education (PAF = 3.9; 95% CI = 2.7, 5.3), and mother's lacking two doses of tetanus injections (PAF = 3.0; 95% CI = 1.9, 3.9). These five modifiable risk factors contributed to 41.4% (95% CI = 35.6, 47.0) of neonatal deaths in the 48 LMICs. Similarly, a combination of these five risk factors contributed to 40.5% of infant deaths. Further, delayed initiation of breastfeeding (PAF = 15.8; 95% CI = 15.2, 16.2), unclean cooking fuel (PAF = 9.6; 95% CI = 8.4, 10.7), mothers lacking formal education (PAF = 7.9; 95% CI = 7.0, 8.9), infrequent ANC visits (PAF = 4.0; 95% CI = 3.3, 4.7), and poor toilet facilities (PAF = 3.4; 95% CI = 2.6, 4.3) were attributed to 40.8% (95% CI = 36.4, 45.2) of under-five deaths.

Conclusions

Given the current global economic climate, policymakers should prioritise these modifiable risk factors. Key recommendations include ensuring that women enter pregnancy in optimal health, prioritising the presence of skilled newborn attendants for timely and proper breastfeeding initiation, and enhancing home-based care during the postnatal period and beyond.

Predicting adverse birth outcome among childbearing women in Sub-Saharan Africa: employing innovative machine learning techniques

BACKGROUND

Adverse birth outcomes, including preterm birth, low birth weight, and stillbirth, remain a major global health challenge, particularly in developing regions. Understanding the possible risk factors is crucial for designing effective interventions for birth outcomes. Accordingly, this study aimed to develop a predictive model for adverse birth outcomes among childbearing women in Sub-Saharan Africa using advanced machine learning techniques. Additionally, this study aimed to employ a novel data science interpretability techniques to identify the key risk factors and quantify the impact of each feature on the model prediction.

METHODS

The study population involved women of childbearing age from 26 Sub-Saharan African countries who had given birth within five years before the data collection, totaling 139,659 participants. Our data source was a recent Demographic Health Survey (DHS). We utilized various data balancing techniques. Ten advanced machine learning algorithms were employed, with the dataset split into 80% training and 20% testing sets. Model evaluation was conducted using various performance metrics, along with hyperparameter optimization. Association rule mining and SHAP analysis were employed to enhance model interpretability.

RESULTS

Based on our findings, about 28.59% (95% CI: 28.36, 28.83) of childbearing women in Sub-Saharan Africa experienced adverse birth outcomes. After repeated experimentation and evaluation, the random forest model emerged as the top-performing machine learning algorithm, with an AUC of 0.95 and an accuracy of 88.0%. The key risk factors identified were home deliveries, lack of prenatal iron supplementation, fewer than four antenatal care (ANC) visits, short and long delivery intervals, unwanted pregnancy, primiparous mothers, and geographic location in the West African region.

CONCLUSION

The region continues to face persistent adverse birth outcomes, emphasizing the urgent need for increased attention and action. Encouragingly, advanced machine learning methods, particularly the random forest algorithm, have uncovered crucial insights that can guide targeted actions. Specifically, the analysis identifies risky groups, including first-time mothers, women with short or long birth intervals, and those with unwanted pregnancies. To address the needs of these high-risk women, the researchers recommend immediately providing iron supplements, scheduling comprehensive prenatal care, and strongly encouraging facility-based deliveries or skilled birth attendance.