Journal of Global Health

IntroductionUndernutrition is a cause of nearly half of all deaths among children under-5. Small-quantity lipid-based nutrient supplements (SQ-LNS) have been shown to prevent child wasting, stunting, anemia and mortality among children 6-23 months of age in low- and middle-income countries (LMICs). Scaling up effective preventive interventions is urgent given the current global food insecurity and nutrition crisis. MethodTo prioritize SQ-LNS scale-up activities, we identified countries with the highest burdens of wasting, stunting, and all-cause mortality among children 6-23 months of age at the national level using the most recent national survey data including the Demographic and Health Survey (DHS) and Multiple Indicator Cluster Surveys (MICS), as well as the Lives Saved Tool (LiST) in LMICs. National-level estimates informed a care cascade model to assess the potential impact of SQ-LNS on all-cause mortality, stunting, and wasting. We also conducted a sub-national level analysis among the 20 highest burden countries with the most recent available survey data to identify the highest burden regions. ResultsOur analysis identified the top 20 countries with the highest burden of the three outcomes as: Niger, South Sudan, Yemen, Sudan, Somalia, Democratic Republic of Congo, Eritrea, Nigeria, Central African Republic, Guinea, Equatorial Guinea, Chad, Papua New Guinea, Benin, Mali, Angola, Pakistan, Timor-Leste, Sierra Leone and Cote dIvoire, although for some countries the survey data were collected > 10 years ago. Some of these countries also ranked high in population estimates of acute food insecurity. The care cascade model demonstrates that a large number of cases of stunting and wasting and deaths could be potentially averted if SQ-LNS is provided. ConclusionMost of the top 20 countries are in Sub-Saharan Africa, with a few in South and Southeast Asia. This geographical concentration underscores the urgent need for targeted interventions in these regions to prevent child malnutrition. What is already known on this topicIt is known that SQ-LNS reduces child wasting, stunting, anemia and mortality among children 6-23 months of age in LMICs. However, survey reports generally estimate stunting, wasting, and mortality rates among all children under 5 years of age, and not specifically for the 6-23 months old age group. What this study addsBy estimating the burden of stunting, wasting and all-cause mortality for the specific age group of 6-23 months old at the national and sub-national level, we identified high burden countries and regions where SQ-LNS will be most impactful. No other study has focused on this age group even though it is a particularly vulnerable period for undernutrition. How this study might affect research, practice or policyOur analysis could help decision-makers and funders to determine where scale-up of SQ-LNS should be prioritized.

Measurement of COVID-19-attributed mortality is vital for public health policy decisions. Unlike high-income countries, the magnitude of COVID-19-related mortality is largely unknown in many low- and middle-income countries due to inadequate COVID-19 testing capacity and a lack of robust civil registration and vital statistics systems. COVID-19-associated excess mortality was investigated in an urban setting in Bangladesh using a cemetery-based death registration dataset. A total of 6,271 deaths (3,790 male and 2,481 female) recorded between January 2015 and December 2021 were analyzed by using the Bayesian structural time series model (BSTS). During the pre-COVID-19 period, the average monthly number of deaths was 69, whereas, during the COVID-19 period, this number significantly increased to 92. Overall, according to model-based results, during COVID-19 period, the number of deaths increased on average by 17% (95% CrI: -18%, 57%): males 29% (95 % CrI: -15%, 75%) and 2.9% for females (95% CrI: -61%, 70%). This first-of-its-kind study in Bangladesh has revealed the excess mortality due to the COVID-19 pandemic (2020-2021) in an urban community. It appears that cemetery-based death registration could help track various crises (e.g., COVID-19), especially when collecting data on the ground is challenging for resource-limited countries.

BackgroundAcute malnutrition remains a major public health challenge among children under five in Malawi due to undetected and untreated cases. While several policies and programmes are in place, they face significant resource input and implementation constraints. In this study, we evaluate the potential health impact and cost-effectiveness of three interventions designed to address constraints along the care pathway in childhood acute malnutrition management. These include improving early recognition of symptoms by caregivers, increasing attendance at routine growth monitoring visits through community outreach, and scaling up the availability of therapeutic food supplements. Methods and FindingsWe use a newly developed model representing the natural history and management of acute malnutrition, implemented within the Thanzi La Onse (TLO) dynamic individual-based simulation framework, which captures the public health system in Malawi. Each of the three interventions is assessed both individually and in combination, translated into seven scenarios which we evaluate in comparison to the status quo. The optimal strategy combines two interventions, improved caregiver awareness of early symptoms with increased availability of therapeutic food supplements. Over five years, this strategy is predicted to avert 840,470 (95% CI: 682,057-998,883) DALYs with total incremental costs of $34 million. This corresponds to an annual health expenditure increase of $0.32 per capita. At a cost-effectiveness threshold of $76 per DALY averted, the strategy results in an incremental net health benefit of 394,252 (95% CI: 235,839-552,665) DALYs averted. ConclusionsThe cost-effective strategy for addressing constraints in childhood acute malnutrition management is simultaneously improving caregiver recognition of early symptoms and expanding therapeutic food supplement availability. Out of the seven scenarios evaluated, this integrated approach was found to be the optimal strategy within the Malawian public health system, yielding substantial health at modest costs. These findings provide critical evidence to inform national policy and guide investment prioritisation for the management of childhood acute malnutrition.

BackgroundUnderstanding the rates and determinants of severe acute malnutrition (SAM) relapse is crucial for stakeholders in Somalia, where evidence is limited. This study aimed to assess SAM relapse rates and associated risk factors among children discharged from outpatient therapeutic programs (OTP) in the Bay and Hiran regions of Somalia. MethodsWe conducted a prospective cohort study of 160 children aged 7-53 months discharged as recovered from OTP SAM treatment between August and September 2023. Children were followed monthly for 6 timepoints post-discharge. Anthropometric measurements, morbidity data, and household information were collected. Survival analysis was used to calculate cumulative incidence of SAM relapse, defined by weight-for-height z-score (WHZ) <-3 SD or mid-upper arm circumference (MUAC) <11.5cm or oedema. Cox proportional hazard models identified factors associated with relapse. ResultsCumulative incidence of SAM relapse at T1=5.2% (CI: 2.5-10.6%)), T2=14.3% (9.4-21.5%) and T6 was 26.0% (CI: 19.3-34.5%) by WHZ and 13.2% (CI: 8.8-19.5%) by MUAC. The relapse rate for combined SAM and MAM by WHZ at T1=26.9% (CI: 19.5-36.3%), T2=36.2% (CI: 28.0-46.1%) and T6=50.1% (CI: 41.0-60.0%). WHZ-based relapse was higher in rural areas (31.4% vs 22.7% urban, p=0.285) and among children with WHZ <-3SD at admission (37.4% vs 21.2%, p=0.029). MUAC-based relapse was higher in urban areas (20.8% vs 4.1% rural, p=0.002), among younger children (19.7% vs 5.5% >2 years, p=0.009), and IDPs (21.8% vs 5.8% non-IDPs, p=0.003). Factors significantly associated with increased relapse risk included WHZ <-3 SD at admission (adjusted HR: 2.22, CI: 1.04-4.72) and longer OTP stay (adjusted HR: 1.02 per day, CI: 1.00-1.04). Participation in a cash assistance program was protective (adjusted HR: 0.44, CI: 0.22-0.90). ConclusionsSAM relapse rates in Somalia are considerable, and varies by indicators, regions, and demographics. cash assistance shows promise for improving outcomes. RegistrationThe cluster-RCT associated with this cohort study is registered at ClinicalTrials.gov, ID: NCT06642012.

BackgroundTo address Somalias high burden of wasting, it is imperative to understand the countrys context-specific drivers of wasting. This study assessed the drivers of wasting among children under 5 (CU5) and mothers in Somalias Bay and Hiran regions to inform strategies to address wasting. MethodsThe data comes from the midline (September 2023) and endline (December 2023) data collection of a randomized controlled trial. Child and maternal outcomes (weight-for-height z-scores (WHZ) and mid-upper arm circumferences (MUAC), respectively) were explored continuously for children via linear regression and as binary outcomes via Poisson regression for children and mothers. A hierarchical model building approach was used, mapping variables into the basic, intervention, underlying, and immediate levels. Separate midline and endline models were analyzed cross-sectionally, comparing drivers by seasonality, and CU5 models were further stratified by region and age. ResultsThe burden of CU5 wasting was 12.9% at midline and 14.4% at endline. The following variables were drivers of low WHZ across different models: child illness, open defecation, low maternal MUAC, no maternal education, having a male-headed household, and living in a household without joint decision-making. Egg/flesh food consumption and higher maternal MUAC were protective of WHZ. Wasting among mothers was 8% at midline and 12% at endline. Household food insecurity, open defecation, and poor waste disposal practices were drivers of mothers wasting, whereas maternal decision-making was protective. ConclusionThis study highlights variation in the key drivers of wasting by region, season, and child age and contributes to an expanding body of evidence on the multifactorial drivers of wasting, encouraging context-specific approaches that address the immediate, underlying, and basic causes of malnutrition. The findings emphasize the importance of maternal nutrition for child nutrition outcomes and the need for interventions considering household food security, sanitation, and gender dynamics in this humanitarian setting. Registration: The cluster-RCT is registered at ClinicalTrials.gov, ID: NCT06642012.

BackgroundThe United Nations Sustainable Development Goals (SDGs) target 3.4 aims to reduce premature mortality attributable to non-communicable diseases (NCDs) by one-third of their 2015 levels by 2030. Although meeting this target leads to longevity, survivors may suffer from long-term disability caused by NCDs. This paper quantifies the potential gains in health-adjusted life expectancy for people aged 30-70 years (HALE[30-70)) by examining the reductions in disability in addition to premature mortality. Additionally, we also assessed the feasibility of meeting the SDGs target 3.4. MethodsWe extracted data from the Global Burden of Disease Study 2019 for all NCDs and four major NCDs (cancers, cardiovascular diseases, chronic respiratory diseases, and diabetes mellitus) in 188 countries from 1990 to 2019. Bayesian age-period-cohort models were used to predict possible premature mortality in 2030. The life table was used to estimate the unconditional probability of death and HALE[30-70). Estimates of the potential gains in HALE[30-70) were based on three alternative future scenarios: a) eliminating all premature deaths and disability from a specific cause, b) meeting SDGs target 3.4, and survivors disability is eliminated, and c) meeting SDGs target 3.4, but survivors remain disabled for the rest of their lives. ResultsIn 2030, the unconditional probability of premature mortality for four major NCDs in most countries remained at more than two-thirds of the 2015 baseline. In all scenarios, the high-income group has the greatest potential gains in HALE[30-70), above the global average of HALE[30-70). In scenario A, the potential gains in HALE[30-70) of reducing premature mortality for four major NCDs are significantly lower than those for all NCDs (range of difference for all income groups: 2.88 - 3.27 years). In scenarios B and C, the potential gains of HALE[30-70) in reducing premature mortality for all NCDs and the four major NCDs are similar (scenario B: 0.14 - 0.22, scenario C: 0.05 - 0.19). In scenarios A and B, countries from the high-income group have the greatest potential gains in HALE[30-70) from cancer intervention, whilst countries from the other income groups result in a greater possible HALE[30-70) gains from cardiovascular diseases control. In scenario C, countries from each income group have the largest potential gains in HALE[30-70) from diabetes reduction and chronic respiratory diseases prevention. ConclusionsAchieving SDGs target 3.4 remains challenging for most countries. The elimination of disability among the population who benefit from the target could lead to a sizable improvement in HALE[30-70). Reducing premature death and disability at once and attaching equal importance to each to in line with the WHO goal of "leaving no one behind".

BackgroundCesarean section (CS) rates have risen dramatically worldwide, with a majority of the countries exceeding the World Health Organizations (WHO) preferred rate of 10-15%. However, disparities exist, with evidence suggesting that socioeconomic disadvantage and geographic location play significant roles. Despite this, comprehensive estimates, especially in Bangladesh, remain scarce. This study aims to determine trends, district-level variations, and socioeconomic disparities in CS rates in Bangladesh. MethodsData from six rounds of Bangladesh Demographic and Health Surveys were analyzed. The considered outcome variables were the occurrence of CS delivery in relation to the mode of delivery and delivery place. Neonatal mortality was also assessed as another outcome variable. Explanatory variables included districts, wealth quintiles, and socio-demographic characteristics. Descriptive statistics were used to provide an over-the-year trend and variation in CS delivery in Bangladesh. Multilevel mixed-effects binary logistic regressions were used to explore predictors of CS delivery and the association between CS and neonatal mortality. ResultsBetween 1999/2000 and 2017/18, hospital births in Bangladesh increased by 42%, primarily driven by a substantial rise in CS delivery, from 30% to 66%. Private healthcare facilities played a significant role, contributing 80% of the countrys total CS delivery in 2017/18, a substantial increase from 45.5% in 1999/2000. In contrast, CS delivery rates in government healthcare facilities decreased from 49.7% to 15.5% during the same period. Deficient use of CS was reported by women in border and hilly districts, as well as those in the poorest wealth quintile. A clear link between a CS delivery and neonatal mortality was not found. ConclusionThe uneven distribution of CS delivery across districts and socioeconomic groups underscores the need for a more nuanced approach to childbirth. While government efforts to curb unnecessary use of CS have fallen short, this study suggests a one-size-fits-all strategy could worsen disparities. Instead, the focus should shift from mere accessibility to ensuring justified and appropriate utilization, with public healthcare facilities playing a key role in offering safe alternatives.

The COVID-19 pandemic and Lebanons ongoing economic crisis exacerbated existing inequalities, including workforce disparities. This study identified predictors of employment attrition during Lebanons concurrent crises and examined the association between chronic conditions and employment attrition. This cross-sectional study recruited adults aged 19-64 years residing in Lebanon through random digit dialing (5 January - 9 July 2024). Data collected included socio-demographics, household characteristics, employment, and self-reported chronic conditions. The outcome was the loss of paid employment (employment attrition) during the crises. Predictors were identified through LASSO regression and model discrimination and calibration were assessed. Logistic regression models, adjusted for covariates identified through directed acyclic graphs, assessed the association between number and types of chronic conditions and employment attrition. Of 2103 participants employed prior to the onset of the concurrent crises (pre-2020), 72.7% were males, 70.1% were Lebanese, and 14.7% became unemployed during the crises. Predictors of employment attrition were: older age, females, non-Lebanese, married, no formal education, having at least one chronic condition, working in a private or non-governmental organization, and having an oral agreement with employer. The predictive model demonstrated a moderate to good discriminative ability and good calibration. Pre-existing chronic conditions, such as cardiovascular disease (aOR: 2.15; 95% CI, 1.27 to 3.64) and diabetes (aOR: 2.52; 95% CI, 1.43 to 4.45), were independently associated with employment attrition. This study underscores the need to address life-course disparities contributing to job loss and to consider proactive job protections to mitigate workforce disruptions during multiple crises, particularly in contexts where social safety nets are absent.

BackgroundMalnutrition imposes enormous costs resulting from lost investments in human capital and increased healthcare expenditures. There is a dearth of research focusing on the prediction of womens body mass index (BMI), and the malnutrition outcomes (underweight, overweight and obesity) in developing countries. This paper attempts to fill out this knowledge gap by predicting the BMI and the risks of malnutrition outcomes for Bangladeshi women of childbearing age from their economic, health, and demographic features. MethodsData from the 2017-18 Bangladesh Demographic and Health Survey and a series of supervised machine learning (SML) techniques are used. Additionally, this study circumvents the imbalanced distribution problem in obesity classification by utilizing an oversampling approach. ResultsStudy findings demonstrate that support vector machine and k-nearest neighbor are the two best-performing methods in BMI prediction based on coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The combined predictor algorithms consistently yield top specificity, Cohens kappa, F1-score, and AUC in classifying the malnutrition status, and their performance is robust to alternative standards. The feature importance ranking based on several nonparametric and combined predictors indicates that socioeconomic status, womens age, and breastfeeding status are the most important features in predicting womens nutritional outcomes. Furthermore, the conditional inference trees corroborate that those three features along with the partners educational attainment and employment significantly predict malnutrition risks. ConclusionTo the best of our knowledge, this is the first study that predicts BMI and one of the pioneer studies to classify all three malnutrition outcomes for women of childbearing age in Bangladesh, let alone in any lower-middle income country, using SML techniques. Moreover, in the context of Bangladesh, this paper is the first to identify and rank features that are critical in predicting nutritional outcomes using several feature selection algorithms. The estimators from this study predict the outcomes of interest most accurately and efficiently compared to other existing studies in the relevant literature. Therefore, study findings can aid policymakers in designing policy and programmatic approaches to address the double burden of malnutrition among Bangladeshi women, thereby reducing the countrys economic burden.

IntroductionDisease burden is a crucial factor in determining healthcare policy and resource allocation. We analyzed the burden of emergency and operative conditions at the subnational level in India from 1980 to 2023. Material and MethodsWe extracted mortality and disability-adjusted life-year (DALY) estimates for 31 Indian states and Union Territories from 1980 to 2023 from the Global Burden of Disease 2023 Study. We used existing expert-consensus-based classifications for defining emergency (30 conditions), operative (57 conditions), and emergency-operative conditions (7 conditions). We investigated rates per 100,000 people and proportions attributable to the above conditions, expressed as percentages of total mortality and DALYs. ResultsIn 2023, emergency (mortality rate: 339 deaths per 100,000; DALY rate: 13,699 DALYs per 100,000), operative (mortality rate: 158 deaths per 100,000; DALY rate: 7,259 DALYs per 100,000), and emergency-operative conditions (mortality rate: 70 deaths per 100,000; DALY rate: 3,823 DALYs per 100,000) accounted for 49.75%, 23.11%, and 10.31% of all-cause mortality and 40.82%, 21.63%, and 11.39% of all-cause DALYs, respectively. Telangana, Chhattisgarh, and Uttarakhand had high mortality and DALY rates for all three condition groups. From 1980 to 2023, mortality and DALY rates generally decreased across all conditions, with the largest decreases in emergency conditions. States such as Kerala and Goa did not show significant decreases in their mortality and DALY rates between 1980 and 2023. ConclusionDespite significant improvements in healthcare nationwide, several states in India have shown a consistent trend over time, reporting a persistently high burden of emergency, operative, and emergency-operative conditions. Given the relatively large share of emergency and operative conditions toward all-cause mortality, surgical care should be a high priority in national and state-level cross-sectoral policymaking.

Background: Maternal mortality remains uneven globally, and the COVID-19 pandemic disrupted maternal health services through direct infection-related risks and indirect health-system pathways. We estimated country-level deviations in maternal deaths and maternal mortality ratios (MMR) during 2020-2023 relative to pre-pandemic trends. Methods: We used WHO/UN MMEIG model-based country-level estimates of maternal deaths and MMR from 2000-2019 to fit an ensemble n-sub-epidemic forecasting model. We generated no-pandemic counterfactual projections for 2020-2023 and compared them with WHO/UN MMEIG estimates for the same years. Excess was defined as the positive difference between the WHO/UN MMEIG estimate and the counterfactual prediction; uncertainty was quantified using bootstrap-based prediction intervals. Results: Globally, estimated cumulative excess maternal deaths were 68,489 (95% UI 34,706-147,118) during 2020-2023, and the aggregate excess MMR was 10,154 (95% UI 4,568-23,744). The largest regional excess death burdens were observed in the South-East Asia Region, Eastern Mediterranean Region, and African Region. Among the eight illustrative high-burden countries, Afghanistan and Somalia had statistically detectable excess maternal deaths, with totals of 2,335 (95% UI 1,148-4,350) and 1,815 (639-3,401), respectively. Liberia had a positive median estimate of 265 excess maternal deaths, but its interval included zero (0-990). Nigeria, Chad, and South Sudan had median totals of zero, although uncertainty intervals indicated that nonzero excess could not be excluded in Chad and South Sudan. Conclusion: Pandemic-period WHO/UN MMEIG estimates deviated heterogeneously from pre-pandemic counterfactual trends. These findings should be interpreted as modeled excess relative to a no-pandemic baseline and may reflect pandemic-related disruptions together with other contemporaneous health-system, political, and social shocks, rather than directly observed deaths or causal effects attributable solely to COVID-19.

Background Bangladesh has experienced a rapid increase in cesarean section (CS) utilization over the past two decades. While previous studies have documented socioeconomic disparities in CS use, evidence on how wealth-related inequalities differ between public and private healthcare facilities remains limited. This study assessed the magnitude and drivers of socioeconomic inequality in CS utilization among facility-based births in Bangladesh. Methods We analyzed data from 3,008 facility-based births reported in the 2022 Bangladesh Demographic and Health Survey (BDHS). Survey-weighted multivariable logistic regression was used to identify factors associated with CS utilization. Wealth-related inequality was assessed using concentration curves and the Erreygers-corrected concentration index (ECCI). Regression-based decomposition of the standard concentration index was performed to quantify the contribution of socioeconomic, demographic, and healthcare-related factors to observed inequalities overall and separately for public and private facilities. Results Overall, 71.2% of facility-based births were delivered by CS, with substantially higher prevalence in private facilities (84.2%) than in public facilities (35.9%). Women delivering in private facilities had markedly higher odds of CS than those delivering in public facilities (adjusted odds ratio [AOR]: 9.07; 95% confidence interval [CI]: 7.17-11.47). Significant pro-rich inequality was observed overall (ECCI: 0.154; 95% CI: 0.117-0.191), with inequality substantially greater in public facilities (ECCI: 0.189; 95% CI: 0.114-0.264) than in private facilities (ECCI: 0.049; 95% CI: 0.014-0.084). Decomposition analysis showed that household wealth was the dominant contributor to inequality, particularly the richest wealth quintile, accounting for 81.5% of overall inequality, 63.8% in public facilities, and 109.7% in private facilities. Conclusions Wealth-related inequalities in CS utilization remain substantial in Bangladesh despite widespread use of the procedure. Although pro-rich inequality exists across both sectors, inequality is considerably greater in public facilities and is driven by different mechanisms across facility types. Policies should simultaneously improve equitable access to medically necessary CS and reduce unnecessary procedures, particularly within the private sector.

Background For children at risk of acute malnutrition, being able to predict and forecast dietary intakes and/or nutritional evolution would support decision-making, particularly in crisis settings where ground data collection is unfeasible or scant. We explored whether statistical models could offer accurate predictions of caloric intake or anthropometric (weight-for-height Z score, WHZ) changes, given intake, household food insecurity and other plausible predictors. Methods We reanalysed data from the Malnutrition and Enteric Disease (MAL-ED) multi-country (Bangladesh, Brazil, India, Nepal, Pakistan, Peru, South Africa, Tanzania) birth cohort (2009-2014), which consistently tracked household food insecurity experience, dietary intake, anthropometry, infectious disease symptoms, breastfeeding and other variables among children 9 to 35 months old. We quantified the performance on cross-validation of three models: (M1) change in WHZ as a function of household food insecurity; (M2) change in WHZ as a function of caloric intake; (M3) caloric intake as a function of household food insecurity. We compared random forests, lasso regressions, additive models and generalised boosted regressions. All models included age, sex, birth weight, urban versus rural residence, breastfeeding status and the longitudinal prevalence of diarrhoea, acute respiratory infection and fever as additional predictors. Results Altogether, M1, M2 and M3 leveraged 2957, 23,651 and 2013 longitudinal child observations, respectively. Both at country and individual level, there was low correlation among the key variables of interest. All three models featured low performance and moderate to extreme regression dilution, even when fitted to each country cohort separately. Discussion This secondary analysis based on data from a rigorous observational study suggests that statistical prediction of key variables along the causal pathway to childhood acute malnutrition may not be feasible. These negative findings may in part be explained by error in predictor measurement and the narrow range of both predictor and outcome values in the MAL-ED cohort, relative to the more extreme scenarios common to crisis settings. They also imply that mechanistic models requiring caloric intake as an input cannot rely on a statistical shortcut of this kind and must instead depend on empirical data or scenario assumptions.

BackgroundIn sub-Saharan Africa (SSA), institutional births have risen rapidly but mortality has remained high. We examined whether there have been increasing births at hospitals, with skilled attendance, and emergency capacity as indicators of comprehensive, higher-quality childbirth services in 21 SSA countries over the last two decades. MethodsWe analysed national household surveys between 2001 and 2022 to examine population trends in birth place (hospital or lower-level), attendant, and Caesarean Section (CS) rates by wealth quintile, and routine health facility data for 2022 on volumes of live births and CS by facility level. Countries were classified based on recent institutional delivery coverage (<65%, 65-85%, >85%), to assess patterns of change and future directions in line with a maternal and neonatal mortality transition model. ResultsInstitutional delivery increases were primarily driven by lower-level facilities, which had low birth volumes and limited CS capacity. Yet countries that reached high delivery coverage saw greater gains in hospital births, attendance by doctors, and CS rates among the poorest. As national coverage rose, more deliveries were conducted at higher-volume CS-capable hospitals. Low population CS rates among the poorest persisted everywhere. ConclusionMajor increases in institutional deliveries have not sufficiently translated into equitable access to comprehensive, life-saving childbirth care in 21 countries of SSA. Shifts towards hospital deliveries in countries that reached high coverage, consistent with the transition model, can provide guidance to those with lower coverage (<85%). Contextualizing strategies to equitably provide high-quality childbirth care will be transformative for womens and newborns health in SSA.

BackgroundAround half of births in Bangladesh occur at home without skilled birth personnel. This study aims to identify the geographical hot spots and cold spots of home delivery in Bangladesh and associated factors. MethodsWe analyzed data from the 2017/2018 Bangladesh Demographic and Health Survey and the 2017 Bangladesh Health Facility Survey. The outcome variable was home delivery without skilled personnel supervision (yes, no). Explanatory variables included individual, household, community, and healthcare facility factors. Morans I was used to determine hot spots and cold spots of home delivery. Geographically weighted regression models were used to identify cluster-specific predictors of home delivery. ResultsThe prevalence of non-supervised and unskilled supervised home delivery was 53.18%. Hot spots of non-supervised and unskilled supervised home delivery were primarily in Dhaka, Khulna, Rajshahi, and Rangpur divisions. Cold spots of home delivery were mainly in Mymensingh and Sylhet divisions. Predictors of higher home births in hot spot areas included womens illiteracy, lack of formal job engagement, higher number of children ever born, partners agriculture occupation, higher community-level illiteracy, and greater distance to the nearest healthcare facility from womens homes. ConclusionsUnskilled supervised home delivery is prevalent in Bangladesh, and the distance between womens homes and healthcare facilities plays a significant role. Awareness-building programs should emphasize the importance of skilled and supervised hospital deliveries, particularly among the poor and disadvantaged groups.

Childhood malnutrition remains a major public health challenge in Ethiopia, where stunting and wasting co-exist but may arise from distinct spatial and etiological processes. Analyses focusing on a single outcome may overlook the interdependence of these conditions and their geographic heterogeneity. This study aimed to disentangle the determinants of stunting and wasting among children under five years of age using a Bayesian bivariate spatial modelling framework. Data from 5,405 children included in the 2019 Ethiopia Mini Demographic and Health Survey were analyzed. Stunting and wasting were modelled as correlated binary outcomes using Bayesian bivariate hierarchical geostatistical models implemented through SPDE-INLA, accounting for child, maternal, household, and environmental covariates, non-linear age effects, and spatial dependence. Model performance was assessed using the deviance information criterion, Watanabe-Akaike information criterion, and marginal log-likelihood. The bivariate model identified shared socio-economic and biological determinants. Multiple births, male sex, low maternal education, a higher number of under-five children, and household poverty were associated with increased risks of both outcomes. Female-headed households were associated with lower odds of stunting but higher odds of wasting. Spatial analysis revealed elevated residual stunting risk in the northern and central highlands, whereas wasting hotspots were concentrated in northeastern pastoralist regions. Residual spatial correlation was weak ({rho} = -0.12), indicating largely independent geographic patterns. These findings suggest that effective child nutrition policies in Ethiopia require outcome-specific and regionally tailored interventions addressing both chronic and acute forms of malnutrition.

ObjectiveThis study aimed to examine the association of regional disparity and socioeconomic determinants with acute malnutrition among children aged 6-59 months attending selected public hospitals in Karachi. MethodsA hospital-based unmatched case-control study was conducted on 394 participants with 197 cases and 197 controls. After developing a self-structured questionnaire based on maternal and child components, wealth quintiles were made using principal component analysis. A univariate and then a binary logistic regression was applied. ResultsThe odds of acute malnutrition were 1.2 times higher in male children. The variables significantly associated with childhood acute malnutrition were regional disparity [AOR=2.3, 95%, C. I (1.4-3.8), p-value <0.01], mothers illiteracy [AOR = 3.6, 95% C. I (1.9-7), p-value <0.001], mothers primary education [AOR =1.1, 95% C. I (0.5-2.2), p-value <0.01], fathers illiteracy [AOR= 2.4, 95% C. I (0.8-4.4), p-value 0.03], fathers primary education [AOR =1.1, 95% C. I (0.4-1.9), p-value <0.01], poorest households [AOR= 2.2, 95% C. I (1.07-4.7), p-value <0.01], and childs age [AOR =0.945, 95% C. I (0.92-0.96), p-value <0.01]. ConclusionRegional disparity was found to be significantly associated with acute malnutrition among children along with household wealth status, mothers illiteracy, and fathers illiteracy. Hence there is a need to direct the focus of policy makers to work on these factors to eradicate acute malnutrition among children.

ObjectivesThis study aimed to globally assess the prevalence and distribution of primary-origin musculoskeletal disorders (MSDs) from 1990 to 2019 to better understand their temporal trends. MethodsUsing data from the 2019 Global Burden of Diseases, prevalence rates of 6 primary-origin MSDs were analysed across sub-regions, age groups, and genders. Raw and age-standardized data were mapped for over 204 countries. Cochran-Armitage trend tests evaluated temporal prevalence trends. The correlation between MSDs prevalence, national income levels, and medical density was explored. ResultsIn 2019, global MSDs prevalence varied significantly among countries. Hip osteoarthritis had a prevalence of 0.56% [95% CI: 0.43-0.70], while low back pain was 8.62% [95% CI: 7.62-9.74]. Most MSDs exhibited an increasing prevalence with age, except for neck pain, which stabilized or decreased after age 45-50. Women generally had higher prevalence rates across all age groups. High-income countries consistently showed higher prevalence rates compared to middle and low-income countries. Over time, most sub-regions experienced a significant increase in MSD prevalence. However, after adjusting for age, the temporal trends for back and neck pain became non-significant, except for hip osteoarthritis, where half of the sub-regions remained significant. Multivariate linear regressions revealed positive associations between MSD prevalence and both national income level and medical density. ConclusionThe global burden of MSDs is increasing due to population ageing, but other factors should be considered. Longitudinal studies with a wider range of MSDs and additional risk factors are needed for improved prevention strategies.

IntroductionResearch on episiotomy practices in Vietnam is limited. This study aimed to describe episiotomy use and identify factors associated with its practice among vaginal births in Central Vietnam, following the implementation of restrictive episiotomy guidelines. MethodsWe used data from a hospital-based, retrospective study conducted at Danang Hospital for Women and Children from April 2015 to March 2016. The study included all singleton, full-term vaginal births. Multivariable logistic regression was used to estimate the odds of episiotomy by selected neonatal or maternal factors. ResultsAmong 3,471 eligible singleton births, 2,770 mothers (79.8%) underwent an episiotomy. The episiotomy rate was significantly higher in first-time births (97.7%) compared to second or subsequent births (61.5%), p<0.001. Multivariable analyses showed that first-time births, higher birth weight, younger maternal age, a less physical active occupation, and a history of miscarriage were significantly associated with higher odds of episiotomy. For example, the odds of episiotomy in first-time births was 24.21 (95% CI: 17.13-34.22) times higher than in second or subsequent births, and the odds for mothers with a history of miscarriage was 1.34 (95%CI: 1.03-1.73) compared to those without. Stratified analysis showed that these associations persisted in multiparous women but were not observed in primiparous women. ConclusionThis study highlights a very high episiotomy rate among primiparous women in Central Vietnam, one year after the implementation of restrictive episiotomy guidelines, despite of other maternal or neonatal factors associated with episiotomy in multiparous women. Comprehensive research and targeted interventions are needed to reduce episiotomy rates, particularly among first-time mothers in Vietnam.

BackgroundEffective coverage cascades have been proposed to understand to what extent populations are able to benefit from interventions to address their health needs. Theoretical effective coverage cascades have been developed for reproductive, maternal, newborn, child, and adolescent health and nutrition (RMNCAH&N), but there is no consensus regarding the methods to estimate effective coverage cascades. We operationalized the proposed effective coverage cascades for selected RMNCAH&N services; this paper presents the overall methods, challenges, and lessons learned. MethodsWe used data from Demographic and Health Surveys, Multiple Indicator Cluster Surveys, Service Provision Assessments, and the Service Availability and Readiness Assessment to estimate effective coverage cascades in seven low- and middle- income countries for the following service areas: antenatal care, care for small and/or sick newborns, postnatal care, sick child care, and maternal and child nutrition. We developed operational definitions for each of the seven steps of the effective coverage cascade and developed readiness, and, where data allowed, process quality indices for each service area. Readiness- and process quality-adjusted coverage were estimated using ecological linking by stratum. We propose approaches for dealing with multiple observations per facility; multiple care-seeking episodes; and empty strata, as well as a jackknife approach to estimate the standard errors for readiness- and process quality-adjusted coverage. ResultsWe were able to estimate effective coverage cascades through intervention coverage (step 4) for postnatal care and through process quality-adjusted coverage (step 5) for antenatal care, sick child care, and maternal and child nutrition. For small and/or sick newborn care, we did not have an appropriate denominator or measure of service contact coverage and had to modify the cascade significantly. Data gaps were the largest barrier to the estimation of effective coverage cascades for RMNCAH&N. Other challenges included accounting for community- and home-based interventions, determining whether the cascade should be nested, and interpreting the cascade. ConclusionsTo make effective coverage cascades feasible for routine use, clear guidance is needed on cascade methods and definitions, accounting for the full spectrum of RMNCAH&N interventions, and developing our understanding of how coverage cascades can be used by stakeholders to improve health systems and programs.