Institutional delivery at birth is an important indicator of improvements in maternal health, which remains one of the targets of sustainable development goals intended to reduce the maternal mortality ratio. The purpose of the present study was to identify the determinants of utilization of institutional delivery in Zambia. A population-based cross-sectional study design was used to examine 9841 women aged 15–49 years from the 2018 Zambia Demographic and Health Survey. A multiple logistic regression was applied to calculate odds ratios (ORs) with 95% confidence intervals (CIs) to identify determinants of utilization of institutional delivery. Sociodemographic factors were significantly associated with institutional delivery: woman’s (OR: 1.76; 95% CI: 1.04–2.99) and husband’s (OR: 1.83; 95% CI: 1.09–3.05) secondary/higher education, higher wealth index (OR: 2.31; 95% CI: 1.27–4.22), and rural place of residence (OR: 0.55; 95% CI: 0.30–0.98). Healthcare-related factors were also significantly associated with institutional delivery: 5–12 visits to antenatal care (OR: 2.33; 95% CI: 1.66–3.26) and measuring blood pressure (OR: 2.15; 95% CI: 1.32–2.66) during pregnancy. To improve institutional delivery and reduce maternal and newborn mortality, policymakers and public health planners should design an effective intervention program targeting these factors.

Diarrhea and fever are prevalent childhood illnesses with potentially severe consequences, especially when they co-occur. This study investigates the prevalence and determinants of diarrhea, fever, and their coexistence among children under-five in Bangladesh. Data from the 2017–2018 Bangladesh Demography and Health Survey (BDHS) were analyzed using multivariable stepwise logistic regression with backward selection. This study found that 5.0% for diarrhea, 34.0% for fever, and 3.0% for the coexistence of both illnesses. Common factors associated with childhood diarrhea and fever included the child’s age (12–23 months), and the mother’s education. Diarrhea was associated with households with improved water sources and children in the Barisal division, while fever was linked to underweight children and those from more affluent backgrounds. The coexistence of both was significantly linked to underweight children, higher birth orders, and children from the Rajshahi division. Notably, child illnesses were associated with parental education, higher socio-economic status, and access to improved drinking water sources. Diarrhea affects one in 20 children, fever affects one in three, and the coexistence of both conditions affects one in 35 children in Bangladesh. The findings need further research and policy reviews to develop effective interventions and improve child health in Bangladesh.

Machine learning (ML) excels over regression by automatically capturing complex, non-linear relationships and interactions, enabling more flexible and accurate predictions without strict assumptions. This study focuses on developing ML-based predictive models for key post-PCI outcomes: 30-day mortality, in-hospital major bleeding, and one-year mortality. Data from 104,665 consecutive PCI cases in the Victorian Cardiac Outcomes Registry (VCOR), collected between 2013 and 2022, will be analyzed. Candidate variables, informed by prior systematic reviews and dataset availability, will undergo multiple imputations for missing values. The Boruta method will be applied to identify influential predictors. Risk-adjusted models will be developed using sophisticated ML algorithms, with performance compared across standard metrics for validation. The dataset will be split, optimized via 10-fold cross-validation, and class imbalance addressed using Adaptive Synthetic resampling technique. SHapley Additive exPlanations will interpret the most influential predictors. The variables from the best model will be converted into simplified numeric scores. External validation will be performed using the Tasmanian dataset or equivalent datasets. This study is expected to identify the most influential variables associated with 30-day all-cause mortality, in-hospital major bleeding, and long-term mortality post-PCI. These variables will form the basis for developing robust risk-scoring models to support clinical decision-making and outcome prediction.

During the COVID-19 pandemic, people’s health behavioral changes have been transposed into a new dimension. Coping with the COVID-19 pandemic may have an impact on sustained health behavior (SHB). Therefore, this study aimed to explore the validity and reliability of the COVID-19 Coping Scale among working-age individuals and to assess whether coping with COVID-19-related stress could influence SHB in this population. A cross-sectional study was conducted based on the population of the city of Dhaka in Bangladesh. A total of 263 working-age individuals (19–65 years) participated in the study. The present study results confirmed the COVID-19 Coping Scale was a valid and reliable instrument for this population. Moreover, the present finding indicated decreased odds of SHB for individuals who rated lower scores on coping with COVID-19 compared to individuals who rated higher scores; the result remained significant after controlling for gender and education (OR 0.68, 95% CI: 0.54–0.87). The present study suggests two important findings: (i) the instrument used in this study was valid and reliable in this population, and (ii) coping with COVID-19-related stress may be an important aspect of practicing SHB. Policymakers may use the highlighted findings to facilitate sustainable health behavior for long-term health benefits and to tackle future pandemics like COVID-19 or in a similar context.