Objectives This study investigated the predictors of infant feeding. The focus was on understanding how maternal sociodemographic, psychosocial, and childbirth factors influence infant feeding practices. Methods An analytical cross-sectional study was conducted between October 2023 and March 2024, using a validated Infant Feeding Practices and Beliefs questionnaire. Mothers of children aged one to 3 years were recruited from 120 healthcare centers across nine Governorates of Oman. Descriptive, inferential, and multivariable linear regression analyses were conducted, with the significance level set at less than 0.05. Results The study analyzed 3,036 surveys. The findings suggested that healthier beliefs, receipt of feeding counselling, and high awareness are associated with better infant feeding practices. Baccalaureate education or more and family income of at least 1000 OMR showed negative influence on practices, although were positively associated with the beliefs. Cesarean delivery was associated with poorer adherence to feeding practices. Conclusion This study offers insights into the factors associated with infant feeding, emphasizing culturally informed health promotion strategies. Comprehensive infant feeding counseling shows promise in improving adherence to infant feeding recommendations.
ObjectivesWe examined associations between prediagnostic smoking trajectories and subtype-specific vascular outcomes.MethodsUsing the National Health Insurance Service Customized Database, we conducted a matched case-control study of 1,106,228 adults in South Korea. The exposure was prediagnostic smoking trajectory group, and the outcomes were cardiovascular and cerebrovascular disease and five subtypes: ischemic heart disease, acute myocardial infarction, cerebrovascular disease, intracranial hemorrhage, and cerebral infarction. Trajectories were identified using group-based trajectory modeling, and conditional logistic regression estimated odds ratios.ResultsTrajectory modeling differentiated never, former, high-decreasing, moderate-stable, and high-stable smoking histories. Overall odds increased with smoking intensity and persistence, from former smoking (OR 1.11) to high-stable smoking (OR 1.97). The high-decreasing group showed lower odds than the moderate-stable and high-stable groups. The strongest associations were observed for acute myocardial infarction (OR 3.51) and cerebral infarction (OR 2.46).ConclusionPrediagnostic smoking trajectories revealed a graded vascular risk pattern shaped by intensity, persistence, and reduction, with the greatest excess risks for ischemic outcomes. Repeated smoking histories in screening data may move risk assessment beyond current smoking status and inform earlier cessation intervention.
Artificial Intelligence (AI) is transforming public health decision-making, shifting it from descriptive epidemiological reporting to predictive, accelerated operational guidance. Yet AI in public health is discussed almost entirely at technical or regulatory levels, leaving a gap in executive-level guidance. Public health operates within a complex landscape of social determinants, political scrutiny, and incomplete information, where algorithmic errors can severely damage public trust. Consequently, executive competency must expand beyond traditional data interpretation to foundational AI literacy. Leaders must differentiate among diverse AI applications and evaluate their risk profiles, including balancing AI trained on individual-level precision medicine models with public health population-level prevention mandate. Continuous human oversight and multidisciplinary validation should be standard for modern public health organizations. Automated systems must equitably advance population health outcomes under final human authority at all times. Public health executives can better prepare to make informed decisions about AI-related risks using the decision points presented in this commentary.
ObjectiveTo conduct a systematic review of the evidence regarding the impact of environmental health factors, such as air pollution, thermal stress, water and soil pollution, agricultural instability, and occupational hazards, on seasonal migration in China.MethodsA systematic review of PubMed, Web of Science, CNKI, Scopus, and Google Scholar (2001–2025) was conducted to identify peer-reviewed articles and institutional reports on environmental exposure, health, and migration outcomes in the Chinese mainland’s populations.ResultsOutmigration trends were found to be associated with each of the four aspects of air pollution, heat stress, water contamination, and agricultural instability during the winter, and, in addition to the above, water and soil contamination, in the summer. The effects were found to be strongest among rural poor and older individuals. At urban destinations, migrants were also at risk of occupational hazards due to silica dust and chemicals.ConclusionThe environment exerts a multidimensional, seasonal influence on internal migration in China. There is a need for coordinated environmental health governance to minimize migrant exposure and promote health equity in this population.
