Plant-based diets can improve health outcomes while reducing food-system environmental impacts. Healthcare organisations can influence dietary practices through food procurement, policy, and education, but the extent to which plant-based foods are embedded in healthcare sustainability policy remains unclear. To map evidence on the role of plant-based foods in healthcare sustainability policies aimed at reducing healthcare-related emissions. The review employed a scoping methodology guided by the PRISMA-ScR framework. A comprehensive search of peer-reviewed literature was conducted across major academic databases including Scopus, Web of Science, and PubMed. Studies were included if they addressed plant-based or sustainable foods, healthcare organisations, sustainability practices, and/or health policy frameworks. Inclusion criteria focused on articles published in English between January 2013 and February 2024. Data were charted and thematically analysed. The search identified 2640 records; 29 sources met inclusion. Evidence supports plant-based food strategies as a lever for health and sustainability, yet explicit integration into healthcare policy was limited and inconsistent. Where present, plant-based approaches were usually embedded within broader nutrition or sustainability initiatives rather than framed as a standalone policy lever. Policy leadership was most evident in the United Kingdom and Qatar; Australia and the United States were described as facing structural and political constraints, including fragmented mandates, industry influence, and limited monitoring and evaluation. Plant-based foods remain underrepresented in healthcare sustainability policy despite strong rationale for emissions reduction and health co-benefits. Clearer policy commitments, measurable targets, evaluation frameworks, and cross-sector collaboration are needed to translate evidence into scalable healthcare practice.

Are local authority green infrastructure strategies in England addressing climate and environmental risks to public health? A policy review

Community engagement and empowerment to address health inequalities: A rapid evidence review.

Household Water Treatment and Safe Storage (HWTS) technologies play a crucial role in ensuring safe drinking water and preventing waterborne diseases. However, among pastoral communities, in Tanzania, the adoption of these technologies remains low due to various socio-economic and cultural challenges. Therefore, the present study examines the factors influencing the adoption of HWTS technologies among rural households in Tanzania. Using a cross-sectional research design, data were collected from 350 respondents across two villages, Oleng’wadu and Ilikirimuni, in Meru district, through structured questionnaires, and analyzed by descriptive and Probit regression model. Findings revealed that most households rely on public tap water (49%), followed by rainwater (22%) and private taps (18%). Adoption of HWTS is mainly due to microbial contamination (32%) and visible dirtiness of water (31%). Non-adoption is linked to absence of illness from water (26%) and perceived water cleanliness (20.7%). Boiling is the most common HWTS method (49.5%), followed by settling (19.7%) and filtering (8.9%). Regression results show HWTS adoption increases with education (0.5039), age (0.2037), and loan access (5.3578). Adoption is also influenced by awareness (0.4372), unsafe water perception (0.4585), and disease experience like amoeba (0.6436). These results suggest that to increase the uptake of HWTS, it is essential to promote community-based education that enhances awareness and corrects risk perceptions about unsafe water. Financial support mechanisms such as subsidies and microloans should be introduced to improve affordability. Strengthening distribution systems will ensure HWTS products are consistently available, especially in rural areas. Lastly, integrating HWTS into public health policies and encouraging community participation will foster long-term adoption.

Randomized Mediterranean diet intervention to reverse metabolic syndrome: Preliminary findings at 6-month follow-up.

Design and validation of a bioethical assessment instrument for public health policies involving behavioral change: A mixed-methods study.

We developed and refined a systematic keyword search process to flag sexual orientation and gender identity indicators in National Violent Death Reporting System suicide narratives (2003-2021). Applying this process to 298 662 records resulted in 2580 records being flagged for review; 1221 (47%) were classified as sexually or gender minoritized (SGM), increasing the sample of identified SGM decedents by 32%. Our findings highlight persistent failures in accurate surveillance, limiting understanding of SGM suicide mortality. Narrative review methods enhance SGM identification and can improve evidence-based suicide prevention and public health policy. (Am J Public Health. 2026;116(6):755-760. https://doi.org/10.2105/AJPH.2026.308460).

Queer relationality and re-orientation through queer kinesthesia: Queer mental health, community, and care during the COVID-19 pandemic

Are the inequalities in dental caries decreasing over time in Brazil? A comparison between the urban population in two National Oral Health Surveys.

Health polices in residential care settings for older adults are essential to align housing and care provision with the needs of ageing populations, ensuring safety, participation, and quality of life. Despite their importance, evidence regarding policy implementation in long-term care (LTC) facilities remains fragmented and inconsistent. This protocol describes an original method for synthesizing health policy documents using the Joanna Briggs Institute (JBI) framework for textual evidence. It aims to identify, analyze, and integrate policy evidence related to LTC facilities for older adults. Following JBI methodological guidance for systematic reviews of text and opinion, this protocol employs the PICo framework to define inclusion criteria and a three-step search strategy. Searches will be conducted in MEDLINE, CINAHL Complete®, and the Virtual Health Library (BVS). Eligible documents include laws, regulations, policy frameworks, and technical guidelines addressing long-term care within publicly regulated systems. Data extraction and quality appraisal will be independently performed by two reviewers using JBI instruments. The review will synthesize existing polices, highlighting their characteristics, implementation strategies, and outcomes. By applying a transparent and replicable JBI-based method, this protocol supports the production of high-quality evidence to inform equitable and effective governance in LTC facilities.

Disaster risk perception and disaster literacy are important factors associated with individuals' preparedness in disaster-prone societies. Nursing students, as future healthcare professionals, play a critical role in disaster management; however, research on the relationship between these variables remains limited. This study examined the relationship between disaster risk perception and disaster literacy among nursing students using a cross-sectional correlational design. Data were collected using the Disaster Risk Perception Scale and the Disaster Literacy Scale, and structural equation modeling was applied. Disaster risk perception was represented by the dimensions of impact, exposure, anxiety, and uncontrollability. Among these subdimensions, impact had the highest mean score, whereas anxiety had the lowest. The results indicated that disaster risk perception was negatively associated with disaster literacy (β = -0.182). These findings suggest that higher levels of perceived risk are related to lower disaster literacy. Strengthening disaster literacy through targeted education and integrating it into nursing curricula and health policy may help support more balanced risk perceptions and improve disaster preparedness.

The widespread use of pacifiers among healthy full-term infants has raised concerns about possible interference with breastfeeding. Conflicting recommendations in clinical practice reflect uncertainty about their impact on breastfeeding outcomes. Pacifier use is common across different cultural and healthcare settings. Despite extensive research, its effects on breastfeeding initiation, exclusivity, and duration remain unclear. Divergent findings and inconsistent professional guidance highlight the need for a comprehensive synthesis of the evidence. To map and describe the literature on pacifier use in healthy full-term infants and its relationship with breastfeeding outcomes, and to explore influencing factors such as timing of introduction, maternal intention, and healthcare practices. A scoping review was conducted following established methodological frameworks and reporting standards. Four databases were searched for studies published between 1994 and 2024. Italian national and international guidelines were also reviewed. Eligible studies included those published in English and involving healthy full-term infants. Data were charted across predefined categories; no critical appraisal was performed. Fifty-four studies were included. Observational studies often reported a negative association between early pacifier use and breastfeeding, whereas randomized trials and systematic reviews did not confirm this association. Contextual factors such as maternal intention, cultural norms and healthcare policies appeared influential. Substantial heterogeneity and inconsistent findings suggest that pacifier use interacts with broader maternal and healthcare factors rather than exerting a simple causal effect. Evidence on the relationship between pacifier use and breastfeeding outcomes remains inconclusive. Timing of pacifier introduction appears critical. Further research should clarify causal mechanisms and guide coherent, evidence-based recommendations.

Cure rate estimation of skin cancer patients in São Paulo, Brazil, through a cure fraction model.

Total Knee Arthroplasty Out-of-Pocket Costs on the Rise: A Nationwide Analysis of Financial Burden and Regional Disparities.

COVID-19 forecasting from U.S. wastewater surveillance data: A retrospective multi-model study (2022-2024).

• Surveyed 1,000 rural households in Mahendragarh (Haryana) and Jhunjhunu (Rajasthan) to assess health impacts of biomass fuel use. • Identified high prevalence of respiratory symptoms, especially among women and children exposed to smoke from wood, dung cakes, and crop residues. • Developed a comprehensive analytical system integrating PM monitoring, weather data, and health surveys to model exposure-risk relationships. • Applied machine learning (Random Forest, XGBoost, LSTM) and causal inference methods (Granger causality, Causal Impact) to predict pollution and health outcomes. • Designed real-time API and dashboard tools for public health alerts and policy support in rural air quality management. This study investigates the spatiotemporal dynamics of ambient air pollution and its health impacts across two semi-urban districts in India- Jhunjhunu and Mahendragarh, using a multidisciplinary approach combining statistical analysis, machine learning, and causal inference. A one-year high-resolution monitoring dataset of PM₁, PM₂.₅, PM₄, and PM₁₀ was integrated with structured household health surveys covering over 1,000 households. High-resolution monitoring of PM₁, PM 2.5 , PM₄, and PM₁₀, along with survey-based health data, was analyzed to explore pollutant behavior, exposure-response relationships, and symptom prevalence. Linear regression models effectively predicted PM 2.5 trends in Jhunjhunu, while advanced models such as Random Forest, XGBoost, and Long Short-Term Memory (LSTM) captured complex variability in Mahendragarh. Models were trained using a 70:30 train–test split with k-fold cross-validation and evaluated using RMSE, MAE, and R² metrics. LSTM and XGBoost achieved the best performance (R² up to 0.87; RMSE reduced by approximately 30% compared to linear regression). SHAP analysis highlighted PM₁ and PM₄ as critical predictors, underscoring the need to expand national air quality standards beyond PM 2.5 and PM₁₀. Explainable machine learning using SHAP identified PM₁ and PM₄ as influential predictors of health-related outcomes, underscoring the need to expand national air quality standards beyond PM2.5 and PM₁₀. Granger-causal links, residual diagnostics, and health symptom anomalies revealed significant associations between particulate pollution and respiratory, cardiovascular, and visual symptoms, particularly in Mahendragarh. Policy insights emphasize cleaner fuel adoption, improved ventilation, and awareness campaigns to mitigate risk among vulnerable, low-income households. By integrating machine learning with epidemiological modeling, this study provides robust, location-specific evidence to support targeted environmental health interventions in under-monitored regions. A key innovation of this study lies in the joint monitoring and modeling of PM₁ and PM₄ alongside conventional PM₂.₅ and PM₁₀ using explainable ML and causal inference. This framework captures nonlinear exposure–response patterns and improves predictive accuracy while providing mechanistic insight into particle-size-specific health risks. The results offer actionable evidence for clean fuel transition, household ventilation improvements, and community-level air quality management in semi-urban and rural settings. Integration of high-resolution particulate monitoring, machine learning, and causal inference reveals strong links between PM₁-PM₁₀ exposure and cardiopulmonary and ocular symptoms in semi-urban India, highlighting PM₁ and PM₄ as key predictors for targeted interventions.

Substance use disorders and related hospitalizations and mortality among gay, bisexual, and other men who have sex with men: A matched cohort study.

Timely discharge of patients who are clinically ready to be discharged from hospitals to the next point of care is a common health system challenge. Ensuring safe and effective discharge holds new urgency, given the backlogs and increased waiting times for inpatient services in many countries after the COVID-19 pandemic. The study aimed to identify policy options for addressing delayed discharges in six European countries (Denmark, France, Germany, the Netherlands, Norway, and Sweden), and to summarise available evidence on their effectiveness. Experts from the Health Systems and Policy Monitor (HSPM) network of the European Observatory on Health Systems and Policies and additional country experts identified relevant policies and initiatives up to November 2023. When evaluations were available, the experts also provided information on their findings. The data collection was followed by a qualitative, cross-country comparative analysis. A total of 17 policies or initiatives were identified in the study countries. Hospital-based initiatives included discharge planning and transitional care. Community and home care initiatives included municipal emergency beds, intensive home rehabilitation, and assisted discharge. Cross-sectoral initiatives ranged from coordination efforts at the regional and municipal levels to decision support systems and financial incentives. Several common factors or principles underpin many of the identified policies and initiatives. These include clarity of responsibility, effective planning and communication, resourcing of community-based capacity, possible unintended consequences of financial penalties, and the need to adopt a systemic approach.

This article offers an interdisciplinary perspective on compulsive consumption by integrating psychoanalytic theory with behavioural economics. Building on Freudian concepts of repression and sublimation, we develop a theoretical model in which unresolved psychological conflict generates inner tension, leading individuals to seek relief through substitute forms of consumption. Empirical support is drawn from a structured survey of 264 French adults with binge-eating behaviour. Findings indicate that emotional triggers such as anxiety, guilt, and shame are closely linked to harmful consumption patterns, particularly when repression mechanisms fail. The model formalises the interplay between unconscious drives, consumption choices, and economic constraints, offering a new lens for understanding self-regulation failure. By bridging economics, psychology, and clinical theory, this study contributes to current debates on mental health, consumer behaviour, and behavioural policy. It provides conceptual tools for scholars and practitioners seeking to address the psychological dimensions of harmful consumption and its broader societal impacts. • Develops a psychoeconomic model of compulsive consumption. • Integrates Freudian repression with economic decision-making theory. • Formalises psychological tension as a driver of substitution in consumption. • Explains harmful overconsumption under internal and budget constraints. • Provides survey evidence linking emotional triggers to binge-eating behaviour.

Better than Nothing? A scoping review on tiny homes and sleeping cabins as a response to homelessness in high-income countries