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Authors: Bhawana Subedi, Kalpana Bhattarai, Brandon A. Kohrt, Claire Henderson, Elaine Brohan, Graham Thornicroft, Ioannis Bakolis, Maya Semrau, Nicole Votruba, Petra C. Gronholm, Uta Ouali, Dristy Gurung

Mental health-related stigma is a barrier to access to mental health services. This mixed-methods pre-post pilot feasibility study aimed to adapt and pilot-test a multi-level anti-stigma intervention across three levels of the health system to assess its feasibility and potential to reduce mental health-related stigma in Nepal. Contact-based interventions integrating people with lived experience in mental health and stigma training packages were adapted and implemented, targeting female community health volunteers (n = 19), primary healthcare workers (n = 9), and psychiatrists and psychiatric nurses (n = 9). Stigma-related knowledge, attitudes, and competency were measured pre- and post-training with follow-up at 3 and 6 months. Qualitative interviews were conducted with 43 participants across levels. In INDIGO Local, knowledge score increased from 20.43 (95% CI: 20.0-20.9) at pre-training to 25.11 (95% CI: 23.83-26.17) at six months. In INDIGO Primary, the intended behavior increased from 16.67 (95% CI: 14.73-19.27) at pre-training to 18.88 (95% CI: 17.69-20.31) at post-training, while SDS mean score decreased from 39.26 to 20.62. The PCP’s helpful behavior increased, and harmful behavior decreased. The knowledge and OSCE mean score in INDIGO READ-MH increased from 12.67 (95% CI: 10.36 -14.97) at baseline to 14.67 (95% CI: 13.28-16.05) at the post-training and from 2 (95% CI: 1.62-2.38) at pre-training to 9 (95% CI: 7.56-10.44) at post- training, respectively. The ASTAD subscale increased at three months, except for motivation and work satisfaction, which decreased at post-training. Qualitative data highlighted the positive attitudinal shift following the interventions, with strong emphasis on PWLE involvement. Overall, the providers perceived the training as feasible and appropriate within Nepal’s health system. However, whilst they reported more positive perceptions towards persons with mental health conditions following the interventions, they also identified barriers to effective mental health care, such as insufficient availability of medicines, limited human resources, or separate counseling rooms.

Source: PLOS Mental Health. DOI: 10.1371/journal.pmen.0000638. Abstract from the publisher’s feed; HTML formatting and repeated author line removed.

Authors: Franck Hervé Akaffou, Salomon Obahoundje, Arona Diedhiou, Kouakou Lazare Kouassi, Dior Diallo, Ernest Amoussou, Daniel Yamegueu, Eric Antwi Ofosu

This study presents a novel five-step ensemble machine learning approach to improve predictive accuracy in assessing climate change impacts on inflow patterns and hydropower generation across seven dam basins in West Africa. The methodology integrates precipitation and temperature using the multi-lag approach. An initial pool of fifteen machine learning models were evaluated, and top-performing models were selected for further refinement through iterative ensemble stacking and weak learner elimination. Historical analysis (1983–2014) utilized CHIRPS and CHIRTS datasets. Future projections employed twelve bias-adjusted CMIP6 models and their ensemble mean (EnsMean) under SSP1-2.6, SSP2-4.5, and SSP5-8.5 for the near (2036–2067) and far (2068–2099) futures. Results showed notable improvements in model accuracy and efficiency across layers, with R² and NSE exceeding 0.6 for all inflow simulations and for selected energy simulations (Bagre, Nangbeto, and Taabo). Projections indicated warming up to 4.5°C and spatially heterogeneous precipitation changes across basins and scenarios, with SSP5-8.5 projecting the most pronounced shifts. Inflow reductions are projected to reach up to 24% at Buyo and 13% at Nangbeto, while hydropower output may decline by up to 19% at Nangbeto and 58% at Taabo in the future. Conversely, Manantali and Taabo are projected to experience inflow increases, and Bagre may see energy gains of up to 42% under SSP5-8.5. These findings highlight heightened vulnerability, as well as contrasting opportunities across the region, underscoring the urgent need for adaptive management strategies, such as enhancing hydropower system resilience, diversifying energy portfolios, and integrating renewable sources to mitigate climate risks. Hydropower managers and policymakers must prioritize proactive measures to ensure energy security and sustainable resource management amid changing climatic conditions.

Source: PLOS Climate. DOI: 10.1371/journal.pclm.0000986. Abstract from the publisher’s feed; HTML formatting and repeated author line removed.

Authors: Orrin D. Ware

Substance use disorders may arise when individuals use substances to cope with adverse mental health symptoms. Conversely, mental health symptoms can develop because of substance use, a category of conditions called substance-induced disorders. This study used two United States-based datasets, the Mental Health Client-Level Data and the Treatment Episode Data Set Admissions, to summarize the number of cases with “alcohol-induced disorders” and “substance-induced disorders” from both mental health and substance use disorder treatment programs from 2013 to 2022. The primary study aim was to identify the percentage of cases with alcohol-induced disorders or substance-induced disorders in these treatment settings. Among all mental health disorder treatment cases, those with an alcohol or substance-induced disorder totaled N = 439,932, making up 0.7% of total cases over the analytic period. During the same period, alcohol or substance-induced disorders totaled N = 459,817, accounting for approximately 2.7% of all substance use disorder treatment cases. The majority (79%) of cases with an alcohol-induced disorder in substance use disorder treatment were identified as reporting alcohol use, with 68% having alcohol listed as their primary substance. Heroin and other opiates and synthetics accounted for the primary substance among 28.8% of cases with a substance-induced disorder in the substance use disorder treatment data. Approximately 1 in 3 cases with an alcohol-induced disorder and 1 in 4 cases with a substance-induced disorder had a depressive disorder diagnosis in mental health treatment. When considering the primary diagnosis, approximately 1 in 4 cases with an alcohol-induced disorder and 15% of cases with a substance-induced disorder had a depressive disorder as the primary diagnosis in mental health treatment. Substance-induced disorders are a significant public health concern, and more research is needed to better understand the clinical needs of this population.

Source: PLOS Mental Health. DOI: 10.1371/journal.pmen.0000706. Abstract from the publisher’s feed; HTML formatting and repeated author line removed.

Authors: Cristian Montenegro, Felipe Szabzon, Sofía Bowen, Delia da Mosto

Psychiatric deinstitutionalisation (PDI) has re-emerged as an important concern in global mental health in response to the persistence of long-term institutionalisation and human rights abuses in institutions. However, contemporary responses continue to frame reform largely in managerial and technocratic terms, prioritising service coverage, bed reductions, workforce expansion, and scalability. We argue that this framing narrows the meaning of reform and its capacity to address the institutional, political, and social conditions through which psychiatric care is organised. Drawing on critical perspectives on PDI and experiences from South America, we show how PDI has developed through distinct relationships between institutional transformation, democratisation, social mobilisation, and citizenship. These trajectories illuminate contemporary dilemmas such as transinstitutionalisation, fragmented systems of care, and the persistence of segregationist and custodial logics within community settings. We propose three lenses for re-engaging with PDI as an ongoing political and ethical project: attending to context and history, decentring dominant geographies of reform, and foregrounding lived experience. Together, these lenses shift attention towards the sites where reform is actually produced—across research, policy, training, activism, and governance— and highlight the contribution of South American experiences to ongoing debates on institutional transformation.

Source: PLOS Mental Health. DOI: 10.1371/journal.pmen.0000699. Abstract from the publisher’s feed; HTML formatting and repeated author line removed.

Authors: Stephanie Jean Tsang, Dandan Wang

Public support for climate action hinges on the integrity of information environments. In an online experiment, U.S. adults used ChatGPT to evaluate climate-related claims. We first conducted computational text analysis of GPT conversation logs, assessing valence, formality, bias, recency, authority cues, and semantic richness of ChatGPT’s responses using language-model based classifiers and link-level metadata. Concurrently, we measured credibility judgments for both the claims and ChatGPT’s responses through participant self-reports. Credibility perceptions were driven primarily by individual differences; greater acceptance of the scientific consensus, attention to climate change, prior familiarity with ChatGPT, and younger age predicted higher perceived credibility. Once these factors were accounted for, authority cues were associated with slightly less extreme climate attitudes, while positive valence and semantic richness correlated positively with perceived credibility. These results support transparent, unbiased, audience-tailored engagement as a pathway to strengthen responsible climate communication and help bridge the gap between scientific consensus and public understanding.

Source: PLOS Climate. DOI: 10.1371/journal.pclm.0000930. Abstract from the publisher’s feed; HTML formatting and repeated author line removed.

Authors: Guanyu Yang, Amy Rodger, Elif Naz Çoker, David Shipworth

Energy, transport, and food sectors contribute substantially to climate change and are key areas where behaviour change can have the greatest impact. International policy learning and transfer are central to government approaches in these sectors. However, policies often show limited effectiveness when applied in varied contexts, likely due to assumptions of population homogeneity and over-reliance on socio-demographic characteristics. Using Multilevel Latent Class Analyses (MLCA) on over 17,000 respondents across 61 regions in nine OECD countries, we found that less sustainable behavioural patterns were prevalent across the three sectors, though with notable heterogeneity in the energy sector, and that socio-demographic characteristics failed to predict segment membership consistently. Individuals engaging in sustainable behaviours in one sector were more likely to do so in others, and unsustainable behaviours had similar positive associations across sectors, suggesting coordinated cross-sector interventions are crucial. Regional distribution of population segments was typically grouped geographically by country, underscoring the importance of contextual influences. However, we identified significant within-country variations in regional distributions, with some regions showing substantially different proportions of people in sustainable segments than neighbouring regions under identical national policies, highlighting the importance of regional context. Moving beyond traditional ad-hoc policy transfer approaches, we proposed a systematic framework for identifying evidence-based transfer opportunities, showing policymakers not only who needs interventions but where successful patterns emerge and which contexts may enable sustainable behaviours. These findings provide actionable insights for the international policy community seeking to accelerate behavioural change at the scale and speed required for climate mitigation.

Source: PLOS Climate. DOI: 10.1371/journal.pclm.0001039. Abstract from the publisher’s feed; HTML formatting and repeated author line removed.

Authors: Md. Mamun Miah, Kabir Hossain, Hafiz T. A. Khan, H. M. Shahadat Ali

Waterborne diseases remain a major public health challenge, especially in low- and middle-income countries like Bangladesh. This study aimed to identify household level predictors of waterborne diseases and develop an optimized, explainable machine learning framework for predicting disease risk in flood-affected communities. This cross-sectional study was conducted in two flood-affected districts in Bangladesh, using face-to-face interviews with 501 respondents selected through simple random sampling to collect household-level data. Following data preprocessing, correlation analysis, variance inflation factor assessment, mutual information analysis, and Recursive Feature Elimination with Cross-Validation (RFECV) were performed for feature selection. Five machine learning algorithms—Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost) were optimized using randomized hyperparameter tuning with five-fold cross-validation. Model performance was evaluated on an independent test set using accuracy, precision, recall, specificity, F1-score, receiver operating characteristic area under the curve (ROC-AUC), bootstrap confidence intervals, and SHAP-based interpretation. The prevalence of waterborne disease in the flood affected area was found to be 63.7% (n=319). RFECV retained ten important predictors: age, gender, monthly household income, household size, flood frequency, flood severity, source of drinking water, availability of medical assistance, water purification method, and community preparedness for preventing waterborne diseases. Among the optimized machine learning models, the Random Forest classifier showed the most balanced predictive performance on the independent test set, achieving an accuracy of 61.4%, precision of 71.9%, recall of 64.1%, specificity of 56.8%, F1-score of 67.8%, and ROC-AUC of 0.643. SHAP analysis identified flood severity, community preparedness, water purification practices, and socio-economic factors as the most influential predictors. Although stacking and weighted soft voting ensemble models were additionally evaluated, neither outperformed the Random Forest. The proposed explainable framework may support targeted interventions and early warning strategies after external validation. All the analyses were conducted using Python.

Source: PLOS Water. DOI: 10.1371/journal.pwat.0000542. Abstract from the publisher’s feed; HTML formatting and repeated author line removed.

Authors: Daniella Myriam Pierre, Rehana Rasul, Reichling St. Sauveur, Kelly Celestin, Vanessa Rouzier, Erline Hilaire, Marie Marcelle Deschamps, Jean William Pape, Lily D. Yan, Anju Ogyu, Catherine Bennett, Margaret L. McNairy, Rodney Sufra, Denis Nash

Cardiovascular disease (CVD) is the leading cause of mortality in low- and middle-income countries, and depression remains an underexplored CVD risk factor in Haiti. We assessed the association between depressive symptoms (DS) and prevalent CVD in urban Haiti and examined sex differences using a cross-sectional analysis of enrollment data from the Haiti Cardiovascular Disease Cohort (adults ≥18 years; March 2019–August 2021). DS were measured using the Patient Health Questionnaire-9 and categorized as none–mild (<10) versus moderate–severe (≥10). Prevalent CVD (angina, myocardial infarction, transient ischemic attack or stroke and heart failure) was adjudicated using epidemiologic definitions aligned with international cohorts. Prevalence ratios (PRs) were estimated using generalized estimating equation Poisson models with a log link, adjusting for age, sex, education, income, food insecurity, smoking, alcohol use, physical activity, stress, and ody Mass Index; effect modification by sex was assessed on multiplicative and additive scales. Among 2,995 participants (mean age 41.9 years; 58.0% female), 16.2% had moderate–severe DS, with higher prevalence in females (22.0%) than males (8.5%). CVD prevalence was higher among those with moderate–severe DS than none–mild DS in both sexes (males: 21.5% vs. 10.6%; females: 23.3% vs. 15.3%). Moderate–severe DS were independently associated with higher CVD prevalence (adjusted PR = 1.36; 95% CI: 1.08–1.71). Sex-stratified estimates were 1.38 (95% CI: 1.06–1.78) for females and 1.25 (95% CI: 0.75–1.99) for males; evidence for additive interaction by sex was limited (RERI = 0.07, 95% CI: −0.74 to 0.88). DS were independently associated with higher CVD prevalence in urban Haiti, with a consistently stronger pattern among women, although formal tests of effect modification was not statistically significant. Integrating depression screening into CVD prevention may help address the growing burden of both conditions in resource-limited settings. Prospective studies are needed to clarify the underlying mechanisms and causal pathways.

Source: PLOS Mental Health. DOI: 10.1371/journal.pmen.0000693. Abstract from the publisher’s feed; HTML formatting and repeated author line removed.

Authors: Giulia Vivaldi, Emeka Chukwusa, Nick Bridge, Michael Smythe, Ioannis Bakolis, Lisa Page, Rhiannon Thompson, Matthew White, Andrea Mechelli

The United Kingdom (UK) Government’s recent 10 Year Health Plan for England aims to fix a “broken” and increasingly unaffordable health system through a shift from sickness to prevention, hospital to community, and analogue to digital, with a focus on mental as well as physical health. However, the 171-page strategy for a healthy nation does not contain a single reference to nature or biodiversity. We review the significant benefits of nature and biodiversity to health and outline how these benefits can and should be incorporated into the 10 Year Health Plan. We provide examples of effective approaches and techniques, describe challenges to implementation, and propose potential solutions. We focus on mental health, although many benefits will also apply to physical health. Harnessing nature and biodiversity could play a substantial role in supporting the UK’s revised goals, particularly preventing illness and enhancing treatment at the community level.

Source: PLOS Mental Health. DOI: 10.1371/journal.pmen.0000647. Abstract from the publisher’s feed; HTML formatting and repeated author line removed.