Pub Date : 2025-12-13DOI: 10.1016/j.ssmph.2025.101897
Yang Zhao , Chao Li , Jiuchang Wei
Objectives
This study aims to investigate the time-dependent pattern of employees’ work stress responses. It seeks to determine whether there is a day-of-the-week effect in employees’ physiological reactions to workplace stressors on an intra-week scale.
Methods
Utilizing seven-year (2017–2023) biometric data from financial sector employees in Eastern China, we analyzed the association between daily stock returns and cardiovascular biomarkers. A counterfactual analysis during holiday periods was conducted to isolate work-cycle-specific effects from general biological rhythms.
Results
Declines in company stock prices significantly elevated employees’ systolic blood pressure and blood lipids from Tuesdays to Thursdays (TTTday), with the effect peaking on Tuesdays, remaining significant on Wednesdays, and failing to reach statistical significance on Thursdays. No significant effects were observed on Mondays or Fridays. Holiday-period analyses confirmed this TTTday vulnerability window as a work-cycle-specific phenomenon.
Conclusions
Employees exhibit rhythmic variations in effort and recovery on the sub-weekly scale, and this leads to varying degrees of physiological responses to workplace stress during the workweek.
{"title":"TTTday window: Intra-week work stress in financial employees’ cardiovascular biomarker trajectories","authors":"Yang Zhao , Chao Li , Jiuchang Wei","doi":"10.1016/j.ssmph.2025.101897","DOIUrl":"10.1016/j.ssmph.2025.101897","url":null,"abstract":"<div><h3>Objectives</h3><div>This study aims to investigate the time-dependent pattern of employees’ work stress responses. It seeks to determine whether there is a day-of-the-week effect in employees’ physiological reactions to workplace stressors on an intra-week scale.</div></div><div><h3>Methods</h3><div>Utilizing seven-year (2017–2023) biometric data from financial sector employees in Eastern China, we analyzed the association between daily stock returns and cardiovascular biomarkers. A counterfactual analysis during holiday periods was conducted to isolate work-cycle-specific effects from general biological rhythms.</div></div><div><h3>Results</h3><div>Declines in company stock prices significantly elevated employees’ systolic blood pressure and blood lipids from Tuesdays to Thursdays (TTTday), with the effect peaking on Tuesdays, remaining significant on Wednesdays, and failing to reach statistical significance on Thursdays. No significant effects were observed on Mondays or Fridays. Holiday-period analyses confirmed this TTTday vulnerability window as a work-cycle-specific phenomenon.</div></div><div><h3>Conclusions</h3><div>Employees exhibit rhythmic variations in effort and recovery on the sub-weekly scale, and this leads to varying degrees of physiological responses to workplace stress during the workweek.</div></div>","PeriodicalId":47780,"journal":{"name":"Ssm-Population Health","volume":"33 ","pages":"Article 101897"},"PeriodicalIF":3.1,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-12DOI: 10.1016/j.ssmph.2025.101898
Jian Song , Peng Li , Jun Cheng , Chao Wang , Rong Song , Weizhuo Yi , Rubing Pan , Xiaoyu Jin , Xulai Zhang , Hong Su
Background
The built environment is a key intervenable target for public health, yet its nonlinear and threshold relationships with schizophrenia incidence remain poorly understood.
Methods
We analyzed township-level schizophrenia incidence (2019–2023) in Anhui, China, using data from the National Severe Mental Disorder Registration System. Built environment features were derived from multi-source geographic data. We characterized spatiotemporal patterns and modeled associations using Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and an interpretable XGBoost model explained by SHAP values.
Results
The mean annual incidence was 13.46 per 100,000, with significant spatial clustering (Moran's I = 0.47, P < 0.001). The SHAP-XGBoost model outperformed both OLS and GWR. Key built environment predictors included population density, NDVI, distance to blue space, street connectivity, and blue space area. These factors exhibited complex nonlinear relationships with schizophrenia risk; for example, population density showed a U-shaped association with a risk threshold around 15,000 persons/km2. Interaction effects between multiple features were also identified.
Conclusion
This study provides robust evidence that the built environment is significantly and nonlinearly linked to schizophrenia incidence. The identified thresholds and interactions offer concrete, actionable guidance for urban planning aimed at mental health promotion.
建筑环境是公共卫生的关键可干预目标，但其与精神分裂症发病率的非线性和阈值关系仍然知之甚少。方法利用国家严重精神障碍登记系统的数据，分析安徽省2019-2023年乡镇精神分裂症发病率。建筑环境特征是由多源地理数据导出的。我们使用普通最小二乘法（OLS）、地理加权回归（GWR）和SHAP值解释的可解释的XGBoost模型来表征时空模式和建模关联。结果年平均发病率为13.46 / 10万，具有显著的空间聚类性（Moran’s I = 0.47, P < 0.001）。SHAP-XGBoost模型优于OLS和GWR。关键的建筑环境预测指标包括人口密度、NDVI、到蓝色空间的距离、街道连通性和蓝色空间面积。这些因素与精神分裂症风险表现出复杂的非线性关系；例如，人口密度与1.5万人/平方公里左右的风险阈值呈u型关系。还确定了多个特征之间的交互效应。结论本研究提供了强有力的证据，表明建筑环境与精神分裂症发病率存在显著的非线性联系。确定的阈值和相互作用为旨在促进心理健康的城市规划提供了具体的、可操作的指导。
{"title":"Nonlinear associations and threshold effects between built environment features and schizophrenia incidence: Implications for healthy city planning","authors":"Jian Song , Peng Li , Jun Cheng , Chao Wang , Rong Song , Weizhuo Yi , Rubing Pan , Xiaoyu Jin , Xulai Zhang , Hong Su","doi":"10.1016/j.ssmph.2025.101898","DOIUrl":"10.1016/j.ssmph.2025.101898","url":null,"abstract":"<div><h3>Background</h3><div>The built environment is a key intervenable target for public health, yet its nonlinear and threshold relationships with schizophrenia incidence remain poorly understood.</div></div><div><h3>Methods</h3><div>We analyzed township-level schizophrenia incidence (2019–2023) in Anhui, China, using data from the National Severe Mental Disorder Registration System. Built environment features were derived from multi-source geographic data. We characterized spatiotemporal patterns and modeled associations using Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and an interpretable XGBoost model explained by SHAP values.</div></div><div><h3>Results</h3><div>The mean annual incidence was 13.46 per 100,000, with significant spatial clustering (Moran's I = 0.47, P < 0.001). The SHAP-XGBoost model outperformed both OLS and GWR. Key built environment predictors included population density, NDVI, distance to blue space, street connectivity, and blue space area. These factors exhibited complex nonlinear relationships with schizophrenia risk; for example, population density showed a U-shaped association with a risk threshold around 15,000 persons/km<sup>2</sup>. Interaction effects between multiple features were also identified.</div></div><div><h3>Conclusion</h3><div>This study provides robust evidence that the built environment is significantly and nonlinearly linked to schizophrenia incidence. The identified thresholds and interactions offer concrete, actionable guidance for urban planning aimed at mental health promotion.</div></div>","PeriodicalId":47780,"journal":{"name":"Ssm-Population Health","volume":"33 ","pages":"Article 101898"},"PeriodicalIF":3.1,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-05DOI: 10.1016/j.ssmph.2025.101893
Huijun Lei , Miaomiao Chen , Haoyu Qu , Liang Li , Mengzhou Xie
Against the backdrop of simultaneous national agendas for green transition and healthy ageing, the nutritional behaviour of older adults is being reshaped by emerging forms of institutional governance. Drawing on four waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS, 2008, 2011, 2014, and 2018), which are linked to the full texts of provincial government work reports from 22 provinces, this study constructs an index of "governmental green development attention" to rigorously assess whether provincial-level prioritization of green development is associated with dietary diversity among older adults. Baseline estimates from two-way fixed-effects models show that, after controlling for both individual- and provincial-level covariates, higher governmental attention to green development is significantly associated with greater dietary diversity in later life. We further identify three channels through which this association appears to operate: (i) heightened agenda salience and social participation; (ii) the framing of "green" as "healthy" and the diffusion of corresponding consumption norms; and (iii) improvements in public environmental conditions and basic service provision. A battery of robustness checks—including multilevel and random-effects specifications, the inclusion of province–year interactions, adjustments to the operationalization of the core explanatory variable, and double machine learning (DML) estimates of causal effects—consistently supports the main findings. Additional analyses show that the supply-side dimension of green transition exerts the strongest influence; that the volume of granted green patents is likewise positively associated with dietary diversity; and that the relationship between policy attention and dietary balance exhibits an inverted U-shape. Heterogeneity analyses indicate that the association is markedly stronger among men, older adults who do not live alone, and residents of provinces with a higher share of secondary industry in GDP, suggesting that certain subgroups are more responsive to institutional environmental signals. Taken together, this study, grounded in the logic of limited attention, traces a "policy signal — cognitive/behavioural response" transmission pathway, offering new evidence on the behavioural health spillovers of environmental governance and informing nutrition-oriented interventions for ageing societies.
{"title":"Can environmental signals influence dietary Behaviours?The impact of governmental green development attention on dietary diversity among older adults","authors":"Huijun Lei , Miaomiao Chen , Haoyu Qu , Liang Li , Mengzhou Xie","doi":"10.1016/j.ssmph.2025.101893","DOIUrl":"10.1016/j.ssmph.2025.101893","url":null,"abstract":"<div><div>Against the backdrop of simultaneous national agendas for green transition and healthy ageing, the nutritional behaviour of older adults is being reshaped by emerging forms of institutional governance. Drawing on four waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS, 2008, 2011, 2014, and 2018), which are linked to the full texts of provincial government work reports from 22 provinces, this study constructs an index of \"governmental green development attention\" to rigorously assess whether provincial-level prioritization of green development is associated with dietary diversity among older adults. Baseline estimates from two-way fixed-effects models show that, after controlling for both individual- and provincial-level covariates, higher governmental attention to green development is significantly associated with greater dietary diversity in later life. We further identify three channels through which this association appears to operate: (i) heightened agenda salience and social participation; (ii) the framing of \"green\" as \"healthy\" and the diffusion of corresponding consumption norms; and (iii) improvements in public environmental conditions and basic service provision. A battery of robustness checks—including multilevel and random-effects specifications, the inclusion of province–year interactions, adjustments to the operationalization of the core explanatory variable, and double machine learning (DML) estimates of causal effects—consistently supports the main findings. Additional analyses show that the supply-side dimension of green transition exerts the strongest influence; that the volume of granted green patents is likewise positively associated with dietary diversity; and that the relationship between policy attention and dietary balance exhibits an inverted U-shape. Heterogeneity analyses indicate that the association is markedly stronger among men, older adults who do not live alone, and residents of provinces with a higher share of secondary industry in GDP, suggesting that certain subgroups are more responsive to institutional environmental signals. Taken together, this study, grounded in the logic of limited attention, traces a \"policy signal — cognitive/behavioural response\" transmission pathway, offering new evidence on the behavioural health spillovers of environmental governance and informing nutrition-oriented interventions for ageing societies.</div></div>","PeriodicalId":47780,"journal":{"name":"Ssm-Population Health","volume":"33 ","pages":"Article 101893"},"PeriodicalIF":3.1,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-05DOI: 10.1016/j.ssmph.2025.101890
Yixin Zhao, Xiaoyan Wang, Shiyao Ling, Kexin Peng, Hongyu Li, Lian Yang
Background
Chinese unique sociocultural context surrounding tobacco reinforces smoking behaviors, potentially through its influence on smokers’ expectancies of smoking-related outcomes. Network analysis effectively explores the intricate relationship between social tobacco cultural attitudes and smoking outcome expectancies among Chinese smokers.
Methods
The study included 1382 current smokers. A mixed graphical model was employed to construct internal and combined networks of social tobacco cultural attitudes and smoking outcome expectancies. Additionally, node strength centrality, edge weights, and stability were analyzed.
Results
In the network of social tobacco cultural attitudes, sharing cigarettes (jingyan or sanyan) was identified as the central node (Str = 0.855). For smoking outcome expectancies, stimulus/state enhancement exhibits the highest strength centrality (Str = 0.860). In the total integrated network of smoking outcome expectancies and social tobacco cultural attitudes, social facilitation outcome expectancies demonstrated the highest strength centrality between the two variables (Str = 0.894).
Conclusions
In this study, social facilitation outcome expectancies were central in the combined network of social tobacco cultural attitudes and smoking outcome expectancies, showing a direct and positive link to multiple tobacco cultural attitudes. This finding illustrates how sociocultural factors are interconnected with individual expectancies of smoking outcomes, identifying the central positioning of social facilitation expectancies variables within the sociocultural attitudes–outcome expectancies network. These insights provide new perspectives for developing culturally adaptive tobacco control interventions, such as reshaping tobacco cultural symbols to promote “smoke-free weddings” and “refusing cigarette gifts.”
{"title":"Unraveling the complexity of associations between tobacco culture in Chinese society and smokers' outcome expectancies: a network perspective","authors":"Yixin Zhao, Xiaoyan Wang, Shiyao Ling, Kexin Peng, Hongyu Li, Lian Yang","doi":"10.1016/j.ssmph.2025.101890","DOIUrl":"10.1016/j.ssmph.2025.101890","url":null,"abstract":"<div><h3>Background</h3><div>Chinese unique sociocultural context surrounding tobacco reinforces smoking behaviors, potentially through its influence on smokers’ expectancies of smoking-related outcomes. Network analysis effectively explores the intricate relationship between social tobacco cultural attitudes and smoking outcome expectancies among Chinese smokers.</div></div><div><h3>Methods</h3><div>The study included 1382 current smokers. A mixed graphical model was employed to construct internal and combined networks of social tobacco cultural attitudes and smoking outcome expectancies. Additionally, node strength centrality, edge weights, and stability were analyzed.</div></div><div><h3>Results</h3><div>In the network of social tobacco cultural attitudes, sharing cigarettes (jingyan or sanyan) was identified as the central node (Str = 0.855). For smoking outcome expectancies, stimulus/state enhancement exhibits the highest strength centrality (Str = 0.860). In the total integrated network of smoking outcome expectancies and social tobacco cultural attitudes, social facilitation outcome expectancies demonstrated the highest strength centrality between the two variables (Str = 0.894).</div></div><div><h3>Conclusions</h3><div>In this study, social facilitation outcome expectancies were central in the combined network of social tobacco cultural attitudes and smoking outcome expectancies, showing a direct and positive link to multiple tobacco cultural attitudes. This finding illustrates how sociocultural factors are interconnected with individual expectancies of smoking outcomes, identifying the central positioning of social facilitation expectancies variables within the sociocultural attitudes–outcome expectancies network. These insights provide new perspectives for developing culturally adaptive tobacco control interventions, such as reshaping tobacco cultural symbols to promote “smoke-free weddings” and “refusing cigarette gifts.”</div></div>","PeriodicalId":47780,"journal":{"name":"Ssm-Population Health","volume":"33 ","pages":"Article 101890"},"PeriodicalIF":3.1,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-04DOI: 10.1016/j.ssmph.2025.101892
Hanyang Shen , Nicole Gladish , Andres Cardenas , Belinda L. Needham , David H. Rehkopf
{"title":"Social support and epigenetic aging at the intersections of race, ethnicity, and gender: findings from NHANES 1999–2002","authors":"Hanyang Shen , Nicole Gladish , Andres Cardenas , Belinda L. Needham , David H. Rehkopf","doi":"10.1016/j.ssmph.2025.101892","DOIUrl":"10.1016/j.ssmph.2025.101892","url":null,"abstract":"","PeriodicalId":47780,"journal":{"name":"Ssm-Population Health","volume":"33 ","pages":"Article 101892"},"PeriodicalIF":3.1,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-04DOI: 10.1016/j.ssmph.2025.101889
Anna Rolleston , Gregory T. Jones , Nikki J. Earle , Sam Gibbs , Anna Pilbrow , Allamanda Faatoese , Katrina K. Poppe , Kimiora Henare , Vicky A. Cameron , Donia Macartney-Coxson , Malcolm E. Legget , Robert N. Doughty
Epigenetic research, particularly DNA methylation (DNAm), holds significant potential for improving cardiovascular disease (CVD) risk prediction, yet its application must be guided by ethical and culturally responsive considerations. This paper examines the integration of a values-based framework to ensure the culturally safe conduct of DNAm research within the Multi-Ethnic New Zealand Study of Acute Coronary Syndromes (MENZACS) cohort. Grounded in Te Tiriti o Waitangi principles and kaupapa Māori methodologies, this study emphasises equity, social accountability, and indigenous data sovereignty. This study was not designed as a discovery epigenome wide analysis, but rather performed, as an exemplar, a SWOT analysis that identified both the potential of DNAm markers, such as cg05575921 in AHRR for smoking exposure assessment, and key risks, including genetic confounding, population-specific variation, and the potential for individual and transgenerational stigma. Findings underscore the importance of ensuring multi-ethnic validation of DNAm markers to prevent exacerbation of health inequities. This paper advocates for the adoption of ethical, culturally attuned research frameworks in epigenetics to enhance equitable health outcomes and support Māori health advancement.
{"title":"Considerations for study design and analysis for ethically and culturally safe DNA methylation research in Aotearoa New Zealand","authors":"Anna Rolleston , Gregory T. Jones , Nikki J. Earle , Sam Gibbs , Anna Pilbrow , Allamanda Faatoese , Katrina K. Poppe , Kimiora Henare , Vicky A. Cameron , Donia Macartney-Coxson , Malcolm E. Legget , Robert N. Doughty","doi":"10.1016/j.ssmph.2025.101889","DOIUrl":"10.1016/j.ssmph.2025.101889","url":null,"abstract":"<div><div>Epigenetic research, particularly DNA methylation (DNAm), holds significant potential for improving cardiovascular disease (CVD) risk prediction, yet its application must be guided by ethical and culturally responsive considerations. This paper examines the integration of a values-based framework to ensure the culturally safe conduct of DNAm research within the Multi-Ethnic New Zealand Study of Acute Coronary Syndromes (MENZACS) cohort. Grounded in Te Tiriti o Waitangi principles and kaupapa Māori methodologies, this study emphasises equity, social accountability, and indigenous data sovereignty. This study was not designed as a discovery epigenome wide analysis, but rather performed, as an exemplar, a SWOT analysis that identified both the potential of DNAm markers, such as cg05575921 in AHRR for smoking exposure assessment, and key risks, including genetic confounding, population-specific variation, and the potential for individual and transgenerational stigma. Findings underscore the importance of ensuring multi-ethnic validation of DNAm markers to prevent exacerbation of health inequities. This paper advocates for the adoption of ethical, culturally attuned research frameworks in epigenetics to enhance equitable health outcomes and support Māori health advancement.</div></div>","PeriodicalId":47780,"journal":{"name":"Ssm-Population Health","volume":"33 ","pages":"Article 101889"},"PeriodicalIF":3.1,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-04DOI: 10.1016/j.ssmph.2025.101891
Aziz Mert Ipekci , Maximilian Filsinger , Diana Buitrago-Garcia , Cristopher I. Kobler Betancourt , Harvy Joy Liwanag , Annika Frahsa , Nicola Low
Objective
Political and affective polarization are different, but related concepts, which can shape trust in authorities, interpretation of health messages, and health behaviors and outcomes. The aim of this study was to systematically review the research literature, exploring how affective and political polarization are associated with COVID-19-related health behaviors and outcomes.
Methods
From January 1, 2019 to November 27, 2024, we searched 12 electronic databases for studies about affective or political polarization and COVID-19-related outcomes, including preventive behaviors such as vaccination, compliance with policies, perceived risk and health outcomes. We included studies reporting primary data from participants of any age and gender, published in any language. Two independent reviewers, from a total of seven, conducted study selection, data extraction and risk of bias assessment. We synthesized findings narratively and reported them according to the PRISMA 2020 statement.
Results
Of 2021 unique articles, we included nine cross-sectional studies, all conducted in the United States of America or Europe from 2020 to 2022. Four studies found associations between higher political polarization and lower COVID-19 vaccine uptake or intent. Reported associations between vaccination and affective polarization were mixed. Four studies of other COVID-19 attitudes or prevention measures found mixed results for both types of polarization. In one study, no association was found between polarization and changes in death in 2020 compared with 2015 to 2019. The risk of selection bias in included studies was high.
Discussion
This systematic review found some evidence of associations between polarization and COVID-19 health-related behaviors and outcomes. Cohort studies are needed to understand the direction of association. More international and interdisciplinary approaches to the study of polarization are needed to generate evidence to inform health and public policy effectively and improve preparedness for future pandemics.
{"title":"Polarization and health-related behaviors and outcomes during the COVID-19 pandemic: a systematic review","authors":"Aziz Mert Ipekci , Maximilian Filsinger , Diana Buitrago-Garcia , Cristopher I. Kobler Betancourt , Harvy Joy Liwanag , Annika Frahsa , Nicola Low","doi":"10.1016/j.ssmph.2025.101891","DOIUrl":"10.1016/j.ssmph.2025.101891","url":null,"abstract":"<div><h3>Objective</h3><div>Political and affective polarization are different, but related concepts, which can shape trust in authorities, interpretation of health messages, and health behaviors and outcomes. The aim of this study was to systematically review the research literature, exploring how affective and political polarization are associated with COVID-19-related health behaviors and outcomes.</div></div><div><h3>Methods</h3><div>From January 1, 2019 to November 27, 2024, we searched 12 electronic databases for studies about affective or political polarization and COVID-19-related outcomes, including preventive behaviors such as vaccination, compliance with policies, perceived risk and health outcomes. We included studies reporting primary data from participants of any age and gender, published in any language. Two independent reviewers, from a total of seven, conducted study selection, data extraction and risk of bias assessment. We synthesized findings narratively and reported them according to the PRISMA 2020 statement.</div></div><div><h3>Results</h3><div>Of 2021 unique articles, we included nine cross-sectional studies, all conducted in the United States of America or Europe from 2020 to 2022. Four studies found associations between higher political polarization and lower COVID-19 vaccine uptake or intent. Reported associations between vaccination and affective polarization were mixed. Four studies of other COVID-19 attitudes or prevention measures found mixed results for both types of polarization. In one study, no association was found between polarization and changes in death in 2020 compared with 2015 to 2019. The risk of selection bias in included studies was high.</div></div><div><h3>Discussion</h3><div>This systematic review found some evidence of associations between polarization and COVID-19 health-related behaviors and outcomes. Cohort studies are needed to understand the direction of association. More international and interdisciplinary approaches to the study of polarization are needed to generate evidence to inform health and public policy effectively and improve preparedness for future pandemics.</div></div>","PeriodicalId":47780,"journal":{"name":"Ssm-Population Health","volume":"33 ","pages":"Article 101891"},"PeriodicalIF":3.1,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.ssmph.2025.101886
Rafael Geurgas , Saul J. Newman , Evelina T. Akimova , Katherine N. Thompson , Robbee Wedow
Identifying individuals at risk for depression early is important for preventing long-term mental health issues. However, the variability in depression severity, duration, and triggers complicates predictions. This study explores whether machine learning models can outperform traditional methods, like Logistic Regression, in predicting self-reported depressive symptoms and clinical depression during adolescence and adulthood. We applied five machine learning models with varying complexity levels – Logistic Regression, Decision Tree, XGBoost, Support Vector Machine, and Neural Networks – using data from a nationally representative longitudinal study of the U.S., which tracked participants for 20 years. The models were trained with early-life predictors (ages 12–18) from Wave I, including environmental factors (family, school, health) and genetic predispositions (polygenic scores) from Wave IV. Models were evaluated on their ability to predict depressive symptoms and clinical diagnoses in both adolescence and adulthood. After evaluating the performance of all five models, XGBoost emerged as the most effective, with a 0.02 increase in ROC-AUC compared to the benchmark Logistic Regression model. While this is a slight performance improvement, overall, Logistic Regression performs about as well as many of our ML models. Early-life data showed strong predictive value for depressive symptoms and clinical diagnoses in adolescence and adulthood, highlighting adolescence as a critical period. Polygenic scores do not add predictive power when combined with environmental data. Feature importance analyses identified self-perception and physical health as key predictors of depressive symptoms, while trauma and life-changing events were more influential for clinical depression.
{"title":"What machine learning teaches us about depression prediction across the life course: An exploratory comparison of predictive models","authors":"Rafael Geurgas , Saul J. Newman , Evelina T. Akimova , Katherine N. Thompson , Robbee Wedow","doi":"10.1016/j.ssmph.2025.101886","DOIUrl":"10.1016/j.ssmph.2025.101886","url":null,"abstract":"<div><div>Identifying individuals at risk for depression early is important for preventing long-term mental health issues. However, the variability in depression severity, duration, and triggers complicates predictions. This study explores whether machine learning models can outperform traditional methods, like Logistic Regression, in predicting self-reported depressive symptoms and clinical depression during adolescence and adulthood. We applied five machine learning models with varying complexity levels – Logistic Regression, Decision Tree, XGBoost, Support Vector Machine, and Neural Networks – using data from a nationally representative longitudinal study of the U.S., which tracked participants for 20 years. The models were trained with early-life predictors (ages 12–18) from Wave I, including environmental factors (family, school, health) and genetic predispositions (polygenic scores) from Wave IV. Models were evaluated on their ability to predict depressive symptoms and clinical diagnoses in both adolescence and adulthood. After evaluating the performance of all five models, XGBoost emerged as the most effective, with a 0.02 increase in ROC-AUC compared to the benchmark Logistic Regression model. While this is a slight performance improvement, overall, Logistic Regression performs about as well as many of our ML models. Early-life data showed strong predictive value for depressive symptoms and clinical diagnoses in adolescence and adulthood, highlighting adolescence as a critical period. Polygenic scores do not add predictive power when combined with environmental data. Feature importance analyses identified self-perception and physical health as key predictors of depressive symptoms, while trauma and life-changing events were more influential for clinical depression.</div></div>","PeriodicalId":47780,"journal":{"name":"Ssm-Population Health","volume":"32 ","pages":"Article 101886"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.ssmph.2025.101887
Hendrik Jürges , Rasheda Khanam
This study investigates whether cognitive ability in early childhood predicts adolescent health-related and risky behaviors, independent of schooling and socioeconomic background. Using longitudinal data from the Kindergarten cohort of the Longitudinal Study of Australian Children (LSAC), we link matrix reasoning scores from ages 6 to 10 to health behaviors at age 16/17. Behaviors include substance use, unsafe driving, nutrition, dental hygiene, and sleep. To reduce dimensionality and interpret behavioral patterns, we derive two composite indices via principal component analysis: a risk-taking index and a health habit index. We find that higher early-life IQ is consistently associated with lower risk-taking and better health habits in adolescence, even after adjusting for a comprehensive set of early life covariates including non-cognitive traits, parental health behaviors, family SES, and regional disadvantages. A Gelbach decomposition shows distinct patterns of confounding: for risk-taking, the attenuation of the IQ-health behavior association is primarily explained by parental health behavior and ethnocultural background; for health habits, socioeconomic disadvantage is more salient. Peer characteristics at age 14/15 explain a substantial share of the IQ-risk-taking relationship, suggesting social environments as potential pathways. Robustness checks using the Cinelli & Hazlett sensitivity framework indicate that the IQ-health habits association is substantively and statistically robust to unobserved confounding. We interpret these findings as support for the hypothesis that early-life IQ may be an important upstream factor of health inequalities before educational differentiation occurs.
{"title":"Childhood IQ and adolescent health behavior","authors":"Hendrik Jürges , Rasheda Khanam","doi":"10.1016/j.ssmph.2025.101887","DOIUrl":"10.1016/j.ssmph.2025.101887","url":null,"abstract":"<div><div>This study investigates whether cognitive ability in early childhood predicts adolescent health-related and risky behaviors, independent of schooling and socioeconomic background. Using longitudinal data from the Kindergarten cohort of the Longitudinal Study of Australian Children (LSAC), we link matrix reasoning scores from ages 6 to 10 to health behaviors at age 16/17. Behaviors include substance use, unsafe driving, nutrition, dental hygiene, and sleep. To reduce dimensionality and interpret behavioral patterns, we derive two composite indices via principal component analysis: a risk-taking index and a health habit index. We find that higher early-life IQ is consistently associated with lower risk-taking and better health habits in adolescence, even after adjusting for a comprehensive set of early life covariates including non-cognitive traits, parental health behaviors, family SES, and regional disadvantages. A Gelbach decomposition shows distinct patterns of confounding: for risk-taking, the attenuation of the IQ-health behavior association is primarily explained by parental health behavior and ethnocultural background; for health habits, socioeconomic disadvantage is more salient. Peer characteristics at age 14/15 explain a substantial share of the IQ-risk-taking relationship, suggesting social environments as potential pathways. Robustness checks using the Cinelli & Hazlett sensitivity framework indicate that the IQ-health habits association is substantively and statistically robust to unobserved confounding. We interpret these findings as support for the hypothesis that early-life IQ may be an important upstream factor of health inequalities before educational differentiation occurs.</div></div>","PeriodicalId":47780,"journal":{"name":"Ssm-Population Health","volume":"32 ","pages":"Article 101887"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a predictive modeling system based on the use of GeoAI to estimate mental health outcomes of wartime in Ukrainian cities, utilizing spatially linked data on the environment, infrastructure, and conflict. Six self-reported psychological outcomes, symptoms of post-traumatic stress disorder, or PSD, anxiety, depression, insomnia, loneliness, and sleep duration, were collected in surveys throughout the war and analyzed in the context of more than 30 spatial predictors: cold exposure, access to heating, power outages, housing insulation, and city-level frequency of drone, missile, artillery, and shelling attacks. Predictor datasets that are derived from incident tracking, World Health Organization, and humanitarian reporting systems, and environmental indicators derived from surveys, which are harmonized using a spatial data integration protocol. In realizing the GeoAI concept, we developed a machine learning pipeline utilizing Ordinary Least Squares, Lasso, Random Forest, Gradient Boosting, and Extreme Gradient Boosting. All models were trained and tested on spatially independent training and testing splits in order to preserve the generalization properties of the models. XG Boost is also shown to be effective, with R2 values often exceeding 0.74 and MAPE values typically less than 7.2 %. Feature importance analysis revealed that key drivers of being exposed to prolonged cold, inadequate insulation, and exposure to drones or artillery were found to be dominant drivers of psychological distress. This GeoAI framework combines the strength of geospatial analytics with artificial intelligence to give precise and high-resolution location-based forecasting of mental health burdens in conflict settings. The method offers a flexible tool for a proactive humanitarian response that can target mental health services spatially based on predictions of mental health vulnerability, in contrast to retrospective clinical information, for relief agencies and public health planners. This work is a step towards incorporating GeoAI in the field of crisis epidemiology, demonstrating the ability to use GeoAI in real-time, place-based mental health planning in war-affected areas.
{"title":"Mapping war trauma: A machine learning approach to predict mental health impacts in Ukraine","authors":"Safiyeh Tayebi , Ayse Sert Oti , Hossein Fathollahian , Ubydul Haque","doi":"10.1016/j.ssmph.2025.101879","DOIUrl":"10.1016/j.ssmph.2025.101879","url":null,"abstract":"<div><div>This paper presents a predictive modeling system based on the use of GeoAI to estimate mental health outcomes of wartime in Ukrainian cities, utilizing spatially linked data on the environment, infrastructure, and conflict. Six self-reported psychological outcomes, symptoms of post-traumatic stress disorder, or PSD, anxiety, depression, insomnia, loneliness, and sleep duration, were collected in surveys throughout the war and analyzed in the context of more than 30 spatial predictors: cold exposure, access to heating, power outages, housing insulation, and city-level frequency of drone, missile, artillery, and shelling attacks. Predictor datasets that are derived from incident tracking, World Health Organization, and humanitarian reporting systems, and environmental indicators derived from surveys, which are harmonized using a spatial data integration protocol. In realizing the GeoAI concept, we developed a machine learning pipeline utilizing Ordinary Least Squares, Lasso, Random Forest, Gradient Boosting, and Extreme Gradient Boosting. All models were trained and tested on spatially independent training and testing splits in order to preserve the generalization properties of the models. XG Boost is also shown to be effective, with R2 values often exceeding 0.74 and MAPE values typically less than 7.2 %. Feature importance analysis revealed that key drivers of being exposed to prolonged cold, inadequate insulation, and exposure to drones or artillery were found to be dominant drivers of psychological distress. This GeoAI framework combines the strength of geospatial analytics with artificial intelligence to give precise and high-resolution location-based forecasting of mental health burdens in conflict settings. The method offers a flexible tool for a proactive humanitarian response that can target mental health services spatially based on predictions of mental health vulnerability, in contrast to retrospective clinical information, for relief agencies and public health planners. This work is a step towards incorporating GeoAI in the field of crisis epidemiology, demonstrating the ability to use GeoAI in real-time, place-based mental health planning in war-affected areas.</div></div>","PeriodicalId":47780,"journal":{"name":"Ssm-Population Health","volume":"32 ","pages":"Article 101879"},"PeriodicalIF":3.1,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号