Annals of Epidemiology最新文献

{"title":"Where you live and where you receive care: Using cross-classified multilevel modeling to examine hospital and neighborhood variation in in-hospital mortality and mortality disparities.","authors":"Alina Schnake-Mahl, Ana V Diez Roux, Bian Liu, Louisa W Holaday, Albert Siu, Edwin McCulley, Usama Bilal, Katherine A Ornstein","doi":"10.1016/j.annepidem.2025.07.021","DOIUrl":"10.1016/j.annepidem.2025.07.021","url":null,"abstract":"<p><strong>Purpose: </strong>Both hospitals and neighborhoods likely play important roles in driving health outcomes and inequities, but there has been limited prior research examining both contexts simultaneously. In this analysis we examine the contributions of these two critical contexts, neighborhoods and hospitals, to variation in in-hospital mortality and mortality disparities.</p><p><strong>Methods: </strong>We used cross-classified multi-level models, a statistical technique that can incorporate data from multiple non-nested levels, to examine the variation in contribution of neighborhoods and hospitals to in-hospital mortality. Our study focuses on COVID-19 in hospital mortality from New York State in 2020, as a methodological case study of cross classified multilevel modeling, given the well documented variation in COVID-19 in-hospital mortality across contexts.</p><p><strong>Results: </strong>We found that nearly one in five patients hospitalized for COVID-19 died, and there was substantial variation in risk of in-hospital mortality by neighborhoods and hospitals, with more variation across hospitals (τ₀₀:0.29) than across neighborhoods (τ₀₀:0.02). Neighborhoods did not explain hospital variability and vice versa: both contexts appeared to contribute independently to in-hospital mortality rates. We also found several hospital, neighborhood, and individual factors were associated with in hospital mortality disparities in fully adjusted models: lower hospital quality and safety-net hospitals, social vulnerability, older age, not having private insurance, and being Hispanic or non-Hispanic other.</p><p><strong>Conclusions: </strong>Our findings suggest the importance of simultaneously considering hospital and neighborhood contexts to understand in-hospital outcome disparities. Understanding the contribution of these critical contexts has important implications for targeting interventions to ensure equitable hospital outcomes despite inequities in neighborhood and hospital contexts.</p>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":" ","pages":"16-22"},"PeriodicalIF":3.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144709758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adverse childhood experiences (ACEs) and adolescent reproductive health: Differentiating household and community adversity.","authors":"Christine M Forke, Laura G Barr, Laura Sinko, Melissa E Dichter, Peter F Cronholm","doi":"10.1016/j.annepidem.2025.07.022","DOIUrl":"10.1016/j.annepidem.2025.07.022","url":null,"abstract":"<p><strong>Purpose: </strong>To add to existing knowledge on relationships between Conventionally-identified Adverse Childhood Experiences (ACEs) and adolescent reproductive health (ARH) outcomes, we identified contributions of Expanded (community-level) ACEs, integrating measures of ACE co-occurrence and burden.</p><p><strong>Methods: </strong>Secondary analysis of 2012-2013 Philadelphia ACEs data from a population-based adult sample. Weighted regressions, adjusted for age, sex, race/ethnicity, and socioeconomic status, tested associations between Conventional and Expanded ACEs (separately and co-occurring) and ACE burden (lowest to highest exposure) with: early sexarche (<15 years), adolescent pregnancy (<19 years), and unintended adolescent pregnancy.</p><p><strong>Results: </strong>Conventional ACEs showed strong dose-response relationships with all outcomes (aOR range: 2.04-4.96, p < 0.05). Expanded ACEs were associated with early sexarche (aOR=2.50; 95 % CI: 1.27, 4.94), adolescent pregnancy (aOR=1.69; 95 % CI: 1.16, 2.46), and unintended adolescent pregnancy (aOR=1.54; 95 % CI: 1.04, 2.29); dose-response patterns were inconsistent. Co-occurring Conventional and Expanded ACEs produced the greatest odds for all outcomes except early sexarche (aOR range: 3.20-14.97, p < 0.05).</p><p><strong>Conclusions: </strong>Conventional and Expanded ACEs are important independently and jointly. ARH outcomes peaked when Conventional and Expanded ACEs co-occurred and both exposures were high. Results suggest that Conventional ACEs may be overestimated when assessed in isolation, highlighting the importance of considering Expanded ACEs to minimize bias and target appropriate interventions.</p>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":" ","pages":"7-15"},"PeriodicalIF":3.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144668907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the link between socioeconomic factors and rheumatoid arthritis: Insights from a large Austrian study.","authors":"Mathias Ausserwinkler, Maria Flamm, Sophie Gensluckner, Kathrin Bogensberger, Bernhard Paulweber, Eugen Trinka, Patrick Langthaler, Christian Datz, Boris Lindner, Bernhard Iglseder, Elmar Aigner, Bernhard Wernly","doi":"10.1016/j.annepidem.2025.07.025","DOIUrl":"10.1016/j.annepidem.2025.07.025","url":null,"abstract":"<p><strong>Introduction: </strong>Austria, a country with a high standard of living and a well-developed healthcare system, still experiences socioeconomic status (SES) disparities that impact health outcomes. Rheumatoid arthritis (RA) is a chronic autoimmune disease associated with significant disability and comorbidities. While SES has been linked to RA prevalence and disease severity, its role in a high-income country like Austria remains underexplored. This study investigates the association between SES factors-education, income, employment status and migration background-and RA prevalence and outcomes.</p><p><strong>Methods: </strong>This population-based study used data from the Paracelsus 10,000 cohort in Salzburg, Austria and a cross-sectional design. A total of 9256 participants aged 40-77 years were analyzed, including 289 individuals diagnosed with RA based on the ACR/EULAR classification criteria. SES was assessed through self-reported education, income, employment status and country of birth. Logistic regression models were used to evaluate the association between SES and RA, adjusting for age, sex, metabolic syndrome, smoking and alcohol consumption.</p><p><strong>Results: </strong>RA prevalence was significantly lower among individuals with higher education (OR = 0.55, 95 % CI: 0.37-0.82 for medium education; OR = 0.41, 95 % CI: 0.25-0.68 for high education). Lower household income correlated with higher RA prevalence. Employment disparities were evident, with RA patients exhibiting higher rates of unemployment and work disability.</p><p><strong>Conclusion: </strong>Despite Austria's high standard of living, SES remains a key determinant of RA prevalence. Lower levels of education, income and employment are associated with higher rates of RA, highlighting the need for targeted public health interventions. Strengthening healthcare access, promoting early screening and offering economic support to vulnerable groups could be important steps toward reducing these disparities. Further research should explore the underlying mechanisms of this association and examine whether socioeconomic disparities also influence disease progression and patient outcomes.</p>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":" ","pages":"66-71"},"PeriodicalIF":3.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144709757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incorporating longitudinal variability in prediction models: A comparison of machine learning and logistic regression in a cohort study with long follow-up.","authors":"L M de Groot, J W R Twisk, A A L Kok, M W Heymans","doi":"10.1016/j.annepidem.2025.07.060","DOIUrl":"10.1016/j.annepidem.2025.07.060","url":null,"abstract":"<p><strong>Purpose: </strong>Clinical prediction models benefit from longitudinal data. While the predictive value of a predictor's mean and change over time is well-established, the role of variability around this change is underexplored. Machine Learning methods can be effective in analyzing longitudinal data with long follow-up periods. This study evaluated the predictive value of mean, change, and variability, comparing Random Forest, Lasso regression, and logistic regression.</p><p><strong>Methods: </strong>We compared models including only mean and change to models also incorporating variability. Predictor selection, interpretability, and performance were compared across methods. Performance was assessed using AUC, sensitivity, specificity, PPV, NPV, and calibration. Data were drawn from the Longitudinal Aging Study Amsterdam to predict depression using 81 longitudinal parameters. Models were trained on 70 % and validated on 30 % of the data. To ensure robustness, analyses were repeated over 500 random splits, and aggregated results were reported.</p><p><strong>Results: </strong>Including variability improved AUCs for all methods. Predictor selection overlapped across models, and regression coefficients aligned with Random Forest partial dependence plots. Lasso showed the highest training AUC but poorer test performance, while logistic regression and Random Forest showed more stable results. Calibration was acceptable, though predicted risks remained below 0.6.</p><p><strong>Conclusion: </strong>Machine Learning methods did not outperform logistic regression. Nonetheless, incorporating variability in longitudinal predictors enhances prediction, especially with expected changes in predictors, e.g., ageing populations.</p>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":" ","pages":"51-65"},"PeriodicalIF":3.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144735095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The rising predictive power of LGBT identity in mental health: An analysis of variable importance","authors":"Masanori Kuroki","doi":"10.1016/j.annepidem.2025.09.022","DOIUrl":"10.1016/j.annepidem.2025.09.022","url":null,"abstract":"<div><h3>Purpose</h3><div>To assess the changing predictive importance of lesbian, gay, bisexual, and transgender (LGBT) status on mental health outcomes between 2014 and 2023.</div></div><div><h3>Methods</h3><div>We utilized data from the Behavioral Risk Factor Surveillance System (BRFSS) and employed two ensemble methods—random forests and gradient boosting—as well as traditional logistic regression, to analyze the predictive power of various factors, including LGBT status, on frequent mental distress. Frequent mental distress was defined as experiencing poor mental health for 14 or more days during the previous 30 days.</div></div><div><h3>Results</h3><div>Our analysis revealed a significant and consistent increase in the predictive importance of LGBT status on frequent mental distress across all three modeling approaches. Specifically, LGBT status consistently rose from the 8th or 13th most important predictor in 2014 to the 3rd or 5th most important in 2023, depending on the model. This trend demonstrates that SOGI has become one of the most influential factors for predicting mental health challenges in recent years.</div></div><div><h3>Conclusions</h3><div>These findings highlight the growing importance of sexual orientation and gender identity (SOGI) as a risk factor for mental health challenges.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"111 ","pages":"Pages 102-106"},"PeriodicalIF":3.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145226342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning in epidemiology: An introduction, comparison with traditional methods, and a case study of predicting extreme longevity.","authors":"Dor Atias, Saar Ashri, Uri Goldbourt, Yael Benyamini, Ran Gilad-Bachrach, Tal Hasin, Yariv Gerber, Uri Obolski","doi":"10.1016/j.annepidem.2025.07.024","DOIUrl":"10.1016/j.annepidem.2025.07.024","url":null,"abstract":"<p><strong>Background: </strong>Healthcare data volume is increasingly expanding, presenting both challenges and opportunities. Traditional statistical methods applied in epidemiology, such as logistic regression (LR), albeit widely used, holds limited ability to handle the complexity and high dimensionality of modern datasets. In contrast, machine learning (ML) methods can model complex, non-linear relationships and are less constrained by parametric assumptions, ideal for uncovering hidden patterns.</p><p><strong>Methods: </strong>In this study, we aim to introduce ML applications for epidemiologic research and explore three predictive models: LR as a traditional modeling approach, and least absolute shrinkage and selection operator (LASSO) regression and eXtreme Gradient Boosting (XGBoost) as ML approaches. We demonstrate how ML approaches, particularly XGBoost, can benefit epidemiologic research through a real-world case study. We present common steps: data preprocessing, model creation and evaluation processes. Additionally, we address the \"black box\" nature of ML models and present post hoc explanation tools to enhance interpretability.</p><p><strong>Results: </strong>We examined the case of near-centenarianism (reaching age of 95 years or older) prediction using midlife predictors (i.e., demographic, clinical, lifestyle, occupational and dietary variables) in a cohort of approximately 10,000 middle-aged working men recruited in 1963 and followed until death or until 2019. Models were fitted and calibrated on a training set, showing good predictive performances on a separate test set. XGboost, LASSO regression, and LR achieved ROC-AUC values of 0.72 (95 % CI: 0.66-0.75), 0.71 (95 % CI: 0.67-0.74) and 0.69 (95 % CI: 0.66-0.73), respectively. Explainability analysis identified key predictors for longevity, including systolic blood pressure, smoking status, and a history of myocardial infarction; consistent with prior studies.</p><p><strong>Conclusions: </strong>In conclusion, our findings highlight the potential of ML to enhance epidemiological studies by handling complex interactions and high-dimensional data, suggesting a complementary approach to traditional methods.</p>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":" ","pages":"23-33"},"PeriodicalIF":3.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differences in cervical cancer stage at diagnosis and survival outcomes among Asian, Native Hawaiian, and other Pacific Islander patients and White patients.","authors":"Zhenyu Ma, Mei Liu, Qipeng Yuan, Ziniu Tang, Peng Shang, Chen Wang, Yueze Li, Jinbo Yue","doi":"10.1016/j.annepidem.2025.07.059","DOIUrl":"10.1016/j.annepidem.2025.07.059","url":null,"abstract":"<p><strong>Purpose: </strong>To explore disparities in cervical cancer diagnosis and outcomes for Asian patients and Native Hawaiian and other Pacific Islanders (NHPIs).</p><p><strong>Methods: </strong>We extracted cervical cancer patient data collected from the Surveillance, Epidemiology, and End Results 17 database. Odds ratios (ORs) for stage and time ratios (TRs) for survival outcomes were estimated using logistic regression and accelerated failure time models, respectively.</p><p><strong>Results: </strong>Of 18770 patients, 15,847 (84.4 %) were White; 2618 (13.9 %) were Asian; and 305 (1.6 %) were NHPI. NHPI patients were less likely than White patients to be diagnosed at an early stage (adjusted OR [aOR]: 0.60; 95 % CI, 0.47-0.77), whereas Asian patients had similar stage-at-diagnosis to White patients (aOR: 0.93; 95 % CI, 0.85-1.02). Asian patients, as a group, had significantly longer overall survival (OS) (adjusted TR [aTR]: 1.46; 95 % CI, 1.33-1.61) and disease-specific survival (DSS) (aTR: 1.35; 95 % CI, 1.21-1.51) than White patients; the opposite was true for NHPIs (OS: aTR, 0.80; 95 % CI, 0.64-1.00; DSS: aTR, 0.75; 95 % CI, 0.59-0.97).</p><p><strong>Conclusions: </strong>We find that NHPI cervical cancer patients tend to be diagnosed later in their disease course than White patients and have shorter survival time post-diagnosis, while Asian patients tend to have longer survival time. These findings support the disaggregation of Asian and NHPI races in cervical cancer investigations.</p>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":" ","pages":"43-50"},"PeriodicalIF":3.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144719075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Migration and cardiovascular disease: A comparative study of prevalence and risk factor profiles in resettlers from the German National Cohort (NAKO)” [Ann Epidemiol 111 (2025) 14–23]","authors":"Glenna Walther ,&nbsp;Tilman Brand ,&nbsp;Nico Dragano ,&nbsp;Claudia Meinke-Franze ,&nbsp;Amand Führer ,&nbsp;Karin Halina Greiser ,&nbsp;Olga Hovardovska ,&nbsp;Jamin Kiekert ,&nbsp;Lilian Krist ,&nbsp;Michael Leitzmann ,&nbsp;Wolfgang Lieb ,&nbsp;Rafael Mikolajczyk ,&nbsp;Ute Mons ,&nbsp;Fiona Niedermayer ,&nbsp;Nadia Obi ,&nbsp;Cara Övermöhle ,&nbsp;Marvin Reuter ,&nbsp;Börge Schmidt ,&nbsp;Ilais Moreno Velasquez ,&nbsp;Henry Völzke ,&nbsp;Volker Winkler","doi":"10.1016/j.annepidem.2025.09.020","DOIUrl":"10.1016/j.annepidem.2025.09.020","url":null,"abstract":"","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"111 ","pages":"Page 87"},"PeriodicalIF":3.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145201620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping access to prenatal care: Geographic disparities in West Virginia’s rural communities","authors":"Madelin Coyne PhD ,&nbsp;Brian Hendricks PhD ,&nbsp;Amna Umer PhD ,&nbsp;Toni Rudisill PhD ,&nbsp;Candice Lefeber MPH ,&nbsp;Collin John MD, MPH ,&nbsp;Christa Lilly PhD","doi":"10.1016/j.annepidem.2025.09.021","DOIUrl":"10.1016/j.annepidem.2025.09.021","url":null,"abstract":"<div><h3>Introduction</h3><div>Adequate prenatal care (PNC) is essential to the overall health of mother and her infant. Previous research has demonstrated that rural areas have a higher risk of inadequate PNC compared to their urban counterparts. No studies to date have applied spatial statistical modeling to understand community level factors related to PNC inadequacy.</div></div><div><h3>Purpose</h3><div>To identify communities where the adjusted rate of PNC inadequacy is high, and the insurance type and drive time driving these geographic differences.</div></div><div><h3>Methods</h3><div>Data were obtained from Project WATCH/Birth Score Program for WV zip codes from May 2018 to March 2022. Stratified spatial regression analyses were conducted for women with public and private insurance to understand the extent to which predictors affected risk of PNC inadequacy, and whether relationships differed depending on insurance type.</div></div><div><h3>Results</h3><div>For both insurance types, 30-minute drive time from a birthing facility had a statistically significant association with risk of inadequate PNC (public IRR:3.83, CI:(2.85,5.18)) (private IRR:4.31, CI:(3.17,5.88). Hot spots of model adjusted inadequate PNC risk were clustered in the mid-eastern and southern parts of WV. Importantly, communities with highest risk of inadequate PNC were located further than 30-minutes from a birthing center.</div></div><div><h3>Discussion</h3><div>This study identified strong associations between restricted access to birthing facilities and inadequacy of PNC for women with public and private insurance. Differences in hotspot locations between public and private insurance groups suggest these groups experience different barriers, such as lack of public transportation and drive time.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"111 ","pages":"Pages 88-93"},"PeriodicalIF":3.0,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145193923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Campus climate and intersectional inequities in eating disorders among U.S. college students: A multilevel analysis of individual heterogeneity and discriminatory accuracy","authors":"Ariel L. Beccia ,&nbsp;Vivienne M. Hazzard ,&nbsp;Rachel F. Rodgers ,&nbsp;Dougie Zubizarreta ,&nbsp;Lauren M. Schaefer ,&nbsp;Natasha L. Burke","doi":"10.1016/j.annepidem.2025.09.014","DOIUrl":"10.1016/j.annepidem.2025.09.014","url":null,"abstract":"<div><h3>Purpose</h3><div>To advance understanding of how contextual factors explain eating disorder (ED) inequities among college students, we examined associations between campus climate – i.e., the extent to which a given school is hostile vs. friendly to students of diverse social/cultural backgrounds – and ED prevalence across intersections of gender, sexual, and racialized identity.</div></div><div><h3>Method</h3><div>Cross-sectional data came from 15,544 students at colleges/universities that participated in the 2018/2019 Healthy Minds Study. We conducted a Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) by grouping participants into 35 intersectional social strata defined by gender, sexual, and racialized identity and fitting multilevel models to obtain stratum-specific prevalence estimates of probable EDs across the range of campus climate ratings (1 = “very hostile” to 5 = “very friendly”).</div></div><div><h3>Results</h3><div>Campus climate was inversely associated with probable EDs; specifically, for every 1-unit increase in ratings (i.e., more friendly climates), odds decreased by 8 %. There were differences in the magnitude of this association across strata, such that multiply marginalized students experienced the largest benefits from attending “very friendly” campuses, and especially those who were cisgender women and/or LGBQ+.</div></div><div><h3>Conclusions</h3><div>Results reveal a complex social patterning of EDs among college students across campus climate ratings and provide preliminary evidence suggesting that hostile campus climates may function as a driver of intersectional inequities in this population.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"111 ","pages":"Pages 94-101"},"PeriodicalIF":3.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145187613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}