Annals of Epidemiology最新文献

{"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":"2025-07-23","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":"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":"2025-07-22","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":"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":"2025-07-22","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":"Imbalanced prediction in epidemiological study: A machine learning-based analysis","authors":"Yafei Wu ,&nbsp;Siyu Duan ,&nbsp;Junmin Zhu ,&nbsp;Ya Fang","doi":"10.1016/j.annepidem.2025.07.023","DOIUrl":"10.1016/j.annepidem.2025.07.023","url":null,"abstract":"<div><h3>Purpose</h3><div>Class imbalance is common in epidemiological studies. To date, no comprehensive investigation has been conducted to evaluate the efficacy of various class-imbalance handling strategies for epidemiological forecasting. Therefore, this study aimed to explore the potential of multiple machine learning techniques in addressing class imbalance through a stroke prediction case study.</div></div><div><h3>Methods</h3><div>A total of 11140 eligible participants (5136 males and 6004 females) aged 45 or above were included from the China Health and Retirement Longitudinal Study (CHARLS). Using 15 predictors, we constructed stroke prediction models based on 3-year follow-up data (2015–2018). The outcome was self-reported doctors’ diagnosis of stroke. Sequential forward selection was used for variable selection. Six machine learning algorithms combined with data resampling, threshold tunning, cost-sensitive learning, ensemble learning, and anomaly detection were used to construct sex-specific stroke prediction models. Accuracy, sensitivity, positive predictive value (PPV), G-mean, and area under the ROC curve (AUROC) were applied to evaluate model performance.</div></div><div><h3>Results</h3><div>The incidence of stroke over a 3-year period was 5.9 % and 5.6 % for men and women, respectively. All models demonstrated suboptimal performance on imbalanced dataset. After using machine learning techniques to address class imbalance, the performance improved significantly, especially for local outlier factor from anomaly detection, with its sensitivity, PPV, and G-mean reaching 0.98, 0.59 and 0.92 for male and 0.93, 0.63, and 0.91 for female.</div></div><div><h3>Conclusions</h3><div>Machine learning showed potential in addressing class imbalance, which would be beneficial for epidemiological prediction studies.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"109 ","pages":"Pages 83-92"},"PeriodicalIF":3.0,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700264","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":"2025-07-21","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":"Editorial: Science, interrupted","authors":"Patrick S. Sullivan,&nbsp;Amy Lansky,&nbsp;Jennifer Evans,&nbsp;Lisa M. Lee","doi":"10.1016/j.annepidem.2025.07.026","DOIUrl":"10.1016/j.annepidem.2025.07.026","url":null,"abstract":"","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"109 ","pages":"Pages 93-95"},"PeriodicalIF":3.0,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144683551","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":"A realist-informed evaluation of the implementation of complex HIV treatment support strategies for female sex workers living with HIV.","authors":"Carly A Comins, Mfezi Mcingana, Becky Genberg, Ntambue Mulumba, Elvin Geng, Sharmistha Mishra, Deliwe R Phetlhu, Sita Lujintanon, Lily Shipp, Joel Steingo, Harry Hausler, Stefan Baral, Sheree Schwartz","doi":"10.1016/j.annepidem.2025.07.016","DOIUrl":"10.1016/j.annepidem.2025.07.016","url":null,"abstract":"<p><strong>Purpose: </strong>In South Africa, female sex workers (FSW) living with HIV have suboptimal treatment outcomes. The Siyaphambili trial tested two strategies to promote viral suppression. This paper identifies why and under what conditions the strategies were appropriate, feasible, implemented with fidelity, and ultimately effective for FSW living with HIV.</p><p><strong>Methods: </strong>Guided by the Consolidated Framework for Implementation Research, we conducted in-depth interviews with 36 Siyaphambili participants using maximum variation sampling and purposively selected 12 key informant implementors. We generated 'Context + Mechanism = Outcome' configurations using deductive coding and retroductive inference.</p><p><strong>Results: </strong>Overall, strategy appropriateness for FSW reflected how \"the needs of innovation recipients\" enhanced/challenged the \"relative advantage\" of the strategies. Feasibility of implementation resulted from the interaction of the \"work infrastructure\", \"available resources\", and access to \"knowledge and resources,\" which activated/dampened the \"design\" of the strategies. Fidelity of implementation relied on how \"partnerships\", \"relational connections\" and \"communication\" influenced strategy \"complexity\" and \"adaptability.\" Strategy effectiveness depended on the influence of FSW \"capability\" on their \"motivation and opportunity.\"</p><p><strong>Conclusions: </strong>Understanding the conditions in which these strategies did or did not work aids in understanding the why this pragmatic trial failed to achieve anticipated results and informs potential success that can be taken forward to better optimize treatment outcomes for FSW.</p>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":" ","pages":"110-118"},"PeriodicalIF":3.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676399","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":"Predictors of migraine prevalence among different age groups in Hong Kong Chinese women: Machine learning analyses on the MECH-HK cohort.","authors":"Yafei Wu, Harry Qin, Shengnan Wang, Qingling Yang, Yan Zhang, Harry Haoxiang Wang, Yao Jie Xie","doi":"10.1016/j.annepidem.2025.07.017","DOIUrl":"10.1016/j.annepidem.2025.07.017","url":null,"abstract":"<p><strong>Purpose: </strong>To identify age-specific predictors of migraine prevalence among Chinese women.</p><p><strong>Methods: </strong>In this cross-sectional analysis, 54 predictors were collected from the MECH-HK cohort. Migraine was assessed by the ICHD 3rd edition. Machine learning was employed to select a streamlined subset of predictors. Participants were categorised as young and middle age group (<60 years) and old age group (≥60 years) for analysis.</p><p><strong>Results: </strong>The mean age of participants was 54.3 years. Migraine prevalence was higher in women under 60 than in older women (10.7 % vs. 6.0 %, P < 0.001). Lasso selected seven (<60 years) and twelve (≥60 years) predictors, respectively. The top three predictors among women under 60 were fatigue, migraine family history, and PSQI, explaining 6.6 %, 5.0 %, and 4.9 % of variation, respectively. Their ORs (95 % CIs) were 1.61 (1.37-1.89), 3.93 (2.77-5.57), and 1.29 (1.12-1.48), respectively. For older women, the top three predictors were experience of hunger, smartphone usage time, and migraine family history, explaining 2.0 %, 1.8 %, and 1.6 % of variation, respectively, with ORs (95 % CIs) of 2.16 (1.21-3.84), 1.24 (1.03-1.48), and 2.26 (1.16-4.40), respectively.</p><p><strong>Conclusion: </strong>Migraine family history and experience of hunger were shared predictors for migraine prevalence in both ages. Other predictors differentially influence migraine prevalence across ages.</p>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":" ","pages":"34-42"},"PeriodicalIF":3.0,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144668908","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":"2025-07-16","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":"The risk of alcohol use disorders in young adults with hyperactivity/inattention problems in early adolescence: UK birth cohort study.","authors":"Berihun Dachew, Getinet Ayano, Rosa Alati","doi":"10.1016/j.annepidem.2025.07.018","DOIUrl":"10.1016/j.annepidem.2025.07.018","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to examine the association between hyperactivity/inattention problems in early adolescence and the risk of alcohol use disorders (AUDs) in young adulthood.</p><p><strong>Methods: </strong>We used data from the Avon Longitudinal Study of Parents and Children (ALSPAC), a population-based prospective cohort based in Bristol, United Kingdom. Logistic regression analyses were used to examine associations. E-values (E) were calculated to estimate the potential impact of unmeasured confounding.</p><p><strong>Results: </strong>Hyperactivity/ inattention problems in early adolescence were associated with a 1.75-fold increased risk of any AUDs (OR = 1.75, 95 % CI: 1.20-2.56; E = 2.90, CI: 1.69) and a 4-fold increased risk of severe AUD at age 24 (OR = 4.35, 95 % CI: 2.00 - 9.47; E = 8.17, CI: 3.58). We also observed a 2.09 (OR = 2.09, 95 % CI: 1.24-3.53; E = 3.60, CI: 1.79) and 1.63-fold (OR = 1.63, 95 % CI: 1.07 - 2.49; E = 2.64, CI: 1.34) increased risk of alcohol dependence symptoms and alcohol abuse symptoms, respectively, at age 24 among those with hyperactivity problems at age 11. These associations did not differ by sex (P > 0.05).</p><p><strong>Conclusions: </strong>Hyperactivity/ inattention problems in early adolescence were associated with an increased risk of AUDs in adulthood. Unmeasured confounders were unlikely to alter the observed associations. Early identification and intervention for behavioural problems may help reduce the risk of AUDs later in life.</p>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":" ","pages":"103-109"},"PeriodicalIF":3.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660986","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}