Annals of Epidemiology最新文献

{"title":"Unmet Health Care Needs Across Canada: A cross-sectional analysis of the intersecting roles of race and ethnicity, immigration, and sex.","authors":"Khandideh K A Williams, Alayne M Adams, Shamara Baidoobonso, Isabelle Leblanc, Aisha K Lofters, Mabel Carabali","doi":"10.1016/j.annepidem.2026.110106","DOIUrl":"https://doi.org/10.1016/j.annepidem.2026.110106","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to evaluate the association between racialized identity and unmet health care needs across Canada, and the modifying effect of intersecting sociodemographic factors.</p><p><strong>Methods: </strong>Data were drawn from the 2019-2020 Canadian Community Health Survey. Quasibinomial regressions with interaction terms were used to evaluate the association between racialized minority identity, immigration status and sex assigned at birth on unmet health care needs. We obtained crude and adjusted risk ratios from overall and province-specific (Ontario, Quebec, British Columbia, and Manitoba) models.</p><p><strong>Results: </strong>Trends towards super-multiplicative and super-additive interaction effects of immigration status and sex assigned at birth were observed. In fully adjusted models, racialized minority immigrants in Quebec faced more than twice the risk of unmet care needs and those in Manitoba experienced a 58% lower risk, compared to their counterparts. In comparison to non-racialized minority males, racialized minority females experienced an almost 3 times higher risk of unmet care needs in Quebec.</p><p><strong>Conclusion: </strong>Racialized minority identity interacts with immigration status and sex assigned at birth to shape risks of unmet needs. These findings highlight the importance of adapting health care provision through intersectional approaches that consider regional context and population nuances in care outcomes towards addressing health care inequities.</p>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":" ","pages":"110106"},"PeriodicalIF":3.0,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147857756","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":"Opportunities for Artificial Intelligence in Epidemiology.","authors":"Tamara R Litwin, Macarius Donneyong, Edward Trapido","doi":"10.1016/j.annepidem.2026.110120","DOIUrl":"https://doi.org/10.1016/j.annepidem.2026.110120","url":null,"abstract":"","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":" ","pages":"110120"},"PeriodicalIF":3.0,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147857758","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":"Evolution of characteristics and outcomes for patients hospitalized for COVID-19 during successive waves of the epidemic in France","authors":"Eric Ouattara MD, PhD ,&nbsp;Aurelie Borde MSc ,&nbsp;Maggie Le-Bourhis MD ,&nbsp;Amelie Bruandet MD, PhD ,&nbsp;Delphine Gabillard MSc, PhD ,&nbsp;Fabienne Seguret MD, PhD ,&nbsp;Florence Binder-Foucard MD ,&nbsp;Joris Muller MD ,&nbsp;Xavier Lenne MSc ,&nbsp;Sophie Tezenas du Montcel MD, PhD ,&nbsp;Pierre Tran Ba Loc MD ,&nbsp;Veronique Gilleron MD, PhD","doi":"10.1016/j.annepidem.2026.110053","DOIUrl":"10.1016/j.annepidem.2026.110053","url":null,"abstract":"<div><h3>Purpose</h3><div>To explore changes in in-hospital mortality, admission to critical care unit (CCU) and clinical characteristics of patients hospitalized for COVID-19 in France.</div></div><div><h3>Methods</h3><div>Hospital discharge data were used to analyse outcomes during epidemic waves: W1 (January-July 2020), W2 (August 2020-June 2021), W3 (July-October 2021),W4 (November 2021-May 2022), W5 (June-September 2022). Join-point, logistic and survival regressions were used for the analyses.</div></div><div><h3>Results</h3><div>Overall, 502,532 patients were included. W3 patients were younger (median age: 61 years; interquartile-range: 44–76) and fewer than 10 % had a Charlson Comorbidity Index ≥ 3. The proportion of patients aged ≥ 75 years old was higher (59 %) during W5. Compared to W1, W3 patients had a higher risk of admission to CCU, adjusted odds ratios ranged from 1.18 (CI: 1.09–1.28) for &lt; 45 years old to 1.30 (1.17–1.34) for ≥ 75 years old. In CCU the risk of death decreased by 47 % during W2 in &lt; 45 years old, increased by 11 % during W4 for 45–74 years old, and increased by 13 % and 10 % during W2 and W3 respectively, for ≥ 75 years old.</div></div><div><h3>Conclusion</h3><div>This analysis confirms the age as a major risk factor for adverse outcomes of COVID-19, and highlights the importance of hospital discharge data for future pandemics.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"116 ","pages":"Article 110053"},"PeriodicalIF":3.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146144661","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":"Suicide risks associated with cardiovascular disease in persons with alcohol use disorder","authors":"Casey Crump ,&nbsp;Jan Sundquist ,&nbsp;Kristina Sundquist ,&nbsp;Alexis C. Edwards","doi":"10.1016/j.annepidem.2026.110052","DOIUrl":"10.1016/j.annepidem.2026.110052","url":null,"abstract":"<div><h3>Purpose</h3><div>People with alcohol use disorder (AUD) have high risks of both cardiovascular disease (CVD) and suicide. We examined how CVD contributes to their long-term suicide risk in a large population-based cohort, which may help guide preventive interventions.</div></div><div><h3>Methods</h3><div>A national cohort study was conducted of all 537,802 people with AUD in Sweden during 1973–2017. CVD and suicide deaths were ascertained from nationwide records through 2018. Cox regression was used to determine hazard ratios (HRs) for suicide death associated with ischemic heart disease, stroke, heart failure, or atrial fibrillation, and a composite outcome (“any CVD”), adjusting for other mental disorders and sociodemographic factors.</div></div><div><h3>Results</h3><div>Any CVD was associated with a 1.4-fold risk of death by suicide (adjusted HR, 1.44; 95 % CI, 1.35–1.54). Adjusted HRs by specific comorbidities were: ischemic heart disease, 1.26 (95 % CI, 1.16–1.38); stroke, 1.45 (1.31–1.61); heart failure, 1.49 (1.31–1.69); and atrial fibrillation, 1.52 (1.36–1.70). These risks were similarly elevated in men and women.</div></div><div><h3>Conclusions</h3><div>In this large national cohort with AUD, CVD was associated with a ∼40 % higher risk of suicide death. Clinical care for persons with AUD should integrate mental health and prevention and treatment of CVD comorbidities to support their long-term health and survival.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"116 ","pages":"Article 110052"},"PeriodicalIF":3.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146144643","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":"Validity and Reliability learning module: Zubizarreta et al. (2025), Context matters: Validity and reliability of a sociopolitical concerns measure for use in population health research on discrimination and health","authors":"Jeb Jones","doi":"10.1016/j.annepidem.2025.12.011","DOIUrl":"10.1016/j.annepidem.2025.12.011","url":null,"abstract":"<div><div>Educational Engagement Modules (EEMs) are teaching materials for educators and students that facilitate a deeper understanding of key epidemiological methods and concepts. Each EEM poses a series of questions using a recently published paper in Annals to further understanding of a specific study design or epidemiological concept and to encourage critical thinking and careful evaluation. This EEM focuses on methods for assessing validity in the following article: Zubizarreta D, Reisner SL, Chen JT, LeBlanc ME, Johnson NR, Krieger N. Context matters: Validity and reliability of a sociopolitical concerns measure for use in population health research on discrimination and health. Ann Epidemiol. 2025 Jul;107:24–28. doi: 10.1016/j.annepidem.2025.05.008. Epub 2025 May 22. PMID: 40412632; PMCID: PMC12191272 <span><span>[1]</span></span>.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"115 ","pages":"Pages 74-75"},"PeriodicalIF":3.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146208231","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":"Is artificial intelligence a friend or foe to epidemiology?","authors":"Emaan Rashidi MHS ,&nbsp;Madeline Brooks MPH ,&nbsp;Ahmed Hassoon MD, MPH ,&nbsp;Shruti Mehta PhD, MPH ,&nbsp;Keri Althoff PhD, MPH ,&nbsp;G. Caleb Alexander MD, MS","doi":"10.1016/j.annepidem.2026.01.003","DOIUrl":"10.1016/j.annepidem.2026.01.003","url":null,"abstract":"<div><div>Epidemiology has long been central to public health, guiding our understanding of the distribution and determinants of disease. As the field has evolved—from John Snow’s cholera investigations to large-scale cohort studies and causal inference frameworks—it now faces a transformative juncture with the advent of artificial intelligence/machine learning (AI/ML). These technologies offer unprecedented opportunities to improve data measurement, inference, and population health insights, yet also pose methodological and ethical challenges. Anchored by the core epidemiologic domains of study population, measurement, and inference, we examine how epidemiologists can use AI/ML effectively. We consider the importance of careful population definition, informed sampling, and external validation to ensure generalizability and minimize bias when AI/ML is used. We also explore the need for rigorous assessment of data quality and model reliability, which strengthens the case for conceptual frameworks in guiding interpretation of scientific investigations. To realize AI/ML’s potential, epidemiology must adapt its training, invest in infrastructure, and promote interdisciplinary collaboration. Doing so will ensure that epidemiologic science remains robust, reproducible, and relevant in a rapidly evolving informational landscape. This moment calls for a strategic integration of AI/ML into the fabric of epidemiologic practice and training to advance both science and public health.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"115 ","pages":"Pages 2-7"},"PeriodicalIF":3.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145985862","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-based LASSO-Cox model for dementia prediction: The role of midlife cardiometabolic, inflammatory, and genetic risk factors in a US cohort","authors":"Longjian Liu MD, PhD, MSc,&nbsp;Jintong Hou, PhD","doi":"10.1016/j.annepidem.2026.01.007","DOIUrl":"10.1016/j.annepidem.2026.01.007","url":null,"abstract":"<div><h3>Purpose</h3><div>We aimed to identify key midlife dementia predictors and develop a novel machine learning (ML) -enabled risk prediction model.</div></div><div><h3>Methods</h3><div>Using data from 9266 Atherosclerosis Risk in Communities study participants (aged 45–64 years at baseline, 1987–1989). Incident dementia was ascertained through December 2019. A ML-based LASSO-Cox model was applied to develop the risk prediction model.</div></div><div><h3>Results</h3><div>Over a 25-year mean follow-up, 2010 participants developed dementia. The LASSO-Cox model identified 12 key predictors and achieved C-indices (95 %CI) of 0.77 (0.75–0.79) in the training set (n = 6182) and 0.78 (0.76–0.81) in the test set (n = 3084). Predictors included age, Digit Symbol Substitution Test, apolipoprotein E ε4, HbA1c, brachial blood pressure, Factor VIII, Delayed Word Recall Test, hypertension, stroke history, C-reactive protein, white blood cell count, and apolipoprotein B. The resulting nomogram demonstrated strong discrimination (AUC 0.77–0.86) and good calibration. LASSO-Cox risk score quartiles effectively stratified participants into low, moderate, high, and very high dementia risk groups.</div></div><div><h3>Conclusions</h3><div>The findings demonstrate that the newly developed machine learning-based LASSO-Cox model provides a robust method to predict individuals at high risk of dementia.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"115 ","pages":"Pages 28-36"},"PeriodicalIF":3.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146046999","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":"Response to “Towards reliable feature interpretation in machine learning-based longevity prediction”","authors":"Dor Atias MD, MPH,&nbsp;Saar Ashri Bsc,&nbsp;Uri Goldbourt PhD,&nbsp;Yariv Gerber PhD,&nbsp;Uri Obolski PhD","doi":"10.1016/j.annepidem.2026.01.006","DOIUrl":"10.1016/j.annepidem.2026.01.006","url":null,"abstract":"","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"115 ","pages":"Page 1"},"PeriodicalIF":3.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982034","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":"Residential mobility during pregnancy and birth outcomes in the United States: The environmental influences on Child Health Outcomes (ECHO) Cohort (2010–2019)","authors":"Angela D’Adamo ,&nbsp;Amii M. Kress ,&nbsp;Rima Habre ,&nbsp;Nissa Towe-Goodman ,&nbsp;Michael R. Desjardins ,&nbsp;Akram Alshawabkeh ,&nbsp;Izzuddin M. Aris ,&nbsp;Carlos A. Camargo Jr. ,&nbsp;Kecia N. Carroll ,&nbsp;Andrea E. Cassidy-Bushrow ,&nbsp;Su H. Chu ,&nbsp;Yolaine Civil ,&nbsp;Alexandrea L. Craft ,&nbsp;Lisa A. Croen ,&nbsp;Sean Deoni ,&nbsp;Viren Dsa ,&nbsp;Anne L. Dunlop ,&nbsp;Amy J. Elliott ,&nbsp;Assiamira Ferrara ,&nbsp;Jody M. Ganiban ,&nbsp;Emily A. Knapp","doi":"10.1016/j.annepidem.2026.01.008","DOIUrl":"10.1016/j.annepidem.2026.01.008","url":null,"abstract":"<div><h3>Purpose</h3><div>To examine factors associated with moving during pregnancy and impacts of assigning nSES at enrollment, delivery, or a time-weighted average on birth outcomes (birthweight, birthweight-for-gestational-age z-score, low birthweight, gestational age, small-for-gestational age, preterm birth).</div></div><div><h3>Methods</h3><div>We used data from the Environmental influences on Child Health Outcomes (ECHO) Cohort Study (2010–2019) with nSES data from the American Community Survey (ACS) matched by time and location to monthly residential histories. We used multivariable logistic models with Generalized Estimating Equations to identify factors associated with moving and quantify exposure misclassification in model estimates.</div></div><div><h3>Results</h3><div>Approximately 7 % of 15,376 participants moved at least once during pregnancy. Maternal age (OR: 0.97, 95 % CI: 0.95, 0.98) and other race vs. White (OR: 0.39, 95 % CI: 0.20, 0.80) were associated with lower odds of moving; lower neighborhood-level education (OR: 1.34, 95 % CI: 1.11, 1.62) and living in urban neighborhoods (OR: 3.03, 95 % CI: 1.39, 6.59) were associated with higher odds. Among movers, estimates between nSES and birth outcomes changed ≥ 16 % by address assignment; birthweight-for-gestational-age z-score was significant only when using nSES at delivery.</div></div><div><h3>Conclusion</h3><div>Sociodemographic and nSES characteristics are associated with moving during pregnancy; movers may experience exposure misclassification and underestimated effects on birth outcomes.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"115 ","pages":"Pages 15-22"},"PeriodicalIF":3.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146004675","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":"School performance following invasive Group B Streptococcus disease in early infancy in Denmark","authors":"Malene Risager Lykke MD ,&nbsp;Henrik Toft Sørensen MD, PhD, DMsc., DSc. ,&nbsp;Joy Elizabeth Lawn MB BS, FRCPCH., MPH, PhD ,&nbsp;Janet L Peacock MSc, PhD ,&nbsp;Erzsébet Horváth-Puhó MSc, PhD","doi":"10.1016/j.annepidem.2026.01.009","DOIUrl":"10.1016/j.annepidem.2026.01.009","url":null,"abstract":"<div><h3>Objectives</h3><div>Better quantification of long-term neurodevelopmental impairments following invasive Group B Streptococcus disease (iGBS) in early infancy can inform prognostication and societal impacts, including children's educational and social care needs.</div></div><div><h3>Study design</h3><div>A population-based observational prevalence study.</div></div><div><h3>Methods</h3><div>Children born 1997–2010, who survived iGBS sepsis or meningitis within the first 89 days after birth and completed public school tests aged 8–15 years were matched 1:20 with a general population comparison group without iGBS by sex and year of birth in Denmark. IGBS was identified using the Danish National Patient Registry covering all Danish hospitals and <em>International Classification of Diseases, Tenth Revision</em> codes. Standardized school test scores from 2010 to 2019 were obtained from the Danish Ministry of Education. Adjusted differences (adj. diff.) in school performance and corresponding 95 % confidence intervals (CIs) were estimated by subject type, grade, sex (at birth), and preterm birth using multivariable linear regression models with robust variance estimators.</div></div><div><h3>Results</h3><div>Among 807 iGBS survivors (90.7 % sepsis, 9.3 % meningitis) and 16,140 comparators, iGBS-sepsis survivors' performance was comparable to children without iGBS across tests, subjects, or grades. However, iGBS-meningitis survivors performed poorer than their matched comparators (adj. diff. −2·74 [95 % CI −5·19; −0·29]). Preterm birth was associated with poorer performance, regardless of a history of iGBS. No difference in test scores was found between sexes.</div></div><div><h3>Conclusion</h3><div>Among Danish school children, no overall difference was observed in school performance between children with a history of iGBS and comparators. However, iGBS-meningitis and preterm birth were linked to lower standardized test scores. This association was not observed in children who had iGBS-sepsis, unless they were also preterm.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"115 ","pages":"Pages 57-63"},"PeriodicalIF":3.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146126947","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}