Pub Date : 2026-03-01Epub Date: 2026-01-12DOI: 10.1016/j.annepidem.2026.01.003
Emaan Rashidi MHS , Madeline Brooks MPH , Ahmed Hassoon MD, MPH , Shruti Mehta PhD, MPH , Keri Althoff PhD, MPH , G. Caleb Alexander MD, MS
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.
{"title":"Is artificial intelligence a friend or foe to epidemiology?","authors":"Emaan Rashidi MHS , Madeline Brooks MPH , Ahmed Hassoon MD, MPH , Shruti Mehta PhD, MPH , Keri Althoff PhD, MPH , 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}
Pub Date : 2026-03-01Epub Date: 2026-01-17DOI: 10.1016/j.annepidem.2026.01.008
Angela D’Adamo , Amii M. Kress , Rima Habre , Nissa Towe-Goodman , Michael R. Desjardins , Akram Alshawabkeh , Izzuddin M. Aris , Carlos A. Camargo Jr. , Kecia N. Carroll , Andrea E. Cassidy-Bushrow , Su H. Chu , Yolaine Civil , Alexandrea L. Craft , Lisa A. Croen , Sean Deoni , Viren Dsa , Anne L. Dunlop , Amy J. Elliott , Assiamira Ferrara , Jody M. Ganiban , Emily A. Knapp
Purpose
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).
Methods
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.
Results
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.
Conclusion
Sociodemographic and nSES characteristics are associated with moving during pregnancy; movers may experience exposure misclassification and underestimated effects on birth outcomes.
{"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 , Amii M. Kress , Rima Habre , Nissa Towe-Goodman , Michael R. Desjardins , Akram Alshawabkeh , Izzuddin M. Aris , Carlos A. Camargo Jr. , Kecia N. Carroll , Andrea E. Cassidy-Bushrow , Su H. Chu , Yolaine Civil , Alexandrea L. Craft , Lisa A. Croen , Sean Deoni , Viren Dsa , Anne L. Dunlop , Amy J. Elliott , Assiamira Ferrara , Jody M. Ganiban , 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}
Pub Date : 2026-03-01Epub Date: 2026-02-03DOI: 10.1016/j.annepidem.2026.01.009
Malene Risager Lykke MD , Henrik Toft Sørensen MD, PhD, DMsc., DSc. , Joy Elizabeth Lawn MB BS, FRCPCH., MPH, PhD , Janet L Peacock MSc, PhD , Erzsébet Horváth-Puhó MSc, PhD
Objectives
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.
Study design
A population-based observational prevalence study.
Methods
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 International Classification of Diseases, Tenth Revision 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.
Results
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.
Conclusion
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.
{"title":"School performance following invasive Group B Streptococcus disease in early infancy in Denmark","authors":"Malene Risager Lykke MD , Henrik Toft Sørensen MD, PhD, DMsc., DSc. , Joy Elizabeth Lawn MB BS, FRCPCH., MPH, PhD , Janet L Peacock MSc, PhD , 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}
Pub Date : 2026-03-01Epub Date: 2026-02-14DOI: 10.1016/j.annepidem.2026.01.012
Haidong Lu , Andrew F. Olshan , Marc L. Serre , Kurtis M. Anthony , Rebecca C. Fry , Nina E. Forestieri , Alexander P. Keil
Birth defects are a leading cause of infant mortality in the United States, but little is known about causes of many types of birth defects. Spatiotemporal disease mapping to identify high-prevalence areas is a potential strategy to narrow the search for potential environmental and other causes that aggregate over space and time. We described the spatial and temporal trends of the prevalence of birth defects in North Carolina during 2003–2015, using data on live births obtained from the North Carolina Birth Defects Monitoring Program. By employing a Bayesian space-time Poisson model, we estimated spatial and temporal trends of non-chromosomal and chromosomal birth defects. During 2003–2015, 52,524 (3.3 %) of 1598,807 live births had at least one recorded birth defect. The prevalence of non-chromosomal birth defects decreased from 3.8 % in 2003–2.9 % in 2015. Spatial modeling suggested a large geographic variation in non-chromosomal birth defects at census-tract level, with the highest prevalence in southeastern North Carolina. The strong spatial heterogeneity revealed in this work allowed us to identify geographic areas with higher prevalence of non-chromosomal birth defects in North Carolina. This variation will help inform future research focused on epidemiologic studies of birth defects to identify etiologic factors.
{"title":"Spatiotemporal trends of birth defects in North Carolina, 2003–2015","authors":"Haidong Lu , Andrew F. Olshan , Marc L. Serre , Kurtis M. Anthony , Rebecca C. Fry , Nina E. Forestieri , Alexander P. Keil","doi":"10.1016/j.annepidem.2026.01.012","DOIUrl":"10.1016/j.annepidem.2026.01.012","url":null,"abstract":"<div><div>Birth defects are a leading cause of infant mortality in the United States, but little is known about causes of many types of birth defects. Spatiotemporal disease mapping to identify high-prevalence areas is a potential strategy to narrow the search for potential environmental and other causes that aggregate over space and time. We described the spatial and temporal trends of the prevalence of birth defects in North Carolina during 2003–2015, using data on live births obtained from the North Carolina Birth Defects Monitoring Program. By employing a Bayesian space-time Poisson model, we estimated spatial and temporal trends of non-chromosomal and chromosomal birth defects. During 2003–2015, 52,524 (3.3 %) of 1598,807 live births had at least one recorded birth defect. The prevalence of non-chromosomal birth defects decreased from 3.8 % in 2003–2.9 % in 2015. Spatial modeling suggested a large geographic variation in non-chromosomal birth defects at census-tract level, with the highest prevalence in southeastern North Carolina. The strong spatial heterogeneity revealed in this work allowed us to identify geographic areas with higher prevalence of non-chromosomal birth defects in North Carolina. This variation will help inform future research focused on epidemiologic studies of birth defects to identify etiologic factors.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"115 ","pages":"Pages 85-90"},"PeriodicalIF":3.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146208305","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}
Pub Date : 2026-03-01Epub Date: 2026-01-31DOI: 10.1016/j.annepidem.2026.01.016
Angela D. Liese PhD, MPH , Brian E. Dixon PhD , Tessa Crume PhD,MSPH , Jasmin Divers PhD , Yi Guo PhD , Annemarie G. Hirsch PhD, MPH , Kristi Reynolds PhD , Levon Utidjian MD , Ibrahim Zaganjor PhD , Marc Rosenman MD , for the DiCAYA Study Group
Purpose
A critical function of public health is to monitor diseases that impede quality of life and burden affected communities. The Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network aims to advance disease monitoring for diabetes using multi-site electronic health record (EHR) data.
Methods
This work involved validating and refining case definitions for accurate identification of type 1 and type 2 diabetes cases to estimate incidence and prevalence of diabetes in children, adolescents, and young adults through age 44 years.
Results
In this essay, we describe the challenges experienced by the Network and lessons learned. Challenges included accessing EHR data, harmonizing EHR data from heterogeneous health systems to a common data model, and developing methods to account for bias introduced by the non-representativeness of health care utilization data. Lessons learned included approaches for data quality assessment, bias correction, and scalability.
Conclusions
As the US continues to evolve its public health data systems and its approach to chronic disease monitoring, the DiCAYA Network offers guidance on factors for success as well as pitfalls to avoid.
{"title":"Public health monitoring of diabetes in the era of electronic health records: Insights from the Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network","authors":"Angela D. Liese PhD, MPH , Brian E. Dixon PhD , Tessa Crume PhD,MSPH , Jasmin Divers PhD , Yi Guo PhD , Annemarie G. Hirsch PhD, MPH , Kristi Reynolds PhD , Levon Utidjian MD , Ibrahim Zaganjor PhD , Marc Rosenman MD , for the DiCAYA Study Group","doi":"10.1016/j.annepidem.2026.01.016","DOIUrl":"10.1016/j.annepidem.2026.01.016","url":null,"abstract":"<div><h3>Purpose</h3><div>A critical function of public health is to monitor diseases that impede quality of life and burden affected communities. The Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network aims to advance disease monitoring for diabetes using multi-site electronic health record (EHR) data.</div></div><div><h3>Methods</h3><div>This work involved validating and refining case definitions for accurate identification of type 1 and type 2 diabetes cases to estimate incidence and prevalence of diabetes in children, adolescents, and young adults through age 44 years.</div></div><div><h3>Results</h3><div>In this essay, we describe the challenges experienced by the Network and lessons learned. Challenges included accessing EHR data, harmonizing EHR data from heterogeneous health systems to a common data model, and developing methods to account for bias introduced by the non-representativeness of health care utilization data. Lessons learned included approaches for data quality assessment, bias correction, and scalability.</div></div><div><h3>Conclusions</h3><div>As the US continues to evolve its public health data systems and its approach to chronic disease monitoring, the DiCAYA Network offers guidance on factors for success as well as pitfalls to avoid.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"115 ","pages":"Pages 45-49"},"PeriodicalIF":3.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146108212","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}
Pub Date : 2026-03-01Epub Date: 2026-01-29DOI: 10.1016/j.annepidem.2026.01.014
Afroza Parvin , Rebecca D. Kehm , Baozhen Qiao , James E. Cone , Mark R. Farfel , Rachel Zeig-Owens , David G. Goldfarb , Moshe Z. Shapiro , Andrew C. Todd , Tabassum Insaf , Charles B. Hall , Paolo Boffetta , Jiehui Li
Purpose
The World Trade Center Health Program (WTCHP) plays a critical role in medical monitoring and treatment to those exposed to the terrorist attacks of September 11, 2001 (9/11). We investigated the association of WTCHP membership with mortality risk among 9/11 responders while controlling for comorbidities using inverse probability weighting.
Methods
We prospectively analyzed 28,430 9/11 responders, followed from the time of their enrollment into the WTCHP or the WTC Health Registry, through 2020. NDI linkage provided death data. Non-cancer comorbidities were self-reported physician-diagnosis and cancer was identified through cancer registry linkage. We estimated the adjusted hazard ratio (aHR) with 95 % confidence interval (CI) for the association between WTCHP membership and all-cause and cause-specific mortality using Cox proportional hazards models and cause-specific hazard regression models, respectively.
Results
A total of 1657 deaths were identified over 444,425 person-years of follow-up. Compared to non-members, WTCHP members had a lower risk of all-cause mortality (aHR=0.87; 95 % CI=0.77–0.98) and smoking-related mortality (aHR=0.83; 0.69–0.99) after adjusting for demographics, WTC exposure, and weights of comorbidities. With the membership-sex interaction included, reduced risk of all-cause mortality remained statistically significant among males only (aHR=0.85; 0.75–0.96). Cancer- and heart-related mortality risk were not significantly different between WTCHP members and non-members.
Conclusions
This study found that WTCHP membership may reduce risks of all-cause and smoking-related mortality among 9/11 responders, even after accounting for underlying medical conditions, underscoring the importance of comprehensive health monitoring and treatment services for disaster-relief workers.
{"title":"Effect of World Trade Center Health Program on mortality among 9/11 responders","authors":"Afroza Parvin , Rebecca D. Kehm , Baozhen Qiao , James E. Cone , Mark R. Farfel , Rachel Zeig-Owens , David G. Goldfarb , Moshe Z. Shapiro , Andrew C. Todd , Tabassum Insaf , Charles B. Hall , Paolo Boffetta , Jiehui Li","doi":"10.1016/j.annepidem.2026.01.014","DOIUrl":"10.1016/j.annepidem.2026.01.014","url":null,"abstract":"<div><h3>Purpose</h3><div>The World Trade Center Health Program (WTCHP) plays a critical role in medical monitoring and treatment to those exposed to the terrorist attacks of September 11, 2001 (9/11). We investigated the association of WTCHP membership with mortality risk among 9/11 responders while controlling for comorbidities using inverse probability weighting.</div></div><div><h3>Methods</h3><div>We prospectively analyzed 28,430 9/11 responders, followed from the time of their enrollment into the WTCHP or the WTC Health Registry, through 2020. NDI linkage provided death data. Non-cancer comorbidities were self-reported physician-diagnosis and cancer was identified through cancer registry linkage. We estimated the adjusted hazard ratio (aHR) with 95 % confidence interval (CI) for the association between WTCHP membership and all-cause and cause-specific mortality using Cox proportional hazards models and cause-specific hazard regression models, respectively.</div></div><div><h3>Results</h3><div>A total of 1657 deaths were identified over 444,425 person-years of follow-up. Compared to non-members, WTCHP members had a lower risk of all-cause mortality (aHR=0.87; 95 % CI=0.77–0.98) and smoking-related mortality (aHR=0.83; 0.69–0.99) after adjusting for demographics, WTC exposure, and weights of comorbidities. With the membership-sex interaction included, reduced risk of all-cause mortality remained statistically significant among males only (aHR=0.85; 0.75–0.96). Cancer- and heart-related mortality risk were not significantly different between WTCHP members and non-members.</div></div><div><h3>Conclusions</h3><div>This study found that WTCHP membership may reduce risks of all-cause and smoking-related mortality among 9/11 responders, even after accounting for underlying medical conditions, underscoring the importance of comprehensive health monitoring and treatment services for disaster-relief workers.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"115 ","pages":"Pages 8-14"},"PeriodicalIF":3.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146097507","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}
Pub Date : 2026-03-01Epub Date: 2026-01-29DOI: 10.1016/j.annepidem.2026.01.013
Romain Brisson
Purpose
This study examined how careless and inconsistent reporting affects adolescent suicidality prevalence and sex differences, a methodological issue often overlooked in self-report epidemiological research.
Methods
I used data from two nationally representative surveys of secondary-school students conducted in 2010 (n = 7640; 49.3 % female) and 2014 (n = 5592; 52.6 % female). Both surveys assessed depressive symptoms, suicidal ideation, suicide plans, suicide attempts, attempt recognition, and attempt disclosure. Three methods of prevalence computation were used: unadjusted estimates (M1); excluding fictitious drug endorsers and treating inconsistencies as missing (M2); and excluding all careless and inconsistent reporters (M3).
Results
About 19 % of respondents were identified as careless or inconsistent. Compared to M1, M2 and M3 yielded lower prevalence estimates for most indicators. The largest reductions involved, on average, reports of unnoticed suicide attempts (-73.8 %), talking to no one about an attempt (-73.3 %), and reporting six or more suicide attempts (-35.9 %). Most sex differences were unaffected, except for the ‘six or more suicide attempts’ category and attempt recognition and disclosure items.
Conclusions
Overlooking misreporting may inflate adolescent suicidality prevalence and distort sex-difference estimates. Incorporating validity checks and data-cleaning procedures can improve the accuracy of epidemiological findings and the effectiveness of prevention programs.
{"title":"Careless and inconsistent reporting inflates suicidality prevalence and biases sex differences","authors":"Romain Brisson","doi":"10.1016/j.annepidem.2026.01.013","DOIUrl":"10.1016/j.annepidem.2026.01.013","url":null,"abstract":"<div><h3>Purpose</h3><div>This study examined how careless and inconsistent reporting affects adolescent suicidality prevalence and sex differences, a methodological issue often overlooked in self-report epidemiological research.</div></div><div><h3>Methods</h3><div>I used data from two nationally representative surveys of secondary-school students conducted in 2010 (<em>n</em> = 7640; 49.3 % female) and 2014 (<em>n</em> = 5592; 52.6 % female). Both surveys assessed depressive symptoms, suicidal ideation, suicide plans, suicide attempts, attempt recognition, and attempt disclosure. Three methods of prevalence computation were used: unadjusted estimates (M1); excluding fictitious drug endorsers and treating inconsistencies as missing (M2); and excluding all careless and inconsistent reporters (M3).</div></div><div><h3>Results</h3><div>About 19 % of respondents were identified as careless or inconsistent. Compared to M1, M2 and M3 yielded lower prevalence estimates for most indicators. The largest reductions involved, on average, reports of unnoticed suicide attempts (-73.8 %), talking to no one about an attempt (-73.3 %), and reporting six or more suicide attempts (-35.9 %). Most sex differences were unaffected, except for the ‘six or more suicide attempts’ category and attempt recognition and disclosure items.</div></div><div><h3>Conclusions</h3><div>Overlooking misreporting may inflate adolescent suicidality prevalence and distort sex-difference estimates. Incorporating validity checks and data-cleaning procedures can improve the accuracy of epidemiological findings and the effectiveness of prevention programs.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"115 ","pages":"Pages 23-27"},"PeriodicalIF":3.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146094875","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}
Pub Date : 2026-03-01Epub Date: 2026-02-14DOI: 10.1016/j.annepidem.2026.01.010
Juan Merlo
Epidemiology has achieved substantial methodological refinement in recent decades, yet its social resonance has not always kept pace. This essay reflects on tendencies within influential sectors of the field toward methodological sophistication that, while yielding genuine intellectual advances, can unintentionally distance epidemiology from its civic and historical roots. By privileging what is analytically tractable, such developments may render broader contextual forces and socially patterned differences between individuals around population averages less visible. Drawing on traditions in social epidemiology, the essay advances a central argument: a substantial share of individual heterogeneity is intrinsically contextual. Differences between individuals are not pre-social deviations to be averaged away, but structured expressions of social, spatial, institutional, and historical contexts. From this perspective, the central challenge facing contemporary epidemiology is not primarily statistical but metaethical. It concerns how analytical choices shape interpretation, how values are embedded in measurement practices, and how these practices delimit the social purposes epidemiology is understood to serve. Crucially, epidemiology is not only a science of causal explanation, but also a discipline concerned with mapping, monitoring, and documenting how health and harm are distributed within populations over time. Even when major determinants of ill health are well established, epidemiology retains a core role in tracking how inequalities persist, change, or respond to policy. Rather than rejecting modern tools, the essay calls for a pluralistic and contextually grounded epidemiology that reconnects analytical rigor with social meaning. By treating individual heterogeneity as contextual rather than residual, epidemiology can reconcile population health and precision approaches and more fully realize its dual role as a scientific enterprise and a civic practice oriented toward equity.
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Pub Date : 2026-03-01Epub Date: 2026-02-01DOI: 10.1016/j.annepidem.2026.01.017
Greta Jianjia Cheng PhD , Christina F. Mair PhD , Jeanine M. Buchanich PhD , Tiffany L. Gary-Webb PhD , C. Elizabeth Shaaban PhD , Andrea L. Rosso PhD
Purpose
Evidence regarding neighborhood socioeconomic status (nSES) as an upstream determinant of cognitive outcomes has largely lacked a life-course perspective. We examined racial differences in the associations between midlife and late-life nSES and cognitive function in a cohort of 330 Black and White older Americans aged 70 + .
Methods
General cognitive function was measured using Modified Mini-Mental State Examination up to a 15-year follow-up. Midlife (age 49–58) and late-life (age 70–79) nSES scores were z-standardized based on five census indicators of tract-level socioeconomic characteristics. Mixed-effects linear regression examined the associations between midlife and late-life nSES and cognitive function.
Results
Higher midlife nSES was associated with higher baseline levels of cognitive function among Black (β: 3.10, 95 % CI: 0.85, 5.33), but not among White participants (β: 0.51, 95 % CI: −0.88, 1.90; p for interaction: 0.037). There were no observed associations between midlife nSES and changes in cognitive function in the overall sample or in either racial group. Late-life nSES was not associated with baseline levels of cognitive function or changes in the overall sample or either racial group.
Conclusions
Midlife may be a critical period in which neighborhood socioeconomic exposure has a greater impact on late-life cognitive health, particularly for Black individuals.
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