Pub Date : 2025-11-01DOI: 10.1016/j.annepidem.2025.08.038
Jeb Jones PhD, MPH, MS
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 and to encourage critical thinking and careful evaluation. This EEM focuses on the use of propensity score matching and multinomial models in a study exploring the association between experiencing a stroke and psychological distress and references the following article: Dagli C, Patel PG, Gonzalez K, Nair M, Al-Antary N, Lin C, Adjei Boakye E. Psychological distress among stroke survivors in the US: An analysis of the National Health Interview Survey. Ann Epidemiol. 2025 Jun 30;109:8–13. doi: 10.1016/j.annepidem.2025.06.019. Epub ahead of print. PMID: 40602697.
教育参与模块（EEMs）是为教育工作者和学生提供的教材，有助于更深入地理解关键的流行病学方法和概念。每个EEM提出一系列问题，使用最近发表在《年鉴》上的一篇论文，以进一步理解特定的研究设计，并鼓励批判性思维和仔细评估。这篇EEM着重于在一项研究中使用倾向得分匹配和多项模型来探索中风与心理困扰之间的关系，并参考了以下文章：Dagli C， Patel PG, Gonzalez K, Nair M, Al-Antary N, Lin C, Adjei Boakye E.美国中风幸存者的心理困扰：全国健康访谈调查的分析。流行病学杂志。2025年6月30日；109:8-13。doi: 10.1016 / j.annepidem.2025.06.019。打印前Epub。PMID: 40602697。
{"title":"Propensity score matching learning module: Dagli et al (2025), Psychological distress among stroke survivors in the US: An analysis of the National Health Interview Survey","authors":"Jeb Jones PhD, MPH, MS","doi":"10.1016/j.annepidem.2025.08.038","DOIUrl":"10.1016/j.annepidem.2025.08.038","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 and to encourage critical thinking and careful evaluation. This EEM focuses on the use of propensity score matching and multinomial models in a study exploring the association between experiencing a stroke and psychological distress and references the following article: Dagli C, Patel PG, Gonzalez K, Nair M, Al-Antary N, Lin C, Adjei Boakye E. Psychological distress among stroke survivors in the US: An analysis of the National Health Interview Survey. Ann Epidemiol. 2025 Jun 30;109:8–13. doi: 10.1016/j.annepidem.2025.06.019. Epub ahead of print. PMID: 40602697.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"111 ","pages":"Pages 193-195"},"PeriodicalIF":3.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145474222","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 : 2025-10-30DOI: 10.1016/j.annepidem.2025.10.017
Gum-Ryeong Park Ph.D. , Jinho Kim Ph.D.
Purpose
Acknowledging the importance of subjective financial measures that objective indicators may not be able to fully capture, this study investigates whether and how perceived economic hardship influences self-rated health among women. Specifically, it examines the cumulative effects of perceived economic hardship while exploring variations across different age groups.
Methods
This study analyzed data from the Korean Longitudinal Survey of Women & Families (2006–2022), including 12,800 participants who experienced varying levels of economic hardship. Economic hardship was assessed based on subjective perceptions reported across consecutive survey waves (ranging from 1 wave to over 4 waves), while self-rated health was measured on a five-point scale. To account for unmeasured individual-level heterogeneity, fixed effects models were employed in the analysis.
Results
Prolonged exposure to economic hardship is associated with greater declines in self-rated health, with longer durations of hardship leading to increasingly severe negative impacts. Also, age differences were observed, as older adults experienced significantly larger declines in self-rated health compared to their younger counterparts as the duration of hardship increased.
Conclusion
The findings on the cumulative effects of perceived economic hardship on health underscore the importance of incorporating subjective measures of economic conditions into research and policy discussions.
{"title":"Cumulative exposure to economic hardship and self-rated health among Korean women: An exploration of age heterogeneity","authors":"Gum-Ryeong Park Ph.D. , Jinho Kim Ph.D.","doi":"10.1016/j.annepidem.2025.10.017","DOIUrl":"10.1016/j.annepidem.2025.10.017","url":null,"abstract":"<div><h3>Purpose</h3><div>Acknowledging the importance of subjective financial measures that objective indicators may not be able to fully capture, this study investigates whether and how perceived economic hardship influences self-rated health among women. Specifically, it examines the cumulative effects of perceived economic hardship while exploring variations across different age groups.</div></div><div><h3>Methods</h3><div>This study analyzed data from the Korean Longitudinal Survey of Women & Families (2006–2022), including 12,800 participants who experienced varying levels of economic hardship. Economic hardship was assessed based on subjective perceptions reported across consecutive survey waves (ranging from 1 wave to over 4 waves), while self-rated health was measured on a five-point scale. To account for unmeasured individual-level heterogeneity, fixed effects models were employed in the analysis.</div></div><div><h3>Results</h3><div>Prolonged exposure to economic hardship is associated with greater declines in self-rated health, with longer durations of hardship leading to increasingly severe negative impacts. Also, age differences were observed, as older adults experienced significantly larger declines in self-rated health compared to their younger counterparts as the duration of hardship increased.</div></div><div><h3>Conclusion</h3><div>The findings on the cumulative effects of perceived economic hardship on health underscore the importance of incorporating subjective measures of economic conditions into research and policy discussions.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"112 ","pages":"Pages 46-52"},"PeriodicalIF":3.0,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145419391","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 : 2025-10-16DOI: 10.1016/j.annepidem.2025.10.015
Yu-Xuan Xiao , Yi-Xin Zou , Zhuo-Ying Li , Qiu-Ming Shen , Da-Ke Liu , Yu-Ting Tan , Hong-Lan Li , Yong-Bing Xiang
Background
Primary liver cancer (PLC) remains a major public health concern, particularly in China where the incidence is high. Existing prediction models often focus on high-risk populations and depend heavily on laboratory data, which limits their utility in general population screening.
Methods
We developed and validated a 15-year PLC risk prediction model using data from two large prospective cohort studies in Shanghai (n = 132,360), including 618 incident PLC cases. Candidate variables encompassed sociodemographic characteristics, lifestyle behaviors, medical history, and dietary factors. Predictor selection was performed using LASSO regression and the Boruta algorithm. Five machine learning models and logistic regression were compared. Model performance was evaluated using AUC, calibration plots and net reclassification improvement (NRI). SHapley Additive exPlanations (SHAP) were used to interpret model predictions. Web-based tools, including a simplified risk calculator, were developed to facilitate practical application.
Results
LightGBM achieved the best discrimination (AUC = 0.766) and excellent calibration. Net reclassification analysis indicated an improved ability to correctly classify low-risk individuals. The model effectively stratified the population: the high-risk group had a 15-year PLC risk that was 39.56 times that of the low-risk group. SHAP analysis revealed biologically meaningful associations. A simplified logistic model with fewer variables also performed well (AUC = 0.762), supporting effective risk stratification.
Conclusion
We developed a questionnaire-based 15-year PLC risk prediction model applicable to the general Chinese population. Both the full and simplified models demonstrated strong performance and interpretability, making them valuable tools for large-scale screening and targeted prevention, especially in resource-limited settings.
{"title":"A machine learning approach for a 15-year prediction model of liver cancer incidence: Results from two large Chinese population cohorts","authors":"Yu-Xuan Xiao , Yi-Xin Zou , Zhuo-Ying Li , Qiu-Ming Shen , Da-Ke Liu , Yu-Ting Tan , Hong-Lan Li , Yong-Bing Xiang","doi":"10.1016/j.annepidem.2025.10.015","DOIUrl":"10.1016/j.annepidem.2025.10.015","url":null,"abstract":"<div><h3>Background</h3><div>Primary liver cancer (PLC) remains a major public health concern, particularly in China where the incidence is high. Existing prediction models often focus on high-risk populations and depend heavily on laboratory data, which limits their utility in general population screening.</div></div><div><h3>Methods</h3><div>We developed and validated a 15-year PLC risk prediction model using data from two large prospective cohort studies in Shanghai (n = 132,360), including 618 incident PLC cases. Candidate variables encompassed sociodemographic characteristics, lifestyle behaviors, medical history, and dietary factors. Predictor selection was performed using LASSO regression and the Boruta algorithm. Five machine learning models and logistic regression were compared. Model performance was evaluated using AUC, calibration plots and net reclassification improvement (NRI). SHapley Additive exPlanations (SHAP) were used to interpret model predictions. Web-based tools, including a simplified risk calculator, were developed to facilitate practical application.</div></div><div><h3>Results</h3><div>LightGBM achieved the best discrimination (AUC = 0.766) and excellent calibration. Net reclassification analysis indicated an improved ability to correctly classify low-risk individuals. The model effectively stratified the population: the high-risk group had a 15-year PLC risk that was 39.56 times that of the low-risk group. SHAP analysis revealed biologically meaningful associations. A simplified logistic model with fewer variables also performed well (AUC = 0.762), supporting effective risk stratification.</div></div><div><h3>Conclusion</h3><div>We developed a questionnaire-based 15-year PLC risk prediction model applicable to the general Chinese population. Both the full and simplified models demonstrated strong performance and interpretability, making them valuable tools for large-scale screening and targeted prevention, especially in resource-limited settings.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"112 ","pages":"Pages 28-37"},"PeriodicalIF":3.0,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145318874","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 : 2025-10-16DOI: 10.1016/j.annepidem.2025.10.005
Melody S. Goodman , Ariana Lopez , Anarina L. Murillo , Kristyn A. Pierce
In many public health and clinical research studies that use regression models for analyses, race is often considered a confounder and "controlled" for in the regression model with simple indicators for race and non-Hispanic White as the reference group, without much introspection from the data analyst. From a health equity perspective, multiple issues exist with this approach. We examine and compare several methods for coding race in linear and logistic regression models. We compare several coding methods using a sample of 8097 participants (≥18 years old) from the 2020 New York City Community Health Survey. To illustrate the importance of coding methods for race, we conducted regression analyses to compare the results from six coding approaches: dummy, simple effect, difference (forward and backward), deviation, and analyst-defined coding. Body mass index measured continuously and diabetes status measured dichotomously were the outcome variables in the linear and logistic regression models. Results showed that selecting a coding method has implications for identifying racial health inequities. The reference group selection is critical to measuring racial inequities in health outcomes. This study emphasizes the need to consider the impact of coding techniques on research study design, particularly when racial health inequities are the research focus.
{"title":"A comparison of methods for coding race in linear and logistic regression models","authors":"Melody S. Goodman , Ariana Lopez , Anarina L. Murillo , Kristyn A. Pierce","doi":"10.1016/j.annepidem.2025.10.005","DOIUrl":"10.1016/j.annepidem.2025.10.005","url":null,"abstract":"<div><div>In many public health and clinical research studies that use regression models for analyses, race is often considered a confounder and \"controlled\" for in the regression model with simple indicators for race and non-Hispanic White as the reference group, without much introspection from the data analyst. From a health equity perspective, multiple issues exist with this approach. We examine and compare several methods for coding race in linear and logistic regression models. We compare several coding methods using a sample of 8097 participants (≥18 years old) from the 2020 New York City Community Health Survey. To illustrate the importance of coding methods for race, we conducted regression analyses to compare the results from six coding approaches: dummy, simple effect, difference (forward and backward), deviation, and analyst-defined coding. Body mass index measured continuously and diabetes status measured dichotomously were the outcome variables in the linear and logistic regression models. Results showed that selecting a coding method has implications for identifying racial health inequities. The reference group selection is critical to measuring racial inequities in health outcomes. This study emphasizes the need to consider the impact of coding techniques on research study design, particularly when racial health inequities are the research focus.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"112 ","pages":"Pages 15-22"},"PeriodicalIF":3.0,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145318839","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 : 2025-10-15DOI: 10.1016/j.annepidem.2025.10.016
Cynthia N. Ramirez , Michael Goodman , Kristine Magnusson , Wendy Leyden , Alexandra N. Lea , Darios Getahun , Courtney McCracken , Suma Vupputuri , Lee Cromwell , Timothy L. Lash , Oumaima Kaabi , Guneet K. Jasuja , Michael J. Silverberg
Purpose
Electronic health records (EHR) offer a unique opportunity to systematically collect sexual orientation and gender identity (SOGI) data. This study examined the prevalence and determinants of SOGI reporting in an EHR-based cohort of transgender and gender diverse (TGD) individuals.
Methods
We identified TGD people with and without SOGI documentation across four Kaiser Permanente health plans from January 1, 2022–2024. TGD status was determined through clinical notes, diagnostic codes, and SOGI data based on a previously established cohort. Factors associated with SOGI reporting were assessed using log-binomial regression, yielding prevalence ratios (PR) and the 95 % confidence intervals (CI).
Results
Among 23,060 TGD individuals, 71 % had SOGI documentation in the EHR. Reporting varied by sociodemographic and clinical characteristics. For example, compared to those < 20 years, SOGI reporting was higher for those aged 21–59 (PRs 1.10–1.21; 95 % CIs 1.06–1.24) and lower for those > 60 (0.93; 0.88–0.99). Documentation was slightly lower for those assigned male at birth (0.98; 0.97–1.00) and varied by race and ethnicity (e.g., Hispanic: 0.97; 0.95–0.99; Other: 1.02; 0.98–1.05 vs. White).
Conclusions
KP’s EHRs captured SOGI data for over 70 % of TGD individuals, though more research is needed to understand factors associated with missing data not captured in structured fields.
{"title":"Availability of sexual orientation and gender identity (SOGI) information in a cohort of transgender and gender diverse people: An analysis of electronic health records","authors":"Cynthia N. Ramirez , Michael Goodman , Kristine Magnusson , Wendy Leyden , Alexandra N. Lea , Darios Getahun , Courtney McCracken , Suma Vupputuri , Lee Cromwell , Timothy L. Lash , Oumaima Kaabi , Guneet K. Jasuja , Michael J. Silverberg","doi":"10.1016/j.annepidem.2025.10.016","DOIUrl":"10.1016/j.annepidem.2025.10.016","url":null,"abstract":"<div><h3>Purpose</h3><div>Electronic health records (EHR) offer a unique opportunity to systematically collect sexual orientation and gender identity (SOGI) data. This study examined the prevalence and determinants of SOGI reporting in an EHR-based cohort of transgender and gender diverse (TGD) individuals.</div></div><div><h3>Methods</h3><div>We identified TGD people with and without SOGI documentation across four Kaiser Permanente health plans from January 1, 2022–2024. TGD status was determined through clinical notes, diagnostic codes, and SOGI data based on a previously established cohort. Factors associated with SOGI reporting were assessed using log-binomial regression, yielding prevalence ratios (PR) and the 95 % confidence intervals (CI).</div></div><div><h3>Results</h3><div>Among 23,060 TGD individuals, 71 % had SOGI documentation in the EHR. Reporting varied by sociodemographic and clinical characteristics. For example, compared to those < 20 years, SOGI reporting was higher for those aged 21–59 (PRs 1.10–1.21; 95 % CIs 1.06–1.24) and lower for those > 60 (0.93; 0.88–0.99). Documentation was slightly lower for those assigned male at birth (0.98; 0.97–1.00) and varied by race and ethnicity (e.g., Hispanic: 0.97; 0.95–0.99; Other: 1.02; 0.98–1.05 vs. White).</div></div><div><h3>Conclusions</h3><div>KP’s EHRs captured SOGI data for over 70 % of TGD individuals, though more research is needed to understand factors associated with missing data not captured in structured fields.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"112 ","pages":"Pages 23-27"},"PeriodicalIF":3.0,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145314098","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 : 2025-10-14DOI: 10.1016/j.annepidem.2025.10.012
Siona Prasad, Sabina A. Murphy, David A. Morrow, Benjamin S. Scirica, Marc S. Sabatine, David D. Berg, Andrea Bellavia
Purpose
Clinical prediction models (CPM) are essential tools for diagnosis and prognosis in clinical epidemiology. Machine learning (ML) and deep learning (DL) approaches provide flexible methods that can complement regression-based methods for CPM when complex predictors such as clinical biomarkers are of interest. However, concerns have been raised on the ability of ML and DL to address desired properties of CPMs such as parsimony, generalizability, and interpretability.
Methods
In this study, we evaluated and applied selected regression-based, ML and DL approaches for time-to-event outcomes in a clinical study integrating protein biomarkers and lipids in an existing CPM for cardiovascular risk.
Results
We observed considerable advantages from the application of gradient boosting machines (GBM: C-statistic=0.72; Brier Score=0.052), which provided the best balance between model flexibility, discrimination, calibration, and parsimony, the latter being directly related to one of the model parameters (shrinking rate). Further, GBM results can be used for individual risk prediction, providing an interpretable tool for CPM implementation.
Conclusions
We compared ML and DL methods for CPM with time-to-event outcomes and discussed practical aspects of their implementation in clinical epidemiology including generalizability and interpretability. Adequately trained ML approaches can provide advantages in prediction modeling, especially when integrating complex predictors.
{"title":"Application of machine learning and deep learning approaches for prediction modeling with time-to-event outcomes in clinical epidemiology. Methods comparison and practical considerations for generalizability and interpretability","authors":"Siona Prasad, Sabina A. Murphy, David A. Morrow, Benjamin S. Scirica, Marc S. Sabatine, David D. Berg, Andrea Bellavia","doi":"10.1016/j.annepidem.2025.10.012","DOIUrl":"10.1016/j.annepidem.2025.10.012","url":null,"abstract":"<div><h3>Purpose</h3><div>Clinical prediction models (CPM) are essential tools for diagnosis and prognosis in clinical epidemiology. Machine learning (ML) and deep learning (DL) approaches provide flexible methods that can complement regression-based methods for CPM when complex predictors such as clinical biomarkers are of interest. However, concerns have been raised on the ability of ML and DL to address desired properties of CPMs such as parsimony, generalizability, and interpretability.</div></div><div><h3>Methods</h3><div>In this study, we evaluated and applied selected regression-based, ML and DL approaches for time-to-event outcomes in a clinical study integrating protein biomarkers and lipids in an existing CPM for cardiovascular risk.</div></div><div><h3>Results</h3><div>We observed considerable advantages from the application of gradient boosting machines (GBM: C-statistic=0.72; Brier Score=0.052), which provided the best balance between model flexibility, discrimination, calibration, and parsimony, the latter being directly related to one of the model parameters (shrinking rate). Further, GBM results can be used for individual risk prediction, providing an interpretable tool for CPM implementation.</div></div><div><h3>Conclusions</h3><div>We compared ML and DL methods for CPM with time-to-event outcomes and discussed practical aspects of their implementation in clinical epidemiology including generalizability and interpretability. Adequately trained ML approaches can provide advantages in prediction modeling, especially when integrating complex predictors.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"111 ","pages":"Pages 186-192"},"PeriodicalIF":3.0,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145309858","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 : 2025-10-11DOI: 10.1016/j.annepidem.2025.10.014
Adam S. Vaughan PhD , Nicholas Sutton MPH , Rebecca C. Woodruff PhD , LaTonia C. Richardson PhD , Janet S. Wright MD , Fátima Coronado MD
Purpose
This study examines national trends in mortality from cardiovascular disease (CVD) and select subtypes among U.S. young adults aged 18–34 years from 2000 to 2023.
Methods
National mortality data from the National Vital Statistics System were used to identify CVD, heart disease, stroke, and hypertension-related CVD deaths among U.S. residents aged 18–34 from 2000 to 2023. Crude and age-standardized death rates were calculated overall and by age group, sex, and race and ethnicity. Temporal trends were calculated as percent change using a log-linear model.
Results
From 2000–2023, age-standardized CVD and heart disease death rates among young adults did not statistically change (percent change: −2.2 % [95 % CI: −7.8, 3.7] and −2.4 % [95 % CI: −8.3 %, 3.8 %], respectively). Stroke death rates decreased (percent change: −15.7 % [-21.0 %, −10.0 %])). However, hypertension-related CVD death rates increased by 78.5 % [95 % CI: 63.6 %, 94.7 %]). Patterns across demographic groups were broadly similar.
Conclusion
Despite stability or modest declines in CVD death rates among young adults, hypertension-related CVD death rates increased sharply during 2000–2023. These findings merit public health action and underscore the need for better identification and management of hypertension and other CVD risk factors among young adults.
{"title":"Cardiovascular disease mortality trends in young adults aged 18–34 years, United States, 2000–2023","authors":"Adam S. Vaughan PhD , Nicholas Sutton MPH , Rebecca C. Woodruff PhD , LaTonia C. Richardson PhD , Janet S. Wright MD , Fátima Coronado MD","doi":"10.1016/j.annepidem.2025.10.014","DOIUrl":"10.1016/j.annepidem.2025.10.014","url":null,"abstract":"<div><h3>Purpose</h3><div>This study examines national trends in mortality from cardiovascular disease (CVD) and select subtypes among U.S. young adults aged 18–34 years from 2000 to 2023.</div></div><div><h3>Methods</h3><div>National mortality data from the National Vital Statistics System were used to identify CVD, heart disease, stroke, and hypertension-related CVD deaths among U.S. residents aged 18–34 from 2000 to 2023. Crude and age-standardized death rates were calculated overall and by age group, sex, and race and ethnicity. Temporal trends were calculated as percent change using a log-linear model.</div></div><div><h3>Results</h3><div>From 2000–2023, age-standardized CVD and heart disease death rates among young adults did not statistically change (percent change: −2.2 % [95 % CI: −7.8, 3.7] and −2.4 % [95 % CI: −8.3 %, 3.8 %], respectively). Stroke death rates decreased (percent change: −15.7 % [-21.0 %, −10.0 %])). However, hypertension<strong>-</strong>related CVD death rates increased by 78.5 % [95 % CI: 63.6 %, 94.7 %]). Patterns across demographic groups were broadly similar.</div></div><div><h3>Conclusion</h3><div>Despite stability or modest declines in CVD death rates among young adults, hypertension-related CVD death rates increased sharply during 2000–2023. These findings merit public health action and underscore the need for better identification and management of hypertension and other CVD risk factors among young adults.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"112 ","pages":"Pages 38-45"},"PeriodicalIF":3.0,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145287638","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}
There are inconsistent findings regarding the associations between lipids and type 2 diabetes mellitus (T2DM), partially due to ignoring the joint effects of longitudinal patterns in lipids simultaneously. This study aimed to investigate the association of joint multi-trajectory of different lipids with the risk of type 2 diabetes.
Methods
We enrolled 71,043 participants free of T2DM from the Kailuan study. Using group-based multi-trajectory modeling, joint multi-trajectory of triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) during 2006–2010 was developed to predict the risk of T2DM during 2010–2019.
Results
Five distinct multi-trajectory groups were identified over 4-year exposure, and 6473 (9.11 %) cases of incident T2DM occurred during a median follow-up of 8.97 years. The highest risk of T2DM was observed in Group 5 with the highest level of TG, optimal-increasing LDL, and high-increasing HDL-C (hazard ratio [HR], 2.14; 95 % confidence interval [CI], 1.89–2.41), followed by Group 3 with the lowest level of HDL-C and an optimal TG and LDL-C (HR, 1.39; 95 % CI, 1.11–1.43), and Group 4 with the highest level of LDL-C, optimal-increasing TG and high-increasing HDL-C (HR, 1,26; 95 % CI, 1.11–1.43), compared to Group 2 with the lowest level of TG and optimal-increasing LDL-C and high-increasing HDL-C. The observed associations existed regardless of baseline lipid levels.
Conclusion
Our results showed the important role of high-increasing TG and low-decreasing HDL-C, rather than high-increasing LDL-C in the development of T2DM, which would help better understand the heterogeneous risk of T2DM and facilitate targeted prevention programs.
{"title":"Longitudinal triglyceride and HDL cholesterol, but not LDL cholesterol associated with the risk of incident type 2 diabetes: Evidence from a multi-trajectory analysis","authors":"Xue Tian , Shuohua Chen , Xue Xia , Qin Xu , Shouling Wu , Anxin Wang","doi":"10.1016/j.annepidem.2025.10.007","DOIUrl":"10.1016/j.annepidem.2025.10.007","url":null,"abstract":"<div><h3>Purpose</h3><div>There are inconsistent findings regarding the associations between lipids and type 2 diabetes mellitus (T2DM), partially due to ignoring the joint effects of longitudinal patterns in lipids simultaneously. This study aimed to investigate the association of joint multi-trajectory of different lipids with the risk of type 2 diabetes.</div></div><div><h3>Methods</h3><div>We enrolled 71,043 participants free of T2DM from the Kailuan study. Using group-based multi-trajectory modeling, joint multi-trajectory of triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) during 2006–2010 was developed to predict the risk of T2DM during 2010–2019.</div></div><div><h3>Results</h3><div>Five distinct multi-trajectory groups were identified over 4-year exposure, and 6473 (9.11 %) cases of incident T2DM occurred during a median follow-up of 8.97 years. The highest risk of T2DM was observed in Group 5 with the highest level of TG, optimal-increasing LDL, and high-increasing HDL-C (hazard ratio [HR], 2.14; 95 % confidence interval [CI], 1.89–2.41), followed by Group 3 with the lowest level of HDL-C and an optimal TG and LDL-C (HR, 1.39; 95 % CI, 1.11–1.43), and Group 4 with the highest level of LDL-C, optimal-increasing TG and high-increasing HDL-C (HR, 1,26; 95 % CI, 1.11–1.43), compared to Group 2 with the lowest level of TG and optimal-increasing LDL-C and high-increasing HDL-C. The observed associations existed regardless of baseline lipid levels.</div></div><div><h3>Conclusion</h3><div>Our results showed the important role of high-increasing TG and low-decreasing HDL-C, rather than high-increasing LDL-C in the development of T2DM, which would help better understand the heterogeneous risk of T2DM and facilitate targeted prevention programs.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"111 ","pages":"Pages 138-145"},"PeriodicalIF":3.0,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269393","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 : 2025-10-10DOI: 10.1016/j.annepidem.2025.10.003
Jacob Englert , Howard Chang
Purpose:
To extend the existing quantile g-computation framework for studying environmental exposure mixtures to estimate local effects of ambient air pollution mixtures on birth weight. This framework has traditionally been applied to estimate global mixture effects without accounting for spatial heterogeneity.
Methods:
First, pregnancy-wide maternal exposure to five common air pollutants is estimated for nearly 1.5 million births occurring in Georgia, USA between 2005 and 2016. Then, a recently developed spatially varying coefficient model based on Bayesian additive regression trees (BART) is applied to estimate spatially heterogeneous mixture effects using quantile g-computation. Results are compared with those obtained from traditional conditional autoregressive models, as well as spatially agnostic modeling approaches.
Results:
We find evidence of county-level spatially varying mixture associations, where for 21 of 159 counties in Georgia, elevated concentrations of a mixture of PM2.5, nitrogen dioxide, sulfur dioxide, ozone, and carbon monoxide were associated with a reduction in birthweight by as much as -14.77 grams (95% credible interval: -21.24, -9.78) per decile increase in all five air pollutants.
Conclusions:
Spatially varying coefficient models based on BART outperform alternative approaches when modeling the relationships between air pollution mixtures and birth weight for the majority of counties in Georgia.
{"title":"Modeling heterogeneity in air pollution mixture effects on birth weight: A spatially varying coefficient approach","authors":"Jacob Englert , Howard Chang","doi":"10.1016/j.annepidem.2025.10.003","DOIUrl":"10.1016/j.annepidem.2025.10.003","url":null,"abstract":"<div><h3>Purpose:</h3><div>To extend the existing quantile g-computation framework for studying environmental exposure mixtures to estimate local effects of ambient air pollution mixtures on birth weight. This framework has traditionally been applied to estimate global mixture effects without accounting for spatial heterogeneity.</div></div><div><h3>Methods:</h3><div>First, pregnancy-wide maternal exposure to five common air pollutants is estimated for nearly 1.5 million births occurring in Georgia, USA between 2005 and 2016. Then, a recently developed spatially varying coefficient model based on Bayesian additive regression trees (BART) is applied to estimate spatially heterogeneous mixture effects using quantile g-computation. Results are compared with those obtained from traditional conditional autoregressive models, as well as spatially agnostic modeling approaches.</div></div><div><h3>Results:</h3><div>We find evidence of county-level spatially varying mixture associations, where for 21 of 159 counties in Georgia, elevated concentrations of a mixture of PM<sub>2.5</sub>, nitrogen dioxide, sulfur dioxide, ozone, and carbon monoxide were associated with a reduction in birthweight by as much as -14.77 grams (95% credible interval: -21.24, -9.78) per decile increase in all five air pollutants.</div></div><div><h3>Conclusions:</h3><div>Spatially varying coefficient models based on BART outperform alternative approaches when modeling the relationships between air pollution mixtures and birth weight for the majority of counties in Georgia.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"111 ","pages":"Pages 180-185"},"PeriodicalIF":3.0,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145276559","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 : 2025-10-10DOI: 10.1016/j.annepidem.2025.10.013
Taryn Lambert , Nikki Stephenson , Janice Skiffington , Donna Slater , Lara M. Leijser , Amy Metcalfe
Purpose
Attrition of participants over time poses a challenge in longitudinal research. This study aimed to explore how partner participation influenced maternal retention.
Methods
Using data from the P3 Cohort (a longitudinal pregnancy cohort), study retention was assessed at each stage of data collection up to 1 year postpartum. Participants were grouped according to their partner's level of participation in the study (participants who did not consent to the study team contacting their partners, participants whose partners were contacted but did not consent to participate, and participants whose partners actively participated). Cox proportional hazards models were used to evaluate the association between partner participation and participant attrition.
Results
Of 2194 eligible participants, 38.9 % did not provide consent for the study team to contact their partner, and 42.1 % of partners that were contacted agreed to participate in the cohort. Retention rates in the cohort were high (97.5 % at 1 year postpartum) but varied by partner participation. Partner participation was associated with a significantly reduced hazard of attrition (HR=0.38, 95 % CI:0.15–0.92).
Conclusions
Active partner participation significantly enhances maternal participant retention. Inclusion of partners in pregnancy research may help reduce attrition and gain a more comprehensive understanding of family dynamics.
{"title":"Impact of couple vs. individual participation in pregnancy research: A comparative analysis of participant characteristics and study retention","authors":"Taryn Lambert , Nikki Stephenson , Janice Skiffington , Donna Slater , Lara M. Leijser , Amy Metcalfe","doi":"10.1016/j.annepidem.2025.10.013","DOIUrl":"10.1016/j.annepidem.2025.10.013","url":null,"abstract":"<div><h3>Purpose</h3><div>Attrition of participants over time poses a challenge in longitudinal research. This study aimed to explore how partner participation influenced maternal retention.</div></div><div><h3>Methods</h3><div>Using data from the P3 Cohort (a longitudinal pregnancy cohort), study retention was assessed at each stage of data collection up to 1 year postpartum. Participants were grouped according to their partner's level of participation in the study (participants who did not consent to the study team contacting their partners, participants whose partners were contacted but did not consent to participate, and participants whose partners actively participated). Cox proportional hazards models were used to evaluate the association between partner participation and participant attrition.</div></div><div><h3>Results</h3><div>Of 2194 eligible participants, 38.9 % did not provide consent for the study team to contact their partner, and 42.1 % of partners that were contacted agreed to participate in the cohort. Retention rates in the cohort were high (97.5 % at 1 year postpartum) but varied by partner participation. Partner participation was associated with a significantly reduced hazard of attrition (HR=0.38, 95 % CI:0.15–0.92).</div></div><div><h3>Conclusions</h3><div>Active partner participation significantly enhances maternal participant retention. Inclusion of partners in pregnancy research may help reduce attrition and gain a more comprehensive understanding of family dynamics.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"111 ","pages":"Pages 163-167"},"PeriodicalIF":3.0,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145281711","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}
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