Annals of Epidemiology最新文献_第7页

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model 利用基于转换器的深度学习模型对不断发展的纵向健康轨迹进行建模。

IF 3 3区医学

Annals of Epidemiology Pub Date : 2025-11-01 Epub Date: 2025-09-11 DOI: 10.1016/j.annepidem.2025.08.025

Hans Moen , Vishnu Raj , Andrius Vabalas , Markus Perola , Samuel Kaski , Andrea Ganna , Pekka Marttinen

{"title":"Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model","authors":"Hans Moen , Vishnu Raj , Andrius Vabalas , Markus Perola , Samuel Kaski , Andrea Ganna , Pekka Marttinen","doi":"10.1016/j.annepidem.2025.08.025","DOIUrl":"10.1016/j.annepidem.2025.08.025","url":null,"abstract":"<div><h3>Purpose:</h3><div>Health registers provide valuable insights into individuals’ health trajectories. This study explores the use of deep learning to model and analyze these trajectories using a nationwide longitudinal dataset containing coded features such as clinical codes, procedures, and drug purchases.</div></div><div><h3>Methods:</h3><div>We introduce <span>Evolve</span>, a transformer-based deep learning model designed to provide continuous multi-label predictions over time. The model predicts disease onsets at each time step conditioned on the health history up to that time step and the time until a given 5-year forecast window. <span>Evolve</span> is evaluated against several baseline models for basic prediction performance. Additionally, we analyze health trajectories by tracking changes in prediction probabilities and in the latent embedding neighborhood to identify important events.</div></div><div><h3>Results:</h3><div><span>Evolve</span> performed comparably to baseline models in disease onset prediction while offering unique trajectory modeling capabilities. The model identified early predictive events and demonstrated that changes in embedding space could indicate shifts in health trajectories. Visualization of evolving health trajectories showed how individuals may become most similar to others with similar profiles and outcomes over time.</div></div><div><h3>Conclusions:</h3><div>The <span>Evolve</span> model seems promising at enabling continuous health monitoring, early disease detection, and retrospective analysis, making it a promising tool for personalized healthcare interventions.</div><div>Code available at: <span><span>https://github.com/hansmoen/evolvehealth</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"111 ","pages":"Pages 30-43"},"PeriodicalIF":3.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145058755","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}

引用次数: 0

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 机器学习和深度学习方法在临床流行病学中具有事件时间结果的预测建模中的应用。方法概括性与可解释性的比较与实践思考。

IF 3 3区医学

Annals of Epidemiology Pub Date : 2025-11-01 Epub Date: 2025-10-14 DOI: 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

{"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":"2025-11-01","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}

引用次数: 0

Longitudinal triglyceride and HDL cholesterol, but not LDL cholesterol associated with the risk of incident type 2 diabetes: Evidence from a multi-trajectory analysis 纵向甘油三酯和高密度脂蛋白胆固醇，而非低密度脂蛋白胆固醇与2型糖尿病发生风险相关：来自多轨迹分析的证据

IF 3 3区医学

Annals of Epidemiology Pub Date : 2025-11-01 Epub Date: 2025-10-10 DOI: 10.1016/j.annepidem.2025.10.007

Xue Tian , Shuohua Chen , Xue Xia , Qin Xu , Shouling Wu , Anxin Wang

{"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":"2025-11-01","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}

引用次数: 0

Cardiovascular disease burden attributable to estimated glomerular filtration rate: Insights from the ATTICA study (2002–2022) 归因于估计肾小球滤过率的心血管疾病负担：来自ATTICA研究的见解（2002-2022）。

IF 3 3区医学

Annals of Epidemiology Pub Date : 2025-11-01 Epub Date: 2025-09-12 DOI: 10.1016/j.annepidem.2025.09.007

Evangelia Damigou , Matilda Florentin , Christina Chrysohoou , Costas Anastasiou , Fotios Barkas , Evangelos Liberopoulos , Petros P. Sfikakis , Costas Tsioufis , Christos Pitsavos , Demosthenes Panagiotakos

{"title":"Cardiovascular disease burden attributable to estimated glomerular filtration rate: Insights from the ATTICA study (2002–2022)","authors":"Evangelia Damigou , Matilda Florentin , Christina Chrysohoou , Costas Anastasiou , Fotios Barkas , Evangelos Liberopoulos , Petros P. Sfikakis , Costas Tsioufis , Christos Pitsavos , Demosthenes Panagiotakos","doi":"10.1016/j.annepidem.2025.09.007","DOIUrl":"10.1016/j.annepidem.2025.09.007","url":null,"abstract":"<div><h3>Purpose</h3><div>This study aimed to estimate cardiovascular disease (CVD) burden attributable to chronic kidney disease (CKD) and whether this burden varies in participants with different population characteristics.</div></div><div><h3>Methods</h3><div>Our sample included 1988 adults free-of-CVD at baseline who took part in the ATTICA study (2002–2022). Estimated glomerular filtration rate (eGFR) was calculated based on the Chronic Kidney Disease Epidemiology Collaboration equation. CKD was defined in 2002, according to Kidney Disease Improving Global Outcomes (KDIGO) guidelines, as an eGFR ≤ 60 mL/min/1.73 m<sup>2</sup>. Combined fatal or non-fatal CVD events were assessed in 2006, 2012 and 2022 based on WHO-ICD-10. Population attributable fractions for multiadjusted models were computed based on Miettinen’s formula. Stratified analyses were also performed.</div></div><div><h3>Results</h3><div>At baseline, CKD prevalence was 4.7 % (n = 94). During the 20-year period, 36.1 % of participants developed CVD. A higher percentage of participants with CKD developed CVD compared to those without (77 % vs. 34 %). Approximately 6 out of a 100 new CVD cases (95 %CI: 1.7 %, 8.1 %) would have been prevented if CKD had been properly managed. Variations in these fractions were observed by sex and presence of comorbidities.</div></div><div><h3>Conclusions</h3><div>Albeit more research is warranted, our study supports that CKD should become a public health priority, and specifically a CVD priority.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"111 ","pages":"Pages 1-8"},"PeriodicalIF":3.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145058725","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}

引用次数: 0

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 倾向得分匹配学习模块：Dagli等人（2025），美国中风幸存者的心理困扰：全国健康访谈调查分析

IF 3 3区医学

Annals of Epidemiology Pub Date : 2025-11-01 Epub Date: 2025-11-07 DOI: 10.1016/j.annepidem.2025.08.038

Jeb Jones PhD, MPH, MS

引用次数: 0

Modeling heterogeneity in air pollution mixture effects on birth weight: A spatially varying coefficient approach 空气污染混合对出生体重影响的异质性建模：一个空间变系数方法。

IF 3 3区医学

Annals of Epidemiology Pub Date : 2025-11-01 Epub Date: 2025-10-10 DOI: 10.1016/j.annepidem.2025.10.003

Jacob Englert , Howard Chang

{"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":"2025-11-01","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}

引用次数: 0

Impact of couple vs. individual participation in pregnancy research: A comparative analysis of participant characteristics and study retention 夫妇与个人参与妊娠研究的影响：参与者特征和研究保留的比较分析。

IF 3 3区医学

Annals of Epidemiology Pub Date : 2025-11-01 Epub Date: 2025-10-10 DOI: 10.1016/j.annepidem.2025.10.013

Taryn Lambert , Nikki Stephenson , Janice Skiffington , Donna Slater , Lara M. Leijser , Amy Metcalfe

{"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":"2025-11-01","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}

引用次数: 0

Artificial intelligence and natural language processing of patient narratives to evaluate semaglutide for weight loss 人工智能和患者叙述的自然语言处理评估西马鲁肽的减肥效果。

IF 3 3区医学

Annals of Epidemiology Pub Date : 2025-11-01 Epub Date: 2025-09-14 DOI: 10.1016/j.annepidem.2025.09.003

Akshaya Srikanth Bhagavathula PhD, FACE

{"title":"Artificial intelligence and natural language processing of patient narratives to evaluate semaglutide for weight loss","authors":"Akshaya Srikanth Bhagavathula PhD, FACE","doi":"10.1016/j.annepidem.2025.09.003","DOIUrl":"10.1016/j.annepidem.2025.09.003","url":null,"abstract":"<div><h3>Purpose</h3><div>This study used artificial intelligence (AI) and natural language processing (NLP) to analyze patient reviews of semaglutide, with the goal of better understanding its real-world effectiveness and safety for weight management.</div></div><div><h3>Methods</h3><div>A retrospective, cross-sectional analysis was conducted on 772 user-generated reviews of semaglutide posted on Drugs.com (July 2021–March 2025). Sentiment analysis was performed using a transformer-based BERT model on a five-point scale. Topic modeling with Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) was used to identify dominant themes. Cluster analysis was applied to segment users based on weight loss outcomes and side effect severity. Reviewers (n = 95) that explicitly reporting both weight loss and treatment duration were analyzed for real-world efficacy and safety.</div></div><div><h3>Results</h3><div>Users who took semaglutide more than 60 days reported a mean weight loss of 32.2 ± 3.1 lbs (14.6 kg). Frequently mentioned side effects included nausea (46.9 %), headache (18.4 %), vomiting (14.3 %), fatigue (9.2 %), and dizziness (4.8 %). The highest sentiment scores were observed in the ≤ 30-day group (mean: 3.38). Topic modeling identified themes such as appetite suppression, medication cost and access, and long-term experiences. Clusters analysis revealed distinct user profile, including super-responder group with substantial weight loss and another with more side effects.</div></div><div><h3>Conclusions</h3><div>AI and NLP methods offer valuable tools for analyzing patient-reported outcomes, revealing semaglutide’s real-world efficacy and safety profile for weight management. These findings contribute to ongoing efforts to integrate patient-reported data into post-marketing surveillance and treatment decision-making.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"111 ","pages":"Pages 9-13"},"PeriodicalIF":3.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145076559","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}

引用次数: 0

The association of preterm birth and small for gestational age with recurrent multisite musculoskeletal pain during early and middle adulthood — The Northern Finland Birth Cohort 1966 Study 1966年芬兰北部出生队列研究：早产和小于胎龄与成年早期和中期复发性多部位肌肉骨骼疼痛的关系

IF 3 3区医学

Annals of Epidemiology Pub Date : 2025-11-01 Epub Date: 2025-09-19 DOI: 10.1016/j.annepidem.2025.09.011

Sandra-Sofia Nieminen , Jaro Karppinen , Eero Kajantie , Paulo Ferreira , Eveliina Heikkala

{"title":"The association of preterm birth and small for gestational age with recurrent multisite musculoskeletal pain during early and middle adulthood — The Northern Finland Birth Cohort 1966 Study","authors":"Sandra-Sofia Nieminen , Jaro Karppinen , Eero Kajantie , Paulo Ferreira , Eveliina Heikkala","doi":"10.1016/j.annepidem.2025.09.011","DOIUrl":"10.1016/j.annepidem.2025.09.011","url":null,"abstract":"<div><h3>Purpose</h3><div>Musculoskeletal (MSK) pain is a leading cause of disability and commonly affects multiple locations. Identifying early-life risk factors could enable targeted prevention of multisite MSK pain. This study investigated associations of preterm birth (<37 weeks) and small for gestational age (SGA) with recurrent multisite MSK pain during early and middle adulthood.</div></div><div><h3>Methods</h3><div>Participants in the NFBC1966 were prospectively followed from gestation to 46 years. Exposure data were based on delivery records; the outcome was self-reported. Multinomial logistic regression estimated odds ratios (ORs) and 95 % confidence intervals (CIs) both unadjusted and adjusted [a] for sex, maternal age at birth, pregnancy disorder, maternal smoking, family socioeconomic status, and parity.</div></div><div><h3>Results</h3><div>Among participants, 12 % born prematurely and 16 % born at term reported recurrent multisite MSK pain between 31 and 46 years (aOR 0.79, 95 % CI 0.43–1.46). Using continuous gestational age, a statistically significant association was observed (aOR 1.07, 95 % CI 1.00–1.14). SGA showed no association (aOR 0.94, 95 % CI 0.84–1.04). No associations were found for onset or resolved multisite MSK pain.</div></div><div><h3>Conclusion</h3><div>Preterm birth and SGA were not negatively associated with adult multisite MSK pain. A protective association appeared with continuous gestational age, but small sample sizes warrant cautious interpretation.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"111 ","pages":"Pages 58-64"},"PeriodicalIF":3.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145114910","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}

引用次数: 0

Body mass index inequities among adults in Spain: An intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy of age, sex/gender, immigration status, and education 西班牙成年人身体质量指数不平等：年龄、性别/性别、移民身份和教育的个体异质性和歧视性准确性的交叉多层次分析。

IF 3 3区医学

Annals of Epidemiology Pub Date : 2025-11-01 Epub Date: 2025-09-19 DOI: 10.1016/j.annepidem.2025.09.013

Luisa N. Borrell , Julia Díez , Nerea Lanborena , Sara Yago-Gonzalez , Elena Rodriguez-Alvarez

{"title":"Body mass index inequities among adults in Spain: An intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy of age, sex/gender, immigration status, and education","authors":"Luisa N. Borrell , Julia Díez , Nerea Lanborena , Sara Yago-Gonzalez , Elena Rodriguez-Alvarez","doi":"10.1016/j.annepidem.2025.09.013","DOIUrl":"10.1016/j.annepidem.2025.09.013","url":null,"abstract":"<div><h3>Purpose</h3><div>We aimed to examine intersectional BMI inequities across age, sex/gender, immigration status, and education among adults in Spain.</div></div><div><h3>Methods</h3><div>We analyzed data from 61,844 adults aged ≥ 18 years from the 2014 and 2020 European Health Interview Surveys in Spain and the 2017 Spanish National Health Survey. Using intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (I-MAIHDA) via linear mixed models, we examined BMI inequities across 180 intersectional strata, defined by age, sex/gender, immigration status, education, and survey year, and quantified the contribution of their intersections to BMI inequities.</div></div><div><h3>Results</h3><div>The intersectional strata explained 9.6 % of BMI inequities, with 87.1 % of the between-strata variance explained by age, sex/gender, immigration status, education, and survey year. On average, BMI was higher in older adults, immigrants, and those with lower education but lower in women. Interaction effects revealed that immigrant women had a higher predicted BMI than Spanish-born women. BMI inequities between immigrant and Spanish-born women were greatest for those aged 55–64 with middle and high education.</div></div><div><h3>Conclusion</h3><div>Although most BMI inequities were explained by the social factors considered as inequity axes, interaction effects were present. The latter calls for a universal public health intervention proportionate to the needs of specific groups in the population. I-MAIHDA revealed complex patterns of BMI inequities in Spain, which may inform the interventions needed to address weight-related outcomes.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"111 ","pages":"Pages 74-81"},"PeriodicalIF":3.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145114859","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}

引用次数: 0