Journal of epidemiology and population health最新文献

{"title":"Predicting hepatocellular carcinoma in people with hepatitis B: a comparison between Cox proportional hazard and machine learning models.","authors":"Clémence Ramier, Marie Guyomard, Camelia Protopopescu, Fabrice Carrat, Marc Bourlière, Patrizia Carrieri, Raquel Urena","doi":"10.1016/j.jeph.2026.203387","DOIUrl":"https://doi.org/10.1016/j.jeph.2026.203387","url":null,"abstract":"<p><strong>Background and aim: </strong>Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, with chronic hepatitis B virus (HBV) infection being a major risk factor. To date, existing predictive scores of HCC are mainly based on traditional Cox proportional hazard (CPH) models. This study aimed to compare the variable selection process and performance of CPH models with those of machine learning (ML) and deep learning (DL) algorithms in predicting HCC among patients with chronic HBV infection.</p><p><strong>Methods: </strong>We used data from 4,370 individuals with chronic HBV infection enrolled in the French prospective multicentre ANRS CO22 HEPATHER cohort, of which 56 (1.3%) developed an HCC. Two published CPH-based scores (ADAPTT and SADAPTT) were compared to Random Survival Forest (RSF), Survival Support Vector Machine (SVM), Survival XGBoost, and DeepSurv algorithms. Models were evaluated using Harrell's C-index, Inverse-Probability-of-Censoring Weighting win ratio statistic, and time-dependent area under the ROC curve at 3, 5, and 8 years. The same set of covariables was used to build all the models.</p><p><strong>Results: </strong>CPH models demonstrated similar or higher performances (C-index [95% confidence interval]: 0.84 [0.82-0.85]) for HCC prediction compared to ML and DL models, with less overfitting. Survival SVM and RSF performed similarly (0.81 [0.79-0.83] and 0.81 [0.79-0.82], respectively) without outperforming CPH models. Variable selection was consistent across top-performing models, though CPH models more effectively captured the predictive value of certain behavioural factors, such as soft drink intake.</p><p><strong>Conclusions: </strong>In this dataset with a limited sample size and strongly imbalanced outcome, traditional CPH models provided robust, interpretable, and computationally efficient predictions for HCC risk. ML and DL methods did not outperform traditional models, reinforcing the validity of traditional statistical approaches in small to medium datasets.</p>","PeriodicalId":517428,"journal":{"name":"Journal of epidemiology and population health","volume":"74 4","pages":"203387"},"PeriodicalIF":0.0,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147655649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Area-based socioeconomic inequalities in cervical cancer screening coverage in France.","authors":"Céline Audiger, Edouard Chatignoux, Irène Rasia, Delphine Serra-Jezewski, Cécile Quintin, Agnès Rogel, Stéphanie Barré, Françoise F Hamers","doi":"10.1016/j.jeph.2026.203350","DOIUrl":"https://doi.org/10.1016/j.jeph.2026.203350","url":null,"abstract":"<p><strong>Background and objectives: </strong>The objective of this study is to assess spatial and socioeconomic disparities in cervical cancer screening (CCS) coverage in France before the implementation of the national organized screening program.</p><p><strong>Materials and methods: </strong>This ecological study analyzed CCS coverage among 17.2 million women aged 25-65 years residing in ∼35,000 municipalities in mainland France from January 2016 to June 2019. Screening coverage was estimated using universal health insurance data. Socioeconomic deprivation was measured using the French Deprivation Index (FDep), and access to care was assessed using the Access to General Practitioner (AGP) index. Descriptive spatial analyses and a generalized hierarchical binomial model were used to examine the impact of deprivation and healthcare provision on screening participation, while accounting for geographic variability.</p><p><strong>Results: </strong>Screening coverage varied widely across departments (47.5%-68.0%) and within departments, with a strong social gradient (62.5% in the least deprived quintile vs. 52.4% in the most deprived). FDep explained 34.1% of municipal variations in screening, but a significant portion remained unexplained (random effect: 21 percentage points). The AGP index was weakly associated with participation (+2.2 percentage points between the 90th and 10th percentiles. The screening gap related to deprivation (11.3 percentage points) was smaller than the unexplained variability (15.6).</p><p><strong>Conclusion: </strong>Our study highlights significant spatial and social disparities in CCS participation before the national program rollout. While organized screening programs were associated with higher participation, inequalities persisted. Addressing these disparities requires targeted interventions and structural changes in the CCS programme. Future evaluations will be essential to determine whether the national CCS program reduces these disparities and improves screening equity.</p>","PeriodicalId":517428,"journal":{"name":"Journal of epidemiology and population health","volume":"74 4","pages":"203350"},"PeriodicalIF":0.0,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147648110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trends in Firearm-related Mortality in Pennsylvania, 2012-2023.","authors":"Sana Muazzam, Geoffrey F Ernst, Suzanne Hoedemaker, A-Mac Harris, Rashad Salters, Jun Yang, Brian Wright","doi":"10.1016/j.jeph.2026.203366","DOIUrl":"https://doi.org/10.1016/j.jeph.2026.203366","url":null,"abstract":"<p><strong>Introduction: </strong>Firearm-related mortality remains a public health concern in the United States (US). This study examines firearm-related mortality trends in Pennsylvania from 2012 to 2023, with an emphasis on demographic disparities.</p><p><strong>Methods: </strong>Firearm-related mortality data was obtained from the Pennsylvania Enterprise Data Dissemination Informatics Exchange, categorizing deaths as intentional (suicide, homicide) or unintentional based on ICD-10 codes. Crude mortality rates were analyzed by sex, age, race/ethnicity, and county. Annual percentage changes were computed to assess trends, and statistical significance was determined using chi-square tests and linear regression models.</p><p><strong>Results: </strong>From 2012 to 2023 in Pennsylvania, there were 19,470 firearm-related deaths, including 11,520 suicides and 7446 homicides. Firearms accounted for 51.4% of all suicides (11,520 of 22,395) and 76.8% of all homicides (7446 of 9691). The proportion of firearm-related suicide was significantly higher among males compared to females (57.2% [10,151/17,740] vs 29.4% [1369/4654]; p < 0.001) and among Whites compared to Blacks (52.7% [10,514/19,939] vs 47.4% [655/1381]; p < 0.001). Among racial subgroups, the firearm-related suicide rate increased among Whites, Blacks, and Hispanics, however, the statistically significant increase over time occurred among Whites (p = 0.011) and Blacks (p < 0.001). The proportion of firearm-related homicide rate mortality was significantly higher among males compared to females (81.3% [6439/7924] vs 57.0% [1007/1766]; p < 0.001) and among Blacks compared to Whites (87.3% [5126/5875] vs 54.5% [1505/2760]). Subgroup analysis showed the highest rate increase was among Black males aged 40-59 years (122.7%).</p><p><strong>Conclusions: </strong>Firearm-related mortality rates increased substantially in Pennsylvania in the past decade, with disparities observed in subgroup populations. Firearm safety policies, mental health support, and violence prevention programs are needed to reduce firearm-related mortality in Pennsylvania.</p>","PeriodicalId":517428,"journal":{"name":"Journal of epidemiology and population health","volume":"74 4","pages":"203366"},"PeriodicalIF":0.0,"publicationDate":"2026-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147624931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The longitudinal impact of childhood vision impairment on health, education, physical activity, and social outcomes: A systematic review.","authors":"Masoud Rahmati, Lee Smith, Mapa Prabhath Piyasena, Sooji Lee, Yi Deun Jeong, Laurent Boyer, Cao Duy Nguyen, Huyen Do, Bach Xuan Tran, Guillaume Fond, Dong Keon Yon, Hayeon Lee, Bastien Boussat, Tarnjit Sehmbi, Shahina Pardhan","doi":"10.1016/j.jeph.2026.203371","DOIUrl":"https://doi.org/10.1016/j.jeph.2026.203371","url":null,"abstract":"<p><strong>Introduction: </strong>This systematic review aims to investigate the broader implications of vision impairment in children, aiming to provide a more comprehensive understanding of the impact of loss, informing development of effective strategies to control preventable vision impairment through a life-course approach. Our comprehensive literature search revealed no existing systematic reviews directly addressing this review question.</p><p><strong>Methods: </strong>We conducted a systematic search of the relevant literature using electronic databases searches including PubMed, MEDLINE (Ovid), CENTRAL, and EMBASE from inception of databases up to 20th March 2024 following PRISMA guidelines. Participants included children with vision impairment. The articles were initially screened for title and abstract, and then for full-text reports by two independent reviewers. Study quality was assessed. The outcomes were mortality, mental health, physical health, education, physical activity, and social outcomes. We synthesized and presented the results narratively to provide a comprehensive and structured interpretation of the findings.</p><p><strong>Results: </strong>Seven articles (57,768 participants, 3-18 years) met the eligibility criteria and were included in the systematic review. Included studies were published between 2000 to 2024 with a median follow-up of 15 years. Vision impairment was linked to a heightened risk of early mortality, and adverse impacts on both physical health and mental health that included increased symptoms of anxiety or depression, and the development of various conditions including metabolic syndrome in adulthood. Vision impairment also had a negative impact on academic performance, lower physical activity, and compromised social outcomes in adolescence and adulthood. The severity of vision impairment was associated with higher magnitude of these effects, with severe impairment having more pronounced impact.</p><p><strong>Conclusion: </strong>Given the negative associations of vision impairment with various aspects of children's lives in the long term, it is vital that the negative effects of vision impairment are understood and mitigated against as they impact beyond childhood.</p>","PeriodicalId":517428,"journal":{"name":"Journal of epidemiology and population health","volume":"74 3","pages":"203371"},"PeriodicalIF":0.0,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147619153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comment on \"Follow-up of cancer incidence associated with smoke-related PM_2.5 exposure due to a coal mine fire\".","authors":"Amit Singh Pawaiya, Sushma Narsing Katkuri, Hariharan Srinivasan","doi":"10.1016/j.jeph.2026.203518","DOIUrl":"https://doi.org/10.1016/j.jeph.2026.203518","url":null,"abstract":"","PeriodicalId":517428,"journal":{"name":"Journal of epidemiology and population health","volume":"74 3","pages":"203518"},"PeriodicalIF":0.0,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147617330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comment on adherence to 24-hour movement guidelines in youth with chronic health conditions.","authors":"Mark A Ferro, Alex Luther, Danielle Fearon","doi":"10.1016/j.jeph.2026.203348","DOIUrl":"https://doi.org/10.1016/j.jeph.2026.203348","url":null,"abstract":"","PeriodicalId":517428,"journal":{"name":"Journal of epidemiology and population health","volume":"74 4","pages":"203348"},"PeriodicalIF":0.0,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147617337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The steps of constructing and validating an algorithm to identify chronic kidney disease patients in medical administrative databases.","authors":"Cécile Couchoud, Guillaume Bouzille, Juliette Piveteau, Mathilde Lassalle, Hélène Lazareth, Aghiles Hamroun, Olivier Moranne, Sahar Bayat","doi":"10.1016/j.jeph.2026.203370","DOIUrl":"10.1016/j.jeph.2026.203370","url":null,"abstract":"<p><p>Chronic kidney disease (CKD) represents a heavy global health burden associated with increased mortality and morbidity and high economic impact. Chronic kidney disease, which is largely asymptomatic and is diagnosed based on laboratory tests, is particularly difficult to identify in medical-administrative databases in the absence of laboratory results and no specific medications or procedures. The aim of this paper is to describe the progressive stages of constructing and validating an algorithm for targeting chronic kidney disease in the French medical administrative databases SNDS . A consortium of experts in nephrology, kidney epidemiology and healthcare claims databases, referred to as group \"REDSIAM Kidney Disease\", collaborated to design a practical algorithm for assessing the probability of chronic kidney disease cases likelihood through a combination of items associated with the CKD care pathway. The performance of the RENALGO-EXPERT algorithm differs significantly depending on the population and the databases used. Sensitivity tends to improve in more at-risk populations. However, at this stage, the results are not very satisfactory. To improve case detection performance and in the hope of capturing weak signals overlooked by experts, a project using machine learning methods was devised, RENALGO-IA.</p>","PeriodicalId":517428,"journal":{"name":"Journal of epidemiology and population health","volume":"74 2","pages":"203370"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147373652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Algorithms for the identification of asthma patients in healthcare administrative databases: A systematic review.","authors":"Raphaëlle Curmin, Yuriko Iwatsubo, Lucile Dheyriat, Laure Com-Ruelle, Marie-Christine Delmas, Camille Taillé, Annick Cohen-Akenine, Sylvie Guillo","doi":"10.1016/j.jeph.2026.203373","DOIUrl":"10.1016/j.jeph.2026.203373","url":null,"abstract":"<p><strong>Background: </strong>Healthcare administrative databases have many advantages for conducting studies on asthma. However, identifying asthma patients in these databases requires using algorithms based on reimbursement data.</p><p><strong>Objective: </strong>The main objective of this study was to describe published algorithms identifying asthma patients in healthcare administrative databases.</p><p><strong>Methods: </strong>We performed a systematic review of the studies using an algorithm to identify asthma patients from healthcare administrative databases, and published between 01/01/2016 and 31/12/2020 in Pubmed®. Data related to the characteristics of the studies, the algorithms used, and their validation were extracted using a standardized form.</p><p><strong>Results: </strong>Three hundred and three studies were selected totaling 471 algorithms including 266 (56.5%) identifying asthma patients without details regarding severity or control, 41 (8.7%) identifying asthma exacerbations and 99 (21.0%) characterizing asthma severity or control. Among the 266 algorithms identifying asthma patients, we found a total of 138 \"different\" algorithms and most of them used only diagnostic codes - from hospital or ambulatory data (n = 64, 46.4%) or combined diagnostic codes with drug dispensations (n = 36, 26.1%). Only 21.1% of the 266 algorithms used were reported as validated.</p><p><strong>Conclusion: </strong>Many algorithms for identifying cases of asthma are available; the choice of an algorithm should be based on its relevance according to the objective of the study, the type of asthma, the type of healthcare administrative database, and its validation.</p>","PeriodicalId":517428,"journal":{"name":"Journal of epidemiology and population health","volume":"74 2","pages":"203373"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147518098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying cancer in the French National Health Data System (SNDS): an updated scoping review of algorithms, validation and applications.","authors":"Audran Hylebos, Juste Goungounga, Lydia Guittet, Hadrien Le Mer, Mohamed Ait-Ali, Aya Ajrouche, Sophie Houzard, Neige Journy, Sofiane Kab, Olivier Laurent, Neda Madani, Choisie Mukakalisa, Mireille Pellicer, Christine Le Bihan, Fabienne Seguret, Romain Varnier","doi":"10.1016/j.jeph.2026.203364","DOIUrl":"10.1016/j.jeph.2026.203364","url":null,"abstract":"<p><strong>Background: </strong>Identifying cancer cases in the French National Health Data System (SNDS) is essential for real-world oncology research, yet operational definitions remain heterogeneous and few algorithms have been validated. An initial ReDSiam review in 2017 mapped early practices. Since then, the use of SNDS data in oncology has expanded considerably, warranting an updated synthesis.</p><p><strong>Methods: </strong>We conducted a scoping review following the PRISMA-ScR guidelines. A comprehensive MEDLINE search identified peer-reviewed studies using SNDS-based algorithms to detect cancer cases up to November 2024. A structured extraction grid captured algorithm characteristics, study applications and validation procedures.</p><p><strong>Results: </strong>Among 233 included studies, publication volume increased sharply over time. Algorithms showed substantial heterogeneity in operational definition. Most studies focused on incident cancer detection using hospital data, while outpatient and long-term illness data were increasingly incorporated. Only 6% of studies reported validation against a gold standard, and validated algorithms remained limited to a few cancer sites. More recent publications more frequently provided code lists and supplementary materials, improving transparency.</p><p><strong>Conclusions: </strong>The use of the SNDS in oncology has expanded substantially, but validated and standardised case-identification algorithms remain scarce. Strengthening validation efforts, improving access to reference data, ensuring regular algorithm updates and fostering collaboration between data producers, methodologists and clinicians are critical to enhance methodological consistency and reproducibility.</p>","PeriodicalId":517428,"journal":{"name":"Journal of epidemiology and population health","volume":"74 2","pages":"203364"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147482986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geographic information in the French national health data system: Key challenges for territorial health studies.","authors":"C Leroy, N Courtejoie, M Espagnacq, G Desjeux, E Laurent, J Chesneau, N Thomas","doi":"10.1016/j.jeph.2026.203374","DOIUrl":"10.1016/j.jeph.2026.203374","url":null,"abstract":"<p><strong>Backgrounds: </strong>Accounting for geographical factors is essential when studying patient care pathways, assessing disease prevalence, and analysing social and territorial inequalities in health. Breaking down health data by geographic areas is therefore a crucial issue for public policy and health management at the local level. However, the characteristics of the geographic information used in the analysis of health data from the French National Health Data System (SNDS) remains poorly documented in studies and largely absent from publications.</p><p><strong>Methods: </strong>A group of experts from the RedSiam network's task force on methodology explored the multiple geographical dimensions within the SNDS (place of residence, place of care, place of death, etc.) by conducting a documentary and technical analysis of its main health administrative databases RESULTS: This collective expertise highlights the challenges associated with the spatialisation of health data, both in terms of quality and reliability. Correction methods and standardisation tools are provided to enhance the use of geographic data and to help users understand their limitations. In addition, the task force's findings have been summarised from a decision-support perspective to guide SNDS users in selecting the geographic information best suited to their objectives in epidemiology or health services research.</p><p><strong>Conclusions: </strong>This work offers a valuable methodological framework for researchers and analysts who incorporate territorial aspects into their public health analyses.</p>","PeriodicalId":517428,"journal":{"name":"Journal of epidemiology and population health","volume":"74 2","pages":"203374"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147494991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}