International journal of epidemiology最新文献

{"title":"Cohort Profile: The Children's Health in London and Luton (CHILL) cohort.","authors":"Helen E Wood, Rosamund E Dove, Jasmine Chavda, Grainne Colligan, Louise Cross, Harpal Kalsi, James Scales, Ivelina Tsocheva, Sean Beevers, Bill Day, Monica Fletcher, W James Gauderman, Jonathan Grigg, Hajar Hajmohammadi, Frank J Kelly, Borislava Mihaylova, Chris Newby, Gurch Randhawa, Aziz Sheikh, Veronica Toffolutti, Florian Tomini, Esther van Sluijs, Ian S Mudway, Chris J Griffiths","doi":"10.1093/ije/dyaf055","DOIUrl":"https://doi.org/10.1093/ije/dyaf055","url":null,"abstract":"","PeriodicalId":14147,"journal":{"name":"International journal of epidemiology","volume":"54 3","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144180503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Change scores and baseline adjustment: splitting the difference (in differences).","authors":"Oliver Dukes, Zach Shahn, Audrey Renson","doi":"10.1093/ije/dyaf042","DOIUrl":"https://doi.org/10.1093/ije/dyaf042","url":null,"abstract":"<p><p>A lively debate exists on how best to incorporate repeated measurements of an outcome in a statistical analysis. Two popular approaches are change-score analyses (the difference in baseline and follow-up outcome measurements are regressed on the exposure) and baseline adjustment (in which baseline outcomes are included as a covariate in a regression model). In this article, we compare both through the lens of the causal inference literature. We draw in particular on the connections between change-score analyses and 'difference-in-differences'-a popular approach for inferring causal effects in economics and social sciences, which has received less attention in epidemiology. We make practical recommendations for which approach to use, based on one's knowledge or belief about the confounding mechanism.</p>","PeriodicalId":14147,"journal":{"name":"International journal of epidemiology","volume":"54 3","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144022743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maternal pre-pregnancy diabetes and risk of all-cause and cause-specific infant mortality.","authors":"Yafeng Wang, Dagfinn Aune, Leandro F M Rezende, Gerson Ferrari, Xiong Chen, Lei Zhang, Chuanhua Yu, Wentao Huang, Ola Didrik Saugstad, Tore Henriksen, Scott M Nelson","doi":"10.1093/ije/dyaf046","DOIUrl":"https://doi.org/10.1093/ije/dyaf046","url":null,"abstract":"<p><strong>Background: </strong>Maternal pre-pregnancy diabetes is associated with a higher risk of adverse pregnancy outcomes. Few large, cohort studies have assessed associations with a wide large range of causes of infant death.</p><p><strong>Methods: </strong>This retrospective cohort study included all singleton live births to women aged 18-49 years in the US National Vital Statistics System from 2011 to 2020. Multivariable Poisson regression models were used to estimate adjusted relative risks (RRs) with 95% confidence intervals (CIs) for all-cause and cause-specific infant mortality.</p><p><strong>Results: </strong>Of 34  918  803 pregnant women with singleton live births, 302  823 had pre-pregnancy diabetes with 3585 corresponding infant deaths [estimated mortality rate per 10 000 infants with 95% CI was 78.60 (75.96-81.25)] compared with 34  615  980 without pre-pregnancy diabetes and 171  989 corresponding infant deaths [42.63 (42.41-42.86)]. The adjusted RR (95% CI) of pre-pregnancy diabetes compared with no pre-pregnancy diabetes was 1.84 (1.78-1.91) for infant death, 1.89 (1.81-1.97) for neonatal death, 1.85 (1.77-1.94) for early neonatal death, 2.04 (1.87-2.23) for late neonatal death, and 1.75 (1.65-1.86) for postneonatal deaths. The association was stronger (RR: 2.03, 95% CI: 1.88-2.20) with lower maternal age (<25 years) than with higher maternal age (≥40 years) (RR: 1.44, 95% CI: 1.28-1.63). Associations varied across maternal race, smoking, and body mass index. Significantly increased risk was observed for 48 out of 73 specific causes of death.</p><p><strong>Conclusion: </strong>Our findings confirm that maternal pre-pregnancy diabetes is an important risk factor for infant death, encompassing a wide range of causes of death, and suggest that these effects may be systemic given the large number of specific causes of death affected.</p>","PeriodicalId":14147,"journal":{"name":"International journal of epidemiology","volume":"54 3","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144101884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Commentary: Navigating symptom and diagnostic overlap in pneumonia and malaria: insights from the field from the PERCH Study.","authors":"David Torres-Fernandez, Quique Bassat","doi":"10.1093/ije/dyaf063","DOIUrl":"https://doi.org/10.1093/ije/dyaf063","url":null,"abstract":"","PeriodicalId":14147,"journal":{"name":"International journal of epidemiology","volume":"54 3","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144182368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cohort Profile: the Cooperative Health Research in South Tyrol study.","authors":"Rebecca Lundin, Roberto Melotti, Laura Barin, Martin Gögele, Stefano Lombardo, Antonio Fanolla, Paola Zuech, Johannes Rainer, David Emmert, Christian Fuchsberger, Deborah Mascalzoni, Alessandro De Grandi, Francisco S Domingues, Andrew A Hicks, Peter P Pramstaller, Cristian Pattaro","doi":"10.1093/ije/dyaf064","DOIUrl":"10.1093/ije/dyaf064","url":null,"abstract":"","PeriodicalId":14147,"journal":{"name":"International journal of epidemiology","volume":"54 3","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12119133/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adherence to the EAT-Lancet reference diet and risk of type 2 diabetes: results from the NutriNet-Santé cohort study.","authors":"Florine Berthy, Benjamin Allès, Léopold K Fezeu, Denis Lairon, Philippe Pointereau, Mathilde Touvier, Serge Hercberg, Pilar Galan, Julia Baudry, Emmanuelle Kesse-Guyot","doi":"10.1093/ije/dyaf011","DOIUrl":"https://doi.org/10.1093/ije/dyaf011","url":null,"abstract":"<p><strong>Background: </strong>In 2019, the EAT-Lancet Commission proposed a planetary, healthy, and reference diet, designed to reduce the global health-environmental double burden of current dietary patterns. We aim to investigate the association between the EAT-Lancet reference diet and type 2 diabetes (T2D) risk.</p><p><strong>Methods: </strong>This observational and prospective web-based study was conducted using data from 88 964 adults participating to the French NutriNet-Santé cohort 2009-22 (78.8% female; mean (SD) baseline age of participants was 43.8 (14.5) years). Dietary intakes were assessed at least by three repeated 24 hour-dietary records (mean = 6.3 (2.8)). The endpoint was incident T2D. The main exposure was the EAT-Lancet diet index (ELD-I) modeled as a continuous variable and sex-specific quintiles (Qs). Hazard ratios (HRs) and confidence intervals (CIs) were assessed by Cox proportional hazards models adjusted for potential confounders. The body mass index (BMI) role as a potential mediating factor in the relationship was also investigated.</p><p><strong>Results: </strong>During follow-up (median = 8.79 years), 812 incident T2D cases occurred. The ELD-I ranged from -192 to 429 points with a median (interquartile range [IQR]) score of 42.8 (29.1) points. For each 25.7-point increase in ELD-I score (1SD), the risk of T2D is reduced by 11% (HR1SD: 0.89; 95% CI: 0.82-0.96; P = .003). The association studied was mediated up to 61% by BMI.</p><p><strong>Conclusions: </strong>In this large cohort study, an increase in adherence to the EAT-Lancet reference diet was associated with a decreased risk of T2D beyond BMI. These results support previous findings and highlights the interest of the EAT-Lancet reference diet as a healthy and sustainable diet.</p>","PeriodicalId":14147,"journal":{"name":"International journal of epidemiology","volume":"54 3","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143986749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Age at first drink as a predictor of all-cause mortality of middle-aged Koreans.","authors":"Horim A Hwang, Dong Jun Kim, Seongju Kim, Hooyeon Lee","doi":"10.1093/ije/dyaf061","DOIUrl":"https://doi.org/10.1093/ije/dyaf061","url":null,"abstract":"<p><strong>Background: </strong>Early alcohol use is hazardous to health and restrictions to age at first drink (AFD) is a cornerstone of public health policy in many nations. We explored the long-term impact of AFD on mortality after early adulthood in community-dwelling Koreans.</p><p><strong>Methods: </strong>A cohort of 36 159 adults aged 35-64 years from the Korean National Health and Nutrition Examination Survey, linked to cause-of-death statistics, was followed up for a median of 9.9 years. Based on AFD with a cutoff at South Korea's legal drinking age, the participants were divided into three groups: AFD < 19, AFD ≥ 19, and lifetime abstainer. The outcome variable was all-cause mortality.</p><p><strong>Results: </strong>A total of 9723 (26.9%) participants reported AFD < 19. During the follow-up, 1073 (3.0%) participants died. AFD < 19 was associated with an increased risk of mortality than lifetime abstainers (adjusted hazards ratio [aHR] 1.29, 95% confidence interval [CI] 1.02-1.64). Female drinkers with AFD < 19 showed an increased risk of mortality than female lifetime abstainers (aHR 2.02, CI 1.26-3.25). Current drinkers with AFD < 19 was associated with a greater risk of mortality than those with AFD ≥ 19 (aHR 1.20, 95%CI 1.01-1.42). Among participants with hazardous drinking habits, AFD < 19 was associated with a greater risk of mortality than AFD ≥ 19 (aHR 1.32, 95%CI 1.07-1.64).</p><p><strong>Conclusion: </strong>Individuals with AFD < 19 were at greater risk of mortality than lifetime abstainers; earlier AFD was associated with a greater mortality risk than later AFD. Prevention and early intervention programs can improve the long-term health of alcohol-consuming population.</p>","PeriodicalId":14147,"journal":{"name":"International journal of epidemiology","volume":"54 3","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144127446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Protocol for improving equity in quantitative big data cleaning: lessons from longitudinal analysis of electronic health records from underrepresented and marginalized communities","authors":"Zeruiah V Buchanan, Scarlett E Hopkins, Bert B Boyer, Alison E Fohner","doi":"10.1093/ije/dyaf013","DOIUrl":"https://doi.org/10.1093/ije/dyaf013","url":null,"abstract":"Background Large biomedical datasets, including electronic health records (EHRs), are a significant source of epidemiologic data. To prepare an EHR for analysis, there are several data-cleaning approaches; here, we focus on data filtering. Common data-filtering methods employ rules that rely on data from socially constructed dominant populations but are inappropriate for marginalized populations, leading to the loss of valuable data and neglect of underrepresented communities. We propose a novel method based on a phenomenological framework that is more equitable and inclusive, leading to culturally responsive research and discoveries. Methods EHRs from the Yukon-Kuskokwim Health Corporation (YKHC) containing 1 262 035 records from 12 402 unique individuals from 2002 to 2012 were cleaned by using the proposed phenomenological (individual) and common (cohort) data-filtering approach. Within the phenomenological framework, we (i) excluded values that were undeniably biologically impossible for any population, (ii) excludes values that fell outside three standard deviations from the mean value for each individual person, and (iii) used two forms of imputation methods for stable quantitative and qualitative values at the individual level when data were missing. Results Compared with common data-filtering practices, the phenomenological approach retained more observations, participants, and a range of outcomes, allowing a truer representation of the priority population. In sensitivity analyses comparing the results of the raw data, the common approach implemented, and the phenomenological approach applied, we found that the phenomenological approach did not compromise the integrity of the results. Conclusion The phenomenological approach to filtering big data presents an opportunity to better advocate for marginalized communities even when using large datasets that require automated rules for data filtering. Our method may empower researchers who are partnering with communities to embrace large datasets without compromising their commitment to community benefit and respect.","PeriodicalId":14147,"journal":{"name":"International journal of epidemiology","volume":"16 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inconsistent consistency: evaluating the well-defined intervention assumption in applied epidemiological research","authors":"Jerzy Eisenberg-Guyot, Katrina L Kezios, Seth J Prins, Sharon Schwartz","doi":"10.1093/ije/dyaf015","DOIUrl":"https://doi.org/10.1093/ije/dyaf015","url":null,"abstract":"Background According to textbook guidance, satisfying the well-defined intervention assumption is key for estimating causal effects. However, no studies have systematically evaluated how the assumption is addressed in research. Thus, we reviewed how researchers using g-methods or targeted maximum likelihood estimation (TMLE) interpreted and addressed the well-defined intervention assumption in epidemiological studies. Methods We reviewed observational epidemiological studies that used g-methods or TMLE, were published from 2000–21 in epidemiology journals with the six highest 2020 impact factors and met additional criteria. Among other factors, reviewers assessed if authors of included studies aimed to estimate the effects of hypothetical interventions. Then, among such studies, reviewers assessed whether authors discussed key causal-inference assumptions (e.g. consistency or treatment variation irrelevance), how they interpreted their findings and if they specified well-defined interventions. Results Just 20% (29/146) of studies aimed to estimate the effects of hypothetical interventions. Of such intervention-effect studies, almost none (1/29) stated ‘how’ the exposure would be intervened upon; among those that did not state a ‘how’, the ‘how’ mattered for consistency (i.e., for treatment variation irrelevance) in 64% of studies (18/28). Moreover, whereas 79% (23/29) of intervention-effect studies mentioned consistency, just 45% (13/29) interpreted findings as corresponding to the effects of hypothetical interventions. Finally, reviewers determined that just 38% (11/29) of intervention-effect studies had well-defined interventions. Conclusions We found substantial deviations between guidelines regarding meeting the well-defined intervention assumption and researchers’ application of the guidelines, with authors of intervention-effect studies rarely critically examining the assumption’s validity, let alone specifying well-defined interventions.","PeriodicalId":14147,"journal":{"name":"International journal of epidemiology","volume":"16 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel tobacco forecasting model by multiple sociodemographic strata in Australia.","authors":"Samantha Howe, Tim Wilson, Coral Gartner, Tony Blakely, Driss Ait Ouakrim","doi":"10.1093/ije/dyaf038","DOIUrl":"10.1093/ije/dyaf038","url":null,"abstract":"<p><strong>Background: </strong>Australia is one of several countries aiming to achieve a commercial tobacco endgame, with a current target of ≤5% daily smoking prevalence by 2030. Like other jurisdictions, the Australian target ignores large variations in smoking across sociodemographic groups and risks perpetuating current smoking-related inequities. To help mitigate this risk, we calculated future smoking rates under business-as-usual for multiple sociodemographic categories and compared them to the endgame target.</p><p><strong>Methods: </strong>We used a simulated annealing optimization approach to estimate historic daily smoking rates in Australia by six dimensions of sex, age, remoteness, index of relative socioeconomic advantage and disadvantage, and Indigenous status, using multiple datasets from 2001 to 2022-23. We applied logistic regression to the modelled outputs to forecast cohort smoking rates for 30 years.</p><p><strong>Results: </strong>At the population level, daily smoking is expected to reach 7.8% by 2030 under business-as-usual. Of the 15 strata combinations of remoteness and socioeconomic status in the model, only two met the ≤5% target by 2030, with smoking prevalence remaining highest (34.6% in 2030) for people living in the most disadvantaged (remote, SES1) areas.</p><p><strong>Conclusions: </strong>Our modelling suggests that if equity is not at the forefront of Australian tobacco policy, ongoing smoking disparities are likely to continue even if the endgame goal is achieved. Our approach offers a crucial baseline for assessing the impact of tobacco control interventions by different sociodemographic dimensions and presents a methodological framework that could be adapted for analysing smoking-related inequities in other jurisdictions. This framework should also be extended, incorporating uncertainty into modelled estimates.</p>","PeriodicalId":14147,"journal":{"name":"International journal of epidemiology","volume":"54 2","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11961201/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}