American journal of epidemiology最新文献_第10页

Invited commentary: 525 600 calories-how do you measure diet in a year? 特邀评论：五十二万五千六百卡路里，如何衡量一年的饮食？

IF 5 2区医学

American journal of epidemiology Pub Date : 2025-02-05 DOI: 10.1093/aje/kwae218

Deirdre K Tobias

引用次数: 0

Invited commentary: influence of incomplete death information on cumulative risk estimates. 特邀评论：不完整死亡信息对累积风险估算的影响。

IF 5 2区医学

American journal of epidemiology Pub Date : 2025-02-05 DOI: 10.1093/aje/kwae227

Judith J Lok

{"title":"Invited commentary: influence of incomplete death information on cumulative risk estimates.","authors":"Judith J Lok","doi":"10.1093/aje/kwae227","DOIUrl":"10.1093/aje/kwae227","url":null,"abstract":"Censoring at death is the only feasible option if death is not recorded and individuals who died simply no longer contribute visits, such as in the setting of Barberio et al (Am J Epidemiol. 2024;193(9):1281-1290) before they acquired access to mortality information. Censoring at death is known to lead to biased estimates of the probability of the event of interest before time $t$. Barberio et al showed through simulations that this bias increases with increasing mortality. However, when analyzing claims data it is often important to not exclude individuals with shorter life expectancies: An important strength of observational studies is that they allow estimation of treatment effects in more varied populations than are typically included in randomized clinical trials. In this commentary, I derive an analytical expression for the bias and provide 2 upper bounds for the bias. The bounds inform the usefulness of obtaining mortality information. If the probability of death before the event is known to be small, wider CIs can be created using the first bound on the bias; an algorithm is provided. If the bias is large, obtaining mortality information is important. Barberio et al show that obtaining mortality information can be essential in practice. This article is part of a Special Collection on Pharmacoepidemiology.","PeriodicalId":7472,"journal":{"name":"American journal of epidemiology","volume":" ","pages":"336-339"},"PeriodicalIF":5.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141756545","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}

引用次数: 0

Exploring the landscape of social epidemiologic research: a scoping review of AJE publications. 探索社会流行病学研究的前景：AJE 出版物范围审查》。

IF 5 2区医学

American journal of epidemiology Pub Date : 2025-02-05 DOI: 10.1093/aje/kwae211

Koichi Sakakibara, Lorraine T Dean

{"title":"Exploring the landscape of social epidemiologic research: a scoping review of AJE publications.","authors":"Koichi Sakakibara, Lorraine T Dean","doi":"10.1093/aje/kwae211","DOIUrl":"10.1093/aje/kwae211","url":null,"abstract":"As social epidemiology is a growing interdisciplinary field with a broad scope, this scoping review investigated its current landscape based on articles published in the American Journal of Epidemiology. Among 1194 extracted records between 2013 and 2022 submitted under the \"social\" category, we identified 178 accepted articles that had a social factor as a primary exposure. We categorized social exposures into 9 major domains and health outcomes into 8 domains. Study design, population, and authorship were also analyzed. Our findings indicate that social epi studies reflect a range of social exposures, including socioeconomic position (37%); neighborhood and built environment (20%); race, racism, and discrimination (16%); and policy and social welfare (12%). The most frequently studied health outcomes were noncommunicable diseases and chronic conditions (42%), mental health (14%), and maternal and child health outcomes (11%). Most studies had quantitative observational designs and focused on high-income countries, particularly the US contexts. Most authors appeared only once, suggesting a range of voices as contributors. Findings suggest that, to enhance knowledge, social epi could benefit from a greater representation of social factors beyond tangible resources, a broader range of health outcomes, study designs and populations, and low- and middle-income countries.","PeriodicalId":7472,"journal":{"name":"American journal of epidemiology","volume":" ","pages":"543-551"},"PeriodicalIF":5.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141858753","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}

引用次数: 0

Correction to "Cardiovascular disease and all-cause mortality in male twins with discordant cardiorespiratory fitness: a nationwide cohort study". 修正“心血管疾病和心肺健康不一致的男性双胞胎的全因死亡率：一项全国性队列研究”。

IF 5 2区医学

American journal of epidemiology Pub Date : 2025-02-05 DOI: 10.1093/aje/kwae311

Marcel Ballin, Anna Nordström, Peter Nordström

引用次数: 0

Can metformin prevent cancer relative to sulfonylureas? A target trial emulation accounting for competing risks and poor overlap via double/debiased machine learning estimators. 相对于磺脲类药物，二甲双胍能预防癌症吗？通过双重/偏差机器学习估计器考虑竞争风险和重叠性差的目标试验模拟。

IF 5 2区医学

American journal of epidemiology Pub Date : 2025-02-05 DOI: 10.1093/aje/kwae217

Shenbo Xu, Bang Zheng, Bowen Su, Stan Neil Finkelstein, Roy Welsch, Kenney Ng, Zach Shahn

{"title":"Can metformin prevent cancer relative to sulfonylureas? A target trial emulation accounting for competing risks and poor overlap via double/debiased machine learning estimators.","authors":"Shenbo Xu, Bang Zheng, Bowen Su, Stan Neil Finkelstein, Roy Welsch, Kenney Ng, Zach Shahn","doi":"10.1093/aje/kwae217","DOIUrl":"10.1093/aje/kwae217","url":null,"abstract":"There is mounting interest in the possibility that metformin, indicated for glycemic control in type 2 diabetes, has a range of additional beneficial effects. Randomized trials have shown that metformin prevents adverse cardiovascular events, and metformin use has also been associated with reduced cognitive decline and cancer incidence. In this paper, we dig more deeply into whether metformin prevents cancer by emulating target randomized trials comparing metformin to sulfonylureas as first-line diabetes therapy using data from the Clinical Practice Research Datalink, a UK primary-care database (1987-2018). We included 93 353 individuals with diabetes, no prior cancer diagnosis, no chronic kidney disease, and no prior diabetes therapy who initiated use of metformin (n = 79 489) or a sulfonylurea (n = 13 864). In our cohort, the estimated overlap-weighted additive separable direct effect of metformin compared with sulfonylureas on cancer risk at 6 years was -1 percentage point (95% CI, -2.2 to 0.1), which is consistent with metformin's providing no direct protection against cancer incidence or substantial protection. The analysis faced 2 methodological challenges: (1) poor overlap and (2) precancer death as a competing risk. To address these issues while minimizing nuisance model misspecification, we develop and apply double/debiased machine learning estimators of overlap-weighted separable effects in addition to more traditional effect estimates. This article is part of a Special Collection on Pharmacoepidemiology.","PeriodicalId":7472,"journal":{"name":"American journal of epidemiology","volume":" ","pages":"512-523"},"PeriodicalIF":5.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141726727","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}

引用次数: 0

Socioeconomic disparities in healthcare access and implications for all-cause mortality among US adults: a 2000-2019 record linkage study. 美国成年人在获得医疗保健方面的社会经济差异及其对全因死亡率的影响：2000-2019 年记录链接研究》。

IF 5 2区医学

American journal of epidemiology Pub Date : 2025-02-05 DOI: 10.1093/aje/kwae202

Ishnaa Gulati, Carolin Kilian, Charlotte Buckley, Nina Mulia, Charlotte Probst

{"title":"Socioeconomic disparities in healthcare access and implications for all-cause mortality among US adults: a 2000-2019 record linkage study.","authors":"Ishnaa Gulati, Carolin Kilian, Charlotte Buckley, Nina Mulia, Charlotte Probst","doi":"10.1093/aje/kwae202","DOIUrl":"10.1093/aje/kwae202","url":null,"abstract":"The United States (US) has witnessed a notable increase in socioeconomic disparities in all-cause mortality since 2000. While this period is marked by significant macroeconomic and health policy changes, the specific drivers of these mortality trends remain poorly understood. In this study, we assessed healthcare access variables and their association with socioeconomic status (SES)-related differences (exposure) in US all-cause mortality (outcome) since 2000. Our research drew upon cross-sectional data from the National Health Interview Survey (NHIS, 2000-2018), linked to death records from the National Death Index (NDI, 2000-2019; n = 486 257). The findings reveal that the odds of a lack of health insurance and unaffordability of needed medical care were over 2-fold higher among individuals with lower education compared to those with high education, following differential time trends. Moreover, elevated mortality risk was associated with lower education (up to 77%), uninsurance (17%), unaffordability (43%), and delayed care (12%). Uninsurance and unaffordability accounted for 4%-6% of the disparities in time to mortality between low- and high-education groups. These findings were corroborated by income-based sensitivity analyses, emphasizing that inadequate healthcare access partially contributed to socioeconomic disparities in mortality. Effective policies promoting equitable healthcare access are imperative to mitigate socioeconomic disparities in mortality.","PeriodicalId":7472,"journal":{"name":"American journal of epidemiology","volume":" ","pages":"432-440"},"PeriodicalIF":5.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141756578","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}

引用次数: 0

Residential natural hazard risk and mental health effects. 居民区自然灾害风险与心理健康影响。

IF 5 2区医学

American journal of epidemiology Pub Date : 2025-02-05 DOI: 10.1093/aje/kwae200

Kaitlyn G Lawrence, Marina R Sweeney, Emily J Werder, Casey Zuzak, Melanie Gall, Christopher T Emrich, Ferdouz V Cochran, Xinlei Deng, Kate E Christenbury, Ian D Buller, W Braxton Jackson Ii, Lawrence S Engel, Dale P Sandler

{"title":"Residential natural hazard risk and mental health effects.","authors":"Kaitlyn G Lawrence, Marina R Sweeney, Emily J Werder, Casey Zuzak, Melanie Gall, Christopher T Emrich, Ferdouz V Cochran, Xinlei Deng, Kate E Christenbury, Ian D Buller, W Braxton Jackson Ii, Lawrence S Engel, Dale P Sandler","doi":"10.1093/aje/kwae200","DOIUrl":"10.1093/aje/kwae200","url":null,"abstract":"Mental health effects are frequently reported following natural disasters. However, little is known about effects of living in a hazard-prone region on mental health. We analyzed data from 9312 Gulf Long-term Follow-up Study participants who completed standardized mental health questionnaires including the Patient Health Questionnaire-9 (depression = score ≥10), Generalized Anxiety Disorder Questionnaire-7 (anxiety = score ≥10), and Primary Care PTSD Screen (PTSD = score ≥3). Geocoded residential addresses were linked to census-tract level natural hazard risk scores estimated using the National Risk Index (NRI). We considered an overall risk score representing 18 natural hazards, and individual scores for hurricanes, heatwaves, coastal flooding, and riverine flooding. Log binomial regression estimated prevalence ratios (PRs) and 95% confidence intervals (CIs) for associations between risk scores (quartiles) and mental health outcomes. Increasing hurricane and coastal flooding scores were associated with all mental health outcomes in a suggestive exposure-response manner. Associations were strongest for PTSD, with PRs for the highest vs lowest quartile of hurricane and coastal flooding risks of 2.29 (95% CI, 1.74-3.01) and 1.59 (95% CI, 1.23-2.05), respectively. High heatwave risk was associated with anxiety (PR = 1.25; 95% CI, 1.12-1.38) and depression (PR = 1.19; 95% CI, 1.04-1.36) and suggestively with PTSD (PR = 1.20; 95% CI, 0.94-1.52). Results suggest that living in areas prone to natural disasters is one factor associated with poor mental health status. This article is part of a Special Collection on Environmental Epidemiology.","PeriodicalId":7472,"journal":{"name":"American journal of epidemiology","volume":" ","pages":"349-361"},"PeriodicalIF":5.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141747155","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}

引用次数: 0

Caregiver burden and risk of epithelial ovarian cancer in the Nurses' Health Studies. 护士健康研究》中护理人员的负担与上皮性卵巢癌的风险。

IF 5 2区医学

American journal of epidemiology Pub Date : 2025-02-05 DOI: 10.1093/aje/kwae185

Mollie E Barnard, Elizabeth M Poole, Tianyi Huang, Anil K Sood, Laura D Kubzansky, Shelley S Tworoger

{"title":"Caregiver burden and risk of epithelial ovarian cancer in the Nurses' Health Studies.","authors":"Mollie E Barnard, Elizabeth M Poole, Tianyi Huang, Anil K Sood, Laura D Kubzansky, Shelley S Tworoger","doi":"10.1093/aje/kwae185","DOIUrl":"10.1093/aje/kwae185","url":null,"abstract":"Psychosocial stress may increase ovarian cancer risk and accelerate disease progression. We examined the association between caregiver burden, a common stressor, and risk of epithelial ovarian cancer. We prospectively followed 67 724 women in the Nurses' Health Study (1992-2012) and 70 720 women in the Nurses' Health Study II (2001-2009) who answered questions on informal caregiving (ie, caregiving outside of work). Women who reported no informal caregiving were considered noncaregivers, while, among women who provided care outside of work, caregiver burden was categorized by time spent caregiving and perceived stress from caregiving. For the 34% of women who provided informal care for ≥15 hours per week, 42% described caregiving as moderately to extremely stressful. Pooled multivariate analyses indicated no difference in ovarian cancer risk for women providing ≥15 hours of care per week compared to noncaregivers (hazard ratio [HR] = 0.96; 95% confidence interval [CI], 0.79-1.18), and no association was evident for women who reported moderate or extreme stress from caregiving compared to noncaregivers (HR = 0.96; 95% CI, 0.75-1.22). Together with prior work evaluating job strain and ovarian cancer risk, our findings suggest that, when evaluating a stressor's role in cancer risk, it is critical to consider how the stressor contributes to the overall experience of distress. This article is part of a Special Collection on Gynecological Cancer.","PeriodicalId":7472,"journal":{"name":"American journal of epidemiology","volume":" ","pages":"362-369"},"PeriodicalIF":5.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11815502/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141553977","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}

引用次数: 0

Identifying signature features of epidemic diseases from 19th century all-cause mortality data. 从 19 世纪全因死亡率数据中识别流行病的特征。

IF 5 2区医学

American journal of epidemiology Pub Date : 2025-02-05 DOI: 10.1093/aje/kwae187

Rasmus Kristoffer Pedersen, Mathias Mølbak Ingholt, Maarten Van Wijhe, Viggo Andreasen, Lone Simonsen

{"title":"Identifying signature features of epidemic diseases from 19th century all-cause mortality data.","authors":"Rasmus Kristoffer Pedersen, Mathias Mølbak Ingholt, Maarten Van Wijhe, Viggo Andreasen, Lone Simonsen","doi":"10.1093/aje/kwae187","DOIUrl":"10.1093/aje/kwae187","url":null,"abstract":"Deadly epidemics leave distinct marks on all-cause mortality. When cause-specific health data are unavailable, studies of all-cause mortality may be necessary for understanding epidemic and pandemic diseases in history. Here, we identify and catalog every major epidemic in Denmark during the 100-year period between 1815 and 1915, based on a recently digitized and compiled data set of all 4 million burials during the period. Although the data set lacks specific information on cause of death, we were able to determine plausible etiology for the majority of 418 identified mortality crises that had more than 50 excess deaths. Epidemiologic methods, data analysis, consultation of historical sources, and investigation of the signature features of age patterns, seasonality, timing, and geography were used. The identified epidemics included, among others, pandemic influenza, cholera outbreaks in 1853 and 1857, and annually repeating epidemics during the period 1826-1832. Although these epidemics have been discussed elsewhere, our work presents a different view of these epidemics, based solely on all-cause mortality. Some of the identified epidemics were caused by pathogens that still affect us in modern times. In low-income modern settings for which representative population health data may be unavailable, the use of mortality data to determine the signature features may guide policy and improve future mitigation strategies.","PeriodicalId":7472,"journal":{"name":"American journal of epidemiology","volume":" ","pages":"460-468"},"PeriodicalIF":5.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11815498/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141615729","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}

引用次数: 0

Invited commentary: deep learning-methods to amplify epidemiologic data collection and analyses. 深度学习--扩大流行病学数据收集和分析的方法。

IF 5 2区医学

American journal of epidemiology Pub Date : 2025-02-05 DOI: 10.1093/aje/kwae215

D Alex Quistberg, Stephen J Mooney, Tolga Tasdizen, Pablo Arbelaez, Quynh C Nguyen

{"title":"Invited commentary: deep learning-methods to amplify epidemiologic data collection and analyses.","authors":"D Alex Quistberg, Stephen J Mooney, Tolga Tasdizen, Pablo Arbelaez, Quynh C Nguyen","doi":"10.1093/aje/kwae215","DOIUrl":"10.1093/aje/kwae215","url":null,"abstract":"Deep learning is a subfield of artificial intelligence and machine learning, based mostly on neural networks and often combined with attention algorithms, that has been used to detect and identify objects in text, audio, images, and video. Serghiou and Rough (Am J Epidemiol. 2023;192(11):1904-1916) presented a primer for epidemiologists on deep learning models. These models provide substantial opportunities for epidemiologists to expand and amplify their research in both data collection and analyses by increasing the geographic reach of studies, including more research subjects, and working with large or high-dimensional data. The tools for implementing deep learning methods are not as straightforward or ubiquitous for epidemiologists as traditional regression methods found in standard statistical software, but there are exciting opportunities for interdisciplinary collaboration with deep learning experts, just as epidemiologists have with statisticians, health care providers, urban planners, and other professionals. Despite the novelty of these methods, epidemiologic principles of assessing bias, study design, interpretation, and others still apply when implementing deep learning methods or assessing the findings of studies that have used them.","PeriodicalId":7472,"journal":{"name":"American journal of epidemiology","volume":" ","pages":"322-326"},"PeriodicalIF":5.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11815488/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141625701","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}

引用次数: 0