Annals of Epidemiology最新文献_第6页

Considerations for Social Networks and Health Data Sharing: An Overview 社交网络和健康数据共享的考虑：概述。

IF 3.3 3区医学

Annals of Epidemiology Pub Date : 2025-02-01 DOI: 10.1016/j.annepidem.2024.12.014

Dana K. Pasquale , Tom Wolff , Gabriel Varela , Jimi Adams , Peter J. Mucha , Brea L. Perry , Thomas W. Valente , James Moody

{"title":"Considerations for Social Networks and Health Data Sharing: An Overview","authors":"Dana K. Pasquale , Tom Wolff , Gabriel Varela , Jimi Adams , Peter J. Mucha , Brea L. Perry , Thomas W. Valente , James Moody","doi":"10.1016/j.annepidem.2024.12.014","DOIUrl":"10.1016/j.annepidem.2024.12.014","url":null,"abstract":"<div><div>The use of network analysis as a tool has increased exponentially as more clinical researchers see the benefits of network data for modeling of infectious disease transmission or translational activities in a variety of areas, including patient-caregiving teams, provider networks, patient-support networks, and adoption of health behaviors or treatments, to name a few. Yet, relational data such as network data carry a higher risk of deductive disclosure. Cases of reidentification have occurred and this is expected to become more common as computational ability increases. Recent data sharing policies aim to promote reproducibility, support replicability, and protect federal investment in the effort to collect these research data by making them available for secondary analyses. However, typical practices to protect individual-level clinical research data may not be sufficiently protective of participant privacy in the case of network data, nor in some cases do they permit secondary data analysis. When sharing data, researchers must balance <em>security, accessibility, reproducibility,</em> and <em>adaptability</em> (suitability for secondary analyses). Here, we provide background about applying network analysis to health and clinical research, describe the pros and cons of applying typical practices for sharing clinical data to network data, and provide recommendations for sharing network data.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"102 ","pages":"Pages 28-35"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142916232","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

Educational Engagement Modules: Furthering the educational mission of the American College of Epidemiology 教育参与模块：促进美国流行病学学院的教育使命

IF 3.3 3区医学

Annals of Epidemiology Pub Date : 2025-02-01 DOI: 10.1016/j.annepidem.2025.01.003

Jeb Jones PhD, Patrick Sean Sullivan DVM, PhD

引用次数: 0

Application of machine learning algorithms in an epidemiologic study of mortality 机器学习算法在死亡率流行病学研究中的应用。

IF 3.3 3区医学

Annals of Epidemiology Pub Date : 2025-02-01 DOI: 10.1016/j.annepidem.2024.12.015

George O. Agogo , Henry Mwambi

{"title":"Application of machine learning algorithms in an epidemiologic study of mortality","authors":"George O. Agogo , Henry Mwambi","doi":"10.1016/j.annepidem.2024.12.015","DOIUrl":"10.1016/j.annepidem.2024.12.015","url":null,"abstract":"<div><h3>Purpose</h3><div>Epidemiologic studies are important in assessing risk factors of mortality. Machine learning (ML) is efficient in analyzing multidimensional data to unravel dependencies between risk factors and health outcomes.</div></div><div><h3>Methods</h3><div>Using a representative sample from the National Health and Nutrition Examination Survey data collected from 2009 to 2016 linked to the National Death Index public-use mortality data through December 31, 2019, we applied logistic, random forests, k-Nearest Neighbors, multivariate adaptive regression splines, support vector machines, extreme gradient boosting, and super learner ML algorithms to study risk factors of all-cause mortality. We evaluated the algorithms using area under the receiver operating curve (AUC-ROC), sensitivity, negative predictive value (NPV) among other metrics and interpreted the results using SHapley Additive exPlanation.</div></div><div><h3>Results</h3><div>The AUC-ROC ranged from 0.80 ─ 0.87. The super learner had the highest AUC-ROC of 0.87 (95 % CI, 0.86 ─ 0.88), sensitivity of 0.86 (95 % CI, 0.84 ─ 0.88) and NPV of 0.98 (95 % CI, 0.98 ─ 0.99). Key risk factors of mortality included advanced age, larger waist circumference, male and systolic blood pressure. Being married, high annual household income, and high education level were linked with low risk of mortality.</div></div><div><h3>Conclusions</h3><div>Machine learning can be used to identify risk factors of mortality, which is critical for individualized targeted interventions in epidemiologic studies.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"102 ","pages":"Pages 36-47"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142933430","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

Disparities in anti-nucleocapsid and anti-spike SARS-CoV-2 antibody prevalence in NYC — April–October 2021 纽约市抗核衣壳和抗刺突SARS-CoV-2抗体流行率的差异- 2021年4 - 10月

IF 3.3 3区医学

Annals of Epidemiology Pub Date : 2025-02-01 DOI: 10.1016/j.annepidem.2024.12.008

Anne Schuster , Erik J. Kopping , Jo-Anne Caton , Emily Spear , Steven Fernandez , Randal C. Fowler , Jing Wu , Scott Hughes , Amber Levanon Seligson , L. Hannah Gould

{"title":"Disparities in anti-nucleocapsid and anti-spike SARS-CoV-2 antibody prevalence in NYC — April–October 2021","authors":"Anne Schuster , Erik J. Kopping , Jo-Anne Caton , Emily Spear , Steven Fernandez , Randal C. Fowler , Jing Wu , Scott Hughes , Amber Levanon Seligson , L. Hannah Gould","doi":"10.1016/j.annepidem.2024.12.008","DOIUrl":"10.1016/j.annepidem.2024.12.008","url":null,"abstract":"<div><h3>Purpose</h3><div>Between April-October 2021, the New York City (NYC) Health Department conducted a serosurvey to assess prevalence of SARS-CoV-2 antibodies in NYC adults as part of continued COVID-19 surveillance efforts. Methods: Whole blood specimens were collected from 1035 adult NYC residents recruited from an annual population-based health surveillance survey. Specimens were tested for the presence of anti-SARS-CoV-2 spike protein (anti-spike) and anti-SARS-CoV-2 nucleocapsid protein (anti-nucleocapsid) antibodies. Results: 91.6 % (95 % CI: 87.45–94.50) had anti-spike antibodies and 30.4 % (95 % CI: 24.78–36.7) had anti-nucleocapsid antibodies. Almost all participants with anti-spike antibodies produced antibodies capable of neutralizing SARS-CoV-2. Overall, anti-spike positivity was lowest (85.9 % [95 % CI: 74.01–92.85) in Hispanic and Latino New York City residents. Anti-nucleocapsid seropositivity was lowest in Asian/Pacific Islander New York City residents (14.1%, 95% CI: 8.0-23.5). Continued disparities persist in SARS-CoV-2 seropositivity regarding ethnic and sociodemographic factors. Conclusions: SARS-CoV-2 seropositivity was high in 2021 in NYC, with evidence of continued inequities associated with seroprevalence.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"102 ","pages":"Pages 1-7"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142903954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Misclassification of opioid-involvement in drug-related overdose deaths in the United States: A scoping review 美国阿片类药物参与药物过量死亡的错误分类：范围审查

IF 3.3 3区医学

Annals of Epidemiology Pub Date : 2025-02-01 DOI: 10.1016/j.annepidem.2024.12.010

Sarah Gutkind , Megan E. Marziali , Emilie Bruzelius , Zachary L. Mannes , Silvia S. Martins , Deborah S. Hasin , Pia M. Mauro

{"title":"Misclassification of opioid-involvement in drug-related overdose deaths in the United States: A scoping review","authors":"Sarah Gutkind , Megan E. Marziali , Emilie Bruzelius , Zachary L. Mannes , Silvia S. Martins , Deborah S. Hasin , Pia M. Mauro","doi":"10.1016/j.annepidem.2024.12.010","DOIUrl":"10.1016/j.annepidem.2024.12.010","url":null,"abstract":"<div><h3>Purpose</h3><div>Most drug-related deaths in the United States (US) in 2022 involved opioids. However, methodological challenges in overdose surveillance may contribute to underestimation of opioid involvement in the overdose crisis. This scoping review aimed to synthesize existing literature to examine the breadth and contributing sources of misclassification of opioid-related overdose deaths.</div></div><div><h3>Methods</h3><div>In October 2022, we searched PubMed, Web of Science, and Scopus for studies on overdose surveillance, death certificates, and medicolegal death investigation (MDI) systems in the US published in 2013–2022. Two reviewers independently screened abstracts, reviewed full-texts, and performed data extraction of study characteristics.</div></div><div><h3>Results</h3><div>We identified 17 studies examining misclassification in drug-related deaths. Across studies, opioid involvement in drug-related deaths was underestimated nationally by 20–35 %. Unspecified drug-related deaths differed by geographic areas and MDI systems and decreased over time. States/counties with coroner MDI systems were more likely to report unspecified overdose deaths than those with medical examiners. Integrating toxicology testing, death scene investigations, and other data with death certificates identified additional opioid-related overdose deaths, particularly those involving heroin.</div></div><div><h3>Conclusions</h3><div>Findings highlight the need for additional resources for surveillance efforts, training for coroners, and data integration to improve reporting of opioid involvement in overdose deaths to inform interventions.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"102 ","pages":"Pages 8-22"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142883191","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 challenges of quantifying the effects of housing on health using observational data 利用观测数据量化住房对健康的影响的挑战。

IF 3.3 3区医学

Annals of Epidemiology Pub Date : 2025-02-01 DOI: 10.1016/j.annepidem.2024.12.013

Ang Li , Kate Mason , Yuxi Li, Rebecca Bentley

引用次数: 0

Associations of early life body size and pubertal timing with breast density and postmenopausal breast cancer risk: A mediation analysis 早期生活体型和青春期时间与乳腺密度和绝经后乳腺癌风险的关联：中介分析。

IF 3.3 3区医学

Annals of Epidemiology Pub Date : 2025-02-01 DOI: 10.1016/j.annepidem.2025.01.004

Dorthe C. Pedersen , Dan Hameiri-Bowen , Julie Aarestrup , Britt W. Jensen , Anne Tjønneland , Lene Mellemkjær , My von Euler-Chelpin , Ilse Vejborg , Zorana J. Andersen , Jennifer L. Baker

{"title":"Associations of early life body size and pubertal timing with breast density and postmenopausal breast cancer risk: A mediation analysis","authors":"Dorthe C. Pedersen , Dan Hameiri-Bowen , Julie Aarestrup , Britt W. Jensen , Anne Tjønneland , Lene Mellemkjær , My von Euler-Chelpin , Ilse Vejborg , Zorana J. Andersen , Jennifer L. Baker","doi":"10.1016/j.annepidem.2025.01.004","DOIUrl":"10.1016/j.annepidem.2025.01.004","url":null,"abstract":"<div><h3>Purpose</h3><div>Whether breast density mediates associations between early life body size and pubertal timing with postmenopausal breast cancer is underexplored.</div></div><div><h3>Methods</h3><div>We studied 33,939 Danish women attending the Capital Mammography Screening Program at ages 50–69 years. Early life anthropometry and pubertal timing information came from the Copenhagen School Health Records Register. Postmenopausal breast cancer information came from the Danish Breast Cancer Group database (n = 833). Breast density (BI-RADS) was categorized as low (n = 25,464; 75 %) or high. Risk ratios (RR) and hazard ratios (HR) were estimated using generalized linear regression and Cox proportional hazards analyses. Counterfactual mediation analyses were conducted.</div></div><div><h3>Results</h3><div>Evidence was limited for associations between birthweight and pubertal timing with breast density or breast cancer. Childhood BMI was inversely associated with high breast density (age 13y, RR=0.77 [0.72–0.81] for a <em>z</em>-score of 0.6 versus 0) and breast cancer (HR=0.90 [0.83–0.96] per <em>z</em>-score). Breast density mediated 37 % (17–170 %) of this association. Although childhood height was associated with breast density and breast cancer, there were few indications of mediation by breast density.</div></div><div><h3>Conclusions</h3><div>Breast density may partially explain the inverse association between childhood BMI and postmenopausal breast cancer, but not the positive association between childhood height and postmenopausal breast cancer.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"102 ","pages":"Pages 68-74"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Duration of exposure to eviction moratoria in the early COVID-19 pandemic and perinatal outcomes: A population-level analysis of US births conceived in March-May 2020 2019冠状病毒病早期大流行期间暂停驱逐的持续时间和围产期结局：对2020年3月至5月美国出生婴儿的人口水平分析

IF 3.3 3区医学

Annals of Epidemiology Pub Date : 2025-02-01 DOI: 10.1016/j.annepidem.2025.01.001

Kaitlyn K. Stanhope PhD , Sara Markowitz PhD , Michael R. Kramer PhD

{"title":"Duration of exposure to eviction moratoria in the early COVID-19 pandemic and perinatal outcomes: A population-level analysis of US births conceived in March-May 2020","authors":"Kaitlyn K. Stanhope PhD , Sara Markowitz PhD , Michael R. Kramer PhD","doi":"10.1016/j.annepidem.2025.01.001","DOIUrl":"10.1016/j.annepidem.2025.01.001","url":null,"abstract":"<div><h3>Objective</h3><div>To estimate associations between the length of state-level eviction moratoria enacted in March and April 2020 in the United States and perinatal outcomes.</div></div><div><h3>Methods</h3><div>We used data from natality files, 2020–2021 to identify individuals with Medicaid or no insurance who conceived in March-May 2020. The exposure was the number of months exposed to a moratorium (0 (referent, no state-level moratoria), 1–2, 3–4, 5 or more). Outcomes included preterm birth (PTB, < 37 weeks gestation), very preterm birth (VPTB, < 32 weeks gestation), low birthweight (LBW, < 2500 g), very low birthweight (VLBW, < 1500 g), primary cesarean, or maternal morbidity. We estimated risk ratios (RRs) using log-binomial regression, including individual, county, and state-level confounders. We conducted several sensitivity analyses to rule out residual state-level confounding including a negative control analysis of 2019 conceptions and difference-in-difference analysis.</div></div><div><h3>Results</h3><div>We included 375,821 births. Following adjustment, having a moratorium in place for 5 or more months was associated with slightly reduced risk of PTB (RR: 0.95, 95 % CI: 0.88, 1.02), VPTB (RR: 0.90, 95 % CI: 0.8–1.01), LBW (RR: 0.95, 95 % CI: 0.9–1.01), and VLBW (RR: 0.91, 95 % CI: 0.81–1.02) compared to states without a moratorium. There was no association with cesarean or maternal morbidity. Sensitivity analyses showed that all or most of the observed associations may be explained by residual state-level confounding.</div></div><div><h3>Conclusions</h3><div>State-level eviction moratoria were associated with improved birth outcomes, yet it is likely that all or most of the observed association is due to other policy actions or characteristics of enacting states.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"102 ","pages":"Pages 48-54"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973140","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

Calculation of lifetime relative risks from summary cohort data and application to calculation of attributable fractions 从汇总队列数据计算终生相对危险度并应用于归因分数的计算。

IF 3.3 3区医学

Annals of Epidemiology Pub Date : 2025-02-01 DOI: 10.1016/j.annepidem.2025.01.008

John Ferguson

{"title":"Calculation of lifetime relative risks from summary cohort data and application to calculation of attributable fractions","authors":"John Ferguson","doi":"10.1016/j.annepidem.2025.01.008","DOIUrl":"10.1016/j.annepidem.2025.01.008","url":null,"abstract":"<div><div>In cohort studies, relative risks are estimated by comparing eventual disease risk in individuals exposed to the risk factor at baseline with similar unexposed individuals. However, such relative risk estimates intrinsically depend on how many of the unexposed individuals develop exposure after baseline and on the ages at which the exposed individuals developed exposure prior to baseline. These factors pertain to the distribution of risk factor incidence in the population, rather than to the causal effect the risk factor has on disease. As such, these cohort relative risk estimates have no straightforward causal interpretation, even after adjustment for confounding. Here, we instead consider initial exposure to the risk factor at differing ages as differing treatments, with corresponding potential outcomes. Subsequently, we define lifetime relative risk as the relative probability of eventual disease comparing initial exposure to the risk factor at differing ages to lifetime non-exposure. We describe a procedure to approximate lifetime relative risks using summary data from published cohort studies, and detail conditions under which such estimation is valid. In addition to being of independent interest, lifetime relative risks are useful in estimating population attributable fractions (PAF)s. In our applied example, we illustrate this connection via application of estimated lifetime relative risks to assess the PAF for incident vascular dementia due to hypertension in the United Kingdom.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"102 ","pages":"Pages 102-113"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143043295","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

Machine learning in public health informatics: Evidence that complex sampling structures may not be needed for prediction models with imbalanced outcomes 公共卫生信息学中的机器学习：结果不平衡的预测模型可能不需要复杂采样结构的证据。

IF 3.3 3区医学

Annals of Epidemiology Pub Date : 2025-02-01 DOI: 10.1016/j.annepidem.2024.12.016

Zhengye Si, Jinpu Li, Emily Leary Ph.D.

{"title":"Machine learning in public health informatics: Evidence that complex sampling structures may not be needed for prediction models with imbalanced outcomes","authors":"Zhengye Si, Jinpu Li, Emily Leary Ph.D.","doi":"10.1016/j.annepidem.2024.12.016","DOIUrl":"10.1016/j.annepidem.2024.12.016","url":null,"abstract":"<div><h3>Purpose</h3><div>The objective of this study is to investigate the predictive ability of machine learning models for imbalanced outcomes from national survey data without the use of sampling weights.</div></div><div><h3>Methods</h3><div>We evaluated the predictive performance of machine learning models on imbalanced outcomes from the US National Health and Nutrition Examination Survey (USNHANES) without using sampling weights. Four machine learning models (support vector machine, random forest, least absolute shrinkage and selection operator regression, and deep neural network) were compared with a logistic model that incorporates the survey's complex sampling design. Three resampling methods (oversampling, undersampling, and combined) were used to address class imbalance during the model training process.</div></div><div><h3>Results</h3><div>For all models, the balanced accuracy was similar (ranging from 0.72 to 0.76) and the specificity was smaller than sensitivity except for random forest. The support vector machine and neural networks performed best with sensitivity (ranging from 0.79 to 0.83), while the random forest had the largest specificity (ranging from 0.86 to 0.96), with one exception. PR-AUC scores and Brier scores were low ranging from 0.2529 to 0.3313 (lower scores worse) and 0.1005–0.3245 (lower scores better), respectively</div></div><div><h3>Conclusions</h3><div>The machine learning models had overall similar predictive capacity to the recommended methods which integrate the complex sampling design for the prediction of osteoarthritis occurrence with USNHANES.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"102 ","pages":"Pages 75-80"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973141","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