Annals of Epidemiology最新文献

{"title":"Validation of ICD-10 diagnostic coding for influenza in the Danish National Patient Registry","authors":"Bo Langhoff Hønge ,&nbsp;Kristoffer Skaalum Hansen ,&nbsp;Marianne Kragh Thomsen ,&nbsp;Lars Østergaard ,&nbsp;Trine Hyrup Mogensen ,&nbsp;Merete Storgaard ,&nbsp;Christian Erikstrup ,&nbsp;Signe Sørup","doi":"10.1016/j.annepidem.2024.12.017","DOIUrl":"10.1016/j.annepidem.2024.12.017","url":null,"abstract":"<div><h3>Background</h3><div>The accuracy of recorded diagnosis codes for hospital admissions due to influenza in the Danish national registries is uncertain. We evaluated positive predictive value (PPV) and sensitivity of ICD-10 codes for influenza by comparing to the reference standard of influenza test results.</div></div><div><h3>Methods</h3><div>Hospital admissions were assessed in the Danish National Patient Registry (DNPR), and influenza test results in the Danish Microbiology Database (MiBa). First, we report the proportion of positive influenza virus tests within seven days of admission among hospital admissions with a discharge influenza ICD-10 code (PPV). Second, we report the proportion with ICD-10 codes for influenza among patients with an admission registered with seven days of a positive influenza virus test (sensitivity).</div></div><div><h3>Results</h3><div>From January 2012 – November 2022 a total of 18,761 admissions were registered with one of the 22 influenza ICD-10 codes in DNPR. Overall, there was a positive influenza test in 16,754 of the admissions (87.9 % = overall PPV, 95 % CI: 87.4–88.3). The PPV was highest for older patient groups (93.7 % in patients &gt;80 years vs. 78.0 % in patients &lt; 11 years), and for admissions that occurred in recent years (95.8 % in 2022 vs. 52.4 % in 2012). Among 33,834 hospitals admissions with a positive influenza test, less than half (n = 16,421, 48.5 % = sensitivity (95 % CI: 48.0 – 49.1 %)) were registered with an influenza ICD-10 code.</div></div><div><h3>Conclusions</h3><div>ICD-10 diagnoses codes have relatively high positive predictive value, but the sensitivity is low. Furthermore, the PPV depend on age and calendar year.</div></div><div><h3>What is new</h3><div><ul><li><span>•</span><span><div>Danish national registries have reasonable positive predictive value for influenza ICD-10 codes.</div></span></li></ul><ul><li><span>•</span><span><div>Positive predictive value varies with time of hospital admission and age of the patient.</div></span></li></ul></div><div><ul><li><span>•</span><span><div>Studies based on ICD-10 codes alone underestimates the number of patients with influenza due to low sensitivity.</div></span></li></ul></div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"102 ","pages":"Pages 62-67"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967122","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}

{"title":"Ecologic study learning module: Gordon et al. (2023), Disparities in preterm birth following the July 1995 Chicago heat wave","authors":"Jeb Jones","doi":"10.1016/j.annepidem.2024.06.010","DOIUrl":"10.1016/j.annepidem.2024.06.010","url":null,"abstract":"<div><div>Educational Engagement Modules (EEMs) are teaching materials for educators and students that facilitate a deeper understanding of key epidemiological methods and concepts. Each EEM poses a series of questions using a recently published paper in <em>Annals of Epidemiology</em> to further understanding of a specific study design and to encourage critical thinking and careful evaluation. This EEM focuses on ecological study designs and references the following article: Gordon M, Casey JA, McBrien H, Gemmill A, Hernández D, Catalano R, Chakrabarti S, Bruckner T. Disparities in preterm birth following the July 1995 Chicago heat wave <span><span>[1]</span></span>.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"102 ","pages":"Pages 122-124"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143334228","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}

{"title":"Cohort and co-sibling study learning module: Crump et al (2023), Preterm or early term birth and risk of attention-deficit/hyperactivity disorder","authors":"Jeb Jones","doi":"10.1016/j.annepidem.2024.06.004","DOIUrl":"10.1016/j.annepidem.2024.06.004","url":null,"abstract":"<div><div>Educational Engagement Modules (EEMs) are teaching materials for educators and students that facilitate a deeper understanding of key epidemiological methods and concepts. Each EEM poses a series of questions using a recently published paper in <em>Annals of Epidemiology</em> to further understanding of a specific study design and to encourage critical thinking and careful evaluation. This EEM focuses on sibling study designs and references the following article: Crump C, Sundquist J, Sundquist K. Preterm or early term birth and risk of attention-deficit/hyperactivity disorder: a national cohort and co-sibling study <span><span>[1]</span></span>.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"102 ","pages":"Pages 119-121"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143334227","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}

{"title":"Educational Engagement Modules: Furthering the educational mission of the American College of Epidemiology","authors":"Jeb Jones PhD,&nbsp;Patrick Sean Sullivan DVM, PhD","doi":"10.1016/j.annepidem.2025.01.003","DOIUrl":"10.1016/j.annepidem.2025.01.003","url":null,"abstract":"","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"102 ","pages":"Pages 114-115"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143334771","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}

{"title":"Application of machine learning algorithms in an epidemiologic study of mortality","authors":"George O. Agogo ,&nbsp;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}

{"title":"Disparities in anti-nucleocapsid and anti-spike SARS-CoV-2 antibody prevalence in NYC — April–October 2021","authors":"Anne Schuster ,&nbsp;Erik J. Kopping ,&nbsp;Jo-Anne Caton ,&nbsp;Emily Spear ,&nbsp;Steven Fernandez ,&nbsp;Randal C. Fowler ,&nbsp;Jing Wu ,&nbsp;Scott Hughes ,&nbsp;Amber Levanon Seligson ,&nbsp;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}

{"title":"Misclassification of opioid-involvement in drug-related overdose deaths in the United States: A scoping review","authors":"Sarah Gutkind ,&nbsp;Megan E. Marziali ,&nbsp;Emilie Bruzelius ,&nbsp;Zachary L. Mannes ,&nbsp;Silvia S. Martins ,&nbsp;Deborah S. Hasin ,&nbsp;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}

{"title":"The challenges of quantifying the effects of housing on health using observational data","authors":"Ang Li ,&nbsp;Kate Mason ,&nbsp;Yuxi Li,&nbsp;Rebecca Bentley","doi":"10.1016/j.annepidem.2024.12.013","DOIUrl":"10.1016/j.annepidem.2024.12.013","url":null,"abstract":"<div><div>Housing is an often overlooked yet fundamental social determinant of health. Like other social epidemiology exposures, housing faces a tension between the promise of modern causal inference methods and the messy reality of complex social processes and reliance on observational data. We use examples from over a decade of research to illustrate some of the key challenges in undertaking causally focused healthy housing research and demonstrate approaches that have been applied to address these challenges. We reflect on the improved understanding these approaches have delivered, and the key gaps and next steps in generating the evidence required to act on housing as a social determinant of health.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"102 ","pages":"Pages 23-27"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142923843","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}

{"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 ,&nbsp;Dan Hameiri-Bowen ,&nbsp;Julie Aarestrup ,&nbsp;Britt W. Jensen ,&nbsp;Anne Tjønneland ,&nbsp;Lene Mellemkjær ,&nbsp;My von Euler-Chelpin ,&nbsp;Ilse Vejborg ,&nbsp;Zorana J. Andersen ,&nbsp;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}

{"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 ,&nbsp;Sara Markowitz PhD ,&nbsp;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, &lt; 37 weeks gestation), very preterm birth (VPTB, &lt; 32 weeks gestation), low birthweight (LBW, &lt; 2500 g), very low birthweight (VLBW, &lt; 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}