Annals of Epidemiology最新文献

英文中文

Validation of ICD-10 diagnostic coding for influenza in the Danish National Patient Registry 丹麦国家患者登记处对ICD-10流感诊断编码的验证。

IF 3.3 3区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Annals of Epidemiology

Pub Date : 2025-02-01 DOI: 10.1016/j.annepidem.2024.12.017

Bo Langhoff Hønge , Kristoffer Skaalum Hansen , Marianne Kragh Thomsen , Lars Østergaard , Trine Hyrup Mogensen , Merete Storgaard , Christian Erikstrup , Signe Sørup

Background

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.

Methods

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).

Results

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 >80 years vs. 78.0 % in patients < 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.

Conclusions

ICD-10 diagnoses codes have relatively high positive predictive value, but the sensitivity is low. Furthermore, the PPV depend on age and calendar year.

What is new

•
Danish national registries have reasonable positive predictive value for influenza ICD-10 codes.

•
Positive predictive value varies with time of hospital admission and age of the patient.

•
Studies based on ICD-10 codes alone underestimates the number of patients with influenza due to low sensitivity.

背景：在丹麦国家登记中，因流感入院的诊断代码记录的准确性是不确定的。通过与流感检测结果参考标准的比较，评价ICD-10编码对流感的阳性预测值（PPV）和敏感性。方法：在丹麦国家患者登记（DNPR）中评估住院情况，并在丹麦微生物数据库（MiBa）中评估流感检测结果。首先，我们报告了出院流感ICD-10代码（PPV）的住院患者入院后7天内流感病毒检测阳性的比例。其次，我们报告了在流感病毒检测呈阳性（敏感性）7天的住院患者中使用ICD-10流感代码的比例。结果：2012年1月至2022年11月，共有18,761例入院患者登记了DNPR的22种流感ICD-10代码之一。总体而言，16754名入院患者的流感检测呈阳性（87.9% =总PPV， 95% CI: 87.4-88.3）。老年患者组的PPV最高（80岁以下患者为93.7%，11岁以下患者为78.0%），近年入院患者的PPV最高（2022年为95.8%，2012年为52.4%）。在流感检测呈阳性的33,834家入院医院中，不到一半（n=16,421, 48.5% =敏感性（95% CI: 48.0 - 49.1%））登记了流感ICD-10代码。结论：ICD-10诊断代码具有较高的阳性预测值，但敏感性较低。此外，PPV取决于年龄和日历年。最新消息：

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引用次数: 0

Ecologic study learning module: Gordon et al. (2023), Disparities in preterm birth following the July 1995 Chicago heat wave

IF 3.3 3区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Annals of Epidemiology

Pub Date : 2025-02-01 DOI: 10.1016/j.annepidem.2024.06.010

Jeb Jones

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 Annals of Epidemiology 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 [1].

引用次数: 0

Cohort and co-sibling study learning module: Crump et al (2023), Preterm or early term birth and risk of attention-deficit/hyperactivity disorder

IF 3.3 3区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Annals of Epidemiology

Pub Date : 2025-02-01 DOI: 10.1016/j.annepidem.2024.06.004

Jeb Jones

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 Annals of Epidemiology 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 [1].

引用次数: 0

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 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

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

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 security, accessibility, reproducibility, and adaptability (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.

网络分析作为一种工具的使用呈指数增长，因为越来越多的临床研究人员看到了网络数据在各种领域（包括患者护理团队、提供者网络、患者支持网络和健康行为或治疗的采用）中对传染病传播建模或转化活动的好处。然而，网络数据等关系数据具有较高的演绎披露风险。重新识别的情况已经发生，随着计算能力的提高，这种情况预计会变得更加普遍。最近的数据共享政策旨在促进再现性，支持可复制性，并通过使这些研究数据可用于二次分析来保护联邦在收集这些研究数据方面的投资。然而，在网络数据的情况下，保护个人层面临床研究数据的典型做法可能不足以保护参与者的隐私，在某些情况下也不允许二次数据分析。当共享数据时，研究人员必须平衡安全性、可访问性、可重复性和适应性（二次分析的适用性）。本文介绍了将网络分析应用于健康和临床研究的背景，描述了将临床数据共享应用于网络数据的典型实践的利弊，并提出了共享网络数据的建议。

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引用次数: 0

Associations of pregnancy timing relative to the COVID-19 pandemic, maternal SARS-CoV-2 infection, and adverse perinatal outcomes

IF 3.3 3区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Annals of Epidemiology

Pub Date : 2025-02-01 DOI: 10.1016/j.annepidem.2025.01.006

Maria Sevoyan , Jihong Liu , Yi-Wen Shih , Peiyin Hung , Jiajia Zhang , Xiaoming Li

Purpose

To examine associations between pregnancy timing relative to the COVID-19 pandemic, maternal SARS-CoV-2 infection, and perinatal outcomes.

Methods

We conducted a retrospective cohort study of 189,097 singleton births in South Carolina (2018–2021). Pregnancy timing relative to the pandemic was classified as pre-pandemic (delivered before March 1, 2020), partial pandemic overlap (conceived before and delivered during the pandemic), or pandemic (conceived and delivered during the pandemic). We examined COVID-19 testing, severity, and timing. Modified Poisson regression models with robust variance were used.

Results

Compared to the pre-pandemic group, the partial overlap group had lower risks of low birthweight (LBW) (aRR=0.93, 95 % CI 0.89–0.97) and preterm birth (PTB) (aRR=0.91, 95 % CI 0.88–0.95). The pandemic group had increased risks of LBW (aRR=1.10, 95 % CI 1.06–1.14), PTB (aRR=1.10, 95 % CI 1.07–1.14), and NICU admissions (aRR=1.13, 95 % CI 1.09–1.17) but a decreased risk of breastfeeding initiation (aRR=0.98, 95 % CI 0.97–0.98). Moderate-to-severe COVID-19 symptoms increased PTB (aRR=1.34, 95 % CI 1.13–1.58). Third-trimester COVID-19 infection increased LBW (aRR=1.23, 95 % CI 1.10–1.37), PTB (aRR=1.18, 95 % CI 1.07–1.30), and NICU admissions (aRR=1.17, 95 % CI 1.05–1.30).

Conclusions

Our findings highlight the importance of considering both maternal COVID-19 infection and pandemic-related factors in optimizing perinatal outcomes.

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引用次数: 0

Suveillance Learning Module: Labgold et al. (2024), Population-based denominators matter: Bias in U.S. Virgin Islands COVID-19 vaccination coverage under changing population counts

IF 3.3 3区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Annals of Epidemiology

Pub Date : 2025-02-01 DOI: 10.1016/j.annepidem.2024.06.005

Jeb Jones

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 Annals of Epidemiology to further understanding of a specific study design and to encourage critical thinking and careful evaluation. This EEM focuses on surveillance study designs and references the following article: Labgold K, Cranford HM, Ekpo LL, Mac VV, Roth J Jr, Stout M, Ellis EM. Population-based denominators matter: Bias in U.S. Virgin Islands COVID-19 vaccination coverage under changing population counts [1].

引用次数: 0

Longitudinal cohort study learning module: Judson et al (2023), Association of protective behaviors with SARS-CoV-2 infection

IF 3.3 3区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Annals of Epidemiology

Pub Date : 2025-02-01 DOI: 10.1016/j.annepidem.2024.06.009

Jeb Jones

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 Annals to further understanding of a specific study design and to encourage critical thinking and careful evaluation. This EEM focuses on observational cohort studies and references the following article: Judson TJ, Zhang S, Lindan CP, Boothroyd D, Grumbach K, Bollyky JB, Sample HA, Huang B, Desai M, Gonzales R, Maldonado Y, Rutherford G; TrackCOVID Consortium. Association of protective behaviors with SARS-CoV-2 infection: results from a longitudinal cohort study of adults in the San Francisco Bay Area [1].

引用次数: 0

Educational Engagement Modules: Furthering the educational mission of the American College of Epidemiology

IF 3.3 3区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

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 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Annals of Epidemiology

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

George O. Agogo , Henry Mwambi

Purpose

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.

Methods

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.

Results

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.

Conclusions

Machine learning can be used to identify risk factors of mortality, which is critical for individualized targeted interventions in epidemiologic studies.

目的：流行病学研究对评估死亡危险因素具有重要意义。机器学习（ML）在分析多维数据以揭示风险因素与健康结果之间的依赖关系方面非常有效。方法：利用2009年至2016年收集的国家健康与营养检查调查数据中的代表性样本，与截至2019年12月31日的国家死亡指数公共使用死亡率数据相关联，应用logistic、随机森林、k-近邻、多元自适应回归样条、支持向量机、极端梯度增强和超级学习者ML算法研究全因死亡率的危险因素。我们使用受试者工作曲线下面积（AUC-ROC）、灵敏度、负预测值（NPV）等指标对算法进行评估，并使用SHapley加性解释对结果进行解释。结果：AUC-ROC范围为0.80 ~ 0.87。超级学习者的AUC-ROC最高，为0.87 (95% CI, 0.86─0.88)，灵敏度最高，为0.86 (95% CI, 0.84─0.88)，净现值最高，为0.98 （95% CI, 0.98─0.99）。死亡的主要危险因素包括高龄、大腰围、男性和收缩压。已婚、高家庭年收入和高教育水平与低死亡率有关。结论：机器学习可用于识别死亡的危险因素，这对于流行病学研究中个性化的针对性干预至关重要。

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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 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

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

Purpose

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.

目的：在2021年4月至10月期间，纽约市卫生局进行了一项血清调查，以评估纽约市成年人中SARS-CoV-2抗体的流行情况，作为持续的COVID-19监测工作的一部分。方法：从每年以人口为基础的健康监测调查中招募的1,035名纽约市成年居民中收集全血标本。检测标本是否存在抗sars - cov -2刺突蛋白（抗刺突）和抗sars - cov -2核衣壳蛋白（抗核衣壳）抗体。结果：91.6% （95% CI: 87.45 ~ 94.50）的患者有抗刺突抗体，30.4% （95% CI: 24.78 ~ 36.7）的患者有抗核衣壳抗体。几乎所有具有抗刺突抗体的参与者都产生了能够中和SARS-CoV-2的抗体。总体而言，纽约市西班牙裔和拉丁裔居民的抗刺突阳性最低（85.9% [95% CI: 74.01-92.85]）。相反，抗核衣壳血清阳性在纽约市黑人居民中最高（39.9%,95% CI: 25.5-49.3）。由于种族和社会人口因素，SARS-CoV-2血清阳性仍然存在差异。结论：2021年纽约市SARS-CoV-2血清阳性水平很高，有证据表明与血清流行率相关的不平等现象仍在继续。

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