Annals of Epidemiology最新文献

{"title":"Prevalence estimates of mental illness among parents in the United States: Results from the National Survey on Drug Use and Health, 2021–2023","authors":"Paul J. Geiger , Lauren Klein Warren , Leyla Stambaugh , Douglas Richesson , Tenecia Smith , Jennifer Hoenig","doi":"10.1016/j.annepidem.2025.01.007","DOIUrl":"10.1016/j.annepidem.2025.01.007","url":null,"abstract":"","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"102 ","pages":"Pages 91-93"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143029811","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":"Statistical harmonization of versions of measures across studies using external data: Self-rated health and self-rated memory","authors":"Yingyan Wu MS ,&nbsp;Eleanor Hayes-Larson PhD, MPH ,&nbsp;Yixuan Zhou ,&nbsp;Vincent Bouteloup PharmD ,&nbsp;Scott C. Zimmerman MPH ,&nbsp;Anna M. Pederson MPH ,&nbsp;Vincent Planche MD, PhD ,&nbsp;Marissa J. Seamans PhD, MSPH ,&nbsp;Daniel Westreich PhD ,&nbsp;M. Maria Glymour ,&nbsp;Laura E. Gibbons PhD ,&nbsp;Carole Dufouil PhD ,&nbsp;Elizabeth Rose Mayeda PhD, MPH","doi":"10.1016/j.annepidem.2025.01.002","DOIUrl":"10.1016/j.annepidem.2025.01.002","url":null,"abstract":"<div><h3>Purpose</h3><div>Harmonizing variables for constructs measured differently across studies is essential for comparing, combining, and generalizing results. We developed and fielded a brief survey to harmonize Likert and continuous versions of measures for two constructs, self-rated health and self-rated memory, for use in studies of French older adults.</div></div><div><h3>Methods</h3><div>We recruited 300 participants from a French memory clinic in 2023 to answer both the Likert and continuous versions of self-rated health and self-rated memory questions. For each construct, we predicted responses to the Likert version with multinomial and ordinal logistic models, varying specifications of continuous version responses (linear or spline) and covariate sets (question order, age, sex/gender, and interactions between the continuous version and covariates). We also implemented a percentiles-based crosswalk sensitivity analysis. We compared Cohen’s weighted kappa values to identify the best statistical harmonization approach.</div></div><div><h3>Results</h3><div>In the final models [multinomial models with continuous version spline, question order (self-rated memory model only), age, sex/gender, and interactions between the continuous version and covariates], weighted kappa values were 0.61 for self-rated health and 0.60 for self-rated memory, reflecting moderate agreement.</div></div><div><h3>Conclusions</h3><div>Primary data collection feasibly facilitates statistical harmonization of variables for constructs measured differently across studies.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"102 ","pages":"Pages 86-90"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973142","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":"Conceptualizing patient-level adverse effects in implementation trials","authors":"Charles W. Goss ,&nbsp;Lindsey M. Filiatreau ,&nbsp;Lisa R. Hirschhorn ,&nbsp;Mark D. Huffman ,&nbsp;Aaloke Mody ,&nbsp;Byron J. Powell ,&nbsp;Emmanuel Tetteh ,&nbsp;Elvin H. Geng ,&nbsp;Mosepele Mosepele","doi":"10.1016/j.annepidem.2024.12.012","DOIUrl":"10.1016/j.annepidem.2024.12.012","url":null,"abstract":"<div><h3>Background</h3><div>Identifying and monitoring adverse effects (AEs) are integral to ensuring patient safety in clinical trials. Research sponsors and regulatory bodies have put into place a variety of policies and procedures to guide researchers in protecting patient safety during clinical trials. However, it remains unclear how these policies and procedures should be adapted for trials in implementation science. As a starting point, we develop a conceptual model that traces causal pathways leading from implementation strategies to AEs, propose a definition and classification of such effects, and provide recommendations for monitoring and oversight.</div></div><div><h3>Main text</h3><div>We propose four major types of adverse effects for implementation trials. First, we characterize implementation strategies that lead to “proper use” of an intervention that align with AEs as conceptualized and reported in clinical trials. Second, we characterize a strategy’s AEs mediated through “misuse” which involves inappropriate utilization of an evidence-based intervention (EBI). Third, we characterize a strategy which focuses on one EBI and may inadvertently cause the inappropriate discontinuation or “disuse” of other EBIs already in place, thus inducing AEs. Finally, we characterize strategies that may cause AEs by reducing the use of an EBI in the target population (i.e., “nonuse”). Based on these considerations, we propose an extended definition of adverse effects that includes harms that are causally related to implementation strategies, termed Implementation strategy Adverse Effects (IAEs). We recommend researchers, oversight committees, sponsors, and other stakeholders work together prior to trials to determine the best approaches for identifying, monitoring, and reporting IAEs.</div></div><div><h3>Conclusions</h3><div>In this paper, we develop a conceptual model to identify four types of AEs in implementation trials clarifying the mechanisms linking implementation strategies to patterns of use of the EBI and potential patient-level harms. We propose a new definition that links implementation strategies to AEs that can be used to guide conceptualization, monitoring, and oversight of potential harms in future implementation trials. Our work represents an important step towards understanding adverse effects in implementation trials and lays the groundwork for future advancement in the conceptualization of other types of adverse effects (e.g., harms to providers) encountered in implementation trials.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"102 ","pages":"Pages 55-61"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142900185","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":"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":"Associations of pregnancy timing relative to the COVID-19 pandemic, maternal SARS-CoV-2 infection, and adverse perinatal outcomes","authors":"Maria Sevoyan ,&nbsp;Jihong Liu ,&nbsp;Yi-Wen Shih ,&nbsp;Peiyin Hung ,&nbsp;Jiajia Zhang ,&nbsp;Xiaoming Li","doi":"10.1016/j.annepidem.2025.01.006","DOIUrl":"10.1016/j.annepidem.2025.01.006","url":null,"abstract":"<div><h3>Purpose</h3><div>To examine associations between pregnancy timing relative to the COVID-19 pandemic, maternal SARS-CoV-2 infection, and perinatal outcomes.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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).</div></div><div><h3>Conclusions</h3><div>Our findings highlight the importance of considering both maternal COVID-19 infection and pandemic-related factors in optimizing perinatal outcomes.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"102 ","pages":"Pages 94-101"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143029810","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":"Considerations for Social Networks and Health Data Sharing: An Overview","authors":"Dana K. Pasquale ,&nbsp;Tom Wolff ,&nbsp;Gabriel Varela ,&nbsp;Jimi Adams ,&nbsp;Peter J. Mucha ,&nbsp;Brea L. Perry ,&nbsp;Thomas W. Valente ,&nbsp;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}

{"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}