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Institutional variation in early mortality following isolated coronary artery bypass graft surgery

International Journal of Cardiology 2022年 362卷 35-41页

作者： Patel, Aayush Ngo, Linh Woodman, Richard J. Aliprandi-Costa, Bernadette Bennetts, Jayme Psaltis, Peter J. Ranasinghe, Isuru Adelaide Medical School The University of Adelaide Adelaide Australia Department of Cardiology The Prince Charles Hospital Brisbane Australia School of Clinical Medicine The University of Queensland Brisbane Australia Cardiovascular Centre E Hospital Hanoi Viet Nam Flinders Centre for Epidemiology and Biostatistics Flinders University SA Australia The Australian Commission on Safety and Quality in Health Care Sydney Australia Department of Cardiothoracic Surgery Flinders Medical Centre Southern Adelaide Local Health Network Adelaide Australia Department of Surgery College of Medicine and Public Health Flinders University Adelaide Australia Department of Cardiology Central Adelaide Local Health Network Adelaide Australia Vascular Research Centre Heart and Vascular Program Lifelong Health Theme South Australian Health and Medical Research Institute Adelaide Australia

Background: Thirty-day mortality following coronary artery bypass grafting (CABG) is a widely accepted marker for quality of care. Although surgical mortality has declined, the utility of this measure to profile quality has not been questioned. We assessed the institutional variation in risk-standardised mortality rates (RSMR) following isolated CABG within Australia and New Zealand (ANZ). Methods: We used an administrative dataset from all public and most private hospitals across ANZ to capture all isolated CABG procedures recorded between 2010 and 2015. The primary outcome was all-cause death occurring in-hospital or within 30-days of discharge. Hospital-specific RSMRs and 95% CI were estimated using a hierarchical generalised linear model accounting for differences in patient characteristics. Results: Overall, 60,953 patients (mean age 66.1 ± 10.1y, 18.7% female) underwent an isolated CABG across 47 hospitals. The observed early mortality rate was 1.69% (n = 1029) with 81.8% of deaths recorded in-hospital. The risk-adjustment model was developed with good discrimination (C-statistic = 0.81). Following risk-adjustment, a 3.9-fold variation was observed in RSMRs among hospitals (median:1.72%, range:0.84–3.29%). Four hospitals had RSMRs significantly higher than average, and one hospital had RSMR lower than average. When in-hospital mortality alone was considered, the median in-hospital RSMR was 1.40% with a 5.6-fold variation across institutions (range:0.57–3.19%). Conclusions: Average mortality following isolated CABG is low across ANZ. Nevertheless, in-hospital and 30-day mortality vary among hospitals, highlighting potential disparities in care quality and the enduring usefulness of 30-day mortality as an outcome measure. clinical and policy interventions, including participating in clinical quality registries, are needed to standardise CABG care. © 2022 Elsevier B.V.

关键词： (short-term) mortality Coronary artery bypass grafting Institutional variation

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Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries (vol 55, pg 89, 2023)

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NATURE GENETICS 2023年第3期55卷 519-520页

作者： Fernandez-Rozadilla, Ceres Timofeeva, Maria Chen, Zhishan Law, Philip Thomas, Minta Schmit, Stephanie Diez-Obrero, Virginia Hsu, Li Fernandez-Tajes, Juan Palles, Claire Sherwood, Kitty Briggs, Sarah Svinti, Victoria Donnelly, Kevin Farrington, Susan Blackmur, James Vaughan-Shaw, Peter Shu, Xiao-ou Long, Jirong Cai, Qiuyin Guo, Xingyi Lu, Yingchang Broderick, Peter Studd, James Huyghe, Jeroen Harrison, Tabitha Conti, David Dampier, Christopher Devall, Mathew Schumacher, Fredrick Melas, Marilena Rennert, Gad Obon-Santacana, Mireia Martin-Sanchez, Vicente Moratalla-Navarro, Ferran Oh, Jae Hwan Kim, Jeongseon Jee, Sun Ha Jung, Keum Ji Kweon, Sun-Seog Shin, Min-Ho Shin, Aesun Ahn, Yoon-Ok Kim, Dong-Hyun Oze, Isao Wen, Wanqing Matsuo, Keitaro Matsuda, Koichi Tanikawa, Chizu Ren, Zefang Gao, Yu-Tang Jia, Wei-Hua Hopper, John Jenkins, Mark Win, Aung Ko Pai, Rish Figueiredo, Jane Haile, Robert Gallinger, Steven Woods, Michael Newcomb, Polly Duggan, David Cheadle, Jeremy Kaplan, Richard Maughan, Timothy Kerr, Rachel Kerr, David Kirac, Iva Bohm, Jan Mecklin, Lukka-Pekka Jousilahti, Pekka Knekt, Paul Aaltonen, Lauri Rissanen, Harri Pukkala, Eero Eriksson, Johan Cajuso, Tatiana Hanninen, Ulrika Kondelin, Johanna Palin, Kimmo Tanskanen, Tomas Renkonen-Sinisalo, Laura Zanke, Brent Mannisto, Satu Albanes, Demetrius Weinstein, Stephanie Ruiz-Narvaez, Edward Palmer, Julie Buchanan, Daniel Platz, Elizabeth Visvanathan, Kala Ulrich, Cornelia Siegel, Erin Brezina, Stefanie Gsur, Andrea Campbell, Peter Chang-Claude, Jenny Hoffmeister, Michael Brenner, Hermann Slattery, Martha Potter, John Tsilidis, Konstantinos Schulze, Matthias Gunter, Marc Murphy, Neil Castells, Antoni Castellvi-Bel, Sergi Moreira, Leticia Arndt, Volker Shcherbina, Anna Stern, Mariana Pardamean, Bens Bishop, Timothy Giles, Graham Southey, Melissa Idos, Gregory McDonnell, Kevin Abu-Ful, Zomoroda Greenson, Joel Shulman, Katerina Lejbkowicz, Flavio Offit, Kenneth Su, Yu-Ru Steinfelder, Robert Keku, Temitope van Guelpen, Bethany Hudson, Thomas Hampel, Heather Pearlman, Edinburgh Cancer Research Centre Institute of Genomics and Cancer University of Edinburgh Edinburgh UK Genomic Medicine Group Instituto de Investigacion Sanitaria de Santiago Santiago de Compostela Spain Colon Cancer Genetics Group Medical Research Council Human Genetics Unit Institute of Genetics and Cancer University of Edinburgh Edinburgh UK Danish Institute for Advanced Study Department of Public Health University of Southern Denmark Odense Denmark Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University Medical Center Nashville TN USA Division of Genetics and Epidemiology Institute of Cancer Research London UK Public Health Sciences Division Fred Hutchinson Cancer Research Center Seattle WA USA Genomic Medicine Institute Cleveland Clinic Cleveland OH USA Population and Cancer Prevention Program Case Comprehensive Cancer Center Cleveland OH USA Colorectal Cancer Group ONCOBELL Program Bellvitge Biomedical Research Institute Barcelona Spain Oncology Data Analytics Program Catalan Institute of Oncology Barcelona Spain Consortium for Biomedical Research in Epidemiology and Public Health Madrid Madrid Spain Department of Clinical Sciences Faculty of Medicine University of Barcelona Barcelona Spain Department of Biostatistics School of Public Health University of Washington Seattle WA USA Institute of Cancer and Genomic Sciences College of Medical and Dental Sciences University of Birmingham Birmingham UK Department of Public Health Richard Doll Building University of Oxford Oxford UK Department of Biomedical Informatics Vanderbilt University School of Medicine Nashville TN USA Department of Preventive Medicine USC Norris Comprehensive Cancer Center Keck School of Medicine University of Southern California Los Angeles CA USA Center for Public Health Genomics Department of Public Health Sciences University of Virginia Charlottesville VA USA Department of Populat

Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and east Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high-confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Crosstissue analyses indicated that over a third of effector genes most probably act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.

关键词： Colorectal cancer Epigenomics Genome-wide association studies Transcriptomics

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Machine Learning for Personalised Risk Assessment of HIV, Syphilis, Gonorrhoea, and Chlamydia: A Systematic Review and Meta-Analysis

SSRN

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SSRN 2025年

作者： Latt, Phyu M. Soe, Nyi N. Fairley, Christopher K. Chow, Eric P.F. Johnson, Cheryl C. Shah, Purvi Maatouk, Ismail Zhang, Lei Ong, Jason J. Artificial Intelligence and Modelling in Epidemiology Program Melbourne Sexual Health Centre Alfred Health Melbourne Australia School of Translational Medicine Faculty of Medicine Nursing and Health Sciences Monash University Melbourne Australia Melbourne Sexual Health Centre Alfred Health Melbourne Australia Centre for Epidemiology and Biostatistics Melbourne School of Population and Global Health The University of Melbourne Melbourne Australia Global HIV Hepatitis and STIs Programmes World Health Organization Geneva Switzerland Regional Support Team Asia Pacific UNAIDS Thailand Clinical Medical Research Centre Children’s Hospital of Nanjing Medical University Jiangsu Province Nanjing China Department of Clinical Research London School of Hygiene and Tropical Medicine London United Kingdom

Background: Despite advances in sexually transmitted infection (STI) prevention, optimising risk estimation remains challenging. While machine learning (ML) approaches hold promise for enhancing STI risk estimation, the empirical evidence supporting their clinical utility remains incompletely understood. This study aimed to systematically evaluate the performance of ML models in estimating individual risk for HIV, syphilis, gonorrhoea, and ***: Following PRISMA guidelines, we systematically searched six electronic databases (Embase, MEDLINE, Scopus, CINAHL, Web of Science, and IEEE), three preprint archives, and conference proceedings from January 2010 to April 2024. Studies were eligible if they reported quantitative performance metrics for supervised ML-based STI risk prediction models. Two independent reviewers assessed the risk of bias using PROBAST (Prediction Model Risk Of Bias Assessment Tool). We employed bivariate random-effects meta-analysis to estimate pooled sensitivity, specificity, and area under the curve (AUC) with 95% confidence intervals. We conducted sequential analyses of studies with complete and reconstructed confusion matrices. Subgroup analyses and meta-regression explored potential sources of heterogeneity, including study population (general versus specific populations such as men who have sex with men (MSM) and other gender-specific groups), sample size (Results: Among 3,877 records screened, 25 studies comprising 45 unique models met inclusion criteria, with sample sizes ranging from 547 to 4,384,178 participants across 14 countries. For HIV risk estimation, the SROC curve analysis of studies reporting complete confusion matrices (7 studies with 9 contingency tables) demonstrated a summary AUC of 0.91 (95% CI: 0.88-0.93), with a pooled sensitivity of 0.84 (0.76-0.90) and specificity of 0.84 (0.70-0.93). The analyses demonstrated substantial heterogeneity (I2 > 98%). While meta-regression identified significant differences in

关键词： Adversarial machine learning

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Preferences for Attributes of an Artificial Intelligence-Based Risk Assessment Tool for HIV and Sexually Transmitted Infections: A Discrete Choice Experiment

SSRN

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SSRN 2024年

作者： Latt, Phyu Mon Soe, Nyi Nyi King, Alicia J. Lee, David Phillips, Tiffany R. Xu, Xianglong Chow, Eric P.F. Fairley, Christopher K. Zhang, Lei Ong, Jason J. Artificial Intelligence and Modelling in Epidemiology Program Melbourne Sexual Health Centre Alfred Health Melbourne Australia School of Translational Medicine Faculty of Medicine Nursing and Health Sciences Monash University Melbourne Australia Melbourne Sexual Health Centre Alfred Health Melbourne Australia School of Public Health Shanghai University of Traditional Chinese Medicine Shanghai China Centre for Epidemiology and Biostatistics Melbourne School of Population and Global Health The University of Melbourne Melbourne Australia Clinical Medical Research Center Children’s Hospital of Nanjing Medical University Jiangsu Province Nanjing China Department of Clinical Research London School of Hygiene and Tropical Medicine London United Kingdom

Introduction: Early detection and treatment of HIV and sexually transmitted infections (STIs) are crucial to their effective control. We developed MySTIRisk, an artificial intelligence (AI)-based risk self-assessment tool that predicts an individual’s risk of HIV and STIs. We examined the attributes of MySTIRisk that encourage potential users to use the ***: Between January and March 2024, we sent text message invitations to the Melbourne Sexual health Centre (MSHC) attendees to participate in an online survey. We also advertised the survey on social media platforms, the clinic's website, and posters in affiliated general practice clinics. This anonymous survey used a discrete choice experiment (DCE) to examine which attributes of MySTIRisk would encourage potential users to use the tool. We analysed the data using random parameters logit (RPL) and latent class analysis (LCA) ***: The median age of 415 participants was 31 years (interquartile range, 26-38 years), with a proportion of participants identifying as straight or heterosexual (31.8%, n=132). The choice to use MySTIRisk was most influenced by two attributes: cost and accuracy, followed by the availability of a pathology request form, level of anonymity, speed of receiving results, and whether the tool was a web or mobile application. LCA revealed two classes: "The Precisionists" (66.0% of respondents), who demanded high accuracy and "The Economists" (34.0% of respondents), who prioritised low cost. Simulations predicted a high uptake (97.7%) for a tool designed with the most preferred attribute levels, contrasting with lower uptake (22.3%) for the least preferred ***: Participants were more likely to use MySTIRisk if it was free, highly accurate, and could send pathology request forms. Tailoring the tool to meet the needs of distinct user segments could further enhance its uptake and effectiveness in promoting early detection and prevention of HIV and ***: EPFC is

关键词： Pathology

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Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

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The Lancet 2023年第10397期402卷 203-234页

作者： Ong, Kanyin Liane Stafford, Lauryn K. McLaughlin, Susan A. Boyko, Edward J. Vollset, Stein Emil Smith, Amanda E. Dalton, Bronte E. Duprey, Joe Cruz, Jessica A. Hagins, Hailey Lindstedt, Paulina A. Aali, Amirali Abate, Yohannes Habtegiorgis Abate, Melsew Dagne Abbasian, Mohammadreza Abbasi-Kangevari, Zeinab Abbasi-Kangevari, Mohsen ElHafeez, Samar Abd Abd-Rabu, Rami Abdulah, Deldar Morad Abdullah, Abu Yousuf Md Abedi, Vida Abidi, Hassan Aboagye, Richard Gyan Abolhassani, Hassan Abu-Gharbieh, Eman Abu-Zaid, Ahmed Adane, Tigist Demssew Adane, Denberu Eshetie Addo, Isaac Yeboah Adegboye, Oyelola A. Adekanmbi, Victor Adepoju, Abiola Victor Adnani, Qorinah Estiningtyas Sakilah Afolabi, Rotimi Felix Agarwal, Gina Aghdam, Zahra Babaei Agudelo-Botero, Marcela Arriagada, Constanza Elizabeth Aguilera Agyemang-Duah, Williams Ahinkorah, Bright Opoku Ahmad, Danish Ahmad, Rizwan Ahmad, Sajjad Ahmad, Aqeel Ahmadi, Ali Ahmadi, Keivan Ahmed, Ali Ahmed, Luai A. Ahmed, Syed Anees Ajami, Marjan Akinyemi, Rufus Olusola Al Hamad, Hanadi Al Hasan, Syed Mahfuz AL-Ahdal, Tareq Mohammed Ali Alalwan, Tariq A. Al-Aly, Ziyad AlBataineh, Mohammad T. Alcalde-Rabanal, Jacqueline Elizabeth Alemi, Sharifullah Ali, Hassam Alinia, Tahereh Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Amare, Firehiwot Ameyaw, Edward Kwabena Amiri, Sohrab Amusa, Ganiyu Adeniyi Andrei, Catalina Liliana Anjana, Ranjit Mohan Ansar, Adnan Ansari, Golnoosh Ansari-Moghaddam, Alireza Anyasodor, Anayochukwu Edward Arabloo, Jalal Aravkin, Aleksandr Y. Areda, Demelash Arifin, Hidayat Arkew, Mesay Armocida, Benedetta Arnlov, Johan Artamonov, Anton A. Arulappan, Judie Aruleba, Raphael Taiwo Arumugam, Ashokan Aryan, Zahra Asemu, Mulu Tiruneh Asghari-Jafarabadi, Mohammad Askari, Elaheh Asmelash, Daniel Astell-Burt, Thomas Athar, Mohammad Athari, Seyyed Shamsadin Atout, Maha Moh'd Wahbi Avila-Burgos, Leticia Awaisu, Ahmed Azadnajafabad, Sina Darshan, B.B. Babamohamadi, Hassan Badar, Muhammad Badawi, Alaa Badiye, Ashish D. Baghcheghi, Nayereh Baghe Institute for Health Metrics and Evaluation School of Medicine General Medicine Service Department of Veterans Affairs Seattle WA United States Department of Health Metrics Sciences School of Medicine Faculty of Medicine France Department of Clinical Governance and Quality Improvement Aleta Wondo Hospital Aleta Wondo Ethiopia Department of Nursing Woldia University Woldia Ethiopia Department of Nursing Department of Orthopedic Surgery Orthopaedic Surgery Social Determinants of Health Research Center Non-communicable Diseases Research Center Epidemiology Department Evidence-Based Practice Center Community and Maternity Nursing Unit University of Duhok Duhok Iraq School of Planning Faculty of Environment Department of Public Health Sciences Penn State College of Medicine Pennsylvania State University Hershey PA United States Biocomplexity Institute Virginia Tech Blacksburg VA United States College of Medicine Department of Public Health Laboratory Technology Sciences Department Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Research Center for Immunodeficiencies Department of Biosciences and Nutrition Karolinska University Hospital Huddinge Sweden Clinical Sciences Department Department of Surgery Department of Surgery Department of Obstetrics & Gynecology College of Graduate Health Sciences University of Tennessee Memphis TN United States Department of Obstetrics & Gynecology Department of Clinical and Psychosocial Epidemiology Department of Anesthesia and Critical Care Centre for Social Research in Health Quality and Systems Performance Unit Cancer Institute NSW Sydney NSW Australia Department of Public Health and Tropical Medicine HIV and Infectious Diseases Department Jhpiego Abuja Nigeria Adolescent Research and Care Department Adolescent Friendly Research Initiative and Care Ado Ekiti Nigeria Department of Epidemiology Department of Epidemiology and Medical Statistics Department of Family Medicine McMaster University

Background: Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods: Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined;these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs;defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims;type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative r

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Factors Affecting Antidepressant Response Trajectories: A Veterans Affairs Augmentation and Switching Treatments for Improving Depression Outcomes Trial Report

Psychiatric Research and Clinical Practice

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Psychiatric research and clinical Practice 2023年第4期5卷 131-143页

作者： Hicks, Paul B. Sevilimedu, Varadan Johnson, Gary R. Tal, Ilanit R. Chen, Peijun Davis, Lori L. Vertrees, Julia E. Zisook, Sidney Mohamed, Somaia Department of Psychiatry Baylor Scott & White Health Temple TX United States Texas A&M College of Medicine Temple TX United States Biostatistics Service Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY United States Yale University School of Public Health New Haven CT United States Cooperative Studies Program Coordinating Center VA Connecticut Healthcare System West Haven CT United States VA San Diego Healthcare System San Diego CA United States Department of Psychiatry VISN10 Geriatric Research Education and Clinical Center VA Northeast Ohio Healthcare System Cleveland OH United States Case Western Reserve University Cleveland OH United States Tuscaloosa VA Medical Center Tuscaloosa AL United States University of Alabama School of Medicine Birmingham AL United States Cooperative Studies Program Clinical Research Pharmacy Coordinating Center Albuquerque NM United States University of California San Diego CA United States Veterans Affairs (VA) New England Mental Illness Research Education and Clinical Center VA Connecticut Healthcare System West Haven CT United States Yale University School of Medicine New Haven CT United States

Background: In this secondary analysis of the VA Augmentation and Switching Treatments for Improving Depression Outcomes (VAST-D) study we used antidepressant response trajectories to assess the association of treatment and multiple clinical/demographic factors with the probability of response. Methods: Using data from VAST-D, a multi-site, randomized, single-blind trial with parallel-assignment to one of three treatment interventions in 1522 Veterans whose major depressive disorder was unresponsive to at least one antidepressant trial, we evaluated response patterns using group-based trajectory modeling (GBTM). A weighted multinomial logistic regression analysis with backward elimination and additional exploratory analyses were performed to evaluate the association of multiple clinical/demographic factors with the probability of inclusion into specific trajectories. Additional exploratory analyses were used to identify factors associated with trajectory group membership that could have been missed in the primary analysis. Results: GBTM showed the best fit for depression symptom change was comprised of six trajectories, with some trajectories demonstrating minimal improvement and others showing a high probability of remission. High baseline depression and anxiety severity scores decreased, and early improvement increased, the likelihood of inclusion into the most responsive trajectory in both the GBTM and exploratory analyses. Conclusion: While multiple factors influence responsiveness, the probability of inclusion into a specific depression symptom trajectory is most strongly influenced by three factors: baseline depression, baseline anxiety, and the presence of early improvement. © 2023 The Authors. Psychiatric research and clinical Practice published by Wiley Periodicals LLC on behalf of American Psychiatric Association.

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Background rates of adverse events of special interest for COVID-19 vaccines: A multinational Global Vaccine Data Network (GVDN) analysis

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Vaccine 2023年第42期41卷 6227-6238页

作者： Phillips, A. Jiang, Y. Walsh, D. Andrews, N. Artama, M. Clothier, H. Cullen, L. Deng, L. Escolano, S. Gentile, A. Gidding, G. Giglio, N. Junker, T. Huang, W. Janjua, N. Kwong, J. Li, J. Nasreen, S. Naus, M. Naveed, Z. Pillsbury, A. Stowe, J. Vo, T. Buttery, J. Petousis-Harris, H. Black, S. Hviid, A. National Centre for Immunisation Research and Surveillance Westmead NSW Australia Department of Statistics University of Auckland New Zealand Global Vaccine Data Network Global Coordinating Centre Auckland New Zealand UK Health Security Agency London United Kingdom Faculty of Social Sciences Tampere University Finland Murdoch Children's Research Institute Parkville VIC Australia Public Health Scotland Scotland Edinburgh United Kingdom Université Paris-Saclay UVSQ Inserm CESP High Dimensional Biostatistics for Drug Safety and Genomics Villejuif France Hospital de Niños Ricardo Gutierrez Epidemiology Department Buenos Aires City Argentina The University of Sydney Northern Clinical School Australia Department of Epidemiology Research Statens Serum Institut Copenhagen Denmark Global Health Program College of Public Health National Taiwan University Taipei Taiwan National Taiwan University Children's Hospital Taipei Taiwan British Columbia Centre for Disease Control Vancouver Canada School of Population and Public Health University of British Columbia Vancouver Canada Centre for Health Evaluation and Outcome Sciences St Paul's Hospital Vancouver Canada ICES Toronto ON Canada Public Health Ontario Toronto ON Canada Department of Family and Community Medicine Temerty Faculty of Medicine and the Dalla Lana School of Public Health University of Toronto Toronto ON Canada Dalla Lana School of Public Health University of Toronto Toronto ON Canada Department of Health Security Finnish Institute for Health and Welfare Helsinki Finland Associate Professor School of Population Health University of Auckland New Zealand Pharmacovigilance Research Center Department of Drug Design and Pharmacology Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark

Background: The Global COVID Vaccine Safety (GCoVS) project was established in 2021 under the multinational Global Vaccine Data Network (GVDN) consortium to facilitate the rapid assessment of the safety of newly introduced vaccines. This study analyzed data from GVDN member sites on the background incidence rates of conditions designated as adverse events of special interest (AESI) for COVID-19 vaccine safety monitoring. Methods: Eleven GVDN global sites obtained data from national or regional healthcare databases using standardized methods. Incident events of 13 pre-defined AESI were included for a pre-pandemic period (2015–19) and the first pandemic year (2020). Background incidence rates (IR) and 95% confidence intervals (CI) were calculated for inpatient and emergency department encounters, stratified by age and sex, and compared between pre-pandemic and pandemic periods using incidence rate ratios. Results: An estimated 197 million people contributed 1,189,652,926 person-years of follow-up time. Among inpatients in the pre-pandemic period (2015–19), generalized seizures were the most common neurological AESI (IR ranged from 22.15 [95% CI 19.01–25.65] to 278.82 [278.20–279.44] per 100,000 person-years);acute disseminated encephalomyelitis was the least common (<0.5 per 100,000 person-years at most sites). Pulmonary embolism was the most common thrombotic event (IR 45.34 [95% CI 44.85–45.84] to 93.77 [95% CI 93.46–94.08] per 100,000 person-years). The IR of myocarditis ranged from 1.60 [(95% CI 1.45–1.76) to 7.76 (95% CI 7.46–8.08) per 100,000 person-years. The IR of several AESI varied by site, healthcare setting, age and sex. The IR of some AESI were notably different in 2020 compared to 2015–19. Conclusion: Background incidence of AESIs exhibited some variability across study sites and between pre-pandemic and pandemic periods. These findings will contribute to global vaccine safety surveillance and research. © 2023

关键词： Adverse event following immunization Background rates COVID-19 Pharmacovigilance Vaccine safety surveillance

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Maintaining the validity of inference from linear mixed models in stepped-wedge cluster randomized trials under misspecified random-effects structures

arXiv

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arXiv 2023年

作者： Ouyang, Yongdong Taljaard, Monica Forbes, Andrew B. Li, Fan Clinical Epidemiology Program Ottawa Hospital Research Institute 1053 Carling Ave OttawaON Canada School of Epidemiology and Public Health University of Ottawa 600 Peter Morand Crescent OttawaON Canada School of Public Health and Preventive Medicine Monash University Melbourne Australia Department of Biostatistics Yale School of Public Health New HavenCT United States Center for Methods in Implementation and Prevention Science Yale School of Public Health New HavenCT United States

Linear mixed models are commonly used in analyzing stepped-wedge cluster randomized trials (SW-CRTs). A key consideration for analyzing a SW-CRT is accounting for the potentially complex correlation structure, which can be achieved by specifying random-effects. The simplest random effects structure is random intercept but more complex structures such as random cluster-by-period, discrete-time decay and more recently, the random intervention structure, have been proposed. Specifying appropriate random effects in practice can be challenging: assuming more complex correlation structures may be reasonable but they are vulnerable to computational challenges. To circumvent these challenges, robust variance estimators (RVE) may be applied to linear mixed models to provide consistent estimators of standard errors of fixed effect parameters in the presence of random-effects misspecification. However, there has been no empirical investigation of RVE for SW-CRT. In this paper, we review six RVEs (both standard and small-sample bias-corrected RVEs) that are available for linear mixed models in R, and then describe a comprehensive simulation study to examine the performance of these RVEs for SW-CRTs with a continuous outcome under different data generators. For each data generator, we investigate whether the use of a RVE with either the random intercept model or the random cluster-by-period model is sufficient to provide valid statistical inference for fixed effect parameters, when these working models are subject to random-effect misspecification. Our results indicate that the random intercept and random cluster-by-period models with RVEs performed adequately. The CR3 RVE (approximate jackknife) estimator, coupled with the number of clusters minus two degrees of freedom correction, consistently gave the best coverage results, but could be slightly conservative when the number of clusters was below 16. We summarize the implications of our results for linear mixed model analysis

关键词： Random errors

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Power calculation for cross-sectional stepped wedge cluster randomized trials with a time-to-event endpoint

arXiv

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arXiv 2023年

作者： Ryan, Mary M. Esserman, Denise Taljaard, Monica Li, Fan Department of Population Health Sciences University of Wisconsin-Madison MadisonWI United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Department of Biostatistics Yale School of Public Health New HavenCT United States Yale Center for Analytical Sciences Yale School of Public Health New HavenCT United States Clinical Epidemiology Program Ottawa Hospital Research Institute OttawaON Canada School of Epidemiology and Public Health University of Ottawa OttawaON Canada Center for Methods in Implementation and Prevention Science Yale University New HavenCT United States

Stepped wedge cluster randomized trials (SW-CRTs) are a form of randomized trial whereby clusters are progressively transitioned from control to intervention, with the timing of transition randomized for each cluster. An important task at the design stage is to ensure that the planned trial has sufficient power to observe a clinically meaningful effect size. While methods for determining study power have been well-developed for SW-CRTs with continuous and binary outcomes, limited methods for power calculation are available for SW-CRTs with censored time-to-event outcomes. In this article, we propose a stratified marginal Cox model to account for secular trend in cross-sectional SW-CRTs, and derive an explicit expression of the robust sandwich variance to facilitate power calculations without the need for computationally intensive simulations. Power formulas based on both the Wald and robust score tests are developed and validated via simulation under different finite-sample scenarios. Finally, we illustrate our methods in the context of a SW-CRT testing the effect of a new electronic reminder system on time to catheter removal in hospital settings. We also offer an R Shiny application to facilitate sample size and power calculations using our proposed methods. © 2023, CC BY-NC-ND.

关键词： Catheters

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Association of Primary Care Continuity With Home Dialysis, Transplantation, and Utilization of Medical Services for Patients Starting Hemodialysis

Kidney Medicine

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Kidney Medicine 2025年第6期7卷 101015-101015页

作者： Wyman, Cole S. Djerboua, Maya Clemens, Kristin K. Harel, Ziv Sood, Manish M. Silver, Samuel A. Queen's University Kingston ON Canada ICES ON Canada Division of Endocrinology and Metabolism and Department of Epidemiology and Biostatistics Western University London ON Canada Division of Nephrology and Li Ka Shing Knowledge Institute St. Michael's Hospital University of Toronto Toronto ON Canada Department of Medicine and Clinical Epidemiology Program of the Ottawa Hospital Research Institute University of Ottawa Ottawa ON Canada Division of Nephrology Kingston Health Sciences Center Queen's University Kingston ON Canada

Rationale & Objective: Primary care may help patients starting dialysis with emotional support and access to health care services. It is unknown whether consistently visiting the same primary care physician (PCP) can strengthen patient confidence to select home dialysis, help facilitate medical appointments for transplantation, or increase care access. Study Design: A population-based retrospective cohort study. Setting & Participants: Patients initiating maintenance hemodialysis from 2007 to 2017 in Ontario, Canada. Exposure: High PCP continuity using the usual provider of care index (an established measure of PCP continuity), defined as >75% of PCP visits with the same PCP in the 2 years before dialysis initiation. Outcomes: Primary outcomes were time to home dialysis (peritoneal or hemodialysis) and transplantation. Secondary outcomes included specialist visits, cancer screening, influenza vaccination, and measures of diabetes care. Analytical Approach: Propensity scores to match patients with high and low PCP continuity. Results: We identified 9,530 matched pairs. High PCP continuity was not associated with increased home dialysis (14.0 events per 100 person-years vs 14.0 events per 100 person-years;subdistribution hazard ratio 1.00;95% CI, 0.97-1.04) or transplantation (4.3 events per 100 person-years vs 4.5 events per 100 person-years;subdistribution hazard ratio 0.97;95% CI, 0.90-1.04). High PCP continuity was associated with greater colon cancer screening (hazard ratio 1.07;95% CI, 1.01-1.14), influenza vaccination (hazard ratio 1.33;95% CI, 1.27-1.39), and comprehensive diabetes care (hazard ratio 1.23;95% CI, 1.14-1.33). Limitations: Residual confounding is possible. Conclusions: High PCP continuity before dialysis initiation was not associated with increased utilization of home dialysis or transplantation but was associated with greater colon cancer screening, influenza vaccination, and comprehensive diabetes care. Additional work is needed to clarify how

关键词： end-stage kidney disease Hemodialysis primary care

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