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Background While evidence-based psychotherapy (EBP) for posttraumatic stress disorder (PTSD) is a first-line treatment, its real-world effectiveness is unknown. We compared cognitive processing therapy (CPT) and prolonged exposure (PE) each to an individual psychotherapy comparator group, and CPT to PE in a large national healthcare system. Methods We utilized effectiveness and comparative effectiveness emulated trials using retrospective cohort data from electronic medical records. Participants were veterans with PTSD initiating mental healthcare (N = 265 566). The primary outcome was PTSD symptoms measured by the PTSD Checklist (PCL) at baseline and 24-week follow-up. Emulated trials were comprised of ‘person-trials,’ representing 112 discrete 24-week periods of care (10/07–6/17) for each patient. Treatment group comparisons were made with generalized linear models, utilizing propensity score matching and inverse probability weights to account for confounding, selection, and non-adherence bias. Results There were 636 CPT person-trials matched to 636 non-EBP person-trials. Completing ⩾8 CPT sessions was associated with a 6.4-point greater improvement on the PCL (95% CI 3.1–10.0). There were 272 PE person-trials matched to 272 non-EBP person-trials. Completing ⩾8 PE sessions was associated with a 9.7-point greater improvement on the PCL (95% CI 5.4–13.8). There were 232 PE person-trials matched to 232 CPT person-trials. Those completing ⩾8 PE sessions had slightly greater, but not statistically significant, improvement on the PCL (8.3-points; 95% CI 5.9–10.6) than those completing ⩾8 CPT sessions (7.0-points; 95% CI 5.5–8.5). Conclusions PTSD symptom improvement was similar and modest for both EBPs. Although EBPs are helpful, research to further improve PTSD care is critical.

Kristin Kostka,1,2 Talita Duarte-Salles,3 Albert Prats-Uribe,4 Anthony G Sena,5,6 Andrea Pistillo,3 Sara Khalid,4 Lana YH Lai,7 Asieh Golozar,8,9 Thamir M Alshammari,10 Dalia M Dawoud,11 Fredrik Nyberg,12 Adam B Wilcox,13,14 Alan Andryc,5 Andrew Williams,15 Anna Ostropolets,16 Carlos Areia,17 Chi Young Jung,18 Christopher A Harle,19 Christian G Reich,1,2 Clair Blacketer,5,6 Daniel R Morales,20 David A Dorr,21 Edward Burn,3,4 Elena Roel,3,22 Eng Hooi Tan,4 Evan Minty,23 Frank DeFalco,5 Gabriel de Maeztu,24 Gigi Lipori,19 Hiba Alghoul,25 Hong Zhu,26 Jason A Thomas,13 Jiang Bian,19 Jimyung Park,27 Jordi Martínez Roldán,28 Jose D Posada,29 Juan M Banda,30 Juan P Horcajada,31 Julianna Kohler,32 Karishma Shah,33 Karthik Natarajan,16,34 Kristine E Lynch,35,36 Li Liu,37 Lisa M Schilling,38 Martina Recalde,3,22 Matthew Spotnitz,14 Mengchun Gong,39 Michael E Matheny,40,41 Neus Valveny,42 Nicole G Weiskopf,21 Nigam Shah,29 Osaid Alser,43 Paula Casajust,42 Rae Woong Park,27,44 Robert Schuff,21 Sarah Seager,1 Scott L DuVall,35,36 Seng Chan You,45 Seokyoung Song,46 Sergio Fernández-Bertolín,3 Stephen Fortin,5 Tanja Magoc,19 Thomas Falconer,16 Vignesh Subbian,47 Vojtech Huser,48 Waheed-Ul-Rahman Ahmed,33,49 William Carter,38 Yin Guan,50 Yankuic Galvan,19 Xing He,19 Peter R Rijnbeek,6 George Hripcsak,16,34 Patrick B Ryan,5,16 Marc A Suchard,51 Daniel Prieto-Alhambra4 1IQVIA, Cambridge, MA, USA; 2OHDSI Center at The Roux Institute, Northeastern University, Portland, ME, USA; 3Fundació Institut Universitari per a la recerca a l’Atenció Primà ria de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain; 4Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK; 5Janssen Research & Development, Titusville, NJ, USA; 6Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands; 7School of Medical Sciences, University of Manchester, Manchester, UK; 8Regeneron Pharmaceuticals, Tarrytown, NY, USA; 9Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; 10College of Pharmacy, Riyadh Elm University, Riyadh, Saudi Arabia; 11National Institute for Health and Care Excellence, London, UK; 12School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; 13Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA; 14Unviersity of Washington Medicine, Seattle, WA, USA; 15Tufts Institute for Clinical Research and Health Policy Studies, Boston, MA, USA; 16Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA; 17Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; 18Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Daegu Catholic University Medical Center, Daegu, South Korea; 19University of Florida Health, Gainesville, FL, USA; 20Division of Population Health and Genomics, University of Dundee, Dundee, UK; 21Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA; 22Universitat Autònoma de Barcelona, Barcelona, Spain; 23O’Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Calgary, Canada; 24IOMED, Barcelona, Spain; 25Faculty of Medicine, Islamic University of Gaza, Gaza, Palestine; 26Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 27Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, South Korea; 28Director of Innovation and Digital Transformation, Hospital del Mar, Barcelona, Spain; 29Department of Medicine, School of Medicine, Stanford University, Redwood City, CA, USA; 30Georgia State University, Department of Computer Science, Atlanta, GA, USA; 31Department of Infectious Diseases, Hospital del Mar, Institut Hospital del Mar d’Investigació Mèdica (IMIM), Universitat Autònoma de Barcelona, Universitat Pompeu Fabra, Barcelona, Spain; 32United States Agency for International Development, Washington, DC, USA; 33Botnar Research Centre, NDORMS, University of Oxford, Oxford, UK; 34New York-Presbyterian Hospital, New York, NY, USA; 35VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA; 36Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA; 37Biomedical Big Data Center, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 38Data Science to Patient Value Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; 39Institute of Health Management, Southern Medical University, Guangzhou, People’s Republic of China; 40Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA; 41Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; 42Real-World Evidence, TFS, Barcelona, Spain; 43Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; 44Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea; 45Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, South Korea; 46Department of Anesthesiology and Pain Medicine, Catholic University of Daegu, School of Medicine, Daegu, South Korea; 47College of Engineering, The University of Arizona, Tucson, AZ, USA; 48National Library of Medicine, National Institutes of Health, Bethesda, MD, USA; 49College of Medicine and Health, University of Exeter, St Luke’s Campus, Exeter, UK; 50DHC Technologies Co. Ltd., Beijing, People’s Republic of China; 51Departments of Biostatistics, Computational Medicine, and Human Genetics, University of California, Los Angeles, CA, USACorrespondence: Daniel Prieto-Alhambra, Botnar Research Centre, Windmill Road, Oxford, OX37LD, UK, Email daniel.prietoalhambra@ndorms.ox.ac.ukPurpose: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD.Patients and Methods: We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services.Results: We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed.Conclusion: We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.Keywords: OHDSI, OMOP CDM, descriptive epidemiology, real world data, real world evidence, open science

BackgroundThe incidence and severity of coronavirus disease 19 (COVID-19) is substantially higher in men. Sex hormones may be a potential mechanism for differences in COVID-19 outcome in men and women. We hypothesized that men treated with androgen deprivation therapy (ADT) have lower incidence and severity of COVID-19.MethodsWe conducted an observational study of male Veterans treated in the Veterans Health Administration from February 15th to July 15th, 2020. We developed a propensity score model to predict the likelihood to undergo Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) testing. We performed multivariable logistic regression modeling adjusted with inverse probability weighting to examine the relationship between ADT and COVID-19 incidence. We conducted logistic regression analysis among COVID-19 patients to test the association between ADT and COVID-19 severity.ResultsWe identified a large cohort of 246,087 VA male patients who had been tested for SARS-CoV-2, of whom 3,057 men were exposed to ADT, and 36,096 men with cancer without ADT. Of these, 295 ADT patients and 2,427 cancer patients not on ADT had severe COVID-19 illness. In the primary, propensity-weighted comparison of ADT patients to cancer patients not on ADT, ADT was associated with decreased likelihood of testing positive for SARS-CoV-2 (adjusted OR, 0.88 [95% CI, 0.81–0.95]; p = 0.001). Furthermore, ADT was associated with fewer severe COVID-19 outcomes (OR 0.72 [95% CI 0.53–0.96]; p = 0.03).ConclusionADT is associated with reduced incidence and severity of COVID-19 amongst male Veterans. Testosterone and androgen receptor signaling may confer increased risk for SARS-CoV-2 infection and contribute to severe COVID-19 pathophysiology in men.

OBJECTIVE Cognitive processing therapy (CPT) and prolonged exposure therapy (PE) were widely disseminated to treat posttraumatic stress disorder (PTSD) in the Veterans Health Administration (VHA). However, few Iraq and Afghanistan war veterans (Operation Enduring Freedom [OEF], Operation Iraqi Freedom [OIF], Operation New Dawn [OND]) diagnosed with PTSD have received CPT/PE and many initiate CPT/PE after substantial delay. Veterans who do not initiate CPT/PE or initiate CPT/PE after delay may have poorer treatment outcomes. This study aimed to identify predictors of CPT/PE initiation and timing. METHODS Participants included OEF/OIF/OND veterans diagnosed with PTSD who received psychotherapy between 2001 and 2017 in the VHA (n = 265,566). Logistic regression analysis was utilized to predict initiating CPT/PE (vs. no CPT/PE). Multinomial logistic regression analysis was utilized to predict not initiating or initiating delayed CPT/PE versus "early CPT/PE" (< 1 year after first mental health visit). Analyzed predictors included demographic, military, and clinical complexity variables (e.g., comorbidities, reported military sexual trauma [MST] history). RESULTS Seventy-Seven percent of veterans did not initiate CPT/PE, with 7.4% initiating early and 15.4% initiating delayed CPT/PE. Reported MST history (odds ratio [OR] = 1.45, 95% CI [1.39, 1.51]) and history of suicidal ideation/attempt (OR = 1.42, 95% CI [1.38, 1.46]) were strong predictors of CPT/PE initiation versus no CPT/PE. Comorbid pain (relative risk ratio [RRR] = 1.35, 95% CI [1.30, 1.42]) and depressive disorders (RRR = 1.37, 95% CI [1.32, 1.43]) were associated with increased likelihood of delayed versus early CPT/PE. CONCLUSIONS Most veterans in our study did not initiate CPT/PE. Generally, clinical complexity variables increased likelihood of initiating CPT/PE and initiating CPT/PE more than 1 year after first mental health visit. Additional research is needed to understand whether CPT/PE delay results from receipt of alternative intervention due to clinical complexity variables. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

© 2017 American Medical Association. All rights reserved. Importance: With improved survival, heart failure (HF) has become a major complication for individuals with human immunodeficiency virus (HIV) infection. It is unclear if this risk extends to different types of HF in the antiretroviral therapy (ART) era. Determining whether HIV infection is associated with HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF), or both is critical because HF types differ with respect to underlying mechanism, treatment, and prognosis. Objectives: To investigate whether HIV infection increases the risk of future HFrEF and HFpEF and to assess if this risk varies by sociodemographic and HIV-specific factors. Design, Setting, and Participants: This study evaluated 98 015 participants without baseline cardiovascular disease from the Veterans Aging Cohort Study, an observational cohort of HIV-infected veterans and uninfected veterans matched by age, sex, race/ethnicity, and clinical site, enrolled on or after April 1, 2003, and followed up through September 30, 2012. The dates of the analysis were October 2015 to November 2016. Exposure: Human immunodeficiency virus infection. Main Outcomes and Measures: Outcomes included HFpEF (EF≥50%), borderline HFpEF (EF 40%-49%), HFrEF (EF