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Abstract The loss and degradation of forests and other ecosystems worldwide threaten both global biodiversity and the livelihoods of people who use natural resources. Understanding how natural resource use impacts landscape provisioning services for both people and wildlife is thus critical for designing comprehensive resource management strategies. We used data from community focus groups, botanical plots and an inventory of plant species consumed by the Critically Endangered red‐ruffed lemur (Varecia rubra) to assess the availability of key provisioning services for people and endemic wildlife on the Masoala Peninsula, a rainforest transformation landscape, in northeastern Madagascar (Masoala National Park and 13 surrounding communities). We constructed Poisson regression mixed models to evaluate the impact of community factors (i.e. community population size, plot distance to community) and changes over time on the count and species richness of timber trees, medicinal plants and red‐ruffed lemur food trees within botanical plots. Over three‐quarters of all plant species could be used for at least one purpose by local communities (n = 238 species). Of the 59 V. rubra food tree species, only 15% had no reported human use. Timber and ruffed lemur food tree availability declined both with community population size and time and were predicted to be lower outside of Masoala National Park. In contrast, medicinal plant availability was not strongly predicted by any tested factors. Provisioning service availability also differed strongly across sites, suggesting that additional, untested proxies of human pressure likely also have an effect. Our results highlight the importance of evaluating natural resource availability from a community‐based perspective and by resource purpose to inform forest landscape restoration efforts that can support both people and wildlife. Read the free Plain Language Summary for this article on the Journal blog.

Objective Venous thromboembolism (VTE) risk reduction strategies include early initiation of chemoprophylaxis, reducing missed doses, weight-based dosing and dose adjustment using anti-Xa levels. We hypothesized that time to initiation of chemoprophylaxis would be the strongest modifiable risk for VTE, even after adjusting for competing risk factors.Methods A prospectively maintained trauma registry was queried for patients admitted July 2017–October 2021 who were 18 years and older and received emergency release blood products. Patients with deep vein thrombosis or pulmonary embolism (VTE) were compared to those without (no VTE). Door-to-prophylaxis was defined as time from hospital arrival to first dose of VTE chemoprophylaxis (hours). Univariate and multivariate analyses were then performed between the two groups.Results 2047 patients met inclusion (106 VTE, 1941 no VTE). There were no differences in baseline or demographic data. VTE patients had higher injury severity score (29 vs 24), more evidence of shock by arrival lactate (4.6 vs 3.9) and received more post-ED transfusions (8 vs 2 units); all p

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 Primària 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

BACKGROUND: Hypofibrinogenemia has been shown to predict massive transfusion and is associated with higher mortality in severely injured patients. However, the role of empiric fibrinogen replacement in bleeding trauma patients remains controversial. We sought to determine the effect of empiric cryoprecipitate as an adjunct to a balanced transfusion strategy (1:1:1). STUDY DESIGN: This study is a subanalysis of patients treated at the single US trauma center in a multicenter randomized controlled trial. Trauma patients (more than 15 years) were eligible if they had evidence of active hemorrhage requiring emergent surgery or interventional radiology, massive transfusion protocol (MTP) activation, and received at least 1 unit of blood. Transfer patients, those with injuries incompatible with life, or those injured more than 3 hours earlier were excluded. Patients were randomized to standard MTP (STANDARD) or MTP plus 3 pools of cryoprecipitate (CRYO). Primary outcomes included all-cause mortality at 28 days. Secondary outcomes were transfusion requirements, intraoperative and postoperative coagulation laboratory values, and quality-of-life measures (Glasgow outcome score-extended). RESULTS: Forty-nine patients (23 in the CRYO group and 26 in the STANDARD group) were enrolled between May 2021 and October 2021. Time to randomization was similar between groups (14 vs 24 minutes, p = 0.676). Median time to cryoprecipitate was 41 minutes (interquartile range 37 to 48). There were no differences in demographics, arrival physiology, laboratory values, or injury severity. Intraoperative and ICU thrombelastography values, including functional fibrinogen, were similar between groups. There was no benefit to CRYO with respect to post-emergency department transfusions (intraoperative and ICU through 24 hours), complications, Glasgow outcome score, or mortality. CONCLUSIONS: In this study of severely injured, bleeding trauma patients, empiric cryoprecipitate did not improve survival or reduce transfusion requirements. Cryoprecipitate should continue as an "on-demand" addition to a balanced transfusion strategy, guided by laboratory values and should not be given empirically. [ABSTRACT FROM AUTHOR]

Operant behavior can reflect the influence of goal‐directed and habitual processes. These can be distinguished by changes to response rate following devaluation of the reinforcing outcome. Whether a response is goal directed or habitual depends on whether devaluation affects response rate. Response rate can be decomposed into frequencies of bouts and pauses by analyzing the distribution of interresponse times. This study sought to characterize goal‐directed and habitual behaviors in terms of bout‐initiation rate, within‐bout response rate, bout length, and bout duration. Data were taken from three published studies that compared sensitivity to devaluation following brief and extended training with variable‐interval schedules. Analyses focused on goal‐directed and habitual responding, a comparison of a habitual response to a similarly trained response that had been converted back to goal‐directed status after a surprising event, and a demonstration of contextual control of habit and goal direction in the same subjects. Across experiments and despite responses being clearly distinguished as goal directed and habitual by total response rate, analyses of bout‐initiation rate, within‐bout rate, bout length, and bout duration did not reveal a pattern that distinguished goal‐directed from habitual responding. [ABSTRACT FROM AUTHOR]