Perivoj dvorca Batthyany

The authors are grateful to the North York General Foundation for financial support through the Exploration Fund. Dr. Greiver holds an investigator award from the Department of Family and Community Medicine, University of Toronto and was supported by a research stipend from North York General Hospital.Patients with chronic obstructive pulmonary disease (COPD) or heart failure (HF) are frequently cared for in hospital and in primary care settings. We studied labeling agreement for COPD and HF for patients seen in both settings in Toronto, Canada. This was a retrospective observational study using linked hospital-primary care electronic data from 70 family physicians. Patients were 20 years of age or more and had at least one visit in both settings between 1 January 2012 and 31 December 2014. We recorded labeling concordance and associations with clinical factors. We used capture-recapture models to estimate the size of the populations. COPD concordance was 34%; the odds ratios (ORs) of concordance increased with aging (OR 1.84 for age 75+ vs. <65, 95% CI 0.92–3.69) and more inpatient admissions (OR 2.89 for 3+ visits vs. 0 visits, 95% CI 1.59–5.26). HF concordance was 33%; the ORs of concordance decreased with aging (OR 0.39 for 75+ vs. <65, 95% CI 0.18–0.86) and increased with more admissions (OR = 2.39; 95% CI 1.33–4.30 for 3+ visits vs. 0 visits). Based on capture-recapture models, 21–24% additional patients with COPD and18–20% additional patients with HF did not have a label in either setting. The primary care prevalence was estimated as 748 COPD patients and 834 HF patients per 100,000 enrolled adult patients. Agreement levels for COPD and HF were low and labeling was incomplete. Further research is needed to improve labeling for these conditions.Publisher PDFPeer reviewe

Our data, our society, our health: A vision for inclusive and transparent health data science in the United Kingdom and beyond

This paper is the work of the first cohort of the Farr Institute's “Future Leaders” scheme. The Future Leaders programme was funded by the Farr Institute and was financially supported by the authors' institutions or grants.The last 6 years have seen sustained investment in health data science in the United Kingdom and beyond, which should result in a data science community that is inclusive of all stakeholders, working together to use data to benefit society through the improvement of public health and well‐being. However, opportunities made possible through the innovative use of data are still not being fully realised, resulting in research inefficiencies and avoidable health harms. In this paper, we identify the most important barriers to achieving higher productivity in health data science. We then draw on previous research, domain expertise, and theory to outline how to go about overcoming these barriers, applying our core values of inclusivity and transparency. We believe a step change can be achieved through meaningful stakeholder involvement at every stage of research planning, design, and execution and team‐based data science, as well as harnessing novel and secure data technologies. Applying these values to health data science will safeguard a social licence for health data research and ensure transparent and secure data usage for public benefit.Publisher PDFPeer reviewe