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      Association between physician characteristics and payments from industry in 2015–2017: observational study

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          Abstract

          Objective

          To investigate the association between physician characteristics and the value of industry payments.

          Design

          Observational study.

          Setting and participants

          Using the 2015–2017 Open Payments reports of industry payments linked to the Physician Compare database, we examined the association between physician characteristics (physician sex, years in practice, medical school attended and specialty) and the industry payment value, adjusting for other physician characteristic and institution fixed effects (effectively comparing physicians practicing at the same institution).

          Main outcome measures

          Our primary outcome was the value of total industry payments to physicians including (1) general payments (all forms of payments other than those classified for research purpose, eg, consulting fees, food, beverage), (2) research payments (payments for research endeavours under a written contract or protocol) and (3) ownership interests (eg, stock or stock options, bonds). We also investigated each category of payment separately.

          Results

          Of 544 264 physicians treating Medicare beneficiaries, a total of $5.8 billion in industry payments were made to 365 801 physicians during 2015–2017. The top 5% of physicians, by cumulative payments, accounted for 91% of industry payments. Within the same institution, male physicians, physicians with 21–30 years in practice and physicians who attended top 50 US medical schools (based on the research ranking) received higher industry payments. Across specialties, orthopaedic surgeons, neurosurgeons and endocrinologists received the highest payments. When we investigated individual types of payment, we found that orthopaedic surgeons received the highest general payments; haematologists/oncologists were the most likely to receive research payments and surgeons were the most likely to receive ownership interests compared with other types of physicians.

          Conclusions

          Industry payments to physicians were highly concentrated among a small number of physicians. Male sex, longer length of time in clinical practice, graduated from a top-ranked US medical school and practicing certain specialties, were independently associated with higher industry payments.

          Related collections

          Most cited references31

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          Scope and impact of financial conflicts of interest in biomedical research: a systematic review.

          Despite increasing awareness about the potential impact of financial conflicts of interest on biomedical research, no comprehensive synthesis of the body of evidence relating to financial conflicts of interest has been performed. To review original, quantitative studies on the extent, impact, and management of financial conflicts of interest in biomedical research. Studies were identified by searching MEDLINE (January 1980-October 2002), the Web of Science citation database, references of articles, letters, commentaries, editorials, and books and by contacting experts. All English-language studies containing original, quantitative data on financial relationships among industry, scientific investigators, and academic institutions were included. A total of 1664 citations were screened, 144 potentially eligible full articles were retrieved, and 37 studies met our inclusion criteria. One investigator (J.E.B.) extracted data from each of the 37 studies. The main outcomes were the prevalence of specific types of industry relationships, the relation between industry sponsorship and study outcome or investigator behavior, and the process for disclosure, review, and management of financial conflicts of interest. Approximately one fourth of investigators have industry affiliations, and roughly two thirds of academic institutions hold equity in start-ups that sponsor research performed at the same institutions. Eight articles, which together evaluated 1140 original studies, assessed the relation between industry sponsorship and outcome in original research. Aggregating the results of these articles showed a statistically significant association between industry sponsorship and pro-industry conclusions (pooled Mantel-Haenszel odds ratio, 3.60; 95% confidence interval, 2.63-4.91). Industry sponsorship was also associated with restrictions on publication and data sharing. The approach to managing financial conflicts varied substantially across academic institutions and peer-reviewed journals. Financial relationships among industry, scientific investigators, and academic institutions are widespread. Conflicts of interest arising from these ties can influence biomedical research in important ways.
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            Pharmaceutical Industry-Sponsored Meals and Physician Prescribing Patterns for Medicare Beneficiaries.

            The association between industry payments to physicians and prescribing rates of the brand-name medications that are being promoted is controversial. In the United States, industry payment data and Medicare prescribing records recently became publicly available.
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              • Article: not found

              Fixed effects, random effects and GEE: what are the differences?

              For analyses of longitudinal repeated-measures data, statistical methods include the random effects model, fixed effects model and the method of generalized estimating equations. We examine the assumptions that underlie these approaches to assessing covariate effects on the mean of a continuous, dichotomous or count outcome. Access to statistical software to implement these models has led to widespread application in numerous disciplines. However, careful consideration should be paid to their critical assumptions to ascertain which model might be appropriate in a given setting. To illustrate similarities and differences that might exist in empirical results, we use a study that assessed depressive symptoms in low-income pregnant women using a structured instrument with up to five assessments that spanned the pre-natal and post-natal periods. Understanding the conceptual differences between the methods is important in their proper application even though empirically they might not differ substantively. The choice of model in specific applications would depend on the relevant questions being addressed, which in turn informs the type of design and data collection that would be relevant. Copyright (c) 2008 John Wiley & Sons, Ltd.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2019
                20 September 2019
                : 9
                : 9
                : e031010
                Affiliations
                [1 ] departmentEpidemiology , UCLA Fielding School of Public Health , Los Angeles, California, USA
                [2 ] departmentCardiology , Massachusetts General Hospital , Boston, Massachusetts, USA
                [3 ] departmentDepartment of Medicine , Harvard Medical School , Boston, Massachusetts, USA
                [4 ] Devoted Health , Waltham, Massachusetts, USA
                [5 ] departmentBiostatistics , UCLA Fielding School of Public Health , Los Angeles, California, USA
                [6 ] departmentDepartment of Medicine Statistics Core , UCLA David Geffen School of Medicine , Los Angeles, California, USA
                [7 ] departmentGeneral Internal Medicine and Health Services Research , UCLA David Geffen School of Medicine , Los Angeles, California, USA
                [8 ] departmentDepartment of Health Policy Management , UCLA Fielding School of Public Health , Los Angeles, California, USA
                Author notes
                [Correspondence to ] Dr Kosuke Inoue; koinoue@ 123456ucla.edu
                Author information
                http://orcid.org/0000-0002-1937-4833
                Article
                bmjopen-2019-031010
                10.1136/bmjopen-2019-031010
                6756347
                31542759
                b251239e-bd9f-4746-8719-e87f9a6d0229
                © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 10 April 2019
                : 26 July 2019
                : 23 August 2019
                Funding
                Funded by: KI was supported by the Burroughs Wellcome Fund Interschool Training Program in Chronic Diseases (BWF-CHIP), a Fellowship in Epidemiology at UCLA, and Heiwa Nakajima Foundation;
                Categories
                Health Services Research
                Original Research
                1506
                1704
                Custom metadata
                unlocked

                Medicine
                open payments,physician compare,physician characteristics,medicare beneficiaries
                Medicine
                open payments, physician compare, physician characteristics, medicare beneficiaries

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