Can peer effects explain prescribing appropriateness? a social network analysis

BMC Medical Research Methodology

Research Article

Nicolas Larrain¹  Sophie Y. Wang²  Oliver Groene³ 

[1] Hamburg Center for Health Economics, Esplanade 36, 20354, Hamburg, Germany;Employment, Labour and Social Affairs, Health Division, OECD, 2 Rue André Pascal, Cedex 16, 75775, Paris, France;Hamburg Center for Health Economics, Esplanade 36, 20354, Hamburg, Germany;OptiMedis AG, Buchardstraße 17, 20095, Hamburg, Germany;Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada;OptiMedis AG, Buchardstraße 17, 20095, Hamburg, Germany;Faculty of Management, Economics and Society, University of Witten, Alfred-Herrhausen-Straße 50, 58455, HerdeckeWitten, Germany;

关键词: Social network analysis;    Inappropriate prescribing;    Physician networks;    Potentially inappropriate medications;    Peer effect;   

DOI  :  10.1186/s12874-023-02048-7

 received in 2022-05-17, accepted in 2023-09-25,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundOptimizing prescribing practices is important due to the substantial clinical and financial costs of polypharmacy and an increasingly aging population. Prior research shows the importance of social relationships in driving prescribing behaviour. Using social network analysis, we examine the relationship between a physician practices’ connectedness to peers and their prescribing performance in two German regions.MethodsWe first mapped physician practice networks using links established between two practices that share 8 or more patients; we calculated network-level (density, average path length) and node-level measures (degree, betweenness, eigenvector). We defined prescribing performance as the total number of inappropriate medications prescribed or appropriate medications not prescribed (PIMs) to senior patients (over the age of 65) during the calendar year 2016. We used FORTA (Fit fOR The Aged) algorithm to classify medication appropriateness. Negative binomial regression models estimate the association between node-level measures and prescribing performance of physician practices controlling for patient comorbidity, provider specialization, percentage of seniors in practice, and region. We conducted two sensitivity analyses to test the robustness of our findings – i) limiting the network mapping to patients younger than 65; ii) limiting the network ties to practices that share more than 25 patients.ResultsWe mapped two patient-sharing networks including 436 and 270 physician practices involving 28,508 and 20,935 patients and consisting of 217,126 and 154,274 claims in the two regions respectively. Regression analyses showed a practice’s network connectedness as represented by degree, betweenness, and eigenvector centrality, is significantly negatively associated with prescribing performance (degree—bottom vs. top quartile aRR = 0.04, 95%CI: 0.035,0.045; betweenness—bottom vs. top quartile aRR = 0.063 95%CI: 0.052,0.077; eigenvector—bottom vs. top quartile aRR = 0.039, 95%CI: 0.034,0.044).ConclusionsOur study provides evidence that physician practice prescribing performance is associated with their peer connections and position within their network. We conclude that practices occupying strategic positions at the edge of networks with advantageous access to novel information are associated with better prescribing outcomes, whereas highly connected practices embedded in insulated information environments are associated with poor prescribing performance.

【 授权许可】

CC BY   
© The Author(s) 2023

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