Roy* 1, 2, L. Wise1, S. Shakir1, 2<\/p>\n\n\n\n
1Drug Safety Research Unit, Southampton, 2University of Portsmouth, Portsmouth, United Kingdom<\/p>\n\n\n\n
Prescribing guidelines influence treatment choice based on patient and healthcare system factors. Multi-level modelling can provide insight into sources of variability in healthcare, especially where nested hierarchical structures exist.[1] A Specialist Cohort Event Monitoring study investigated the safety and use of rivaroxaban in clinical use, with a warfarin cohort for context.<\/p>\n\n\n\n
Study to investigate prescribing variability using Multi-level modelling.<\/p>\n\n\n\n
Data on NHS acute trusts in England\/Wales (e.g. population size, trust type) were linked to study patient demographic and drug utilization data, and prescriber details (e.g. degree, specialty).<\/p>\n\n\n\n
Using Multi-level modelling we explored the influence of patient, prescriber and trust characteristics on prescribing variability in 2106 rivaroxaban (59%) vs. 1468 warfarin (41%) adult patients nested in 780 prescribers, nested in 73 trusts. The majority of patients had an indication of DVT\/PE (56.4%) or non-valvular AF (AF) (41.2%). The binary outcome was rivaroxaban or warfarin treatment.<\/p>\n\n\n\n
Variance components estimate the variability accounted for by each level in the model and are expressed as: Median Odd Ratios (MOR \u2013 median relative increase in odds of rivaroxaban treatment if patient changed prescriber (PR) or trust (T)); Proportional Change in Variance (PCV) between models when successively adding fixed effects.<\/p>\n\n\n\n
DVT\/PE group:<\/p>\n\n\n\n
Adjusting for patient factors, MORT=6.9 (PCV=-0.6%); MORPR=2.8 (PCV=3.8%).<\/p>\n\n\n\n
Adjusting for patient and prescriber factors, MORT=6.8 (PCV=-2.9%); MORPR=2.6 (PCV=-15.4%).<\/p>\n\n\n\n
Adjusting for patient, prescriber and trust factors, MORT=4.9 (PCV=-30.2%); MORPR=2.6 (PCV=3.4%).<\/p>\n\n\n\n
Differences between trusts and prescribers (in trusts) are important in treatment choice; trust being more influential. Some patient factors had a relatively large effect on odds of treatment choice although the absolute number of patient impacted was often small. Trust type, was shown to be associated with the odds of treatment choice [Final model: foundation vs acute trusts OR 4.0 (95%CI 1.5, 9.9)].<\/p>\n\n\n\n
Data on AF and all indications (combined) will be included in the final presentation.<\/p>\n\n\n\n
This study highlights the utility of Multi-level modelling in exploring patient and non-patient factors in nested hierarchical healthcare settings. Prescribing variability appears dominated by differences between trusts and prescribers (in trusts). Some patient factors were important in treatment choice, but PCV between models suggest that accounting for patient differences does not fully explain the variance between prescribers (in trusts) and between trusts.<\/p>\n\n\n\n
1. Zuidgeest MG, van Dijk L, Spreeuwenberg P, Smit HA, Brunekreef B, Arets HG, et al. What drives prescribing of asthma medication to children? A multilevel population-based study. Ann Fam Med. 2009;7(1):32-40.<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"_acf_changed":false},"categories":[45],"tags":[],"class_list":["post-4813","abstract","type-abstract","status-publish","hentry","category-isop-18th-annual-meeting-geneva-november-2018"],"acf":[],"yoast_head":"\n