Biology subjects

Curtis, H. J.

Publications and source records attributed to Curtis, H. J..

3 recordsLinked to original sources

bioRxiv2018

Measuring the Impact of an Open Online Prescribing Data Analysis Service on Clinical Practice: a Cohort Study in NHS England Data

BackgroundOpenPrescribing is a freely accessible service that enables any user to view and analyse NHS primary care prescribing data at the level of individual practices. This tool is intended to improve the quality, safety, and cost-effectiveness of prescribing.\n\nObjectivesWe set out to measure the impact of OpenPrescribing being viewed on subsequent prescribing.\n\nMethodsHaving pre-registered our protocol and code, we measured three different metrics of prescribing quality (mean percentile across 34 existing OpenPrescribing quality measures, available \"price-per-unit\" savings, and total \"low-priority prescribing\" spend) to see if they changed after CCG and practice pages were viewed. We also measured whether practices whose data were viewed on OpenPrescribing differed in prescribing, prior to viewing, to those who were not. We used fixed effects and between effects linear panel regression, to isolate change over time and differences between practices respectively. We adjusted for month of prescribing in the fixed effects model, to remove underlying trends in outcome measures.\n\nResultsWe found a reduction in available price-per-unit savings for both practices and CCGs after their pages were viewed. The saving was greater at the practice level (-{pound}40.42 per thousand patients per month, 95% confidence interval -54.04 to -26.01) than at CCG level (-{pound}14.70 per thousand patients per month, 95% confidence interval -25.56 to -3.84). We estimate a total saving since launch of {pound}243k at practice level and {pound}1.47m at CCG level between the feature launch and end of follow-up (August to November 2017) among practices viewed. If the observed savings from practices viewed were extrapolated to all practices, this would generate {pound}26.8m in annual savings for the NHS, approximately 20% of the total possible savings from this method. The other two measures were not different after CCGs/practices were viewed. Practices which were viewed had worse prescribing quality scores overall, prior to viewing.\n\nConclusionsWe found a clinically significant positive impact from use of OpenPrescribing, specifically for the class of savings opportunities that can only be identified by using this tool. We also show that it is possible to conduct a robust analysis of the impact of such an online service on clinical practice.

epidemiology↗

bioRxiv2017

Trends, geographic variation, and factors associated with prescribing of gluten-free foods in English primary care: a cross sectional study

BackgroundThere is substantial disagreement about whether gluten-free foods should be prescribed on the NHS. We aim to describe time trends, variation and factors associated with prescribing gluten-free foods in England.\n\nMethodsWe described long-term national trends in gluten-free prescribing, and practice and Clinical Commissioning Group (CCG) level monthly variation in the rate of gluten-free prescribing (per 1000 patients) over time. We used a mixed effect poisson regression model to determine factors associated with gluten-free prescribing rate.\n\nResultsThere were 1.3 million gluten-free prescriptions between July 2016 and June 2017, down from 1.8 million in 2012/13, with a corresponding cost reduction from {pound}25.4m to {pound}18.7m. There was substantial variation in prescribing rates among practices (range 0 to 148 prescriptions per 1000 patients, interquartile range 7.3 to 31.8), driven in part by substantial variation at the CCG level, likely due to differences in prescribing policy. Practices in the most deprived quintile of deprivation score had a lower prescribing rate than those in the highest quintile (incidence rate ratio 0.89, 95% confidence interval 0.87-0.91). This is potentially a reflection of the lower rate of diagnosed coeliac disease in more deprived populations.\n\nConclusionGluten-free prescribing is in a state of flux, with substantial clinically unwarranted variation between practices and CCGs.\n\nStrengths and weaknesses of the studyO_LIWe were able to measure the prescribing of gluten-free foods across all prescribing in England, eliminating bias. We also removed seasonal variation by aggregating savings over 12 months.\nC_LIO_LIAs well as gluten-free prescribing variation at practice and CCG level, we have described long-term prescribing trends at national level, back to 1998.\nC_LIO_LIUsing the available data, we were unable to look at gluten-free prescribing at prescriber level, or investigate factors associated with prescribing to individual patients\nC_LI

epidemiology↗

bioRxiv2017

Trends and variation in Prescribing of Low-Priority Medicines Identified by NHS England: A Cross-Sectional Study and Interactive Data Tool in English Primary Care

BackgroundRoutine accessible audit of prescribing data presents significant opportunities to identify cost-saving opportunities. NHS England recently announced a consultation seeking to discourage use of medicines it considers to be low-value. We set out to produce an interactive data resource to show savings in each NHS general practice, and to assess the current use of these medicines, their change in use over time, and the extent and reasons for variation in such prescribing.\n\nResultsThe total NHS spend on all low-value medicines identified by NHS England was {pound}153.5m in the last year, across 5.8m prescriptions (mean {pound}26 per prescription). Among individual medications, liothyronine had the highest prescribing cost at {pound}29.6m, followed by trimipramine ({pound}20.2m) and gluten-free foods ({pound}18.7m). Over time, the overall total number of low-value prescriptions decreased, but the cost increased, although this varied greatly between medications. Annual practice level spending varied widely (median, {pound}2,262 per thousand patients, IQR {pound}1,439 to {pound}3,298). The proportion of patients over 65 showed the strongest association with low-value prescribing; CCG was also strongly associated. Our interactive data tool was deployed to OpenPrescribing.net where monthly updated figures and graphs can be viewed.\n\nConclusionsPrescribing of low-value medications is extensive but varies widely by medication, geographic area and individual practice. Despite a fall in prescription numbers, the overall cost of prescribing for low-value items has risen. Prescribing behaviour is clustered by CCG, which may represent variation in medicines optimisation efficiency, or in some cases access inequality.\n\nAbbreviations

epidemiology↗