Search results
Found 18097 matches for
We lead multidisciplinary applied research and training to rethink the way health care is delivered in general practice and across the community.
Comparison of methods for predicting COVID-19-related death in the general population using the OpenSAFELY platform.
BACKGROUND: Obtaining accurate estimates of the risk of COVID-19-related death in the general population is challenging in the context of changing levels of circulating infection. METHODS: We propose a modelling approach to predict 28-day COVID-19-related death which explicitly accounts for COVID-19 infection prevalence using a series of sub-studies from new landmark times incorporating time-updating proxy measures of COVID-19 infection prevalence. This was compared with an approach ignoring infection prevalence. The target population was adults registered at a general practice in England in March 2020. The outcome was 28-day COVID-19-related death. Predictors included demographic characteristics and comorbidities. Three proxies of local infection prevalence were used: model-based estimates, rate of COVID-19-related attendances in emergency care, and rate of suspected COVID-19 cases in primary care. We used data within the TPP SystmOne electronic health record system linked to Office for National Statistics mortality data, using the OpenSAFELY platform, working on behalf of NHS England. Prediction models were developed in case-cohort samples with a 100-day follow-up. Validation was undertaken in 28-day cohorts from the target population. We considered predictive performance (discrimination and calibration) in geographical and temporal subsets of data not used in developing the risk prediction models. Simple models were contrasted to models including a full range of predictors. RESULTS: Prediction models were developed on 11,972,947 individuals, of whom 7999 experienced COVID-19-related death. All models discriminated well between individuals who did and did not experience the outcome, including simple models adjusting only for basic demographics and number of comorbidities: C-statistics 0.92-0.94. However, absolute risk estimates were substantially miscalibrated when infection prevalence was not explicitly modelled. CONCLUSIONS: Our proposed models allow absolute risk estimation in the context of changing infection prevalence but predictive performance is sensitive to the proxy for infection prevalence. Simple models can provide excellent discrimination and may simplify implementation of risk prediction tools.
Trends and variation in prescribing of suboptimal statin treatment regimes: a cohort study in English primary care
Objectives We set out to describe trends and variation in statin prescribing in England that breaches 2014 national guidance on “high-intensity” statins. We identify factors associated with breaching; and assess the feasibility of rapid prescribing behaviour change. Design, Setting and Participants Retrospective cohort study in NHS primary care in England, including all 8,142 standard general practices from August 2010 to March 2019. Main Outcome Measures We categorised statins as high or low/medium-intensity based on two different thresholds, and calculated the proportion prescribed below these thresholds across all practices. We plotted trends and geographical variation, carried out mixed effects logistic regression to identify practice characteristics associated with breaching guidance, and used indicator saturation to identify practices exhibiting sudden changes in prescribing. Results We included all 8,142 practices across the study period. The proportion of statin prescriptions below the recommended 40% LDL-lowering threshold decreased gradually since 2012 from 80% to 45%; the proportion below a pragmatic 37% threshold decreased from 30% to 18%. The 2014 guidance had minimal impact on these trends. We found wide variation between practices (interdecile ranges 20% to 85% and 10% to 30% respectively in 2018). Mixed effects logistic regression did not identify practice characteristics strongly associated with breaching guidance. Indicator saturation identified several practices exhibiting sudden changes in prescribing towards greater guideline compliance. Conclusions Breaches of English guidance on choice of statin remain common, with substantial variation between GP practices. Some practices and regions have implemented rapid change, indicating the feasibility of rapid prescribing behaviour change. We discuss the potential for a national strategic approach, using data and evidence to optimise care, including targeted education alongside audit and feedback to outliers through services such as OpenPrescribing. Summary What is already known on this topic English national guidance recommends the use of a high-intensity statin, capable of reducing LDL (low-density lipoprotein) cholesterol by 40% or more. Adherence at the time of guideline release was low, but has not been documented since. What this study adds Adherence is improving, but breaches of national guidance remain common, with 45% of prescriptions below the recommended strength, and there is very substantial variation between practices. Some practices have exhibited rapid positive change in prescribing, which indicates that better adherence could readily be achieved. We have produced a live data tool allowing anyone to explore any practice’s current statin prescribing behaviour.
During the COVID-19 pandemic 20 000 prostate cancer diagnoses were missed in England
Objectives: To investigate the effect of the COVID-19 pandemic on prostate cancer incidence, prevalence, and mortality in England. Patients and Methods: With the approval of NHS England and using the OpenSAFELY-TPP dataset of 24 million patients, we undertook a cohort study of men diagnosed with prostate cancer. We visualised monthly rates in prostate cancer incidence, prevalence, and mortality per 100 000 adult men from January 2015 to July 2023. To assess the effect of the pandemic, we used generalised linear models and the pre-pandemic data to predict the expected rates from March 2020 as if the pandemic had not occurred. The 95% confidence intervals (CIs) of the predicted values were used to estimate the significance of the difference between the predicted and observed rates. Results: In 2020, there was a drop in recorded incidence by 4772 (31%) cases (15 550 vs 20 322; 95% CI 19 241–21 403). In 2021, the incidence started to recover, and the drop was 3148 cases (18%, 17 950 vs 21 098; 95% CI 19 740–22 456). By 2022, the incidence returned to the levels that would be expected. During the pandemic, the age at diagnosis shifted towards older men. In 2020, the average age was 71.6 (95% CI 71.5–71.8) years, in 2021 it was 71.8 (95% CI 71.7–72.0) years as compared to 71.3 (95% CI 71.1–71.4) years in 2019. Conclusions: Given that our dataset represents 40% of the population, we estimate that proportionally the pandemic led to 20 000 missed prostate cancer diagnoses in England alone. The increase in incidence recorded in 2023 was not enough to account for the missed cases. The prevalence of prostate cancer remained lower throughout the pandemic than expected. As the recovery efforts continue, healthcare should focus on finding the men who were affected. The research should focus on investigating the potential harms to men diagnosed at older age.
Measuring the Impact of an Open Online Prescribing Data Analysis Service on Clinical Practice: a Cohort Study in NHS England Data
Background OpenPrescribing 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. Objectives We set out to measure the impact of OpenPrescribing being viewed on subsequent prescribing. Methods Having 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. Results We 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 (−£40.42 per thousand patients per month, 95% confidence interval −54.04 to −26.01) than at CCG level (−£14.70 per thousand patients per month, 95% confidence interval −25.56 to −3.84). We estimate a total saving since launch of £243k at practice level and £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 £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. Conclusions We 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.
Why did some practices not implement new antibiotic prescribing guidelines on urinary tract infection? A cohort study and survey in NHS England primary care
Objectives To describe prescribing trends and geographic variation for trimethoprim and nitrofurantoin; to describe variation in implementing guideline change; and to compare actions taken to reduce trimethoprim use in high- and low-using Clinical Commissioning Groups (CCGs). Design A retrospective cohort study and interrupted time series analysis in English NHS primary care prescribing data; complemented by information obtained through Freedom of Information Act requests to CCGs. The main outcome measures were: variation in practice and CCG prescribing ratios geographically and over time, including an interrupted time-series; and responses to Freedom of Information requests. Results The amount of trimethoprim prescribed, as a proportion of nitrofurantoin and trimethoprim combined, remained stable and high until 2014, then fell gradually to below 50% in 2017; this reduction was more rapid following the introduction of the Quality Premium. There was substantial variation in the speed of change between CCGs. As of April 2017, for the 10 worst CCGs (with the highest trimethoprim ratios): 9 still had trimethoprim as first line treatment for uncomplicated UTI (one CCG had no formulary); none had active work plans to facilitate change in prescribing behaviour away from trimethoprim; and none had implemented an incentive scheme for change in prescribing behaviour. For the 10 best CCGs: 2 still had trimethoprim as first line treatment (all CCGs had a formulary); 5 (out of 7 who answered this question) had active work plans to facilitate change in prescribing behaviour away from trimethoprim; and 5 (out of 10 responding) had implemented an incentive scheme for change in prescribing behaviour. 9 of the best 10 CCGs reported at least one of: formulary change, work plan, or incentive scheme. None of the worst 10 CCGs did so. Conclusions Many CCGs failed to implement an important change in antibiotic prescribing guidance; and report strong evidence suggesting that CCGs with minimal prescribing change did little to implement the new guidance. We strongly recommend a national programme of training and accreditation for medicines optimisation pharmacists; and remedial action for CCGs that fail to implement guidance; with all materials and data shared publicly for both such activities.
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
Background Routine 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. Results The total NHS spend on all low-value medicines identified by NHS England was £153.5m in the last year, across 5.8m prescriptions (mean £26 per prescription). Among individual medications, liothyronine had the highest prescribing cost at £29.6m, followed by trimipramine (£20.2m) and gluten-free foods (£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, £2,262 per thousand patients, IQR £1,439 to £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. Conclusions Prescribing 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. Abbreviations GP General Practice NHS National Health Service NICE National Institute for Health and Care Excellence PCA Prescription cost Analysis QOF Quality Outcomes Framework
Trends, geographic variation, and factors associated with prescribing of gluten-free foods in English primary care: a cross sectional study
Background There 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. Methods We 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. Results There 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 £25.4m to £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. Conclusion Gluten-free prescribing is in a state of flux, with substantial clinically unwarranted variation between practices and CCGs. Strengths and weaknesses of the study We 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. As well as gluten-free prescribing variation at practice and CCG level, we have described long-term prescribing trends at national level, back to 1998. Using the available data, we were unable to look at gluten-free prescribing at prescriber level, or investigate factors associated with prescribing to individual patients
The clinician impact and financial cost to the NHS of litigation over pregabalin: a cohort study in English primary care
Objectives Following litigation over pregabalin’s second-use medical patent for neuropathic pain NHS England were required by the court to instruct GPs to prescribe the branded form (Lyrica) for pain. Pfizer’s patent was found invalid in 2015; a ruling subject to ongoing appeals. If the Supreme Court appeal in February 2018 is unsuccessful, the NHS can reclaim excess prescribing costs. We set out to describe the variation in prescribing of pregabalin as branded Lyrica, geographically and over time; to determine how clinicians responded to the NHS England instruction to GPs; and to model excess costs to the NHS attributable to the legal judgments. Setting English primary care Participants English general practices Primary and secondary outcome measures Variation in prescribing of branded Lyrica across the country before and after the NHS England instruction, by practice and by Clinical Commissioning Group (CCG); excess prescribing costs. Results The proportion of pregabalin prescribed as Lyrica increased, from 0.3% over six months before the NHS England instruction (September 2014-February 2015) to 25.7% afterwards (April - September 2015). Although 70% of pregabalin is estimated to be for neuropathic pain, only 11.6% of practices prescribed Lyrica at this level; the median proportion prescribed as Lyrica was 8.8% (IQR 1.1-41.9%). If pregabalin had come entirely off patent in September 2015, and Pfizer had not appealed, we estimate the NHS would have spent £502m less on pregabalin to July 2017. Conclusion NHS England instructions to GPs regarding branded prescription of pregabalin were widely ignored, and have created much debate around clinical independence in prescribing. Protecting revenue from “skinny labels” will pose a challenge. If Pfizer’s final appeal on the patent is unsuccessful the NHS can seek reimbursement of excess pregabalin prescribing costs, potentially £502m.
A New Mechanism To Identify Cost Savings in NHS Prescribing: Minimising “Price-Per-Unit”
Background Minimising prescription costs while maintaining quality is a core element of delivering high value healthcare. There are various strategies to achieve savings, but almost no research to date on determining the most effective approach. We describe a new method of identifying potential savings due to large national variations in drug cost, including variation in generic drug cost; and compare these with potential savings from an established method (generic prescribing). Methods We used English NHS Digital prescribing data, from October 2015 to September 2016. Potential cost savings were calculated by determining the price-per-unit (e.g. pill, ml) for each drug and dose within each general practice. This was compared against the same cost for the practice at the lowest cost decile, to determine achievable savings. We compared these price-per-unit savings to the savings possible from generic switching; and determined the chemicals with the highest savings nationally. A senior pharmacist manually assessed whether a random sample of savings were practically achievable. Results We identified a theoretical maximum of £410M of savings over 12 months. £273M of these savings were for individual prescribing changes worth over £50 per practice per month; this compares favorably with generic switching, where only £35M of achievable savings were identified. The biggest savings nationally were on glucose blood testing reagents (£12M), fluticasone propionate (£9M) and venlafaxine (£8M). Approximately half of all savings were deemed practically achievable. Discussion We have developed a new method to identify and enable large potential cost savings within NHS community prescribing. Given the current pressures on the NHS, it is vital that these potential savings are realised. Our tool enabling doctors to achieve these savings is now launched in pilot form. However savings could potentially be achieved more simply through national policy change. Abbreviations BNF British National Formulary CCG Clinical Commissioning Group GP General Practice MR Modified Release NHS National Health Service NIC Net Ingredient Cost NP8 Non-Part VIII, i.e. drugs not listed in Part VIII of the Drug Tariff PPU Price-Per-Unit
Accuracy of parents’ subjective assessment of paediatric fever with thermometer measured fever in a primary care setting
Background: Fever is a common symptom of benign childhood illness but a high fever may be a sign of a serious infection. Temperature is often used by parents to check for illness in their children, and the presence of a high temperature can act as a prompt to consult a healthcare professional. It would be helpful for GPs to understand how well parental assessment of the presence of fever correlates with temperature measurement in the clinic in order to incorporate the history of the child’s fever into their clinical assessment. Methods: Secondary analysis of a cross-sectional diagnostic method comparison study. Parents were asked whether they thought their child had fever before their temperature was measured by a researcher. Fever was defined as a temperature of 38 °C and higher using either an axillary or tympanic thermometer. Results: Of 399 children recruited, 119 (29.8%) were believed by their parents to be febrile at the time of questioning and 23 (6.3%) had a fever as measured by a researcher in the clinic. 23.5% of children with a parental assessment of fever were found to have a fever in the clinic. Less than 1% of children whose parents thought they did not have a fever were found to be febrile in the clinic. Having more than one child did not improve accuracy of parents assessing fever in their child. Conclusions: In the GP surgery setting, a child identified as afebrile by their parent is highly likely to be measured as such in the clinic. A child identified as febrile by their parent is less likely to be measured as febrile.