Rachael Williams

Cardiovascular safety of vildagliptin in patients with type 2 diabetes: A European multi-database, non-interventional post-authorization safety study

The aim of this non‐interventional, multi‐database, analytical cohort study was to assess the cardiovascular (CV) safety of vildagliptin vs other non‐insulin antidiabetic drugs (NIADs) using real‐world data from 5 European electronic healthcare databases. Patients with type 2 diabetes aged ≥18 years on NIAD treatment were enrolled. Adjusted incidence rate ratios (IRRs) and 95% confidence intervals (CIs) for the outcomes of interest (myocardial infarction [MI], acute coronary syndrome [ACS], stroke, congestive heart failure [CHF], individually and as a composite) were estimated using negative binomial regression. Approximately 2.8% of the enrolled patients (n = 738 054) used vildagliptin at any time during the study, with an average follow‐up time of 1.4 years, resulting in a cumulative current vildagliptin exposure of 28 330 person‐years. The adjusted IRRs (vildagliptin [±other NIADs] vs other NIADs) were in the range of 0.61 to 0.97 (MI), 0.55 to 1.60 (ACS), 0.02 to 0.77 (stroke), 0.49 to 1.03 (CHF), and 0.22 to 1.02 (composite CV outcomes). The IRRs and their 95% CIs were close to 1, demonstrating no increased risk of adverse CV events, including the risk of CHF, with vildagliptin vs other NIADs in real‐world conditions.

The healthcare costs of heart failure during the last five years of life: A retrospective cohort study

BACKGROUND Evidence on the economic impact of heart failure (HF) is vital in order to predict the cost-effectiveness of novel interventions. We estimate the health system costs of HF during the last five years of life. METHODS We used linked primary care and mortality data accessed through the Clinical Practice Research Datalink (CPRD) to identify 1555 adults in England who died with HF in 2012/13. We used CPRD and linked Hospital Episode Statistics to estimate the cost of medications, primary and hospital healthcare. Using GLS regression we estimated the relationship between costs, HF diagnosis, proximity to death and patient characteristics. RESULTS In the last 3months of life, healthcare costs were £8827 (95% CI £8357 to £9296) per patient, more than 90% of which were for inpatient or critical care. In the last 3months, patients spent on average 17.8 (95% CI 16.8 to 18.8) days in hospital and had 8.8 (95% CI 8.4 to 9.1) primary care consultations. Most (931/1555; 59.9%) patients were in hospital on the day of death. Mean quarterly healthcare costs in quarters after HF diagnosis were higher (£1439; [95% CI £1260 to £1619]) than in quarters preceding diagnosis. Older patients and patients with lower comorbidity scores had lower costs. CONCLUSIONS Healthcare costs increase sharply at the end of life and are dominated by hospital care. There is potential to save money by implementation and evaluation of interventions that are known to reduce hospitalisations for HF, particularly at the end of life.

PLEASANT: Preventing and Lessening Exacerbations of Asthma in School-age children Associated with a New Term - a cluster randomised controlled trial and economic evaluation

BACKGROUND Asthma episodes and deaths are known to be seasonal. A number of reports have shown peaks in asthma episodes in school-aged children associated with the return to school following the summer vacation. A fall in prescription collection in the month of August has been observed, and was associated with an increase in the number of unscheduled contacts after the return to school in September. OBJECTIVE The primary objective of the study was to assess whether or not a NHS-delivered public health intervention reduces the September peak in unscheduled medical contacts. DESIGN Cluster randomised trial, with the unit of randomisation being 142 NHS general practices, and trial-based economic evaluation. SETTING Primary care. INTERVENTION A letter sent (n = 70 practices) in July from their general practitioner (GP) to parents/carers of school-aged children with asthma to remind them of the importance of taking their medication, and to ensure that they have sufficient medication prior to the start of the new school year in September. The control group received usual care. MAIN OUTCOME MEASURES The primary outcome measure was the proportion of children aged 5-16 years who had an unscheduled medical contact in September 2013. Supporting end points included the proportion of children who collected prescriptions in August 2013 and unscheduled contacts through the following 12 months. Economic end points were quality-adjusted life-years (QALYs) gained and costs from an NHS and Personal Social Services perspective. RESULTS There is no evidence of effect in terms of unscheduled contacts in September. Among children aged 5-16 years, the odds ratio (OR) was 1.09 [95% confidence interval (CI) 0.96 to 1.25] against the intervention. The intervention did increase the proportion of children collecting a prescription in August (OR 1.43, 95% CI 1.24 to 1.64) as well as scheduled contacts in the same month (OR 1.13, 95% CI 0.84 to 1.52). For the wider time intervals (September-December 2013 and September-August 2014), there is weak evidence of the intervention reducing unscheduled contacts. The intervention did not reduce unscheduled care in September, although it succeeded in increasing the proportion of children collecting prescriptions in August as well as having scheduled contacts in the same month. These unscheduled contacts in September could be a result of the intervention, as GPs may have wanted to see patients before issuing a prescription. The economic analysis estimated a high probability that the intervention was cost-saving, for baseline-adjusted costs, across both base-case and sensitivity analyses. There was no increase in QALYs. LIMITATION The use of routine data led to uncertainty in the coding of medical contacts. The uncertainty was mitigated by advice from a GP adjudication panel. CONCLUSIONS The intervention did not reduce unscheduled care in September, although it succeeded in increasing the proportion of children both collecting prescriptions and having scheduled contacts in August. After September there is weak evidence in favour of the intervention. The intervention had a favourable impact on costs but did not demonstrate any impact on QALYs. The results of the trial indicate that further work is required on assessing and understanding adherence, both in terms of using routine data to make quantitative assessments, and through additional qualitative interviews with key stakeholders such as practice nurses, GPs and a wider group of children with asthma. TRIAL REGISTRATION Current Controlled Trials ISRCTN03000938. FUNDING DETAILS This project was funded by the NIHR Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 20, No. 93. See the HTA programme website for further project information.

Quality of recording of diabetes in the UK: how does the GP's method of coding clinical data affect incidence estimates? Cross-sectional study using the CPRD database

Objective To assess the effect of coding quality on estimates of the incidence of diabetes in the UK between 1995 and 2014. Design A cross-sectional analysis examining diabetes coding from 1995 to 2014 and how the choice of codes (diagnosis codes vs codes which suggest diagnosis) and quality of coding affect estimated incidence. Setting Routine primary care data from 684 practices contributing to the UK Clinical Practice Research Datalink (data contributed from Vision (INPS) practices). Main outcome measure Incidence rates of diabetes and how they are affected by (1) GP coding and (2) excluding ‘poor’ quality practices with at least 10% incident patients inaccurately coded between 2004 and 2014. Results Incidence rates and accuracy of coding varied widely between practices and the trends differed according to selected category of code. If diagnosis codes were used, the incidence of type 2 increased sharply until 2004 (when the UK Quality Outcomes Framework was introduced), and then flattened off, until 2009, after which they decreased. If non-diagnosis codes were included, the numbers continued to increase until 2012. Although coding quality improved over time, 15% of the 666 practices that contributed data between 2004 and 2014 were labelled ‘poor’ quality. When these practices were dropped from the analyses, the downward trend in the incidence of type 2 after 2009 became less marked and incidence rates were higher. Conclusions In contrast to some previous reports, diabetes incidence (based on diagnostic codes) appears not to have increased since 2004 in the UK. Choice of codes can make a significant difference to incidence estimates, as can quality of recording. Codes and data quality should be checked when assessing incidence rates using GP data.

Characterisation of Data Quality in Electronic Healthcare Records

The quality of information depends on the quality of data from which it is derived, but data are of high quality only if they are fit for their intended use. Although electronic healthcare records are collected primarily for patient care and audits, their use in research can also greatly benefit the quality of life of patients. We aim to discuss the challenges and issues involved with measuring data quality in electronic health records for epidemiological and clinical research using the Clinical Practice Research Datalink as a model. We also will share our experiences of assessing data quality in medical primary care database CPRD GOLD and discuss a suggested framework that can help ensure compatibility of data quality measures for different European Electronic Healthcare Records.

Study investigating the generalisability of a COPD trial based in primary care (Salford Lung Study) and the presence of a Hawthorne effect

Introduction Traditional phase IIIb randomised trials may not reflect routine clinical practice. The Salford Lung Study in chronic obstructive pulmonary disease (SLS COPD) allowed broad inclusion criteria and followed patients in routine practice. We assessed whether SLS COPD approximated the England COPD population and evidence for a Hawthorne effect. Methods This observational cohort study compared patients with COPD in the usual care arm of SLS COPD (2012–2014) with matched non-trial patients with COPD in England from the Clinical Practice Research Datalink database. Generalisability was explored with baseline demographics, clinical and treatment variables; outcomes included COPD exacerbations in adjusted models and pretrial versus peritrial comparisons. Results Trial participants were younger (mean, 66.7 vs 71.1 years), more deprived (most deprived quintile, 51.5% vs 21.4%), more current smokers (47.5% vs 32.1%), with more severe Global initiative for chronic Obstructive Lung Disease stages but less comorbidity than non-trial patients. There were no material differences in other characteristics. Acute COPD exacerbation rates were high in the trial population (98.37th percentile). Conclusion The trial population was similar to the non-trial COPD population. We observed some evidence of a Hawthorne effect, with more exacerbations recorded in trial patients; however, the largest effect was observed through behavioural changes in patients and general practitioner coding practices.

Cancer recording in patients with and without type 2 diabetes in the Clinical Practice Research Datalink primary care data and linked hospital admission data: a cohort study

Objectives and setting Conflicting results from studies using electronic health records to evaluate the associations between type 2 diabetes and cancer fuel concerns regarding potential biases. This study aimed to describe completeness of cancer recording in UK primary care data linked to hospital admissions records. Design Patients aged 40+ years with insulin or oral antidiabetic prescriptions in Clinical Practice Research Datalink (CPRD) primary care without type 1 diabetes were matched by age, sex and general practitioner practice to non-diabetics. Those eligible for linkage to Hospital Episode Statistics Admitted Patient Care (HES APC), and with follow-up during April 1997–December 2006 were included. Primary and secondary outcome measures Cancer recording and date of first record of cancer were compared. Characteristics of patients with cancer most likely to have the diagnosis recorded only in a single data source were assessed. Relative rates of cancer estimated from the two datasets were compared. Participants 53 585 patients with type 2 diabetes matched to 47 435 patients without diabetes were included. Results Of all cancers (excluding non-melanoma skin cancer) recorded in CPRD, 83% were recorded in HES APC. 94% of cases in HES APC were recorded in CPRD. Concordance was lower when restricted to same-site cancer records, and was negatively associated with increasing age. Relative rates for cancer were similar in both datasets. Conclusions Good concordance in cancer recording was found between CPRD and HES APC among type 2 diabetics and matched controls. Linked data may reduce misclassification and increase case ascertainment when analysis focuses on site-specific cancers.