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INTRODUCTION: This retrospective database study explored treatment patterns and potential off-label prescribing among patients newly prescribed fluticasone furoate/vilanterol (FF/VI) in a UK primary care setting. METHODS: In Europe, FF/VI is approved in two strengths: 100/25 µg for adults with chronic obstructive pulmonary disease (COPD) and 100/25 µg or 200/25 µg for treatment of asthma in patients aged 12 or older. Using electronic health records from the Clinical Practice Research Datalink, new users of FF/VI or other inhaled corticosteroid/long-acting beta-agonist fixed-dose combination products were identified and classified into one of three groups: COPD diagnosis, asthma diagnosis, and other diagnosis (not COPD or asthma). RESULTS: During 2014-2015, 4373 patients initiated FF/VI: 3380 on FF/VI 100/25 (65% in the COPD diagnosis group) and 993 on FF/VI 200/25 (51% in the asthma diagnosis group). During up to 12 months of follow-up, the median number (interquartile range) of prescriptions of the index strength issued per patient was 7 (2-8) for FF/VI 100/25 and 5 (2-8) for FF/VI 200/25; most new users did not change from the index strength prescribed (93.0% COPD; 89.7% asthma, of all patients initiating treatment with FF/VI). Potential off-label FF/VI prescribing in children < 12 years old was rare (< 0.29% in the combined asthma and other diagnosis groups), and up to one in five new users of FF/VI with COPD were potentially prescribed FF/VI 200/25 off-label during the study period. Much of the potential off-label prescribing in COPD occurred in patients with a history of asthma, those presenting with greater disease severity, and/or prior treatment with high-dose steroids. CONCLUSIONS: The prescription of FF/VI is rare in children under 12 years of age in the UK, according to our findings, but up to one in five COPD patients in the UK may have been prescribed FF/VI 200/25, some of which may have been off-label. FUNDING: This study was funded by GlaxoSmithKline plc (study 205052). STUDY REGISTRATION: GlaxoSmithKline plc Clinical Trial Registry study number 205052.
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BACKGROUND: Research into diabetes mellitus (DM) often requires a reproducible method for identifying and distinguishing individuals with type 1 DM (T1DM) and type 2 DM (T2DM). OBJECTIVES: To develop a method to identify individuals with T1DM and T2DM using UK primary care electronic health records. METHODS: Using data from The Health Improvement Network primary care database, we developed a two-step algorithm. The first algorithm step identified individuals with potential T1DM or T2DM based on diagnostic records, treatment, and clinical test results. We excluded individuals with records for rarer DM subtypes only. For individuals to be considered diabetic, they needed to have at least two records indicative of DM; one of which was required to be a diagnostic record. We then classified individuals with T1DM and T2DM using the second algorithm step. A combination of diagnostic codes, medication prescribed, age at diagnosis, and whether the case was incident or prevalent were used in this process. We internally validated this classification algorithm through comparison against an independent clinical examination of The Health Improvement Network electronic health records for a random sample of 500 DM individuals. RESULTS: Out of 9,161,866 individuals aged 0-99 years from 2000 to 2014, we classified 37,693 individuals with T1DM and 418,433 with T2DM, while 1,792 individuals remained unclassified. A small proportion were classified with some uncertainty (1,155 [3.1%] of all individuals with T1DM and 6,139 [1.5%] with T2DM) due to unclear health records. During validation, manual assignment of DM type based on clinical assessment of the entire electronic record and algorithmic assignment led to equivalent classification in all instances. CONCLUSION: The majority of individuals with T1DM and T2DM can be readily identified from UK primary care electronic health records. Our approach can be adapted for use in other health care settings.
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OBJECTIVE: To describe the characteristics of pregnant women with and without pregestational diabetes and to estimate the prevalence of pregestational diabetes in pregnant women recorded in a UK primary care database. METHODS: The data source for this study is The Health Improvement Network (THIN) primary care database. Pregnant women with and without diabetes aged 16 years and over were identified using diagnostic Read codes and prescriptions for antidiabetics from medical records. Data were examined on: age, body mass index (BMI), social deprivation, smoking, ethnicity and glycaemic control. The prevalence of pregestational diabetes was calculated by diabetes type and calendar year between 1995 and 2012. RESULTS: Data from 400,434 pregnancies suggests that women with pregestational diabetes were: older (median 29, 32 vs 29 years for type 1, type 2 and without diabetes, respectively), had higher BMI (median 25.0, 30.4 vs 23.9 k/m(2) for type 1, type 2 and without diabetes, respectively) and were registered with a general practice for longer than pregnant women without diabetes. The prevalence of type 1 diabetes in pregnancy increased from 1.56 to 4.09 per 1000 pregnancies between 1995 and 2015. For type 2 diabetes the increase was from 2.34 to 5.09 per 1000 pregnancies between 1995 and 2008 followed by a more rapid increase to 10.62 per 1000 pregnancies by 2012. CONCLUSIONS: Pregnant women with pregestational diabetes were older, had higher BMI and were registered for longer than women without diabetes. The prevalence of type 1 and type 2 diabetes increased in pregnancy. The prevalence of type 2 diabetes rose more rapidly with a marked increase after 2008.
