97 research outputs found

    Influenza vaccination reduced myocardial infarctions in UK older adults: a prior event rate ratio study

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    This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordOBJECTIVE: We aimed to estimate the real-world effectiveness of the influenza vaccine against myocardial infarction (MI) and influenza in the decade since adults aged ≥65y were first recommended the vaccine. STUDY DESIGN AND SETTING: We identified annual cohorts, 1997 to 2011, of adults aged ≥65y, without previous influenza vaccination, from UK general practices, registered with the Clinical Practice Research Datalink. Using a quasi-experimental study design to control for confounding bias, we estimated influenza vaccine effectiveness on hospitalisation for MI, influenza and antibiotic prescriptions for lower respiratory tract infections. RESULTS: Vaccination was moderately effective against influenza, the prior event rate ratio (PERR)-adjusted hazard ratios [HR] ranging from 0.70 in 1999 to 0.99 in 2001. PERR-adjusted HRs demonstrated a protective effect against MIs, varying between 0.40 in 2010 to 0.89 in 2001. Aggregated across the cohorts, influenza vaccination reduced the risk of MIs by 39% (95%confidence interval: 34%, 44%) . CONCLUSIONS: Effectiveness of the flu vaccine in preventing MIs in older UK adults is consistent with the limited evidence from clinical trials. Similar trends in effectiveness against influenza and against MIs suggest the risk of influenza mediates the effectiveness against MIs, although divergence in some years implies the mechanism may be complex.Medical Research CouncilNational Institute for Health ResearchNational Institute for Health Researc

    Uptake and impact of the English National Health Service digital diabetes prevention programme: observational study

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    This is the final version. Available from BMJ Publishing Group via the DOI in this record. Data are available on reasonable request. MDS and SAP data may be made available on reasonable request to the corresponding author. No patient data are available for sharing.INTRODUCTION: 'Healthier You', the National Health Service (NHS) diabetes prevention programme (DPP) offers adults in England at high risk of type 2 diabetes (T2DM) an evidence-based behavioral intervention to prevent or delay T2DM onset. This study assesses the impact of a pilot digital stream of the DPP (DDPP) on glycated hemoglobin (HbA1c) and weight. RESEARCH DESIGN AND METHODS: A service evaluation employing prospectively collected data in a prospective cohort design in nine NHS local pilot areas across England. Participants were adults with non-diabetic hyperglycemia (NDH) (HbA1c 42-47 mmol/mol or fasting plasma glucose 5.5-6.9 mmol/L) in the 12 months prior to referral. The DDPP comprised five digital health interventions (DHI). Joint primary outcomes were changes in HbA1c and weight between baseline and 12 months. HbA1c and weight readings were recorded at referral (baseline) by general practices, and then at 12-month postregistration. Demographic data and service variables were collected from the DHI providers. RESULTS: 3623 participants with NDH registered for the DDPP and of these, 2734 (75%) were eligible for inclusion in the analyses. Final (12-month) follow-up data for HbA1c were available for 1799 (50%) and for weight 1817 (50%) of registered participants. Mean change at 12 months was -3.1 (-3.4 to -2.8) kg, p<0.001 for weight and -1.6 (-1.8 to -1.4) mmol/mol, p<0.001 for HbA1c. Access to peer support and a website and telephone service was associated with significantly greater reductions in HbA1c and weight. CONCLUSIONS: Participation in the DDPP was associated with clinically significant reductions in weight and HbA1c. Digital diabetes prevention can be an effective and wide-reaching component of a population-based approach to addressing type 2 diabetes prevention.NHS EnglandNational Institute for Health Research Applied Research Collaboration South West Peninsul

    Development of a treatment selection algorithm for SGLT2 and DPP-4 inhibitor therapies in people with type 2 diabetes: a retrospective cohort study

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    This is the final version. Available from Elsevier via the DOI in this record. Data sharing: CPRD data are available by application to the CPRD Independent Scientific Advisory Committee and clinical trial data are accessible by application to the Yale University Open Data Access Project and Vivli.Background Current treatment guidelines do not provide recommendations to support the selection of treatment for most people with type 2 diabetes. We aimed to develop and validate an algorithm to allow selection of optimal treatment based on glycaemic response, weight change, and tolerability outcomes when choosing between SGLT2 inhibitor or DPP-4 inhibitor therapies. Methods In this retrospective cohort study, we identified patients initiating SGLT2 and DPP-4 inhibitor therapies after Jan 1, 2013, from the UK Clinical Practice Research Datalink (CPRD). We excluded those who received SGLT2 or DPP-4 inhibitors as first-line treatment or insulin at the same time, had estimated glomerular filtration rate (eGFR) of less than 45 mL/min per 1·73 m2, or did not have a valid baseline glycated haemoglobin (HbA1c) measure (<53 or ≥120 mmol/mol). The primary efficacy outcome was the HbA1c value reached 6 months after drug initiation, adjusted for baseline HbA1c. Clinical features associated with differential HbA1c outcome on the two therapies were identified in CPRD (n=26 877), and replicated in reanalysis of 14 clinical trials (n=10 414). An algorithm to predict individual-level differential HbA1c outcome on the two therapies was developed in CPRD (derivation; n=14 069) and validated in head-to-head trials (n=2499) and CPRD (independent validation; n=9376). In CPRD, we further explored heterogeneity in 6-month weight change and treatment discontinuation. Findings Among 10 253 patients initiating SGLT2 inhibitors and 16 624 patients initiating DPP-4 inhibitors in CPRD, baseline HbA1c, age, BMI, eGFR, and alanine aminotransferase were associated with differential HbA1c outcome with SGLT2 inhibitor and DPP-4 inhibitor therapies. The median age of participants was 62·0 years (IQR 55·0–70·0). 10 016 (37·3%) were women and 16 861 (62·7%) were men. An algorithm based on these five features identified a subgroup, representing around four in ten CPRD patients, with a 5 mmol/mol or greater observed benefit with SGLT2 inhibitors in all validation cohorts (CPRD 8·8 mmol/mol [95% CI 7·8–9·8]; CANTATA-D and CANTATA-D2 trials 5·8 mmol/mol [3·9–7·7]; BI1245.20 trial 6·6 mmol/mol [2·2–11·0]). In CPRD, predicted differential HbA1c response with SGLT2 inhibitor and DPP-4 inhibitor therapies was not associated with weight change. Overall treatment discontinuation within 6 months was similar in patients predicted to have an HbA1c benefit with SGLT2 inhibitors over DPP-4 inhibitors (median 15·2% [13·2–20·3] vs 14·4% [12·9–16·7]). A smaller subgroup predicted to have greater HbA1c reduction with DPP-4 inhibitors were twice as likely to discontinue SGLT2 inhibitors than DPP-4 inhibitors (median 26·8% [23·4–31·0] vs 14·8% [12·9–16·8]). Interpretation A validated treatment selection algorithm for SGLT2 inhibitor and DPP-4 inhibitor therapies can support decisions on optimal treatment for people with type 2 diabetes.BHF-Turing Cardiovascular Data Science AwardMedical Research Counci

    Per protocol analyses produced larger treatment effect sizes than intention to treat: a meta-epidemiological study

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    This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordObjective: To undertake meta-analysis and compare treatment effects estimated by the intention-to-treat (ITT) method and per-protocol (PP) method in randomized controlled trials (RCTs). PP excludes trial participants who are non-adherent to trial protocol in terms of eligibility, interventions, or outcome assessment. Study design and setting: Five high impact journals were searched for all RCTs published between July 2017 to June 2019. Primary outcome was a pooled estimate that quantified the difference between the treatment effects estimated by the two methods. Results are presented as ratio of odds ratios (ROR). Meta-regression was used to explore the association between level of trial protocol non-adherence and treatment effect. Sensitivity analyses compared results with varying within-study correlations and across various study characteristics. Results: Random-effects meta-analysis (N = 156) showed that PP estimates were on average 2% greater compared to the ITT estimates (ROR: 1.02, 95% CI: 1.00–1.04, P = 0.03). The divergence further increased with higher degree of protocol non-adherence. Sensitivity analyses reassured consistent results with various within-study correlations and across various study characteristics. Conclusion: There was evidence of larger treatment effect with PP compared to ITT analysis. PP analysis should not be used to assess the impact of protocol non-adherence in RCTs. Instead, in addition to ITT, investigators should consider randomization based casual method such as Complier Average Causal Effect (CACE)

    Prior event rate ratio adjustment produced estimates consistent with randomized trial: a diabetes case study

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    Objectives: Electronic health records (EHR) provide a valuable resource for assessing drug side-effects, but treatments are not randomly allocated in routine care creating the potential for bias. We conduct a case study using the Prior Event Rate Ratio (PERR) Pairwise method to reduce unmeasured confounding bias in side-effect estimates for two second-line therapies for type 2 diabetes, thiazolidinediones, and sulfonylureas. Study design and settings: Primary care data were extracted from the Clinical Practice Research Datalink (n = 41,871). We utilized outcomes from the period when patients took first-line metformin to adjust for unmeasured confounding. Estimates for known side-effects and a negative control outcome were compared with the A Diabetes Outcome Progression Trial (ADOPT) trial (n = 2,545). Results: When on metformin, patients later prescribed thiazolidinediones had greater risks of edema, HR 95% CI 1.38 (1.13, 1.68) and gastrointestinal side-effects (GI) 1.47 (1.28, 1.68), suggesting the presence of unmeasured confounding. Conventional Cox regression overestimated the risk of edema on thiazolidinediones and identified a false association with GI. The PERR Pairwise estimates were consistent with ADOPT: 1.43 (1.10, 1.83) vs. 1.39 (1.04, 1.86), respectively, for edema, and 0.91 (0.79, 1.05) vs. 0.94 (0.80, 1.10) for GI. Conclusion: The PERR Pairwise approach offers potential for enhancing postmarketing surveillance of side-effects from EHRs but requires careful consideration of assumptions.This article is freely available via Open Access. Click on the Publisher URL to access it via the publisher's site.The MASTERMIND (MRC APBI Stratification and Extreme Response Mechanism IN Diabetes) consortium is funded by the U.K Medical Research Council funded study grant number MR/N00633X/1. The funder had no role in study design, data collection, data analysis, data interpretation, or writing of the report. IQVIA provided some funding for this project.published version, accepted version (12 month embargo), submitted versio

    Clusters provide a better holistic view of type 2 diabetes than simple clinical features – Authors' reply

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    This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.CorrespondenceMedical Research Council (MRC)National Institute for Health Research (NIHR

    Time trends in prescribing of type 2 diabetes drugs, glycaemic response and risk factors: a retrospective analysis of primary care data, 2010–2017

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    Aim: To describe population‐level time trends in prescribing patterns of type 2 diabetes therapy, and in short‐term clinical outcomes (glycated haemoglobin [HbA1c], weight, blood pressure, hypoglycaemia and treatment discontinuation) after initiating new therapy.Materials and methods: We studied 81 532 people with type 2 diabetes initiating a first‐ to fourth‐line drug in primary care between 2010 and 2017 inclusive in United Kingdom electronic health records (Clinical Practice Research Datalink). Trends in new prescriptions and subsequent 6‐ and 12‐month adjusted changes in glycaemic response (reduction in HbA1c), weight, blood pressure and rates of hypoglycaemia and treatment discontinuation were examined.Results: Use of dipeptidyl peptidase‐4 inhibitors as second‐line therapy near doubled (41% of new prescriptions in 2017 vs. 22% in 2010), replacing sulphonylureas as the most common second‐line drug (29% in 2017 vs. 53% in 2010). Sodium‐glucose co‐transporter‐2 inhibitors, introduced in 2013, comprised 17% of new first‐ to fourth‐line prescriptions by 2017. First‐line use of metformin remained stable (91% of new prescriptions in 2017 vs. 91% in 2010). Over the study period there was little change in average glycaemic response and in the proportion of people discontinuing treatment. There was a modest reduction in weight after initiating second‐ and third‐line therapy (improvement in weight change 2017 vs. 2010 for second‐line therapy: −1.5 kg, 95% confidence interval [CI] −1.9, −1.1; P &lt; 0.001), and a slight reduction in systolic blood pressure after initiating first‐, second‐ and third‐line therapy (improvement in systolic blood pressure change 2017 vs. 2010 range: −1.7 to −2.1 mmHg; all P &lt; 0.001). Hypoglycaemia rates decreased over time with second‐line therapy (incidence rate ratio 0.94 per year, 95% CI 0.88, 1.00; P = 0.04), mirroring the decline in use of sulphonylureas.Conclusions: Recent changes in prescribing of therapy for people with type 2 diabetes have not led to a change in glycaemic response and have resulted in modest improvements in other population‐level short‐term clinical outcomes.</br

    Predicting Incident Multimorbidity.

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    This is the author accepted manuscript. The final version is available from Annals of Family Medicine via the DOI in this record.PURPOSE: Multimorbidity is associated with adverse outcomes, yet research on the determinants of its incidence is lacking. We investigated which sociodemographic, health, and individual lifestyle (eg, physical activity, smoking behavior, body mass index) characteristics predict new cases of multimorbidity. METHODS: We used data from 4,564 participants aged 50 years and older in the English Longitudinal Study of Aging that included a 10-year follow-up period. Discrete time-to-event (complementary log-log) models were constructed for exploring the associations of baseline characteristics with outcomes between 2002-2003 and 2012-2013 separately for participants with no initial conditions (n = 1,377) developing multimorbidity, any increase in conditions within 10 years regardless of initial conditions, and the impact of individual conditions on incident multimorbidity. RESULTS: The risks of developing multimorbidity were positively associated with age, and they were greater for the least wealthy, for participants who were obese, and for those who reported the lowest levels of physical activity or an external locus of control (believing that life events are outside of one's control) for all groups regardless of baseline conditions (all linear trends <.05). No significant associations were observed for sex, educational attainment, or social detachment. For participants with any increase in conditions (n = 4,564), a history of smoking was the only additional predictor. For participants with a single baseline condition (n = 1,534), chronic obstructive pulmonary disease (COPD), asthma, and arrhythmia showed the strongest associations with subsequent multimorbidity. CONCLUSIONS: Our findings support the development and implementation of a strategy targeting the prevention of multimorbidity for susceptible groups. This approach should incorporate behavior change addressing lifestyle factors and target health-related locus of control.There was no direct funding for this study. Dr Mounce was supported by the National Health Service, Cambridgeshire, and through an National Institute for Health Research Clinical Scientist Award granted to Dr Valderas

    Evaluating associations between the benefits and risks of drug therapy in type 2 diabetes: a joint modeling approach

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    John M Dennis,1 Beverley M Shields,2 Angus G Jones,2 Ewan R Pearson,3 Andrew T Hattersley,2 William E Henley1 On behalf of the MASTERMIND consortium 1Health Statistics Group, University of Exeter Medical School, Exeter, UK; 2National Institute for Health Research Exeter Clinical Research Facility, University of Exeter Medical School, Exeter, UK; 3Division of Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK Objective: Precision medicine drug therapy seeks to maximize efficacy and minimize harm for individual patients. This will be difficult if drug response and side effects are positively associated, meaning that patients likely to respond best are at increased risk of side effects. We applied joint longitudinal&ndash;survival models to evaluate associations between drug response (longitudinal outcome) and the risk of side effects (survival outcome) for patients initiating type 2 diabetes therapy. Study design and setting: Participants were randomized to metformin (MFN), sulfonylurea&nbsp;(SU), or thiazolidinedione (TZD) therapy in the A Diabetes Outcome Progression Trial (ADOPT) drug efficacy trial (n=4,351). Joint models were parameterized for 1) current HbA1c response (change from baseline in HbA1c) and 2) cumulative HbA1c response (total HbA1c change). Results: With MFN, greater HbA1c response did not increase the risk of gastrointestinal events (HR per 1% absolute greater current response 0.82 [95% CI 0.67, 1.01]; HR per 1% higher cumulative response 0.90 [95% CI 0.81, 1.00]). With SU, greater current response was associated with an increased risk of hypoglycemia (HR 1.41 [95% CI 1.04, 1.91]). With TZD, greater response was associated with an increased risk of edema (current HR 1.45 [95% CI 1.05, 2.01]; cumulative 1.22 [95% CI 1.07, 1.38]) but not fracture. Conclusion: Joint modeling provides a useful framework to evaluate the association between response to a drug and the risk of developing side effects. There may be great potential for widespread application of joint modeling to evaluate the risks and benefits of both new and established medications. Keywords: diabetes mellitus, type 2, drug-related side effects, HbA1c, hypoglycemia, joint model, precision medicine, thiazolidinediones, metformin, sulfonylurea compounds, ADOPT, edem

    Evidence from a quasi-experimental study for the effectiveness of the influenza vaccination against myocardial infarction in UK adults aged at least 65 y

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    This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record Abstracts of the 34th International Conference on Pharmacoepidemiology & Therapeutic Risk Management, Prague Congress Centre, Prague, Czech Republic, August 22–26, 2018Medical Research Council (MRC)National Institute for Health Research (NIHR
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