Effectiveness of sitagliptin vs sulphonylureas for managing type 2 diabetes mellitus in clinical practice

One challenging prescribing decision in type 2 diabetes mellitus (T2DM) is when clinicians must choose between sitagliptin and sulphonylureas as add-on to metformin based on effectiveness. Evidence on effectiveness of sitagliptin versus sulphonylureas as add-on to metformin was therefore systematically searched and revealed no study evaluating “real-world” comparative effectiveness of these treatments, particularly in older, more comorbid individuals. To address this gap, The Health Improvement Network, UK primary care database was used to extract a cohort of 26,844 individuals with T2DM prescribed these treatments and four cohort studies were undertaken to evaluate their comparative effectiveness. The first two studies demonstrated no difference in HbA1c reduction, approximately 12 months after initiating either treatment as add-on to metformin, however a significant comparative weight reduction with sitagliptin in those aged 18-75 (-2.26kg 95%CI -2.48 to -2.04) and ≥75 (-1.31kg 95%CI -1.96 to -0.66) was found. Two further studies revealed individuals prescribed sitagliptin were 11% more likely to record an undesirable HbA1c >58mmol/mol (Hazard Ratio 1.11 95%CI 1.06-1.16), however nearly twice as likely to record an anti-diabetic treatment change (HR 1.98 95%CI 1.86-2.10) compared to sulphonylurea initiators. This analysis on treatment change also highlighted an underlying inertia in both groups, as 66.4% of those prescribed sitagliptin and 83.7% prescribed sulphonylureas had no treatment change introduced despite recording a HbA1c >58 mmol/mol. This thesis provides “real-world” evidence that both sitagliptin and sulphonylureas are equally effective in lowering HbA1c and achieving glycaemic targets in a population that includes individuals aged ≥75 and with significant comorbidity. Sitagliptin is preferable for weight reduction. There is however, a substantial inertia in changing treatment when targets are not met, which is greater among sulphonylurea initiators. There remains a need to eliminate barriers preventing clinicians changing treatment when these two add-on medications prove inadequate, and further evaluate their longer-term comparative effectiveness

Borrow from Anywhere: Pseudo Multi-modal Object Detection in Thermal Imagery

Can we improve detection in the thermal domain by borrowing features from rich domains like visual RGB? In this paper, we propose a pseudo-multimodal object detector trained on natural image domain data to help improve the performance of object detection in thermal images. We assume access to a large-scale dataset in the visual RGB domain and relatively smaller dataset (in terms of instances) in the thermal domain, as is common today. We propose the use of well-known image-to-image translation frameworks to generate pseudo-RGB equivalents of a given thermal image and then use a multi-modal architecture for object detection in the thermal image. We show that our framework outperforms existing benchmarks without the explicit need for paired training examples from the two domains. We also show that our framework has the ability to learn with less data from thermal domain when using our approach. Our code and pre-trained models are made available at https://github.com/tdchaitanya/MMTODComment: Accepted at Perception Beyond Visible Spectrum Workshop, CVPR 201

Initiation of antidepressant medication in people with type 2 diabetes living in the UK – a retrospective cohort study

INTRODUCTION: Depression is a common comorbidity in people with type 2 diabetes and it is associated with poorer outcomes. There is limited data on the treatments used for depression in this population. The aim of this study was to explore the rates of initiation of antidepressant prescriptions in people with type 2 diabetes in the UK and identify those most at risk of needing such treatment. RESEARCH DESIGN AND METHODS: This was a retrospective cohort study using data from IQVIA Medical Research Data (IMRD)-UK data. Data from general practices in IMRD-UK between January 2008 and December 2017 were used for this study. RESULTS: The overall rates of antidepressant prescribing were stable over the study period. The rate of initiation of antidepressant medication in people with type 2 diabetes was 22.93 per 1000 person years at risk (PYAR) with a 95%CI 22.48 to 23.39 compared to 16.89 per 1000 PYAR (95%CI 16.77 to 17.01) in an age and gender matched cohort. The risk of being prescribed anti-depressant medication with age had a U-shaped distribution with the lowest risk in the 65-69 age group. The peak age for antidepressant initiation in men and women was 40-44, with a rate in men of 32.78 per 1000 PYAR (95% CI 29.57 to 36.34) and a rate in women of 46.80 per 1000 PYAR (95% CI 41.90 to 52.26). People with type 2 diabetes with in the least deprived quintile had an initiation rate of 19.66 per 1000 PYAR (95%CI 18.67 to 20.70) compared to 27.19 per 1000 PYAR (95%CI 25.50 to 28.93) in the most deprived quintile, with a 32% increase in the risk of starting antidepressant medication (95%CI 1.22 to 1.43). CONCLUSIONS: People with type 2 diabetes were 30% more likely to be started on antidepressant medication than people without type 2 diabetes. Women with type 2 diabetes were 35% more likely than men to be prescribed antidepressants and the risks increased with deprivation and in younger or older adults, with the lowest rates in the 65-69 year age band. The rates of antidepressant prescribing were broadly stable over the 10-year period in this study. The anti-depressant medications prescribed changed slightly over time with sertraline becoming more widely used and fewer prescriptions of citalopram

Ethnic Differences in the Prevalence of Type 2 Diabetes Diagnoses in the UK: Cross-Sectional Analysis of the Health Improvement Network Primary Care Database.

AIMS/HYPOTHESIS: Type 2 diabetes mellitus is associated with high levels of disease burden, including increased mortality risk and significant long-term morbidity. The prevalence of diabetes differs substantially among ethnic groups. We examined the prevalence of type 2 diabetes diagnoses in the UK primary care setting. METHODS: We analysed data from 404,318 individuals in The Health Improvement Network database, aged 0-99 years and permanently registered with general practices in London. The association between ethnicity and the prevalence of type 2 diabetes diagnoses in 2013 was estimated using a logistic regression model, adjusting for effect of age group, sex, and social deprivation. A multiple imputation approach utilising population-level information about ethnicity from the UK census was used for imputing missing data. RESULTS: Compared with those of White ethnicity (5.04%, 95% CI 4.95 to 5.13), the crude percentage prevalence of type 2 diabetes was higher in the Asian (7.69%, 95% CI 7.46 to 7.92) and Black (5.58%, 95% CI 5.35 to 5.81) ethnic groups, while lower in the Mixed/Other group (3.42%, 95% CI 3.19 to 3.66). After adjusting for differences in age group, sex, and social deprivation, all minority ethnic groups were more likely to have a diagnosis of type 2 diabetes compared with the White group (OR Asian versus White 2.36, 95% CI 2.26 to 2.47; OR Black versus White 1.65, 95% CI 1.56 to 1.73; OR Mixed/Other versus White 1.17, 95% CI 1.08 to 1.27). CONCLUSION: The prevalence of type 2 diabetes was higher in the Asian and Black ethnic groups, compared with the White group. Accurate estimates of ethnic prevalence of type 2 diabetes based on large datasets are important for facilitating appropriate allocation of public health resources, and for allowing population-level research to be undertaken examining disease trajectories among minority ethnic groups, that might help reduce inequalities

Borrow from Anywhere: Pseudo Multi-modal Object Detection in Thermal Imagery

Can we improve detection in the thermal domain by borrowing features from rich domains like visual RGB? In this paper, we propose a ‘pseudo-multimodal’ object detector trained on natural image domain data to help improve the performance of object detection in thermal images. We assume access to a large-scale dataset in the visual RGB domain and relatively smaller dataset (in terms of instances) in the thermal domain, as is common today. We propose the use of well-known image-to-image translation frameworks to generate pseudo-RGB equivalents of a given thermal image and then use a multi-modal architecture for object detection in the thermal image. We show that our framework outperforms existing benchmarks without the explicit need for paired training examples from the two domains. We also show that our framework has the ability to learn with less data from thermal domain when using our approac

OpenSAFELY NHS Service Restoration Observatory 2: changes in primary care clinical activity in England during the COVID-19 pandemic

BACKGROUND: The COVID-19 pandemic has disrupted healthcare activity across a broad range of clinical services. The NHS stopped non-urgent work in March 2020, later recommending services be restored to near-normal levels before winter where possible. AIM: To describe changes in the volume and variation of coded clinical activity in general practice across six clinical areas: cardiovascular disease, diabetes, mental health, female and reproductive health, screening and related procedures, and processes related to medication. DESIGN AND SETTING: With the approval of NHS England, a cohort study was conducted of 23.8 million patient records in general practice, in situ using OpenSAFELY. METHOD: Common primary care activities were analysed using Clinical Terms Version 3 codes and keyword searches from January 2019 to December 2020, presenting median and deciles of code usage across practices per month. RESULTS: Substantial and widespread changes in clinical activity in primary care were identified since the onset of the COVID-19 pandemic, with generally good recovery by December 2020. A few exceptions showed poor recovery and warrant further investigation, such as mental health (for example, for 'Depression interim review' the median occurrences across practices in December 2020 was down by 41.6% compared with December 2019). CONCLUSION: Granular NHS general practice data at population-scale can be used to monitor disruptions to healthcare services and guide the development of mitigation strategies. The authors are now developing real-time monitoring dashboards for the key measures identified in this study, as well as further studies using primary care data to monitor and mitigate the indirect health impacts of COVID-19 on the NHS

Manufacturing and Supply Chain Flexibility: Building an Integrative Conceptual Model Through Systematic Literature Review and Bibliometric Analysis

The purpose of this study is twofold: first, to establish the current themes on the topic of manufacturing and supply chain flexibility (MSCF), assess their level of maturity in relation to each other, identify the emerging ones and reflect on how they can inform each other, and second, to develop a conceptual model of MSCF that links different themes connect and highlight future research opportunities. The study builds on a sample of 222 articles published from 1996 to 2018 in international, peer-reviewed journals. The analysis of the sample involves two complementary approaches: the co-word technique to identify the thematic clusters as well as their relative standing and a critical reflection on the papers to explain the intellectual content of these thematic clusters. The results of the co-word analysis show that MSCF is a dynamic topic with a rich and complex structure that comprises five thematic clusters. The value chain, capability and volatility clusters showed research topics that were taking a central role in the discussion on MSCF but were not mature yet. The SC purchasing practices and SC planning clusters involved work that was more focused and could be considered more mature. These clusters were then integrated in a framework that built on the competence–capability perspective and identified the major structural and infrastructural elements of MSCF as well as its antecedents and consequences. This paper proposes an integrative framework helping managers keep track the various decisions they need to make to increase flexibility from the viewpoint of the entire value chain