Diabetes, metabolic health, and the development of frozen shoulder: a cohort study in UK electronic health records

Objective Estimate the effect of type 2 diabetes on the development of frozen shoulder and investigate whether the effect is mediated by other metabolic factors. Methods Primary care medical record-based cohort study containing 43,977 patients newly diagnosed with type 2 diabetes and 43,977 without diabetes. Variables were identified using established Read codes. A weighting approach with Cox regression was used to decompose the total effect into the direct effect and indirect effect, mediated by metabolic health (which was defined as the number of metabolic factors developed during follow-up). Estimates were expressed as hazard ratios (HR). Confounders were identified using a DAG. Sensitivity to unmeasured confounding, extreme weights, and missing data were tested. Results The total effect of type 2 diabetes on the development of frozen shoulder was HR = 4.38 (95% CI: 3.70–5.21), the natural indirect effect (mediated through metabolic health) was HR = 0.98 (95% CI: 0.93–1.03) and the natural direct effect was HR = 4.46 (95% CI: 3.68–5.41). Results were robust to unmeasured confounding, extreme weights, and missing data. Conclusions This study suggests that type 2 diabetes may be a cause of frozen shoulder but does not support the hypothesis that the effect is mediated by metabolic health. Clinicians should remain alert that shoulder pain in patients with diabetes could be indicative of a frozen shoulder. This study should raise awareness that, despite often being overlooked, musculoskeletal conditions can be complications of diabetes and should be considered during clinical conversations with patients

The XGTDL family of survival distributions

Non-PH parametric survival modelling is developed within the framework of the mul tiple logistic function. The family considered comprises three basic models: (a) a PH model, (b) an accelerated life model and (c) a model which is non-proportional haz ards and non-accelerated life. The last model, the generalised time-dependent logistic model was described first by the author in 1996 and this model gives its name to the entire family. The family is generalised by means of a Gamma frailty extension which is shown to accommodate crossing hazards data. A further generalisation is the inclusion of a dispersion model. These extensions lead naturally to the concept of a multi-parameter regression model described by Burke and MacKenzie in which the scale and shape parameters are modelled simultaneously as functions of covariates. Where possible, we include the MPR extension in the XGTDL family. Following a simulation study, the new models are used to analyse two sets survival data and the methods are discussed

Are patients with newly diagnosed frozen shoulder more likely to be diagnosed with type 2 diabetes? A cohort study in UK electronic health records

Aim: To estimate the association between newly diagnosed frozen shoulder and a subsequent diagnosis of type 2 diabetes in primary care. Methods: We conducted an age‐, gender‐ and practice‐matched cohort study in UK primary care electronic medical records containing 31 226 adults diagnosed with frozen shoulder, matched to 31 226 without frozen shoulder. Patients with pre‐existing diabetes were excluded. Variables were identified using established Read codes. A hazard ratio (HR) for the association between incident frozen shoulder and a subsequent type 2 diabetes diagnosis was estimated using shared frailty Cox regression, adjusted for age and gender. To determine whether the association could be explained by increased testing for type 2 diabetes based on other risk factors, a secondary analysis involved re‐running the Cox model adjusting for the mean number of consultations per year, hyperlipidaemia, hypertension, obesity, thyroid dysfunction, ethnicity, deprivation, age, and gender. Results: Participants with frozen shoulder were more likely to be diagnosed with type 2 diabetes (1559 out of 31 226 patients [5%]) than participants without frozen shoulder (88 out of 31 226 patients [0.28%]). The HR for a diagnosis of type 2 diabetes in participants with frozen shoulder versus people without frozen shoulder was 19.4 (95% confidence interval [CI] 15.6–24.0). The secondary analysis, adjusting for other factors, produced similar results: HR 20.0 (95% CI 16.0–25.0). Conclusions: People who have been newly diagnosed with frozen shoulder are more likely to be diagnosed with type 2 diabetes in the following 15.8 years. The value of screening patients presenting with frozen shoulder for type 2 diabetes at presentation, alongside more established risk factors, should be considered in future research