Evangelical Visitor- October 2, 1911. Vol. XXV. No. 20.

Evangelical Visitor published in Harrisburg, Pa., for the exposition of true, practical piety and devoted to the spread of evangelical truths and the unity of the church. Published in the interest of the church of the Brethren in Christ on October 2, 1911. Vol. XXV. No. 20

The impact of the UK COVID-19 lockdown on the screening, diagnostics and incidence of breast, colorectal, lung and prostate cancer in the UK: a population-based cohort study

Introduction: The COVID-19 pandemic had collateral effects on many health systems. Cancer screening and diagnostic tests were postponed, resulting in delays in diagnosis and treatment. This study assessed the impact of the pandemic on screening, diagnostics and incidence of breast, colorectal, lung, and prostate cancer; and whether rates returned to pre-pandemic levels by December, 2021. Methods: This is a cohort study of electronic health records from the United Kingdom (UK) primary care Clinical Practice Research Datalink (CPRD) GOLD database. The study included individuals registered with CPRD GOLD between January, 2017 and December, 2021, with at least 365 days of clinical history. The study focused on screening, diagnostic tests, referrals and diagnoses of first-ever breast, colorectal, lung, and prostate cancer. Incidence rates (IR) were stratified by age, sex, and region, and incidence rate ratios (IRR) were calculated to compare rates during and after lockdown with rates before lockdown. Forecasted rates were estimated using negative binomial regression models. Results: Among 5,191,650 eligible participants, the first lockdown resulted in reduced screening and diagnostic tests for all cancers, which remained dramatically reduced across the whole observation period for almost all tests investigated. There were significant IRR reductions in breast (0.69 [95% CI: 0.63-0.74]), colorectal (0.74 [95% CI: 0.67-0.81]), and prostate (0.71 [95% CI: 0.66-0.78]) cancer diagnoses. IRR reductions for lung cancer were non-significant (0.92 [95% CI: 0.84-1.01]). Extrapolating to the entire UK population, an estimated 18,000 breast, 13,000 colorectal, 10,000 lung, and 21,000 prostate cancer diagnoses were missed from March, 2020 to December, 2021. Discussion: The UK COVID-19 lockdown had a substantial impact on cancer screening, diagnostic tests, referrals, and diagnoses. Incidence rates remained significantly lower than pre-pandemic levels for breast and prostate cancers and associated tests by December, 2021. Delays in diagnosis are likely to have adverse consequences on cancer stage, treatment initiation, mortality rates, and years of life lost. Urgent strategies are needed to identify undiagnosed cases and address the long-term implications of delayed diagnoses

Unsupervised learning to understand patterns of comorbidity in 633,330 patients diagnosed with osteoarthritis

With the advent of big data in healthcare, machine learning has rapidly gained popularity due to its potential to analyse large volumes of complex data from a variety of sources. Unsupervised learning can be used to mine data and discover patterns such as sub-groups within large patient populations. However challenges with implementation in large-scale datasets and interpretability of solutions in a real-world context remain. This work presents an application of unsupervised clustering techniques for discovering patterns of comorbidities in a large dataset of osteoarthritis patients with a view to discover interpretable and clinically-meaningful patterns

Predicting imminent fractures in patients with a recent fracture or starting oral bisphosphonate therapy: development and international validation of prognostic models

The availability of anti-osteoporosis medications with rapid onset and high potency requires tools to identify patients at high imminent fracture risk (IFR). There are few tools that predict a patient's IFR. We aimed to develop and validate tools for target patients with a recent fracture or initiating oral bisphosphonate therapy. Models for two separate cohorts, those with incident fragility fracture (IFx) and with incident oral bisphosphonate prescription (OBP), were developed in primary care records from Spain (SIDIAP database), UK (Clinical Practice Research Datalink GOLD), and Denmark (Danish Health Registries). Separate models were developed for hip, major, and any fracture outcomes. Only variables present in all databases were included in Lasso regression models for the development and logistic regression models for external validation. Discrimination was tested using area under curve (AUC) and calibration was assessed using observed vs predicted risk plots stratified by age, sex, and previous fracture history. The development analyses included 35,526 individuals in the IFx and 41,401 in OBP cohorts, with 671,094 in IFx and 330,256 in OBP for the validation analyses. Both the IFx and OBP models demonstrated similarly good performance for hip fracture at 1 year (with AUCs of 0.79 (95% CI 0.75 to 0.82) and 0.87 (0.83 to 0.91) in Spain, 0.71 (0.71 to 0.72) and 0.73 (0.72 to 0.74) in the UK, and 0.70 (0.70 to 0.70) and 0.69 (0.68 to 0.70) in Denmark), and lower discrimination for major osteoporotic and any fracture sites. Calibration was good across all three countries with similar discrimination and calibration for the 2-year models. The proposed IFR prediction models could be used to identify more precisely patients at high imminent risk of fracture and inform anti-osteoporosis treatment selection. The freely available model parameters permit local validation and implementation

Classification of patients with osteoarthritis through clusters of comorbidities using 633,330 individuals from Spain

Objectives: To explore clustering of comorbidities among patients with a new diagnosis of osteoarthritis (OA) and estimate the 10-year mortality risk for each identified cluster. Methods: This is a population-based cohort study of individuals with first incident diagnosis of OA of the hip, knee, ankle/foot, wrist/hand, or ‘unspecified’ site between 2006 and 2020, using SIDIAP (a primary care database representative from Catalonia, Spain). At the time of OA diagnosis, conditions associated with OA in the literature that were found in ≥1% of the individuals (n=35) were fitted into two cluster algorithms, K-means and latent class analysis (LCA). Models were assessed using a range of internal and external criteria evaluation procedures. Mortality risk of the obtained clusters was assessed by survival analysis using Cox proportional hazards. Results: We identified 633,330 patients with a diagnosis of OA. Our proposed best solution used LCA to identify four clusters: ‘Low-morbidity (relatively low number of comorbidities), ‘Back/neck pain plus mental health’, ‘Metabolic syndrome’ and ‘Multimorbidity’ (higher prevalence of all study comorbidities). Compared to the ‘Low-morbidity, the ‘Multimorbidity’ cluster had the highest risk of 10-year mortality (adjusted HR: 2.19 [95%CI: 2.15-2.23]), followed by ‘Metabolic syndrome’ (adjusted HR: 1.24 [95%CI: 1.22-1.27]]) and ‘Back/neck pain plus mental health’ (adjusted HR: 1.12 [95%CI: 1.09-1.15]). Conclusion: Patients with a new diagnosis of OA can be clustered into groups based on their comorbidity profile, with significant differences in 10-year mortality risk. Further research is required to understand the interplay between OA and particular comorbidity groups, and the clinical significance of such results.</p