IMPACT OF THE COVID-19 PANDEMIC ON ADHERENCE TO DISEASE MODIFYING DRUGS AMONG PATIENTS WITH RHEUMATIC DISEASES: A POPULATION-BASED, INTERRUPTED TIME SERIES ANALYSIS

BackgroundAlthough studies have quantified adherence to medications among patients with rheumatic diseases (RD) during the COVID-19, lack of direct pre-pandemic comparison precludes understanding of impact of the pandemic.ObjectivesOur objective was to evaluate the effect of the COVID-19 pandemic on adherence to disease modifying drugs (DMARDs) including conventional synthetic (csDMARDs) and targeted synthetic (tsDMARDs).MethodsWe linked population-based health data on all physician visits, hospital admissions, and all dispensed medications, regardless of payer in British Columbia from 01/01/1996 to 3/31/2021. We identified prescriptions for csDMARDs (including methotrexate, hydroxychloroquine) and tsDMARDs, namely anti-TNFs (including infliximab, etanercept, adalimumab) and rituximab using drug identification numbers among indicated individuals with RD. We defined March 11, 2020, as the ‘index date' which corresponded to the date that mitigation measures for the COVID-19 pandemic were first introduced. We assessed adherence as proportion days covered (PDC), calculated monthly in the 12 months before and 12 months after the index date. We used interrupted time-series models, namely segmented regression to estimate changes and trends in adherence before and after the index date.ResultsOur analysis showed that the mean PDCs for all included DMARDs stayed relatively steady in the 12 months before and after mitigation measures were introduced (see Table 1). Adherence was highest among anti-TNFs, methotrexate, and azathioprine. Anti-TNFs were on a downward trajectory 12 months prior to the index date. Interrupted time-series modeling demonstrated statistically significant differences in the trends in PDCs post- vs. pre-mitigation measures for all anti-TNFS (slope [∂]: 1.38, standard error [SE]: 0.23), infliximab (∂: 1.35, SE: 0.23), adalimumab (∂: 0.82, SE: 0.25), and etanercept (∂: 1.07, SE: 0.25) (see Figure 1a). Conversely, the csDMARDs were on a flatter trajectory, and methotrexate (∂: -0.53, SE: 0.16), leflunomide (∂: 0.43, SE: 0.08), mycophenolate (∂: -1.26, SE: 0.48), cyclophosphamide (∂: 0.29, SE: 0.05), minocycline (∂: 0.04, SE: 0.02), chloroquine (∂: 0.02, SE: 0.00) showed statistically significant changes in estimated PDC trajectory after mitigation measures were introduced (see Figure 1b).ConclusionThis population-based study demonstrates that messaging and pandemic mitigation measures did not affect adherence to DMARDs.Table 1.Mean PDC 1 year before and after mitigation measures for the COVID-19 pandemic were introduced.MedicationMean PDC (%) 12 months before index dateMean PDC (%) 12 months after index datecsDMARDsmethotrexate28.926.8azathioprine21.819.5sulfasalazine16.214.9leflunomide14.313.0cyclosporine13.711.5hydroxychloroquine10.59.6mycophenolate4.52.9antimalarials4.43.9penicillamine3.53.4cyclophosphamide1.50.7chlorambucil1.20.4minocycline1.10.9gold0.50.2chloroquine0.10.0tsDMARDsanti-TNFs52.149.2infliximab41.838.3adalimumab40.336.8etanercept31.828.9rituximab3.42.9REFERENCES:NIL.Acknowledgements:NIL.Disclosure of InterestsNone Declared.

The Relevance of Applied Education to Urban Sustainability: A Case Study of a Degree Programme in Horticulture, Arboriculture and Landscape Management in Hong Kong

Applied degree programme is an innovative form of vocational education and training. The aim of this chapter is to examine the challenges and opportunities of the implementation of an existing vocational degree programme and its transformation into an applied degree. Bachelor of Science in Horticulture, Arboriculture and Landscape Management, BSc(HALM), which is a degree programme in Technological and Higher Education Institute of Hong Kong is used throughout as a case study. The purposes of BSc(HALM), and the methods to achieve them would be examined. Knowledge classification, workplace training and cultivation of transferable skills would be explored. The process of incorporating the positive impacts of COVID-19 would be elaborated to evince the revolution of applied degree programmes. The challenges and potential solutions, at individual, programme, industry and social levels, would be identified and discussed. Teaching and learning experiences would serve as evidence to support the arguments in this chapter. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images

Coronavirus Disease 2019 (COVID-19) has spread aggressively across the world causing an existential health crisis. Thus, having a system that automatically detects COVID-19 in tomography (CT) images can assist in quantifying the severity of the illness. Unfortunately, labelling chest CT scans requires significant domain expertise, time, and effort. We address these labelling challenges by only requiring point annotations, a single pixel for each infected region on a CT image. This labeling scheme allows annotators to label a pixel in a likely infected region, only taking 1-3 seconds, as opposed to 10-15 seconds to segment a region. Conventionally, segmentation models train on point-level annotations using the cross-entropy loss function on these labels. However, these models often suffer from low precision. Thus, we propose a consistency-based (CB) loss function that encourages the output predictions to be consistent with spatial transformations of the input images. The experiments on 3 open-source COVID-19 datasets show that this loss function yields significant improvement over conventional point-level loss functions and almost matches the performance of models trained with full supervision with much less human effort. Code is available at: https://github.com/IssamLaradji/covid19_weak_supervision

Radiological Findings in COVID-19 and Adaptive Approaches for Radiology Departments: Literature Review and Experience Sharing

Radiological investigations play an important role in the treatment course of patients with coronavirus disease 2019 (COVID-19) and radiologists should be familiar with the imaging characteristics. Being an integral component of the healthcare system, radiology departments have made adaptations to enhance infection control and strengthen the service. In this article, we review the radiological features of COVID-19 on chest radiography and computed tomography, and share experiences on the adaptive approach of radiology departments amidst the COVID-19 pandemic.