Understanding the Psychological Impact of COVID-19 Pandemic on Patients With Cancer, Their Caregivers, and Health Care Workers in Singapore.

PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has had a global impact, and Singapore has seen 33,000 confirmed cases. Patients with cancer, their caregivers, and health care workers (HCWs) need to balance the challenges associated with COVID-19 while ensuring that cancer care is not compromised. This study aimed to evaluate the psychological effect of COVID-19 on these groups and the prevalence of burnout among HCWs. METHODS: A cross-sectional survey of patients, caregivers, and HCWs at the National Cancer Centre Singapore was performed over 17 days during the lockdown. The Generalized Anxiety Disorder-7 and Maslach Burnout Inventory were used to assess for anxiety and burnout, respectively. Self-reported fears related to COVID-19 were collected. RESULTS: A total of 624 patients, 408 caregivers, and 421 HCWs participated in the study, with a response rate of 84%, 88%, and 92% respectively. Sixty-six percent of patients, 72.8% of caregivers, and 41.6% of HCWs reported a high level of fear from COVID-19. The top concern of patients was the wide community spread of COVID-19. Caregivers were primarily worried about patients dying alone. HCWs were most worried about the relatively mild symptoms of COVID-19. The prevalence of anxiety was 19.1%, 22.5%, and 14.0% for patients, caregivers, and HCWs, respectively. Patients who were nongraduates and married, and caregivers who were married were more anxious. The prevalence of burnout in HCWs was 43.5%, with more anxious and fearful HCWs reporting higher burnout rates. CONCLUSION: Fears and anxiety related to COVID-19 are high. Burnout among HCWs is similar to rates reported prepandemic. An individualized approach to target the specific fears of each group will be crucial to maintain the well-being of these vulnerable groups and prevent burnout of HCWs.

Bayesian Multiparameter Evidence Synthesis to Inform Decision Making: A Case Study in Metastatic Hormone-Refractory Prostate Cancer.

In health technology assessment, decisions are based on complex cost-effectiveness models that require numerous input parameters. When not all relevant estimates are available, the model may have to be simplified. Multiparameter evidence synthesis combines data from diverse sources of evidence, which results in obtaining estimates required in clinical decision making that otherwise may not be available. We demonstrate how bivariate meta-analysis can be used to predict an unreported estimate of a treatment effect enabling implementation of a multistate Markov model, which otherwise needs to be simplified. To illustrate this, we used an example of cost-effectiveness analysis for docetaxel in combination with prednisolone in metastatic hormone-refractory prostate cancer. Bivariate meta-analysis was used to model jointly available data on treatment effects on overall survival and progression-free survival (PFS) to predict the unreported effect on PFS in a study evaluating docetaxel with prednisolone. The predicted treatment effect on PFS enabled implementation of a 3-state Markov model comprising stable disease, progressive disease, and dead states, while lack of the estimate restricted the model to a 2-state model (with alive and dead states). The 2-state and 3-state models were compared by calculating the incremental cost-effectiveness ratio (which was much lower in the 3-state model: £22,148 per quality-adjusted life year gained compared to £30,026 obtained from the 2-state model) and the expected value of perfect information (which increased with the 3-state model). The 3-state model has the advantage of distinguishing surviving patients who progressed from those who did not progress. Hence, the use of advanced meta-analytic techniques allowed obtaining relevant parameter estimates to populate a model describing disease pathway in more detail while helping to prevent valuable clinical data from being discarded.

Presentational approaches used in the UK for reporting evidence synthesis using indirect and mixed treatment comparisons.

OBJECTIVES: To establish current guidance and practice in UK on presentation of indirect comparison and mixed treatment comparison analyses; to provide recommendations to improve indirect comparison/mixed treatment comparison reporting and to identify research priorities for improved presentation. METHODS: Existing institutional guidance for conducting indirect comparison/mixed treatment comparison alongside current practice in health technology assessment was reviewed. Reports published in UK by the Health Technology Assessment programme since 1997, which utilized indirect comparison/mixed treatment comparison methods, were reviewed with respect to the presentation of study data, statistical models and results. Recommendations for presentation were developed. RESULTS: Guidance exists that provide the details necessary to conduct a successful indirect comparison/mixed treatment comparison analysis but recommendations on presentation are limited. Of 205 health technology assessment reports that contained evidence synthesis for effectiveness, 19 used indirect comparison/mixed treatment comparison methods. These reports utilized numerous presentational formats from which the following key components were identified: network table/diagram for presenting data; model description to allow reproducibility; and tables, forest plots, matrix tables and summary forest plots for presenting a range of results. Recommendations were developed to ensure that reporting is explicit, transparent and reproducible. Approaches most understandable by non-technical decision makers, and areas where future research is required, are outlined. CONCLUSIONS: There is no standard presentational style used in UK for reporting indirect comparison/mixed treatment comparison, and the use of graphical tools is limited. Standardization of reporting and innovation in graphical representation of indirect comparison/mixed treatment comparison results is required.