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Objective: This study examined how best to identify modifiable protective and risk factors for burnout in healthcare workers in the face of the COVID-19 pandemic. Individual, occupational, organizational and social factors were investigated. The study also assessed the impact of these factors on post-traumatic stress disorder (PTSD), anxiety, and depression. Methods: Healthcare workers in the Quebec (Canada) healthcare system were recruited between May 21 to June 5, 2020. Participants answered an electronic survey 3 months after the COVID-19 epidemic outbreak began in Canada. Using the Maslach Burnout Inventory, PTSD Checklist for DSM-5, and Hospital Anxiety and Depression Scale, we studied the prevalence of burnout, PTSD, anxiety and depression in this cohort. Multivariable logistic or linear regression models including resilience, social and organizational support, workload and access to mental health help, simulation techniques and protective personal equipment (PPE) as well as perception of PPE security were conducted for each outcome. Results: In mid-June 2020, 467 participants completed the survey. We found that half (51.8%) of the respondents experienced burnout characterized by emotional exhaustion and/or depersonalization at least once a week. In total, 158 healthcare workers (35.6%) displayed severe symptoms of at least one of the mental health disorders (24.3% PTSD, 23.3% anxiety, 10.6% depression). Resilience (OR = 0.69, 95% CI: [0.55–0.87]; p = 0.002) and perceived organizational support (OR = 0.75, 95% CI: [0.61–0.93]; p = 0.009) were significantly associated with burnout and other outcomes. Social support satisfaction, perception of PPE security, work type and environment, mental health antecedents and reassignment were associated with PTSD and/or anxiety and/or depression, but not burnout. Conclusion: Future studies should address primarily resilience and perceived organizational support to promote mental health and prevent burnout, PTSD, anxiety and depression. [ABSTRACT FROM AUTHOR]

Background: Comorbidity indexes derived from administrative databases are essential tools of research in global health. We sought to develop and validate a novel cardiac-specific comorbidity index, and to compare its accuracy with the generic Charlson-Deyo and Elixhauser comorbidity indexes.Methods: We derived the cardiac-specific comorbidity index from consecutive patients who were admitted to hospital at a tertiary-care cardiology hospital in Quebec. We used logistic regression analysis and incorporated age, sex and 22 clinically relevant comorbidities to build the index. We compared the cardiac-specific comorbidity index with refitted Charlson-Deyo and Elixhauser comorbidity indexes using the C-statistic and net reclassification improvement to predict in-hospital death, and the Akaike information criterion to predict length of stay. We validated our findings externally in an independent cohort obtained from a provincial registry of coronary disease in Alberta.Results: The novel cardiac-specific comorbidity index outperformed the refitted generic Charlson-Deyo and Elixhauser comorbidity indexes for predicting in-hospital mortality in the derivation population (n = 10 137): C-statistic 0.95 (95% confidence interval [CI] 0.94-0.9) v. 0.81 (95% CI 0.77-0.84) and 0.86 (95% CI 0.82-0.89), respectively. In the validation population (n = 17 877), the cardiac-specific comorbidity index was similarly better: C-statistic 0.92 (95% CI 0.89-0.94) v. 0.76 (95% CI 0.71-0.81) and 0.82 (95% CI 0.78-0.86), respectively, and also numerically outperformed the Charlson-Deyo and Elixhauser comorbidity indexes for predicting 1-year mortality (C-statistic 0.78 [95% CI 0.76-0.80] v. 0.75 [95% CI 0.73-0.77] and 0.77 [95% CI 0.75-0.79], respectively). Similarly, the cardiac-specific comorbidity index showed better fit for the prediction of length of stay. The net reclassification improvement using the cardiac-specific comorbidity index for the prediction of death was 0.290 compared with the Charlson-Deyo comorbidity index and 0.192 compared with the Elixhauser comorbidity index.Interpretation: The cardiac-specific comorbidity index predicted in-hospital and 1-year death and length of stay in cardiovascular populations better than existing generic models. This novel index may be useful for research of cardiology outcomes performed with large administrative databases. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has put chronic pressure on worldwide healthcare systems. While the literature regarding the prevalence of psychological distress and associated risk factors among healthcare workers facing COVID-19 has exploded, biological variables have been mostly overlooked. 467 healthcare workers from Quebec, Canada, answered an electronic survey covering various risk factors and mental health outcomes three months after the onset of the COVID-19 pandemic. Of them, 372 (80%) provided a hair sample, providing a history of cortisol secretion for the three months preceding and following the pandemic's start. We used multivariable regression models and a receiver operating characteristic curve to study hair cortisol as a predictor of burnout and psychological health, together with individual, occupational, social, and organizational factors. As expected, hair cortisol levels increased after the start of the pandemic, with a median relative change of 29% (IQR = 3–59%, p < 0.0001). There was a significant association between burnout status and change in cortisol, with participants in the second quarter of change having lower odds of burnout. No association was found between cortisol change and post-traumatic stress disorder, anxiety, and depression symptoms. Adding cortisol to individual-occupational-socio-organizational factors noticeably enhanced our burnout logistic regression model's predictability. Change in hair cortisol levels predicted burnout at three months in health personnel at the onset of the COVID-19 pandemic. This non-invasive biological marker of the stress response could be used in further clinical or research initiatives to screen high-risk individuals to prevent and control burnout in health personnel facing an important stressor. • Hair cortisol levels increased at the pandemic onset in healthcare workers. • There was a significant association between burnout status and change in cortisol. • No association was found between cortisol and PTSD/anxio/depressive symptoms. • Cortisol enhanced the burnout logistic regression model's predictability. [ABSTRACT FROM AUTHOR]