Impacts of mental health in the sleep pattern of healthcare professionals during the COVID-19 pandemic in Brazil

Abstract

Background: After >2 years of the Coronavirus Disease-19 (COVID-19) pandemic, it is well established how sleep symptoms are rising, especially among healthcare workers (HCW). The aim of this study is to evaluate what features are associated with sleep disturbances in the HCW population.

Methods: Cross-sectional and longitudinal analysis of social and clinical variables associated with sleep problems and insomnia incidence in HCW in a large, national-level cohort. The measurement of sleep problems was assessed by self-report using Jenkins Sleep Scale (JSS). A multivariate analysis was used in the cross-sectional design and generalized linear models were used in the longitudinal design.

Results: 10,467 HCW were analyzed in the cross-sectional analysis, 3313 participants were analyzed in the three timepoints of the study. Sex, previously diagnosed mental illness and frontline work with COVID-19 were associated with higher scores in JSS in the univariate analysis. In the multivariate analysis, only previous diagnosis of mental illness was related with sleep difficulties, especially previously diagnosed insomnia. The longitudinal analysis concluded that previous diagnosis of mental illnesses was associated with higher levels of insomnia development (OR = 11.62). The self-reported disorders found to be major risk factors were addiction (OR = 7.69), generalized anxiety disorder (OR = 3.67), social anxiety (OR = 2.21) and bipolar disorder (OR = 2.21).

Limitations: Attrition bias.

Conclusions: Previous diagnosis of mental illness was strongly related to insomnia development in HCW during the COVID-19 pandemic. Strategies that focus on this population are advised.

Keywords: COVID-19; Health professionals; Insomnia; Mental illness; Sleep.

Fig. 1

Periods of data collection (blue) and number of deaths due to Covid-19 per million of inhabitants in Brazil (green). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Raincloud plot of JSS scores by previously diagnosed insomnia stated in timepoint 1. JSS: Jenkins Sleep Scale.

Fig. 3

Risk factors found in the cross-sectional arm and their OR for insomnia development in the first model of the multivariate analysis of the longitudinal arm. OR: odds ratio; empty dot: OR; continuous line: confidence interval (95 %).

Fig. 4

Previously diagnosed mental illnesses reported in timepoint 1 and their OR for insomnia development in the adjusted model of the multivariate analysis of the longitudinal arm. ADHD: attention deficit/hyperactivity disorder; MDD: major depressive disorder; OR: odds ratio; PSTD: post-traumatic stress disorder; empty dot: OR; continuous line: confidence interval (95 %).

Fig. 5

JSS score means by timepoint in different groups of insomnia diagnosis patterns in HCW. G0: individuals who did not report any previous diagnosis of insomnia in all the timepoints (n = 2402); G1: individuals who reported previous diagnosis of insomnia in timepoint 1 (n = 47); G2: individuals who did not report previous diagnosis of insomnia in timepoint 1 but reported it in timepoint 2 (n = 158); G3: individuals who did not report previous diagnosis of insomnia in both timepoints 1 and 2 but reported it in timepoint 3 (n = 73). The scale of JSS score was enlarged for better visualization.

Fig. 6

Cross-influence between GSI index of BSI and insomnia development in timepoints 1, 2 and 3. GSI: Global Severity Index; BSI: Brief Symptoms Inventory; INS_1: Individuals who reported insomnia in timepoint 1; INS_2: Individuals with newly diagnosed insomnia in timepoint 2; INS_3: Individuals with newly diagnosed insomnia in timepoint 3; GSI_1: GSI score in timepoint 1; GSI_2: GSI score in timepoint 2; GSI_3: GSI score in timepoint 3. **p < 0.01; ***p < 0.001.