Association of Burnout With Depression and Anxiety in Critical Care Clinicians in Brazil.

IMPORTANCE

It is unclear whether burnout, anxiety, and depression constitute the same or different constructs. Better understanding of these constructs is important for diagnosis and treatment for intensive care unit (ICU) clinicians.

OBJECTIVE

To determine the associations and distinctiveness of burnout, depression, and anxiety in a sample of ICU clinicians.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used baseline data from the ICU Visits Study, a cluster-randomized crossover clinical trial conducted from April 2017 to July 2018 in 36 mixed public and private nonprofit ICUs in Brazil. ICU clinicians, including day-shift physicians, nurses, nurse technicians, and physiotherapists working in an ICU at least 20 hours per week, were enrolled. Data were analyzed from December 27, 2019, to October 10, 2020.

MAIN OUTCOMES AND MEASURES

The main outcome measures were burnout, depression, and anxiety measured with the Maslach Burnout Inventory (MBI; range, 0-6, with high scores indicating more burnout) and the Hospital Depression and Anxiety Scale (HADS; range, 0-3, with higher scores indicating more depression or anxiety). Internal consistencies were satisfactory.

RESULTS

The total sample included 715 ICU clinicians (median [interquartile range] age, 34.8 [30.2-39.3] years; 520 [72.7%] women), including 96 physicians (13.4%), 159 nurses (22.2%), 358 nurse technicians (50.1%), and 102 physiotherapists (14.3%). Clinicians reported low levels of emotional exhaustion (mean [SD] score, 1.84 [1.18]), depersonalization (mean [SD] score, 0.98 [1.03]), and personal accomplishment (mean [SD] score, 5.05 [0.87]) on the MBI, and similarly low levels of depression (mean [SD] score, 0.54 [0.40]) and anxiety (mean [SD] score, 0.70 [0.45]) on the HADS. Confirmatory factor analyses consistently showed improved fit separating latent burnout dimensions from depression and anxiety. An exploratory graph analysis combining gaussian graphical model with clustering algorithms for weighted networks suggested 3 clusters, with distinct burnout, anxiety, and depression clusters. This structure was confirmed using a bootstrap with 1000 random samples, in which the 3-cluster solution emerged in 625 samples (62.5%). Both latent variable loadings and network statistics suggested 3 key indicators (ie, feeling burned out from work, worrying thoughts, and reverse-scored reporting feeling cheerful) that can be used for short screening instruments.

CONCLUSIONS AND RELEVANCE

These findings suggest that burnout and clinical symptoms of depression and anxiety were empirically distinct in a large sample of ICU clinicians, highlighting the importance of screening for burnout and clinical symptoms to allow fast access to adequate support and treatment in health professionals at high risk of burnout.