Burnout syndrome among Anesthesia providers in Rwanda: A cross sectional survey

Authors: Eugene Tuyishime1,2,3, MD, MSc; Paulin Ruhato Banguti1, MMED ; Daniel I McIsaac5 MD, MPH, FRCPC , Dylan Bould4, MD, Med, FRCA Institutions: 1.University of Rwanda, 2.Centre Hospitalier Universitaire de Kigali, 3. OhioHealth Learning, Riverside Methodist Hospital, Columbus, Ohio, 4. Children's Hospital of Eastern Ontario 5. Departments of Anesthesiology & Pain Medicine, University of Ottawa and The Ottawa Hospital; Ottawa Hospital Research Institute; School of Epidemiology & Public Health, University of Ottawa Background Burnout is a negative emotional and psychological response to the stresses of work. It is commonly defined by the following three dimensions: emotional exhaustion, depersonalization, and reduced personal accomplishment.1 Emotional exhaustion refers to feeling a lot of stress and fatigue. Depersonalization is a coping mechanism involving withdrawal from work and feeling of cynicism.2 Reduced personal accomplishment characterizes a feeling of frustration towards work and lack of achievement.3 Burnout is a major problem for anesthesia providers. The prevalence of burnout among anesthesiologists vary between 10.4 and 52.7%.4-10 The common factors leading to burnout include increased workload, role-related conflict, lack of community and teamwork, and value-related conflicts. 11 Burnout has many negative consequences for health care providers, health system, and patients. Some of these consequences include tiredness, impaired alertness, mood disturbances such as irritability, strained interpersonal relationships at work, substance misuse and suicidal ideation, low staff recruitment, frequent staff transfers, frequent medical errors, and reduced quality of service to patients. 3, 11 Little is known about burnout among anesthesiologists in low income countries. As the number of anesthesiologists is very low, most of anesthesia care is provided by non-physician anesthetists (NPAs) working in remote areas without adequate equipment and supervision. This may lead to even higher prevalence of burnout. Few studies on burnout among anesthesia providers have been conducted in Africa. Van der Walt et al. and Mumbwe et al. found respectively a burnout prevalence of 21% and 51.3% respectively for South Africa and Zambia. 10, 12 Rwanda has made great investment in training healthcare providers in the last decade. However, burnout can jeopardize capacity building and patient safety efforts in Rwanda. To our knowledge there is no previous study done in Rwanda to evaluate burnout among anesthesia providers in Rwanda. Therefore our study had the following objectives: 1) to determine the prevalence of burnout syndrome among anesthesia providers working in Rwandan hospitals. 2) to determine which sociodemographic and occupational factors were associated with increased risk of burnout among anesthesia providers in Rwandan hospitals. Methods Design This study was approved (No 056/CMHS-IRB/2018) by the University of Rwanda/College of Medicine and Health Sciences institutional review board. Study design was a cross-sectional study using survey methodology. Reporting is consistent with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for the reporting of observational research.13 Population Our sampling frame included all anesthesia providers in Rwanda (19 anesthesiologists, 30 anesthesia residents, and 270 non-physician anesthesia providers (with either advanced diploma or bachelor’s degree in anesthesia). Recruitment and Data collection Due to the practical considerations of reaching all anesthesia providers in Rwanda, who work in geographically diverse settings, we developed an electronic google form (distributed via WhatsApp or email to anesthesia providers who agreed to participate in the study) and a paper-based version of the questionnaire. Participants were asked to choose the most convenient method for their circumstances. From September to November 2018, the data collector (ET) went to all 4 teaching hospitals as they were easily accessible and approached participants from other hospitals during their participation in the Vital Anesthesia Simulation Training (VAST) course (https://vastcourse.org/ ) that was organized for 6 district hospitals (Masaka, Nyamata, Gahini, Nyagatare, Rwinkwavu, and Kiziguro), 1 provincial hospital (Rwamagana), and 1 referral hospitals (Kibungo). Outcome Our primary outcome was prevalence of burnout, which we measured using the validated and widely used Maslach Burnout Inventory Human Services Survey (MBI-HSS). The questionnaire had both English and Kinyarwanda versions available to minimize any language barrier from respondents. The translation was done following the WHO steps for translation guidelines (forward translation, expert panel back-translation, pre-testing and cognitive interviewing, and final version). 14 The MBI-HSS is a 22-item questionnaire (see Supplemental Digital Content, Appendix 1) that assesses burnout in 3 dimensions: (1) emotional exhaustion, (2) depersonalization, and (3) personal achievement. Multiple questions within each dimension are scored on a 7-point Likert scale. Following standard scoring practices, burnout was defined as being present if (1) an individual scored more than 27 on the emotional exhaustion subscale (high emotional exhaustion) and (2) either more than 10 on the depersonalization subscale (high depersonalization) or less than 40 on the low personal accomplishment scale. 12, 15 A secondary prevalence assessment was made for participants who did not meet the above standard criteria but did score high levels of emotional exhaustion, high depersonalization, or low personal accomplishment on a unique subscale basis. Scores on the emotional exhaustion dimension were defined as high 26–54, medium 16–25, and low 0–15. Scores on the depersonalization subscale were defined as high 9–30, medium 3–8, and low as 1–2. Scores on the personal accomplishment subscale were defined as high 43–48, medium 34–42, and low 0–33, as performed by Mangu, Natalia, and Raluca. 12,16 Other Variables Collected Participants also completed a 2-part questionnaire (see Supplemental Digital Content, Appendix 2) to allow self-report of sociodemographic data (age, gender, marital status, and number of dependents) and occupational factors (job position, team work perception, and availability of equipment, frequency of negative outcomes, vacation days, and remuneration). Considering the local context, this questionnaire was adapted from a tool used in a similar setting (Zambia) by Mumbwe and colleagues. Statistical Analysis Data will be analyzed using SPSS (version 24; IBM, Armonk, NY) and SAS (version 9.4, SAS Institute, Cary, NC). Descriptive statistics Descriptive statistics will be calculated as • percentages for binary and categorical data, or • mean and standard deviation for normally distributed continuous variable or median and interquartile range (IQR) for skewed continuous variables. Distributions of data will be determined by visually inspecting histograms of each variable. • The proportion of participants who had burnout will be calculated as a proportion Inferential statistics We will use a Bayesian framework for analysis so that we can incorporate prior knowledge about burnout in similar jurisdictions with the new data provided from the current study to generate estimates of the probability of non-null associations between provider and practice characteristics and burnout. This approach will allow generation of 95% credible intervals (i.e., intervals with 95% probability of containing the true effect estimate given the data and prior knowledge) as well as probability estimates that a given predictor has a non-zero association with outcome. Bayesian analyses will be conducted using the R statistical programming language (R Foundation for Statistical Computing, Vienna, Austria) and the ‘brms’ and ‘rmsb’ packages. We will use two approaches to our Bayesian analyses. The first will be to employ weakly-informative prior distributions (i.e., prior distributions that limit Markov Chain Monte Carlo (MCMC) draws to plausible values without strongly influencing point estimates) and the second will use highly-informative prior distributions based on previously published associations. All analyses will employ logistic regression models to estimate odds ratios, 95% credible intervals and probabilities of non-null associations. Best practices will be followed to ensure adequate mixing of chains and limit autocorrelation between MCMC estimates. Analyses will include univariable (i.e., unadjusted) and multivariable models. The multivariable approach will consider the following preditors: 1. whether physician or non-physician (binary) 2. rural vs urban practice (binary) 3. having the right team to carry out work to an appropriate standard (dichotomized to a binary variable) 4. years spent in independent practice (reported as a continuous variable). 5. gender (as a binary variable) 6. vacation (dichotomized to a binary variable, either taking vacation or not) 7. having the right equipment to perform work to an appropriate standard (dichotomized to a binary variable) 8. workload in hours per week (self-reported as a continuous variable) • Confidence intervals for the proportion of participants with burnout will be determined using Wilson's method. • Independent variables in anesthesia providers with burnout will be compared to those without burnout using χ2 or Fisher exact test for nominal/dichotomous data and the Mann-Whitney U test for continuous data that was not normally distributed. • Unadjusted associations between associated variables and burnout will be calculated using univariable logistic regression. • To evaluate the adjusted contribution of variables on burnout, we will pre-specify a set of variables likely to be associated with burnout based on literature review and local knowledge

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