Table 3.

Characteristics of studies on burnout among combined populations of healthcare workers in sub-Saharan Africa (N = 18)

First Author, Year	Country	Study population	Burnout assessment	Reported burnout	Main findings
Bhagavathula, 2018 [96]	Ethiopia	Healthcare workers at a teaching hospital (N = 248)	MBI	Emotional exhaustion (mean ± SD): 5.4 ± 1.2 Inefficacy: 5.1 ± 1.7 Cynicism: 4.8 ± 2.0 13.7% reported overall burnout.	Burnout was associated with age (p = 0.008), number of patients treated per day (p < 0.001), and shift work (p < 0.001). In multivariable analyses, sex, marital status, profession, and work experience were significantly associated with burnout subscales (p < 0.01).
Biksegn, 2016 [97]	Ethiopia	Healthcare workers at a teaching hospital (N = 334)	CBI	50.3 ± 17.2 (mean ± SD)	Nurses had the highest prevalence (82.8%) of burnout and laboratory technicians had the lowest (2.8%). Job insecurity, history of physical illness, low interest in profession, poor relationship status with managers, worry of contracting infection or illness and physical/verbal abuse were predictors of burnout.
Bonenberger, 2014 [98]	Ghana	Healthcare workers (N = 256)	Instrument to measure motivation with 7 outcome constructs, including burnout	3.3 ± 1.0 (mean ± SD)	Motivation and job satisfaction were significantly associated with career development (OR = 0.56, 95% CI: 0.36–0.86), workload (OR = 0.58, 95% CI: 0.34–0.99), management (OR = 0.51, 95% CI: 0.30–0.84), organizational commitment (OR = 0.36, 95% CI: 0.19–0.66), and burnout (OR = 0.59, 95% CI: 0.39–0.91).
Crabbe, 2004 [99]	South Africa	Healthcare workers in trauma unit of a hospital (N = 38)	MBI	61% had high emotional exhaustion, 50% high depersonalization, and 50% high reduced personal accomplishment	At least half of respondents reported high professional burnout in all 3 MBI subscales.
Fiadzo, 1997 [100]	Ghana	Healthcare workers (N = 287)	MBI	Not reported	Study provides support for burnout progression model
Kim, 2018 [101]	Malawi	Healthcare workers providing clinical care for HIV-positive patients (N = 520)	MBI	62% met criteria for total burnout, with 55% reporting moderate-high emotional exhaustion, 31% moderate-high depersonalization, and 46% low-moderate personal accomplishment.	Burnout was associated with self-reported suboptimal patient care (OR = 3.22, 95% CI: 2.11–4.90; p < 0.0001)
Kokonya, 2014 [102]	Kenya	Healthcare workers at a national hospital (N = 345)	Compassion Fatigue Self-Test	95.4% reported high burnout	96.7% of medical practitioners and 94.1% of nurses reported high burnout. Burnout was not significantly associated with participants’ sex, age, marital status, religion, education, or number of years as a healthcare provider.
Kruse, 2009 [103]	Zambia	Healthcare providers (N = 483 active clinical staff completed questionnaire; N = 50 in focus groups, N = 4 interviews)	Occupational burnout measured on 5-item scale	51% of respondents reported occupational burnout	Occupational burnout was associated with having another job (RR = 1.4, 95% CI: 1.2–1.6) and knowing a co-worker who left in the last year (RR = 1.6, 95% CI: 1.3–2.2).
Ledikwe, 2018 [104]	Botswana	Healthcare workers at a public health facility (N = 1348)	MBI-GS	Professional efficacy (mean ± SD): 4.9 ± 1.1 Exhaustion: 2.3 ± 1.7 Cynicism: 2.4 ± 1.4	Overall job satisfaction assessed by the Job In General Scale was significantly higher for healthcare workers who participated in 7 or more activities as part of the Botswana’s Workplace Wellness Program (WWP) compared with those who did not participate in any activities (p = 0.004). Stress levels (p = 0.006), measured on the Stress in General scale, and exhaustion (p < 0.001), measured on the MBI, were significantly lower among those with high participation in WWP activities.
Madede, 2017 [105]	Mozambique	Healthcare workers (quantitative: N = 92 baseline and 49 post-intervention; N = 17 qualitative interviews)	MBI	At baseline, 67.1% low, 15.9% moderate, and 17.1% high burnout. After intervention, 71.1% low, 17.8% moderate, 11.1% high burnout.	There were no significant differences in emotional exhaustion between baseline and post intervention, for any intervention groups. Job satisfaction, emotional exhaustion and work engagement showed no significant differences between baseline and post intervention.
McAuliffe, 2009 [106]	Malawi	Healthcare workers in public and private facilities (N = 153)	MBI	31% reported high emotional exhaustion, 5% reported high depersonalization, and 45% reported low personal accomplishment	The adequate resources subscale of the Health Care Providers Work Index correlates with emotional exhaustion on the MBI.
Mutale, 2013 [107]	Zambia	Healthcare workers from health facilities (N = 96)	“I feel emotionally drained at the end of the day” and “Sometimes when I get up in the morning, I dread having to face another day at work.”	Not reported	Burnout was higher among women as compared to men in 2 of the 3 districts. Linear regressions showed major determinants of higher motivation were female (p = 0.008) and working in non-clinical areas (for example, pharmacists or laboratory technicians, p = 0.039).
Ndetei, 2008 [108]	Kenya	Healthcare workers at a psychiatric hospital (N = 121)	MBI-HSS and MBI-GS	Emotional exhaustion (mean ± SD): 17.2 ± 9.8 Depersonalization: 7.3 ± 5.8 Personal accomplishment: 29.3 ± 10.3	Emotional exhaustion was significantly associated with younger age (p < 0.001), number of children (p = 0.003), number of years worked (p = 0.049), heavy workload (p < 0.001) and low morale (p = 0.001). Depersonalization was significantly associated with heavy workload (p = 0.034). Reduced personal accomplishment was associated with younger age (p = 0.03).
Nel, 2013 [109]	South Africa	Healthcare workers at public and private hospitals (N = 511)	MBI-HSS	Emotional exhaustion (mean ± SD): 15.2 ± 7.2 Mental distance: 13.6 ± 9.3	The proposed structural model shows paths between job demands and job resources; job demands, emotional intelligence and work wellness; job resources, emotional intelligence and work wellness.
Ojedokun, 2013 [110]	Nigeria	Healthcare workers working in AIDs care (N = 242)	MBI	66.4 ± 21.5 (mean ± SD)	Burnout was significantly associated with aggressive tendency and perceived fear of AIDS (p < 0.01)
Olley, 2003 [111]	Nigeria	Healthcare workers at a teaching hospital (N = 260)	MBI	Not reported	Nurses reported higher scores on burnout subscales compared to other healthcare providers (p < 0.05). Significant differences were found between nurses and other healthcare providers on the General Health Questionnaire-12 (p < 0.01) and the State Trait Anxiety Inventory (p < 0.05).
Thorsen, 2011 [112]	Malawi	Healthcare workers in a referral hospital (N = 101)	MBI-HSS	Emotional exhaustion (mean ± SD): 23.1 ± 9.7 Depersonalization: 6.2 ± 4.8 Personal accomplishment: 37.8 ± 7.5	Sociodemographic characteristics were not associated with the emotional exhaustion subscale of burnout. For the depersonalization and personal accomplishment subscales, number of children was the only significant predictor (p < 0.05).
Weldegebriel, 2016 [113]	Ethiopia	Healthcare workers at public hospitals (N = 304)	Organizational burnout measured as a subdimension of motivation	3.6 ± 1.3 (mean ± SD)	Performance review was the only significant predictor of the burnout dimension of motivation. Respondents who never had a performance review conducted had an average decrease of 0.155 units (95% CI: −0.875 to −0.122) in burnout motivation score as compared to those with formal performance assessment.

Abbreviations: MBI Maslach Burnout Inventory, MBI-HSS Maslach Burnout Inventory - Human Services Survey, MBI-GS Maslach Burnout Inventory - General Survey