Prepandemic Mental Health and Well-being: Differences Within the Health Care Workforce and the Need for Targeted Resources : Journal of Occupational and Environmental Medicine

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Prepandemic Mental Health and Well-being

Differences Within the Health Care Workforce and the Need for Targeted Resources

Silver, Sharon R. MS; Li, Jia MS; Marsh, Suzanne M. MPA; Carbone, Eric G. PhD

Author Information
Journal of Occupational and Environmental Medicine 64(12):p 1025-1035, December 2022. | DOI: 10.1097/JOM.0000000000002630
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Occupational stress and diminished levels of overall well-being among health care workers were issues of concern even before the coronavirus disease 2019 (COVID-19) pandemic, which exacerbated existing stressors and introduced new challenges to this workforce.1,2 Mental health concerns that have been the focus of attention in this workforce include depression, anxiety, substance use disorders, posttraumatic stress disorder (PTSD), burnout, compassion fatigue, and suicide.

Health care workers have long faced a convergence of stressors that are less common in other types of work. These stressors include the emotional burden of dealing with individuals who are seriously ill or dying; witnessing traumatic events, which is associated with PTSD, particularly among first responders3,4; secondary traumatic stress after exposure to traumatized patients, particularly in emergency departments5,6; witnessing or being the target of workplace violence,7 which can result in adverse physical, psychological, social, and emotional effects7–10; and workplace bullying.10–12 The prevalence of these problems has been reported to be particularly pronounced in emergency and psychiatry departments within hospitals, in the home health setting, and in nursing care facilities.7,9,13 Among the additional stressors for many health care workers are poor job design, management challenges, suboptimal safety climate/safety culture, high caseloads, the effects of shift work and long work hours, and exposure to pathogens.

Most research on mental health in the health care workforce has focused on physicians (including physicians in training) and nurses. Problems noted among physicians include depression, anxiety, substance use disorders, burnout, and suicide.13–17 As with the general public, estimated prevalences of depression among health care workers vary depending on the case definition and characteristics of the measurement instrument (eg, criteria met for depressive disorder versus presence of subclinical depressive symptoms, self-reported symptoms versus provider-diagnosed depression, current depressive symptomatology versus incidence in the last 12 months), as well as demographic characteristics of the respondents (eg, variation by age).14–16,18,19 Depression has also been reported among physicians in training; a systematic review noted estimates of the prevalence of depression or depressive symptoms among residents ranging from 21% to 43%, depending on the case ascertainment variables listed previously,16 whereas a prospective study found an increase in depression based on Patient Health Questionnaire-9 (PHQ-9) scores from 3.9% before the internship year to more than 20% during each quarter of the internship.20 As with depression, suicide risk appears to accelerate during the physician training period.19 Beyond the training period, female physicians have been found to have higher rates of completed suicide, at 1.4 to 2.3 times the rate in the general population,18 although a recently published analysis suggests that the overall suicide risk among physicians is not significantly different from that of the general population.21 Identified risk factors for mental health issues among physicians are both individual and occupational, with the latter including the stress of patient interactions and expectations, easy access to medication, heavy workload, adverse work schedules, and problematic or limited social interaction in the workplace.13

A high prevalence of depression has been noted among registered nurses (RNs).22 A survey of nurses employed by hospitals reported rates of depressive symptomatology of 18%, approximately twice that of the US population, with job satisfaction, body mass index, number of health problems, mental well-being, and health-related productivity significantly associated with depression scores.23 The prevalence of depression among RNs is reported to be highest among those who are young, female, or working in intensive care or psychiatric units.22 Nurses appear to be at higher risk for suicide than both physicians and the general public.24,25

Mental health concerns in the health care workforce are not restricted to physicians and nurses. However, information about the prevalence of mental health problems among other health care occupations is more limited. Rates of depression, stress, and PTSD have been reported for emergency services personnel, but estimates vary widely.26 The scant literature on health care support workers (eg, patient care aides; occupational, physical, and dental aides; phlebotomists) has found that care and support workers have worse mental health than the general working population27 and that patient care aides are more likely to report depression than nurses.28 Female health care support workers have also been found to have elevated rates of suicide.25 Janitors across all industries have a higher prevalence of depression than other workers.29 Although ancillary health care workers such as housekeeping staff do not have direct patient care responsibilities, they frequently work in patient care areas.

Mental health is also a concern for social workers, counselors, psychologists, and others who are tasked with directly addressing the mental health needs of others.30,31 Male human service workers have been found to have higher levels of antidepressant use than other workers at the same skill level.32 Elevated suicide rates have been reported among male welfare support workers, social workers, and female welfare support workers.33

Although the pandemic has led to new mental health challenges for workers globally, health care workers have been particularly at risk because of increased emotional, physical, and organizational demands, as well as increased risk of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19. However, fully addressing the long-term mental health needs of this workforce also requires understanding the levels of baseline, prepandemic mental health issues. To assess baseline mental health and well-being among health care workers in different occupations, we examined 2017 to 2019 data from the Behavioral Risk Factor Surveillance System (BRFSS). Although BRFSS does not include prevalence data on the full range of mental health conditions and is limited to self-reported conditions and diagnoses, it does sample from a wide range of health care (and other) industries and occupations.

To our knowledge, this is the first study to evaluate prepandemic mental health and well-being of the health care workforce using a broad definition of this workforce (health care industry workers who have patient care responsibilities or who work in patient care areas) and including low-wage health care workers. The purpose of this study was to identify segments of the health care workforce that had the highest prepandemic prevalences of selected adverse conditions related to mental health and well-being, as they might require additional services during and after the pandemic.


Study Population

The BRFSS is a national survey of the noninstitutionalized US adult population (18 years or older) administered by state and jurisdictional health departments.34 Respondents are selected for the survey using random digit dialing techniques on both cellular phones and landlines. Overall response rates for this survey for landlines and cellphones, respectively, by year were 45.3% and 44.5% (2017); 53.3% and 43.4% (2018); and 53.5% and 45.9% (2019). Response rates overall and by state can be found at

In addition to a core survey, the BRFSS includes modules that states can opt to include. One of these modules is sponsored by the National Institute for Occupational Safety and Health and collects the industry and occupation of respondents who are employed for wages, out of work for less than 1 year, or self-employed. Occupation and industry are collected through open-ended questions: “What kind of work do you do?” followed by “What kind of business or industry do you work in?” This module is not implemented by the same states each year. A total of 33 states included this module during at least 1 year between 2017 and 2019 (22 states in 2017, 30 in 2018, and 25 in 2019, with 17 states participating all 3 years). We used the 3 most recent years of prepandemic data to enhance reportability for smaller health care occupations.

During BRFSS survey years 2017 to 2019, 314,078 respondents reported that they were employed or self-employed. A total of 51,895 respondents (16.5%) were excluded because of missing or uncodable industry or occupation, active-duty military status, or conflicting employment status information (respondents who reported being employed but whose responses to the industry or occupation question indicated they were unpaid workers, disabled, or retired). Industry and occupation free-text responses were autocoded to 2010 US Census Bureau industry and occupation codes by the National Institute for Occupational Safety and Health Industry and Occupation Computerized Coding System or, for items that could not be coded automatically, by human coders using computer-assisted coding.35

Although we provide results for all health care industry workers combined (all organizationally and self-employed workers with census industry codes 7970 to 8270), the focus of this study was on workers who interact directly with patients, as well as those who work in patient care areas as part of their duties, such as janitors and maids. Non–health care workers (employed outside both health care industries and health care occupations) comprised the comparison group for this work. A third, smaller set of workers are employed in health care occupations but outside the health care industries (eg school nurses, dieticians employed in the sports industry); we excluded them from reporting. Within the health care industry, we present results for occupational groups that had reportable results (denominator size ≥50 and relative standard error for prevalence estimates ≤30%) for at least 3 of the 6 conditions of interest.


We calculated distributions of demographic characteristics for each health care occupation. We also calculated unadjusted and adjusted prevalences of 6 health conditions elicited in the survey (Table 1). Because well-being and physical and mental health are not independent,36 in addition to conditions that explicitly concern mental health, we assessed prevalences of self-rated overall health, frequent physical distress, and insufficient sleep. Conditions evaluated were self-rated health (fair or poor general health), frequent physical distress (physical health not good at least 14 of past 30 days), frequent mental distress (mental health not good at least 14 of past 30 days), activity limitations (poor physical or mental health preventing usual activities for at least 14 of past 30 days), diagnosed depression, and insufficient sleep (<7 hours average sleep per 24-hour period; elicited only in 2018 BRFSS survey). Because responses for the items reported as number of days cluster at 0 and at multiples of 5 and 7, we did not treat them as continuous variables, instead dichotomizing them.

TABLE 1 - BRFSS Survey Questions Related to Mental Health and Well-being, 2017–2019
Metric Title BRFSS Question Cut Point
Poor self-rated health Would you say that in general your health is: excellent, very good, good, fair or poor? Fair or poor = poor self-rated health
Frequent physical distress Now thinking about your physical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health not good? ≥14 d = yes
Frequent mental distress Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good? ≥14 d = yes
Activity limitations During the past 30 days, for about how manydays did poor physical or mental health keep
you from doing your usual activities, such as self-care, work, or recreation?
≥14 d = yes
Diagnosed depression Has a doctor, nurse, or other health professional ever told you that you had a depressive
disorder (including depression, major depression, dysthymia, or minor depression)?
Insufficient sleep* On average, how many hours of sleep do you get in a 24-hour period? < 7 h average per 24-h period
*Elicited only in 2018.


To account for the complex survey design and incorporate respondent sampling weights in BRFSS, we used SAS version 9.4 (SAS Institute Inc, Cary, NC) and SAS-callable SUDAAN version 11.0 (RTI International, Research Triangle Park, NC). To estimate population counts and weighted unadjusted prevalences for all variables, we used the SURVEYFREQ procedure. We identified differences in health conditions by health care worker occupations or industries using the RLOGISTIC procedure. We compared health care workers to non–health care workers by performing logistic regressions and estimating adjusted prevalence ratios (aPRs) and their 95% confidence intervals (CIs). Non–health care workers served as the comparison group for the full group of health care workers, as well as for specific subgroups of health care workers. We considered CIs for aPRs that do not span the null to be statistically significant.

Adjustment variables in the primary regression models were sex, race/ethnicity combined (classified as white non-Hispanic, black non-Hispanic, other non-Hispanic, Hispanic), age in years (18–34, 35–54, ≥55), and marital status (collapsed to married or part of an unmarried couple [as a proxy for level of social support] vs all other). All estimates in this report were weighted. Because of the complex relations between income and demographics, occupation/industry, and health outcomes,37,38 we did not adjust for household income.


The 37,685 BRFSS respondents who worked in health care industries were the focus of the study (Table 2). Another 4627 health care workers were employed in non–health care industries; results for this group are not further reported. The 219,871 non–health care workers comprised the comparison population. The largest subset of the 37,685 health care respondents were from the hospital industry (47%), followed by ambulatory care (29%), nursing care facilities (10%), home health (8%), dental offices (4%), and other health care industries (2%).

TABLE 2 - Distribution of Workers From Health Care Occupations Across Health Care Industries, BRFSS 2017–2019
2010 Census Occupation Codes Sample
Weighted n (times 1000) Health Care Industry
(U.S. Census Industry Codes)
(Census 8180)
Dental Office
(Census 7980)
Home Health
(Census 8170)
Ambulatory Care
(Census 7970, 7990, 8070–8090)
(Census 8190)
Nursing Care Facilities
(Census 8270)
Non–health care workers† 219,871 74,862
Health care workers in non–health care industries‡ 4627 1330
Health care industry workers§ 37,685 12,051 3.7 8.4 29.4 46.5 10.1 1.9
Health care occupation grouping∥
Community and social service occupations 2000–2060 1288 317 NR 2.8 55.6 34.5 5.9 NR
 Counselors 2000 516 142 0.0 NR 77.9 17.7 NR NR
 Social workers 2010 554 125 0.0 4.1 46.3 40.5 8.9 NR
Health care practitioners and technical occupations 3000–3540 18,598 5646 3.5 4.5 26.6 55.8 6.9 2.8
Health diagnosing practitioners 4100 1235 6.9 1.1 52.2 37.9 1.5 NR
 Physicians and surgeons 3060 2445 758 NR NR 58.1 40.6 NR NR
Health treating practitioners # 10,737 3126 NR 6.5 20.4 63.7 9.2 NR
 Physical therapists 3160 529 152 0.0 10.5 52.7 26.8 10.0 0.0
 Registered nurses 3255 8959 2626 NR 6.8 17.1 66.6 9.4 NR
Miscellaneous health technologists and technicians 3300–3535 3679 1257 8.6 2.9 16.2 53.7 6.7 11.9
 Clinical laboratory technologists and technicians 3300 600 192 0.0 NR 8.4 68.0 NR 18.7
 Health practitioner support technologists and technicians 3420 325 119 NR NR 11.1 86.7 NR NR
 Licensed practical and licensed vocational nurses 3500 788 251 0.0 13.6 23.0 36.0 27.5 0.0
 Miscellaneous health technologists and technicians, other 3535 403 132 NR NR 36.2 59.7 NR NR
Health care support occupations 3600–3655 5313 1931 7.2 20.7 22.2 24.2 25.0 0.7
 Nursing, psychiatric, and home health aides 3600 3436 1223 0.0 31.6 7.5 24.3 36.6 NR
 Dental assistants 3640 385 144 95.8 0.0 NR NR 0.0 0.0
 Medical assistants 3645 823 356 NR NR 74.8 23.2 NR NR
 Phlebotomists 3649 154 54 0.0 0.0 NR 63.4 0.0 19.5
Food preparation and serving 4000–4160 470 117 0.0 NR 6.6 50.7 41.4 0.0
Building and grounds cleaning and maintenance occupations 4200–4250 777 262 NR NR 16.3 60.3 21.1 NR
 Janitors and building cleaners 4220 393 143 NR NR 26.1 58.8 NR NR
 Maids and housekeeping cleaners 4230 338 109 0.0 NR NR 62.6 31.3 0.0
Personal care and service occupations 4300–4650 1336 476 NR 58.5 12.0 14.4 14.9 NR
 Personal care aides 4610 1156 432 NR 63.6 11.0 13.7 11.5 NR
Office and administrative support occupations 5000–5940 3731 1280 5.5 2.1 43.4 44.1 3.7 1.2
Trades** 6200–9750 679 225 NR NR 24.2 50.5 10.1 7.3
Heavier shading indicates broader occupational grouping.
NR, not reported because relative standard error of estimates is >30%.
†Respondents with census industry codes (0170-7890 or 8290-9500) and census occupation not in (3000–3655).
‡Respondents with census industry codes (0170-7890 or 8290-9550) and census occupation in (3000–3655).
§Respondents with census industry codes 7970 to 8270.
∥Within health care industry, includes health care occupational groups with at least 3 reportable mental health related outcomes (see Table 3).
¶3000, 3010, 3040, 3050, 3060, 3110, 3120, 3140, 3230, 3250, and 3258.
#3030, 3150, 3160, 3200, 3210, 3220, 3235, 3245, 3255, 3256, 3257, and 3260.
**Construction, extraction, maintenance, production, and transportation and materials moving industries.

Demographics of Health Care Workers by Occupation

Demographic characteristics of respondents differed markedly by health care occupation. Although approximately 65% of health care diagnosing and treating practitioners were White, most health care support workers (55%) were non-White, primarily non-Hispanic African American or Hispanic (Table 3). Age distributions also varied by occupation. Educational attainment generally tracked with educational requirements for the occupation: 90% of health diagnosing practitioners and 64% of health treating practitioners had completed college, but only 28% of health technicians and technologists and 14% of health care support workers had done so. Income distribution and home ownership levels followed patterns similar to those observed for education.

TABLE 3 - Demographic Characteristics (Percentages) of Health Care Workers by Occupation, BRFSS 2017–2019
Age, %* Sex, %* Race/Ethnicity, %* Educational Attainment, %* Household Income, %*
18–34 y 35–54 y ≥55 y Male Female Non-Hispanic White Non-Hispanic African American Non-Hispanic Other Hispanic High School or Less Some College/Technical School College Graduate or More <$35 k $35–<$50 k $50–<$75 k ≥$75 k
Non–health care industries† 32.3 43.3 24.4 59.5 40.5 60.8 10.8 8.4 19.9 38.6 29.8 1431.6 26.6 12.4 15.8 45.2
Health care industry workers‡ 29.0 45.8 25.2 25.0 75.0 60.3 16.2 11.6 11.9 19.3 34.9 45.8 20.1 11.0 16.8 52.1
Health care occupation grouping§
Community and social service occupations 26.2 50.0 23.8 24.8 75.2 63.4 25.8 4.0 6.8 5.9 17.6 76.5 14.0 12.9 19.7 53.4
 Counselors 24.8 53.1 22.1 31.3 68.7 58.8 29.9 NR NR 5.1 19.9 75.0 18.5 14.5 21.6 45.4
 Social workers 29.7 45.6 24.6 14.7 85.3 69.5 21.1 NR 4.7 NR 10.2 83.8 8.5 11.7 15.5 64.3
Health care practitioners and technical occupations 26.2 49.1 24.7 24.5 75.5 66.2 12.4 12.9 8.4 6.5 32.0 61.6 7.4 8.2 16.8 67.6
Health diagnosing practitioners 17.6 52.8 29.6 47.3 52.7 64.1 6.8 21.6 7.5 3.4 6.3 90.3 3.2 3.3 5.2 88.3
 Physicians and surgeons 14.6 52.9 32.4 56.2 43.8 62.4 6.0 25.1 6.4 3.2 6.2 90.6 3.9 2.8 4.0 89.3
Health treating practitioners 26.8 48.8 24.4 13.0 87.0 66.6 14.0 10.8 8.7 4.2 32.1 63.7 5.8 7.5 18.6 68.1
 Physical therapists 25.4 54.0 20.6 23.8 76.2 77.8 NR NR NR NR 12.7 86.2 NR NR 12.5 83.6
 Registered nurses 26.8 48.2 25.0 11.6 88.4 65.9 14.9 10.6 8.6 4.5 34.2 61.2 5.6 7.7 18.6 68.1
Miscellaneous health technologists and technicians 32.7 46.6 20.8 30.2 69.8 67.3 14.0 9.8 8.8 15.2 56.9 28.0 14.9 14.7 23.8 46.5
 Clinical laboratory technologists and technicians 40.3 36.4 23.3 28.4 71.6 71.5 12.1 9.9 NR NR 43.7 40.1 NR 12.1 26.0 39.1
 Health practitioner support technologists and technicians 42.0 43.1 NR 35.9 64.1 60.1 25.4 NR NR 17.4 61.9 20.7 15.9 22.1 31.4 30.5
 Licensed practical nurses/LVNs 25.3 53.7 20.9 11.3 88.7 55.6 25.3 NR NR NR 66.9 20.5 15.3 23.6 28.6 32.6
 Miscellaneous health technologists and technicians, other 27.9 50.8 21.3 41.1 58.9 56.1 14.4 NR 6.1 20.7 41.7 37.6 19.0 15.4 22.4 43.2
Health care support occupations 41.8 37.7 20.5 12.0 88.0 44.9 27.1 10.7 17.3 40.2 45.9 13.9 51.8 16.8 15.0 16.4
 Nursing, psychiatric, and home health aides 39.4 36.1 24.4 11.8 88.2 39.5 33.8 11.7 14.9 51.3 38.0 10.7 60.1 16.1 12.1 11.6
 Dental assistants 47.1 38.9 14.1 NR 97.1 54.3 NR NR 32.1 31.1 57.4 11.5 32.2 12.7 21.8 33.4
 Medical assistants 47.0 44.1 9.0 8.9 91.1 48.4 19.4 9.2 23.0 16.3 63.2 20.5 42.4 19.8 17.9 19.8
 Phlebotomists 51.0 35.7 13.3 NR 83.4 48.7 26.7 NR 19.4 20.8 59.3 19.9 43.5 28.2 NR NR
Food preparation and serving 31.9 40.6 27.5 36.6 63.4 48.0 27.7 3.5 20.8 67.4 27.3 5.3 56.5 14.8 15.7 NR
Building and grounds cleaning and maintenance occupations 31.7 42.9 25.4 46.1 53.9 45.2 24.3 NR 20.2 74.0 20.4 5.6 60.8 14.3 14.0 NR
 Janitors and building cleaners 33.4 43.1 23.5 63.7 36.3 54.9 20.7 NR 19.6 66.4 27.0 NR 49.8 19.5 NR NR
 Maids and housekeeping cleaners 31.3 42.8 25.9 22.2 77.8 31.5 28.5 NR 22.1 84.6 11.8 NR 78.4 NR NR NR
Personal care and service occupations 33.2 41.7 25.2 18.4 81.6 45.4 20.6 11.5 22.6 47.2 38.3 14.5 63.4 15.0 11.5 10.1
 Personal care aides 34.4 42.0 23.6 18.3 81.7 43.3 20.8 12.1 23.8 49.4 37.1 13.6 66.8 13.1 11.6 8.5
Office and administrative support occupations 31.2 41.9 26.9 13.2 86.8 59.6 16.3 8.4 15.7 25.5 51.2 23.3 24.4 16.3 23.8 35.5
Trades∥ 32.3 33.9 33.8 75.4 24.6 58.7 11.0 NR 24.6 49.3 33.0 17.7 26.2 17.9 20.9 35.0
Heavier shading indicates broader occupational grouping.
LVN, licensed vocational nurse; NR, not reported because relative standard error of estimates is >30%.
†Respondents with census industry codes (0170-7890 or 8290-9500) and census occupation not in (3000–3655).
‡Respondents with census industry codes 7970 to 8270.
§Within health care industry, includes health care occupational groups with at least 3 reportable mental health related outcomes.
∥Construction, extraction, maintenance, production, and transportation and materials moving industries.

Prevalence of Adverse Health Conditions by Health Care Occupation

Across the health care workforce, insufficient sleep and diagnosed depression were the most commonly reported issues, with prevalences of 41.0% and 18.9%, respectively (Table 4). For both conditions, health care workers had statistically significant elevated aPRs compared with the non–health care workers; prevalences among the latter were 36.5% for insufficient sleep and 14.2% for depression. Although health care workers had a higher prevalence of depression than non–health care workers (aPR, 1.11; 95% CI, 1.06–1.19), health care workers were slightly less likely to report frequent mental distress (aPR, 0.88; 95% CI, 0.81–0.94). Health care workers were also significantly less likely than non–health care workers to report poor self-rated health (aPR, 0.76; 95% CI, 0.70–0.83) and marginally less likely to report frequent physical distress (aPR, 0.90; 95% CI, 0.81–1.00).

TABLE 4 - Health-Related Metrics by Health Care Occupation: Unadjusted Prevalences and aPRs*, BRFSS 2017–2019
Fair or Poor Self-rated Health
% (95% CI)
aPR (95% CI)
Frequent Physical Distress
% (95% CI)
aPR (95% CI)
Frequent Mental Distress
% (95% CI)
aPR (95% CI)
Activity Limitations
% (95% CI)
aPR (95% CI)
% (95% CI)
aPR (95% CI)
Insufficient Sleep†
% (95% CI)
aPR (95% CI)
Non–health care workers‡ 11.9 (11.6–12.3)
6.7 (6.5–6.9)
10.1 (9.8–10.4)
3.8 (3.6–4.0)
14.2 (13.9–14.6)
36.5 (35.7–37.3)
Health care industry workers§ 8.6 (8.0–9.3) 6.3 (5.8–7.0) 9.8 (9.1–10.4) 3.7 (3.2–4.2) 18.9 (18.0–19.8) 41.0 (39.0–43.1)
Health care occupation grouping∥ 0.76 (0.70–0.83) 0.90 (0.81–1.00) 0.88 (0.81–0.94) 0.91 (0.79–1.06) 1.11 (1.061.18) 1.13 (1.07–1.19)
Community and social service occupations 7.4 (5.3–10.0)
0.70 (0.52–0.95)
5.7 (3.8–8.2)
0.84 (0.58–1.22)
10.1 (6.6–14.5)
0.93 (0.63–1.37)
4.8 (2.6–8.1)
27.3 (21.9–33.1)
1.65 (1.322.05)
36.5 (27.7–46.0)
1.03 (0.81–1.30)
 Counselors 9.1 (5.3–14.2)
0.83 (0.53–1.31)
7.0 (3.7–11.9)
1.04 (0.60–1.80)
NR NR 34.7 (25.3–45.1)
2.21 (1.623.02)
42.7 (27.3–59.1)
1.18 (0.83–1.68)
 Social workers 6.1 (3.7–9.5)
0.62 (0.40–0.96)
5.1 (2.6–8.9)
0.77 (0.43–1.36)
9.1 (5.4–14.2)
0.81 (0.51–1.30)
NR 20.5 (15.6–26.2)
1.14 (0.87–1.50)
32.0 (19.9–46.1)
0.94 (0.65–1.37)
Health care practitioners and technical occupations 5.1 (4.5–5.8)
0.49 (0.43–0.56)
5.1 (4.3–6.1)
0.75 (0.64–0.89)
7.7 (6.9–8.6)
0.72 (0.64–0.81)
2.5 (2.0–3.1)
0.64 (0.51–0.80)
17.9 (16.6–19.2)
1.05 (0.98–1.14)
41.5 (38.4–44.7)
1.16 (1.07–1.25)
Health diagnosing practitioners 3.3 (2.3–4.5)
0.33 (0.24–0.45)
2.5 (1.7–3.5)
0.38 (0.27–0.54)
5.0 (3.8–6.5)
0.57 (0.44–0.75)
1.4 (0.8–2.4)
0.40 (0.24–0.68)
12.6 (10.7–14.6)
0.91 (0.79–1.05)
35.1 (29.0–41.7)
0.99 (0.83–1.17)
 Physicians and surgeons 3.5 (2.4–5.0)
0.35 (0.25–0.50)
2.5 (1.7–3.5)
0.38 (0.22–0.54)
5.0 (3.4–7.0)
0.61 (0.43–0.86)
1.5 (0.8–2.7)
0.45 (0.26–0.79)
13.6 (10.9–16.5)
1.07 (0.89–1.29)
32.5 (25.5–40.1)
0.92 (0.74–1.14)
Health treating practitioners 5.0 (4.1–5.9)
0.48 (0.40–0.57)
5.2 (4.1–6.5)
0.74 (0.59–0.94)
8.1 (6.9–9.4)
0.72 (0.61–0.84)
2.8 (2.0–3.8)
0.71 (0.52–0.98)
18.6 (17.0–20.4)
1.03 (0.93–1.13)
44.5 (40.4–48.7)
1.25 (1.14–1.37)
 Physical therapists NR NR 2.6 (1.4–4.4)
0.24 (0.14–0.41)
NR 12.0 (6.8–19.2)
0.68 (0.42–1.10)
24.3 (14.6–36.4)
0.72 (0.47–1.10)
 Registered nurses 5.1 (4.2–6.1)
0.49 (0.41–0.59)
5.3 (4.1–6.7)
0.75 (0.59–0.97)
8.4 (7.0–10.0)
0.75 (0.63–0.89)
3.1 (2.2–4.3)
0.78 (0.56–1.09)
18.6 (16.9–20.4)
1.02 (0.93–1.13)
46.6 (42.0–51.2)
1.31 (1.19–1.44)
Miscellaneous health technologists and technicians 7.3 (5.7–9.1)
0.70 (0.56–0.87)
7.7 (5.6–10.3)
1.16 (0.86–1.55)
9.5 (7.8–11.4)
0.84 (0.69–1.02)
2.7 (1.8–3.8)
0.67 (0.47–0.97)
21.3 (17.9–25.0)
1.24 (1.041.48)
41.8 (34.1–49.7)
1.15 (0.96–1.38)
 Clinical laboratory technologists and technicians 9.8 (5.6–15.8)
0.81 (0.48–1.37)
16.7 (8.9–27.5)
0.90 (0.51–1.59)
48.9 (21.8–76.4)
1.34 (0.76–2.35)
 Health practitioner support technologists and technicians NR NR 14.5 (8.4–22.8)
1.20 (0.76–1.90)
NR 29.7 (18.5–42.9)
1.77 (1.142.76)
51.5 (30.7–71.9)
1.40 (0.95–2.07)
 Licensed practical and licensed vocational nurses 7.5 (4.1–12.3)
0.67 (0.40–1.12)
NR 9.0 (5.7–13.3)
0.76 (0.50–1.15)
NR 23.6 (17.5–30.6)
1.32 (1.011.71)
43.3 (30.8–56.4)
1.15 (0.86–1.55)
 Miscellaneous health technologists and technicians (other) 9.4 (5.2–15.2)
0.92 (0.51–1.47)
12.9 (6.6–22.0)
2.01 (1.17–3.44)
14.1 (7.9–22.5)
1.44 (0.89–2.34)
NR 18.9 (9.5–32.0)
1.29 (0.73–2.28)
34.5 (19.1–52.8)
0.93 (0.57–1.51)
Health care support occupations 14.5 (12.6–16.5)
1.17 (1.021.34)
8.3 (6.8–9.9)
1.14 (0.94–1.38)
15.6 (13.6–17.7)
1.18 (1.021.35)
6.5 (5.1–8.2)
1.46 (1.141.86)
21.5 (19.3–23.8)
1.16 (1.041.29)
46.5 (42.0–51.0)
1.22 (1.10–1.35)
 Nursing, psychiatric, and home health aides 16.8 (14.3–19.5)
1.31 (1.121.54)
9.4 (7.4–11.7)
1.26 (1.001.59)
16.8 (14.1–19.8)
1.29 (1.091.53)
7.3 (5.4–9.7)
1.62 (1.202.18)
22.0 (19.1–25.1)
1.22 (1.061.40)
47.7 (41.8–53.6)
1.22 (1.07–1.40)
 Dental assistants NR NR 13.3 (7.7–20.9)
0.97 (0.59–1.59)
NR 20.5 (13.7–28.8)
0.99 (0.68–1.43)
33.9 (18.3–52.6)
0.97 (0.61–1.54)
 Medical assistants 10.8 (7.2–15.4)
0.90 (0.63–1.30)
6.7 (4.2–10.1)
0.98 (0.65–1.47)
14.9 (11.2–19.2)
1.08 (0.82–1.44)
5.3 (3.0–8.6)
1.21 (0.73–2.01)
21.1 (16.7–26.2)
1.08 (0.86–1.36)
51.1 (41.0–61.2)
1.40 (1.15–1.71)
 Phlebotomists 18.1 (9.1–30.6)
1.52 (0.86–2.70)
NR 10.3 (5.3–17.6)
0.73 (0.42–1.29)
NR 26.9 (16.5–39.5)
1.44 (0.93–2.23)
56.2 (37.8–73.4)
1.44 (1.02–2.04)
Food preparation and serving 18.8 (12.4–26.7)
1.43 (0.97–2.11)
12.7 (7.7–19.4)
1.72 (1.112.66)
11.7 (7.6–17.0)
0.99 (0.67–1.47)
NR 16.2 (11.3–22.1)
0.98 (0.69–1.39)
53.4 (39.067.4)
1.37 (1.03–1.82)
Building and grounds cleaning and maintenance occupations 22.1 (14.4–31.6)
1.70 (1.132.55)
9.9 (5.4–16.2)
1.38 (0.82–2.31)
10.1 (6.2–15.3)
0.94 (0.62–1.44)
NR 14.3 (9.7–20.1)
0.99 (0.69–1.39)
36.5 (23.8–50.6)
0.96 (0.67–1.38)
 Janitors and building cleaners 23.5 (11.5–39.7)
1.84 (0.99–3.41)
NR NR NR 11.1 (5.8–18.7)
0.80 (0.48–1.34)
29.2 (14.1–48.8)
0.75 (0.43–1.33)
 Maids and housekeeping cleaners 20.8 (12.6–31.1)
1.55 (0.98–2.46)
NR 10.6 (5.9–17.3)
0.96 (0.58–1.59)
NR 18.5 (10.7–28.8)
1.21 (0.75–1.93)
47.5 (29.7–65.8)
1.28 (0.87–1.88)
Personal care and service occupations 22.7 (16.5–30.0)
1.53 (1.221.98)
10.0 (6.1–15.4)
1.35 (0.88–2.07)
18.2 (13.8–23.3)
1.57 (1.232.01)
7.5 (4.3–12.1)
1.73 (1.062.82)
30.8 (24.9–37.2)
1.87 (1.552.25)
47.7 (37.0–58.7)
1.35 (1.11–1.65)
 Personal care aides 22.6 (16.0–30.5)
1.48 (1.161.89)
10.0 (5.8–15.9)
1.35 (0.85–2.15)
18.6 (13.8–24.3)
1.60 (1.232.08)
7.8 (4.3–12.9)
1.80 (1.083.00)
32.3 (25.8–39.3)
1.96 (1.622.38)
48.6 (37.2–60.1)
1.39 (1.13–1.70)
Office and administrative support occupations 9.2 (7.2–11.5)
0.76 (0.61–0.94)
5.5 (3.9–7.4)
0.69 (0.51–0.93)
9.9 (7.9–12.2)
0.84 (0.68–1.04)
3.6 (2.2–5.4)
0.74 (0.49–1.12)
20.9 (17.9–24.2)
1.13 (0.97–1.33)
31.6 (26.0–37.6)
0.87 (0.72–1.05)
Trades¶ 15.8 (10.0–23.3)
1.24 (0.86–1.79)
14.3 (8.8–21.5)
2.00 (1.303.05)
9.7 (5.3–15.9)
1.05 (0.63–1.74)
5.2 (2.9–8.6)
1.43 (0.87–2.35)
10.8 (6.6–16.4)
0.89 (0.58–1.35)
51.1 (38.2–63.9)
1.40 (1.10–1.78)
Italics indicate statistically significantly elevated aPR. Heavier shading indicates broader occupational grouping.
aPR, adjusted prevalence ratio; CI, confidence interval; NR, not reported because relative standard error of estimates is >30%; Ref, reference group.
*Adjusted prevalences given for non–health care workers. For each health care occupation, the unadjusted prevalence is given for each condition, followed by the aPR comparing adjusted prevalence in that occupation to the adjusted prevalence for non–health care workers. Results are adjusted for age (18–34, 35–54, ≥55 years), sex (male, female), and race/ethnicity (non-Hispanic White, non-Hispanic African American, non-Hispanic other, Hispanic).
†Elicited only in 2018 BRFSS questionnaire.
‡Respondents with census industry codes (0170-7890 or 8290-9500) and census occupation not in (3000–3655).
§Respondents with census industry codes 7970 to 8270.
∥Within health care industry, includes health care occupational groups with at least three reportable mental health related outcomes.
¶Construction, extraction, maintenance, production, and transportation and materials moving industries.

Workers in community and social service occupations had an elevated prevalence of diagnosed depression (compared with the prevalence observed in non–health care workers, the reference group for all comparisons), with an aPR of 1.65 (95% CI, 1.32–2.05). This result was driven largely by counselors in the health care industry; this group had the highest unadjusted prevalence estimate (34.7%) for depression of all health care occupation groups reported, as well as an aPR above 2. Social workers had a small elevation for depression that did not attain statistical significance (aPR, 1.14; 95% CI, 0.87–1.50). Community and social service occupations workers were significantly less likely than non–health care workers to report poor self-rated health (aPR, 0.70; 95% CI, 0.52–0.95).

In the broad grouping of health care practitioners and technical occupations, only the prevalence of insufficient sleep was significantly elevated (aPR, 1.16; 95% CI, 1.07–1.25). Health diagnosing practitioners had lower prevalences than non–health care workers for every condition except insufficient sleep, and the only significantly elevated prevalence was for insufficient sleep among nurse practitioners (prevalence of insufficient sleep, 58.7 [95% CI, 36.4–78.7]; aPR, 1.58 [95% CI, 1.11–2.26]; results for other groups of health diagnosing practitioners not shown). Results for health treating practitioners were similar, with a significant elevation only for insufficient sleep (primarily among RNs). The prevalence of diagnosed depression among treating practitioners was higher than for diagnosing practitioners but was not significantly elevated compared with the prevalence among non–health care workers. Health technologists and technicians (and particularly licensed practical nurses/licensed vocational nurses), a group with somewhat lower wages than health diagnosing and treating practitioners, had significant elevations of diagnosed depression.

Adverse health conditions were most common among the lowest-wage health care workers with patient care responsibilities. The health care support occupations grouping had statistically significant elevations of every condition except frequent physical distress. With the exception of insufficient sleep (which was most common among phlebotomists), these results are driven by the nursing, psychiatric, and home care aide occupation. The duties of nursing, psychiatric, and home health aides substantially overlap those of an occupation outside health support: the personal care aides and service occupation. Like health care aides, personal care aides had statistically significant elevations of almost every outcome: poor self-rated health, frequent mental distress, activity limitations, diagnosed depression, and insufficient sleep. For most of these outcomes, point estimates were higher than those for patient care aides. In addition, personal care aides had the highest unadjusted prevalence estimates among of frequent mental distress and activity limitations of any group assessed.

Ancillary support occupations (food preparation and serving, janitors, maids and housekeepers, trades) had elevated prevalences for some outcomes. Among these groups, the prevalence of poor self-rated health was at least 4 times the prevalence among health care diagnosing and treating practitioners. Both food preparation and serving workers and trades workers had statistically significant elevations of frequent physical distress and insufficient sleep.

Health Conditions and Behaviors by Health Care Industry

Prevalences of adverse health conditions also differed by industry (Table 5). Workers in the home health industry had the highest prevalences of most adverse health conditions: poor self-reported health, frequent physical distress, activity limitations, and diagnosed depression. Moreover, aPRs comparing prevalences of these conditions among home health workers to prevalences in non–health care workers were statistically significant. Home health and nursing care facility workers had the highest prevalences of frequent mental distress. Hospital workers had significantly lower prevalences of poor self-rated health (aPR, 0.62; 95% CI, 0.54–0.71) and frequent physical (aPR, 0.77; 95% CI, 0.66–0.90) and mental distress (aPR, 0.75; 95% CI, 0.68–0.84) than non–health care workers but did have a statistically significant elevation for insufficient sleep (aPR, 1.16; 95% CI, 1.07–1.25).

TABLE 5 - Health-Related Metrics for Health Care Workers by Industry: Prevalences and aPRs*, BRFSS 2017–2019
Fair or Poor Self-rated Health
% (95% CI)
aPR (95% CI)
Frequent Physical Distress
% (95% CI)
aPR (95% CI)
Frequent Mental Distress
% (95% CI)
aPR (95% CI)
Activity Limitations
% (95% CI)
aPR (95% CI)
% (95% CI)
aPR (95% CI)
Insufficient Sleep†
% (95% CI)
aPR (95% CI)
Non–health care workers‡ 11.9 (11.6–12.3)
6.7 (6.5–6.9)
10.1 (9.8–10.4)
3.8 (3.6–4.0)
14.2 (13.9–14.6)
36.5 (35.7–37.3)
Health care industry workers§ 8.6 (8.0–9.3)
0.76 (0.70–0.83)
6.3 (5.8–7.0)
0.90 (0.81–1.00)
9.8 (9.1–10.4)
0.88 (0.81–0.94)
3.7 (3.2–4.2)
0.91 (0.79–1.06)
18.9 (18.0–19.8)
1.11 (1.061.18)
41.0 (39.0–43.1)
1.13 (1.07–1.19)
Industry group (US Census industry codes)
Dental office (7980) 5.5 (3.4–8.4)
0.5 (0.34–0.78)
NR 8.5 (6.0–11.7)
0.74 (0.53–1.03)
2.9 (1.5–5.1)
0.75 (0.42–1.32)
12.5 (9.7–15.9)
0.67 (0.53–0.86)
28.6 (20.9–37.3)
0.84 (0.64–1.09)
Home health (8170) 20.2 (16.6–24.2)
1.45 (1.23–1.72)
12.5 (9.6–15.8)
1.62 (1.27–2.07)
15.9 (13.0–19.1)
1.38 (1.15–1.65)
7.9 (5.5–10.8)
1.80 (1.29–2.51)
26.3 (22.9–30.0)
1.56 (1.36–1.78)
42.1 (35.2–49.2)
1.15 (0.98–1.35)
Other ambulatory health care settings (7970, 7990, 8070–8090) 6.7 (5.7–7.8)
0.62 (0.53–0.72)
5.9 (4.9–7.0)
0.84 (0.70–1.00)
9.0 (7.9–10.3)
0.85 (0.74–0.97)
3.3 (2.5–4.2)
0.81 (0.62–1.05)
19.6 (18.0–21.3)
1.17 (1.07–1.28)
38.4 (34.8–42.0)
1.07 (0.98–1.18)
Hospital (8190) 6.8 (5.9–7.7)
0.62 (0.54–0.71)
5.3 (4.6–6.2)
0.77 (0.66–0.90)
8.3 (7.5–9.2)
0.75 (0.68–0.84)
3.2 (2.5–4.0)
0.80 (0.64–1.01)
17.0 (15.7–18.3)
1.01 (0.93–1.09)
42.0 (38.9–45.3)
1.16 (1.07–1.25)
Nursing care facilities (8270) 14.7 (12.4–17.2)
1.23 (1.05–1.45)
7.2 (5.7–9.0)
0.99 (0.78–1.24)
14.4 (12.2–16.9)
1.17 (0.99–1.37)
4.6 (3.3–6.2)
1.05 (0.77–1.42)
21.8 (19.3–24.4)
1.22 (1.08–1.37)
46.2 (40.8–51.7)
1.23 (1.09–1.39)
Italics indicate statistically significantly elevated aPR.
aPR, adjusted prevalence ratio; CI, confidence interval; NR, not reported because relative standard error of estimates is >30%; Ref, reference group.
*Adjusted prevalences given for non–health care workers. For each health care occupation, the unadjusted prevalence is given for each condition, followed by the aPR comparing adjusted prevalence in that occupation to the adjusted prevalence for non–health care workers. Results are adjusted for age (18–34, 35–54, ≥55 years), sex (male, female), and race/ethnicity (non-Hispanic White, non-Hispanic African American, non-Hispanic other, Hispanic).
†Elicited only in 2018 BRFSS questionnaire.
‡Respondents with census industry codes (0170-7980 or 8290-9500) and census occupation not in (3000-3655).
§Respondents with census industry codes 7970 to 8270.


Although much of the research on adverse mental health conditions among health care workers has focused on physicians and nurses, our study assessed mental health and well-being among multiple health care industry workforces and found that health care support workers bore the greatest burden of these conditions before the COVID-19 pandemic. Among low-wage workers, patient and personal care aides were particularly at risk, with higher prevalences of adverse mental health conditions and poorer well-being compared with both other health care workers and the non–health care workforce. These findings by occupation were reflected in health care industry results: workers in the home health and nursing care facility industries, where the majority of patient and personal care aides work, had higher prevalences of adverse health conditions than their counterparts in hospitals and ambulatory care settings.

Workforces with either a very high prevalence of a single condition or many conditions with significantly elevated aPRs can be considered to have high mental health burdens. By these metrics, the occupations with the highest burdens have workforces that are largely female (nursing, psychiatric, and home health aides; counselors; personal care aides), have relatively high percentages of non-Hispanic African American workers (nursing, psychiatric, and home health aides; counselors; food preparation and serving), have low educational attainment (nursing, psychiatric, and home health aides; patient and personal care aides; janitors; food preparation and serving; trades), and/or have low household incomes (nursing, psychiatric, and home health aides; patient and personal care aides; janitors; food preparation and serving). Many workforces with these demographic characteristics are disproportionately subject to multiple stressors, including discrimination and restricted occupational options.39,40

Previous literature has noted that self-reported health and the prevalence of mental health issues differ across demographic characteristics. Women report more mental health symptoms, both in general and in the workplace context,18 and they report more physical health repercussions from burnout.31 Socioeconomic status and race/ethnicity have been reported to affect self-rating of health in the general population, perhaps reflecting differing expectations41 or experiences.42 In an older study of mental health workers, non-White respondents reported lower levels of both emotional exhaustion and personal accomplishment, whereas higher education and salary were positively associated with both outcomes.30 This observation may stem from different configurations of job demand and control, as well as other occupational and nonoccupational stressors. Within the physician occupation, a recent study found no significant differences in prevalence of depressive symptoms by race/ethnicity.43 Of interest is that our study did not observe significantly increased adverse health conditions for occupations with the highest prevalences of Hispanic workers. Although Hispanic workers are disproportionately found in several low-wage health care occupations, they have a substantial presence in professional occupations (eg comprising 20% of dentists). Notably, the prevalence of depression in the general population is inversely related to income, with nearly 16% of adults with family income below the federal poverty limit reporting depression during the past 2 weeks, compared with 3.5% of adults from families with incomes at least 4 times the federal poverty limit.44 All of the demographic and occupational findings in our study of mental health and well-being should be considered within the context of complex relations between discrimination, income, educational opportunities, and occupational opportunities, as well as reporting differences. Although presentation of separate results by demographic group was beyond the scope of this scan of mental health outcomes by health care occupation, further research into demographic differences within specific occupations is warranted.

Mental health conditions among health care workers not only adversely affect the workers themselves and their families but also can also impact patient care.45 A systematic review found that common mental disorders in nurses were strongly associated with multiple adverse work themes: general errors, medication errors, near misses, and decreased patient safety and satisfaction.46 Self-reported exhaustion due to long-term stress has been associated with poor job performance and absence due to illness among health care and social insurance workers.47 Depression among physicians is also associated with lower quality medical care48; although research on the effects of depression on care quality among low-wage health care workers is lacking, there is little reason to believe that results would differ. The elevated burden of adverse health conditions observed among home health and nursing care facility industries in the current study may be linked to observed high staff turnover in these industries.

Multiple groups of health care workers reported insufficient sleep. The prevalence of insufficient sleep was elevated in the health care industry as a whole and specifically in the hospital and nursing care facility home industries and among workers in specific health care occupations: RNs, patient care aides, personal care aides, medical assistants, phlebotomists, food preparation and serving workers, and workers in the trades. Whether insufficient sleep primarily reflects long working hours and shift work, or is a function of insomnia (from physical or mental health conditions) or mental health issues or conditions (eg, anxiety, depression) likely varies by individual, as well as industry and occupation, and could not be determined in this cross-sectional study. Among all industries, shift work has been associated with increased risk of adverse mental health outcomes, with results varying by sex and shift type.49 Shift work is associated with insufficient sleep,50 which in turn has been associated with increased odds of poor self-rated health,51 burnout,52 and depressive symptoms.53 The mechanisms of relations between insufficient sleep and some adverse effects may be complex: one study found that, although long working hours appear to be linked to depression in physicians, the association disappeared after stratification for an occupational stress metric.54 However, the high prevalence of insufficient sleep across the health care workforce is concerning.

This study has a number of limitations. Foremost is that BRFSS questions related to mental health are not comprehensive. Although depression and “poor mental health days” are included, the survey does not specifically assess other common conditions such as anxiety. In addition, the “diagnosed depression” variable provides no specific information on severity or duration and is a single summary metric, with none of the detail included in survey instruments designed to ascertain symptoms or severity of depression. All information in BRFSS is self-reported and subject to social desirability and recall bias, with the former likely to lead to underestimated prevalences of adverse health conditions. Of the basic demographic characteristics, income was omitted most frequently (11%) for our study population. The results for several of the adverse health conditions we evaluated could also be affected by the stigma associated with mental health issues (resulting in underreporting); the level of stigma may differ by demographic and occupational group. Finally, as the BRFSS industry and occupation module is optional and is not administered by every locality, these results are not nationally representative. Despite these limitations, the current findings are useful for identifying groups within the health care workforce in most need of resources and interventions to address adverse mental health issues.

Prevention of the upstream (including organizational and structural) factors leading to mental health issues among health care workers, along with subsequent assessment, intervention, and treatment, is key. However, research on the efficacy of workplace mental health and well-being programs, practices, and policies (including those that are individually, group, and organizationally focused) has been characterized as sparse, methodologically weak, or failing to account adequately for differences in demographic or occupational groups.30,31,55,56 The results of the current study highlight the need for understanding and improving working conditions that may impact health care workers' mental health and well-being. Research on interventions among health care support staff and other low-wage health care workers, groups with the highest prevalences of adverse outcomes in this study, has been particularly limited. Such research is particularly important in light of the finding in previous research that the suicide rate in female health care support workers is significantly higher than that of all female workers.25

Other barriers to addressing mental health issues include stigmatization of acknowledging and seeking help for mental health issues, as well as access to affordable care. Stigma has been noted particularly for physicians, who have concerns about the professional implications of accessing mental health care.13 The need for specialized service providers who are aware of these concerns has been noted.13,18 Incorporating education about mental illness into medical training is recommended.48 Another barrier, access to affordable care, is most salient for lower-wage workers, such as patient and personal care aides; low-wage workers are more likely to lack health insurance and to be unable to afford health care visits.27

One potential approach to circumventing the stigma of seeking mental health assistance might be to focus on addressing burnout, a construct that is sometimes conflated with mental health concerns such as depression and anxiety. The nature of the relationship between burnout and mental health concerns is contested, with some research finding them indistinguishable57 and other analyses suggesting that depression and anxiety are distinct from burnout58 or that only specific characteristics of burnout are linked to depression59 or anxiety.60 Investigators have expressed concern that burnout is taken less seriously than the overlapping or coextensive diagnosis of depression.57 However, the possibility that burnout may be less stigmatized and may thus present a more acceptable reason for seeking treatment should be explored. Unfortunately, interventions around burnout have limitations similar to those described for other mental health and well-being concerns. Public health and health delivery systems should strive to implement evidence-based programs that (1) meet the needs of specific workforces to support employee mental health and well-being and (2) simultaneously address organizational impediments to the success of these programs through measures such as ensuring easy and affordable access, employee privacy, and supportive work cultures.


In these prepandemic survey data, elevated prevalences of the broadest range of mental health-related concerns were seen among low-wage health care workers. More recent work has documented the effects of both occupational and personal stressors associated with COVID-19 on a range of health care workers.1,2,61,62 Among the general public, the prevalence of depression has increased markedly from prepandemic levels, particularly for more severe depression,63 although whether this increase will be sustained is unclear. Regardless, the current emotional support needs of the health care workforce are likely greater than those indicated by this study. At the same time, mental health treatment resources have been heavily strained by the pandemic and its repercussions and are not available to all who would benefit from them. Moreover, many lower-income health care workers, such as the health care support group observed in this study to have high prevalences of multiple adverse health conditions, may not have access to affordable mental health treatment. A concerted effort to develop, implement, and evaluate occupation- and industry-specific, culturally competent prevention, intervention, and mitigation strategies addressing both organizational and personal conditions that lead to mental health issues is critical to ensuring a robust health care workforce.


The authors wish to thank the following: Thomas Cunningham, Marie Haring Sweeney, Jennifer Cornell, CDC; Pamela Schumacher-Young (work performed under General Informatics, now retired), Susan Burton, Synergy; Katrina Bicknaver, Matt Hirst, Rebecca Purdin, Elizabeth Smith, Surprese Watts (work performed under General Informatics, now employed by CeTechs); Jeff Purdin, Maximus (formerly ATTAIN); and state BRFSS coordinators.


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mental health; well-being; health care workforce; health care support workers; counselors; depression; insufficient sleep; occupation; industry

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