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Published in final edited form as: Rural Ment Health. 2025 Apr 14;49(3):288–303. doi: 10.1037/rmh0000299 Search in PMC Search in PubMed View in NLM Catalog Add to search Sociodemographic Factors and Mental Health in Health Care Professionals and Patients During COVID-19 Anna K Radin Anna K Radin 1. St. Luke’s Health System, Applied Research Division, Boise, ID, USA Find articles by Anna K Radin 1 , Zihan Zheng Zihan Zheng 2. University of Washington, Department of Biostatistics, Seattle, WA, USA Find articles by Zihan Zheng 2 , Jenny Shaw Jenny Shaw 1. St. Luke’s Health System, Applied Research Division, Boise, ID, USA Find articles by Jenny Shaw 1 , Siobhan P Brown Siobhan P Brown 2. University of Washington, Department of Biostatistics, Seattle, WA, USA Find articles by Siobhan P Brown 2 , Elizabeth McCue Elizabeth McCue 1. St. Luke’s Health System, Applied Research Division, Boise, ID, USA Find articles by Elizabeth McCue 1 , Tara Fouts Tara Fouts 1. St. Luke’s Health System, Applied Research Division, Boise, ID, USA Find articles by Tara Fouts 1 , Anton Skeie Anton Skeie 1. St. Luke’s Health System, Applied Research Division, Boise, ID, USA Find articles by Anton Skeie 1 , Cecelia Peña Cecelia Peña 1. St. Luke’s Health System, Applied Research Division, Boise, ID, USA Find articles by Cecelia Peña 1 , Jonathan Youell Jonathan Youell 1. St. Luke’s Health System, Applied Research Division, Boise, ID, USA Find articles by Jonathan Youell 1 , Hilary Flint Hilary Flint 1. St. Luke’s Health System, Applied Research Division, Boise, ID, USA Find articles by Hilary Flint 1 , Amelia Doty-Jones Amelia Doty-Jones 3. St. Luke’s Health System, Behavioral Health Service Line, Boise, ID, USA Find articles by Amelia Doty-Jones 3 , Jacob Wilson Jacob Wilson 3. St. Luke’s Health System, Behavioral Health Service Line, Boise, ID, USA Find articles by Jacob Wilson 3 , Kwun CG Chan Kwun CG Chan 2. University of Washington, Department of Biostatistics, Seattle, WA, USA Find articles by Kwun CG Chan 2 , Martina Fruhbauerova Martina Fruhbauerova 4. University of Kentucky, Department of Psychology, Lexington, KY, USA Find articles by Martina Fruhbauerova 4 , Michael Walton Michael Walton 3. St. Luke’s Health System, Behavioral Health Service Line, Boise, ID, USA Find articles by Michael Walton 3 , Katherine Anne Comtois Katherine Anne Comtois 5. University of Washington, Department of Psychiatry and Behavioral Sciences, Seattle, WA, USA Find articles by Katherine Anne Comtois 5 Author information Article notes Copyright and License information 1. St. Luke’s Health System, Applied Research Division, Boise, ID, USA 2. University of Washington, Department of Biostatistics, Seattle, WA, USA 3. St. Luke’s Health System, Behavioral Health Service Line, Boise, ID, USA 4. University of Kentucky, Department of Psychology, Lexington, KY, USA 5. University of Washington, Department of Psychiatry and Behavioral Sciences, Seattle, WA, USA ✉ Corresponding Author: Anna K. Radin, [email protected] , 190 E Bannock St., Boise, ID 83712 Issue date 2025 Jul. PMC Copyright notice PMCID: PMC12456348 NIHMSID: NIHMS2064592 PMID: 40995334 The publisher's version of this article is available at Rural Ment Health Abstract The COVID-19 pandemic worsened mental health. This study aimed to: (1) measure the prevalence of loneliness, anxiety, depression, suicidal ideation or behavior, and stress in primary care patients, and physicians, healthcare providers, and healthcare staff; and (2) measure the association of socio-demographic and employment characteristics with mental health measures. A cross-sectional survey measured the prevalence of mental health outcomes in healthcare providers and staff and primary care patients in Idaho during the COVID-19 pandemic from January 18 - July 7, 2021. Descriptive statistics and multivariate linear regression models with robust standard errors were used to assess the association between mental health measures and demographic characteristics, employment status, healthcare occupation type, financial security, and local COVID-19 hospitalizations. Each outcome was modeled separately. 3,646 participants completed the survey, including 1,687 patients and 1,959 healthcare providers and staff. Participants were 73.5% female, 92.5% White, and 6.6% Hispanic, with a mean age of 46.8 (SD: 15.6). Overall, 32.2% of patients and 29% of healthcare providers and staff reported moderate to high levels of at least one measure of mental distress. Male sex, older age, larger household size, not caring for dependents, being a healthcare provider or staff, and being financially secure were associated with better mental health conditions, with financial security being the association of largest magnitude. Understanding which sociodemographic factors were associated with disproportionate adverse mental health outcomes from COVID-19 may inform how mental health support is prioritized in future public health emergencies. Keywords: Mental health, COVID-19, Depression, Anxiety, Suicide, Health personnel, loneliness Adverse mental health conditions are common in the United States and worsened during the COVID-19 pandemic. By late June of 2020, nearly 41% of 5,470 US adults who completed a nationally representative survey reported adverse mental health conditions, with essential workers, young adults, unpaid caregivers, and racial/ethnic minorities being disproportionately affected ( Czeisler et al., 2020 ). The prevalence of anxiety symptoms was three times higher and depression symptoms were four times higher than pre-pandemic levels reported in 2019 ( Czeisler et al., 2020 ) Physicians, healthcare clinicians, and staff faced additional stress during the COVID-19 pandemic due to their increased exposure to infected patients and the burden of bearing witness to the devastating effects of this disease ( Kisely et al., 2020 ; Lai et al., 2020 ). Many sociodemographic factors influenced health outcomes during the pandemic. Differences in COVID-19 infection rates and mortality rates were observed across urban and rural areas, with rural areas generally experiencing lower infection rates but higher mortality ( Ramírez & Lee, 2020 ; Zhang & Schwartz, 2020 ). Suicide rates in rural areas are twice as high as in large urban areas in 2020 ( Ehlman et al., 2022 ) and compared to urban areas, rural residence is generally associated with lower socioeconomic status and worse overall health outcomes ( Centers for Disease Control and Prevention, 2013 ). COVID-19 had a significant impact on the economy, leading to widespread job loss and financial stress ( Panchal et al., 2020 ). Financial stress is associated with an elevated risk for depression, suicide, and anxiety ( Panchal et al., 2020 ). Living situation and the number of people at home may have influenced risk of COVID-19 exposure and degree of loneliness experienced. Homeless and transient individuals were at a greater risk of infection due to their environment ( Holmes et al., 2020 ; Tsai & Wilson, 2020 ). Healthcare and other essential workers may have experienced stress about infecting others in their homes after exposure at work ( Kisely et al., 2020 ). Having many people in a household may have offered social support which is protective against loneliness ( Zhao et al., 2018 ), but could also have contributed to stress, interpersonal conflict, and domestic violence ( Humphreys et al., 2020 ). Unpaid caregivers of children, older adults, or other family members who may have physical or mental disabilities were twice as likely to have experienced elevated mental distress compared with non-caregivers ( Czeisler et al., 2020 ). Both parents and youth were impacted by the changes in school environment and extracurricular activities due to COVID-19 ( Salari et al., 2020 ). Understanding which socio-demographic and employment characteristics were associated with elevated risk for adverse mental health conditions may inform how mental health resources are prioritized in future public health emergencies. Objectives This study aimed to (1) measure the prevalence of loneliness, anxiety, depression, suicidal ideation or behavior, and stress in two strata: (a) primary care patients and (b) health care professionals (HCP), including physicians, healthcare clinicians, and patient- and non-patient-facing healthcare staff; and (2) measure the association of socio-demographic and employment characteristics with mental health measures during the COVID-19 pandemic. Methods Study Design This study involved a cross-sectional survey of primary care patients and HCP to measure the prevalence of various measures of mental distress and their association with socio-demographic and employment characteristics and local COVID-19 hospitalizations. The St. Luke’s Health System Institutional Review Board approved and oversaw the study. Written informed consent was obtained from all participants included in this study. Setting This study was conducted in a rural state in the Intermountain West Region ( U.S. Department of the Interior, U. S. Geological Survey, 2016 ) of the United States (Idaho) that was highly impacted by COVID-19. Hospitals and health systems in all or parts of the state operated under crisis standards of care between September 2021 and February 2022 ( Idaho Department of Health and Welfare, 2022 ). Participants were recruited from St. Luke’s Health System (St. Luke’s), which is a large, non-profit health system based in Southern Idaho. St. Luke’s employs over 16,000 people and serves nearly half of the state’s population. Approximately 77% of St. Luke’s employees are female. In 2020, the state population in Idaho was 91% White, 13% Hispanic ethnicity, and 32% had a bachelor’s degree or higher ( US Census Bureau, 2022 ). Idaho is considered one of the most rural US states, with 80% of counties classified as rural, and half of those classified as frontier ( US Health Resources & Services Administration (HRSA), 2022) . All but one of Idaho’s counties are federally designated healthcare provider shortage areas ( Idaho Department of Health and Welfare, 2023b ). Timing and COVID-19 Surveys were completed virtually between January 18 and July 7, 2021, using a web-based software platform, Research Electronic Data Capture (REDCap) ( Harris, &. et al., 2009 ; Harris, P. A. et al., 2019 ) Data were collected at the tail end of the Alpha variant surge (January 2021) or between the Alpha and Delta variant surges of COVID-19 in Idaho (February – July 2021) ( Idaho Department of Health and Welfare, 2023a ). Participants The source population for this study included (a) primary care patients and (b) HCP from St. Luke’s Health System. Eligible patients attended a primary care visit within the past 12 months, were current mobile electronic health record patient portal account users and were at least 12 years of age. Eligible HCP were employees or contracted physicians or clinicians of St. Luke’s Health System and at least 18 years of age. All participants were proficient in written and spoken English and provided informed consent to participate. Complete lists of St. Luke’s HCP (n=16,108) and eligible patients (n=243,218) were obtained. The study statistician generated a list of random numbers which were used to randomize the order of patients and HCP on those lists. Invitations to participate in the survey were sent in waves to patients and HCP based on the randomized order. Selected HCP were sent an electronic survey link via their work email accounts. Selected patients were sent an electronic survey link via a mobile electronic health record patient portal message and (when available) the email listed in their medical record. Measures All measures were self-reported. Mental Health Measures Loneliness. Loneliness is a well-established risk factor for suicide ( Van Orden et al., 2012 ), depression ( Cacioppo et al., 2006 ; Loades et al., 2020 ; Wang et al., 2017 ; Yanguas et al., 2018 ), psychological stress ( Wang et al., 2017 ; Yanguas et al., 2018 ), and anxiety ( Loades et al., 2020 ; Wang et al., 2017 ). Loneliness was measured using the NIH Toolbox Social Relationship Scales loneliness measure. The Loneliness measure of the NIH Toolbox Emotion battery for adults 18+ comprises five items with rating scale responses of never, rarely, sometimes, usually, and always. Responses are used to calculate a raw score which is converted to a t-score ( Health Measures, 2020 ). The version for adolescents includes 7 items. The loneliness measures are brief, validated, and psychometrically sound ( Gershon et al., 2013 ; Salsman et al., 2013 ). Suicidal ideation or behavior. Suicidal ideation or behavior was measured using the 6-item Columbia Suicide Severity Rating Scale (C-SSRS) self-assessment screener ( Interian et al., 2018 ; Matarazzo et al., 2019 ; Posner et al., 2009 ). Past 3-months suicidal behavior was included in the scoring; lifetime suicidal behavior was excluded from the scoring to exclude suicidal ideation or behavior that may have occurred prior to the pandemic. Individuals who reported high risk for suicide were contacted by the study team to assess safety and provide appropriate support. Depression. Depression was assessed using the Patient Health Questionnaire-9 (PHQ-9), a nine-item self-report scale ( Arroll et al., 2010 ; Kroenke et al., 2001 ; Löwe et al., 2004 ; Manea et al., 2015 ; Richardson et al., 2010 ). This study utilized the PHQ-9 adult and PHQ-A adolescent versions for the corresponding age groups. Psychological Stress. Psychological stress was assessed using the 11-item (adult) and 10-item (adolescent) NIH Toolbox Stress and Self-Efficacy Scales Perceived Stress measure ( Salsman et al., 2013 ). Anxiety. Anxiety was assessed using the Generalized Anxiety Disorder Scale (GAD-7), a 7-item self-report scale frequently used in the identification of Generalized Anxiety Disorder ( Plummer et al., 2016 ; Spitzer et al., 2006 ). Exposure Variables The following exposure variables were included in the survey: age, sex assigned at birth, race, ethnicity, zip code (to determine urban/rural residence based on the Health Resources & Service Administration Federal Office of Rural Health Policy’s urban-rural designations for census tracts ( Cook, 2014 ), housing type and stability ( United States Census Bureau, 2022 ), number of people at home, caregiving for one or more dependents in the home ( Johns Hopkins University Bloomberg School of Public Health, 2021 ), employment status, occupation, highest level of education completed ( United States Census Bureau, 2022 ), financial security (delaying purchase of essential items like medication, food, or clothing for financial reasons), and a count of daily hospitalizations for COVID-19 at St. Luke’s during the study period. HCP occupation categories included (a) physicians and advanced practice providers (including physicians, physicians assistants, nurse practitioners, and other advanced practice providers), (b) pharmacy, patient access specialists/admin/customer support/clerical, laboratory, therapists/rehab, clinical technologists/technicians, medical assistants, Air St. Luke’s, and ambulance services, (c) nursing (including registered nurses, nursing leaders, nursing graduates and apprentices, certified nurse anesthetists, and licensed practical nurses), (d) non-patient-facing (including professional workers, revenue cycle, health information management, human resources, information technology, digital and analytics, and research) (e) mental health & social services (including psychologists, social workers, counselors, social services, spiritual services, and support services) and (f) other, including categories that were combined due to the small number of participants (senior management / executives, support services/environmental services, trades, food services, construction, and engineering/maintenance). Statistical Power and Sample Size The sample size was up to 2,000 HCP and up to 2,000 patients, which was determined based on the need to identify an adequate number of participants with elevated mental distress for a related clinical trial (planned n = 330 providers and staff and n = 330 patients) ( Radin, 2022 ; Radin et al., 2023 ). Participants were invited to enroll and complete surveys in waves, and invitations were stopped once the clinical trial target sample size was reached for both cohorts. With the actual sample of 1,959 in the HCP cohort, the power to detect a correlation of 0.1 or higher is above 99%; in the actual sample of 1,687 patients in the patient cohort, the power to detect a correlation of 0.1 or higher is 98%. Data Collection & Management Participants responded to study questionnaires via Research Electronic Data Capture( Harris, &. et al., 2009 ; Harris, P. A. et al., 2019 ) (REDCap), a secure, HIPAA-compliant web application, which was used for building and managing online surveys, capturing written informed consent, and as a database for this research. Analytical and Statistical Approaches Descriptive statistics were used to summarize cross sectional survey results as well as baseline socio-demographic and other exposure variables by strata (HCP and patients). Individuals who were both a patient and HCP were included in the HCP stratum. Only completed surveys were included. To assess how different cadres of HCP and primary care patients were coping with life during the COVID-19 pandemic, multivariate linear regression models were used to assess the association between the prevalence of study mental health measures and demographic characteristics, employment status (currently employed vs unemployed), occupation type (HCP only), financial security, and local COVID-19 hospitalizations. Robust standard errors were used to allow for departures of the observed error distribution from classic model assumptions such as heteroskedasticity. Each outcome was modeled separately. All exposure variables were included in each model and the HCP and patient strata were combined for the regression analyses. Variance inflation factors were used to evaluate level of multicollinearity; no changes were made as a result. Local COVID-19 hospitalizations was defined as the total number of COVID-19-related hospitalizations at St. Luke’s Health System on the day when participants signed the study informed consent form and completed their survey. Financial security was measured by the frequency of putting off buying necessities due to financial constraints in the prior year. Data analysis was completed with R version 4.1.2 ( R Core Team, 2021 ) using an alpha criterion of 0.05 (two-sided) to determine statistical significance. Employee categories included: physicians and advanced practice providers; nursing; mental health and social support, including psychologists; other patient-facing roles including pharmacy, medical assistants, and technicians; non-patient-facing roles including office workers and educators; senior management; and other. Results A total of 3,902 individuals provided written informed consent to participate, and of these 3,646 participants (93.4%) completed the survey, including 1,687 patients and 1,959 HCP. Results are reported for the 3,646 participants who completed the survey. The survey population was mostly female (73.5%), 6.6% reported Hispanic ethnicity, and 92.5% reported Caucasian or white race. Participants had a mean age of 46.8 (SD: 15.6), ranging from 12 to 99 years old. Table 1 summarizes demographic characteristics of study participants. Table 1: Socio-Demographic Characteristics of MHAPPS Survey Participants Patients HCP N = 1687 N = 1959 Gender Female 1133 (67.2%) 1548 (79%) Ethnicity Hispanic or Latino 98 (5.8%) 143 (7.3%) Age Mean (SD) 53.1 (17.3) 41.4 (11.6) Median (min, max) 55 (12,99) 40 (19,78) Race American Indian/Alaska Native 9 (0.5%) 10 (0.5%) Asian 16 (0.9%) 25 (1.3%) Black or African American 6 (0.4%) 4 (0.2%) Native Hawaiian or Pacific Islander 3 (0.2%) 3 (0.2%) Caucasian or White 1581 (93.7%) 1791 (91.4%) Other or Multiple 42 (2.5%) 77 (3.9%) Unknown 30 (1.8%) 49 (2.5%) Currently Employed 903 (53.5%) 1959 (100%) Education High school or less 173 (20.8%) 80 (4.1%) Some college credit but no degree 351 (20.8%) 350 (17.9%) Associate degree 159 (9.4%) 234 (11.9%) Bachelor’s degree 520 (30.8%) 742 (37.9%) Master’s degree 278 (16.5%) 327 (16.7%) PhD or equivalent 116 (6.9%) 190 (9.7%) Prefer not to disclose 3 (0.2%) 0 (0%) Put off buying necessities due to financial constraints Never 1057 (62.7%) 1185 (60.5%) Rarely 276 (16.4%) 434 (22.2%) Occasionally 189 (11.2%) 227 (11.6%) Frequently 104 (6.2%) 70 (3.6%) All the time 53 (3.1%) 38 (1.9%) Prefer not to disclose 8 (0.5%) 5 (0.3%) One or More Dependents 554 (32.8%) 999 (51.0%) Size of household – mean (SD) 2.6 (1.3) 3.1 (1.4) Rural resident 383 (22.7%) 406 (20.7%) Number of daily COVID-19 hospitalizations at St. Luke’s Health System when survey was completed Mean (SD) 28.5 (7.5) 30.7 (7.7) Median (min, max) 55 (13,69) 40 (12,69) Open in a new tab Abbreviations: HCP: Health Care Professionals; MHAPPS: Mental Health Among Patients, Providers, and Staff Prevalence of Mental Distress Table 2 summarizes the prevalence of five measures of mental distress in the patient and HCP cohorts. Overall, 32.2% of patients and 29% of HCP reported moderate to high levels of at least one measure of mental distress. Loneliness was the most common form of mental distress, with 24.7% of patients and 20.5% of HCP reporting elevated levels of loneliness. Stress was the next most common, with 17.0% of patients and 15.2% of HCP reporting elevated stress levels. Moderate to high levels of depressive symptoms were reported by 15.7% of patients and 10.9% of HCP. Moderate to high levels of anxiety were reported by 11% of patients and 8.6% of HCP. Suicidal ideation (any level) was reported by 7.9% of patients and 4.9% of HCP. Table 2: Prevalence of Self-Reported Measures of Mental Distress in Primary Care Patients and Health Care Professionals (HCP) during the COVID-19 Pandemic, MHAPPS Survey Patients HCP N = 1687 N = 1959 Loneliness (NIH Loneliness Scale) Mean (SD) 50.8 (12.3) 49.4 (11.5) Mod/High n (%) (score ≥ 60.7 adult, ≥ 60 youth) 417 (24.7%) 401 (20.5%) Suicidal Ideation & Behavior (C-SSRS) Mean (SD) 0.1 (0.6) 0.1 (0.5) Any suicidal ideation 134 (7.9%) 96 (4.9%) Mod/High n (%) (score ≥ 3) 28 (1.7%) 23 (1.2%) Anxiety (GAD-7) Mean (SD) 4.1 (4.7) 4.0 (4.2) Mod/High n (%) (score ≥ 11) 185 (11%) 168 (8.6%) Depression (PHQ-9) Mean (SD) 4.6 (5.3) 4 (4.4) Mod/High n (%) (score ≥ 10) 265 (15.7%) 214 (10.9%) Stress (NIH Perceived Stress Scale) Mean (SD) 48.5 (11.5) 49.2 (10) Mod/High n (%) (≥ score 60.5 adult, ≥ 60.8 youth) 286 (17%) 298 (15.2%) Any Mental Distress Moderate or High n (%) 544 (32.2%) 567 (29%) Open in a new tab Abbreviations: C-SSRS: Columbia-Suicide Severity Rating Scale; GAD-7: General Anxiety Disorder 7; HCP: Health Care Professionals; MHAPPS: Mental Health Among Patients, Providers, and Staff; NIH: National Institutes of Health; PHQ-9: Patient Health Questionnaire-9. Scoring for Outcome Measures: NIH Loneliness and Perceived Stress scale scores are converted to T-scores, with a T-score of 50 representing the mean of the US general population (based on the 2010 Census) and 10 T-score units represents one standard deviation. Higher scores indicate higher loneliness/stress. C-SSRS screener scores range from 0 to 6, with higher scores indicative of higher suicide risk. GAD7 scores range from 0–21, with higher scores indicating a greater severity of anxiety symptoms. PHQ-9 scores range from 0–27 with higher scores indicating a greater degree of depression. There were differences in the prevalence of mental health measures by age category, with adolescents and young adults aged 12–24 more than three times as likely as adults aged 65 or older to report moderate to high levels of one or more adverse mental health outcomes (61.8% versus 18.3%). Being employed for pay (or parental employment for minor participants) was also associated with differences in mental health. A substantial proportion of the total sample (including adult patients and parents of minor patients) were not employed for pay (46.3%). Among 562 patients of retirement age (at least 65 years old), 451 (80.3%) were not employed for pay. Among 1,023 patients between the ages of 25 and 64, 279 (27.3%) were not employed for pay. Adults aged 25–64 who were employed for pay reported better mental health than adults aged 25–64 who were not employed for pay, with 35.2% (employed) and 41.6% (unemployed) reporting moderate to high levels of mental distress. Mental health outcomes for patients are summarized by age and employment status in Table 3 . Table 3: MHAPPS Survey: Mental Distress Survey Results among Patients by Age & Employment Status Adolescents and Young Adults (≤24), employed or unemployed Adults (25–64), employed Adults (25–64), unemployed Adults ≥65, employed or unemployed N = 102 N = 744 N = 279 N = 562 Loneliness (NIH Loneliness Scale) Mean (SD) 57.7 (13.5) 51.3 (11.9) 53.8 (13.1) 47.5 (11.2) Mod/High n (%) (score ≥60.7 adult, ≥ 60 youth) 50 (49.0%) 191 (25.7%) 90 (32.3%) 86 (15.3%) Suicidal Ideation & Behavior (C-SSRS) Mean (SD) 0.3 (0.9) 0.1 (0.6) 0.2 (0.8) 0.1 (0.5) Any suicidal ideation 19 (18.6%) 58 (7.8%) 32 (11.5%) 25 (4.4%) Mod/High n (%) (score ≥ 3) 4 (3.9%) 10 (1.3%) 8 (2.9%) 6 (1.0%) Anxiety (GAD-7) Mean (SD) 7.9 (5.7) 4.7 (4.7) 5.0 (5.3) 2.2 (3.3) Mod/High n (%) (score ≥ 11) 31 (30.4%) 100 (13.4%) 39 (14.0%) 15 (2.7%) Depression (PHQ-9) Mean (SD) 8.7 (7.1) 4.9 (5.2) 6.0 (6.1) 2.8 (3.8) Mod/High n (%) (score ≥ 10) 40 (39.2%) 126 (16.9%) 57 (20.4%) 42 (7.5%) Stress (NIH Perceived Stress Scale) Mean (SD) 57.1 (10.9) 50.5 (10.7) 50.9 (11.7) 42.9 (10.1) Mod/High n (%) (score ≥ 60.5 adult, ≥ 60.8 youth) 40 (39.2%) 147 (19.8%) 67 (24.0%) 32 (5.7%) Any Mental Distress Moderate or High n (%) 63 (61.8%) 262 (35.2%) 116 (41.6%) 103 (18.3%) Open in a new tab Abbreviations: C-SSRS: Columbia-Suicide Severity Rating Scale; GAD-7: General Anxiety Disorder 7; MHAPPS: Mental Health Among Patients, Providers, and Staff; NIH: National Institutes of Health; PHQ-9: Patient Health Questionnaire-9. Scoring for Outcome Measures: NIH Loneliness and Perceived Stress scale scores are converted to T-scores, with a T-score of 50 representing the mean of the US general population (based on the 2010 Census) and 10 T-score units represents one standard deviation. Higher scores indicate higher loneliness/stress. C-SSRS screener scores range from 0 to 6, with higher scores indicative of higher suicide risk. GAD7 scores range from 0–21, with higher scores indicating a greater severity of anxiety symptoms. PHQ-9 scores range from 0–27 with higher scores indicating a greater degree of depression. Within the healthcare provider and staff cohort, there were 179 physicians and advanced practice providers; 327 employees in pharmacy, patient access, laboratory, specialists, technicians, and medical assistants; 677 nurses; 588 non-patient-facing staff; 104 psychologists, social workers, and other employees in mental health and social support; and 84 in other employment categories. The highest percentage of employees reporting moderate to high levels of loneliness were in mental health and social support (25%), nursing (23%) and non-patient-facing staff (21.8%), followed by the “other” category (17.9%), pharmacy, patient access, lab, specialists, technicians, and medical assistants (17.7%) and physicians and advanced practice providers (10.1%). Non-patient-facing staff were the most likely to report moderate to high levels of any form of mental distress (32.3%), along with nursing (31.8%) and mental health and social support (28.8%). Reported mental health measures for HCP are summarized by employee type in Table 4 . Table 4: Number and Percentage of Health Care Professionals (HCP) and Employed Primary Care Patients Reporting Moderate to High Levels of Study Measures of Mental Distress by Strata and Employment Type in the MHAPPS Cross-Sectional Survey Loneliness (NIH Toolbox Loneliness Scale) Suicidal Ideation & Behavior (C-SSRS) Anxiety (GAD-7) Depression (PHQ-9) Stress (NIH Toolbox Stress Scale) Moderate/High Levels of Any Measure of Mental Distress St. Luke’s Health Care Professionals (HCP) N = 1,959 Physicians & APPs N = 179 18 (10.1%) 1 (0.6%) 8 (4.5%) 6 (3.4%) 13 (7.3%) 29 (16.2%) Pharmacy, PAS, Lab, Specialists, Technicians, MAs N = 327 58 (17.7%) 5 (1.5%) 25 (7.6%) 27 (8.3%) 40 (12.2%) 83 (25.4%) Nursing (registered nurses, nursing leaders) N = 677 156 (23%) 10 (1.5%) 67 (9.9%) 79 (11.7%) 105 (15.5%) 215 (31.8%) Non-Patient-Facing (office workers, IT, educators) N = 588 128 (21.8%) 4 (0.7%) 51 (8.7%) 78 (13.3%) 108 (18.4%) 190 (32.3%) Mental Health & Social Services (psychologists, SW) N = 104 26 (25%) 3 (2.9%) 10 (9.6%) 15 (14.4%) 20 (19.2%) 30 (28.8%) Healthcare Employee, Other N = 84 15 (17.9%) 0 (0%) 7 (8.3%) 10 (11.9%) 13 (15.5%) 21 (25%) Employed Primary Care Patients N=876 Business Management and Administration N = 49 8 (16.3%) 1 (2%) 4 (8.2%) 5 (10.2%) 6 (12.2%) 10 (20.4%) Education and Training N = 122 34 (27.9%) 2 (1.6%) 12 (9.8%) 22 (18%) 20 (16.4%) 49 (40.2%) Finance N = 63 11 (17.5%) 0 (0%) 3 (4.8%) 8 (12.7%) 9 (14.3%) 15 (23.8%) Food Preparation or Service N = 35 13 (37.1%) 3 (8.6%) 12 (34.3%) 12 (34.3%) 14 (40%) 17 (48.6%) Government and Public Administration N = 41 8 (19.5%) 0 (0%) 4 (9.8%) 7 (17.1%) 7 (17.1%) 13 (31.7%) Health, Healthcare, or Health Sciences N = 135 39 (28.9%) 2 (1.5%) 22 (16.3%) 28 (20.7%) 31 (23%) 51 (37.8%) Marketing, Business, Sales, or Service N = 38 14 (36.8%) 0 (0%) 9 (23.7%) 11 (28.9%) 12 (31.6%) 15 (39.5%) Retail Sales N = 39 11 (28.2%) 1 (2.6%) 4 (10.3%) 6 (15.4%) 9 (23.1%) 13 (33.3%) Employed, Other N = 354 91 (25.7%) 5 (1.4%) 48 (13.6%) 52 (14.7%) 66 (18.6%) 125 (35.3%) Open in a new tab Abbreviations: APPs: Advanced Practice Providers; C-SSRS: Columbia-Suicide Severity Rating Scale; GAD-7: General Anxiety Disorder 7; HCP: Health Care Professionals; IT: Information Technology; MAs: Medical Assistants; MHAPPS: Mental Health Among Patients, Providers, and Staff ; NIH: National Institutes of Health; PAS: Patient Access Specialists; PHQ-9: Patient Health Questionnaire-9; SW: Social Workers. Among the 876 adult patients who are currently employed, there were 49 working in business management and administration; 122 in education and training; 63 in finance; 35 in food preparation or service; 41 in government and administration; 135 in health, healthcare, or health sciences, 38 in marketing, business sales or service, 39 in retail sales, and 354 in other employment categories. The highest percentage of patients reporting moderate to high levels of loneliness were found in food preparation or service (37.1%) and marketing, business sales or service (36.8%), followed by health, healthcare, and health sciences (28.9%), retail sales (28.2%), education and training (27.9%), and the “other” category (25.7%). Patients working in food preparation or service were the most likely to report moderate to high levels of any form of mental distress (48.6%), followed by education and training (40.2%), and marketing, business sales or service (39.5%). Mental health outcomes for patients are summarized by employment type in Table 4 . Demographic Factors & Mental Health Outcomes Multiple demographic factors were significantly associated with mental distress. Results of the regression analyses are summarized in Table 5 . Compared to females, males had better mental health outcomes overall. Regression coefficients indicated male sex was associated with significantly lower loneliness (−2.70, 95% CI: (−3.54, −1.86), p<0.001), depression (−0.71, 95% CI: (−1.04, −0.39), p<0.001), anxiety (−0.83, 95% CI: (−1.12, −0.54), p<0.001), and stress (−3.10, 95% CI: (−3.85, −2.35), p<0.001). Table 5: MHAPPS Survey Regression Results: Association Between Mental Health Distress and Demographics Characteristics, Financial Security, and Local COVID-19 Prevalence Loneliness (NIH Toolbox, t-score) Suicidal Ideation & Behavior (C-SSRS) Depression (PHQ-9) Anxiety (GAD-7) Stress (NIH Toolbox, t-score) Estimate (95% CI) Estimate (95% CI) Estimate (95% CI) Estimate (95% CI) Estimate (95% CI) Age (per 10 years) −0.45 (−0.75, −0.15) −0.03 (−0.04, −0.01) −0.37 (−0.49, −0.26) −0.54 (−0.65, −0.43) −1.37 (−1.62, −1.12) Male Sex (ref: female) −2.70 (−3.54, −1.86) −0.003 (−0.04, 0.04) −0.71 (−1.04, −0.39) −0.83 (−1.12, −0.54) −3.10 (−3.85, −2.35) Race/Ethnicity (ref: non-Hispanic White) Hispanic, any race −0.86 (−2.54, 0.81) 0.07 (−0.04, 0.17) 0.002 (−0.66, 0.67) 0.08 (−0.53, 0.69) 0.59 (−0.74, 1.91) Non-Hispanic, non-White race −0.18 (−1.89, 1.54) −0.02 (−0.13, 0.08) −0.29 (−1.05, 0.47) −0.15 (−0.84, 0.53) 0.27 (−1.22, 1.76) Rural Residence (ref: urban) −0.48 (−1.37, 0.40) 0.001 (−0.05, 0.05) −0.14 (−0.48, 0.21) −0.27 (−0.58, 0.05) −0.20 (−0.95, 0.54) Size of Household (per each additional person in household) −1.41 (−1.78, −1.05) 0.001 (−0.02, 0.02) −0.20 (−0.35, −0.06) −0.18 (−0.32, −0.05) −0.37 (−0.67, −0.08) St. Luke’s Provider/Staff (ref: primary care patient) −1.79 (−2.64, −0.93) −0.05 (−0.10, −0.01) −0.90 (−1.25, −0.55) −0.68 (−0.99, −0.36) −1.11 (−1.84, −0.39) Education (ref: high school or less education) Some college credit but no degree 0.56 (−1.15, 2.26) 0.01 (−0.10, 0.12) 0.39 (−0.33, 1.12) −0.15 (−0.82, 0.53) −0.35 (−1.76, 1.06) Associate degree −0.06 (−1.93, 1.80) 0.02 (−0.10, 0.14) 0.17 (−0.63, 0.96) −0.23 (−0.98, 0.51) −1.38 (−2.92, 0.16) Bachelor’s degree 1.18 (−0.43, 2.80) −0.002 (−0.10, 0.10) −0.09 (−0.77, 0.58) −0.11 (−0.75, 0.53) −0.44 (−1.79, 0.91) Master’s degree 1.08 (−0.64, 2.79) −0.01 (−0.11, 0.09) −0.17 (−0.87, 0.53) −0.24 (−0.90, 0.42) −0.27 (−1.72, 1.18) PhD or equivalent 0.73 (−1.17, 2.63) −0.04 (−0.13, 0.06) −0.48 (−1.22, 0.27) −0.03 (−0.76, 0.69) −0.33 (−1.94, 1.29) Put off buying necessities due to financial constraints (ref: never) Rarely 4.01 (3.03, 4.99) 0.01 (−0.03, 0.06) 1.44 (1.06, 1.83) 1.44 (1.08, 1.80) 4.98 (4.17, 5.80) Occasionally 5.54 (4.28, 6.79) 0.08 (0.01, 0.15) 2.45 (1.89, 3.00) 2.23 (1.73, 2.73) 6.66 (5.67, 7.65) Frequently 12.09 (9.96, 14.22) 0.30 (0.12, 0.48) 6.01 (5.03, 6.98) 4.79 (3.94, 5.64) 11.78 (10.26, 13.31) All the time 14.38 (11.22, 17.54) 0.38 (0.13, 0.64) 6.71 (5.18, 8.23) 6.33 (5.00, 7.66) 14.27 (12.07, 16.47) One or More Dependents (ref: no dependents) 2.15 (1.20, 3.09) −0.07 (−0.13, −0.02) 0.47 (0.09, 0.84) 0.54 (0.19, 0.89) 1.94 (1.13, 2.74) COVID-19 Hospitalizations (per additional patient hospitalized due to COVID-19 at St. Luke’s Health System) 0.03 (−0.02, 0.08) 0.001 (−0.001, 0.004) 0.01 (−0.01, 0.03) 0.01 (−0.01, 0.03) 0.01 (−0.03, 0.05) Open in a new tab Abbreviations: C-SSRS: Columbia-Suicide Severity Rating Scale; GAD-7: General Anxiety Disorder 7; MHAPPS: Mental Health Among Patients, Providers, and Staff ; NIH: National Institutes of Health; PHQ-9: Patient Health Questionnaire-9. Scoring for Outcome Measures: NIH Loneliness and Perceived Stress scale scores are converted to T-scores, with a T-score of 50 representing the mean of the US general population (based on the 2010 Census) and 10 T-score units represents one standard deviation. Higher scores indicate higher loneliness/stress. C-SSRS screener scores range from 0 to 6, with higher scores indicative of higher suicide risk. GAD7 scores range from 0–21, with higher scores indicating a greater severity of anxiety symptoms. PHQ-9 scores range from 0–27 with higher scores indicating a greater degree of depression. Older age was generally associated with better mental health outcomes. Each ten-year increase in age was associated with small but significantly lower levels of loneliness (−0.45, 95% CI: (−0.75, −0.15), p<0.004), suicidal ideation (−0.03, 95% CI: (−0.04, −0.01), p<0.001), depression (−0.37, 95% CI: (−0.49, −0.26), p<0.001), anxiety (−0.54, 95% CI: (−0.65, −0.43), p<0.001), and stress (−1.37, 95% CI: (−1.62, −1.12), p<0.001). Compared to patients, HCP reported significantly lower levels of loneliness (−1.79, 95% CI: (−2.64, −0.93), p<0.001), depression (−0.90, 95% CI: (−1.25, −0.55), p<0.001), anxiety (−0.68, 95% CI: (−0.99, −0.36), p<0.001), stress (−1.11, 95% CI: (−1.84, −0.39), p=0.003), and a statistically significant but only slightly lower level of suicidal ideation (−0.05, 95% CI: (−0.10, −0.01), p=0.02). Compared to those who did not care for a dependent, people who cared for at least one dependent child or adult in the last 12 months had a significant but only slightly lower level of suicidal ideation (−0.07, 95% CI: (−0.13, −0.02), p=0.01) and significantly higher loneliness (2.15, 95% CI: (1.20, 3.09), p<0.001), depression (0.47, 95% CI: (0.09, 0.84), p=0.01), anxiety (0.54, 95% CI: (0.19, 0.89), p=0.003), and stress (1.94, 95% CI: (1.13, 2.74), p<0.001). Household size was also associated with mental wellbeing. With each additional person residing in their household, respondents reported significantly lower loneliness (−1.41, 95% CI: (−1.78, −1.05), p<0.001) and statistically significant but only slightly lower levels of depression (−0.20, 95% CI: (−0.35, −0.06), p=0.005), anxiety (−0.18, 95% CI: (−0.32, −0.05), p=0.008), and stress (−0.37, 95% CI: (−0.67, −0.08), p=0.01). In summary, male sex, older age, larger household size, and employment as a healthcare provider or staff were associated with better mental health measures in general. Caring for a dependent was associated with slightly lower level of suicidal ideation, but higher levels of loneliness, depression, anxiety, and perceived stress. The magnitude of each of these differences was small and likely not clinically important as independent factors but may be meaningful in aggregate. Financial Security & Mental Health Outcomes Table 5 includes the regression results for the association between financial security and mental health outcomes. Compared with participants who never put off buying necessities due to financial constraints in the past year, those who faced these financial constraints reported significantly higher levels of loneliness, depression, anxiety, stress. The magnitude of many of these differences were clinically significant based on the minimal clinically important differences specified for a related clinical trial ( Radin et al., 2023 ). Additionally, reporting lower levels of financial security was associated with statistically significant but only slightly higher reported levels of suicidal ideation or behavior. Local COVID-19 Hospitalizations & Mental Health Outcomes We did not find a significant association between any mental distress measure and COVID-19 hospitalizations at St. Luke’s Health System. Results are included in Table 5 . Discussion Overview This study was designed to measure the prevalence of various mental distress measures in patients and HCP during the COVID-19 pandemic and to determine which sociodemographic factors were associated with mental health measures. Mental distress varied among patients by employment status and age and varied by healthcare employee type. Several demographic factors were significantly associated with mental health outcomes: financial security, male sex, older age, increased household size, employment at St. Luke’s as a healthcare provider/staff, and not caring for dependent adults or children were generally associated with better mental health. Financial security had the most significant associations (in magnitude) with each of the five mental health measures, and a dose-response relationship was observed, meaning the more frequently people had to put off buying necessities, the worse their reported mental health outcomes. This finding is consistent with other literature on the established bi-directional association between poverty or financial insecurity and adverse mental health outcomes ( American Psychiatric Association, 2022 ). It is important to note that the mental burden of COVID-19 likely fluctuated during the course of the pandemic as infections, hospitalizations, and deaths rose and fell, and preventive measures such as vaccines were introduced and eventually delivered at scale. In Idaho, some of the most intense periods - such as when crisis standards of care were rolled out statewide - occurred after this survey was conducted. It is likely that the levels of distress reported by both patients and healthcare professionals in this survey is lower than that experienced at the peaks of the pandemic when morbidity and mortality were highest and health systems were most severely strained. Mental Health Among Patients Among patient participants, unemployed individuals of retirement age (65+ years) reported the best mental health, with 18.3% reporting moderate to high levels of mental distress. Adolescents and young adults reported the worst mental health, with 61.8% reporting moderate to high levels of at least one measure of mental distress. Among adults 25–64 years old, those who were employed reported better mental health than those who were not employed for pay, some of whom may have been seeking work. There were important differences in mental health by occupation type among employed patients, with teachers and educators, food service, and retail sales employees reporting some of the worst mental health conditions. However, sample sizes for several of the employment categories were limited and these findings should be considered hypothesis-generating. Mental Health Among HCP Overall, mental health among HCP was better than employed or unemployed patients, except for retirement age unemployed patients, who had the best mental health outcomes. Among healthcare employees, nursing, non-patient-facing staff (e.g.: office workers and educators), and mental health and social support staff (e.g. psychologists, social workers, chaplains, and counselors) reported worse mental health outcomes compared to other cadres of healthcare employees. The elevated level of mental distress among nurses was expected, given their role in providing front line care for patients in addition to the added stress of the COVID-19 pandemic on the healthcare system. Similarly, mental health and social support employees, who are also patient-facing, were asked to care for and comfort patients, families, and staff as deaths resulting from COVID-19 exceeded pre-pandemic mortality rates. The high prevalence of mental distress among non-patient-facing staff relative to other health system employees was unexpected, as this group is not patient-facing, and was assumed to be buffered to some extent from experiencing firsthand the death and severe illness that frontline healthcare workers witnessed daily. However, non-patient-facing employees of the health system were likely aware of the trauma frontline healthcare workers faced, yet were unable to directly intervene, which may have resulted in an increase in adverse mental health conditions ( Jennings, 1990 ). It is also plausible that non-patient-facing staff working from home may have experienced more severe and consistent social isolation than frontline healthcare workers. Working long hours and dealing with stress related to critical work such as designing new electronic health record modules to facilitate and monitor vaccine deployment or managing supply chain for key commodities and drugs during periods of intense shortages during the pandemic may have contributed to adverse mental health conditions in this group. Additionally, several health systems in the state were laying off employees during the pandemic ( KTVB Staff, 2020 ). Patient-facing teams who were clearly in high demand may have faced less stress about job security than non-patient-facing employees. Mental distress among non-patient-facing healthcare workers during public health crises has been under-studied and our findings suggest this group may warrant additional research and mental health support during and following periods of crisis. The relatively low rate of mental distress among physicians and advanced practice providers was unexpected. Other published studies found high rates of mental distress among physicians ( Czeisler et al., 2020 ; Lai et al., 2020 ). This could possibly be explained by the fact that the quoted studies were published in 2019 and 2020 – early in the pandemic, when the lethality of the virus was poorly understood, and vaccination rates were lagging. By 2021 most healthcare providers were vaccinated and fearing loss of life was likely less common. It is also possible that the physicians and advanced practice providers who were struggling most were least likely to participate in this research, or that they had a higher sense of agency (locus of control) ( Jennings, 1990 ) during the pandemic than other HCP, which may have led to better mental health outcomes. Strengths This cross-sectional study was completed at the tail end of the Alpha variant surge and just prior to the start of the Delta variant surge of COVID-19 in a state and region of the country where data on the prevalence of mental health and associated risk and protective factors are limited. The sample size is large relative to the target population of healthcare workers and primary care patients in southern Idaho. This study includes both rural and urban populations, allowing comparison of mental health outcomes across those settings within a rural state. This study reports the mental health status for non-patient-facing healthcare workers, an important population often excluded in health services research. Limitations The study population was mostly female, white, and non-Hispanic, with higher educational attainment than average; generalizability of the findings may be limited to other rural populations with similar demographics. Minors were eligible to participate but few enrolled and the study cannot draw meaningful conclusions about adolescents. Limited data were collected about employment status, and we could not distinguish between individuals who were retired, unemployed but not seeking work, unemployed and seeking work, or working in the home but not for pay. Selection bias may have been present in the study sample in the patient cohort (just 12% of invited patients enrolled in the study, and nearly half of the sample of patients were retired or not employed for pay; 54.2% of the sample of patients had a bachelor’s degree or higher, compared to 32.3% of Idaho residents overall) ( US Census Bureau, 2022 ), and in physicians and advanced practice providers (just 20% of those invited completed the survey, compared to 36% of nurses and 47% of non-patient-facing healthcare staff). Finally, without pre-pandemic baseline data on mental health in the target population, the study cannot determine the extent to which adverse mental health outcomes may have changed during the COVID-19 pandemic. Conclusions The prevalence of adverse mental health conditions during the COVID-19 pandemic in a large sample of Idaho HCP and primary care patients varied significantly based on age, sex, household size, care for dependents, employment status, occupation, type of employment, and financial security. Targeting mental health resources to those most in need should be a priority during public health emergencies and the recovery period that follows. Further research is warranted to address the needs of priority populations within healthcare settings and beyond. Public Health Significance. This study of primary care patients and healthcare professionals in Idaho found that the mental health impacts of the COVID-19 pandemic varied based on sociodemographic factors. Adolescents and young adults, females, people with dependents, and people who had to put off buying things they needed, like food, medication, or clothing, reported the worst mental health outcomes. Prioritizing mental health support for these populations may be warranted in future public health emergencies. Acknowledgements We gratefully acknowledge the support of our team at the Patient-Centered Outcomes Research Institute (PCORI), our research coordinators, our follow-up specialists at the Idaho Crisis and Suicide Hotline, and the whole St. Luke’s team, including Executive Leaders, Communications, the Research Department, and the Behavioral Health Service Line. Finally, we wish to recognize the contributions of members of our People with Lived Experience with Suicide Advisory Board, and our participants, who prioritized participating in this study during an exceptionally challenging time. Funding Research reported in this publication was funded through a Patient-Centered Outcomes Research Institute ® (PCORI ® ) Award (HIS-2018C3-14695). The information presented in this publication is solely the responsibility of the authors and does not necessarily represent the views of the Patient-Centered Outcomes Research Institute ® (PCORI ® ), its Board of Governors or Methodology Committee. Data and Safety Monitoring was provided by the Institute of Translational Health Sciences at the University of Washington, supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1 TR002319. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. 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