Mental Health Impact of COVID-19 among Spanish Healthcare Workers.
A 3-year follow-up study
JordiAlonso
PhD, MSc
1✉,2,3,43Phone(+34)933 160 760Email
GemmaVilagut
MSc
1,2
F.Amigo
MPH
4
M.Ruiz-Rivera
MD, MPH, PhD
1
M.Ferrer
MD, PhD, A., MD, PhD
1,2,3,5,6
E.Aragonès7,8
M.Campos
MD, PhD
9
I.
del
Cura-González5,9,10,11
I.Urreta
PhD
2,12
M.JForjaz
MD, PhD.
13,14
A.GonzálezPinto
MD, PhD
15,16
J.M.Haro
MD, PhD
16,17
N.LópezFresneña
MD, PhD
18,19
A.Martínez
de
Salázar
MD, PhD
20
J.D.Molina
MD, PhD
16,21,22,23
R.M.Ortí Lucas24,25
M.Parellada
MD, MPH
18,25
J.M.Pelayo-Terán
MD, PhD
16,26,27
AuroraPérezZapata
MD
28
M.N.Plana
MD, MSC, PhD
29
T.Puig
MD, PhD
30,31,32,33
CristinaRius2,34
CarmenRodriguez-Blazquez
PhD
13,35
FerranSanz
MD, PhD
3,36,37
ConsolSerra
PhD
2,38,39
R.C.Kessler
PhD
40
R.Bruffaerts
MD, PhD
41
EduardVieta
MD, PhD
16,42
V.Pérez-Solá
MD, MSc, PhD
16,32
P.Mortier1,2
KarolinskaInstitutet1
HospitalUniversitarioAraba-Santiago1
Vitoria-Gasteiz1
Spain1
ItxasoAlayo1
ManuelAlonso1
RosaPla1
NievesPlana1
CoroPerezAznar1
BeatrizPerezGomez1
JoseIgnacioPijoan1
ElenaPolentinos1
BeatrizPuertolas1
MariaTeresaPuig1
AlexQuílez1
M.JesusQuintana1
AntonioQuiroga1
DavidRentero1
CristinaRey1
M.JoseRojas1
YaminaRomero1
GabrielRubio1
MercedesRumayor1
PedroRuiz1
MargaritaSaenz1
JesusSanchez1
IgnacioSanchez-Arcilla1
VictoriaSerra-Sutton1
ManuelaSerrano1
SilviaSola1
SaraSolera1
MiguelSoto1
AlejandraTarrago1
NatividadTolosa1
MireiaVazquez1
MargaritaViciola1
SaraYago1
JesusYañez1
YolandaZapico1
LuisMariaZorita1
IñakiZorrilla1
SaioaL.Zurbano1
VictorPerez-Solá1
1Health Services Research UnitHospital de Mar Research InstituteBarcelonaSpain
2CIBER Epidemiología y Salud Pública (CIBERESP/ISCIII)MadridSpain
3Department of Medicine and Life SciencesUniversitat Pompeu FabraBarcelonaSpain
4Department of Population Health SciencesDuke University School of MedicineDurhamNCUSA
5Sub-Directorate of Public Health SurveillanceCommunity of MadridMadridSpain
6Fundación Investigación e Innovación Biosanitaria de APComunidad de MadridMadridSpain
7Institut d’Investigació en Atenció Primària IDIAP Jordi GolBarcelonaSpain
8Atenció Primària Camp de TarragonaInstitut Català de la SalutTortosaSpain
9Occupational Health and Safety Officer, Medical Emergency SystemGeneralitat de CatalunyaBarcelonaSpain
10Research Unit, Primary Care ManagementMadrid Health ServiceMadridSpain
11Department of Medical Specialities and Public HealthKing Juan Carlos UniversityMadridSpain
12Donostialdea Integrated Health Organisation, Clinical Epidemiology UnitOsakidetza Basque Health Service, Donostia University HospitalSan SebastiánSpain
13National Center of Epidemiology, Instituto de Salud Carlos III (ISCIII)MadridSpain
14Health Services Research Network on Chronic Diseases (REDISSEC)MadridSpain
15Hospital Universitario Araba-Santiago, BIOARABA, UPV-EHUVitoria-GasteizSpain
16CIBER Salud Mental (CIBERSAM)MadridSpain
17Parc Sanitari Sant Joan de DéuBarcelonaSpain
18Hospital General Universitario Gregorio MarañónMadridSpain
19Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM)MadridSpain
20UGC Salud Mental, Hospital Universitario TorrecárdenasAlmeríaSpain
21Clinical Management Area of Psychiatry and Mental Health, Psychiatric ServiceVillaverde Mental Health Center, Hospital Universitario 12 de OctubreMadridSpain
22Research Institute Hospital 12 de Octubre (i+12)MadridSpain
23Faculty of Health SciencesFrancisco de Vitoria UniversityMadridSpain
24Hospital Clínic UniversitariValenciaSpain
25Universitat Catòlica de ValènciaValenciaSpain
26Servicio de Psiquiatría y Salud Mental, Hospital el BierzoGerencia de Asistencia Sanitaria del Bierzo (GASBI), Gerencia Regional de Salud de Castilla y Leon (SACYL)Ponferrada, LeónSpain
27Área de Medicina Preventiva y Salud PúblicaUniversidad de LeónLeónSpain
28Príncipe de Asturias University HospitalAlcalá de Henares, MadridSpain
29
A
A
A
Health Technology Assessment UnitRamón y Cajal University HospitalIRYCIS
30Department of Epidemiology and Public HealthHospital de la Santa Creu i Sant PauBarcelonaSpain
31Biomedical Research Institute Sant Pau (IIB Sant Pau)BarcelonaSpain
32Universitat Autònoma de BarcelonaBarcelonaSpain
33CIBER Enfermedades Cardiovasculares (CIBERCV)MadridSpain
34Agència de Salut Pública de BarcelonaBarcelonaSpain
35CIBER Enfermedades Neurodegenerativas (CIBERNED)MadridSpain
36Research Progamme on Biomedical Informatics (GRIB)Hospital del Mar Medical Research Institute (IMIM)BarcelonaSpain
37Instituto Nacional de Bioinformática – ELIXIR-ESBarcelonaSpain
38Servicio de Salud LaboralHospital del MarBarcelonaSpain
39CiSAL-Centro de Investigación en Salud Laboral, Hospital del Mar Research Institute, Universitat Pompeu FabraBarcelonaSpain
40Department of Health Care PolicyHarvard Medical SchoolBostonMAUSA
41Center for Public Health PsychiatryUniversitair Psychiatrisch Centrum, KU LeuvenLeuvenBelgium
42Institut de Neurociencies, Universitat de Barcelona, Hospital Clinic, Fundació Clínic per a la Recerca Biomèdica, IDIBAPSBarcelonaSpain
43Department of Medicine and Life SciencesUniversitat Pompeu FabraCarrer del Dr. Aiguader, 88, edifici PRBB08003Barcelona, Address, BARCELONASpain, Spain
MD, PhD, J. Alonso1,2,3 *, PhD, MSc, G. Vilagut1,2 *, MSc, F. Amigo4,MPH, M. Ruiz-Rivera1, MD, MPH, PhD, M. Ferrer1,2,3, MD, PhD, A. Aragón-Peña5.6, MD, PhD, E. Aragonès7,8, M. Campos9, MD, PhD, I. del Cura-González5,9,10,11,12, I. Urreta2,13, PhD, M.J Forjaz14,15, MD, PhD. A. González Pinto16,17, MD, PhD, J. M. Haro18, 17, MD, PhD, N. López Fresneña19,20, MD, PhD, A. Martínez de Salázar21, MD, PhD, J. D. Molina22,23,24,17, MD, PhD, R. M. Ortí Lucas25,26, M. Parellada19,26, MD, MPH J. M. Pelayo-Terán27,28,17, MD, PhD, A. Pérez Zapata29, MD, M.N. Plana30, MD, MSC, PhD, T. Puig31,32,33,34, MD, PhD, C. Rius35,2, C. Rodriguez-Blazquez36,37 ,PhD, F. Sanz38,3,39, MD, PhD, C. Serra 40,41,2, PhD, R. C. Kessler42, PhD, R. Bruffaerts43, MD, PhD, E. Vieta44,17, MD, PhD, V. Pérez-Solá 45,33,17 †, MD, MSc, PhD, P. Mortier 1,2 *†, on behalf of MINDCOVID Working group‡
* Equally contributing first author
† Equally contributing senior author
1 Health Services Research Unit, Hospital de Mar Research Institute, Barcelona, Spain;
2 CIBER Epidemiología y Salud Pública (CIBERESP/ISCIII), Madrid, Spain;
3 Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain;
4 Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA
5 Sub-Directorate of Public Health Surveillance, Community of Madrid, Madrid, Spain;
6 Fundación Investigación e Innovación Biosanitaria de AP, Comunidad de Madrid, Madrid, Spain;
7 Institut d’Investigació en Atenció Primària IDIAP Jordi Gol, Barcelona, Spain;
8 Atenció Primària Camp de Tarragona, Institut Català de la Salut, Tortosa, Spain;
9 Occupational Health and Safety Officer, Medical Emergency System, Generalitat de Catalunya, Barcelona, Spain;
10 Research Unit, Primary Care Management, Madrid Health Service, Madrid, Spain;
11 Department of Medical Specialities and Public Health, King Juan Carlos University, Madrid, Spain;
12 Research network on chronic conditions, primary care, and health promotion. RICAPSS. IISGM. ARC Karolinska Institutet;
13 Osakidetza Basque Health Service, Donostialdea Integrated Health Organisation, Donostia University Hospital, Clinical Epidemiology Unit, San Sebastián, Spain;
14 National Center of Epidemiology, Instituto de Salud Carlos III (ISCIII), Madrid, Spain;
Hospital Universitario Araba-Santiago, Vitoria-Gasteiz, Spain;
15 Health Services Research Network on Chronic Diseases (REDISSEC), Madrid, Spain;
16 Hospital Universitario Araba-Santiago, BIOARABA, UPV-EHU,Vitoria-Gasteiz, Spain;
17 CIBER Salud Mental (CIBERSAM), Madrid, Spain;
18 Parc Sanitari Sant Joan de Déu, Barcelona, Spain;
19 Hospital General Universitario Gregorio Marañón, Madrid, Spain;
20 Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain;
21 UGC Salud Mental, Hospital Universitario Torrecárdenas, Almería, Spain;
22 Villaverde Mental Health Center, Clinical Management Area of Psychiatry and Mental Health, Psychiatric Service, Hospital Universitario 12 de Octubre, Madrid, Spain;
23 Research Institute Hospital 12 de Octubre (i + 12), Madrid, Spain;
24 Faculty of Health Sciences, Francisco de Vitoria University, Madrid, Spain;
25 Hospital Clínic Universitari, Valencia, Spain;
26 Universitat Catòlica de València, Valencia, Spain;
27 Servicio de Psiquiatría y Salud Mental, Hospital el Bierzo, Gerencia de Asistencia Sanitaria del Bierzo (GASBI), Gerencia Regional de Salud de Castilla y Leon (SACYL), Ponferrada, León, Spain;
28 Área de Medicina Preventiva y Salud Pública, Universidad de León, León, Spain;
29 Príncipe de Asturias University Hospital, Alcalá de Henares, Madrid, Spain;
30 Health Technology Assessment Unit. Ramón y Cajal University Hospital, IRYCIS
31 Department of Epidemiology and Public Health, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain;
32 Biomedical Research Institute Sant Pau (IIB Sant Pau), Barcelona, Spain;
33 Universitat Autònoma de Barcelona, Barcelona, Spain;
34 CIBER Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain;
35 Agència de Salut Pública de Barcelona, Barcelona, Spain;
36 National Center of Epidemiology, Instituto de Salud Carlos III (ISCIII), Madrid, Spain;
37 CIBER Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain;
38 Research Progamme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain;
39 Instituto Nacional de Bioinformática – ELIXIR-ES, Barcelona, Spain;
40 Servicio de Salud Laboral, Hospital del Mar, Barcelona, Spain;
41 CiSAL-Centro de Investigación en Salud Laboral, Hospital del Mar Research Institute/Universitat Pompeu Fabra, Barcelona, Spain;
42 Department of Health Care Policy, Harvard Medical School, Boston, MA, USA;
43 Center for Public Health Psychiatry, Universitair Psychiatrisch Centrum, KU Leuven, Leuven, Belgium;
44 Institut de Neurociencies, Universitat de Barcelona, Hospital Clinic, Fundació Clínic per a la Recerca Biomèdica, IDIBAPS, Barcelona, Spain
45 Parc de Salut Mar PSMAR, Barcelona;
‡ The MINDCOVID Working Group is formed by:
Jordi Alonso, Itxaso Alayo, Manuel Alonso, Mar Álvarez, Benedikt Amann, Franco F. Amigo, Gerard Anmella, Andres Aragón, Nuria Aragonés, Enric Aragonès, Ana Isabel Arizón, Angel Asunsolo, Alfons Ayora, Laura Ballester, Puri Barbas, Josep Basora, Elena Bereciartua, Inés Bravo Ignasi Bolibar, Xavier Bonfill, Alberto Cotillas, Andres Cuartero, Concha de Paz, Isabel del Cura, Maria Jesus del Yerro, Domingo Diaz, Jose Luis Domingo, Jose I. Emparanza, Mireia Espallargues, Meritxell Espuga, Patricia Estevan, M. Isabel Fernandez, Tania Fernandez, Montse Ferrer, Yolanda Ferreres, Giovanna Fico, M. Joao Forjaz, Rosa Garcia Barranco, J. Manuel Garcia TorrecillasC. Garcia-Ribera, Araceli Garrido, Elisa Gil, Marta Gomez, Javier Gomez, Ana Gonzalez Pinto, Josep Maria Haro, Margarita Hernando, Maria Giola Insigna, Milagros Iriberri, Nuria Jimenez, Xavi Jimenez, Amparo Larrauri, Fernando Leon, Maria Nieves Lopez- Fresneña, Carmen Lopez, Mayte Lopez-Atanes Juan Antonio Lopez-Rodriguez, German Lopez-Cortacans, Alba Marcos, Jesus Martin, Vicente Martin, Mercedes Martinez- Cortés, Raquel Martinez-Martinez, Alma D. Martinez de Salazar, Isabel Martinez, Marco Marzola, Nelva Mata, Josep Maria Molina, Juan de Dios Molina, Emilia Molinero, Philippe Mortier, Carmen Muñoz, Andrea Murru, Jorge Olmedo, Rafael M. Ortí, Rafael Padrós, Meritxell Pallejà, Raul Parra, Julio Pascual, Jose Maria Pelayo, Rosa Pla, Nieves Plana, Coro Perez Aznar, Beatriz Perez Gomez, Aurora Perez Zapata, Jose Ignacio Pijoan, Elena Polentinos, Beatriz Puertolas, Maria Teresa Puig, Alex Quílez, M. Jesus Quintana, Antonio Quiroga, David Rentero, Cristina Rey, Cristina Rius, Carmen Rodriguez-Blazquez, M. Jose Rojas, Yamina Romero, Gabriel Rubio, Mercedes Rumayor, Pedro Ruiz, Margarita Saenz, Jesus Sanchez, Ignacio Sanchez-Arcilla, Ferran Sanz, Consol Serra, Victoria Serra-Sutton, Manuela Serrano, Silvia Sola, Sara Solera, Miguel Soto, Alejandra Tarrago, Natividad Tolosa, Mireia Vazquez, Margarita Viciola, Eduard Vieta, Gemma Vilagut, Sara Yago, Jesus Yañez, Yolanda Zapico, Luis Maria Zorita, Iñaki Zorrilla, Saioa L. Zurbano, and Victor Perez-Solá.
CORRESPONDING AUTHOR: Jordi Alonso, Health Services Research Unit, Hospital de Mar Research Institute, Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP/ISCIII), Madrid, Spain; Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain. Address; Carrer del Dr. Aiguader, 88, edifici PRBB. 08003 BARCELONA, Spain.
Phone: (+ 34) 933 160 760. (jordi.alonso@upf.edu)
ABSTRACT
Purpose
We assessed mental health trajectories of Spanish healthcare workers (HCWs) during three years following the first COVID-19 wave, focusing on distal and proximal risk factors.
Methods
A
A prospective cohort of HCWs from 18 Spanish institutions was assessed seven times between May 2020 and June 2023 through institutional web-based surveys. Outcomes included: (1) any probable mental disorder (MDx)—depression (PHQ-8 ≥ 10), anxiety (GAD-7 ≥ 10), panic attack, post-traumatic stress disorder (PCL-5 ≥ 7), or substance use disorder (CAGE-AID ≥ 2); and (2) any suicidal thoughts or behaviors (STB, CSSRS). Risk factors comprised 11 distal (sociodemographic, professional, health-related) and 13 proximal (COVID-19 infection, work, health, financial, and family stressors) variables. Latent Class Growth Analysis identified trajectories.
Results
A
4,809 were followed. Most were women (77.3%), hospital workers (57.6%), and nurses/nurse assistants (44.4%). Prevalence of any MDx declined from 45.5% (May–Sept 2020) to 31.2% (Apr–Jun 2023), while STB increased slightly from 8.4% to 9.4% (not significant). Common risk factors for MDx and STB were previous mental disorders, female gender, nurse assistant role, foreign-born status, primary care work, and physical comorbidities. Four trajectory classes were identified for both MDx and STB: persistent high (19.4%, 5.0%), decreasing from high prevalence (28.5%, 5.2%), increasing from low (5.9%, 4.2%), and persistent low (46.2%, 85.5%). Persistent high classes were associated with interpersonal stress, loved ones’ health, lack of preparedness/protective equipment, and ethically challenging care decisions.
Conclusions
Mental health problems remained substantial among Spanish HCWs during three years of follow-up, about one in five showing persistently high burden. Continued monitoring and support are warranted.
Keywords:
COVID-19
Healthcare Workers
Mental Health Impact
Suicidal Thoughts and Behaviors
Risk Factors
Trajectories
A
A
A
A
A
INTRODUCTION
Multiple studies and systematic reviews show that mental health problems among healthcare workers (HCWs) were highly prevalent during the first wave of the COVID-19 pandemic [1], [2], [3], [4], [5], [6]. Reported outcomes included major depression, generalized anxiety, posttraumatic stress disorders, insomnia, burnout and related symptoms. Prevalence estimates range from 37–42% for anxiety, 33–36% for depression [6], [7], 14–32% for PTSD[7], [8], and 37% for symptoms of burnout[7]. However, there is substantial variability in results and timing across studies.
A considerable number of studies assessed the evolution of mental health associated with the initial stages of the COVID-19 pandemic. The majority of longitudinal studies have used short follow-up periods, with only a few reaching 24 months [9], [10], [11], [12], [13], [14], [15], [16], [17], [18]. The four studies reporting on adverse mental health among HCWs during more than 24 months ended up with small sample sizes [19], [20], [21], [22]. Furthermore, longitudinal results are not always consistent. Some studies reported that distress and fear/worry of COVID-19 remained high or even increased in follow-up surveys[17], [23], [24], [25], [26], while others reported that depression and anxiety tended to decrease at follow-up [27], [28], [29], [30], [31], [32], [33]. In particular, among the four longitudinal studies with more than 2-years follow-up of HCWs, one showed a tendency to decreasing all adverse mental health outcomes [22], while the rest report selected decrease in some disorders such as anxiety and PTSD but not in others, such as psychological distress or depression[19], [20], [21]. Overall, there is a limited number of longer term follow-up studies with larger samples more representative of the HCWs.
Therefore, more robust evidence is needed of longer-term mental health impact of COVID-19 among HCWs, representative of the whole collective of HCWs, including higher number of participants, and focusing on relevant mental health outcomes. This study describes the course of adverse mental health outcomes of HCWs up to 3-years after the initiation of the COVID-19 pandemic, and their association with both distal and proximal stressors.
METHODS
Study design, population sampling and follow-up
A
We conducted a multicenter observational cohort study of HCWs across 18 healthcare institutions in 6 Spanish Autonomous Communities, including hospitals, primary care, and public health centers. Institutions were selected by convenience, to ensure geographic and healthcare setting diversity. All employed HCWs were invited via institutional email lists (census sampling) to complete a web-based survey (Qualtrics.com). Reminder emails were sent within a 2–4-week.
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Informed consent was obtained online.
HCWs completing the first assessment interview (T1, May 5th 2020-September 7th 2020, post-first wave peak) providing an email were invited to follow-up assessments: T2 (October- December 2020) and T3 (March-April 2021). Participants in T3, were subsequently invited to T4 (October-November 2021), and T5 (March-April 2022). Participated in the T5, were further invited to two final assessments: T6 (November 2022) and T7 (April-June 2023). Median follow-up times since the initial assessment were 119; 269, 479, 654, 862 and 1,049 days. More information is available elsewhere [27], [29], [34].
Measures
Probable current mental disorders (MDx)
Validated screening instruments for five MDx were used. Probable Major Depressive Disorder (MDD): we used the Spanish version of the Patient Health Questionnaire (PHQ-8), with a cut-off point > = 10 of the sum score to indicate current MDD. The PHQ-8 shows high reliability (> 0.8) and good diagnostic accuracy for MDD (AUC > 0.90)[35]. Nevertheless, the instrument can overestimate the prevalence of depressive disorders [36]. Probable Generalized Anxiety Disorder (GAD): we used the seven-item GAD scale (GAD-7), which has a good performance to detect anxiety (AUC > 0.8)[37], [38] with a cut-off point of 10 or more. Probable Panic attacks, we used the self-reported number of panic attacks in the 30 days prior to the interview, assessed with an item from the World Mental Health-International College Student (WMH-ICS) [39], [40]. A dichotomous variable indicated the presence/absence of panic attacks. Probable Posttraumatic Stress Disorder (PTSD): was assessed using the 4-item version of the PTSD Checklist for DSM-5 (PCL-5) [41], which generates diagnoses that closely parallel those of the full PCL-5 (AUC > 0.9), making it well-suited for screening [42]. We used the Spanish version (https://cptforptsd.com/cpt-resources/), and considered a cut-off point of 7 to indicate current PTSD [41]. Probable Substance Use Disorder (SUD) was assessed with the CAGE Adapted to Include Drugs questionnaire (CAGE-AID). It consists of 4 items is proven useful in helping to make a diagnosis of alcoholism and SUD [43]. The questionnaire was adapted into Spanish [44]. A cut-off point of 2 + was used to indicate current SUD [45].
Suicidal Thoughts and Behaviors (STB)
STB in the 30 days before the initial and follow-up assessments was assessed using adapted items from the Columbia Suicide Severity Rating Scale [46]. Dichotomous questions asked about passive suicidal ideation (SI), active SI, suicide plans and suicide attempts.
Associated factors
We used the conceptual framework of Boden [1] to study the mental health impact of COVID-19 considering: (i) current mental health conditions; (ii) proximal risk factors (pandemic-related stressful experiences), assessed at baseline and (iii) distal (pre-pandemic) risk factors, assessed retrospectively.
Distal (pre-pandemic) risk factors
Eleven distal (pre-pandemic) risk factors were assessed at baseline: age, gender, country of birth (Spain vs other), marital status (married/cohabiting vs single/divorced/separated/widowed), having children in care, profession (doctor, nurse, nurse assistant, other with or without patient care), workplace setting (hospital, primary care, or other), lifetime pre-pandemic mental disorders (via a checklist based on the CIDI;[47], and seven pre-pandemic physical conditions[48].
Proximal risk factors (pandemic-related stressful experiences)
Proximal stressors, assessed at baseline, were grouped into five domains. The first included eight work-related stressors: (1) weekly hours worked (≤ 40, 41–50, ≥ 51); (2) changes in work conditions (none, team/functions, location); (3) frequency of direct exposure to COVID-19 patients (0–4 scale); (4) perceived lack of preparedness (0–4); (5) perceived lack of protective equipment (0–4); (6) decisions on prioritizing care (doctors and nurses); and (7) having patients who died from COVID-19 (for HCWs involved in care). The second domain assessed health-related stressors (0–4 scale): (1) personal and (2) for loved ones. The third addressed financial stressors: (1) income loss and (2) financial stress (0–4). A fourth domain captured interpersonal stress (0–4). All stress scales ranged from 0 (none) to 4 (highest). Finally, COVID-19 infection-related experiences included: (1) personal infection status (hospitalization or non-hospitalized diagnosis) and (2) infection among loved ones. Only infection-related experiences were analyzed as time-varying exposures.
Analysis
Missing data and weighting
To address bias from loss to follow-up, inverse probability weighting (IPW) was applied using the inverse probability of completing each follow-up survey[49]. Weights were estimated from baseline covariates via logistic regression. Post-stratification weights (raking) adjusted for deviations from target distributions by age, gender, profession, and healthcare center. Minimal item-level missing data at each follow-up were handled using multiple imputation by chained equations[50] with 10 iterations.
Statistical analyses
Sample characteristics at baseline are reported as weighted percentages and standard errors (SE). In each of the 7 different assessments, the prevalence of any MDx and any STB was estimated for the whole sample and then stratified by proximal and distal factors, and differences tested with the modified Rao-Scott (R-S) χ² test.
Latent Classes Growth Analysis (LCGA) was conducted to identify different trajectories in the mental health outcomes assessed (MDx or STB). The model identified participants’ responses patterns to outcomes over the seven time points. Multiple LCGA models were built specifying different number of latent classes. Model fit was evaluated to select the optimal number of classes using the following criteria: low Akaike’s Information Criterion (AIC), Bayesian Information Criterion (BIC) parameters, and sample size adjusted BIC (ssa-BIC), high Shannon’s entropy and significant Lo-Mendell-Rubin-adjusted likelihood ratio test (LMRA-LRT).
After identifying the optimal class solution, individuals were assigned to their most probable class. Multinomial logistic regressions were then used to examine associations between proximal covariates and class membership, adjusting for distal factors. Analyses were conducted using SAS Studio[51]and Mplus v8.11[52].
RESULTS
Of the 8,996 healthcare workers participating in the initial evaluation (T1), 7,318 provided permission to be re-contacted. Of those, 4,809 responded to the first follow-up (T2), 4,484 to T3, 3,468 to T4, 2,960 to T5, 2,424 to T6, and 2,167 to T7. Response rates in the follow-ups ranged between 61.27% and 81.89% (Supplementary Fig. 1).
At baseline (Supplementary Table 1), most HCWs were female (77.3%), aged ≥ 30 (89.2%), and married (53%). The majority (59.1%) had no children in care. Nurses (30.8%) and physicians (26.2%) were most common professions. Over half worked in hospitals (57.6%) and 35.6% in primary care. Most had no pre-pandemic mental (59.5%) or physical (74.6%) condition. 63.2% worked ≤ 40 h/week; 45.2% reported no workplace change, 34.0% changed team/functions, and 20.8% changed location due to COVID-19. High exposure to COVID-19 patients was reported by 43.7%. Frequent lack of preparedness (34.1%) and/or protective equipment (38.0%) was common. 16.4% had to prioritize care, and 36.9% had patients who died from COVID-19. Financial stress was reported by 16.5%, and stress about loved ones by 26.2%. Most (82.7%) were not diagnosed with COVID-19, but 58.9% had infected close contacts. Further details in Supplementary Table 1.
At T1, the prevalence of any MDx was 45.8% (95% CI: 42.3–49.3), decreasing gradually over follow-ups: 42.6%, 41.4%, 33.5%, 32.9%, 31.1%, and 31.2%, respectively (Fig. 1.A; Supplementary Tables 4–8). Specific disorders followed a similar slow decline, with a moderate drop by October 2021 (3rd follow-up), except substance use, which remained stable and lower throughout.
Fig. 1
Prevalence of any probable mental disorders (A) and any STB (B) by distal factors, among healthcare workers participating in the MINDCOVID project.
Click here to Correct
Any MDx prevalence was consistently higher among women, younger participants, those with lower income, and especially nurse assistants. It was markedly higher among those with pre-pandemic mental or physical health conditions.
As shown in Table 1, working > 40 hours/week or reassignment to COVID-19 services was linked to higher MDx prevalence (e.g., 54.4% at baseline). Perceived lack of preparedness or protective equipment showed a dose-response effect (68.9% and 63.8% in highest categories). Those prioritizing COVID-19 patient care had a prevalence of 51.9%. Prevalence exceeded 55% among those reporting health, financial, or interpersonal stress.
Table 1
Prevalence of any probable mental disorder (MDx) according proximal factors among healthcare workers participating in the MINDCOVID project, along all the 3-year assessments.
 
TIME 1
TIME 2
TIME 3
TIME 4
TIME 5
TIME 6
TIME 7
 
n
%
SE
n
%
SE
n
%
SE
n
%
SE
n
%
SE
n
%
SE
n
%
SE
TOTAL
4059
45.8
1.8
1856
42.6
1.9
1688
41.4
1.2
995
33.5
1.3
865
32.9
1.4
654
31.1
1.7
615
31.2
1.6
≤ 40 h/week worked on average
2281
43
1.8
1070
41.2
2
998
39.3
1.6
580
31.8
1.9
535
32.9
2.1
399
30.7
2.1
367
30
2
41–50 h/week worked on average
1074
51.5
2.8
477
46
3.6
421
46.7
2.8
245
36.4
2.5
207
35.4
2.2
152
33
2.6
164
37.1
3.1
≥ 51 h/week worked on average
704
49.5
1.9
309
43.3
2
269
42.3
2.3
170
36.7
2.3
123
28.9
1.7
103
30.4
3.6
84
27.2
3
No changes at workplace
1518
39.7
1.8
726
38.4
2.1
673
38.1
1.8
415
31.4
2.1
365
31.1
2.1
296
30.6
2.4
275
31.5
1.9
Changed team or functions
1424
48.7
1.9
675
45.6
1.9
608
41.8
2.5
360
34.5
1.6
309
32.8
2.2
224
30.8
1.4
211
28.8
2.7
Changed to specific COVID-19 related work location
1117
54.4
2.7
455
46.8
3.6
407
47.5
1.7
220
36.3
2.5
191
36.6
2.9
134
32.6
2
129
34.4
2.2
Perceived frequency of lack of protective equipment
                     
Never
279
26.5
1.6
133
27.2
2.3
135
29.9
2.3
73
21.6
2.9
65
22.3
3.2
55
24.9
2.9
58
25.4
2.2
1
395
31.5
1.6
177
28.7
2.1
177
29
1.6
110
24.8
2.7
98
24.1
2.8
75
22.3
2.6
71
21.3
2.1
2
1405
43.7
1.7
666
40.8
2.3
604
38.3
1.5
363
30.2
1.7
311
30
1.5
231
28
2.8
227
28.5
2
3
1273
56.3
1.9
556
49.4
1.7
513
47.9
1.7
288
38.7
2.3
252
37.3
1.8
198
35.9
2.4
170
35.6
1.8
Always
707
63.8
1.6
324
61.5
4.4
259
60.8
2.5
161
51.4
4.5
139
53
2.6
95
46.5
4
89
48.4
3
Perceived lack of preparedness
                     
Never
167
22.9
2.9
74
26.7
4.5
80
26.5
2.8
35
15.5
3.4
31
18
4.5
19
15.1
4.7
21
18.1
4.2
1
574
32.1
2
268
30.2
2.3
260
30.7
1.5
147
23.7
2.4
132
22.6
1.7
101
22.1
1.9
102
21.3
3.1
2
1423
44.8
1.9
637
41.5
2.4
596
40.1
1.5
362
33.3
1.6
293
30.9
2.3
240
30.9
2.9
225
31.6
1.9
3
1325
58.4
2
595
51.7
1.9
531
50.3
1.5
315
39.1
2.5
297
43.7
2.6
208
36.3
2.7
190
36.7
2.6
Always
570
68.9
1.7
282
63.7
3.9
221
59
2
136
53.8
5
112
46.8
5
86
51.3
5.1
77
49.9
3.8
Not having to make decisions to prioritize care among COVID-19 patients
3223
44.6
1.8
1457
41.4
1.8
1346
41.1
1.2
811
33.7
1.5
703
33.2
1.5
520
30.9
1.8
498
31.6
1.6
Having to make decisions to prioritize care among COVID-19 patients
836
51.9
2.8
399
48.8
1.9
342
42.6
2
184
32.3
2.2
162
31.4
2.6
134
32.3
2.1
117
29.6
2.3
Not having patient(s) that died from COVID-19 infection
2285
43.1
1.7
1039
40.8
1.8
981
40.3
1.4
591
33
2.2
518
33
1.9
373
29.9
2.3
371
31.6
2.2
Having patient(s) that died from COVID-19 infection
1774
50.4
3.1
817
45.6
2.5
707
43.1
2.5
404
34.2
1.7
347
32.7
1.7
281
32.9
1.6
244
30.8
1.8
Frequency of direct exposure to COVID-19 patients
                     
Never
349
28.1
1.4
209
30.3
1.6
164
29.6
2.8
99
23.4
3
79
20.4
2.7
66
23.3
3.1
60
23.1
3.1
1
414
35
2.1
246
30.9
2
185
34.4
1.6
106
27.8
2.6
95
25.8
2.4
70
23.6
2.7
80
26.4
2.9
2
1086
44.9
1.9
499
42.3
1.8
456
38.3
0.9
291
32.9
1.1
256
32.7
1.7
177
28
1.8
173
28.3
2
3
924
50.8
1.8
407
48.8
2.6
397
44.8
1.9
223
34.1
2
213
37.8
2.4
161
36.9
2.3
148
36.8
2.1
Always
1286
58.3
2.5
490
58.6
2.4
486
52.5
1.6
276
42.1
2.1
222
39.1
2.1
180
38.1
2.1
154
36.5
3.1
Personal health-related stress
                     
None
73
13.9
1.9
38
14.1
2.3
42
18.9
3.2
22
12.5
2.8
18
14.3
3.3
11
10.8
2.8
15
10.4
2.5
1
589
24.5
1.4
296
22.5
2
328
26.5
1.8
205
22
2
195
22.5
1.8
148
20.9
2
152
22.6
1.6
2
1407
45.1
1.4
660
42.2
2.2
588
40
1.4
358
32.5
1.2
315
33.1
1.6
230
29.1
1.9
222
30.3
1.9
3
1351
63
1.7
580
57.9
1.9
504
54.9
1.5
285
45.3
2.4
229
40.4
2.5
182
42.1
3.8
151
38.5
3.7
Very intense
639
79
1.9
282
71.5
2.5
226
62.5
3.5
125
48.4
4.1
108
52.7
3.7
83
52.6
4.4
75
54.3
3.6
Health-related stress loved ones
                     
None
4
2.9
1.7
4
6.7
4.1
5
14
7.2
0
.
.
1
6.5
6.3
0
.
.
0
.
.
1
121
14.8
1.4
58
13.7
2.4
72
20.2
1.6
50
17.3
2.6
47
18
2.9
36
14
2.8
37
14.3
2.2
2
699
29.5
1
367
29.1
1.6
357
28.9
1.8
214
23.9
1.9
191
23.4
2
137
21.3
2.4
138
22.9
1.8
3
1314
47.4
1.3
581
45.2
2.4
536
42.6
1.2
331
34.7
1.5
263
31.4
1.8
203
31.3
2.2
192
31.6
2.1
Very intense
1921
68.4
1.9
846
60.5
2.1
718
57.6
1.4
400
45.8
2.5
363
47.7
2.7
278
46
2.4
248
44.8
2
No significant loss of personal or familial income due to COVID-19
3124
43.5
1.7
1467
40.7
1.9
1342
39.5
1.3
785
32.3
1.3
708
32.2
1.2
520
29.3
1.4
496
30.3
1.3
Significant loss of personal or familial income due to COVID-19
935
55.2
2.3
389
50.2
2.5
346
48.8
2.8
210
38.1
2.8
157
35.8
3.6
134
39
3.6
119
35.1
4
Financial stress
                     
None
891
32.1
1.9
456
28.9
1.6
418
28
1.6
253
21.6
1.5
243
23.5
1.7
164
19.8
1.8
181
23.5
1.7
1
1300
43.5
1.8
601
41.1
1.8
560
39.9
1.3
347
34
1.6
295
32.3
2.3
231
31.3
2.2
208
30.4
2.2
2
976
53.2
1.9
427
51.1
2.7
394
51.2
2.4
218
38.7
2.6
187
39.7
2.2
145
36.6
3.8
131
35.7
4.8
3
578
61.7
3
251
55.9
3.8
211
51.8
2.7
125
47.1
2.9
94
39.5
3.9
80
45
3.8
68
40.5
2.9
Very intense
314
72.7
2.8
121
64.9
3.3
105
62.5
3.6
52
48.2
6.7
46
53
7
34
53.7
3.7
27
50.8
7.1
Stress related to relations with others
                     
None
117
14.4
1.7
68
16.7
3.1
68
16.5
2
45
14.5
2.1
38
16.1
2
24
11.7
2.9
22
10.8
2.3
1
765
26.1
1.2
372
25.9
1.8
385
26.9
2
232
23.3
1.3
196
21.5
2
159
21.4
1.9
165
22.8
2.2
2
1536
50.6
1.9
696
46.2
2.4
618
44.2
1.6
360
32.6
2.2
335
37.3
1.6
253
34.2
1.8
226
34.2
2.3
3
1305
72.6
1.8
588
66.2
2.1
513
63.5
1.7
296
53.4
3.4
242
47.6
2.7
175
44.9
3.3
171
46.3
2.5
Very intense
336
81.1
2.5
132
67.6
4.3
104
62.8
4.9
62
50.7
7.4
54
45
5.7
43
58.4
7
31
39.4
6.9
No positive COVID-19 test/diagnosis
3263
45
1.6
1310
41
1.9
1196
40.7
1.4
670
31.6
1.2
443
32
1.5
148
29.7
2.4
115
30.7
2.9
Positive COVID-19 test/diagnosis
796
49.8
2.7
542
48
1.8
490
43.1
1.7
325
38.1
2.6
422
33.9
2.3
506
31.6
2
500
31.4
1.7
No close ones infected with COVID-19
1426
43
2.2
738
37.6
2.3
413
37.8
1.8
209
35.8
2
53
37.1
2.9
24
42.1
8.2
7
26.9
11
Close ones infected with COVID-19
2633
47.8
1.8
1114
47
1.4
1272
42.8
1.2
786
32.8
1.5
812
32.6
1.5
629
30.7
1.8
608
31.3
1.7
[Figure 1, and Table 1 about here]
Unlike MDx, the prevalence of any STB did not decline over time, rising slightly (but non-significantly) from 8.4% (95% CI: 7.6–9.2) to 9.4% (95% CI: 8.2–10.6) by April–June 2023 (Table 2). STB was associated with the same distal factors as any MDx: female gender, younger age, lower income, nurse assistant role, and pre-pandemic physical—especially mental—conditions (Fig. 1.B; Supplementary Table 2). STB was more frequent among those relocated to COVID-19 services (10.3%) and those consistently reporting lack of protective equipment or preparedness (> 14% across assessments, Table 2).
Table 2
Prevalence of Suicidal thoughts and behaviors (STB) among healthcare workers participating in the MINDCOVID project by proximal factors, along all the 3-year assessments.
 
TIME 1
TIME 2
TIME 3
TIME 4
TIME 5
TIME 6
TIME 7
 
 
n
%
SE
n
%
SE
n
%
SE
n
%
SE
n
%
SE
n
%
SE
n
%
SE
 
TOTAL
732
8.4
0.4
374
8.4
0.6
343
8.9
0.6
274
8.6
0.7
245
8.7
0.6
188
9.1
0.8
177
9.4
0.6
 
≤ 40 h/week worked on average
434
8.4
0.5
220
8.1
0.8
203
8.8
0.9
163
8.3
1
159
9.2
0.7
122
9.6
0.9
119
10.2
0.7
 
41–50 h/week worked on average
174
8.6
0.7
85
8.1
0.9
76
9
1.4
58
8.6
1.3
54
8.9
1.2
32
7.3
1.2
40
9.3
1.3
 
≥ 51 h/week worked on average
124
8
1.1
69
10.2
1.5
64
9.4
2
53
10.3
1.8
32
6
0.9
34
9.9
1.6
18
5.9
1.7
 
No changes at workplace
259
7.2
0.7
135
7.2
0.5
142
9.3
1.2
123
8.4
0.7
105
9.2
1
86
9.5
1
78
9.4
0.6
 
Changed team or functions
256
8.7
0.6
140
9.1
1
130
8.7
1.1
106
8.8
1.3
97
8.6
0.7
73
10
1
69
9.8
1.4
 
Changed to specific COVID-19 related work location
217
10.3
1.3
99
9.8
1.3
71
8.4
1
45
8.8
1.5
43
7.7
1.3
29
7.1
1.2
30
8.7
1.3
 
Perceived frequency of lack of protective equipment
                      
Never
50
5.1
0.7
23
4.6
1.7
30
7.1
2.6
25
6
1.9
23
6.4
2
17
9
3.2
11
5.8
1.4
 
1
59
4.6
0.6
30
5.2
1
34
5.8
1.4
29
8
1.6
34
8.1
1.4
17
6.8
2
21
7.2
1.4
 
2
244
7.8
0.6
131
8.2
0.7
128
8.4
0.8
99
7.2
1
85
7.4
0.7
71
9
0.9
68
9.1
0.9
 
3
224
9.7
0.4
110
9.1
1
89
9.1
1.4
66
8.5
1
62
9.6
1.2
49
8.5
1.3
49
9.9
1.5
 
Always
155
14.6
1.6
80
14.4
2.3
62
15.1
2.1
55
16
3.2
41
13.7
2.4
34
14
2.6
28
14.8
2.6
 
Perceived lack of preparedness
                      
Never
25
3.9
0.7
15
5.1
1.6
8
4.1
2
12
5.4
1.9
7
3.7
0.8
7
5.1
1.5
8
6.9
1.8
 
1
82
4.5
0.6
46
4.9
0.8
52
6.5
0.9
37
5.9
1.5
33
5.7
1.1
20
5.5
1.2
22
5.3
1.3
 
2
228
7.3
0.6
112
6.6
0.8
119
8.4
0.9
99
8.8
1
85
8.7
0.8
67
9.3
1.3
65
10.1
0.8
 
3
255
11.4
0.7
139
13.1
0.9
114
11.3
0.9
78
8.8
1.3
86
10.9
1.5
68
10.6
1.5
66
11.6
1.1
 
Always
142
17.6
1.9
62
12.9
1.9
50
14.2
2
48
16.4
3.5
34
13.8
2.6
26
16.8
3.3
16
12.1
2.4
 
Not having to make decisions to prioritize care among COVID-19 patients
572
8
0.5
299
8.4
0.6
288
9.5
0.6
232
9.1
0.8
202
8.9
0.7
148
9.2
1.1
149
9.9
0.7
 
Having to make decisions to prioritize care among COVID-19 patients
160
10.3
0.8
75
8.6
1.4
55
6.1
1.6
42
6
1.2
43
7.6
1.3
40
9
1.8
28
7
1.5
 
Not having patient(s) that died from COVID-19 infection
394
7.7
0.5
216
8.5
0.6
210
9.5
0.8
172
9.3
0.9
153
8.9
0.7
109
9.2
1.3
113
10
0.7
 
Having patient(s) that died from COVID-19 infection
338
9.5
0.6
158
8.2
0.9
133
7.9
1.1
102
7.6
1.4
92
8.3
1.1
79
9
1.2
64
8.5
0.8
 
Frequency of direct exposure to COVID-19 patients
                      
Never
63
6.1
1
41
5.9
0.9
38
9.4
2.4
34
8.2
1.2
27
6.8
1.3
16
5.9
1.9
16
6.4
1.4
 
1
57
4.6
0.7
58
6.1
0.9
41
7.9
1.4
25
6.4
1.6
27
7.2
1.4
14
5.7
1.9
14
5.6
1.5
 
2
209
8.2
0.5
91
8
0.9
97
8
0.8
77
7.4
1.1
74
8.6
0.9
60
8.9
0.9
58
10
1
 
3
168
9.3
0.7
80
9.1
1.2
74
8.8
1.1
65
9.4
1.2
59
10
1.6
50
12.1
1.3
45
10.9
1.4
 
Always
235
11.2
0.7
103
12.8
1.8
93
10.5
1
73
11.1
1.5
58
9.4
1.3
48
10.3
1.6
44
10.8
1.8
 
Personal health-related stress
                      
None
25
5.7
1.2
13
4.4
1.2
12
4.4
1
8
3.8
1.1
11
6.4
1.4
8
8.9
3.3
9
7
2.1
 
1
132
5.7
0.6
77
6.3
0.9
75
6.4
1
69
6.4
0.9
71
8
1.1
50
7.5
1.9
43
6.5
1
 
2
245
7.7
0.5
121
7.4
0.6
120
8
0.8
94
8.8
1.3
89
8.9
0.9
63
8.3
0.7
68
10.7
1.8
 
3
223
10.8
0.6
95
9.2
1.1
88
11.7
1.4
66
10.1
1.6
49
8.4
1.6
44
10.4
2.2
39
9.9
2.4
 
Very intense
107
13.4
1.2
68
17.2
2.7
48
14.1
2.7
37
12.4
3.5
25
11.6
3.5
23
13.9
3.9
18
12.3
3.6
 
Health-related stress loved ones
                      
None
1
0.6
0.6
1
0.9
0.9
2
3.6
2.9
0
.
.
1
1.3
1.5
1
1.5
1.7
1
1.5
1.6
 
1
24
3.1
0.8
20
4.2
1
18
3.9
1
14
4
1.1
18
6
1.4
10
6.5
3.4
10
4.6
1.9
 
2
130
5.7
0.4
73
6.6
0.7
71
5.7
0.7
62
6
0.9
62
7.9
1.1
40
6.3
0.8
39
6.5
0.8
 
3
247
8.7
0.6
99
6.8
1.1
118
9.7
0.9
93
9.7
1.3
76
8
1.1
55
8.4
1
65
12
1.2
 
Very intense
330
12.1
0.9
181
12.8
1.3
134
12.5
1.6
105
11.3
1.5
88
11
1.3
82
13.4
1.7
62
11.1
1.8
 
No significant loss of personal or familial income due to COVID-19
568
7.9
0.4
282
7.5
0.5
268
8.1
0.4
210
7.7
0.7
191
8
0.6
149
8.8
1.1
140
8.7
0.6
 
Significant loss of personal or familial income due to COVID-19
164
10.2
1
92
11.8
1.7
75
12
2.2
64
12.4
2
54
11.5
1.8
39
10.5
2.3
37
12.1
1.8
 
Financial stress
                      
None
144
4.5
0.5
67
3.6
0.5
82
5.6
0.6
61
4.6
0.9
63
5.3
0.7
41
5.1
1.1
39
5.4
0.6
 
1
216
7.5
0.6
120
8
1
108
7.7
0.7
101
8.3
1.1
88
9.2
1.1
72
10.4
1.9
70
10.8
0.8
 
2
189
10.5
0.8
95
10.9
1.1
79
9.5
1.2
67
11.6
1.5
56
10.3
1.7
43
10.2
1.8
37
9.9
2.2
 
3
112
12.8
1.7
56
12.1
0.8
47
14.8
2.6
28
9.5
1.9
28
12.2
2.4
20
10.3
2
24
14.4
2
 
Very intense
71
17.2
1.9
36
20.6
3.8
27
20.5
5.7
17
19.1
4
10
12.2
4.2
12
21
5.5
7
13.4
5
 
Stress related to relations with others
                      
None
12
0.8
0.3
8
1.4
0.6
11
2.3
0.7
9
2.5
0.9
6
1.6
0.7
3
2
1.2
5
1.6
0.7
 
1
101
3.5
0.4
59
3.8
0.5
56
4.1
0.6
64
6
0.6
54
6.2
0.8
35
5
0.7
40
6
1.3
 
2
258
8.8
0.6
143
9.3
0.8
139
10.3
0.7
112
9.4
1.3
110
11.3
1.1
79
10.1
1.3
75
11.7
1.3
 
3
276
15.7
1
126
14.4
1.3
110
15
1.8
70
13.1
1.6
58
9.8
1
56
15.2
2.3
50
14.2
1.4
 
Very intense
85
18.6
1.8
38
18.6
2.4
27
15.9
3.7
19
11.8
3.9
17
17
3.5
15
19.7
5.7
7
8
2.8
 
No positive COVID-19 test/diagnosis
569
7.9
0.4
269
8.2
0.7
250
8.7
0.8
186
8.3
0.7
127
8.5
0.6
43
9.9
1.6
42
11.8
1.8
 
Positive COVID-19 test/diagnosis
163
10.7
1.3
105
9.3
0.9
93
9.4
1.3
88
9.5
1.3
118
8.8
0.8
145
8.9
0.9
135
8.8
0.6
 
No close ones infected with
COVID-19
266
7.9
0.4
142
6.8
1
75
7.8
0.9
48
7
0.8
11
8.9
3
12
20.4
9.4
5
23.1
11
 
Close ones infected with COVID-19
466
8.7
0.5
230
9.6
0.7
265
9.3
0.9
226
9.1
0.8
234
8.7
0.6
175
8.6
0.8
172
9.1
0.6
 
Table 3. Likelihood of belonging to other trajectories (classes*) other than persistent none probable mental disorder (MDx) and than none STB, according to baseline proximal factors, adjusted by baseline distal factors
    
Any probable mental disorder (MDx)
Any STB
 
n
%
SE
Class i vs iv
OR (95% CI)
Class ii vs iv
OR (95% CI)
Class iii vs iv
OR (95% CI)
Class i vs iv
OR (95% CI)
Class ii vs iv
OR (95% CI)
Class iii vs iv
OR (95% CI)
≤ 40 h/week worked on average (reference)
1,144
63
1.4
1.00
1.00
1.00
1.00
1.00
1.00
41–50 h/week worked on average
429
23
1.2
1.43
(1.04–1.98)*
1.09
(0.81–1.46)
1.44
(0.89–2.33)
1.47
(0.86–2.52)
0.98
(0.56–1.71)
0.44
(0.22–0.88)*
≥ 51 h/week worked on average
292
14
0.9
1.10
(0.73–1.67)
1.29
(0.91–1.81)
0.97
(0.51–1.84)
1.85
(0.98–3.46)
1.39
(0.75–2.57)
0.53
(0.22–1.29)
No changes at work (reference)
866
46
1.4
1.00
1.00
1.00
1.00
1.00
1.00
Changed of team or assigned functions
629
33
1.3
1.16
(0.84–1.59)
1.41
(1.07–1.85)*
1.03
(0.64–1.68)
1.03
(0.63–1.68)
1.11
(0.69–1.78)
1.10
(0.63–1.91)
Changed to specific COVID-19 related work location
370
22
1.2
1.35
(0.93–1.94)
1.22
(0.88–1.69)
1.07
(0.60–1.88)
0.80
(0.40–1.61)
0.58
(0.28–1.20)
1.00
(0.54–1.88)
Perceived lack of preparedness (0 never − 4 always)
   
1.97
(1.71–2.27)*
1.59
(1.41–1.80)*
1.38
(1.12–1.70)*
1.53
(1.23–1.91)*
1.46
(1.18–1.81)*
1.08
(0.85–1.36)
Perceived frequency of lack of protective equipment (0 never − 4 always)
   
1.55
(1.36–1.76)*
1.45
(1.30–1.62)*
1.27
(1.05–1.54)*
1.23
(1.00-1.51)*
1.31
(1.07–1.60)*
1.25
(1.00-1.56)
Having to make decisions regarding prioritizing care among COVID-19 patients (Ref: not having to)
356
17
1.0
1.77
(1.20–2.61)*
1.38
(0.99–1.93)
0.69
(0.36–1.33)
1.72
(0.88–3.34)
0.74
(0.37–1.48)
0.67
(0.30–1.49)
Having patient(s) in care that died from COVID-19 infection (Ref: not having them)
758
41
1.4
1.42
(1.04–1.94)*
1.07
(0.82–1.40)
0.94
(0.59–1.51)
1.25
(0.73–2.14)
0.86
(0.51–1.47)
1.13
(0.66–1.92)
Personal health-related stress (0 none − 4 very intense)
   
2.27
(1.96–2.63)*
1.79
(1.58–2.03)*
1.36
(1.10–1.69)*
1.33
(1.07–1.65)*
1.75
(1.40–2.20)*
0.90
(0.71–1.14)
Health-related stress loved ones (0 none − 4 very intense)
   
2.52
(2.14–2.96)*
1.98
(1.73–2.26)*
1.37
(1.10–1.69)*
1.66
(1.29–2.14)*
1.69
(1.31–2.19)*
1.01
(0.80–1.29)
Significant loss of personal or familial income due to COVID-19 – Yes (Ref: No)
316
20
1.2
1.43
(1.03–1.99)*
1.36
(1.01–1.82)*
2.09
(1.32–3.34)*
1.85
(1.11–3.09)*
2.07
(1.28–3.34)*
1.67
(0.98–2.83)
Stress related to financial issues (0 none − 4 very intense)
   
1.43
(1.26–1.61)*
1.39
(1.25–1.55)*
1.52
(1.27–1.81)*
1.52
(1.25–1.83)*
1.53
(1.27–1.83)*
1.12
(0.92–1.37)
Interpersonal - Stress related to relations with others (0 none − 4 very intense)
   
2.56
(2.19–2.99)*
2.12
(1.85–2.42)*
1.36
(1.09–1.69)*
1.94
(1.53–2.45)*
2.09
(1.65–2.65)*
1.29
(1.02–1.64)*
Positive COVID-19 test or medical diagnosis (Ref: no positive test or diagnosis)
360
19
1.1
1.22
(0.88–1.70)
1.17
(0.87–1.57)
0.95
(0.55–1.63)
1.23
(0.73–2.08)
1.01
(0.59–1.72)
0.71
(0.37–1.36)
Close ones infected with COVID-19 (Ref: no close ones infected)
1,247
61
1.4
1.55
(1.17–2.05)*
1.20
(0.94–1.52)
1.57
(1.01–2.41)*
1.30
(0.82–2.05)
1.00
(0.64–1.54)
1.55
(0.94–2.56)
*Class i: high persistent MDx or STB; Class ii: decreasing MDx or STB; Class iii: increasing MDx or STB ; and Class iv: low persistent MDx or STB.
[Table 2 about here]
Latent class growth analyses were conducted for any MDx and any STB, identifying distinct prevalence trajectories over the 3-year follow-up. For both outcomes, the best-fitting model was the 4-class solution (Supplementary Table 9). The final models are shown in Fig. 2. For any MDx (Fig. 2.A), the classes were: high persistent prevalence (19.4%); decreasing (28.5%); increasing (5.2%); and low persistent (46.2%). For STB (Fig. 2.B), the classes were similar, although with a different distribution: high persistent (5.0%); decreasing (5.2%); increasing (4.2%); and low or very low persistent prevalence (85.5%).
Fig. 2
Estimated probabilities of any probable mental disorder (A) and any STB (B) for each latent class through time.
Click here to Correct
[Figure 2 about here]
Table 3 shows the likelihood of belonging to each latent class (i–iii) versus the low persistent prevalence class (iv), by proximal risk factors—first for any MDx, then for STB. Estimates are ORs adjusted for distal (but not proximal) factors. All but four proximal factors were associated with MDx class i (high persistent), with strongest associations for interpersonal stress (OR = 2.56; 95% CI: 2.19–2.99), health-related stress for loved ones (OR = 2.52; 95% CI: 2.14–2.96) and self (OR = 2.27; 95% CI: 1.96–2.63), perceived lack of protective equipment (OR = 1.97; 95% CI: 1.71–2.27), and prioritizing care decisions (OR = 1.77; 95% CI: 1.20–2.61). Testing positive or having a COVID-19 diagnosis was not associated with any class. Similar associations were seen for any MDx classes ii and iii. For STB (Table 3, second half), associations were generally weaker but significant. Income loss was strongly linked to the increasing-STB trajectory. On the other hand, HCWs working 41–50h/week were less likely (OR = 0.44; 95% CI: 0.22–0.88) to belong to the increasing-STB class than to the low persistent (class iv).
[Table 3 about here]
DISCUSSION
This 3-year longitudinal study of Spanish HCWs yields five key findings. First, prevalence of any MDx was initially very high and decreased moderately by year two; by year three, one in five HCWs had probable depression–well above general population rates [53], [54]. Second, STB prevalence remained high (8–9%) throughout, double the 4.5% reported in June 2020 [55]. Third, probable MDx and STB were consistently linked to pandemic-related stressors, including poor working conditions, financial strain, and health- or relationship-related stress. Perceived lack of coordination, inadequate protection, and long hours were repeatedly associated with poorer mental health, underscoring the need for improved crisis management. Fourth, distal factors—especially pre-existing mental and physical conditions—predicted higher long-term prevalence. Finally, trajectory analysis revealed subgroups with persistent or delayed-onset adverse mental health: (25.3% for MDx and 9.2% for STB). Overall, a significant portion of Spanish HCWs experienced sustained psychological distress, likely affecting the healthcare system’s human capital.
Comparison with previous studies
Our results are consistent with published meta-analyses on anxiety, depression and stress disorders among healthcare workers internationally [6], [7], although most of those studies were based on cross-sectional studies. Among longitudinal studies of HCWs, similar differences in prevalence of mental disorders according to gender, age, profession, and pre-existing medical conditions and time trends had been reported [18]. But studies included small sample sizes of healthcare workers and/or focused on specific HCWs professionals [19], [56].
In our study, suicidality among Spanish HCWs during the COVID-19 pandemic remained consistently high through the 3 years of follow-up. This is consistent with previous cross-sectional studies showing an overall growing trend of prevalence of suicidal ideation during the pandemic in HCWs and medical students [57], [58], [59], [60]. And it adds to the higher risk of suicidality in this population.
The number of pre-pandemic mental disorders was positively associated with any probable MDx and STB, showing a clear dose-response. A similar pattern, though weaker, was found for pre-pandemic physical conditions. These findings suggest that prior health - especially mental - makes HCWs more vulnerable during intense stress, such as the COVID-19 pandemic, consistent with evidence on differential impacts in vulnerable populations [61]. Importantly, the pandemic's impact varied by professional role: compared to doctors, other HCWs, particularly nurse assistants, showed consistently higher rates of MDx and STB over the 3-year period, identifying them as a highly vulnerable subgroup.
A
Among proximal stressor factors, work-related conditions showed strong, consistent association with adverse mental health outcomes. The robust association of perceived lack of protective equipment or preparedness with probable MDx and STB, supports the ILO's 2022 resolution recognizing a safe and healthy work environment as a shared responsibility [62]. Decisions to prioritize patient care and witnessing patient deaths were also strongly associated with MDx, especially early in the pandemic—likely reflecting moral injury[63] and intense frontline exposure. Working in COVID-specific settings was linked to the highest MDx prevalence at baseline, aligning with evidence of increased stress, burnout, and poor sleep among frontline HCWs[64], [65]. These stressors may lead to compassion and decision fatigue with lasting effects on wellbeing [66]. Financial and personal stress were also associated with MDx and STB, underscoring the need to address non-work factors. This supports a holistic view of HCWs’ wellbeing, integrating occupational and external domains [67].
Working > 40 hours/week at baseline was linked to higher odds of persistent mental disorder. HCWs working 41–50 hours had 43% greater odds of belonging to the high persistent MDx class, consistent with prior findings on long hours and poor mental health [65]. Lack of preparedness and frequent perception of insufficient protective equipment were associated with all MDx latent classes (vs persistent low prevalence). Specifically, those perceiving low preparedness at baseline were 97% and 38% more likely to fall into the high persistent or increasing MDx classes, respectively. These results highlight the need for healthcare systems to recognize worker vulnerability and implement long-term mental health care and prevention strategies [68].
Strengths and limitations
This study has several strengths.
A
First, it involved 18 healthcare institutions across six Spanish Autonomous Communities in Spain covering hospitals, primary care, emergency, and public health settings. Although selected by convenience, all registered professionals were invited, and sampling weights were applied to improve representativeness. Second, we followed a large HCW sample for 3 years—an unprecedented duration in this research—providing valuable insight into the long-term mental health impact of the pandemic [69]. Third, the analytic approach enabled identification of latent mental health trajectories, enhancing the study robustness and relevance.
A
Limitations include a low initial response rate (12%). Nevertheless, follow-up retention was higher (61–82%), and weighting adjusted for demographic and professional distributions. Second, mental disorders were assessed via self-reported screening tools. While validated (e.g., PHQ-8; [70]), they likely overestimate prevalence, as seen in HCW samples[71], capturing psychological distress rather than clinical diagnoses. However, using consistent tools across waves supports comparability. Finally, the observational design limits causal inference, despite adjusting for many variables. Also, we only considered COVID-19 infection/disease status and vaccination as time-varying variables.
Conclusions
Adverse mental health outcomes among Spanish HCWs remained high over the 3-year follow-up. While some symptoms declined after year one, a significant subgroup showed persistent symptoms through October 2023, and substance use disorders remained unchanged. Vulnerable subgroups included younger, female HCWs, those with prior mental or physical conditions, and especially nurse assistants and other non-doctor roles. Personal, occupational, and financial COVID-related stressors were strongly linked to probable mental disorders and suicidal thoughts and behaviors. This study advances understanding of factors associated with persistently high MDx and STB. Timely interventions might have mitigated these effects. Given HCWs’ essential role, routine mental health monitoring is crucial to identify and support those in need.
A
Acknowledgement
The authors would like to sincerely thank all healthcare workers participating in the study. They also thank Puri Barbas for the management of the project and helping with the manuscript preparation.
Conflict of interest.
E. A. reports personal fees from Lündbeck and Boehringer Ingelheim. E. V. reports personal fees from Abbott, Allergan, Angelini, Lundbeck, Sage and Sanofi, grants from Novartis and Ferrer and grants and personal fees from Janssen, Otsuka, Rovi and Viatris, outside the submitted work. J. D. M. reports personal fees from Janssen and Angelini, personal fees and non-financial support from Otsuka, Lundbeck and Accord, outside the submitted work. J. M. P. T. reports personal fees from Angelini, Janssen and Lunbeck, and grants from Janssen, outside the submitted work. R. C. K. was a consultant for Datastat, Inc., Holmusk, RallyPoint Networks, Inc., Sage Therapeutics and he has stock options in Mirah, P. Y. M., and Roga Sciences. All other authors reported no conflict of interest. N.L. reports personal fees from GSK.
Ethical standards.
A
A
A
The study complies with the Declaration of Helsinki and the Code of Ethics and was approved by the IRB Parc de Salut Mar (2020/9203/I) and by the corresponding IRBs of all the participating centers. The study protocol is registered at ClinicalTrials.gov (https://clinicaltrials.gov/ct2/show/NCT04556565), where more ethical information is available.
A
Data Availability
The completely anonymized individual participant data as along with data dictionaries and the statistical code (SAS, MPlus) used in this study, will be available upon publication and upon reasonable request to the equally contributing first authors (Jordi Alonso: jordi.alonso@upf.edu; Gemma Vilagut: [gvilagut@researchmar.net](mailto:gvilagut@researchmar.net) ). Data will be shared for the sole purpose of replicating the analysis and findings as reported in this paper, without investigator support, following approval of a proposal and the signing of a data access and use agreement.
Electronic Supplementary Material
Below is the link to the electronic supplementary material
A
A
Author Contribution
-J. Alonso, G. Vilagut. and P. Mortier (Conceptualization, Data curation, Funding acquisition, Methodology, Writing- original draft).-J. Alonso., G. Vilagut., P. Mortier., M. Ferrer., E. Aragonès., V. Pérez-Solà, J. M. Haro, R. C.Kessler. and R. Bruffaerts (Investigation, Methodology, Writing- review and editing).-E. Aragonès., J. D. Molina., N. López, T. Puig, J. M. Pelayo-Terán, MJ. Fojaz, N. PLana, A. González-Pinto, C. Rius, I. Cura-González, A. Aragón-Peña, M. Campos, A. Pérez-Zapata., E. Vieta, C. Serra, I. Urreta-Barallobre, A.D. Martínez de Salazar, R. Ortí, M. Parellada, C.Rodríguez-Blázquez, F. Sanz and V. Pérez-Solà. (Writing- review and editing, Investigation)-G. Vilagut., F. Amigo., M. Ruiz-Rivera, and P. Mortier (Formal analysis).
Declarations
A
Competing Interests
Declaration conflict of interest. E. A. reports personal fees from Lündbeck and Boehringer Ingelheim. E. V. reports personal fees from Abbott, Allergan, Angelini, Lundbeck, Sage and Sanofi, grants from Novartis and Ferrer and grants and personal fees from Janssen, Otsuka, Rovi and Viatris, outside the submitted work. J. D. M. reports personal fees from Janssen and Angelini, personal fees and non-financial support from Otsuka, Lundbeck and Accord, outside the submitted work. J. M. P. T. reports personal fees from Angelini, Janssen and Lunbeck, and grants from Janssen, outside the submitted work. R. C. K. was a consultant for Datastat, Inc., Holmusk, RallyPoint Networks, Inc., Sage Therapeutics and he has stock options in Mirah, P. Y. M., and Roga Sciences. All other authors reported no conflict of interest. N.L. reports personal fees from GSK.
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Yes
Total words in MS: 7675
Total words in Title: 9
Total words in Abstract: 247
Total Keyword count: 6
Total Images in MS: 2
Total Tables in MS: 2
Total Reference count: 71