European Journal of Psychotraumatology

ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/zept20

A longitudinal study of risk and protective factors
for symptoms of adjustment disorder during the
COVID-19 pandemic
Annett Lotzin, Katharina Stahlmann, Elena Acquarini, Dean Ajdukovic,
Marina Ajdukovic, Xenia Anastassiou-Hadjicharalambous, Vittoria Ardino,
Kristina Bondjers, Maria Bragesjö, Maria Böttche, Małgorzata Dragan,
Margarida Figueiredo-Braga, Odeta Gelezelyte, Piotr Grajewski, Jana Darejan
Javakhishvili, Evaldas Kazlauskas, Lonneke Lenferink, Chrysanthi Lioupi,
Brigitte Lueger-Schuster, Trudy Mooren, Luisa Sales, Lela Tsiskarishvili, Irina
Zrnic Novakovic, Ingo Schäfer & ADJUST Study Consortium
To cite this article: Annett Lotzin, Katharina Stahlmann, Elena Acquarini, Dean Ajdukovic,
Marina Ajdukovic, Xenia Anastassiou-Hadjicharalambous, Vittoria Ardino, Kristina Bondjers,
Maria Bragesjö, Maria Böttche, Małgorzata Dragan, Margarida Figueiredo-Braga, Odeta
Gelezelyte, Piotr Grajewski, Jana Darejan Javakhishvili, Evaldas Kazlauskas, Lonneke
Lenferink, Chrysanthi Lioupi, Brigitte Lueger-Schuster, Trudy Mooren, Luisa Sales, Lela
Tsiskarishvili, Irina Zrnic Novakovic, Ingo Schäfer & ADJUST Study Consortium (2024) A
longitudinal study of risk and protective factors for symptoms of adjustment disorder during
the COVID-19 pandemic, European Journal of Psychotraumatology, 15:1, 2318944, DOI:
10.1080/20008066.2024.2318944
To link to this article: https://doi.org/10.1080/20008066.2024.2318944© 2024 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group

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EUROPEAN JOURNAL OF PSYCHOTRAUMATOLOGY
2024, VOL. 15, NO. 1, 2318944
https://doi.org/10.1080/20008066.2024.2318944

BASIC RESEARCH ARTICLE

A longitudinal study of risk and protective factors for symptoms of adjustment
disorder during the COVID-19 pandemic
Annett Lotzin a,b, Katharina Stahlmannc, Elena Acquarinid, Dean Ajdukovice, Marina Ajdukovicf,
Xenia Anastassiou-Hadjicharalambousg, Vittoria Ardinod, Kristina Bondjersh,i, Maria Bragesjö j,Maria Böttche k, Małgorzata Draganl, Margarida Figueiredo-Bragam,n, Odeta Gelezelyteo, Piotr Grajewskil,
Jana Darejan Javakhishvilip, Evaldas Kazlauskaso, Lonneke Lenferinkq, Chrysanthi Lioupig,
Brigitte Lueger-Schusterr, Trudy Moorens, Luisa Salesn,t, Lela Tsiskarishviliu, Irina Zrnic Novakovicr,
Ingo Schäfera and ADJUST Study Consortium*a

Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; bDepartment of
Psychology, MSH Medical School Hamburg, Hamburg, Germany; cInstitute of Medical Biometry and Epidemiology, University Medical
Center Hamburg-Eppendorf, Hamburg, Germany; dDISCUI, University of Urbino, Urbino, Italy; eDepartment of Psychology, Faculty of
Humanities and Social Sciences, University of Zagreb, Zagreb, Croatia; fDepartment of Social Work, Faculty of Law, University of Zagreb,
Zagreb, Croatia; gPsychology Program, School of Ηumanities, Social Sciences and Law, University of Nicosia, Nicosia, Cyprus; hNational
Centre for Disaster Psychiatry, Department of Medical Sciences, Uppsala University, Uppsala, Sweden; iNational Centre for Violence andTraumatic Stress Studies, Oslo, Norway; jDepartment of Clinical Neuroscience, Centre for Psychiatric Research, Karolinska Institutet,
Stockholm, Sweden; kDivision of Clinical Psychological Intervention, Freie Universität Berlin, Berlin, Germany; lFaculty of Psychology,
University of Warsaw, Warsaw, Poland; mDepartment of Clinical Neurosciences and Mental Health, Faculty of Medicine, University ofPorto, Porto, Portugal; nTrauma Observatory, Centre for Social Studies (CES), University of Coimbra, Coimbra, Portugal; oCenter for
Psychotraumatology, Institute of Psychology, Vilnius University, Vilnius, Lithuania; pFaculty of Arts and Science, Institute of Addiction
Studies, Ilia State University, Tbilisi, Georgia; qDepartment of Psychology, Health, & Technology, Faculty of Behavioural, Management, andSocial Sciences, University of Twente, Enschede, the Netherlands; rUnit of Psychotraumatology, Department of Clinical and Health
Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria; sDepartment of Clinical Psychology, Utrecht University, Utrecht,
The Netherlands; tUnit of Psychiatry, Hospital Militar, Coimbra, Portugal; uFaculty of Arts and Science, Ilia State University, Tbilisi, Georgia

CONTACT Annett Lotzin
A.lotzin@uke.de
Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Martinistr.
52, Hamburg 20246, Germany*ADJUST Consortium:
Annett Lotzin, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department of
Psychology, MSH Medical School Hamburg
Elena Acquarini, DISCUI, University of Urbino, Italy
Dean Ajdukovic, Department of Psychology, Faculty of Humanities and Social Sciences, University of Zagreb, Croatia
Xenia Anastassiou-Hadjicharalambous, Psychology Program, School of Ηumanities, Social Sciences and Law, University of Nicosia
Vittoria Ardino, DISCUI, University of Urbino, Italy
Filip Arnberg, National Centre for Disaster Psychiatry, Department of Neuroscience, Uppsala University, Uppsala, Sweden
Maria Bragesjö, Department of Clinical Neuroscience, Centre for psychiatric research, Karolinska Institutet, Stockholm, Sweden
Małgorzata Dragan, Faculty of Psychology, University of Warsaw
Piotr Grajewski, Faculty of Psychology, University of Warsaw
Margarida Figueiredo-Braga, Department of Clinical Neurosciences and Mental Health, Faculty of Medicine, University of Porto, Portugal; Trauma Observatory, Centre for Social Studies (CES), University of Coimbra, Portugal, & Unit of Psychiatry, Hospital Militar, Coimbra, Portugal
Luisa Sales, Trauma Observatory, Centre for Social Studies (CES), University of Coimbra, Portugal & Unit of Psychiatry, Hospital Militar, Coimbra, Portugal;
Unit of Psychiatry, Hospital Militar, Coimbra, Portugal
Odeta Gelezelyte, Center for Psychotraumatology, Institute of Psychology, Vilnius University, Lithuania
Jana Darejan Javakhishvili, Institute of Addiction Studies, Ilia State University, Tbilisi, Georgia
Evaldas Kazlauskas, Center for Psychotraumatology, Institute of Psychology, Vilnius University, Lithuania
Lonneke I.M. Lenferink, Department of Psychology, Health, & Technology, Faculty of Behavioural, Management, and Social Sciences, University of Twente,
The Netherlands
Chrysanthi Lioupi, Psychology Program, School of Ηumanities, Social Sciences and Law, University of Nicosia
Brigitte Lueger-Schuster, Unit of Psychotraumatology, Faculty of Psychology, University of Vienna, Austria
Nino Makhashvili, Institute of Addiction Studies, Ilia State University, Tbilisi, Georgia
Trudy Mooren, Department of Clinical Psychology, Utrecht University, The Netherlands
Ingo Schäfer, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
Aleksandra Stevanovic, Department of Psychiatry and Psychological Medicine, Faculty of Medicine, University of Rijeka, Croatia
Irina Zrnic Novakovic, Unit of Psychotraumatology, Faculty of Psychology, University of Vienna, Austria© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which
permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been
published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

2

A. LOTZIN ET AL.

ABSTRACTARTICLE HISTORY

Background: The COVID-19 pandemic caused multiple stressors that may lead to symptoms ofadjustment disorder.
Objective: We longitudinally examined relationships between risk and protective factors,
pandemic-related stressors and symptoms of adjustment disorder during the COVID-19
pandemic, as well as whether these relationships diﬀered by the time of assessment.
Method: The European Society for Traumatic Stress Studies (ESTSS) ADJUST Study included
N = 15,169 participants aged 18 years and above. Participants from 11 European countries
were recruited and screened three times at 6-month intervals from June 2020 to January
2022. Associations between risk and protective factors (e.g. gender), stressors (e.g. fear of
infection), and symptoms of adjustment disorder (AjD, ADNM-8) and their interaction withtime of assessment were examined using mixed linear regression.
Results: The following predictors were signiﬁcantly associated with higher AjD symptom
levels: female or diverse gender; older age; pandemic-related news consumption >30 min aday; a current or previous mental health disorder; trauma exposure before or during the
pandemic; a good, satisfactory or poor health status (vs. very good); burden related togovernmental crisis management and communication; fear of infection; restricted social
contact; work-related problems; restricted activity; and diﬃcult housing conditions. The
following predictors were associated with lower AjD levels: self-employment or retirement;
working in healthcare; and face-to-face contact ≥ once a week with loved ones or friends.
The eﬀects of the following predictors on AjD symptoms diﬀered by the time of assessmentin the course of the pandemic: a current or previous mental disorder; burden related togovernmental crisis management; income reduction; and a current trauma exposure.
Conclusions: We identiﬁed risk factors and stressors predicting AjD symptom levels at diﬀerent
stages of the pandemic. For some predictors, the eﬀects on mental health may change at
diﬀerent stages of a pandemic.

Received 12 September 2023
Revised 22 December 2023
Accepted 28 December 2023

Estudio longitudinal sobre factores protectores y de riesgo para lossíntomas del trastorno de adaptación durante la pandemia por
COVID-19
Antecedentes: La pandemia por COVID-19 fue causa de múltiples factores estresantes que
puedan haber condicionado un trastorno de adaptación.
Objetivo: Se evaluó de forma longitudinal la relación entre factores protectores y factores deriesgo, factores estresantes asociados a la pandemia y síntomas del trastorno de adaptación
durante la pandemia por COVID-19. Además, se evaluó si estas relaciones variaban según latemporalidad de la evaluación.
Métodos: El estudio ADJUST de la Sociedad Europea para el Estudio de Estrés Traumático
incluyó a N = 15.169 participantes de 18 años o más. Se reclutó a participantes de 11 países
europeos y fueron evaluados tres veces en intervalos de 6 meses desde junio del 2020 hasta
enero del 2022. Las asociaciones entre los factores protectores (e. g. género) y de riesgo, losfactores estresantes (e. g. miedo a contagiarse), los síntomas del trastorno de adaptación(AjD por sus siglas en inglés; empleando el Cuestionario para el Trastorno de Adaptación –Nuevo Módulo 8 o ADNM-8 por sus siglas en inglés) y sus interacciones según latemporalidad de la evaluación fueron analizados mediante una regresión linear mixta.
Resultados: Se asociaron con niveles signiﬁcativamente mayores de síntomas del AjD lossiguientes: género femenino o diverso; edad avanzada; consumo de noticias relacionadas
con la pandemia mayor a 30 minutos al día; trastorno mental actual o previo; exposición atrauma antes o durante la pandemia; estado de salud bueno, satisfactorio o pobre (encomparación con uno muy bueno); carga asociada al manejo y comunicación
gubernamentales de la crisis; miedo a infectarse; contacto social restringido; problemaslaborales; actividad restringida; y diﬁcultades asociadas a la vivienda. Se asociaron con
menores niveles de síntomas del AjD los predictores siguientes: trabajo autónomo ocondición de jubilado; trabajo en el sector salud; contacto en persona más de una vez por
semana con amigos o seres queridos. Los efectos sobre los niveles de los síntomas del AjD
que cambiaron según la temporalidad de la evaluación durante la pandemia fueron lossiguientes: Trastorno mental actual o previo; carga relacionada con el manejo
gubernamental de la crisis; reducción de ingresos; exposición actual al trauma.
Conclusiones: Se identiﬁcaron factores protectores y de riesgo predictores de niveles desíntomas del AjD en diferentes momentos de la pandemia. Para algunos predictores, losefectos sobre la salud mental impresionan cambiar en diferentes etapas de una pandemia.

1. Introduction
The COVID-19 pandemic resulted in profoundpsychological, societal, and economic disruptions(Dubey et al., 2020). It has caused illness, deaths,

KEYWORDS

COVID-19; pandemic;
coronavirus; adjustment
disorder; mental health;stressors; stress-relateddisorders; post-traumaticstress; risk factors; protective
factors
PALABRAS CLAVE

COVID-19; pandemia;
coronavirus; trastorno deadaptación; salud mental;
factores estresantes;
trastornos relacionados con
el estrés; estrés
postraumático; factores deriesgo; factores protectores
HIGHLIGHTS• We longitudinally
examined predictors ofsymptoms of adjustment
disorder in 15,563 adults
during the COVID-19
pandemic.
• We found stressors, risk,
and protective factors
predicting adjustment
disorder symptom levels at
diﬀerent stages of thepandemic.
• For some predictors, theeﬀects appear to change in
diﬀerent phases of apandemic.

and strain on healthcare and economic systems. Entire
populations have experienced multiple pandemicspeciﬁc stressors, such as fear of contracting or suﬀering from COVID-19, quarantine and physicalEUROPEAN JOURNAL OF PSYCHOTRAUMATOLOGY

distancing, home schooling and remote work. Some
individuals faced income or work loss, which can
additionally strain mental health and wellbeing(Thomson et al., 2022).
In view of the cumulative stress factors speciﬁc to
the pandemic, those aﬀected could be at increased
risk of adjustment disorders (AjD). According to theeleventh version of the International Classiﬁcation of
Diseases (ICD-11; WHO, 2021), AjD is deﬁned as a
failure to adapt to stressors, such as illness or disability, socio-economic problems, and conﬂicts at home
(WHO, 2021). The main features of AjD include preoccupation with the stressor or its consequences,recurrent and distressing thoughts about the stressor,
or constant rumination about its consequences. Symptoms of AjD cause signiﬁcant impairment in personal,
family, social, educational, occupational functioning(WHO, 2021). During a pandemic, where multiple
stressors may overtax coping abilities, developmentof AjD is plausible.
While many studies looked at the relationships
between pandemic-related stressors and depressiveor anxiety symptoms during the COVID-19 pandemic(Daniali et al., 2023), few studies examined therelationships with AjD symptom levels (e.g. BenEzra et al., 2021; Dragan et al., 2021; Rossi et al.,2020). The studies on AjD syptoms reported crosssectional associations between higher AjD symptom
levels and younger age (Rossi et al., 2020), female gender (Ben-Ezra et al., 2021; Dragan et al., 2021; Rossi
et al., 2020), general health problems, social isolation,and caregiving problems (Ben-Ezra et al., 2021).
Relationships with AjD symptom levels were also
found for unemployment (Dragan et al., 2021) andjob loss (Zelviene et al., 2020), which are importantrisk factors for AjD (Lorenz et al., 2018).
A crosssectional analysis of the ﬁrst wave of The
European Society for Traumatic Stress Studies(ESTSS) ADJUST study reported associations between
higher AjD symptom levels and a number of risk
fators, including older age, female gender, fear of
infection, trauma exposure, a previous mental healthdisorder, exposure to COVID-19 related news, andrestricted social contact (Lotzin et al., 2021). Whilethese ﬁndings provide insights into cross-sectional
associations between risk factors and AjD symptom
levels during the early phase of the pandemic, it isstill unclear whether these relationships remain stableor may change over the diﬀerent phases of the pandemic. Some of the pandemic-related stressors werecompletely new in the initial phase of the pandemic,
but people may have learned to deal with them adaptively after an initial adjustment.
We are only aware of one previous study that examined longitudinal associations between pandemicrelated risk factors and AjD symptoms at diﬀerent
time points of the COVID-19 pandemic among the3

general population. The study examined associations
between female gender and health-related stressors(deterioration in health, diagnosis of an illness, andphysical injury or disability) and AjD symptoms
before and after the second lockdown during the pandemic in the Israeli population (Hamama-Raz et al.,2021). Signiﬁcant relationships between these variables were found at both timepoints.
Addional research on predictors of AjD symptoms
during the COVID-19 pandemic was conductedamong patient samples. German patients with diﬀerent mental health disorders were cross-sectionallyassessed during the ﬁrst year of the pandemic (Belzet al., 2022). AjD symptoms were evaluated retrospectively before the pandemic, during the moststrict lockdown conditions, and after the most strict
lockdown conditions (Belz et al., 2022). Psychosocial
problems and media consumption were related tohigher AjD symptom levels (Belz et al., 2022).
Whether these relationships varied at diﬀerent time
points over the course of the pandemic was notassessed.
In sum, a few cross-sectional studies (Belz et al.,2022; Ben-Ezra et al., 2021; Dragan et al., 2021; Rossi
et al., 2020) and one longitudinal study (HamamaRaz et al., 2021) reported signiﬁcant relationships
between risk factors and pandemic-related stressors
and AjD symptom levels, including both younger
and older age, a COVID-19 infection, health-related
stressors, female gender, psychosocial stressors, social
isolation, caregiving problems, unemployment, andmedia consumption. We are unaware of research
examining whether the relationships between theserisk factors orstressors and AjD symptom levels
change over time. Given the high and prolonged
psychological burden of the COVID-19 pandemic, it
seems essential to better understand the relationships
between these factors and and symptoms of AjD overdiﬀerent phases of the COVID-19 pandemic to be able
to design adequate intervention strategies for future
pandemics.
Based on an adapted COVID-19 framework ofdeterminants of health (Lotzin et al., 2020), thisresearch examined the relationships between risk
and protective factors (e.g. age, gender, income), pandemic-speciﬁc stressors (e.g. fear of infection,
restricted social contact), and symptoms of AjD atthree diﬀerent time points over 18 months of the
COVID-19 pandemic in a large sample of more than
15,000 participants from the general populations ofeleven European countries. The very few studies conducted on AjD symptoms during the pandemic did
not include such a large sample and only assessed
one speciﬁc country. The knowledge gained fromthis study may help identify potential risk and protective factors for AjD symptoms at diﬀerent time pointsof the pandemic.

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A. LOTZIN ET AL.

2. Methods
2.1. Study design and setting
The ADJUST study was initiated by the ESTSS. The
study investigated longitudinal associations between
risk and protective factors, stressors, and symptomsof AjD during the COVID-19 pandemic in the general
populations of eleven European countries (Austria,
Croatia, Georgia, Germany, Greece, Italy, Lithuania,
the Netherlands, Poland, Portugal, and Sweden). Participants who consented to study participation and tomultiple data collections were assessed at three timepoints, with 6-months intervals from June 2020 to January 2022. A 6-months interval was chosen as
COVID-infection rates and the corresponding lockdown measures were expected to change between
diﬀerent seasons (e.g. from summer to winter). The
study was pre-registered in a study registry (OSF registry, https://osf.io/8xhyg).

2.2. Participants
We recruited participants from the general populations of the above mentioned eleven countries.
Inclusion criteria were (1) at least 18 years of age,(2) ability to read and write in the respective language,and (3) willingness to participate in the study.

2.3. Measures
2.3.1. Symptoms of adjustment disorder
We assessed symptoms of AjD with the Adjustment
Disorder – New Module 8 scale (ADNM-8; Kazlauskas
et al., 2018), a self-report measure of AjD according to
the eleventh version of the International Classiﬁcationof Diseases (ICD-11; WHO, 2021). The participants
were asked to ‘indicate what was the most straining
event(s) during the Coronavirus-pandemic’, and toevaluate for the most stressful event how often therespective statement applied to them. AjD symptoms
are assessed by eight items (1 = never, 2 = rarely, 3 =sometimes, 4 = often). A total score, ranging from 8
to 32, is calculated by summing the item scores. A
cut-oﬀ score of >22 indicates probable AjD. The
ADNM-8 is the short form of the ADNM-20; bothmeasures have been widely used in ICD-11 AjD
research (Kazlauskas et al., 2017). The ADNM-8 has
proven structural and convergent validity (Ben-Ezra
et al., 2018; Kazlauskas et al., 2017) and excellent accuracy of diagnosing AjD according to ICD-11 (BenEzra et al., 2018).
2.3.2. Risk and protective factors
We used the WHO framework for social determinantsof health (Solar & Irwin, 2010) for selecting risk andprotective factors, which we adapted to the speciﬁc

circumstances of the COVID-19 pandemic (Lotzin
et al., 2020). Risk and protective factors included
sociodemographic (e.g. age, gender), pandemicrelated (Table 1, e.g. reduced income due to pandemic), and health-related characteristics (Table 2,e.g. current health condition). Items were self-constructed except for trauma exposure, as validated
measured were unavailable at the time of survey development. For the self-constructed items, the core item
set was developed in English language. The standard
procedure for translation was as follows: A native
speaker of the respective study site translated theself-constructed variables. A second native speakerbacktranslated the translation and checked the correctness of the translation. Any discrepancies werediscussed within the team and a ﬁnal decision on thecorrect translation was made upon consensus.
Trauma exposure was measured using the Criterion
A section of the PTSD Checklist for DSM-5 (PCL-5;
Weathers et al., 2013). Participants were asked
whether they experienced stressful event(s) involving
actual or threatened death, serious injury, or sexual
violence before or during the COVID-19 pandemic.

2.3.2.1. Pandemic-related stressors. We assessed theburden of pandemic-related stressors within the lastmonth by a self-constructed 30-item questionnaire
named Pandemic Stressor Scale (PaSS). The measure,
item construction and psychometric properties aredescribed elsewhere (Lotzin et al., 2022). Participants
indicate on 5-point scales (0 = ‘Not at all burdened’;
1 = ‘Somewhat burdened’; 2 = ‘Moderately burdened’;
3 = ‘Strongly burdened, 4 = ‘Does not apply to me’)how much diﬀerent stressors have burdened them
due to the pandemic. The PaSS measures pandemicrelated stressors in nine domains: Crisis management
and communication (e.g. poor information from thegovernment); Fear of infection (e.g. fear of getting
infected with the coronavirus); Burden of infection(e.g. own infection with the coronavirus); Restricted
Face-to-face contact (e.g. restricted face-to-face contact with loved ones); Restricted activity (e.g. restricted
everyday activity such as shopping); Restricted leisureactivity (e.g. restaurant visit); Work-related problems(e.g. not being able to work); Diﬃcult housing conditions, (e.g. restricted housing conditions such as little space); Restricted access to resources (e.g. restricted
access to goods, e.g. food, water, clothing). Subscalescores for the diﬀerent domains were calculated by
the average of the scores of the respective items. Before
calculating subscores, we recoded the ‘Does not apply
to me’ category to ‘Not at all burdened’. The nine-factor structure yielded in the German sample of the
ADJUST study could be replicated in a conﬁrmatory
factor analysis using the data of the Austrian sample(Lotzin et al., 2022).

EUROPEAN JOURNAL OF PSYCHOTRAUMATOLOGY

2.3.3. Lockdown stringency
To adjust for possible diﬀerences in the strictness oflockdown measures during the COVID-19 pandemic,
the Oxford stringency index (https://ourworldindata.
org/covid-stringency-index) was included in theanalysis on the person-level. We included the stringency value at the time of the data assessment foreach participant separately, except for Croatia, where
we used the start date of the assessment, as individual
dates were unavailable. To adjust for between-country
diﬀerences, a variable indicating the diﬀerent
countries was constructed.
2.3.4. Procedure
Data were collected between June 2020 and January2022 at three time intervals (T1 June 2020 to December 2020; T2 December 2020 to June 2021; T3 June
2021 to January 2022). Symptoms of adjustment disorder as well as the measured risk and protective factors were collected repeatedly at T1, T2 and T3. Due tothe lockdown situation, recruitment was predominantly conducted online. We applied diﬀerent recruitment strategies to increase sample variability in termsof gender, age, and education. We promoted the study
via social platforms, leisure and interest groups, newsletters, and advertisements in newspapers and magazines. We also disseminated the study information
through universities, stakeholders, and professionalorganizations. The study site in Poland recruited participants via a professional panel service. A moredetailed description of the recruitment strategy canbe found elsewhere (Lotzin et al., 2021). Interested
individuals received a study invitation by a website
link to the survey. After providing consent, they
could complete an online survey on web-based platforms following the EU data protection regulations.
2.3.5. Data analysis
The data analysis set of N = 15,196 included all casesthat had at least one value in the dependent variable
at either T1, T2 or T3. Individuals with missing values
in the dependent variable at all three timepoints(7.6%) were excluded, as the imputation of missing
values in the dependent variable is not recommended.
We imputed missing values of all independent variables using multiple imputation following the guidelines by White and colleagues (2011). We generated
50 imputed datasets by using the mice (multivariate
imputations by chained equations) package in R
(van Buuren & Groothuis-Oudshoorn, 2011) and thepredictive mean matching option as imputation
method. All independent and dependent variables ofthis data analysis were included in the imputation
model. Additional variables were selected for the
imputation model based on their correlation with
the incomplete variables.

5

A linear mixed regression model with the AjD
symptom score as dependent variable (ADNM-8)
was performed. As ﬁxed eﬀects, risk and protective
factors (see methods section), the nine PaSS subscales, and the timpoint of assessment were includedin the model. The stringency index was included asﬁxed eﬀect to adjust for possible diﬀerences in stringency of the applied lockdown measures. The participant identiﬁcator and the country of the dataassessment were included as random eﬀects. Descriptive statistics were computed for all variables
included in the model. Mean and standard deviation
were calculated, as appropriate, for the continuous
variables; absolute and relative frequencies werecomputed for categorical variables. As a sensitivity
analysis, the same analysis was performed using thedataset with all imputed values including the dependen variable, which relevealed comparable results.
All analyses were conducted with R-4.2.2 forWindows.
2.3.6. Results
Out of the N = 15,169 participants, n = 6006 (36.6%)
completed all three waves of the survey whereas n =8389 (51.12%) and n = 2016 (12.28%) dropped out at
T2 and T3, respectively. Cases were complete (i.e. no
missing values in the variables used in analysis) inn = 1680 (10.2%) participants. Missing values ranged
from 0.01% (gender at T1) to 69.31% (burden related
to reduced social contact at T2) in the independent
variables.
2.3.7. Sample characteristics
Two third of the sample were female (n = 10,402,
68.6% female; n = 4712, 31.1% male; n = 54, 0.4%
diverse). Age (M = 42.7, SD = 15.3, range 18–96
years) and income (n = 1209, 8.5% very low income;n = 2709, 19.1% low income; n = 5105, 36.0% medium
income; n = 5162, 36.4% high income) varied. The
sample was predominantly high-educated (n = 368,
2.5% less than 10 years of schooling; n = 3466,
23.3% ≥ 10 years of schooling; n = 2143, 14.4% vocational studies, n = 8886, 59.8% completed studies).
About half of the sample (n = 7578, 50.1%) was
employed full time, 15.8% (n = 2381) was employed
part-time, 15.6% (n = 2359) seeked work, and 10.6%(n = 1605) were retired. The remaining participants
were self-employed (n = 876, 5.8%), freelancer (n =553, 4.2%), in training or study (n = 874, 5.8%) or
had another occupation (n = 817, 7.2%). Work areas
varied across the sample (n = 1913, 14.6% healthcare;n = 1796, 13.7% education; n = 856, 6.6% retail or services; n = 749, 5.7% public security; n = 420, 3.2%
maintenance, repair, or construction; n = 4790,
36.7% other work area; n = 2539, 19.4% not working).
Lockdown stringency was lower at T1 and T3 compared to T2 (Table 1). The percentage of individuals

6

A. LOTZIN ET AL.

Table 1. Pandemic-related and clinical characteristics (N =15,169).

Table 3. Pandemic-related stressors (N = 15,169).

Characteristic

PaSS Domain

T1

T2

T3

Lockdown stringencya

M (SD)
M (SD)
M (SD)
56.87 (9.32)
72.10 (8.69)
55.72 (10.10)
n (%)
n (%)
n (%)
Reduced income due to pandemicb
No10291 (68.1%) 4568 (75.8%) 4607 (78.3%)
Yes4823 (31.9%) 1462 (24.2%) 1275 (21.7%)
Face-to-face contactc
No contact
1557 (10.3%)
402 (7.6%)
219 (4.2%)<once a week
3609 (23.8%) 1323 (25.1%) 1010 (19.4%)
once a week
2144 (14.1%)
785 (14.9%)
737 (14.2%)
1–2 times a week
3183 (21.0%)928 (17.6%) 1082 (20.8%)
3–6 times a week/
4663 (30.8%) 1828 (34.7%) 2159 (41.5%)
everyday
Digital social contactd
No contact
494 (3.3%)
210 (3.0%)
198 (3.4%)<once a week
1417 (9.3%)
813 (11.7%)
772 (13.2%)
once a week
1316 (8.7%)753 (10.8%)708 (12.1%)
1–2 times a week
2803 (18.5%) 1381 (19.9%) 1199 (20.5%)
3–6 times a week/
9126 (60.2%) 3800 (54.6%) 2959 (50.7%)
everyday
News consumptione
I do not watch, read
1091 (7.2%)
614 (8.8%)705 (12.1%)
or listen to news
<30 min a day
7385 (48.7%) 3671 (52.8%) 3530 (60.5%)
30–60 min a day
3980 (26.3%) 1789 (25.7%) 1087 (18.6%)
1–2 h a day
1515 (10.0%)563 (8.1%)
326 (5.6%)
2–3 h a day
622 (4.1%)
175 (2.5%)
98 (1.7%)>3 h a day
563 (3.7%)
145 (2.1%)
90 (1.5%)
Notes. Lockdown stringency according to the Oxford Stringency Index.
Reduced income = Reduced monthly household income due to the
COVID-19 pandemic. Face-to-face contact = Face-to-face contact withloved ones or friends. Digital social contact = Digital social contact
with loved ones or friends, e.g. by phone, Skype, or Zoom. News consumption = Hours a day watching, reading, or listening to the news orother information about the COVID-19 pandemic.
Missing values: T1: an = 0, bn = 55, cn = 13, dn = 13, en = 13. T2: an = 8065,bn = 9139, cn = 9903, dn = 8212, en = 8212. T3: an = 9328, bn = 9287,
cn = 9962, dn = 9333, en = 9333.

with a COVID-19 infection, veriﬁed by a test,
increased over time (Table 2; T1 n = 296; 2.0%; T2,
n = 632, 9.1%, T3 n = 752, 12.9%).
Table 2. Health-related characteristics (N = 15,169).
Characteristic

T1

T2

T3

Current health statusa
Very good
4406 (29.1%) 1396 (20.1%) 1263 (21.6%)
Good6620 (43.7%) 3069 (44.1%) 2634 (45.1%)
Satisfactory
3299 (21.8%) 1910 (27.5%) 1467 (25.1%)
Bad
827 (5.5%)583 (8.4%)
472 (8.1%)
b
COVID-19 infection
No14856 (98.0%) 6325 (90.9%) 5083 (87.1%)
Yes
296 (2.0%)
632 (9.1%)
752 (12.9%)cDiagnosis of mental disorderNo
11932 (78.7%) 5210 (74.9%) 4323 (74.1%)
Yes, recovered
1940 (12.8%) 1082 (15.6%) 972 (16.7%)
Yes, currently aﬀected
1280 (8.4%)
665 (9.6%)
541 (9.3%)dTrauma during pandemic
No13705 (92.3%) 5923 (89.1%) 4982 (88.6%)
Yes1150 (7.7%)
721 (10.9%) 642 (11.4%)
Trauma before pandemiceNo10582 (71.3%)
Yes4263 (28.7%)2826 (18.7%) 1444 (21.5%) 962 (17.1%)
Probable adjustment
disorderf (ADNM-8 > 22)g
M (SD)
M (SD)
M (SD)
ADNM-8 total score16.03 (6.45) 16.51 (6.65) 15.41 (6.47)
Notes: COVID-19 infection = Infected (i.e. tested positive) with COVID-19.
ADNM-8 = Adjustment Disorder – New Module 8.
Missing values: T1: an = 156, bn = 17, cn = 159, dn = 1547, en = 1557,
fn = 1295, gn = 51. T2: a n = 9446, bn = 8212, cn = 9447, dn = 9766,fn = 9690, gn = 8447. T3: a n = 10,575, bn = 9334, cn = 10,575, dn =10,787, fn = 10,769, gn = 9526.

T1
M (SD)T2
M (SD)T3
M (SD)

1.53 (0.90) 1.59 (0.86) 1.21 (0.86)
Restricted social contacta
0.38 (0.83) 0.32 (0.75) 0.27 (0.70)
Problems with childcareb
0.62 (0.83) 0.48 (0.77) 0.40 (0.71)
Work-related problemsc
Fear of infectiond
1.65 (0.78) 1.69 (0.77) 1.51 (0.81)e
0.64 (0.84) 0.71 (0.78) 0.63 (0.73)
Burden of infectionfRestricted activity
1.56 (0.85) 1.63 (0.83) 1.34 (0.85)
1.21 (0.87) 1.43 (0.89) 1.28 (0.90)
Crisis management/
communicationg
h0.83 (0.79) 0.91 (0.77) 0.75 (0.76)
Restricted access to resources
0.60 (0.77) 0.54 (0.71) 0.45 (0.65)
Diﬃcult housing conditioni
Notes. PaSS = Pandemic Stressor Scale. 0 = ‘Not at all burdened’; 1= ‘Somewhat burdened’; 2 = ‘Moderately burdened’; 3 = ‘Strongly burdened’.
Missing values: T1: a n = 38, bn = 38, cn = 981, dn = 25, en = 25, fn = 33, gn= 22, hn = 22, in = 38. T2: a n = 10,135, bn = 8319, cn = 8397, dn = 8289,
en = 8288, fn = 8303, gn = 8281, hn = 8281, in = 8319. T3: a n = 9515,bn = 9421, cn = 9591, dn = 9403, en = 9403, fn = 9409, gn = 9400, hn =9400, in = 9421.

The AjD symptom level was moderate at all threemeasurement points: T1 (M = 16.03, SD = 6.45), T2(M = 16.51, SD = 6.65), and T3 (M = 15.41, SD =6.47), on average. The prevalence of probable AjD
was 18.7%, 21.5%, and 17.1% at T1, T2 and T3,
respectively (Table 2). At all time points, the highest
burden was related to the pandemic stressors ’Fearof infection’, ’Restricted activity’ and ’Restricted social
contact’ (Table 3).

2.3.8. Predictors of AjD symptoms
2.3.8.1. Risk factors. The following sociodemographic, pandemic- or health-related variables weresigniﬁcantly associated with higher AjD symptom
levels (Figure 1, for eﬀect estimates and p-values
see Table 4): female or diverse gender (vs. male gender), older age (vs. younger age), pandemic-related
news consumption of more than 30 min a day (vs.
no news consumption); diagnosis of a current mental health disorder; diagnosis of a previous mental
health disorder; trauma exposure during the pandemic (vs. no trauma exposure); trauma exposure
before the pandemic (vs. no trauma exposure);‘good’, ‘satisfactory’ or ‘poor’ health status (vs.
‘very good’ health status). The following of theabove reported eﬀects did not diﬀer by time ofassessment: female or diverse gender (vs. male gender), older age (vs. younger age), pandemic-related
news consumption of more than 30 min a day (vs.
no news consumption); trauma exposure before thepandemic (vs. no trauma exposure); ‘good’, ‘satisfactory’ or ‘poor’ health status (vs. ‘very good’ healthstatus). In contrast, a signiﬁcant interaction eﬀect
with time was found for the following variables:
The eﬀect of income reduction on AjD symptom
levels was signiﬁcantly greater at T2 vs. T1 (Figure 2).
The eﬀect of a current mental health disorder on
AjD symptom levels was signiﬁcantly greater at T1
vs. T2 and T3. The eﬀect of a previous mental healthEUROPEAN JOURNAL OF PSYCHOTRAUMATOLOGY

7

Figure 1. Eﬀect estimates of the linear mixed regression analysis.
Notes. ≥10 years = 10 or more years of schooling. Reduced income = Reduced monthly household income due to the coronavirus. Financial support =Receiving ﬁnancial support from the government. Maintenance/repair/etc. = Maintenance, repair, construction. More at home = Spent more time at home
due to the coronavirus pandemic. Social dist. = Spent more time at home as a precautionary measure (social distancing). Self-isolation = Stayed at home inself-isolation because of self-infection. Quarantine = Stayed at home due to contact with infected people or being in risk areas. Face contact = Face-to-facecontact with loved ones or friends. Face contact at work = Work involves (almost) daily face-to-face contact with other people. Digital contact = Digitalcontact with loved ones or friends, e.g. by phone, Skype, or Zoom. News = Hours a day watching, reading, or listening to the news or other information
about the coronavirus pandemic. COVID-19 inf. = Infected (i.e. tested positive) with the coronavirus.
Reference categories: aMale. bLess than 10 years of schooling. cVery low. dNo. eOther. fNo. gI have no personal contact with other people. hI have no contact
by phone, skype, etc. iI do not watch, read or listen to news about the coronavirus pandemic. jVery good. kNo.

8

A. LOTZIN ET AL.

Table 4. Eﬀect estimates of risk factors and stressors on symptoms of adjustment disorder (N = 15,169).
Term

b

SE

p

95% CI Low

95% CI Up

Intercept
Age (in years)
Gender (ref. male)
FemaleDiverse
Education (ref. < 10 yearsa)≥10 years
Vocational studies
Completed studies
Income (ref. very low)
Low
MediumHigh
Reduced income (ref. no)Yes
T2 × Yes
T3 × YesWork status (ref. no work)
Training/study: Yes
Employed part-time: Yes
Employed full-time: Yes
Self-employed: Yes
Freelancer: Yes
Retired: Yes
Seeking work: Yes
Other: Yes
Work areae (ref. other area)
Health care
Public security
Retail, Services
Maintenance/repair/etc.
Education
Not working
Face-to-face contact (ref. no contactb)<once a week
once a week
1–2 times a week
3–6 times a week or every day
Virtual social contact (ref. no contactc)<once a week
once a week
1–2 times a week
3–6 times a week
News consumption (ref. no consumpt.)<30 min. a day
30–60 min a day
1–2 h a day
2–3 h a day>3 h a day
Current health status (ref. very good)
Good
Satisfactory
Poor
Diagnosis of mental disorder (ref. no)
Yes, recovered
Yes, currently aﬀected
T2 × Yes, recovered
Yes, currently aﬀected
T3 × Yes, recovered
Yes, currently aﬀected
COVID-19 infection (ref. no)
Yes
Trauma during pandemic (ref. no)Yes
T2 × Yes
T3 × Yes
Trauma before pandemic (ref. no)
Yes
PaSS: Restricted face-to-face contact
PaSS: Problems with childcare
PaSS: Work-related problems
PaSS: Fear of infection
PaSS: Burden of infection
PaSS: Restricted activity
PaSS: Crisis management/commun.
T2 × Crisis management
T3 × Crisis management/commun.

7.37
0.02

0.56
0.00

<.001
<.001

6.26
0.02

8.47
0.03

0.92
1.43

0.08
0.58

<.001
.014

0.75
0.29

1.08
2.58

0.33
0.33
0.22

0.24
0.26
0.24

.180
.208
.368

−0.15
−0.18
−0.26

0.81
0.83
0.70

−0.03
0.02
−0.18

0.16
0.16
0.17

.845
.897
.278

−0.35
−0.30
−0.52

0.29
0.34
0.15

0.16
0.42
0.28

0.10
0.16
0.17

.115
.009
.104

−0.04
−0.11
−0.06

0.35
0.73
0.61

−0.18
−0.09
−0.23
−0.44
−0.01
−0.75
−0.16
−0.11

0.14
0.14
0.15
0.17
0.21
0.29
0.28
0.18

.202
.515
.113
.012
.948
.011
.579
.531

−0.46
−0.37
−0.52
−0.78
−0.41
−1.33
−0.71
−0.24

0.10
0.19
0.05
−0.10
0.39
−0.17
0.40
0.46

−0.25
0.02
0.04
0.05
−0.05
0.21

0.12
0.19
0.17
0.22
0.13
0.27

.042
.924
.817
.810
.709
.439

−0.48
−0.35
−0.30
−0.39
−0.30
−0.32

−0.01
0.39
0.38
0.50
0.20
0.73

−0.35
−0.38
−0.48
−0.33

0.13
0.14
0.14
0.13

.006
.009
<.001
.014

−0.61
−0.66
−0.75
−0.59

−0.10
−0.09
−0.21
−0.07

−0.12
−0.22
−0.22
−0.31

0.19
0.19
0.18
0.17

.529
.256
.217
.075

−0.49
−0.59
−0.58
−0.65

0.25
0.16
0.13
0.03

−0.06
0.41
0.77
1.09
1.53

0.12
0.13
0.15
0.20
0.21

.600
.001
<.001
<.001
<.001

−0.29
0.16
0.47
0.69
1.12

0.17
0.66
1.07
1.48
1.94

0.95
2.16
4.11

0.08
0.10
0.15

<.001
<.001
<.001

0.79
1.97
3.82

1.10
2.35
4.40

1.06
3.20
−0.21
−0.87
−0.54
−0.68

0.12
0.15
0.18
0.23
0.20
0.25

<.001
<.001
.261
<.001
.006
.006

0.82
2.90
−0.58
−1.33
−0.922
−1.17

1.31
3.50
0.16
−0.42
−0.16
−0.19

−0.02

0.14

.907

−0.28

0.25

0.97
0.50
0.66

0.16
0.24
0.24

<.001
.033
.006

0.67
0.04
0.19

1.27
0.96
1.14

0.38
1.03
−0.23
0.89
1.21
0.01
0.42
0.63
0.00
−0.31

0.08
0.05
0.05
0.06
0.05
0.05
0.05
0.05
0.08
0.08

<.001
<.001
<.001
<.001
<.001
.851
<.001
<.001
.972
<.001

0.21
0.93
−0.32
0.77
1.11
−0.08
0.32
0.53
−0.15
−0.47

0.54
1.13
−0.13
1.00
1.32
0.10
0.51
0.74
0.15
−0.15
(Continued)

EUROPEAN JOURNAL OF PSYCHOTRAUMATOLOGY

9

Table 4. Continued.
Term

b

SE

p

95% CI Low

95% CI Up

PaSS: Restricted resources
.063
.050
.206
−0.04
0.16
PaSS: Diﬀ. housing condition
1.20
0.06
<.001
1.09
1.31
Lockdown stringency
−0.01
0.00
.006
−0.02
−0.00
Notes. Ref. = reference category. Interaction eﬀects with time (T2, T3) were included if signiﬁcant. a < 10 years = Less than 10 years of schooling. b No
personal face-to-face contact with other people. c No digital contact by phone, skype, etc. with other people.

Figure 2. Interaction between time and reduced monthly
household income.

disorder was signiﬁcantly greater at T1 vs. T3. In
contrast, the eﬀect of trauma exposure during the
pandemic on AjD symptoms was signiﬁcantly
greater at T2 and T3 vs. T1.
2.3.8.2. Protective factors. The following variables
were signiﬁcantly related to lower AjD symptom

Figure 4. Interaction between time and trauma exposure.

levels: self-employment or retirement (vs. having nowork); working in healthcare (vs. other work area);
and a higher face-to-face contact of more than oncea week (vs. no face-to-face contact; Figure 1, foreﬀect estimates and p-values see Table 4). The
reported signiﬁcant eﬀects did not diﬀer by time ofassessment.
2.3.8.3. Pandemic stressors. Six out of nine pandemic
stressor domains were positively related to AjD symptom levels (all p < .001, Figure 1, Table 4). These
included ‘Fear of infection,’ ‘Governmental crisis
communication and management’, ‘Restricted faceto-face contact’, ‘Work-related problems’, ‘Restricted(leisure and everyday) activity’, and ‘Diﬃcult housing conditions’. ‘Problems with childcare’ was negatively associated with AjD symptom levels. The
analysis was controlled for lockdown stringency,
which was related to AjD symptom levels. Signiﬁcantinteraction eﬀects with time were found for theeﬀect of ‘Governmental crisis communication/management’, which was higher at T1 vs. T3. For theremaining variables, eﬀects did not diﬀer by thetime of assessment.

3. Discussion
Figure 3. Interaction between time and mental healthdisorder.

This study examined predictors of AjD symptoms,
including risk and protective factors and pandemic-

10

A. LOTZIN ET AL.

related stressors, in the general populations of elevencountries over 18 months of the COVID-19 pandemic.
The prevalence rate of self-reported probable AjD was
18.7% at the ﬁrst assessment (June 2020 to December2020), 21.5% at the second assessment (December
2020 to June 2021), and 17.1% at the third assessment(June 2021 to January 2022). The higher lockdown
stringency at the second assessment might have contributed to an increased pandemic-related burden
and a higher risk of AjD.
3.1. Sociodemographic factors
A number of sociodemographic factors, including
female or diverse gender and age, were signiﬁcantly
related to AjD symptom levels. Female gender was
associated with higher AjD symptom levels at all threeassessments. Meta-analytic evidence indicates thatwomen showed greater depressive and anxiety symptoms during the pandemic compared to men (Robinson
et al., 2022). Women may have experienced more pandemic-related stressors, as they are more often responsible for childcare than men (Petts et al., 2021). They
also might have been more aﬀected by domestic violence and job loss (Flor et al., 2022), thereby aggravating
existing pre-pandemic mental health gender inequalities. On the other hand, gender diﬀerences in AjD
symptoms could reﬂect higher biological stress vulnerability. There are gender-speciﬁc diﬀerences in the
hypothalamic-pituitary-adrenal (HPA) stress system
and its responses, which could explain gender-speciﬁc
diﬀerences in AjD between women and men. For
example, stressors activate the HPA axis more quickly
and releases a greater amount of stress hormones inwomen compared to men (Goel et al., 2014).
Diverse gender (vs. male) was related to higher AjD
symptom levels in our study, although the sample sizeof this subgroup was small. Associations between
divere gender and higher distress during the pandemic
were reported in earlier studies (Hunt et al., 2021).
Gender diverse individuals may receive less social support and gender-aﬃrming interventions (Jones et al.,2021), which may contribute to higher AjD levels.
Older age (vs. younger age) was associated with higher
AjD symptom levels, consistent with earlier researchduring the COVID-19 pandemic (Dworakowski et al.,2022). Older age might be related to poorer health, ahigher risk for a severe or life-threatening COVID-19
infection, and reduced face-to-face contact compared
to younger age, which may be factors related to AjD.
While earlier research reported relationships between
younger age and increased psychological distress (Breslau et al., 2023), we did not ﬁnd this relationship withregard to AjD symptoms over the three assessed
timepoints.
In contrast to our expectations, working in healthcare (vs. other work area) was related to lower AjD

symptoms. However, the chosen category ‘working
in healthcare’ might have been too broad to identify
high-risk individuals, such as healthcareworkers whocared for COVID-19 infected patients. Future studies
should examine diﬀerent types of healthcare professions more in depth to diﬀerentiate between highand low-risk groups.
The eﬀects of the sociodemographic variables
female and diverse gender, age, work area and work
status did not diﬀer by the time of assessment, indicating that the relationships with AjD symptom levels
remained stable over the pandemic.
3.1.1. Income reduction
While the main eﬀect of income reduction (vs. no
income reduction) on AjD symptom levels did notreach signiﬁcance, time of assessment moderated therelationship between income reduction and AjD
symptom levels. Income reduction was related to agreater increase in AjD symptoms at the second
assessment compared to the ﬁrst assessment. Income
reduction could be particularly burdensome if it lasts
for a longer period of time (Gupta et al., 2020; Midões
& Seré, 2022), which could have been the case for ourstudy participants at the second compared to the ﬁrst
assessment. Another factor that diﬀered at the second
assessment was the degree of lockdown stringency,
which was highest at this time period. Consistent
with our ﬁndings, earlier research found that income
was more greatly reduced during phases of high lockdown stringency (Midões & Seré, 2022). In thesephases, the reduction in income could have reduced
the ﬁnancial resources for the necessary food andhousing expenses (Midões & Seré, 2022), whichmight have increased distress. Earlier pre-pandemic
research found that low income was associated withhigher distress (Breslau et al., 2023), depression andanxiety symptoms (Blasco-Belled et al., 2022).
3.2. Pandemic-related factors
The frequency of face-to-face contact with loved ones
or friends was related to lower AjD symptom levels
over the three assessments, while the frequency of digital social contact with loved ones or friends was unrelated to AjD symptom levels. These ﬁndings are in linewith earlier research reporting that face-to-face contact had a stronger positive aﬀect on mental healthand wellbeing than digital contact during the pandemic (Newson et al., 2021; Stieger et al., 2023).
Research has indicated that communication perceived
as more natural was related to lower levels of loneliness and sadness and higher levels of happiness compared to less natural communication (Petrova &Schulz, 2022). Face-to-face communication may help
to reduce distress and may increase positive emotions
that may buﬀer against the stressors of the pandemic.

EUROPEAN JOURNAL OF PSYCHOTRAUMATOLOGY

Consumption of news related to COVID-19 of atleast 30 min per day (vs. no news consumption) was
associated with higher AjD symptom levels, while
news consumption of less than 30 min a day was unrelated to AjD symptom levels. There was a doseresponse relationship between the variables, i.e. thelonger the duration of news consumption, the higher
were the reported AjD symptom levels. Pandemicrelated news consumption may not be harmful at alower dosis, but may lead to AjD symptoms at a higher
dosis.
3.3. Health-related factors
A current health status perceived as less than very
good (i.e. good, satisfactory, or poor) was related tohigher AjD symptom levels. The worse the health status, the higher were the AjD symptom levels. Individuals with a poor health condition reported the highest
levels of AjD symptoms. Previous studies reported
associations between medical problems or physicaldiseases and higher levels of distress, anxiety, anddepression during the COVID-19 pandemic (Mazzaet al., 2020; Özdin & Bayrak Özdin, 2020). Poor healthcan lead to a variety of stressors, such as worrying
about caring for the health condition and accessing
needed treatment or medication (Boyle et al., 2020).
These additional stressors may overwhelm thecapacity to cope with the numerous other stressors
during a pandemic.
A current or previous diagnosis of a mental healthdisorder (vs. no mental health disorder) was related
to higher AjD symptom levels, in line with an earlier
cross-sectional study reporting associations between
a previous mental health diagnosis and higher levelsof AjD symptoms (Rossi et al., 2020). Previous
research found strong associations between pre-existing mental health disorders and higher levels ofanxiety or depressive symptom during the pandemic(Neelam et al., 2021). Pre-existing mental health disorders might lead to additional burden and lower abilities to cope with the stressors during a pandemic.
The eﬀects of a current or previous mental healthdisorder on AjD symptoms were signiﬁcantly greaterat the ﬁrst assessment compared to the following
assessments. A mental health disorder might reducethe capacities to cope with the multiple novel stressors
during the early phase of a pandemic. In addition,
many health support facilities were closed in the earlier phase of the pandemic which reduced mental
health support (Duden et al., 2022).
Trauma exposure before the pandemic and trauma
exposure during the pandemic (vs. no trauma
exposure) were related to higher AjD symptom levels.
People with trauma exposure during the pandemic
showed the highest AjD symptom levels at all timepoints. It is well documented that trauma exposure11

is a transdiagnostic risk factor for mental health disorders (Hogg et al., 2023).
The eﬀect of trauma exposure during the pandemic
on AjD symptom levels – but not trauma exposure
before the pandemic – was greater at the second andthird assessment compared to the ﬁrst assessment.
At the third assessment, AjD symptom levels werelower in both the trauma and non-trauma group compared to the second assessment, but the AjD symptom
levels stayed relatively high in the trauma-exposuregroup. These ﬁndings may indicate that the eﬀect of
trauma exposure on AjD symptom levels is more pronounced when lockdown stringency is high.
3.3.1. Work status
Self-employment and retirement were associated withlower AjD symptom levels compared to having nowork. Retirement might be related to fewer workrelated stressors, such as risk of COVID-19 infectionin the workplace. Self-employment, i.e. income froman independent pursuit of economic activity, might
be related to a lower risk of income loss, and a higher
level of control of one’s own ﬁnancial situation.
Employment and freelancing were unrelated to AjD
symptoms compared to having no work, which may
indicate similar levels of distress during the pandemic.
3.4. Pandemic-related stressors
Out of the nine assessed stressor domains, six domains
were signiﬁcantly associated with AjD symptoms. Burden due to ineﬃcient Crisis management and communication of the government was related to higher
AjD symptom levels, pointing to the importance ofeﬃcient and clear governmental communication andmanagement for the mental health of the general
population. In times of a global pandemic, crisis communication is essential to reduce fear and uncertainty
and motivate individuals to act together against healththreats (Su et al., 2021). Inadequate crisis communication, such as ongoing news about COVID-19 infection and death rates is associated with fear anduncertainty (Li et al., 2021; Su et al., 2021). Due tothe increased anxiety, individuals could experience
the pandemic as less manageable and therefore may
have an increased risk of developing AjD. Adequate
governmental management and communication
seems to be particularly important at the beginningof a pandemic, as we found that the eﬀect of this factor
was lower at the last assessment (vs. the ﬁrst).
Distress due to Fear of infection was associated withhigher AjD symptom levels. COVID-19 related fear
could be addressed by preventive interventions to
reduce distress and AjD symptoms. The perceived
Burden of infection was unrelated to AjD symptom
levels, which might be since a low proportion of participants (i.e. 13% at the last assessment) contracted

12

A. LOTZIN ET AL.

COVID-19 in our study. Individuals severely aﬀectedby a COVID-19 infection might have not been able toparticipate in the survey. Distress related to Restricted
face-to-face contact was associated with higher AjD
symptom levels. Earlier research found associations
between social support and lower levels of depression
and anxiety symptoms during the COVID-19 pandemic (Li et al., 2021). Social support can mitigate distress (Cohen & Hoberman, 1983) and is associatedwith better mental health during the COVID-19 pandemic (Li et al., 2021).
Restricted activity including restricted shopping,restaurant visits, and private travelling, was related
to higher AjD symptom levels. Leisure activities signiﬁcantly reduced distress during the ﬁrst lockdownof the COVID-19 pandemic (Takiguchi et al., 2023).
Engaging in activities may be helpful to reduce AjD
symptoms, as they may promote functional coping,social support and opportunities for positive environmental reinforcement (Arends et al., 2012). Preventive
interventions in pandemic contexts may therefore
include the planning and implementation of safe andpermitted activities in public and private spaces.
Distress related to Diﬃcult housing conditions, suchas restricted room space and conﬂicts at home, wasassociated with higher AjD symptom levels. Previous
studies reported associations between poorer housing
conditions and increased levels of depressive symptoms during the COVID-19 pandemic (Amerio
et al., 2020). No access to outdoor space and household crowding were associated with higher levels ofanxiety symptoms (Keller et al., 2022). Hence, diﬃcult
housing conditions seem to contribute to poor mental
health during a pandemic.
Contrary to our expectations, distress due to Problems with childcare (i.e. loss of childcare, diﬃculties
with combining work with childcare) was negatively
related to AjD symptoms, although an earlier study
found associations between diﬃculties obtaining
childcare and depression and anxiety symptoms
during the pandemic (Racine et al., 2021). Parents
who use childcare (and have problems with it)
may be more priviledged in terms of ﬁnancial
ressources compared to parents not using childcare,
which
might
reduceburdenduringapandemic. Moreover, spending time with children
might foster family bonding, which might inturn buﬀer the eﬀects of other pandemic-related
stressors.
Overall, we identiﬁed signiﬁcant predictors of AjD
symptoms among the general populations of elevencountries that included risk and protective factors, aswell as pandemic-related stressors over a period of
18 months of the COVID-19 pandemic. The eﬀect
sizes were in the small to moderate range, with a current poor or satisfactory health condition (vs. very
good health condition) and a current diagnosis of amental health disorder (vs. no mental health disorder)
showing greater eﬀect sizes, followed by news consumption more than 3 h a day (vs. no news consumption), diverse gender, fear of infection, restricted faceto-face contact, and a diﬃcult housing situation. Dueto their comparatively greater impact, these factors
should be prioritized in prevention and intervention
eﬀorts.
The eﬀects of the predictors were mostly additive,
i.e. the size of the eﬀect on AjD symptom levels did
not diﬀer by the time of assessment, relative to therespective comparison group. According to our
results, most of the predictors of AjD symptoms
remained stable over time, independently from diﬀerent levels of lockdown stringency and the chronicity ofthe pandemic.
Nevertheless, we did ﬁnd speciﬁc predictors forwhich the size of the eﬀect on AjD symptoms
depended on the time of assessment. While theeﬀects of a previous mental health disorder and governmental communication and management weremore pronounced at the earlier phase of the pandemic,
the eﬀects of income reduction and trauma exposure
during the pandemic were more pronounced at later
phases of the pandemic. Addressing the identiﬁed predictors of AjD symptoms at an optimal time window,
i.e. when they have stronger eﬀects, may help toreduce AjD symptoms and may improve preparedness
for future pandemics.
3.5. Strengths and limitations
The longitudinal study design, the large sample size
and the use of a well-established measure as thedependent variable are strengths of this study. The
use of a non-probabilistic sample is a weakness of
this study, as it overrepresents individuals with higher
education and women, and underrepresents individuals with no or poor internet accessibility and those
severely aﬀected by a COVID-19 infection. Moreover,the study might have overrepresented individuals withhigher psychological distress as they might be more
inclined to participate in surveys on mental healthproblems (Groves et al., 2004). On the other hand,
individuals with a severe mental health disordermight be underrepresented as they might be morehesitant to participate in a survey (Pierce et al.,2020). Furthermore, the use of self-report measures
could have introduced systematic bias.

4. Conclusions
We found several risk factors and stressors longitudinally associated with AjD symptom levels over the
COVID-19 pandemic. The risk factors and stressors
may help identify individuals with high-risk proﬁles.
These may include individuals with a less than very

EUROPEAN JOURNAL OF PSYCHOTRAUMATOLOGY

good health status, fear of COVID-19 infection, amental health disorder, trauma exposure, lack of social
support, work-related problems, and restricted housing conditions.
Most of the eﬀects of the risk factors and stressors
were relatively stable over the ﬁrst two years of thepandemic, i.e. independent from lockdown stringency
and duration of the pandemic. Few risk factors andstressors showed diﬀerential eﬀects depending on
the time of assessment during the pandemic. Theserisk factors and stressors should be particularly
addressed at the time windows in which they might
have stronger eﬀects. The identiﬁed predictors may
be targeted by psychosocial interventions to reduce
AjD symptoms and to prevent more severe mental
health disorders in future pandemics.

Acknowledgments
The authors thank the collaborators for their support andcontribution to the present paper: Ozan Demirok (teamAustria); Marina Ajdukovic, Helena Bakic, Ines Rezo Bagaric, Tanja Franciskovic (team Croatia); Nino Makhashvili
and Sophio Vibliani (team Georgia); Eleftheria Eugeniou,
George Fevgas, Kostas Messas, Marianna Philippidou,
Eleni Papathanasiou, Anastasia Selidou (team Cyprus/
Greece); Ilaria Cinieri, Alessandra Gallo and Chiara Marangio (team Italia); Monika Kvedaraite and Auguste Nomeikaite (team Lithuania); Joanne Mouthaan, Suzan Soydas,
Marloes Eidhof, Marie José van Hoof and Simon Groen(team the Netherlands); Magdalena Skrodzka and Monika
Folkierska-Żukowska (team Poland); Aida Dias, Camila
Borges, Diana Andringa, Guida Manuel, Joana Beker andJoão Veloso, Francisco Freitas (team Portugal); Kristina
Bondjers, Joseﬁn Sveen, Rakel Eklund, Kerstin Bergh Johannesson and Ida Hensler (team Sweden). We greatly thankthe study team of the coordinating site at University Medical
Center Hamburg-Eppendorf (team Germany) that conducted the overall data management: Laura Kenntemich,Leonie von Huelsen, Sven Buth, Eike Neumann-Runde,Ronja Ketelsen, Lennart Schwierzke, Julia Groß, Laura
Gutewort, and Emily Price.

Author contributions
AL designed the study in cooperation with all members of
the ADJUST consortium formed by the representatives of
the ESTSS countries. All authors recruited study participants and contributed to the data management of therespective site. KS conducted the data analysis. AL drafted
the ﬁrst version of the manuscript. All authors revised sections of the manuscript and approved the ﬁnal version of
the manuscript.

13

Data availability
The detailed sociodemographic information of the dataset
does not fully protect the anonymity of the respondents.
For this reason, the entire dataset cannot be made publicly
available. However, excerpts of the data on a higher aggregation level can be provided upon justiﬁed request by the ﬁrst
author.

Disclosure statement
No potential conﬂict of interest was reported by the authors.

Funding
The data assessment in Poland was supported by the Facultyof Psychology, University of Warsaw, from the funds
awarded by the Ministry of Science and Higher Education,
Poland in the form of a subsidy for the maintenance anddevelopment of research potential in 2020 (501-D125-011250000 zlec.5011000248, 501-D125-20-0004318). The
data assessment in the Netherlands was supported by Two
Dutch insurance companies: DSW and CZ . The remaining
countries did not receive speciﬁc funding for this research.

Ethics, consent, and permissions
Each country obtained ethical approval of the study: Ethics
Committee of the University of Vienna, 00554. Ethics Committee of the University of Urbino ‘Carlo Bo’, 34, 22 July
2020. Ethics Committee of the Department of Psychology,
Faculty of Humanities and Social Sciences, University ofZagreb, 21 May 2020. Ethics Review Board of the Facultyof Social and Behavioural Sciences, Utrecht University, 20360. Ilia State University Faculty of Arts and Science
Research Ethics Committee, 12 June 2020. Local Psychological Ethics Committee at the Centre for Psychosocial Medicine, LPEK-0149. Social Sciences Ethics Review Board(SSERB), University of Nicosia, SSERB 00109. The Swedish
Ethical Review Authority, 2020-03217. Vilnius University
Ethics Committee of Research in Psychology, 44. Ethics
Committee of the Faculty of Psychology, University of Warsaw, 6 July 2020. Ethics Committee of the Medical Faculty,
University of Porto and Centro Hospitalar São João, Porto,
CE 201-20. The National Ethical Review Board in Sweden,
2020-03217. All participants provided informed consent
before taking part in the study. Participants were informedthat they were under no obligation to participate and thatthey could withdraw at any time from the study without
consequences.

ORCID
Annett Lotzin http://orcid.org/0000-0002-2834-8047
Maria Bragesjö http://orcid.org/0000-0003-2246-3842
Maria Böttche http://orcid.org/0009-0002-7568-3937

Data protection and quality assurance

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Research, CIAR, University of Hamburg). Data handlingfollowed the EU General Data Protection Regulation(DSGVO); data will be stored for at least 10 years.

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