Worry and Mental Health in the COVID-19 Pandemic: Vulnerability Factors in the General Norwegian Population

Worry and Mental Health in the COVID-19 Pandemic: Vulnerability Factors in the General Norwegian Population | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research article Worry and Mental Health in the COVID-19 Pandemic: Vulnerability Factors in the General Norwegian Population Ines Blix, Marianne Skogbrott Birkeland, Siri Thoresen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-192098/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background There is an urgent need for knowledge about the mental health consequences of the ongoing pandemic. The aim of this study was to identify vulnerability factors for psychological distress and reduced life satisfaction in the general population. Furthermore, we aimed to assess the role of COVID-related worries for psychological distress and life satisfaction. Methods A presumed representative sample for the Norwegian population (n=1041, response rate=39.9%) responded to a web-survey in May 2020. The participants were asked about potential vulnerability factors including increased risk for severe illness from COVID-19 (underlying illness, older age), socioeconomic disadvantage (living alone, unemployment, economic problems), and pre-existing mental health vulnerability (recent exposure to violence, previous mental health challenges). Additional measures included COVID-related worry, psychological distress, and life satisfaction. Results More than one out of four reported current psychological distress over the threshold for clinically significant symptoms. Socioeconomic disadvantages, including living alone and pre-existing economic challenges, and pre-existing mental health vulnerabilities, including recent exposure to violence and previous mental health problems, were associated with a higher level of psychological distress and a lower level of life satisfaction. Conclusion This study identified several vulnerability factors for mental health problems in the pandemic. Individuals recently exposed to violence and individuals with pre-existing mental health problems are at particular risk. Worrying about the consequences of the pandemic contributes negatively to current mental health. However, worry cannot explain the excess distress in vulnerable groups. Future research should focus on how COVID-related strains contribute to mental health problems for vulnerable groups. Health Economics & Outcomes Research Infectious Diseases Health Policy mental health mental health pandemic COVID Background Emerging evidence shows that the COVID-19 pandemic not only poses a threat to physical health but also to mental health in the community. The pandemic has profound effects on our daily life, and will for many cause major stressors, including the fear of the disease itself, social distancing, and isolation due to the mitigation strategies implemented by the authorities, as well as economic consequences of the mitigation strategies. These stressors may cause worries and influence mental health. A recent review showed high prevalence rates for stress, anxiety, and depression in the general population, across studies more than a third of the respondents scored above the threshold for anxiety and depression( 1 ). One study from Norway reported two to threefold increases in anxiety and depression symptoms, compared to pre-pandemic levels ( 2 ). Although most individuals show resilience and do not experience substantial psychological distress due to the pandemic, some groups in our society may carry a heavier psychosocial burden. As pointed out by scholars in the field, there is an urgent need for research that focuses on the mental health consequences of the ongoing pandemic for the general population, and particularly for vulnerable groups( 3 ). Increased vulnerability for serious illness due to COVID-19, and socioeconomic disadvantages or pre-existing mental health problems are likely to increase the psychosocial burden during the pandemic. A study conducted in Spain during the lockdown in March 2020, showed that previous diagnoses of mental health problems or neurological disorders, having symptoms associated with the virus or having a close relative infected, and female gender were associated with higher levels of anxiety, depression, and posttraumatic stress reactions. Older age and economic stability were associated with lower levels of anxiety, depression, and posttraumatic stress reactions( 4 ). A study from Ireland reported that lost income, younger age, and female gender were significant predictors for anxiety or depression during the lockdown ( 5 ). In a recent review, Xiong et al.( 6 ) reported that among the risk factors associated with mental distress during the COVID-19 pandemic were female gender, younger age (≤ 40 years), and chronic/psychiatric illnesses. Despite the high variability between countries in the burden of the pandemic and the countermeasures, the research so far seems to underscore similarities in vulnerability factors for mental health problems, at least when it comes to young age and female gender. Further research is necessary to identify more specific vulnerability factors and their potential variation across time and place. Research on vulnerability factors provides important knowledge about the groups that carry a higher risk for psychological distress in the ongoing pandemic. However, it is not clear to what extent these vulnerability factors are specific for the pandemic, or in what way the pandemic constitutes an additional burden for vulnerable groups. In this study, we take this research one-step further by examining the relative significance of several vulnerability factors, as well as the significance of worry for mental health and life satisfaction during the pandemic. Several studies have shown that COVID-related worry is associated with psychological distress in the pandemic ( 7 – 9 ). Worry refers to problem-focused thoughts about the future, and these types of thoughts can motivate us to search for goals or solutions ( 10 )and are initially an adaptive response to a threatening situation like the current pandemic. However, when worry becomes excessive or difficult to control, these thoughts can be experienced as negative, and lead to psychological distress( 11 ). COVID-related worry and psychological distress in the population are likely to vary across different places and different phases of the pandemic. Indeed, one study reported significant regional differences across the US, with a higher level of fear and worry in highly affected areas ( 12 ). Hence, there is a need to specify factors associated with psychological distress in different places and different phases of the pandemic to prioritise and target efforts to prevent distress. The COVID-19 pandemic can be long lasting, and there is a need to prevent long-lasting mental health problems. The aim of the present study was to identify vulnerability factors associated with higher levels of psychological distress and lower life satisfaction in the general population. More specifically, we wanted to examine the role of increased risk for severe illness from COVID-19 (due to underlying illness or older age), socioeconomic disadvantage (due to living alone, unemployment, or economic problems), and pre-existing mental health vulnerability (due to exposure to violence or previous mental health challenges). Furthermore, we wanted to assess the contribution of COVID-related worry to psychological distress and life satisfaction. The data in the current study were collected in May 2020. At the time, the COVID-19 situation was described as under control in the Norwegian society, and the government had recently started easing the countermeasures. Schools were gradually opening for more than the four youngest cohorts from May 11th, although most schools did not open for full-day activity until the beginning of June. Most leisure activities were still closed (gyms, cinemas, museums, theatres), however many institutions aimed to reopen or partly reopen during the coming month. The government upheld the rule of physical distance to other people and the advice against non-essential public transport. Employees were instructed to work from home if possible but allowed to attend the office if necessary pending COVID-19 adaptations at the workplace. Thus, our study may reflect a snapshot of the population in a time characterized by 'opening up' the society. Methods Participants and procedure The web-survey was performed by the data collection agency Kantar/Gallup Norway in their panel consisting of approximately 46,000 participants. The panel was constructed to represent the Norwegian general population in miniature. Recruitment to the panel was done by probability sampling, not self-recruitment. The panel is considered representative of the Norwegian 'internet population' (everyone who has access to the internet), which constitutes about 97% of the total Norwegian population. Sampling and weighting were performed based on official statics from Statistics, Norway. Sociodemographic information on panel members is updated each year. Panel members received points for their participation according to the number of minutes estimated to complete the questions. In the current study, estimated to 20 minutes completion time, participants were rewarded 20 points (equals 20 Norwegian Kroner, 1.9 Euro, or 2.2 USD). The data collection was performed within one week (19–26 May 2020). In the present web survey, Kantar/Gallup approached a total of 2612 individuals stratified on gender, age, education, and area of residence. In total, 39.9% (N = 1041) completed the survey, 55.8 (N = 1457) did not respond, 2.7% (N = 71) started but did not complete, 1.6% (N = 41) clicked on the link to participate but did not confirm agreement to the terms of the study, and 0.1% (N = 2) withdrew from the study. Our study participants did not differ from non-responders in gender or education (Table 1 ), but the sample was highly skewed toward older age, with a mean age of 54.1 in responders and 43.3 in non-responders. Subgroup analyses of various age groups showed a steady trend from a poor participation rate of 17.0% in the youngest age group (age 18–29) increasing to 57.1% in the oldest (60 years and above). According to Kantar (personal communication, 2020), the problem of recruiting young adults is a general survey trend and not specific to the current study. However, caution should be taken when interpreting results for the youngest age group. Table 1 Sample Characteristics for Responders and Non-Responders. Characteristics Respondents % (N) / mean (SD) Non-respondents % (N) / mean (SD) X2/t-test p value Gender: Female 49.0% (510) 50.5% (794) 0.438 Age (mean) 54.1 (15.9) 43.3 (17.2) < 0.001 Age groups 18–29 (n = 524) 17.0% (89) 83.0% (435) 30–44 (n = 664) 31.7% (211) 68.3% (454) 45–59 (n = 659) 46.3% (305) 53.7% (354) 60 and above (n = 764) 57.1% (436) 42.9% (328) < 0.001 Education level: College/university 35.9% (374) 36.3% (570) 0.853 Currently working or studying* 60.9% (633) 77.2% (1203) < 0.001 Retired from work 27.2% (283) 11.4% (178) < 0.001 Living alone ** 22.3% (229) 19.4% (294) 0.075 Table 1 Sample Characteristics in Responders and Non-Responders. *0.5% (n = 14) missing, **2.5% (n = 64) missing The Regional Committee for Medical and Health Research Ethics approved the study (Registration number 133226/2020). The questionnaire was briefly piloted before the data collection. The survey included an open box for comments and feedback on the study. The vast majority of comments were positive or even grateful, although several individuals remarked that some work-related questions were not suitable for those who had retired. Measures Increased vulnerability for serious illness due to COVID-19 (age and underlying disease) The participants were asked if they had a chronic illness or a health condition that constituted an increased risk for severe COVID-19 illness. Socioeconomic disadvantages (living alone, pre-existing economic challenges, COVID-related unemployment) Pre-existing economic problems were measures by asking the participants whether their economic situation (before the pandemic) was better than most people, like most people, or worse than most people. The participants were also asked if they had lost their job or being temporarily laid off due to the pandemic, if yes on any of these, this was coded as “unemployed”. Pre-existing mental health vulnerability (previous/pre-existing mental health challenges, recent exposure to violence) : The participants were asked whether or not they had previously received treatment for mental health problems. To measure exposure to violence the last month, the participants were asked if they had experienced that someone had: 1) repeatedly ridiculed you, put you down, ignored you, or told you that you were no good 2) slapped, pinched, pulled, or shook you violently, 3) hit you with a fist or a hard object, kicked, strangulated, beaten up, threatened with a weapon, or physically attacked in other ways, 4) exposed you to any form of sexual assault or violation. COVID-related worry The participants were asked to indicate their level of worry on a scale from 1 (not worried) to 7(very worried) for 12 questions about COVID-related worries These questions were adapted from the COSMO study ( 13 ). In this study, we wanted to capture an underlying tendency to worry about COVID-19-related issues. Therefore, we conducted a preliminary confirmatory factor analysis (CFA) of our 12 proposed worries related to COVID-19. The one-factor CFA with all 12 items showed poor model fit, and that several of the proposed items loaded poorly (see Table 2 ). We excluded items one by one until the model fit indices showed acceptable fit and all factor loadings were appropriate. We considered values of CFI above .95 and RMSEA above .08 to indicate acceptable model fit( 14 , 15 ). Factor loadings above .70 were considered excellent .63 very good, .55 good, and .45 fair ( 16 ). When a one-factor model showed acceptable fit, six items remained. We computed a total mean score of these six COVID-related worries and used this in the further analyses. Table 2 COVID-related worry Mean (SD), factor loadings for 12 and 6 items. Worries (1-don’t worry at all- 7-worry a lot) Mean (SD) Factor loadings 12-item one- factor model Factor loadings 6-item one- factor model *Losing someone I love 3.95 (1.63) .70 .74 *Becoming seriously ill from the virus 3.29 (1.55) .66 .68 *Infecting others 3.71 (1.67) .62 .65 Not being able to get the medicines or treatment that I need 2.90 (1.64) .62 *Health system being overloaded 3.70 (1.55) .74 .70 Economic recession in Norway 4.60 (1.47) .52 Become unemployed 2.33 (1.70) .37 Not be able to carry out plans that are important to me 3.54 (1.65) .53 *Not be able to visit people who depend on me 4.07 (1.71) .66 .62 The society will become more egoistic 3.79 (1.66) .52 The welfare society will collapse? 3.54 (1.65) .56 *A new outbreak of COVID-19 4.30 (1.52) .77 .79 Model fit χ 2 683.51 59.38 df 54 9 CFI 0.85 0.98 RMSEA 0.11 0.07 Psychological distress the last two weeks was measured by an abbreviated 5-item version of the Hopkins Symptom Checklist-25 (HSCL) ( 17 ): Feeling hopeless about the future; feeling blue; worrying too much about things; feeling fearful; feeling tense or worked up. Participants responded on a scale from 0 (not bothered) to 3 (bothered a great deal). This abbreviated version of the HSCL has shown good psychometric properties and has previously been found to correlate highly (r = 0.92) with the HSCL-25 in a general population sample ( 18 ). We used a cut-off value of > 2, which has achieved the best combination of specificity, sensitivity, and predictive values ( 19 ). Cronbach’s Alpha for the 5-item HSCL was 0.91 in the present study. Life satisfaction was measured by a single question from the European Social Survey ( https://www.europeansocialsurvey.org/ ): ‘All things considered, how satisfied are you with your life as a whole nowadays?’ Participants responded on a scale from 0 (Extremely dissatisfied) to 10 (Extremely satisfied). All the questions in this study can be found in the supplementary file. Statistical analyses Univariate and multivariate linear regression analyses were performed with psychological distress and life satisfaction as the dependent variables. Univariate linear regression analyses were performed with pre-existing health problems, age older than 65, living alone, pre-existing economic challenges, becoming unemployed during the pandemic, exposure to violence the last month, previous mental health challenges, and female gender entered separately as independent variables and psychological distress and life satisfaction as dependent variables. The same independent variables were included simultaneously in two multivariate linear regression analyses, with psychological distress and life satisfaction as dependent variables. COVID-related worry was added as an independent variable together with the above-mentioned variables. Statistical analyses were conducted with SPSS version 26 for Windows (SPSS, Inc.) and Mplus 8.3. Results Of the 1041 participants, 49.0% (n = 510) were females. The age range was between 18 and 89 years old, with a mean of 54.1 (SD = 15.9). A percentage of 35.9% (n = 374) had college/university education. 25.7% (268 participants) reported current psychological distress over the threshold for clinically significant symptoms. The CFA for COVID-related worries showed an acceptable fit and all factor loadings were appropriate when six covid related worries were included in the one-factor model. Means, standard deviations, model fit, and factor loadings of the 12-item one-factor model, and the final 6-item one-factor model can be seen in Table 2 . As can be seen in Table 2 , the highest mean level of worry was about a new outbreak of COVID-19, not be able to visit people who depend on me, and the economic recession in Norway. Table 3 shows the mean number and percentage of the participants that reported each of the potential vulnerability factors. Old age (> 65 years), underlying illness, living alone, and pre-existing mental health problems were the most frequent potential vulnerability factors. For each of the vulnerability factors, and the sample in total, mean scores for psychological distress, COVID-related worry, and life satisfaction are reported. Individuals who had recently been exposed to violence had pre-existing mental health problems, or pre-existing economic challenges reported particularly high levels of psychological distress. Table 3 Vulnerability factors, n (%), mean (SD) for psychological distress, COVID-related worry and life satisfaction. Vulnerability factors N (%) COVID-related worry, mean (SD) Psychological distress, mean (SD) Life satisfaction (1–10), mean (SD) At risk for severe illness from COVID-19 Underlying illness 255 (24.5) 4.07 (1.31) 1.62 (.66) 7.69 (1.80) Old age (> 65 years) 307 (29.5) 3.67 (1.29) 1.42 (.45) 8.29 (1.55) Socioeconomic disadvantage Living alone 231 (22.2) 3.90(1.31) 1.76 (.74) 7.42 (1.98) Pre-existing economic challenges 129 (12.5) 4.17 (1.20) 2.05 (.84) 6.61 (1.98) COVID-related unemployed 109 (10.5) 3.80 (1.27) 1.80 (.82) 7.72 (2.11) Pre-existing mental health vulnerability Recent violence exposure 59 (5.7) 4.45 (1.35) 2.46 (.77) 6.78 (2.41) Pre-existing mental health problems 223 (21.4) 4.01 (1.30) 2.06 (.81) 7.14 (2.01) Total sample 4.30 (1.52) 1.61 (.67) 7.84 (1.80) Linear regression analyses showed that living alone, pre-existing economic challenges, being exposed to violence within the last month, previous mental health problems, and female gender, were significantly associated with a higher level of psychological distress. Conversely, age higher than 65 years was negatively associated with psychological distress. When mutually adjusted (model 1, Table 4 ), all the same factors, except COVID-related unemployment, were independently associated with a higher level of psychological distress. Table 4 Unadjusted and adjusted linear regression analyses for vulnerability factors and COVID-related worries predicting psychological distress. Unadjusted Model 1: adjusted Model 2: adjusted, worry as predictor B 95% CI p β B 95% CI p β B 95% CI p β At risk for severe illness from COVID-19 Underlying illness .03 − .07,.12 .591 .02 .03 − .06, .12 .497 .03 − .03 − .11,.05 .467 − .02 Older age (> 65 years) − .28 − .36,-.19 < .001 − .18 − .16 − .25, − .08 < .001 − .11 − .13 − .21-.05 .002 − .09 Socioeconomic disadvantage Living alone .19 .09,.29 < .001 .12 .11 .02,.20 .018 .07 .12 .03,.20 .028 .07 Pre-existing economic challenges .52 .40,.64 < .001 .26 .37 .26,.48 < .001 .18 .32 .22,.43 < .001 .16 COVID-related unemployed .21 .08,.34 .002 .10 .04 − .08,.16 .641 .04 .06 − .06,.17 .318 .03 Pre-existing mental health vulnerability Recent violence exposure .91 .74,1.08 < .001 .31 .70 .54,.87 < .001 .24 .57 .42,.73 < .001 .20 Pre-existing mental health problems .57 .47,.66 < .001 .35 .42 .33,.51 < .001 .26 .41 .32,.49 < .001 .26 Female gender .17 .09,.26 < .001 .13 .12 .05,.19 .001 .09 .06 − .01,.13 .113 .04 COVID-related worries .17 .14,.20 < .001 .31 R square .25 .33 *Unadjusted: all predictors were entered separately. Model 1: all predictors except were included simultaneously Model 2: COVID-related worry was entered while adjusting for all the vulnerability factors. Adding COVID-related worry as a predictor in the multivariate regression analysis (model 2, Table 4 ) did not substantially attenuate the associations between the suggested vulnerability factors and psychological distress. All the same factors, except female gender, were still significantly associated with distress. The magnitude of the regression coefficient for recent violence exposure seemed to be lower in the adjusted model, but the confidence intervals were overlapping. A higher level of COVID-related worry was uniquely associated with a higher level of psychological distress. Linear regression analyses showed that living alone, pre-existing economic challenges, being exposed to violence within the last month and previous mental health problems, were associated with lower levels of reported life satisfaction. Age higher than 65 years was associated with a higher level of life satisfaction. When mutually adjusted (model 1, Table 5 ), all the same factors and underlying illness were independently associated with a lower level of life satisfaction. Table 5 Unadjusted and adjusted linear regression analyses for vulnerability factors and COVID-related worries predicting life satisfaction Unadjusted Model 1: adjusted Model 2: adjusted, worry as predictor B 95% CI p β B 95% CI p β B 95% CI p β At risk for severe illness from COVID-19 Underlying illness − .22 − .47,.04 .095 − .05 − .27 − .52,-.03 .029 − .07 − .20 − .44,.05 .113 − .05 Older age (> 65 years) .63 .40,.87 < .001 .16 .52 .28,.77 < .001 .13 .46 .22,.70 < .001 .13 Socioeconomic disadvantage Living alone − .56 − .82,-.30 < .001 − .13 − .45 − .70,-.19 .001 .10 − .43 − .68,-.18 < .001 − .10 Pre-existing economic challenges -1.41 -1.72,-.1.10 < .001 − .27 -1.18 -1.49,-.86 < .001 − .22 -1.11 -1.41,-.80 < .001 − .21 COVID-related unemployed − .14 − .50,.22 .453 − .02 .04 − .30,-.38 .818 .01 .01 − .33,.34 .980 .01 Pre-existing mental health vulnerability Recent violence exposure -1.13 − .1.60,-.66 < .001 − .15 − .56 -1.03,-.10 .017 − .07 − .39 − .85,.07 .096 − .05 Pre-existing mental health problems − .90 -1.17,-.64 < .001 − .21 − .59 − .85,-.33 < .001 − .14 − .58 − .84,-.33 < .001 − .14 Female gender − .21 − .43,.01 .057 − .06 − .10 − .31,.11 .334 − .03 − .03 − .23,.18 .816 − .01 COVID-related worries − .24 − .32,.-15 < .001 − .16 R square .14 .16 *Unadjusted: all predictors were entered separately. Model 2: all predictors except were included simultaneously Model 3: COVID-related worry was entered while adjusting for all the vulnerability factors. When COVID-related worry was added as a predictor in the multivariate regression analysis (model 2) the association between recent violence exposure and life satisfaction the confidence intervals were somewhat attenuated but the confidence intervals were overlapping. When adjusting for worry the association between underlying illness and life satisfaction was also lower, but with overlapping confidence intervals. Adding worry as a predictor did not considerably attenuate the associations between the other independent variables and life satisfaction. Age higher than 65 years was associated with a higher level of life satisfaction. The results showed that a higher level of COVID-related worry was uniquely associated with a lower level of life satisfaction. We performed the same analyses, weighted on gender, age, education, and area of residence, and these showed very similar results. Discussion A substantial proportion of the general Norwegian population experienced significant psychological distress in the first opening-up phase of the COVID-19 pandemic. Indeed, more than one out of four reported current psychological distress over the threshold for clinically significant symptoms. Whereas population data from 2019 show that 14% scored above the clinical threshold ( 20 ). Similar findings have been reported in studies from Ireland ( 5 ), the UK ( 21 ), and Denmark ( 22 ). It should be noted that at the time of the data collection, in May 2020, Norway was not hit particularly hard by the pandemic compared to other countries as indicated by a relatively low number of fatalities ( 23 ) and sufficient hospital intensive care capacity to handle the comparably modest number of covid-19 patients. Additionally, the countermeasures were not among the strictest in a European context, for example, a curfew was not implemented. Thus, the present study adds to the literature by showing that the pandemic can lead to increased psychological distress in the population, even in a situation with low infection rates and moderate countermeasures. Some groups may carry a heavier psychosocial burden in the pandemic. In this study, we examined the role of three types of vulnerability factors: increased risk for severe illness from COVID-19 (due to underlying illness or older age), socioeconomic disadvantage (due to living alone, unemployment or economic problems), and pre-existing mental health vulnerability (due to exposure to violence or previous mental health challenges). Taken together, our findings showed that socioeconomic disadvantage and pre-existing mental health problems, recent violence exposure, but no increased risk for severe illness, was associated with a higher level of psychological distress and a lower level of life satisfaction. Contrary to our expectations, we found no evidence for an association between increased risk for severe illness from COVID-19, and a higher level of psychological distress. This contrasts with previous studies from Turkey conducted in April 2020 ( 24 ), from Spain conducted in March 2020 ( 25 ) and from Italy conducted in March 2020 ( 26 ), which all reported that chronic disease was associated with higher levels of psychological distress. However, it is important to note that the context and phase of the pandemic were different across these studies. Whereas our study was performed in an opening up phase, in a context of moderate countermeasures, and low levels of fatalities, the studies in Italy, Spain, and Turkey were conducted in a situation where the pandemic was rising, the intensive care capacities were challenged and there was a high level of fatalities. Likely, individuals living with increased risk for severe illness due to COVID-19 would experience a higher level of perceived threat in a situation where the pandemic is currently not under control. Older age was not identified as a vulnerability factor for psychological distress. On the contrary, the results showed that older age was associated with a lower level of psychological distress, and a higher level of life satisfaction. Similar findings were reported by González-Sanguino an colleagues( 4 ). Other studies conducted during the COVID-19 pandemic have also reported that younger, but not older age is associated with a higher level of psychological distress ( 6 ). In a diary study, from March/April 2020, Klaiber, Wen, DeLongis, and Sin ( 27 ) reported that older and younger participants experienced comparable levels of stress, but younger participants expressed more worry. It should be mentioned that the tendency for older adults to worry less than younger adults have also been found in previous studies conducted outside a pandemic context ( 28 , 29 ). Furthermore, several studies have also shown that life satisfaction increases with older age in adulthood ( 30 ).Maybe older individuals are more able to cope with the uncertain situation that the COVID-19 pandemic represents and that more is at stake for young people and their future. Indeed, older adults are more experienced in coping with problems and it has been suggested that older adults are more capable of regulating their emotions ( 31 ). The present results showed that socioeconomic disadvantages, including living alone and pre-existing economic challenges, were associated with a higher level of psychological distress, and a lower level of life satisfaction. This illustrates the disproportionate impact of the pandemic and the significance of where pre-existing resources. Some individuals have lower access to the safety net of economic and other resources, and for these people, the pandemic constitutes a risk for further losses and over time, and further social marginalisation. Pre-existing mental health vulnerabilities, including recent exposure to violence and previous mental health problems, were associated with a higher level of psychological distress and a lower level of life satisfaction. Previous studies have also shown that previous or pre-existing mental health problems are a vulnerability factor during the pandemic (for a review see Xiong et al. 2020), but to our knowledge, the present study was the first to investigate recent exposure to violence as a vulnerability factor. It has been discussed whether lockdown, closed schools, and social isolation may result in domestic violence. Although this study could not compare violence exposure to previous levels, our results underscore the importance of targeting this group for prevention purposes. Many of the violence-exposed individuals may already be known within health services and child protection services, and outreach services may be suitable to ensure their safety and support their coping. These present findings highlight the need to prevent discontinuation of activities or treatment for individuals with pre-existing mental health vulnerabilities. As pointed out by Galea et al. ( 32 ), efforts should be made to facilitate connection with individuals at risk for social marginalisation during the pandemic. Although worrying initially is an adaptive response to the pandemic, the present results suggest that COVID-related worry can play an important role in psychological distress and life satisfaction in the pandemic. Even when adjusting for all the vulnerability factors, a higher level of COVID-related worry was significantly associated with a higher level of psychological distress, and a lower level of life satisfaction. This is in line with previous studies reporting a link between COVID-related worry and psychological distress ( 7 – 9 ).The present study extends these findings by examining the role of worry for psychological distress in vulnerable groups. However, the results showed that COVID-related worry can only to a small extent explain the higher levels of psychological distress and lower level of life satisfaction among vulnerable groups. The vulnerability factors were independently associated with psychological distress and lower level of life satisfaction. Individuals with these vulnerabilities may also have less access to resources such as social support, which might have an alleviating effect on the strains of the pandemic. Thus, both individuals with and without vulnerabilities can worry about the pandemic so much that it brings about psychological distress and lessen their quality of life. COVID-related worry seems to partly explain why women report more distress in the pandemic. However, the present results do not suggest that COVID-related worry can explain the excess distress in vulnerable groups. For example, our results do not indicate that it because of COVID-related worry that an individual exposed to recent violence is more distressed. Other factors, not included in our study, maybe more important, such as loss of social network, loss of treatment contacts, unavailability of child protection services, or increased threat to financial security. The present study has some strengths and limitations. A stratified probability sample, with a 39.9% response rate adds to the strengths of the study. Although we cannot exclude a self-selection bias, we could assess the representability by comparing demographic characteristics between responders and non-responders. We found that our sample was skewed in terms of high age. However, analyses weighted for age provided similar results. It should also be mentioned that systematic response rate biases are more likely to affect levels of vulnerability factors, COVID-related worries, psychological distress, and life satisfaction than estimates of their associations. Additionally, it is important to emphasise that the present study is cross-sectional and only shows a snapshot of COVID-related worries, life satisfaction, and psychological distress at a particular time and place. In the present study, we initially asked about different types of worries related to COVID-19, and the results of the CFA identified health-related worries as a unitary factor. However, other types of worries that we did not measure are probably also important for psychological distress and life satisfaction. Different types of worries will probably affect different vulnerable groups, and the most prevalent worries will probably change with time, local context, political and economic circumstances. Conclusion In conclusion, our study identified several unique vulnerability factors for psychological distress and lower life satisfaction in the pandemic. In particular, individuals recently exposed to violence, and individuals with pre-existing mental health problems were at risk. Worrying about the consequences of the pandemic can contribute negatively to current mental health. However, worry does not seem to explain the excess distress in vulnerable groups. The COVID-19 pandemic is still ongoing and is likely to continue to affect us for a long time to come. An understanding of how the pandemic develops through several phases and how vulnerabilities and worries may change throughout the pandemic will benefit people who struggle with strains and worries. Interventions should specifically target vulnerable groups to prevent long-term suffering. Future research should make an effort to identify which COVID-related strains make life so difficult for vulnerable groups in the pandemic. Abbreviations CFA- Confirmatory factor analysis COVID-corona virus disease HSCL- Hopkins Symptom Checklist Declarations Funding statement: This research received no specific grant from any funding agency, commercial or not-for-profit sectors. Competing interests: The authors declare that they have no competing interests. Ethics approval and consent to participate: The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. The Regional Committee for Medical and Health Research Ethics approved the study (Registration number 133226/2020). Written informed consent was obtained from all participants. Consent for publication: Not applicable Availability of data and materials: The data are not publicly available due to them containing information that could compromise research participant privacy and consent. Authors’ contributions: I.B., M.S.B and S.T. developed the study concept, the study design and performed the data collection. I.B. performed the data analysis and interpretation, and drafted the paper. S.T. and M.S.B. provided critical revisions. All authors approved the final version of the paper for submission. Acknowledgements: Not applicable References Salari N, Hosseinian-Far A, Jalali R, Vaisi-Raygani A, Rasoulpoor S, Mohammadi M, et al. Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: a systematic review and meta-analysis. Globalization health. 2020;16(1):1–11. Ebrahimi OV, Hoffart A, Johnson SU. The mental health impact of non-pharmacological interventions aimed at impeding viral transmission during the COVID-19 pandemic in a general adult population and the factors associated with adherence to these mitigation strategies. 2020. Holmes EA, O'Connor RC, Perry VH, Tracey I, Wessely S, Arseneault L, et al. Multidisciplinary research priorities for the COVID-19 pandemic: a call for action for mental health science. The Lancet Psychiatry. 2020. González-Sanguino C, Ausín B, ÁngelCastellanos M, Saiz J, López-Gómez A, Ugidos C, et al Mental health consequences during the initial stage of the 2020 Coronavirus pandemic (COVID-19) in Spain. Brain, Behavior, and Immunity. 2020. Hyland P, Shevlin M, McBride O, Murphy J, Karatzias T, Bentall RP, et al. Anxiety and depression in the Republic of Ireland during the COVID-19 pandemic. Acta Psychiatr Scand. 2020;142(3):249–56. Xiong J, Lipsitz O, Nasri F, Lui LM, Gill H, Phan L, et al. Impact of COVID-19 pandemic on mental health in the general population: A systematic review. Journal of affective disorders. 2020. El-Gabalawy R, Sommer J. We are at risk too: The disparate impacts of the pandemic on younger generations. medRxiv. 2020. Elmer T, Mepham K, Stadtfeld C. Students under lockdown: Assessing change in students’ social networks and mental health during the COVID-19 crisis. 2020. Kampfen F, Kohler IV, Ciancio A, de Bruin WB, Maurer J, Kohler H-P. Predictors of Mental Health during the early Covid-19 Pandemic in the US: role of economic concerns, health worries and social distancing. medRxiv. 2020. Sweeny K, Dooley MD. The surprising upsides of worry. Soc Pers Psychol Compass. 2017;11(4):e12311. Muris P, Roelofs J, Rassin E, Franken I, Mayer B. Mediating effects of rumination and worry on the links between neuroticism, anxiety and depression. Personality Individ Differ. 2005;39(6):1105–11. Fitzpatrick KM, Harris C, Drawve G. Fear of COVID-19 and the mental health consequences in America. Psychological trauma: theory, research, practice, and policy. 2020. Betsch C, Wieler LH, Habersaat K. Monitoring behavioural insights related to COVID-19. The Lancet. 2020;395(10232):1255–6. Browne MW, Cudeck R. Alternative ways of assessing model fit. Sociological Methods Research. 1992;21(2):230–58. Lt Hu, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal. 1999;6(1):1–55. Comrey A, Lee H. A First Course in Factor Analysis (2nd edn.) Lawrence Earlbaum Associates. Publishers: Hillsdale, New Jersey. 1992. Derogatis LR, Lipman RS, Rickels K, Uhlenhuth EH, Covi L. The Hopkins Symptom Checklist (HSCL): A self-report symptom inventory. Behavioral science. 1974;19(1):1–15. Tambs K, Moum T. How well can a few questionnaire items indicate anxiety and depression? Acta Psychiatr Scand. 1993;87(5):364–7. Strand BH, Dalgard OS, Tambs K, Rognerud M. Measuring the mental health status of the Norwegian population: a comparison of the instruments SCL-25, SCL-10, SCL-5 and MHI-5 (SF-36). Nord J Psychiatry. 2003;57(2):113–8. Norway S. Utdanning-statistikk-SSB.[Education-statistics-SSB]. 2019. Shevlin M, McBride O, Murphy J, Miller JG, Hartman TK, Levita L, et al. Anxiety, Depression, Traumatic Stress, and COVID-19 Related Anxiety in the UK General Population During the COVID-19 Pandemic. 2020. Sønderskov KM, Dinesen PT, Santini ZI, Østergaard SD. The depressive state of Denmark during the COVID-19 pandemic. Acta neuropsychiatrica. 2020:1–3. Worldometers. COVID-19 Corona virus pandemic. 2020 [November 09, 2020:[Available from: https://www.worldometers.info/coronavirus/ . Özdin S, Bayrak Özdin Ş. Levels and predictors of anxiety, depression and health anxiety during COVID-19 pandemic in Turkish society: The importance of gender. International Journal of Social Psychiatry. 2020:0020764020927051. Ozamiz-Etxebarria N, Idoiaga Mondragon N, Dosil Santamaría M, Picaza Gorrotxategi M. Psychological symptoms during the two stages of lockdown in response to the COVID-19 outbreak: an investigation in a sample of citizens in Northern Spain. Frontiers in psychology. 2020;11:1491. Mazza C, Ricci E, Biondi S, Colasanti M, Ferracuti S, Napoli C, et al. A nationwide survey of psychological distress among italian people during the COVID-19 pandemic: Immediate psychological responses and associated factors. International Journal of Environmental Research Public Health. 2020;17(9):3165. Klaiber P, Wen JH, DeLongis A, Sin NL. The ups and downs of daily life during COVID-19: Age differences in affect, stress, and positive events. The Journals of Gerontology: Series B; 2020. Basevitz P, Pushkar D, Chaikelson J, Conway M, Dalton C. Age-related differences in worry and related processes. The International Journal of Aging Human Development. 2008;66(4):283–305. Gonçalves DC, Byrne GJ. Who worries most? Worry prevalence and patterns across the lifespan. Int J Geriatr Psychiatry. 2013;28(1):41–9. Baird BM, Lucas RE, Donnellan MB. Life satisfaction across the lifespan: Findings from two nationally representative panel studies. Soc Indic Res. 2010;99(2):183–203. Mather M, Carstensen LL. Aging and motivated cognition: The positivity effect in attention and memory. Trends Cogn Sci. 2005;9(10):496–502. Galea S, Merchant RM, Lurie N. The mental health consequences of COVID-19 and physical distancing: The need for prevention and early intervention. JAMA internal medicine. 2020;180(6):817–8. Cite Share Download PDF Status: Under Review Version 1 posted Review # 2 received at journal 28 Feb, 2021 Editorial decision: Major revision 28 Feb, 2021 Review # 1 received at journal 16 Feb, 2021 Reviewer # 4 agreed at journal 09 Feb, 2021 Reviewer # 3 agreed at journal 08 Feb, 2021 Reviewer # 2 agreed at journal 05 Feb, 2021 Editor assigned by journal 28 Jan, 2021 Reviewers invited by journal 28 Jan, 2021 Reviewer # 1 agreed at journal 28 Jan, 2021 Submission checks completed at journal 28 Jan, 2021 Editor invited by journal 28 Jan, 2021 First submitted to journal 17 Dec, 2020 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-192098","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":9768364,"identity":"44bf4f26-3559-474c-a58a-c48a0d472cff","order_by":0,"name":"Ines Blix","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACNhDB2GDBw8DewEySFgkeBp4DQC0JxFoF1MLAIJFApBY+6ebDHxh3SMgY3Hx72Jj3Rx2DOXsDAYfJHEuTYDwjwSM5Oy85mSfhMINlzwECWiRyzBgY2yR4+KVzjA/OSDjAYHCDgOvYJPI/fwBpYZM8A9JSR4yWHAYJsC0SPMYJH4BBQISWNDOJRJBfenKMDT6kHeYxOEPAL/Izkh9/+LjDxt7g+BljiQSbOjmD4w34tYABskt4iFA/CkbBKBgFo4AQAACmCDh97WSD0AAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-1603-6281","institution":"Norwegian Centre for Violence and Traumatic Stress Studies","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ines","middleName":"","lastName":"Blix","suffix":""},{"id":9768365,"identity":"ccb5c82c-d0c1-4c3d-9746-cc7330abaa06","order_by":1,"name":"Marianne Skogbrott Birkeland","email":"","orcid":"","institution":"Norwegian centre for violence and traumatic stress studies","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Marianne","middleName":"Skogbrott","lastName":"Birkeland","suffix":""},{"id":9768366,"identity":"7b5eda27-f460-4bc6-894f-9fbc76338833","order_by":2,"name":"Siri Thoresen","email":"","orcid":"","institution":"Norwegian centre for violence and traumatic stress studies","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Siri","middleName":"","lastName":"Thoresen","suffix":""}],"badges":[],"createdAt":"2021-01-30 21:09:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-192098/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-192098/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":13653979,"identity":"805d722b-97dc-4c8d-8bc2-d6c3891b7147","added_by":"auto","created_at":"2021-09-17 09:55:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":436312,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-192098/v1/4728e6bd-ca5d-4ccc-bca5-fcba4c841ac0.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eWorry and Mental Health in the COVID-19 Pandemic: Vulnerability Factors in the General Norwegian Population\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003eEmerging evidence shows that the COVID-19 pandemic not only poses a threat to physical health but also to mental health in the community. The pandemic has profound effects on our daily life, and will for many cause major stressors, including the fear of the disease itself, social distancing, and isolation due to the mitigation strategies implemented by the authorities, as well as economic consequences of the mitigation strategies. These stressors may cause worries and influence mental health. A recent review showed high prevalence rates for stress, anxiety, and depression in the general population, across studies more than a third of the respondents scored above the threshold for anxiety and depression(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). One study from Norway reported two to threefold increases in anxiety and depression symptoms, compared to pre-pandemic levels (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Although most individuals show resilience and do not experience substantial psychological distress due to the pandemic, some groups in our society may carry a heavier psychosocial burden. As pointed out by scholars in the field, there is an urgent need for research that focuses on the mental health consequences of the ongoing pandemic for the general population, and particularly for vulnerable groups(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIncreased vulnerability for serious illness due to COVID-19, and socioeconomic disadvantages or pre-existing mental health problems are likely to increase the psychosocial burden during the pandemic. A study conducted in Spain during the lockdown in March 2020, showed that previous diagnoses of mental health problems or neurological disorders, having symptoms associated with the virus or having a close relative infected, and female gender were associated with higher levels of anxiety, depression, and posttraumatic stress reactions. Older age and economic stability were associated with lower levels of anxiety, depression, and posttraumatic stress reactions(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). A study from Ireland reported that lost income, younger age, and female gender were significant predictors for anxiety or depression during the lockdown (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In a recent review, Xiong et al.(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) reported that among the risk factors associated with mental distress during the COVID-19 pandemic were female gender, younger age (\u0026le;\u0026thinsp;40\u0026nbsp;years), and chronic/psychiatric illnesses. Despite the high variability between countries in the burden of the pandemic and the countermeasures, the research so far seems to underscore similarities in vulnerability factors for mental health problems, at least when it comes to young age and female gender. Further research is necessary to identify more specific vulnerability factors and their potential variation across time and place.\u003c/p\u003e \u003cp\u003eResearch on vulnerability factors provides important knowledge about the groups that carry a higher risk for psychological distress in the ongoing pandemic. However, it is not clear to what extent these vulnerability factors are specific for the pandemic, or in what way the pandemic constitutes an additional burden for vulnerable groups. In this study, we take this research one-step further by examining the relative significance of several vulnerability factors, as well as the significance of worry for mental health and life satisfaction during the pandemic. Several studies have shown that COVID-related worry is associated with psychological distress in the pandemic (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Worry refers to problem-focused thoughts about the future, and these types of thoughts can motivate us to search for goals or solutions (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)and are initially an adaptive response to a threatening situation like the current pandemic. However, when worry becomes excessive or difficult to control, these thoughts can be experienced as negative, and lead to psychological distress(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCOVID-related worry and psychological distress in the population are likely to vary across different places and different phases of the pandemic. Indeed, one study reported significant regional differences across the US, with a higher level of fear and worry in highly affected areas (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Hence, there is a need to specify factors associated with psychological distress in different places and different phases of the pandemic to prioritise and target efforts to prevent distress. The COVID-19 pandemic can be long lasting, and there is a need to prevent long-lasting mental health problems. The aim of the present study was to identify vulnerability factors associated with higher levels of psychological distress and lower life satisfaction in the general population. More specifically, we wanted to examine the role of increased risk for severe illness from COVID-19 (due to underlying illness or older age), socioeconomic disadvantage (due to living alone, unemployment, or economic problems), and pre-existing mental health vulnerability (due to exposure to violence or previous mental health challenges). Furthermore, we wanted to assess the contribution of COVID-related worry to psychological distress and life satisfaction.\u003c/p\u003e \u003cp\u003eThe data in the current study were collected in May 2020. At the time, the COVID-19 situation was described as under control in the Norwegian society, and the government had recently started easing the countermeasures. Schools were gradually opening for more than the four youngest cohorts from May 11th, although most schools did not open for full-day activity until the beginning of June. Most leisure activities were still closed (gyms, cinemas, museums, theatres), however many institutions aimed to reopen or partly reopen during the coming month. The government upheld the rule of physical distance to other people and the advice against non-essential public transport. Employees were instructed to work from home if possible but allowed to attend the office if necessary pending COVID-19 adaptations at the workplace. Thus, our study may reflect a snapshot of the population in a time characterized by 'opening up' the society.\u003c/p\u003e "},{"header":"Methods","content":" \u003cp\u003eParticipants and procedure\u003c/p\u003e \u003cp\u003eThe web-survey was performed by the data collection agency Kantar/Gallup Norway in their panel consisting of approximately 46,000 participants. The panel was constructed to represent the Norwegian general population in miniature. Recruitment to the panel was done by probability sampling, not self-recruitment. The panel is considered representative of the Norwegian 'internet population' (everyone who has access to the internet), which constitutes about 97% of the total Norwegian population. Sampling and weighting were performed based on official statics from Statistics, Norway. Sociodemographic information on panel members is updated each year. Panel members received points for their participation according to the number of minutes estimated to complete the questions. In the current study, estimated to 20 minutes completion time, participants were rewarded 20 points (equals 20 Norwegian Kroner, 1.9\u0026nbsp;Euro, or 2.2 USD). The data collection was performed within one week (19\u0026ndash;26 May 2020).\u003c/p\u003e \u003cp\u003eIn the present web survey, Kantar/Gallup approached a total of 2612 individuals stratified on gender, age, education, and area of residence. In total, 39.9% (N\u0026thinsp;=\u0026thinsp;1041) completed the survey, 55.8 (N\u0026thinsp;=\u0026thinsp;1457) did not respond, 2.7% (N\u0026thinsp;=\u0026thinsp;71) started but did not complete, 1.6% (N\u0026thinsp;=\u0026thinsp;41) clicked on the link to participate but did not confirm agreement to the terms of the study, and 0.1% (N\u0026thinsp;=\u0026thinsp;2) withdrew from the study. Our study participants did not differ from non-responders in gender or education (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), but the sample was highly skewed toward older age, with a mean age of 54.1 in responders and 43.3 in non-responders. Subgroup analyses of various age groups showed a steady trend from a poor participation rate of 17.0% in the youngest age group (age 18\u0026ndash;29) increasing to 57.1% in the oldest (60\u0026nbsp;years and above). According to Kantar (personal communication, 2020), the problem of recruiting young adults is a general survey trend and not specific to the current study. However, caution should be taken when interpreting results for the youngest age group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample Characteristics for Responders and Non-Responders.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRespondents\u003c/p\u003e \u003cp\u003e% (N) / mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-respondents\u003c/p\u003e \u003cp\u003e% (N) / mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eX2/t-test p value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender: Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49.0% (510)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.5% (794)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.1 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.3 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;29 (n\u0026thinsp;=\u0026thinsp;524)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.0% (89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.0% (435)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;44 (n\u0026thinsp;=\u0026thinsp;664)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.7% (211)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.3% (454)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;59 (n\u0026thinsp;=\u0026thinsp;659)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46.3% (305)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.7% (354)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60 and above (n\u0026thinsp;=\u0026thinsp;764)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57.1% (436)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.9% (328)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level: College/university\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.9% (374)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.3% (570)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrently working or studying*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60.9% (633)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.2% (1203)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetired from work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.2% (283)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.4% (178)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving alone **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.3% (229)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.4% (294)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Sample Characteristics in Responders and Non-Responders.\u003c/p\u003e \u003cp\u003e*0.5% (n\u0026thinsp;=\u0026thinsp;14) missing, **2.5% (n\u0026thinsp;=\u0026thinsp;64) missing\u003c/p\u003e \u003cp\u003eThe Regional Committee for Medical and Health Research Ethics approved the study (Registration number 133226/2020). The questionnaire was briefly piloted before the data collection. The survey included an open box for comments and feedback on the study. The vast majority of comments were positive or even grateful, although several individuals remarked that some work-related questions were not suitable for those who had retired.\u003c/p\u003e \u003cp\u003eMeasures\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eIncreased vulnerability for serious illness due to COVID-19 (age and underlying disease)\u003c/strong\u003e \u003cp\u003eThe participants were asked if they had a chronic illness or a health condition that constituted an increased risk for severe COVID-19 illness.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSocioeconomic disadvantages (living alone, pre-existing economic challenges, COVID-related unemployment)\u003c/strong\u003e \u003cp\u003ePre-existing economic problems were measures by asking the participants whether their economic situation (before the pandemic) was better than most people, like most people, or worse than most people. The participants were also asked if they had lost their job or being temporarily laid off due to the pandemic, if yes on any of these, this was coded as \u0026ldquo;unemployed\u0026rdquo;.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003ePre-existing mental health vulnerability (previous/pre-existing mental health challenges, recent exposure to violence)\u003c/em\u003e: The participants were asked whether or not they had previously received treatment for mental health problems. To measure exposure to violence the last month, the participants were asked if they had experienced that someone had: 1) repeatedly ridiculed you, put you down, ignored you, or told you that you were no good 2) slapped, pinched, pulled, or shook you violently, 3) hit you with a fist or a hard object, kicked, strangulated, beaten up, threatened with a weapon, or physically attacked in other ways, 4) exposed you to any form of sexual assault or violation.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCOVID-related worry\u003c/strong\u003e \u003cp\u003eThe participants were asked to indicate their level of worry on a scale from 1 (not worried) to 7(very worried) for 12 questions about COVID-related worries These questions were adapted from the COSMO study (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). In this study, we wanted to capture an underlying tendency to worry about COVID-19-related issues. Therefore, we conducted a preliminary confirmatory factor analysis (CFA) of our 12 proposed worries related to COVID-19. The one-factor CFA with all 12 items showed poor model fit, and that several of the proposed items loaded poorly (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We excluded items one by one until the model fit indices showed acceptable fit and all factor loadings were appropriate. We considered values of CFI above .95 and RMSEA above .08 to indicate acceptable model fit(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Factor loadings above .70 were considered excellent .63 very good, .55 good, and .45 fair (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). When a one-factor model showed acceptable fit, six items remained. We computed a total mean score of these six COVID-related worries and used this in the further analyses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCOVID-related worry Mean (SD), factor loadings for 12 and 6 items.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorries (1-don\u0026rsquo;t worry at all- 7-worry a lot)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactor loadings 12-item one- factor model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFactor loadings 6-item one- factor model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*Losing someone I love\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.95 (1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*Becoming seriously ill from the virus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.29 (1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*Infecting others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.71 (1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot being able to get the medicines or treatment that I need\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.90 (1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*Health system being overloaded\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.70 (1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic recession in Norway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.60 (1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBecome unemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.33 (1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot be able to carry out plans that are important to me\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.54 (1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*Not be able to visit people who depend on me\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.07 (1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe society will become more egoistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.79 (1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe welfare society will collapse?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.54 (1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*A new outbreak of COVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.30 (1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e683.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003ePsychological distress the last two weeks\u003c/em\u003e was measured by an abbreviated 5-item version of the Hopkins Symptom Checklist-25 (HSCL) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e): Feeling hopeless about the future; feeling blue; worrying too much about things; feeling fearful; feeling tense or worked up. Participants responded on a scale from 0 (not bothered) to 3 (bothered a great deal). This abbreviated version of the HSCL has shown good psychometric properties and has previously been found to correlate highly (r\u0026thinsp;=\u0026thinsp;0.92) with the HSCL-25 in a general population sample (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). We used a cut-off value of \u0026gt;\u0026thinsp;2, which has achieved the best combination of specificity, sensitivity, and predictive values (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Cronbach\u0026rsquo;s Alpha for the 5-item HSCL was 0.91 in the present study.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLife satisfaction\u003c/em\u003e was measured by a single question from the European Social Survey (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.europeansocialsurvey.org/\u003c/span\u003e\u003c/span\u003e): \u0026lsquo;All things considered, how satisfied are you with your life as a whole nowadays?\u0026rsquo; Participants responded on a scale from 0 (Extremely dissatisfied) to 10 (Extremely satisfied).\u003c/p\u003e \u003cp\u003eAll the questions in this study can be found in the supplementary file.\u003c/p\u003e \u003cp\u003eStatistical analyses\u003c/p\u003e \u003cp\u003eUnivariate and multivariate linear regression analyses were performed with psychological distress and life satisfaction as the dependent variables. Univariate linear regression analyses were performed with pre-existing health problems, age older than 65, living alone, pre-existing economic challenges, becoming unemployed during the pandemic, exposure to violence the last month, previous mental health challenges, and female gender entered separately as independent variables and psychological distress and life satisfaction as dependent variables. The same independent variables were included simultaneously in two multivariate linear regression analyses, with psychological distress and life satisfaction as dependent variables. COVID-related worry was added as an independent variable together with the above-mentioned variables. Statistical analyses were conducted with SPSS version 26 for Windows (SPSS, Inc.) and Mplus 8.3.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":" \u003cp\u003eOf the 1041 participants, 49.0% (n\u0026thinsp;=\u0026thinsp;510) were females. The age range was between 18 and 89\u0026nbsp;years old, with a mean of 54.1 (SD\u0026thinsp;=\u0026thinsp;15.9). A percentage of 35.9% (n\u0026thinsp;=\u0026thinsp;374) had college/university education. 25.7% (268 participants) reported current psychological distress over the threshold for clinically significant symptoms.\u003c/p\u003e \u003cp\u003eThe CFA for COVID-related worries showed an acceptable fit and all factor loadings were appropriate when six covid related worries were included in the one-factor model. Means, standard deviations, model fit, and factor loadings of the 12-item one-factor model, and the final 6-item one-factor model can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. As can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the highest mean level of worry was about a new outbreak of COVID-19, not be able to visit people who depend on me, and the economic recession in Norway.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the mean number and percentage of the participants that reported each of the potential vulnerability factors. Old age (\u0026gt;\u0026thinsp;65\u0026nbsp;years), underlying illness, living alone, and pre-existing mental health problems were the most frequent potential vulnerability factors. For each of the vulnerability factors, and the sample in total, mean scores for psychological distress, COVID-related worry, and life satisfaction are reported. Individuals who had recently been exposed to violence had pre-existing mental health problems, or pre-existing economic challenges reported particularly high levels of psychological distress.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVulnerability factors, n (%), mean (SD) for psychological distress, COVID-related worry and life satisfaction.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVulnerability factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOVID-related worry,\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePsychological distress,\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLife satisfaction (1\u0026ndash;10),\u003c/p\u003e \u003cp\u003emean (SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAt risk for severe illness from COVID-19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderlying illness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e255 (24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.07 (1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.62 (.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.69 (1.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOld age (\u0026gt;\u0026thinsp;65\u0026nbsp;years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e307 (29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.67 (1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42 (.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.29 (1.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocioeconomic disadvantage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e231 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.90(1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.76 (.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.42 (1.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-existing economic challenges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.17 (1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.05 (.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.61 (1.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOVID-related unemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.80 (1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.80 (.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.72 (2.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePre-existing mental health vulnerability\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecent violence exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.45 (1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.46 (.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.78 (2.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-existing mental health problems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e223 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.01 (1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.06 (.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.14 (2.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal sample\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.30 (1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.61 (.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.84 (1.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cp\u003eLinear regression analyses showed that living alone, pre-existing economic challenges, being exposed to violence within the last month, previous mental health problems, and female gender, were significantly associated with a higher level of psychological distress. Conversely, age higher than 65\u0026nbsp;years was negatively associated with psychological distress. When mutually adjusted (model 1, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), all the same factors, except COVID-related unemployment, were independently associated with a higher level of psychological distress.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnadjusted and adjusted linear regression analyses for vulnerability factors and COVID-related worries predicting psychological distress.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eModel 1: adjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c15\" namest=\"c12\"\u003e \u003cp\u003eModel 2: adjusted,\u003c/p\u003e \u003cp\u003eworry as predictor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"15\" nameend=\"c15\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAt risk for severe illness from COVID-19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderlying illness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.07,.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.06, .12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.11,.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOlder age (\u0026gt;\u0026thinsp;65\u0026nbsp;years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.36,-.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.25, \u0026minus;\u0026thinsp;.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.21-.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"15\" nameend=\"c15\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocioeconomic disadvantage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.09,.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.02,.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.03,.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-existing economic challenges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.40,.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.26,.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.22,.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOVID-related unemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.08,.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.08,.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.06,.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"15\" nameend=\"c15\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePre-existing mental health vulnerability\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecent violence exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.74,1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.54,.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.42,.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-existing mental health problems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.47,.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.33,.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.32,.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.09,.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.05,.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.01,.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOVID-related worries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.14,.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003e\u003cem\u003e*Unadjusted: all predictors were entered separately. Model 1: all predictors except were included simultaneously Model 2: COVID-related worry was entered while adjusting for all the vulnerability factors.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAdding COVID-related worry as a predictor in the multivariate regression analysis (model 2, Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) did not substantially attenuate the associations between the suggested vulnerability factors and psychological distress. All the same factors, except female gender, were still significantly associated with distress. The magnitude of the regression coefficient for recent violence exposure seemed to be lower in the adjusted model, but the confidence intervals were overlapping. A higher level of COVID-related worry was uniquely associated with a higher level of psychological distress.\u003c/p\u003e \u003c/div\u003e \u003cp\u003eLinear regression analyses showed that living alone, pre-existing economic challenges, being exposed to violence within the last month and previous mental health problems, were associated with lower levels of reported life satisfaction. Age higher than 65\u0026nbsp;years was associated with a higher level of life satisfaction. When mutually adjusted (model 1, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), all the same factors and underlying illness were independently associated with a lower level of life satisfaction.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnadjusted and adjusted linear regression analyses for vulnerability factors and COVID-related worries predicting life satisfaction\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eModel 1: adjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c15\" namest=\"c12\"\u003e \u003cp\u003eModel 2: adjusted,\u003c/p\u003e \u003cp\u003eworry as predictor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"15\" nameend=\"c15\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAt risk for severe illness from COVID-19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderlying illness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.47,.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.52,-.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.44,.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOlder age (\u0026gt;\u0026thinsp;65\u0026nbsp;years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.40,.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.28,.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.22,.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"15\" nameend=\"c15\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocioeconomic disadvantage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.82,-.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.70,-.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.68,-.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-existing economic challenges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.72,-.1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.49,-.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-1.41,-.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOVID-related unemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.50,.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.30,-.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.33,.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"15\" nameend=\"c15\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePre-existing mental health vulnerability\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecent violence exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.1.60,-.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.03,-.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.85,.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-existing mental health problems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.17,-.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.85,-.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.84,-.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.43,.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.31,.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.23,.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOVID-related worries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.32,.-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003e\u003cem\u003e*Unadjusted: all predictors were entered separately. Model 2: all predictors except were included simultaneously Model 3: COVID-related worry was entered while adjusting for all the vulnerability factors.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen COVID-related worry was added as a predictor in the multivariate regression analysis (model 2) the association between recent violence exposure and life satisfaction the confidence intervals were somewhat attenuated but the confidence intervals were overlapping. When adjusting for worry the association between underlying illness and life satisfaction was also lower, but with overlapping confidence intervals. Adding worry as a predictor did not considerably attenuate the associations between the other independent variables and life satisfaction. Age higher than 65\u0026nbsp;years was associated with a higher level of life satisfaction. The results showed that a higher level of COVID-related worry was uniquely associated with a lower level of life satisfaction.\u003c/p\u003e \u003c/div\u003e \u003cp\u003eWe performed the same analyses, weighted on gender, age, education, and area of residence, and these showed very similar results.\u003c/p\u003e \u003c/div\u003e "},{"header":"Discussion","content":" \u003cp\u003eA substantial proportion of the general Norwegian population experienced significant psychological distress in the first opening-up phase of the COVID-19 pandemic. Indeed, more than one out of four reported current psychological distress over the threshold for clinically significant symptoms. Whereas population data from 2019 show that 14% scored above the clinical threshold (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Similar findings have been reported in studies from Ireland (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), the UK (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), and Denmark (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). It should be noted that at the time of the data collection, in May 2020, Norway was not hit particularly hard by the pandemic compared to other countries as indicated by a relatively low number of fatalities (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) and sufficient hospital intensive care capacity to handle the comparably modest number of covid-19 patients. Additionally, the countermeasures were not among the strictest in a European context, for example, a curfew was not implemented. Thus, the present study adds to the literature by showing that the pandemic can lead to increased psychological distress in the population, even in a situation with low infection rates and moderate countermeasures.\u003c/p\u003e \u003cp\u003eSome groups may carry a heavier psychosocial burden in the pandemic. In this study, we examined the role of three types of vulnerability factors: increased risk for severe illness from COVID-19 (due to underlying illness or older age), socioeconomic disadvantage (due to living alone, unemployment or economic problems), and pre-existing mental health vulnerability (due to exposure to violence or previous mental health challenges). Taken together, our findings showed that socioeconomic disadvantage and pre-existing mental health problems, recent violence exposure, but no increased risk for severe illness, was associated with a higher level of psychological distress and a lower level of life satisfaction.\u003c/p\u003e \u003cp\u003eContrary to our expectations, we found no evidence for an association between increased risk for severe illness from COVID-19, and a higher level of psychological distress. This contrasts with previous studies from Turkey conducted in April 2020 (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), from Spain conducted in March 2020 (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) and from Italy conducted in March 2020 (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), which all reported that chronic disease was associated with higher levels of psychological distress. However, it is important to note that the context and phase of the pandemic were different across these studies. Whereas our study was performed in an opening up phase, in a context of moderate countermeasures, and low levels of fatalities, the studies in Italy, Spain, and Turkey were conducted in a situation where the pandemic was rising, the intensive care capacities were challenged and there was a high level of fatalities. Likely, individuals living with increased risk for severe illness due to COVID-19 would experience a higher level of perceived threat in a situation where the pandemic is currently not under control.\u003c/p\u003e \u003cp\u003eOlder age was not identified as a vulnerability factor for psychological distress. On the contrary, the results showed that older age was associated with a lower level of psychological distress, and a higher level of life satisfaction. Similar findings were reported by Gonz\u0026aacute;lez-Sanguino an colleagues(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Other studies conducted during the COVID-19 pandemic have also reported that younger, but not older age is associated with a higher level of psychological distress (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In a diary study, from March/April 2020, Klaiber, Wen, DeLongis, and Sin (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) reported that older and younger participants experienced comparable levels of stress, but younger participants expressed more worry. It should be mentioned that the tendency for older adults to worry less than younger adults have also been found in previous studies conducted outside a pandemic context (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Furthermore, several studies have also shown that life satisfaction increases with older age in adulthood (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).Maybe older individuals are more able to cope with the uncertain situation that the COVID-19 pandemic represents and that more is at stake for young people and their future. Indeed, older adults are more experienced in coping with problems and it has been suggested that older adults are more capable of regulating their emotions (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present results showed that socioeconomic disadvantages, including living alone and pre-existing economic challenges, were associated with a higher level of psychological distress, and a lower level of life satisfaction. This illustrates the disproportionate impact of the pandemic and the significance of where pre-existing resources. Some individuals have lower access to the safety net of economic and other resources, and for these people, the pandemic constitutes a risk for further losses and over time, and further social marginalisation. Pre-existing mental health vulnerabilities, including recent exposure to violence and previous mental health problems, were associated with a higher level of psychological distress and a lower level of life satisfaction. Previous studies have also shown that previous or pre-existing mental health problems are a vulnerability factor during the pandemic (for a review see Xiong et al. 2020), but to our knowledge, the present study was the first to investigate recent exposure to violence as a vulnerability factor. It has been discussed whether lockdown, closed schools, and social isolation may result in domestic violence. Although this study could not compare violence exposure to previous levels, our results underscore the importance of targeting this group for prevention purposes. Many of the violence-exposed individuals may already be known within health services and child protection services, and outreach services may be suitable to ensure their safety and support their coping. These present findings highlight the need to prevent discontinuation of activities or treatment for individuals with pre-existing mental health vulnerabilities. As pointed out by Galea et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), efforts should be made to facilitate connection with individuals at risk for social marginalisation during the pandemic.\u003c/p\u003e \u003cp\u003eAlthough worrying initially is an adaptive response to the pandemic, the present results suggest that COVID-related worry can play an important role in psychological distress and life satisfaction in the pandemic. Even when adjusting for all the vulnerability factors, a higher level of COVID-related worry was significantly associated with a higher level of psychological distress, and a lower level of life satisfaction. This is in line with previous studies reporting a link between COVID-related worry and psychological distress (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).The present study extends these findings by examining the role of worry for psychological distress in vulnerable groups.\u003c/p\u003e \u003cp\u003eHowever, the results showed that COVID-related worry can only to a small extent explain the higher levels of psychological distress and lower level of life satisfaction among vulnerable groups. The vulnerability factors were independently associated with psychological distress and lower level of life satisfaction. Individuals with these vulnerabilities may also have less access to resources such as social support, which might have an alleviating effect on the strains of the pandemic. Thus, both individuals with and without vulnerabilities can worry about the pandemic so much that it brings about psychological distress and lessen their quality of life. COVID-related worry seems to partly explain why women report more distress in the pandemic. However, the present results do not suggest that COVID-related worry can explain the excess distress in vulnerable groups. For example, our results do not indicate that it because of COVID-related worry that an individual exposed to recent violence is more distressed. Other factors, not included in our study, maybe more important, such as loss of social network, loss of treatment contacts, unavailability of child protection services, or increased threat to financial security.\u003c/p\u003e \u003cp\u003eThe present study has some strengths and limitations. A stratified probability sample, with a 39.9% response rate adds to the strengths of the study. Although we cannot exclude a self-selection bias, we could assess the representability by comparing demographic characteristics between responders and non-responders. We found that our sample was skewed in terms of high age. However, analyses weighted for age provided similar results. It should also be mentioned that systematic response rate biases are more likely to affect levels of vulnerability factors, COVID-related worries, psychological distress, and life satisfaction than estimates of their associations. Additionally, it is important to emphasise that the present study is cross-sectional and only shows a snapshot of COVID-related worries, life satisfaction, and psychological distress at a particular time and place. In the present study, we initially asked about different types of worries related to COVID-19, and the results of the CFA identified health-related worries as a unitary factor. However, other types of worries that we did not measure are probably also important for psychological distress and life satisfaction. Different types of worries will probably affect different vulnerable groups, and the most prevalent worries will probably change with time, local context, political and economic circumstances.\u003c/p\u003e "},{"header":"Conclusion","content":" \u003cp\u003eIn conclusion, our study identified several unique vulnerability factors for psychological distress and lower life satisfaction in the pandemic. In particular, individuals recently exposed to violence, and individuals with pre-existing mental health problems were at risk. Worrying about the consequences of the pandemic can contribute negatively to current mental health. However, worry does not seem to explain the excess distress in vulnerable groups. The COVID-19 pandemic is still ongoing and is likely to continue to affect us for a long time to come. An understanding of how the pandemic develops through several phases and how vulnerabilities and worries may change throughout the pandemic will benefit people who struggle with strains and worries. Interventions should specifically target vulnerable groups to prevent long-term suffering. Future research should make an effort to identify which COVID-related strains make life so difficult for vulnerable groups in the pandemic.\u003c/p\u003e"},{"header":"Abbreviations","content":" \u003cp\u003eCFA- Confirmatory factor analysis\u003c/p\u003e \u003cp\u003eCOVID-corona virus disease\u003c/p\u003e \u003cp\u003eHSCL- Hopkins Symptom Checklist\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding statement: This research received no specific grant from any funding agency, commercial or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eCompeting interests: The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. \u003c/em\u003eThe Regional Committee for Medical and Health Research Ethics approved the study (Registration number 133226/2020).\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003eConsent for publication: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials: \u003c/strong\u003eThe data are not publicly available due to them containing information that could compromise research participant privacy and consent.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions: I.B., M.S.B and S.T.\u0026nbsp;developed the study concept, the study design and performed the data collection. I.B. performed the data analysis and\u0026nbsp;interpretation,\u0026nbsp;and\u0026nbsp;drafted the paper. S.T.\u0026nbsp;and M.S.B. provided critical revisions. All authors approved the final version of the paper for submission.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgements: Not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSalari N, Hosseinian-Far A, Jalali R, Vaisi-Raygani A, Rasoulpoor S, Mohammadi M, et al. Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: a systematic review and meta-analysis. Globalization health. 2020;16(1):1\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eEbrahimi OV, Hoffart A, Johnson SU. 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Acta Psychiatr Scand. 2020;142(3):249\u0026ndash;56.\u003c/li\u003e\n\u003cli\u003eXiong J, Lipsitz O, Nasri F, Lui LM, Gill H, Phan L, et al. Impact of COVID-19 pandemic on mental health in the general population: A systematic review. Journal of affective disorders. 2020.\u003c/li\u003e\n\u003cli\u003eEl-Gabalawy R, Sommer J. We are at risk too: The disparate impacts of the pandemic on younger generations. medRxiv. 2020.\u003c/li\u003e\n\u003cli\u003eElmer T, Mepham K, Stadtfeld C. Students under lockdown: Assessing change in students\u0026rsquo; social networks and mental health during the COVID-19 crisis. 2020.\u003c/li\u003e\n\u003cli\u003eKampfen F, Kohler IV, Ciancio A, de Bruin WB, Maurer J, Kohler H-P. Predictors of Mental Health during the early Covid-19 Pandemic in the US: role of economic concerns, health worries and social distancing. medRxiv. 2020.\u003c/li\u003e\n\u003cli\u003eSweeny K, Dooley MD. The surprising upsides of worry. 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The Journals of Gerontology: Series B; 2020.\u003c/li\u003e\n\u003cli\u003eBasevitz P, Pushkar D, Chaikelson J, Conway M, Dalton C. Age-related differences in worry and related processes. The International Journal of Aging Human Development. 2008;66(4):283\u0026ndash;305.\u003c/li\u003e\n\u003cli\u003eGon\u0026ccedil;alves DC, Byrne GJ. Who worries most? Worry prevalence and patterns across the lifespan. Int J Geriatr Psychiatry. 2013;28(1):41\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eBaird BM, Lucas RE, Donnellan MB. Life satisfaction across the lifespan: Findings from two nationally representative panel studies. Soc Indic Res. 2010;99(2):183\u0026ndash;203.\u003c/li\u003e\n\u003cli\u003eMather M, Carstensen LL. Aging and motivated cognition: The positivity effect in attention and memory. Trends Cogn Sci. 2005;9(10):496\u0026ndash;502.\u003c/li\u003e\n\u003cli\u003eGalea S, Merchant RM, Lurie N. The mental health consequences of COVID-19 and physical distancing: The need for prevention and early intervention. JAMA internal medicine. 2020;180(6):817\u0026ndash;8.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"mental health, mental health, pandemic, COVID","lastPublishedDoi":"10.21203/rs.3.rs-192098/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-192098/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThere is an urgent need for knowledge about the mental health consequences of the ongoing pandemic. The aim of this study was to identify vulnerability factors for psychological distress and reduced life satisfaction in the general population. Furthermore, we aimed to assess the role of COVID-related worries for psychological distress and life satisfaction. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003e\u003c/p\u003e\u003cp\u003eA presumed representative sample for the Norwegian population (n=1041, response rate=39.9%) responded to a web-survey in May 2020. The participants were asked about potential vulnerability factors including increased risk for severe illness from COVID-19 (underlying illness, older age), socioeconomic disadvantage (living alone, unemployment, economic problems), and pre-existing mental health vulnerability (recent exposure to violence, previous mental health challenges). Additional measures included COVID-related worry, psychological distress, and life satisfaction. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eMore than one out of four reported current psychological distress over the threshold for clinically significant symptoms. Socioeconomic disadvantages, including living alone and pre-existing economic challenges, and pre-existing mental health vulnerabilities, including recent exposure to violence and previous mental health problems, were associated with a higher level of psychological distress and a lower level of life satisfaction.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThis study identified several vulnerability factors for mental health problems in the pandemic. Individuals recently exposed to violence and individuals with pre-existing mental health problems are at particular risk. Worrying about the consequences of the pandemic contributes negatively to current mental health. However, worry cannot explain the excess distress in vulnerable groups. Future research should focus on how COVID-related strains contribute to mental health problems for vulnerable groups.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","manuscriptTitle":"Worry and Mental Health in the COVID-19 Pandemic: Vulnerability Factors in the General Norwegian Population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-02-01 18:55:15","doi":"10.21203/rs.3.rs-192098/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2021-03-01T00:00:00+00:00","index":2,"fulltext":"Recommendation: Major revisions required\nForm responses:\n---\n\nComments to Author:\n---\nWe do need studies looking at COVID-19 and the impact on mental health, and this paper are therefore relevant.\nHowever i feel that perhaps it was written a bit in a haste, which made that parts of explaining the research questions, and motivating the aim have been left out. The same goes with method, I get so many questions reading these parts. Why only report these groups for example. Most things are easily fixed with a couple of sentences here and there.\n\nClarify research question in introduction. Looking at your aim I cannot understand table 2 usage in relation to your aim, why this isn't just a method question.\n\nCan you actually see the increased risk for severe illness, or the perception of the risk for this? \n\nMeasures age and underlying illness- yes no or open question or list of things?\n\nCovid related worry. COSMO study, hest you could give example of questions and also give the anchors (scale) .\n\nwhen talking about these 12 questions, there are no reference to it being reported in table 2. So I am left confused here. Arguably table 2 should have been a part of this instead and left out of results. even if perhaps a motivation why you created a scale of 12 items but chose not to use all. Why did you include these 12 items then? how was this scale created?\nLooking at pre-existing mental health vulnerability, again only exposure to violence are explained.\n\nAnd why treatment for mental health problems previously (ever in life or? And why with violence not with underlying illness. Not explained enough.\n\nThis goes thru instruments, these are not to well explained how did you create the cutoff of more than 2 regarding psychological distress. Having more than 2 is that actually what is seem as clinically significant symptoms? Or is this measured some other way? As this part of methods aren't transparent and clear there is a risk of misunderstanding.\n\nSocioeconomic disadvantages, there you only look if the situation was better, like or worse than most people. But what was considered as most people aren't explained. And only if they lost their job due to COVID where measured regarding the important SES variable regarding employment. Yet when looking at table one you have currently working or studying as a variable, which mean that you do have this information.\n\nIn table one you have two * ** that aren't explained under the tab\nTable three with percent and mean SD for one group but not all group? Knowing the mean for underlying illness is only meaningful if I get a comparison in the same table. That table need to have more information\n\nIf we look at the underlying factors of table 3, why were these decided? You do mention that these are the ones that you will be focusing on in the aim, but I am missing a good motivation why, that would have been able to be visible in table 3 as you could show that the mean would differ in these groups.\n\n\n\n\n\n\n\n* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons after the final decision on the manuscript has been made. If you have already registered at Publons, they will notify you of the receipt of this review and update your profile as per your settings and their policy. If you are not registered with Publons, you will receive an email from them asking you to register in order for them to be able to recognize your review on your new profile page. Publons may use the Review Data to generate derivative metadata for the benefit of Publons and you as a reviewer, carefully considering the sensitivity of such information. For example, Publons may verify your record as a reviewer by updating your profile published on its webservice if you have registered for such service or help editors to identify candidate reviewers. Please find the details of processing in Publons’ privacy policy https://publons.com/about/terms: **Yes**\n* Declaration of competing interests: **I declare that I have no competing interests**\n* Reviewer Publication Consent. I agree for my report to be made available under an Open Access Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0) if this manuscript is accepted for publication. Any comments that I do not wish to be included in the published report have been included as confidential comments to the editor, which will not be published.: **I agree to the terms of the CC-BY 4.0 license; please publish my name with my report.**\n* Is the study design appropriate to answer the research question (including the use of appropriate controls), and are the conclusions supported by the evidence presented?: **Yes**\n* Are the methods sufficiently described to allow the study to be repeated?: **No**\n* Is the use of statistics and treatment of uncertainties appropriate?: **Yes**\n* Is the presentation of the work clear?: **No**\n* Are the images in this manuscript (including electrophoretic gels and blots) free from apparent manipulation?: **Yes**\n"},{"type":"decision","content":"Major revision","date":"2021-03-01T00:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-02-17T00:00:00+00:00","index":1,"fulltext":"Recommendation: Accept after minor essential revisions\nForm responses:\n---\n\nComments to Author:\n---\nI am glad to judge the creative article entitled, \"Authorized, Clear and Timely Communication of Risk to Guide Public Perception and Action: Lessons of COVID-19 from China.\"\n\nThis study states that there is an urgent need for knowledge about the mental health consequences of the ongoing pandemic. The aim of this study was to identify vulnerability factors for psychological distress and reduced life satisfaction in the general population. Furthermore, we aimed to assess the role of COVID-related worries for psychological distress and life satisfaction.\n\nI found this article interesting and it has explored the hot topic on the COVID-19 pandemic. My overall impression about this current study is that it is thorough, informative, reflects a regulatory-based perspective, and the authors presented the topic in a clear manner.\n\nAvoid passive style in the Abstract and it must be high quality. I suggest authors to limit Abstract within 250 to 300 words. Abstract must reflect high quality, as it the \"FACE\" of the study.\n\nLiterature section\nI suggest authors to add a paragraph about infectious diseases caused by epidemics and pandemics, such as SARS and the COVID-19 pandemic and read the suggested studies and cite them in the literature. As the current situation of the pandemic COVID-19 has increased the use of medicines. I am suggesting some excellent studies related to this topic, which been published in leading \"Web of Science\" journals. I advise authors to revisit their literature section of the recommended studies and cite these studies to improve their study to reach the scientific merit for publication.\n\nSu, Z., McDonnell, D., Wen, J., Kozak, M., Abbas, J., Šegalo, S., . . . Xiang, Y.-T. (2021). Mental health consequences of COVID-19 media coverage: the need for effective crisis communication practices. Globalization and Health, 17(1), 4. doi:10.1186/s12992-020-00654-4\n\nShuja, K. H., Aqeel, M., Jaffar, A., \u0026 Ahmed, A. (2020, Spring). COVID-19 Pandemic and Impending Global Mental Health Implications. Psychiatr Danub, 32(1), 32-35. https://doi.org/10.24869/psyd.2020.32\n\nSu, Z., Wen, J., Abbas, J., McDonnell, D., Cheshmehzangi, A., Li, X., Ahmad, J., Šegalo, S., Maestro, D., \u0026 Cai, Y. (2020, 2020/12/01/). A race for a better understanding of COVID-19 vaccine non-adopters. Brain, Behavior, \u0026 Immunity - Health, 9, 100159. https://doi.org/https://doi.org/10.1016/j.bbih.2020.100159\n\nAbbas, J. (2020, Autumn - Winter). The Impact of Coronavirus (SARS-CoV2) Epidemic on Individuals Mental Health: The Protective Measures of Pakistan in Managing and Sustaining Transmissible Disease. Psychiatr Danub, 32(3-4), 472-477. https://doi.org/10.24869/psyd.2020.472\n\nShuja, K. H., Shahidullah, Aqeel, M., Khan, E. A., \u0026 Abbas, J. (2020, Sep 8). Letter to highlight the effects of isolation on elderly during COVID-19 outbreak. Int J Geriatr Psychiatry, n/a(n/a). https://doi.org/10.1002/gps.5423\n\nYoosefi Lebni, J., Abbas, J., Moradi, F., Salahshoor, M. R., Chaboksavar, F., Irandoost, S. F., Nezhaddadgar, N., \u0026 Ziapour, A. (2020, Jul 2). How the COVID-19 pandemic effected economic, social, political, and cultural factors: A lesson from Iran. Int J Soc Psychiatry, 20764020939984. https://doi.org/10.1177/0020764020939984\n\nMethods and Results\nI suggest you to read the suggested studies and cite them to refine your methods and result sections. These studies are informative and helpful to improve the study.\n\nYoosefi Lebni, J., Abbas, J., Khorami, F., Khosravi, B., Jalali, A., \u0026 Ziapour, A. (2020, 2020-August-14). Challenges Facing Women Survivors of Self-Immolation in the Kurdish Regions of Iran: A Qualitative Study [Original Research]. Front Psychiatry, 11(778), 778. https://doi.org/10.3389/fpsyt.2020.00778\n\nAbbas, J., Aman, J., Nurunnabi, M., \u0026 Bano, S. (2019). The Impact of Social Media on Learning Behavior for Sustainable Education: Evidence of Students from Selected Universities in Pakistan. Sustainability, 11(6), 1683. https://www.mdpi.com/2071-1050/11/6/1683\n\nNeJhaddadgar, N., Ziapour, A., Zakkipour, G., Abbas, J., Abolfathi, M., \u0026 Shabani, M. (2020, 2020/11/13). Effectiveness of telephone-based screening and triage during COVID-19 outbreak in the promoted primary healthcare system: a case study in Ardabil province, Iran. Journal of Public Health. https://doi.org/10.1007/s10389-020-01407-8\n\nConclusion\nThis section is satisfactory. However, I recommend the authors to expand it to 500 words. The authors need to highlight the study's creativity and scientific contribution to the body of knowledge briefly in the discussion section. I want to see this paper published as it presents a good idea. I accept and endorse this manuscript for publication after the suggested corrections.\n* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons after the final decision on the manuscript has been made. If you have already registered at Publons, they will notify you of the receipt of this review and update your profile as per your settings and their policy. If you are not registered with Publons, you will receive an email from them asking you to register in order for them to be able to recognize your review on your new profile page. Publons may use the Review Data to generate derivative metadata for the benefit of Publons and you as a reviewer, carefully considering the sensitivity of such information. For example, Publons may verify your record as a reviewer by updating your profile published on its webservice if you have registered for such service or help editors to identify candidate reviewers. Please find the details of processing in Publons’ privacy policy https://publons.com/about/terms: **Yes**\n* Declaration of competing interests: **NONE**\n* Reviewer Publication Consent. I agree for my report to be made available under an Open Access Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0) if this manuscript is accepted for publication. Any comments that I do not wish to be included in the published report have been included as confidential comments to the editor, which will not be published.: **I agree to the terms of the CC-BY 4.0 license; please do not publish my name with my report. 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