Rapid assessment of psychological and epidemiological correlates of COVID-19 concern, financial strain, and health-related behavior change in a large online sample.

COVID-19 emerged in November 2019 leading to a global pandemic that has not only resulted in widespread medical complications and loss of life, but has also impacted global economies and transformed daily life. The current rapid response study in a convenience online sample quickly recruited 2,065 participants across the United States, Canada, and Europe in late March and early April 2020. Cross-sectional findings indicated elevated anxiety and depressive symptoms compared to historical norms, which were positively associated with COVID-19 concern more strongly than epidemiological data signifying risk (e.g., world and country confirmed cases). Employment loss was positively associated with greater depressive symptoms and COVID-19 concern, and depressive symptoms and COVID-19 concern were significantly associated with more stringent self-quarantine behavior. The rapid collection of data during the early phase of this pandemic is limited by under-representation of non-White and middle age and older adults. Nevertheless, these findings have implications for interventions to slow the spread of COVID-19 infection.

Introduction

In November 2019 the novel severe acute respiratory syndrome coronavirus 2, which causes the coronavirus disease 2019 (COVID-19), emerged in Wuhan, China. Since this time COVID-19 has rapidly spread around the world leading to a pandemic that has so far resulted not only in medical complications and loss of life, but has also led to the largest global economic impact and transformation to daily life, since past global events, such as the Great Depression. As of April 9, 2020 there have been 1,696,139 confirmed cases and 102,669 confirmed deaths [1]. Recently, researchers at the CDC estimated that COVID-19 infectiousness or median R0 is 5.7 (95% CI 3.8–8.9) [2], higher than prior estimates that ranged from 2.0–2.6 ([3]; Note that any R0 value above 1.0 indicates that cases will continue to grow). Furthermore, international COVID-19 case fatality rate (CFR) estimates range from 1.0% to as high as 7.2% in particular countries, such as Italy which resulted from hospital resources becoming overwhelmed [4]. For comparison, the common flu has an R0 of 1–1.5 and a CFR of .01%. Overall, some initial epidemiological models based on an “unmitigated epidemic” (e.g., absence of individual behavior change and systemic control measures) that doesn’t account for the potential of overwhelmed healthcare systems have predicted that despite R0 and CRP estimates, there could be up to 1.1–1.2 million deaths in the United States and up to 250,000 deaths in Great Britain [3].

Mental health during a global pandemic

While the psychological fallout of past epidemics, such as SARS and Polio have been documented [5], currently, there is a lack of psychological literature directly related to global pandemics [6]. The last pandemic, the 1918 influenza pandemic, occurred prior to modern psychological science. Therefore, the potential mental health effects of COVID-19 might be gleaned from other areas of inquiry including 1) primary effects of epidemic disease outbreaks, as well as the secondary effects of 2) economic recessions/depressions, and 3) loneliness, quarantine, and social isolation.

Disease outbreaks

Prior epidemics have consistently led to increased mental health difficulties [5]. For example, Polio symptoms and treatment conditions led to trauma [7]. Moreover, the Australian outbreak of equine influenza was associated with psychological distress [8], and the 2003 SARS epidemic in Hong Kong was associated with increased psychological burden, distress, depressive symptoms, fear, and restless sleep [9] as well as elderly suicide [10]. Preliminary research from China on COVID-19 has found higher depression, anxiety, and posttraumatic stress symptoms [11, 12] and non-peer reviewed research has found increased sleep in the United States [13]. Similarly, recent preprints studies have found increased internet mental health-related keyword searches using the Google search engine [14] and an association between social distancing and past-month suicidal ideation and suicide attempts [15].

Economic recession

Due to behavioral restrictions on movement around the world to curb the spread of COVID-19, many nations have completely shut down “non-essential” business sectors impacting global economies causing massive disruption [16]. For example, between January 2, 2020 and March 23, 2020 the S&P 500 fell 31.32% and within a single week that ended on March 28, 2020, 6,648,000 Americans filed unemployment claims, which is the highest number of seasonally adjusted initial filed unemployment claims in US history [17], indicating that 10% of the US workforce became unemployed [18]. Some calculations indicate that the United States could see unemployment surpass rates during the Great Depression [19] and the world could see 195,000,000 lost jobs [20] with double digit declines in imports/exports during 2020 likely leading to the largest decrease in world trade since the 2008 Financial Crisis [16].

Economic recessions have reliably been positively associated with mental health degradation and increased negative coping behaviors (e.g., substance use; [21]). For example, experiencing an impact to financial, housing, or employment during the Great Recession of 2008 in the United States was positively associated with increased anxiety, depression, and substance use up to 3–4 years post-recession [21]. Furthermore, country level unemployment, poverty, and foreclosure were positively associated with suicide rates during the 2008 recession [22, 23].

Quarantine, social isolation, and loneliness

Quarantine measures of past outbreaks have resulted in higher depression, anxiety, post-traumatic stress symptoms [5], psychological burden, distress, restless sleep [9], and suicide among the elderly, possibly due to increased disconnection and loneliness [10]. Furthermore, social isolation and loneliness have a negative impact on physical health that is on par with well-known behavioral health variables (e.g., physical inactivity, obesity, substance abuse), such that loneliness, social isolation, and living alone are positively associated with a 26%, 29%, and 32% increased risk for mortality, respectively [24].

Overall, there are objective threats to societal health that COVID-19 poses, which indicate that there is a pressing need to characterize the current impact the COVID-19 pandemic is having on the general public’s mental health, financial concern, and to identify variables that are positively associated with successful adherence to self-quarantine recommendations.

Current study

The current study was cross-sectional and used open materials, including code and deidentified data, available on Open Science Framework (https://osf.io/vtnca/) to 1) characterize whether current levels of individual transdiagnostic mental health symptoms (i.e., anxiety and depression) are elevated when compared to historical normative data, 2) determine whether individual differences in transdiagnostic mental health symptoms or epidemiological data indicating objective risk (e.g., cumulative country and world cases) explain more variance in psychological concern about COVID-19, 3) identify associations between financial difficulties, mental health symptoms, and COVID-19 concern, and 4) delineate whether transdiagnostic mental health symptoms, COVID-19 concern, or epidemiological data explain more variance in the degree of adherence with self-quarantine.

Methods and materials

Participants

Due to the time sensitive nature of identifying the initial impact of COVID-19, the current study utilized a rapid response design based on a convenience online sample. The study recruited 2,443 participants around the world to participate in a survey between March 19, 2020 and April 10, 2020. The final sample included 2,065 participants (see Fig 1) after limiting participation to predominantly English speaking countries and geographic regions for which we had a sizable sample (n > 100 per region), which led to selecting participants from the United States (n = 1683), Canada (n = 137), and Europe (n = 245). In addition, participants were removed if they did not meet age criteria of being 18 or older (n = 12), if they did not successfully complete an attention check (n = 130), or if they had an invalid IP address (n = 4). For breakdown of participants by individual countries see S1 File. Inclusion criteria required that participants had to be 18 years old or older (mean age = 34.40 years, SD = 11.49, Range 18–77 years; see S1 File). Our sample identified as predominantly White (80.19%), Non-Hispanic (90.07%), and female (69.20%). The most common household income was $20,000-$49,000 and the most common level of education was some college or higher. See Fig 2 below for full demographic break down of race, ethnicity, gender, political orientation and health. See Fig 3 below for a demographic breakdown of Income, Employment, Financial Strain, and Health Behavior Change. The study was approved by the University of Oregon Institutional Review Board.

Fig 1

Participant location.

A. United States, Canada, and Europe, B. Zoomed Image of United States and Canada, C. Zoomed Image of Europe.

Fig 2

Description of participant demographics.

Fig 3

Description of COVID-19 health variables.

Recruitment

Participants were recruited through promoted social media ads on Twitter (n = 403) and Facebook (n = 36), Instagram (n = 6), and the survey was submitted to a call for Covid-19 related studies on Reddit (n = 970) [25]. Smaller numbers of participants were recruited through word of mouth (n = 16, i.e., by sharing the study link with friends), other methods (n = 150), and 484 participants did not provide a response regarding their method of recruitment.

Assessment procedures

Participants consented via Qualtrics and were then asked to complete a set of questionnaires (see OSF for materials https://osf.io/vtnca/) to assess current transdiagnostic symptoms of mental health, demographics, COVID-19 related behaviors, and COVID-19 concern. The questionnaire was completed in a median of 5 minutes and 18 seconds. Participants were not compensated for survey completion.

Measures

Symptom measures

Generalized Anxiety Disorder-2 (GAD-2). The GAD-2 has been shown to be a valid measure of anxiety symptoms [26-28]. The GAD-2 consists of the first two questions of the GAD-7 has been shown to be just as informative as the GAD-7 for identifying GAD and other anxiety disorder diagnosis [29]. A cut-off score ≥ 3 has been established for identifying likely Generalized Anxiety Disorder with a sensitivity (i.e., true positive rate) value of 0.86 (0.76–0.93) and a specificity (i.e., true negative rate) value of 0.83 (0.80–0.85) [27], although a more recent meta-analysis identified a lower pooled sensitivity (i.e., true positive rate) value of 0.80 (0.62–0.91) and a lower pooled specificity (i.e., true negative rate) value of 0.81 (0.65–0.91) [28]. In a sample of over 5,000 individuals from the general population prior to the COVID-19 pandemic the average GAD-2 score was 0.82 (SD = 1.10) [30].

Patient Health Questionnaire-2 (PHQ-2). The PHQ-2 consists of the first two questions of the PHQ-9 and has been shown to be a valid measure of depressive symptoms [26, 31, 32] with a cut-off score ≥ 3 for identifying likely Major Depressive Disorder with a sensitivity value of 0.83 and a specificity value of 0.92 [31], although a more recent meta-analysis identified a lower pooled sensitivity value of 0.76 (0.68–0.82) and a lower pooled specificity value of 0.87 (.82–0.90) [32]. In a sample of over 5,000 individuals from the general population prior to the COVID-19 pandemic the average PHQ-2 score was 0.94 (SD = 1.20) [30].

COVID-19 concern. We created a measure of COVID-19 concern that stated, “How concerned do you feel about COVID-19?” with a 5-point Likert scale ranging from “Not at all concerned” to “Extremely concerned.”

COVID-19 measures. We created a list of COVID-19 measures including items associated with personal flu symptoms, COVID-19 testing, hospitalization and known relationship with someone with COVID-19 symptoms, testing, hospitalization and/or death, and behavioral questions included change in behavior and self-quarantine due to COVID-19. Specifically, we assessed change in behavior by asking, “Have you made any changes to your daily lifestyle due to COVID-19?” with the following responses: a. Yes, I have made changes to my daily schedule to reduce risk or b. No, I have not made changes to my daily schedule to reduce risk. Furthermore, we assessed self-quarantine by asking, “How much are you self-quarantining?” with the following responses: a. None of the time. I am continuing my normal daily schedule, b. Some of the time. I have reduced some of the time that I am in public spaces, social gatherings, and work, c. Most of the time. I only leave for food, doctor appointments, and other essentials, or d. All of the time. I am staying home almost all of the time. For the complete list of questions and item responses, please see OSF (https://osf.io/vtnca/).

Financial strain. We created a measure of COVID-associated financial strain included questions associated with lost or change in job, income, and financial comfort. Annual income (prior to COVID-19) and highest education was also obtained.

Epidemiological data

Confirmed cases, deaths, recovered

Epidemiological data on confirmed cases were extracted from the Johns Hopkins University Center for Systems Science and Engineering github (https://github.com/CSSEGISandData/COVID-19). Data on daily confirmed world and country cases were used and merged with participant data on date that participant filled out the questionnaire.

Statistical analyses

All statistical analyses were conducted with R Studio, version 1.1.463. See S1 File on Open Science Framework (OSF; https://osf.io/vtnca/) for statistical code and packages used for analyses. Statistical significance was defined using 95% confidence intervals and p-values. A series of multilevel model delineating correlations between measures of interest were used to assess 1) the associations between transdiagnostic mental health symptoms and objective epidemiological risk (i.e., confirmed world and country cases) with COVID-19 concern; 2) the associations between financial strain, transdiagnostic mental health symptoms, and COVID-19 concern; and 3) the associations between COVID-19 concern, transdiagnostic mental health symptoms, and objective epidemiological risk (i.e., confirmed world and country cases) with degree of adherence to self-quarantine recommendations. For each model, intercepts were allowed to vary by country, and models controlled for age, gender, and date participants filled out the questionnaire. For each analysis we ran a set of models 1) unadjusted and adjusted transdiagnostic mental health symptoms and 2) unadjusted and adjusted epidemiological variables before 3) a final single and more stringent model was run including transdiagnostic mental health symptoms, epidemiological variables, and covariates in order to identify which specific variable accounted for the most variance in the model. In addition, bar chart figures were created with the ggstatsplot package [33] to visually depict the percent of participants reaching anxiety and depression diagnostic cutoff scores by level of COVID-19 concern. Confirmed world and country cases were log transformed to correct for skew. See OSF for tables of results presented below.

Results

Descriptives

Descriptive statistics revealed high levels of COVID-19 concern, such that that 30.51% (n = 630) were extremely concerned, 37.43% (n = 773) were very concerned, 25.62% (n = 529) were moderately concerned, 5.57% (n = 115) were a little concerned, and 0.73% (n = 15) were not at all concerned. Furthermore, 27.61% (n = 569) of participants reported experiencing flu symptoms, while 51.31% (n = 1058) of participants reported knowing someone that was exhibiting flu symptoms. Lastly, 10.94% (n = 226) of the participants were hospitalized due to COVID-19 and 3.83% (n = 79) personally knew of someone that had passed away due to COVID-19 (see Fig 3).

Descriptives also revealed that 98.50% of participants (n = 2034) had made lifestyle changes with varying degrees of self-quarantine, such that 41.50% (n = 857) spending all the time, 51.14% (n = 1056) most of the time, 6.39% (n = 132) some of the time, and 0.77% (n = 16) none of the time in self-quarantine (see Fig 4).

Fig 4

Behavioral changes due to COVID-19.

Lastly, 32.88% (n = 679) of participants had lost income and 13.56% (n = 280) had lost their job due to COVID-19. In terms of financial security for each month, 38.50% (n = 795) of participants reported being comfortable with extra, 37.09% (n = 766) reported having enough, but no extra, 19.13% (n = 395) reporting they had to cut back, and 5.13% (n = 106) reporting that they could not make ends meet. In terms of those reporting food security in the last 12 months (e.g., whether they ran out of food and didn’t have money to buy more), 91.33% (n = 1886) reported that this was never true, 7.41% (n = 153) reported sometimes true, and 1.11% (n = 23) reported often true (see Fig 5).

Fig 5

Financial strain.

Anxiety and depressive symptoms compared to historical averages

In the current study and as shown in Fig 6, the average GAD-2 score was 3.31 (SD = 1.97) and the average PHQ-2 score was 2.59 (SD = 1.80), which indicates significant elevations in anxiety symptoms, t(2061) = 57.287, p < .001, 95% CI (3.221, 3.392), and depressive symptoms, t(2061) = 41.717, p < .001, 95% CI (2.516, 2.671) during the COVID-19 pandemic as compared to past normative data from the general population where the average GAD-2 score was 0.82 (SD = 1.10) and the average PHQ-2 score was 0.94 (SD = 1.20) [30].

Fig 6

Mean a) GAD-2 and b) PHQ-2 Total Scores and Clinical Diagnostic Cutoff Compared to Historical General Population Mean Total Scores. Note: Solid Black Line = GAD-2 and PHQ-2 Historical General Population Mean Total Scores; Dotted Red Line = Threshold for Clinical Diagnosis; Grey Dashed Line error bars = Standard Error; GAD-2 = Generalized Anxiety Disorder-2; PHQ-2 = Patient Health Questionnaire-2; *** = p < .001.

Mental health and epidemiological correlates of COVID-19 concern

Mental health correlates of COVID-19 concern

Results showed that greater anxiety (B = 0.219, SE = 0.009, p < 0.001, 95% CI [0.201 – 0.236]) and depressive symptoms (B = 0.151, SE = 0.011, p < 0.001, 95% CI [0.133 – 0.175]) were both positively associated with COVID-19 concern. When anxiety and depressive symptoms were entered into the same model, greater anxiety symptoms were significantly positively associated with COVID-19 concern (B = 0.211, SE = 0.012, p < 0.001, 95% CI [0.188 – 0.233]), while depressive symptoms were not (B = 0.014, SE = 0.013, p = 0.283, 95% CI [-0.011 – 0.039]), indicating that anxiety symptoms are a more strongly related to COVID-19 concern than depressive symptoms. Fig 7 displays percent of participants that met cutoff score by level of COVID-19 concern.

Fig 7

A. GAD-2 and B. PHQ-2 diagnostic threshold by level of COVID-19 concern.

Epidemiological correlates of COVID-19 concern

Results indicated that higher confirmed world cases at time survey was filled out (B = 0.665, SE = 0.332, p = 0.045, 95% CI [0.014 – 1.316]) and higher confirmed country cases at time survey was filled out (B = 0.053, SE = 0.018, p = 0.003, 95% CI [0.018 – 0.088]) were also positively associated with COVID-19 concern. When world and country confirmed cases were entered into the same model, higher country confirmed cases (B = 0.042, SE = 0.019, p = 0.015, 95% CI [0.006 – 0.079]), but not world confirmed cases (B = 0.584, SE = 0.343, p = 0.089, 95% CI [-0.088 – 1.256]) were significantly associated with increased COVID-19 concern, indicating that regional cases are more strongly positively associated with COVID-19 concern when compared to global cases.

Combined mental health and epidemiological model

Lastly, when both transdiagnostic mental health symptoms and epidemiological data of confirmed world and country cases were included in the same model, greater anxiety was significantly positively associated with higher COVID-19 concern (B = 0.209, SE = 0.012, p < 0.001, 95% CI [0.186 – 0.232]). In contrast, depressive symptoms (B = 0.014, SE = 0.013, p = 0.259, 95% CI [-0.011 – 0.039]), confirmed world cases (B = 0.289, SE = 0.303, p = 0.340, 95% CI [-0.304 – 0.882]), and confirmed country cases (B = 0.018, SE = 0.017, p = 0.283, 95% CI [-0.015 – 0.050]) was not significantly associated with COVID-19 concern. It is important to note that older age (range 18–77) was also positively associated with increased COVID-19 concern (B = 0.014, SE = 0.002, p < 0.001, 95% CI [0.011 – 0.018]).

Relationship between financial strain, mental health, and COVID-19 concern

Loss of employment was positively associated with greater COVID-19 concern (B = 0.179, SE = 0.042, p < 0.001, 95% CI [0.096 – 0.262]), greater depressive symptoms (B = 0.456, SE = 0.084, p < 0.001, 95% CI [0.291 – 0.622]) and greater anxiety symptoms (B = 0.346, SE = 0.093, p < 0.001, 95% CI [0.165 – 0.528]).

Correlates of adherence to stay at home orders

Anxiety and depressive symptom correlates of self-quarantine behavioral adherence

Greater anxiety (B = 0.032, SE = 0.007, p < 0.001, 95% CI [0.018 – 0.046]) and depressive (B = 0.040, SE = 0.008, p < 0.001, 95% CI [0.025 – 0.056]) symptoms were significantly related to adherence to more stringent self-quarantine recommendations. When anxiety and depressive symptoms were entered into the same model, greater depressive symptoms (B = 0.029, SE = 0.010, p = 0.003, 95% CI [0.010 – 0.049]) were positively associated with adherence to more stringent self-quarantine recommendations, but anxiety symptoms were not (B = 0.016, SE = 0.009, p = 0.077, 95% CI [-0.002 – 0.034]).

COVID-19 concern correlates of self-quarantine behavioral adherence

Greater COVID-19 concern was positively associated with more strict adherence to self-quarantine recommendations (B = 0.136, SE = 0.016, p < 0.001, 95% CI [0.106 – 0.167]).

Epidemiological correlates of self-quarantine behavioral adherence

Neither confirmed country (B = 0.005, SE = 0.011, p = 0.633, 95% CI [-0.017 – 0.028]) nor world cases (B = 0.341, SE = 0.236, p = 0.148, 95% CI [-0.121 – 0.804]) were associated with more strict behavioral quarantine recommendations.

Combined mental health, COVID-19 concern, and epidemiological model

Lastly, when transdiagnostic mental health symptoms, COVID-19 concern, and Epidemiological variables of confirmed world and country cases were included in the same model greater depressive symptoms (B = 0.028, SE = 0.010, p = 0.005, 95% CI [0.008 – 0.047]) and COVID-19 concern (B = 0.132, SE = 0.018, p < 0.001, 95% CI [0.097 – 0.166]) were significantly positively associated with increased degree of adherence to self-quarantine recommendations. In contrast, anxiety symptoms (B = -0.012, SE = 0.010, p = 0.219, 95% CI [-0.031 – 0.007]), confirmed country cases (B = -0.001, SE = 0.011, p = 0.913, 95% CI [-0.024 – 0.021]), and confirmed world cases (B = 0.225, SE = 0.235, p = 0.338, 95% CI [-0.236 – 0.686]) were not significantly positively associated with degree of adherence to self-quarantine recommendations.

Discussion

The current study recruited 2,065 participants across the United States, Canada, and Europe to investigate the initial impact the COVID-19 pandemic on the level of psychological concern about the pandemic, mental health symptoms, financial stability, and degree of adherence to self-quarantine health behavior.

Data provided compelling evidence of increased anxiety and depression symptoms compared to historical normative data, indicating a clinically significant increase in societal mental health difficulties. These finding converge with prior research on local epidemic and disease outbreaks [5, 7, 8, 10] and are consistent with recent poll data from 150,000 Americans [13].

Results also indicated that individual differences in mental health symptoms and epidemiological data signifying objective world/country confirmed cases were significantly positively associated with increased COVID-19 concern, although when all variables were placed into the same model, then anxiety symptoms were the strongest correlate of COVID-19 concern. Although our sample skewed toward higher education and was predominantly White, our findings through online recruitment were consistent with recent and more representative poll data of over 16,000 American individuals indicating COVID-19 concern [34]. In the current study models, age was significantly positively associated with COVID-19 concern, which is consistent with increased case severity and CFR for older individuals [35].

Loss of employment was positively associated with increased COVID-19 concern and mental health symptoms, the latter of which has been previously documented during global recessions [21]. These results coincide with historical increases in unemployment [17, 18], likely increasing concern related to COVID-19 as individuals not only worry about immediate health, but also secondary economic implications, yet the data collected were cross-sectional, which precludes any ability to make directional claims with data from the current study.

Last, depressive symptoms and COVID-19 concern were both related to having more stringent self-quarantine behaviors, while this was not true of epidemiological variables representing confirmed world and country cases, which may partially relate to objective risk for participants in specific areas around the world. These finding coincide with past research indicating that emotional risk perceptions are often stronger determinants of behavior change than objective risk [36, 37] as well as research showing that successful public health interventions for 2009 H1N1 Swine Flu were impacted by individual risk perception, indicating that risk perception is a critical driver of protective behavior [38]. Alternatively, it is possible that individuals that were under more stringent self-quarantine, for whatever reason, may have higher depressive symptoms due to the strain of social isolation and the lack of social interactions as depression is associated with social withdrawal. Similarly, loss of employment was associated with depressive symptoms and perhaps the reason these participants were quarantining was because they had nowhere to go. Again, data presented here were correlational and in no way allow us to make directional claims. Future longitudinal studies will be required to parse apart directionality.

These findings may inform public health interventions designed to slow infection. Given that there is currently no vaccine for the virus, the most effective intervention is population wide self-quarantine and “physical distancing”. Conformity with these guidelines, however, is effortful, economically damaging, and conflicts with the powerful human motivation for social contact. As such, understanding correlates of adherence to guidelines is essential information for informing more effective public health campaigns. Findings presented here indicate that although public education about objective measures of infection and death rates, especially at the country level, do correlate with psychological concern with COVID-19, the strongest associations of both COVID-19 concern and conformity with self-quarantine measures are measures of individual differences in mental health symptoms. These findings, while correlational that in no way imply causation, may suggest that the propensity for worry and sadness is somewhat adaptive in the current environment during which circumstances are objectively threatening and defensive behavior is in both personal and public interest. However, previous fear-based behavior change campaigns have been controversial [39]. It is likely that some level of fear and sadness is adaptive in objectively threatening circumstances, as long as it’s not severe enough to induces behavioral paralysis, and is combined with self-efficacy [40]. Public education may need to focus on low fear and sadness individuals. Furthermore, older age was positively associated with increased COVID-19 concern, so public education may also benefit from focusing increasing self-quarantine among younger aspects of the population as they seem to have lower levels of COVID-19 concern.

Limitations

While the present study had a number of significant strengths such as rapidly collecting comprehensive psychosocial, health, and economic data on 2,065 participants across the United States, Canada, and Europe during the initial stages of the COVID-19 global pandemic, there are a number of limitations to note. First, online recruitment has been found to have variable demographic and political representation [41]. Our sample was overwhelmingly White with political views that tended to lean moderate to left, limiting the ability to generalize findings to individuals of other races and political orientations. This limitation is an important limitation as preliminary data have shown that African Americans have disproportionately contracted and passed away due to COVID-19 [42-45] and recent poll data have found that 76% of conservatives believe the media has have exaggerated COVID-19 risks, potentially indicating less COVID-19 concern [46]. Second, the study was limited to the United States, Canada, and Europe and therefore was strongly skewed to White individuals, which may preclude these data from generalizing to other non-Western countries. Third, the study was cross-sectional limiting the ability to address changes in mental health symptoms, COVID-19 concern, and financial stability across time. The current study is continuing data collection each month for 12 months, which will allow for longitudinal assessment of the dynamics of psychological adjustment during global pandemics. Fourth, the current study did not collect other psychosocial variables, such as social support and coping that may moderate effects found in the present study. Again, these important data will be collected at follow-ups with the current sample. Fifth, the current study conceptualized confirmed world and country cases with objective risk, which is a potential oversimplification that has the potential to lead to misinterpretation of findings. For example, the current study did not collect data on household presence of, or caretaker responsibilities for, high risk individuals, which would be a key factor that would greatly increase objective risk. Future studies should collect additional factors that would allow researchers to identify those at objectively higher risk for COVID-19 transmission. Sixth, and related to the prior point, results showed that mental health symptoms explained more variance in COVID-19 concern as compared to variables that we defined as indicating objective risk (e.g., confirmed world and country cases). It is possible that other factors related to being in a high risk group (e.g., preexisting medical complications, which themselves highly covary with mental health symptoms) or even differences in personality (e.g., conscientiousness or neuroticism) may have been an unexplained third variable that may have led to higher COVID-19 concern and stringent self-quarantine behaviors. Future research should collect these variables to provide a more comprehensive evaluation of participant behavior change.

Conclusion

Our cross-sectional study recruited 2,065 participants across the United States, Canada, and Europe to investigate whether current levels of anxiety and depressive symptoms are elevated compared to historical normative data, determine the strength of psychological and epidemiological associations with COVID-19 concern, identify correlates of financial strain, and identify associations with these variables and engagement in more stringent self-quarantine behaviors. Findings indicated that current anxiety and depressive symptoms are elevated compared to historical norms, these mental health symptoms explain more variance in COVID-19 concern when compared to epidemiological data signifying confirmed world and country cases. In addition, loss of employment was positively associated with greater depressive symptoms and COVID-19 concern, and that COVID-19 concern and depressive symptoms explained the most variance in adhering to more stringent self-quarantine behavior, which have implications for slowing the spread of the COVID-19 pandemic.

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13 Jul 2020

PONE-D-20-11961

Rapid Assessment of Psychological and Epidemiological Correlates of COVID-19 Concern, Financial Strain, and Health-Related Behavior Change in a Large Online Sample

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Reviewers' comments:

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #1: Partly

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: This review is of a manuscript under consideration for publication in PLOS-One by Nelson et al., entitled “Rapid assessment of psychological and epidemiological correlates of COVID-19 concern, financial strain, and health-related behavior change in a large online sample.” The paper describes a cross-sectional study conducted by self-report of Americans during late March/early April 2020, during the coronavirus (COVID-19) pandemic. The authors conclude that mental health symptoms are more highly associated with COVID-19-related concern and quarantine behaviors and make leaps of interpretation regarding possible impacts on behavioral changes. The paper should benefit from rapid publication, due to the sensitivity of the timing of information related to the pandemic; however, the reviewer does find major concerns that must be addressed prior to publication. With that in mind, the reviewer wishes to stress that there are no “fatal flaw” concerns here; the listed problems should be able to be addressed without extensive additional work, and the length of the comments are primarily intended to make clear the reviewer’s thoughts.

Major comments:

1. The paper equates confirmed world and or country cases with objective risk, an oversimplification that leads to an overreach of conclusions and a possible misinterpretation of findings. The survey does not allow the authors, as far as the reviewer can tell, to measure household presence of, or caretaker responsibilities for, high risk individuals, a key factor that greatly modifies objective risk in this scenario and which affects a large number of individuals. Caretakers for, or individuals of, high risk must be more vigilant than the average person under normal circumstances and thus, were almost certainly more attuned to the danger of COVID-19 early in the pandemic. Additionally, it was known fairly early that age of over 60 or 65 increased risk, greatly widening the number of people falling into the high risk category and the number of people directly affected by the health of high-risk individuals. Indeed, the authors report that age is positively correlated with COVID-19 concern, making this scenario possible. The question is, are individuals affected by higher risk driving the findings relating to stringent quarantine behavior and or concern about the virus? The authors conclude that mental health symptoms (anxiety and depression) explain more variance in COVID-19 concern than objective risk, but using world/country case numbers to indicate objective risk is not an equivalent representation for a large number of people. The authors say their findings coincide with studies suggesting that emotional risk perceptions are stronger determinants of behavior change than objective risk. However, if their findings are driven by high risk-affected individuals, who are ostensibly at greater risk for infection, complications and death, this interpretation isn’t appropriate. If by late March/early April, the U.S. had still had a negligible number of cases, perhaps equating country cases to objective risk would be fine here, but that was not the case. The reviewer suggests a reconsideration of interpretations with this in mind. At the very least, there should be space dedicated to this possibility in the discussion and the authors’ longer-term follow up should explore this avenue. The authors should be careful to also change abstract and conclusion wording accordingly to best reflect their final interpretation.

2. The paper implies that correlation, or association, implies causation and without much discussion of other possibilities, they imply a direction of causation. Specifically, they say “These findings suggest that the propensity for worry and sadness is somewhat adaptive in the current environment…”, and suggest that fear and sadness are causing protective behaviors. Related to the reviewers concerns in the separate point above, it could be that individuals at high risk or with direct ties to or caretaking roles for high risk individuals (their children and/or parents) are experiencing mental health symptoms and are taking the virus more seriously due to their more prominent, objective risk. Likewise, the authors seem to favor that perhaps depressive symptoms are improving quarantine behavior, but another interpretation is that individuals under more stringent quarantine, for whatever reason, have increased symptoms of depression due to strain of isolation, including a lack of social interactions. The reviewer suggests that the authors think more carefully about the implications of their work; they may wish to keep some of their points about public education, but need to round those arguments out carefully and consider other implications their work may have and how that information could impact interventions for the public.

3. There are many spelling, grammatical, and other errors that should be corrected to meet the quality standards of the journal and to best convey the information.

a. Some examples:

i. “inflection” is found at least twice, instead of “infection”

ii. Case fatality rate (CFR) should be defined properly on Line 63. Later “CFR” is used alone.

iii. Line 85: “recent preprints have found” – should be something like “studies in recent preprints show”

iv. Line 90 “casing” – should be “causing”; also the “and” before “casing” should be removed, as it is not a complete sentence as is

v. Line 100 – perhaps missing word “been”

vi. Line 110 – change “do” to “due”

vii. Lines 110-114 – awkward sentence. Suggest, “Furthermore, the impact of social isolation and loneliness on physical health is on par with that of…”

viii. Line 115-120

1. use of “due to” twice in same sentence

2. “and” is placed before #3 and #4

3. Also, this is a gigantic sentence that needs to be broken into two separate sentences

ix. Line 122-124 – awkward sentence

x. Line 124-125 – should be “This cross-sectional study…”

xi. Line 135 – if number will be used to start a sentence, should be spelled out

xii. Line 141 – should be “IP address”

xiii. Line 141 – when referring to people, it is “who” or “whom” instead of “that”

xiv. Not certain about format used for some citations (e.g., Reddit, ProPublica, CDC, Johns Hopkins).

xv. Line 172-174 – awkward sentence uses “and has been shown to be” twice

xvi. Some sentences start with “This” and do not reference a noun. Example Line 199-200.

xvii. “epidemiological of risk” is used a few times – not clear what is meant, maybe a typo? Examples Line 217, Line 213.

xviii. Line 259-263 – run-on sentence (“which indicates…which indicates…” within same sentence)

xix. Line 303 – “associate” should be “associated”

xx. Line 413 – “…with of COVID-19 concern…” – need to remove “of”

xxi. Line 414 – need to add the word “with” – sentence not complete

4. At times, the manuscript leaves out important details, and in general, would benefit from re-wording to convey information more accurately. Listed are instances of concern as examples, and it would likely be beneficial if the authors would use the same eye to re-examine the manuscript a bit more broadly.

a. Introduction: “Overall, some epidemiological models have predicted that there will be 1.1 – 1.2 million deaths in the United States and 250,000 deaths in Great Britain (Ferguson et al., 2020).” This sentence comes at the end of a paragraph that does a nice job of covering ranges of estimates concerning COVID-19’s R0 and fatality rate. Then suddenly, the authors use an unmitigated estimate of deaths, citing Ferguson et al., which is meant to model mortality in the “… absence of any control measures or spontaneous changes in individual behaviour…”. There are other relevant considerations of the model, including that it does not account “for the potential negative effects of health systems being overwhelmed on mortality.” The authors need to include more of this information to give the reader an accurate sense of its relevance, particularly in light of the moderate way they have approached the other estimates.

b. Line 71: Is use of “Spanish flu” conventional in the field? Perhaps “1918 influenza pandemic” or “1918 H1N1 pandemic” or other wording is more accurate and informative.

c. Line 85: “…increased Google mental health searches…” Consider rewording to say “internet mental health-related keyword searches using the Google search engine,” or similar.

d. The authors use “in modern times” in multiple places. Use of this phrase is probably fine, but more meaning would be gleaned if they gave a little more information, such as “since a [particular year or event].” The example in Line 99, “…leading to the largest decrease in world trade and GDP in modern times.” There are major events last century (WWII, Great Depression, etc.) that are familiar to most readers—is there a semi-recent event that can be accurately pointed to instead as a comparison?

e. “Associated” is used throughout the manuscript, but “positively” or “negatively” correlated or similar would be more informative.

i. Example Line 105

f. Lines 136-141 – sentence is too long and implies that limiting participation to certain countries led to removing participants based on age, etc. Suggest starting a new sentence after “Europe (n = 245)” on Line 139.

g. Related to above, Lines 139-141 concerning how participants were excluded is confusing. The authors simultaneously describe inclusion criteria (“18 or older”) and the number of individuals removed for not meeting those criteria (“n=12”), so that it seems like 12 people were kept in because they were 18 or older. Also consider the following sentence where inclusion criteria are mentioned again. This information can be more streamlined.

h. Mean age and range should include the unit (years)

i. Line 200 – “year income” – probably meant “annual income”

j. Line 354-356 – confusing sentence – suggest, “Our sample demographic was skewed toward higher education and was predominantly Caucasian, consistent with online recruitment studies assessing larger…”

k. The very next sentence starts with “in these models” and it is unclear to the reader whether the authors mean the larger online recruitment studies that they just referred to or their own models in the current paper.

l. Lines 354-356 seem out of place. If Lines 356-358 do refer to the larger studies they should be moved with Lines 354-356; see last comment)

5. General corrections need to be made

i. Some figures have legend-style descriptions (e.g., Figs. 6, 7), while others have very minimal information (only a title?) (e.g., Figs. 2, 3, 4). Some figure legends use “a)” and others “A”, etc.

ii. Figure 7 has “covid_concern” in panel A, which seems like it should be updated to “Covid-19 Concern” similar to panel B.

iii. more stats explanation is needed

1. the authors say they used models and give a link to the statistical code, which will be good for future use, but the reader has little idea how to evaluate these statistical analyses. Throughout the paper, the authors say that a particular variable is “associated” with an outcome, but is their finding similar to a correlation? If so, can they say positively or negatively associated with…? For example, Lines 304-305, “…older age (range 18-77) was associated with COVID-19 concern (B = 0.014, SE = …). More description about the statistical models used would be helpful.

2. what is the “B” value given throughout?

These comments are also uploaded as a separate file for the authors.

Reviewer #2: The study is well designed with a good rationale and good choice of brief symptom measures. The manuscript is exceptionally well written, clear and easy to follow. For the most part, I have only minor suggestions.

Main Issue: The main conceptual issue that I would like to see addressed relates to the interpretation of the findings regarding greater depression symptoms being associated with adherence to more stringent self-quarantine recommendations, despite anxiety symptoms being the strongest correlate of COVID-19 concern. This begs the question as to whether depression is really driving adherence to COVID-19 restrictions per se, or if it’s a spurious correlation related to depressive behavior, not COVID-19 related behavior. Under normal circumstances depression is be associated with social withdrawal. It appears that people in the sample who lost their jobs were also more depressed. Perhaps the reason they are “quarantining” is because they lost their job and have no where to go? To help me understand this better, I wondered specifically how adherence to COVID-19 restrictions was assessed. For example, is this staying home/self-isolating only, or does the “change in behavior” also include COVID-19 specific behaviors such as wearing a mask in public, physical distancing, cleaning hands frequently? If it is only staying home, this may be better explained by depression and job loss and not really specific to COVID-19 which is implied. I tried to find supplemental information specifying the questions used to measure “change in behavior” and “self-quarantine due to COVID-19” however these measures did not appear to be included in the supplemental material attached to the submission, nor were the questions easily located at the provided OSF website. Please clarify in the manuscript how "change in behavior" and "self-quarantine due to COVID-19" was measured.

Related to the above:

Line 307 – loss of employment was associated with both greater COVID-19 concern, greater depressive symptoms and greater anxiety symptoms. If you entered loss of employment into the same model with depressive symptoms and anxiety symptoms, do you get the same results as when you have depression and anxiety symptoms alone? (Line 317-320)

Minor Points

Future directions – consider including measures of personality, in particular the Big 5 Personality traits (NEO-PI). One wonders if personality traits such as conscientiousness or neuroticism might also predict adherence to COVID-19 restrictions?

Line 286 – Figure 7. I don’t think you can use dx = diagnosis when you only had 2 items from the PHQ. It’s an indicator of depressive symptoms that has been shown to be sensitive and specific, but on it’s own would not be sufficient to make a diagnosis.

Line 386 – do you think public education should be specifically geared towards younger population since your results also found that older age was associated with increased COVID-19 concern? (Line 305) ie, younger age associated with lower concern?

**********

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Reviewer #1: Yes: Laura Smith

Reviewer #2: No

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4 Sep 2020

Reviewer #1: This review is of a manuscript under consideration for publication in PLOS-One by Nelson et al., entitled “Rapid assessment of psychological and epidemiological correlates of COVID-19 concern, financial strain, and health-related behavior change in a large online sample.” The paper describes a cross-sectional study conducted by self-report of Americans during late March/early April 2020, during the coronavirus (COVID-19) pandemic. The authors conclude that mental health symptoms are more highly associated with COVID-19-related concern and quarantine behaviors and make leaps of interpretation regarding possible impacts on behavioral changes. The paper should benefit from rapid publication, due to the sensitivity of the timing of information related to the pandemic; however, the reviewer does find major concerns that must be addressed prior to publication. With that in mind, the reviewer wishes to stress that there are no “fatal flaw” concerns here; the listed problems should be able to be addressed without extensive additional work, and the length of the comments are primarily intended to make clear the reviewer’s thoughts.

Major comments:

1. The paper equates confirmed world and or country cases with objective risk, an oversimplification that leads to an overreach of conclusions and a possible misinterpretation of findings. The survey does not allow the authors, as far as the reviewer can tell, to measure household presence of, or caretaker responsibilities for, high risk individuals, a key factor that greatly modifies objective risk in this scenario and which affects a large number of individuals. Caretakers for, or individuals of, high risk must be more vigilant than the average person under normal circumstances and thus, were almost certainly more attuned to the danger of COVID-19 early in the pandemic. Additionally, it was known fairly early that age of over 60 or 65 increased risk, greatly widening the number of people falling into the high risk category and the number of people directly affected by the health of high-risk individuals. Indeed, the authors report that age is positively correlated with COVID-19 concern, making this scenario possible. The question is, are individuals affected by higher risk driving the findings relating to stringent quarantine behavior and or concern about the virus? The authors conclude that mental health symptoms (anxiety and depression) explain more variance in COVID-19 concern than objective risk, but using world/country case numbers to indicate objective risk is not an equivalent representation for a large number of people. The authors say their findings coincide with studies suggesting that emotional risk perceptions are stronger determinants of behavior change than objective risk. However, if their findings are driven by high risk-affected individuals, who are ostensibly at greater risk for infection, complications and death, this interpretation isn’t appropriate. If by late March/early April, the U.S. had still had a negligible number of cases, perhaps equating country cases to objective risk would be fine here, but that was not the case. The reviewer suggests a reconsideration of interpretations with this in mind. At the very least, there should be space dedicated to this possibility in the discussion and the authors’ longer-term follow up should explore this avenue. The authors should be careful to also change abstract and conclusion wording accordingly to best reflect their final interpretation.

Thank you for this important point. As requested, we have now added the conceptualization of confirmed world and country cases with objective risk to the limitations section. Specifically, we have stated,

“Fifth, the current study conceptualized confirmed world and country cases with objective risk, which may be an oversimplification that has the potential to lead to misinterpretation of findings. For example, the current study did not collect data on household presence of, or caretaker responsibilities for, high risk individuals, which would be a key factor that would significantly increase objective risk. Future studies should collect additional data such as these that would allow researchers to identify those at objectively higher risk for COVID-19 transmission.”

In terms of the possibility that age is driving the association with stringent quarantine behaviors, participant age was included in the model and was not a significant covariate, indicating that this aspect of being high risk may not have translated to more stringent quarantine behaviors. None-the-less, we have added the following to the limitations section:

“Sixth, and related to the prior point, results showed that mental health symptoms explained more variance in COVID-19 concern than did variables that we defined as indicating objective risk (e.g., confirmed world and country cases). It is possible that other factors related to being in a high risk group (e.g., preexisting medical complications, which themselves highly covary with mental health symptoms) may have been an unexplained third variable that may have led to higher COVID-19 concern and stringent self-quarantine behaviors. Future research should collect these variables to provide a more comprehensive evaluation of participant behavior change.”

Lastly, we have changed our abstract and conclusion wording to describe that we compared mental health symptoms to epidemiological data signifying confirmed world and country cases, rather than use language about objective risk.

2. The paper implies that correlation, or association, implies causation and without much discussion of other possibilities, they imply a direction of causation. Specifically, they say “These findings suggest that the propensity for worry and sadness is somewhat adaptive in the current environment…”, and suggest that fear and sadness are causing protective behaviors. Related to the reviewers concerns in the separate point above, it could be that individuals at high risk or with direct ties to or caretaking roles for high risk individuals (their children and/or parents) are experiencing mental health symptoms and are taking the virus more seriously due to their more prominent, objective risk. Likewise, the authors seem to favor that perhaps depressive symptoms are improving quarantine behavior, but another interpretation is that individuals under more stringent quarantine, for whatever reason, have increased symptoms of depression due to strain of isolation, including a lack of social interactions. The reviewer suggests that the authors think more carefully about the implications of their work; they may wish to keep some of their points about public education, but need to round those arguments out carefully and consider other implications their work may have and how that information could impact interventions for the public.

Thank you for this important point. We have tempered our language in the discussion to highlight the correlational nature of the findings and explicitly state that our findings in no way can imply causation. Specifically, in regards to the quote that you cite, we state:

“These findings, while correlational that in no way imply causation, may suggest if confirmed in a longitudinal study that the propensity for worry and sadness is somewhat adaptive in the current environment, where circumstances are objectively threatening and defensive behavior is in both personal and public interest.”

As proposed by the reviewers, we have also added in other possibilities for results (e.g., association between depressive symptoms and quarantine behaviors) indicating that directionality cannot be determined in this study due to the cross-sectional nature of the study design. Specifically, we state,

“Alternatively, it is possible that individuals that were under more stringent self-quarantine, for whatever reason, may have higher depressive symptoms due to the strain of social isolation and the lack of social interactions. Again, data presented here were correlational and in no way allow us to make directional claims. Future longitudinal studies will be required to parse apart directionality.”

Although we appreciate the reviewer’s suggestion that we expand on the implications of this study, we are also mindful of the additional material we have already added in response to other reviewer comments, and as such we have been conservative about responding to this issue. However, as suggested by Reviewer 2, we have added some material about the implications of the findings for targeting public education towards specific age groups.

3. There are many spelling, grammatical, and other errors that should be corrected to meet the quality standards of the journal and to best convey the information.

a. Some examples:

i. “inflection” is found at least twice, instead of “infection”

This has been edited.

ii. Case fatality rate (CFR) should be defined properly on Line 63. Later “CFR” is used alone.

This has been edited.

iii. Line 85: “recent preprints have found” – should be something like “studies in recent preprints show”

This has been edited

iv. Line 90 “casing” – should be “causing”; also the “and” before “casing” should be removed, as it is not a complete sentence as is

This has been edited.

v. Line 100 – perhaps missing word “been”

This has been edited

vi. Line 110 – change “do” to “due”

This has been edited

vii. Lines 110-114 – awkward sentence. Suggest, “Furthermore, the impact of social isolation and loneliness on physical health is on par with that of…”

This has been edited

viii. Line 115-120

1. use of “due to” twice in same sentence

This has been edited

2. “and” is placed before #3 and #4

This has been edited

3. Also, this is a gigantic sentence that needs to be broken into two separate sentences

Sentence reduced in size

ix. Line 122-124 – awkward sentence

This has been edited

x. Line 124-125 – should be “This cross-sectional study…”

This has been edited

xi. Line 135 – if number will be used to start a sentence, should be spelled out

This has been edited

xii. Line 141 – should be “IP address”

This has been edited

xiii. Line 141 – when referring to people, it is “who” or “whom” instead of “that”

This has been edited

xiv. Not certain about format used for some citations (e.g., Reddit, ProPublica, CDC, Johns Hopkins).

Thank you, we have checked formatting.

xv. Line 172-174 – awkward sentence uses “and has been shown to be” twice

This has been edited

xvi. Some sentences start with “This” and do not reference a noun. Example Line 199-200.

This has been edited.

xvii. “epidemiological of risk” is used a few times – not clear what is meant, maybe a typo? Examples Line 217, Line 213.

This has been edited

xviii. Line 259-263 – run-on sentence (“which indicates…which indicates…” within same sentence)

This has been edited

xix. Line 303 – “associate” should be “associated”

This has been edited

xx. Line 413 – “…with of COVID-19 concern…” – need to remove “of”

This has been edited

xxi. Line 414 – need to add the word “with” – sentence not complete

This has been edited

4. At times, the manuscript leaves out important details, and in general, would benefit from re-wording to convey information more accurately. Listed are instances of concern as examples, and it would likely be beneficial if the authors would use the same eye to re-examine the manuscript a bit more broadly.

a. Introduction: “Overall, some epidemiological models have predicted that there will be 1.1 – 1.2 million deaths in the United States and 250,000 deaths in Great Britain (Ferguson et al., 2020).” This sentence comes at the end of a paragraph that does a nice job of covering ranges of estimates concerning COVID-19’s R0 and fatality rate. Then suddenly, the authors use an unmitigated estimate of deaths, citing Ferguson et al., which is meant to model mortality in the “… absence of any control measures or spontaneous changes in individual behaviour…”. There are other relevant considerations of the model, including that it does not account “for the potential negative effects of health systems being overwhelmed on mortality.” The authors need to include more of this information to give the reader an accurate sense of its relevance, particularly in light of the moderate way they have approached the other estimates.

Thank you for raising this point. As suggested, we have now added that this is one of the initial epidemiological models that is based on an “unmitigated epidemic” (e.g., absence of individual behavior change and systemic control measures) that doesn’t account for the potential of overwhelmed healthcare systems.

b. Line 71: Is use of “Spanish flu” conventional in the field? Perhaps “1918 influenza pandemic” or “1918 H1N1 pandemic” or other wording is more accurate and informative.

Thank you for this suggestion. We have reworded this according to your suggestion.

c. Line 85: “…increased Google mental health searches…” Consider rewording to say “internet mental health-related keyword searches using the Google search engine,” or similar.

We have rephrased the sentence as suggested.

d. The authors use “in modern times” in multiple places. Use of this phrase is probably fine, but more meaning would be gleaned if they gave a little more information, such as “since a [particular year or event].” The example in Line 99, “…leading to the largest decrease in world trade and GDP in modern times.” There are major events last century (WWII, Great Depression, etc.) that are familiar to most readers—is there a semi-recent event that can be accurately pointed to instead as a comparison?

We have made appropriate edits to provide names of major events, rather than referring to “modern times.”

e. “Associated” is used throughout the manuscript, but “positively” or “negatively” correlated or similar would be more informative.

We have revised our wording to include the directionality of associations throughout the manuscript.

i. Example Line 105

We have revised our wording to include the directionality of the association in this example.

f. Lines 136-141 – sentence is too long and implies that limiting participation to certain countries led to removing participants based on age, etc. Suggest starting a new sentence after “Europe (n = 245)” on Line 139.

We have revised this sentence by starting a new sentence as suggested.

g. Related to above, Lines 139-141 concerning how participants were excluded is confusing. The authors simultaneously describe inclusion criteria (“18 or older”) and the number of individuals removed for not meeting those criteria (“n=12”), so that it seems like 12 people were kept in because they were 18 or older. Also consider the following sentence where inclusion criteria are mentioned again. This information can be more streamlined.

Thank you for these points. We have revised this sentence to indicate that we removed 12 subjects who were younger than 18 years of age, which was required for our inclusion criteria. We have also clarified the other exclusion criteria.

h. Mean age and range should include the unit (years)

We have now specified that age is in years.

i. Line 200 – “year income” – probably meant “annual income”

This has been revised as suggested.

j. Line 354-356 – confusing sentence – suggest, “Our sample demographic was skewed toward higher education and was predominantly Caucasian, consistent with online recruitment studies assessing larger…”

This has been edited as suggested.

k. The very next sentence starts with “in these models” and it is unclear to the reader whether the authors mean the larger online recruitment studies that they just referred to or their own models in the current paper.

We have added language to clarify this point by stating,

“In the current study models, age was significantly positively associated with COVID-19 concern, which is consistent with increased case severity and CFR for older individuals (CDC, 2020b).”

l. Lines 354-356 seem out of place. If Lines 356-358 do refer to the larger studies they should be moved with Lines 354-356; see last comment)

Thank you for this suggestion. We have reworded this sentence.

5. General corrections need to be made

i. Some figures have legend-style descriptions (e.g., Figs. 6, 7), while others have very minimal information (only a title?) (e.g., Figs. 2, 3, 4). Some figure legends use “a)” and others “A”, etc.

The figures with extra information were automatically generated by a separate package to that used to generate the other descriptive figures. The package is meant to visualize different statistical models and therefore provides information that is not readily available in (nor necessarily relevant to) the package used to make the other figures. If the reviewer provides the additional statistics or information that they consider to be important to display on the other figures, then the authors are happy to provide that information.

ii. Figure 7 has “covid_concern” in panel A, which seems like it should be updated to “Covid-19 Concern” similar to panel B.

We have made the change to Figure 7.

iii. more stats explanation is needed

The authors are more than happy to provide extra explanations for the statistics used, but would need more specific guidance on what the reviewer would like to be included. For example, some of the figures are merely presentations of descriptive data, rather than statistical analyses per se, so we are unsure what else to include at this time. If the reviewer would be willing to identify which concepts need clarification, we are more than willing to add these to the manuscript.

1. the authors say they used models and give a link to the statistical code, which will be good for future use, but the reader has little idea how to evaluate these statistical analyses. Throughout the paper, the authors say that a particular variable is “associated” with an outcome, but is their finding similar to a correlation? If so, can they say positively or negatively associated with…? For example, Lines 304-305, “…older age (range 18-77) was associated with COVID-19 concern (B = 0.014, SE = …). More description about the statistical models used would be helpful.

We have now provided a description that we used multilevel models to delineate correlations between measures of interest. Specifically, we state,

“A series of multilevel model delineating correlations between measures of interest were used to assess 1) the associations between transdiagnostic mental health symptoms and objective epidemiological risk (i.e., confirmed world and country cases) with COVID-19 concern; 2) the associations between financial strain, transdiagnostic mental health symptoms, and COVID-19 concern; and 3) the associations between COVID-19 concern, transdiagnostic mental health symptoms, and objective epidemiological risk (i.e., confirmed world and country cases) with degree of adherence to self-quarantine recommendations.”

In the results, we have also added directionality every time we mention any associations to highlight that the statistical models were looking at correlations.

2. what is the “B” value given throughout?

The B values are the beta values or the degree of change in the outcome variable for every 1 unit of change in the predictor variable.

Reviewer #2: The study is well designed with a good rationale and good choice of brief symptom measures. The manuscript is exceptionally well written, clear and easy to follow. For the most part, I have only minor suggestions.

Main Issue: The main conceptual issue that I would like to see addressed relates to the interpretation of the findings regarding greater depression symptoms being associated with adherence to more stringent self-quarantine recommendations, despite anxiety symptoms being the strongest correlate of COVID-19 concern. This begs the question as to whether depression is really driving adherence to COVID-19 restrictions per se, or if it’s a spurious correlation related to depressive behavior, not COVID-19 related behavior. Under normal circumstances depression is be associated with social withdrawal. It appears that people in the sample who lost their jobs were also more depressed. Perhaps the reason they are “quarantining” is because they lost their job and have no where to go? To help me understand this better, I wondered specifically how adherence to COVID-19 restrictions was assessed. For example, is this staying home/self-isolating only, or does the “change in behavior” also include COVID-19 specific behaviors such as wearing a mask in public, physical distancing, cleaning hands frequently? If it is only staying home, this may be better explained by depression and job loss and not really specific to COVID-19 which is implied. I tried to find supplemental information specifying the questions used to measure “change in behavior” and “self-quarantine due to COVID-19” however these measures did not appear to be included in the supplemental material attached to the submission, nor were the questions easily located at the provided OSF website. Please clarify in the manuscript how "change in behavior" and "self-quarantine due to COVID-19" was measured.

We strongly agree with the reviewer’s suggestion and have added an alternative explanation for the finding of an association between depressive symptoms and self-quarantine. Specifically, we state,

“Alternatively, it is possible that individuals that were under more stringent self-quarantine, for whatever reason, may have higher depressive symptoms due to the strain of social isolation and the lack of social interactions as depression is associated with social withdrawal. Similarly, loss of employment was associated with depressive symptoms, and perhaps the reason these participants were quarantining was because they had nowhere to go. Again, data presented here were correlational and in no way allow us to make directional claims. Future longitudinal studies will be required to parse apart directionality.”

We also addressed the issue of self-quarantining by asking, “How much are you self-quarantining?” with the following responses.

a. None of the time. I am continuing my normal daily schedule.

b. Some of the time. I have reduced some of the time that I am in public spaces, social gatherings, and work.

c. Most of the time. I only leave for food, doctor appointments, and other essentials.

d. All of the time. I am staying home almost all of the time.

In addition, we assessed behavior change by asking, “Have you made any changes to your daily lifestyle due to COVID-19?” with the following responses.

a. Yes, I have made changes to my daily schedule to reduce risk.

b. No, I have not made changes to my daily schedule to reduce risk.

Therefore, we did assess physical distancing, but did not assess wearing a mask in public or cleaning hands frequently.

Lastly, we have now included the questions related to behavior change and self-quarantine to the methods section and uploaded a list of all study questions to OSF.

Related to the above:

Line 307 – loss of employment was associated with both greater COVID-19 concern, greater depressive symptoms and greater anxiety symptoms. If you entered loss of employment into the same model with depressive symptoms and anxiety symptoms, do you get the same results as when you have depression and anxiety symptoms alone? (Line 317-320)

We ran the requested analysis (i.e., with loss of employment in the same model with depressive symptoms) and anxiety symptoms had the same result as when we had depression and anxiety symptoms alone. Also, we found that loss of employment was significantly related to COVID-19 concern (p = 0.021).

Minor Points

Future directions – consider including measures of personality, in particular the Big 5 Personality traits (NEO-PI). One wonders if personality traits such as conscientiousness or neuroticism might also predict adherence to COVID-19 restrictions?

We have now included this in our limitations section. Specifically, we state,

“Sixth, and related to the prior point, results showed that mental health symptoms explained more variance in COVID-19 concern as compared to variables that we defined as indicating objective risk (e.g., confirmed world and country cases). It is possible that other factors related to being in a high risk group (e.g., preexisting medical complications, which themselves highly covary with mental health symptoms) or even differences in personality (e.g., conscientiousness or neuroticism) may have been an unexplained third variable that may have led to higher COVID-19 concern and stringent self-quarantine behaviors. Future research should collect these variables to provide a more comprehensive evaluation of participant behavior change.”

Line 286 – Figure 7. I don’t think you can use dx = diagnosis when you only had 2 items from the PHQ. It’s an indicator of depressive symptoms that has been shown to be sensitive and specific, but on its own would not be sufficient to make a diagnosis.

We have changed the figure legend to reflect an anxious and depressed group rather than a diagnosis per se.

Line 386 – do you think public education should be specifically geared towards younger population since your results also found that older age was associated with increased COVID-19 concern? (Line 305) ie, younger age associated with lower concern?

Thank you for this great point! We agree and have added the following language to the discussion section,

“Furthermore, older age was positively associated with increased COVID-19 concern, so public education may also benefit from focusing increasing self-quarantine among younger aspects of the population as they seem to have lower levels of COVID-19 concern.”

Submitted filename: Response to Reviewers RR1 Final.docx

Click here for additional data file.

26 Oct 2020

Rapid Assessment of Psychological and Epidemiological Correlates of COVID-19 Concern, Financial Strain, and Health-Related Behavior Change in a Large Online Sample

PONE-D-20-11961R1

Dear Dr. Nelson,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Comments to the Author

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: The reviewer appreciates the authors efforts to correct and improve the submitted manuscript and their important contribution to the literature. Concerns were sufficiently mitigated or resolved via author comments/clarifications and through additions and changes to the manuscript. In particular, improvements are noted in changes to the graphical representations and mitigation of wording regarding interpretations. The reviewer recommends to accept the manuscript as is. Below some additional minor comments may be considered by the authors.

•Fig 6. - consider adding “historical” to the population average scores on the graphs (as it is in the legend)

•Fig 7. – the reviewer doesn’t see “dx” in the figure, as is mentioned in the legend

•Line 401 – typo makes meaning difficult to discern; thing the word “and” may need to be removed

•Line 404-411 – The reviewer suggests mild refinement and tightening of the writing in this section. The sentiments conveyed here should be maintained, but the wording is somewhat confusing and awkward, and generally does not align with the writing capability and tone used in the rest of the manuscript.

Reviewer #2: I am satisfied that my comments based on the first draft of this manuscript have been adequately addressed in the revision of this paper. I could question whether age was included as a predictor in all regressions, as is implied in the discussion but is not clear from the description of the results. It would appear that age was used in a separate regression and not included in the omnibus regression. However, the authors do acknowledge that age is a significant predictor of COVID-19 concern, and aiming interventions at younger populations who may not take precautions as seriously which was my feedback on the original paper. Given the time sensitive nature of this paper, I would not require an additional revision to clarify exactly which regressions age was included in; I would be satisfied to accept the revision as is.

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Reviewer #1: Yes: Laura N. Smith

Reviewer #2: No

29 Oct 2020

PONE-D-20-11961R1

Rapid Assessment of Psychological and Epidemiological Correlates of COVID-19 Concern, Financial Strain, and Health-Related Behavior Change in a Large Online Sample

Dear Dr. Nelson:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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