Design and study population
We conducted a cohort study of university students in Stockholm, Sweden before and during the outbreak of the COVID-19. The study is nested within a large on-going dynamic cohort study of university students: the Sustainable University Life (SUN-study) (ClinicalTrial.gov ID: NCT04465435).
All full-time undergraduate students enrolled at Karolinska Institutet (KI), Sophiahemmet University (SHH) and The Scandinavian College of Naprapathic Manual Medicine (NPH) with at least one year left to complete their degree were eligible for inclusion in the study. We also invited all students from the architectural program at Royal Institute of Technology (KTH), students in the bachelor program Business and Economics from the Stockholm School of Economics (SCE) and targeted bachelor programs at The Swedish School of Health and Sports Sciences (GIH) to enroll in the study. Data collection started in August 2019 and is still ongoing.
Data collection
The data was collected online. Students received information about the study through in-class presentations by study staff. Students were invited to complete the baseline survey and provided with access links to the study questionnaire via e-mail. All participants provided informed consent electronically before entering the study. Information about the study was also given in relevant social media channels (e.g. student union social media channels), and through on-campus information sites. Included students were followed with web surveys every three months starting in November 2019. Participants not responding to the follow-up received reminders by email, phone text-message and one phone call over the following month. The study was approved by the Swedish Ethical Review Authority (reference number: 2019-03276, 2020-01449).
The data collected from December 1, 2019 to February 28, 2020 was used as baseline information “before pandemic” (except for the demographic variables for participants from SHH and NPH which were collected August-September 2019). Data collected from March 1 to May 20, 2020 provided follow-up information “during pandemic” (see Table 1). This categorization is based on the facts that Sweden had very few cases of COVID-19 throughout February, with an accelerating spread in March.
Table 1. Participants baseline characteristics and a comparison of the odds of being lost to follow-up between levels of characteristics.
|
All participants
n = 1658
|
Participants at follow-up
n = 1354
|
Participants lost to follow-up
N=304
|
Crude OR of dropping out (95 % CI)
|
DASS-21 Depression, mean (SD)
|
4.70 (4.75)
|
4.63 (4.67)
|
5.04 (5.06)
|
1.07 (0.81 to 1.41)
|
DASS-21 Anxiety, mean (SD)
|
3.17 (3.51)
|
3.14 (3.48)
|
3.32 (3.61)
|
1.12 (0.82 to 1.50)
|
DASS-21 Stress, mean (SD)
|
6.58 (4.67)
|
6.53 (4.62)
|
6.78 (4.88)
|
1.32 (1.0 to 1.74)
|
Age, mean (SD)
|
26.45 (6.9)
|
26.59 (7.01)
|
25.84 (6.39)
|
0.83 (0.64 to 1.06)
|
Females, n (%)
|
1225 (73.84 %)
|
1020 (75.33%)
|
205 (67.43)
|
0.68 (0.52 to 0.89)
|
University, n (%)
KI
SHH
NPH
SSE
KTH
GIH
|
1064 (64.17 %)
218 (13.15 %)
136 (8.20%)
119 (7.18%)
80 (4.83%)
41 (2.47 %)
|
902 (66.62 %)
175 (12.92 %)
110 (8.12%)
84 (6.10%)
52 (3.84%)
31 (2.29%)
|
162 (53.29%)
43 (14.14%)
26 (8.55%)
35 (11.51%)
28 (9.21%)
10 (3.29%)
|
Ref
1.37 (0.93 to 1.97)
1.32 (0.82 to 2.05)
2.32 (1.50 to 3.53)
3 (1.82 to 4.85)
1.8 (0.82 to 3.61)
|
Lonely, n (%)
|
641 (38.66 %)
|
518 (38.26 %)
|
123 (40.46%)
|
1.1 (0.85 to 1.41)
|
Moderate mental health problems, n (%)
|
664 (40.05 %)
|
541 (39.96%)
|
123 (40.46 %)
|
1.02 (0.79 to 1.31)
|
Poor sleep, n (%)
|
943 (56.88%)
|
773 (57.09 %)
|
170 (55.92)
|
0.95 (0.74 to 1.23)
|
Year of Study, n (%)
1st
2nd
3rd
Masters
|
623 (37.58%)
415 (25.03 %)
273 (16.47%)
347 (20.93 %)
|
484 (35.75%)
339 (25.04%)
230 (16.99 %)
301 (22.23 %)
|
139 (45.72%)
76 (25%)
43 (14.14%)
46 (15.13%)
|
Ref
0.78 (0.57 to 1.06)
0.65 (0.44 to 0.94)
0.53 (0.37 to 0.76)
|
At least one parent with university education, n (%)
|
1189 (71.71 %)
|
972 (71.79 %)
|
217 (71.38%)
|
0.98 (0.75 to 1.30)
|
Country of Origin, n (%)
Sweden
Scandinavia
Europe
Outside Europe
|
1337 (80.64%)
78 (4.70%)
85 (5.13%)
158 (9.53%)
|
1098 (81.09%)
67 (4.95%)
71 (5.24%)
118 (8.71%)
|
239 (78.62%)
11 (3.62%)
14 (4.61%)
40 (13.16%)
|
Ref
0.75 (0.37 to 1.39)
0.91 (0.48 to 1.59)
1.56 (1.05 to 2.27)
|
Month of observation
December
January
February
March
April
May
|
83 (5.00%)
952 (57.42%)
623 (37.58%)
..
..
..
|
..
..
..
70 (5.17%)
997 (73.63%)
287 (21.20%)
|
..
|
..
|
Karolinska Instituet (KI), Sophiahemmet University (SHH), The Scandinavian College of Naprapathic Manual Medicine (NPH), Stockholm School of Economics (SCE), Royal Institute of Technology (KTH), The Swedish School of Health and Sports Sciences (GIH). For the attrition analysis age has been dichotomized by the median value 24 years (<24 years is the reference category). DASS-21 values have been dichotomized at the level for moderate symptoms, see Methods section (below cut-off is the reference category).
Measurement of loneliness, sleep quality and pre-existing mental health problems
Loneliness was measured using the UCLA Three-Item Loneliness Scale [25] with a total score ranging from 3-9 points. In accordance with previous research, we used a cut-off of ≥ 6/9 to define loneliness [26]. UCLA Three-Item Loneliness Scale has acceptable internal consistency (Cronbach’s α= 0.72) and high correlation (r=0.82) with the 20 item Revised UCLA Loneliness Scale [25].
Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI) [27]. A score of > 5/21 is used to classify poor sleep quality. This cut-off has shown a sensitivity of 89.6 % and a specificity of 86.5 % for differentiating between good and poor sleepers [28]. The PSQI has adequate internal consistency (Cronbach’s α =0.82) and test-retest reliability (r=0 .82) over one month [28].
Pre-existing mental health problems were measured with the Depression, Anxiety and Stress Scale (DASS-21; see psychometrics under ‘Outcomes’) [29] and classified as moderate symptoms if scoring above cut-off for on any of the three subscales ( ≥ 7 on the depression subscale or ≥ 6 on the anxiety subscale or ≥ 10 on the stress scale) [30]. Loneliness, sleep quality and pre-existing mental health problems were all measured before the pandemic.
Outcomes
We measured symptoms of depression, anxiety, and stress with DASS-21 [29]. DASS-21 has good psychometric properties, with convergent and divergent validity distinguishing between subscales, and Cronbach’s α of 0.82-0.90 for the three subscales [29]. The primary outcomes are scores of depression, anxiety, and stress, respectively. For each participant, these are measured before and during the COVID-19 pandemic.
Statistical analyses
Participants baseline characteristics are presented in Table 1 as number of participants and percentages or as means with SDs for all participants, participants completing the follow-up and participants lost to follow-up.
We used Generalized Estimating Equations (GEE) to model mental health symptoms during two time periods, before and during the pandemic. GEE models treat correlation between observations from the same individual as nuisance parameters and provide estimates of the marginal population mean of the outcome. Our data was not normally distributed, which was one reason for choosing GEE since the model do not rely on the assumption of normally distributed outcome measures or the normality of residuals. We built three separate models, one each for symptoms of depression, anxiety, and stress, to assess overall mean differences in symptoms from before to during the pandemic. These models included only time-point (before vs. during pandemic) as the predictor. Since the models evaluated differences over time for the full group, no covariates were used in these models. An exchangeable correlation matrix was specified, although with only two time points, it makes no difference which structure is used.
Subsequently nine separate models were fitted to assess if differences between the outcomes from before to during the pandemic varied by loneliness, poor sleep quality or pre-existing mental health problems. These models included an exposure variable of dichotomized loneliness, sleep quality or pre-existing mental health problems and time-point as predictors. A two-way interaction term between exposure and time point was included letting the differences over time vary by exposure level. These models were adjusted for age, female gender, and highest parental education level (for unadjusted coefficients see Online Resource 1). An exchangeable working correlation structure was used in all models.
We conducted a sensitivity analysis using the same methods described above in a sample of 496 participants followed from August-September 2019 to November 2019-January 2020 to compare the effect modification of the exposures during the pandemic to those of an earlier time period (Online Resource 2). All analyses were performed using RStudio version 1.2.5001, the packages ‘geepack’ and ‘emmeans’ were used to perform GEE analyses, and to derive estimated marginal means from the models.
Three items of the PSQI were missing (5b, 5f and 5j) for the first 333 included participants, due to initial technical problems with the web survey. We imputed these missing variables by imputing the individual mean values from observed items 5b-5j on the PSQI. No other measures had missing items.
We investigated whether loss to follow-up was random, or systematic by investigating the association between baseline characteristics and dropping out of the study by building a series of logistic regression models. We report crude odds ratios (ORs) of being lost to follow-up (Table 1).