Sampling

Participants were recruited among the students of the Martin Luther University Halle-Wittenberg using convenience sampling. We aimed to include all students enrolled at this university at the time of the survey and used a number of mailing lists kept by different university departments and student organizations to distribute the invitations. In addition, we advertised the study on the university’s online learning platform, and a number of counselling offices shared the invitation on social media. Since we have no way to establish how many students were reached through these channels, we are unable to calculate the response proportion.

Questionnaire

For the purpose of this study, the questionnaire collected three types of data: First, it measured respondents’ health-related quality of life using the German version of the SF-12 [31]. The choice of the SF-12 was motivated by its widespread use and the resulting option to directly compare our findings among university students during the pandemic with previous research in this target group before the pandemic [3] but also with other, non-student populations [32, 33].

Second, it gathered sociodemographic information such as age, gender, nationality and migration background (the latter defined as having at least one parent with non-German nationality), and information on the respondents’ university studies. Third, it assessed a number of potential stressors that might impair students’ quality of life. To that end, respondents were asked whether they considered themselves to be members of a group (or groups) of people potentially affected by discrimination. If they responded in the affirmative, they were asked to specify the group it in an open question. In addition, they were asked if they had actually experienced discrimination in the context of university life. We also collected data on whether participants had children, were affected by a chronic illness or disability, and whether they considered their financial means to be sufficient.

Statistical analyses

The SF-12 was evaluated using standard procedures [31] for separately calculating the Mental Component Summary (MCS) and the Physical Component Summary (PCS), whereby each score can reach values between 0 and 100, and higher values signal better health-related quality of life [34]. We report the average MCS and PCS values and their 95% confidence intervals. To assess potential determinants of quality of life, we performed stratified analyses in which we compared MCS and PCS across different sociodemographic groups.

In a last analytic step, we aimed to account for multiple variables associated with quality of life and accordingly performed linear regression with the physical and the mental dimension of quality of life as dependent variables and the different sociodemographic characteristics and stressors as independent variables. Hereby, we first conducted simple regression analyses; then in a second step, we calculated one multivariable model for each dependent variable, in which the majority of independent variables were included. We did not include nationality and membership of a potentially discriminated group in order to avoid collinearity with migration background, gender, religion, nationality and experience of discrimination. We report regression coefficients and their respective 95% confidence intervals.

Ethics

The Institutional Review Board of Martin Luther University Halle-Wittenberg granted ethical approval for the study (registration number: 2020–144). Data collection was anonymous and in accordance with the respective data protection laws. Before starting the survey, respondents were informed about the aim of the study, anonymity, and measures of data protection and gave their consent to participate by clicking the respective field.

Quality of life in different groups of students

Overall, the average PCS was 51.2 and the MCS was 36.3. Female students showed a slightly lower MCS than male students, while PCS was very similar. German nationals had higher PCS and MCS, compared to respondents with other nationalities. Similarly, students without a migration background showed a higher quality of life in PCS and MCS than students with a migration background.

The subject at university was also associated with quality of life, with medical students having the highest PCS followed by students of Law and Educational Science. For MCS, Law students had the highest score followed by medical students and students of Educational Science.

Students’ experience of discrimination was also associated with their quality of life, whereby the differences were particularly pronounced for MCS: Students who reported belonging to at least one group potentially affected by discrimination had an MCS of 33.9 compared to 51.0 among students who reported no membership of a potentially discriminated group. Students with multiple group memberships had substantially lower MCSs than those with only one and those with none. Similarly, students who experienced discrimination in the context of university had lower PCS and MCS than those who did not. More details are displayed in Table 3.

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Table 3. Health-related quality of life (measured using SF-12).

https://doi.org/10.1371/journal.pone.0310378.t003

Regression analysis of determinants of quality of life

In the simple regression analyses, low quality of life in the physical dimension measured by PCS was most strongly associated with studying Educational science and studying Law, with having insufficient financial means and with the experience of discrimination at university.

When adjusting for potential confounding in the multivariable analysis, we saw for PCS that the association with the experience of discrimination and of lacking sufficient financial means decreased, and the association with gender, religion and migration background increased. However, as displayed in Table 4, confidence intervals in the multivariable model were wide and included the null value, therefore interpretation of these findings needs to be cautious.

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Table 4. Regression coefficients in simple and multivariable linear regression of the physical dimension of quality of life (PCS) and its determinants.

https://doi.org/10.1371/journal.pone.0310378.t004

A poor outcome in the mental dimension of quality of life (MCS) was most strongly associated with having a migration background and a nationality other than German, studying a subject grouped under other (such as sociology, political science or pharmacology), and belonging to a potentially discriminated group.

In the multivariable model, migration background and the academic subject displayed the strongest associations, while the influence of financial means, religion and discrimination experience were inverted from a negative to a positive association with the mental dimension of quality of life. For the influence of insufficient financial means, exploratory stepwise regression showed that adjustment for migration background seems to cause this inversion. Again, confidence intervals in the multivariable model were wide and often included the null value, therefore interpretation of these findings needs to be cautious. The details are displayed in Table 5.

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Table 5. Regression coefficients in univariable and multivariable linear regression of the mental dimension of quality of life (MCS) and its determinants.

https://doi.org/10.1371/journal.pone.0310378.t005

Quality of life before and during the pandemic

When comparing our findings to other studies that measured university students’ health-related quality of life, our findings align with the literature in two dimensions: First, a comparatively low quality of life especially in the mental dimension is a consistent finding among university students. These low levels remained consistent before and during the pandemic at a similar level. For instance, German studies among fourth-semester medical students found an MCS of 44 in 2008 [35] and of 38.5 in 2016 [36], with both values being substantially lower than the mean of 53 for a German reference population aged 21 to 30 years [35]. Similarly, a pre-pandemic study among students in different colleges and universities in the US found an MCS of 38, which is about one standard deviation below the mean value in the US adult population [37]. In comparison, an Italian study among medical students measured an overall PCS of 54 and an MCS of 41 during the pandemic [38].

It thus seems that students’ health-related quality of life did not differ substantially between the times before and during the pandemic, while at both points in time it was considerably lower than other populations’ quality of life.

This low mental health-related quality of life in our population is even more striking when compared to patient populations. While at first glance, we would expect patients suffering from chronic conditions to have a much lower health-related quality of life compared to university students, patients’ values are of a similar magnitude or even better. A study among mentally ill patients measured an MCS of 37 [39], while a study among diabetes patients found an MCS of 48 [32] and a meta-analysis of studies on quality of life among chronic heart failure patients found an MCS of 51 [40].

The reasons for students’ low quality of life and their high burden of mental health issues are not yet fully understood. Studies already established that they are not the result of age or generation effects, since students also have a lower quality of life than non-student peers of the same generation and age [35, 41]. Meanwhile, it seems that students’ social integration could partly explain their mental health: Social disconnection, homesickness and separation from parents have been shown to predict mental health problems in students, according to a recent systematic review by Sheldon et al. [42] and other studies [43], and might be more common among university students than among their peers who pursue vocational training or start work.

Differing results in mental health and quality of life among university students

With respect to the associations of sociodemographic characteristics with quality of life, we see some differences between our study and previous studies. These differences mostly pertain to the mental dimension of quality of life: We found that for the physical dimension of quality of life, the confidence intervals of the regression coefficients in the regression models were large and included the null value, which implies a considerable uncertainty in interpreting the findings.

In contrast, for the mental dimension of quality of life, the model identified migration background and studying a subject other than Medicine as risk factors associated with impaired mental health. There was no relevant association with gender, religion, and with the experience of discrimination.

These findings partly contradict other studies, which usually find substantially higher quality of life in men compared to women [3, 44, 45] and where medical students often have the lowest quality of life [3, 46]. While the reasons for the comparatively good quality of life among female students in our sample are unclear, the relatively good quality of life among medical students could–at least partly–be explained as an effect of our sampling strategy: Due to the research team’s affiliation with the Medical Faculty, it is possible that medical students were more likely to receive an invitation and participate in the study than students from other faculties, which suggests that the medical students participating in the study are more representative of the overall population of medical students. Meanwhile, students of other faculties were mainly reached through student associations and counselling offices, which might have oversampled students who sought help from these institutions and might have a higher risk for mental health issues and lower quality of life.

Differences to the literature are also seen with respect to the importance of financial precarity: In the multivariable model, lack of financial means was associated with a better quality of life in the mental dimension in our sample, which is in contrast to most other studies, which find an association between financial stress and impaired mental health [4]. While in simple regression analysis, insufficient financial means were–as in most other studies–associated with lower MCS, exploratory stepwise regression showed that adjustment for migration background is the reason for the conversion from a negative association in simple regression analysis to a positive association in multivariable analysis. A similar effect is seen when adjusting for nationality.

The relevance of migration background for mental health

In sum, migration background is not only the most important predictor of low quality of life in university students but also seems to be partially mediated by the influence of financial precarity.

To make sense of this finding, we would like to offer two hypotheses: First, the lack of association between financial situation and mental health after adjustment for migration status might be due to the fact that migrants are often excluded from government student loans (which is reserved for German nationals and certain categories of non-Germans). Resultantly, migrants are more likely to be in a financially insecure situation than students without a migration background. Migration background therefore partly seems to function as a surrogate parameter for financial precarity in our model. This hypothesis is supported by other studies that similarly show that students with a migration background more often report lacking financial means [47].

Beyond that, our analysis points to the importance of migration background for both the mental and physical dimension of quality of life. Hereby, the association of migration background with poor quality of life is stronger in the multivariable analysis than in the simple regression analysis. At the same time, membership of a potentially discriminated group is also associated with a lower quality of life in the simple regression model for MCS. This is in line with many other studies that find effects of minority status or the experience of discrimination for a vast array of health-related outcomes [42, 48, 49], which is usually explained by increased stress-levels stemming from the experience of exclusion and relative deprivation [50] and from the physiological reaction to discrimination [51].

Similarly, many studies also show that the experience of discrimination leads to worse subjective health, lower quality of life, worse mental health and higher mortality [51]; this trend does not strongly resonate in our data. In our study, this association is rather weak and the confidence intervals include the null value. We therefore hypothesize that the association of discrimination experiences with low quality of life is confounded by migration background and thus ceases after adjusting for it.

In addition, we hypothesize that the strong association between migration background and low quality of life is only partially mediated by the experience of discrimination and more strongly influenced by social integration and a sense of belonging. A recent systematic review has shown that a sense of social belonging at university is an important predictor of mental health, and that students with a migration background are more likely to feel like they are not belonging [47]. These findings have recently been confirmed by a study specifically for the context of German university [52]. Correspondingly, the establishment or increase of peer support groups has been shown to be effective for improving mental health among international students in the USA [53] and among Black women studying at predominantly white institutions [54].

But also beyond the situation of student minority groups, studies have shown that social support and social integration at university are important influences [42, 47, 55] and therefore can be used as starting points for interventions to increase university students’ resilience e.g. through communal activities and student unions. Also, it has been shown that peer support through mentoring programs is effective in reducing students’ stress and facilitating their transition into university [56] while students’ mental health can be improved by targeted eHealth interventions, as shown in a recent umbrella review [57].

Since we did not gather any data on students’ sense of belonging or their social network, we cannot test this hypothesis but suggest that it will be validated in further studies.

Strengths and limitations

To our knowledge, this is one of very few recent studies investigating the health-related quality of life of university students across different subjects and faculties in a fairly large sample, while also analyzing associated risk factors under the conditions of the COVID-19 pandemic. Hereby, the use of standard instruments ensured a good data quality and the regression models achieved a high R².

Still, some limitations need to be considered: First, we were not able to distribute invitations for this survey via the university’s mailing list, so that we cannot know how many students were actually reached by our invitation; therefore, we cannot calculate a response proportion. Secondly, our invitation might have reached some faculties’ students better than others, so that in our realized sample, not all faculties might be represented in proper proportion. Thirdly, this survey investigated the situation at our own university, which might have prompted students towards socially desirable answers. Fourth, some students might counteract the stress they experience using stress management strategies [8] or receive professional counselling. Since we did not collect data on students’ coping strategies, we cannot rule out that some of the differences we find between groups of students come about by differences in the use of stress coping techniques.