The COVID-19 pandemic and well-being in Switzerland - worse for young people?

doi:10.21203/rs.3.rs-3911291/v1

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Research Article

The COVID-19 pandemic and well-being in Switzerland - worse for young people?

https://doi.org/10.21203/rs.3.rs-3911291/v1

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Background

The key objective of our study was to describe the population-average trajectories of wellbeing, spanning the period of 2017-2022, comparing young people with other age groups. Additionally, we aimed to describe population-average trajectories of stress and psychosomatic symptoms among young people in 2017-2022 and to identify subgroups who experienced disproportionate changes in wellbeing, stress or psychosomatic symptoms.

Methods

We used longitudinal data from six waves (2017-2022) of the Swiss Household Panel (n individuals = 11,224; n observations = 49,032). We described the trajectories employing piecewise growth curve analysis.

Results

Young people (age 14-25) experienced a steady decline in positive affect and life satisfaction throughout the entire period, with the greatest change occurring before the pandemic (2017-2019). The trajectories in this outcome were largely stable in other age groups. Moreover, young individuals showed a more pronounced increase in negative affect, particularly in the pre-pandemic years, compared to older groups. Negative affect increased during the pandemic, followed by a subsequent decline post-pandemic, observed similarly across all age groups. Among young people specifically, stress followed as similar trajectory as in negative affect. However, issues such as sleep problems, weakness, weariness, and headaches continued to increase in this population from 2017 to 2022. We also found evidence for a greater increase in negative affect during the pandemic in young women and those not in employment or education.

Conclusions

Given the fact that the decline in young people's well-being in Switzerland started two years before the pandemic, our study emphasises the importance of to consideing their well-being within a broader systemic context beyond pandemic-related changes.

Wellbeing

life satisfaction

positive affect

negative affect

young people

trajectories

Covid-19 pandemic

1.1. Mental health and wellbeing before and during the Covid-19 pandemic – young people

There has been an enormous amount of evidence of the Covid-19 pandemic on wellbeing and mental health. Overall, the evidence suggests that mental health and wellbeing declined to some extent during the pandemic in Western countries [1–4]. However, these changes are typically described as “small” in magnitude, with most of the population showing resiliency [1–4]. For instance, a systematic review of 137 distinct studies involving 134 cohorts revealed no changes for general mental health or anxiety symptoms, but depression symptoms worsened minimally during the pandemic compared with pre-pandemic (2018–2019) [3]. However, large inconsistencies persist in the evidence, with studies showing negative, null, or even positive effects of the pandemic [1–4]. This may be due to differential timing of measurement, sociodemographic characteristics of the population, definition and measurement of mental health problems [5].

The most consistent finding emerging from reviews is that mental health and wellbeing of young people were affected to a greater extent than that of the general population [1–4]. In Switzerland, young people (under 25-years-old) experienced lower life satisfaction and higher negative affect compared to pre-pandemic levels, despite no pre- vs pandemic differences across all ages combined [6, 7]. A longitudinal survey of over 1000 Swiss young adults (with average age around 20 years) also indicated greater levels of depression symptoms and anxiety in 2021 compared with 2018 [8]. Young people may not be equipped with skills to deal with stressors of the pandemic, such as severe disease or death, worry about health [9, 10]. Adolescence and young adulthood are critical developmental stages, characterised by transitions across multiple life domains [11]. These transitions could be especially difficult during the pandemic, translating into a greater decline in wellbeing in this age group. However, there is some evidence that that mental health and wellbeing among young people has been declining already before the pandemic. For instance, one study using repeated cross-sectional analysis from the UK Household Longitudinal Study found that psychological distress had been already increasing since 2014 [12]. Therefore, mental health of young people has been high on the political agenda both internationally and in Switzerland [13, 14]. Hence, the key objective of our study was to describe population-average trajectories of wellbeing among young people in 2017–2022, comparing them to older adults, in Switzerland. We aimed to produce more representative values on wellbeing in the Swiss context, than previous studies, and on a longer time trend.

There were two additional secondary objectives of our study. First, we aimed to describe population-average trajectories in other wellbeing-related outcomes such as stress and psychosomatic symptoms (i.e., headaches and sleeping problems) in 2017–2022. This is in recognition that the Covid-19 pandemic may have constituted a traumatic experience, and these outcomes are typically more strongly associated with trauma and hence potentially more responsive to the pandemic than wellbeing [15]. For instance, psychosomatic issues and stress substantially increased during the pandemic in China [16]. However, they have received less attention than psychological distress or wellbeing. Providing a more holistic picture of how well young people did, particularly during the pandemic, across a comprehensive set of indicators can also help to speculate about determinants of wellbeing. The latter secondary objective was to identify groups of young individuals particularly vulnerable during the observation period, with the key focus on the pandemic, using typically available sociodemographic indicators. This was due to previous research indicating large heterogeneity in the extent to which various population groups were affected by the pandemic [1–4, 17]. For instance, women, socioeconomically disadvantaged individuals, or migrants have been found to have disproportionately higher psychological distress during the pandemic, however the findings were not always consistent [1–4].

1.2. Covid-19 pandemic in Switzerland – context

To contextualise the study objectives, the first case of infection in Switzerland was confirmed on 25 February 2020. Most educational institutions and shops were closed in Switzerland on 16 March 2020, with public gatherings including more than five people being banned on 20 March [18]. Individuals were recommended to stay at home, while outdoor activities were allowed in groups up to five with adequate physical distance. Despite efforts to reduce the spread of the virus, intensive care units were close to full occupancy by the end of March. A gradual easing of preventive measures began at the end of April, with a complete opening on 8 June 2020, but new measures were imposed in October as cases surged again. The vaccination campaign started in Switzerland on 23 December 2020, reaching a vaccination rate of 69% by February 2022. From 13 September 2021 until 17 February 2022 access to indoor public spaces was only permitted with a valid Covid certificate. The pandemic had a significant impact on the economy, with a record high decline of the gross domestic product by 10.5 percent in the first six months of 2020, while roughly 30,000 individuals lost their jobs in March and April, resulting in the rise of unemployment nearly as high as in all of 2010 after the financial crisis [19]. Nonetheless, Switzerland suffered the consequences of the Covid-19 pandemic to a lesser degree than most other European countries. For instance, the gross domestic product declined by 6 percent more on average in the European Union than in Switzerland [20]. In addition, as opposed to some European countries, such as Italy or Spain, no strict confinements were introduced in Switzerland [21]. Hence, the “milder” lockdown may have had less of an impact on mental health than in other countries.

1.3. Study objectives

To summarise, the key objective of our study was to describe population-average trajectories of wellbeing among young people in 2017–2022, comparing them to older adults, in Switzerland. There were two additional secondary objectives of our study. First, we aimed to describe population-average trajectories of young people in other wellbeing-related outcomes such as stress and psychosomatic symptoms (i.e., headaches and sleeping problems) in 2017–2022. The latter secondary objective was to identify groups of individuals particularly vulnerable during the observation period, with the key focus on the pandemic, using typically available sociodemographic indicators.

2.1. Data

This paper draws on longitudinal data from six waves (2017–2022) of the Swiss Household Panel [22]. The SHP is a nationally representative household-based panel study that collects information yearly on different aspects of life from each household member at the time of the interview [22]. The data collection period for each study wave was relatively spread out, typically ranging from September to March, with around 90% of the interviews conducted by November. We define our sample as those who were at least 14 years old in 2017, were eligible to participate in all waves between 2017 and 2022 and had at least one valid composite measure of wellbeing between 2017 and 2022 (n individuals = 11,224; n observations = 49,032).

The individual questionnaire was administered mainly by Computer Assisted Telephone Interviewing (CATI) (> 95% for waves 2017–2019, and about 75% for 2020–2022), with Computer Assisted Web Interviewing (CAWI) used for less than 5% prior to 2020 and about 25% after. The difference in modes over time is due to the start of a mixed-mode refreshment sample in 2020 with a 50/50 division between CATI and CAWI. Only few participants changed survey mode over time. Very few participants were interviewed through Computer Assisted Personal Interviewing (CAPI) (< 0.4%).

2.2. Measures

2.2.1. Wellbeing

Wellbeing (also referred to as subjective wellbeing) is defined as “a person’s cognitive and affective evaluations of his or her life” [23]. As the SHP does not include a validated battery measuring wellbeing, we selected items that measured various aspects of life satisfaction, positive and negative affect, following the framework proposed by Diener [23]. These selected items were consistently present across all survey waves from 2017 to 2022.

We selected 10 potentially relevant items, described in more detail in Table 1. The scale of each item ranged from 0 to 10. A higher score indicated greater satisfaction in each life domain and more frequent positive and negative affect. We derived components of wellbeing – positive affect and life satisfaction (PALS) and negative affect (NA) – by summing up the relevant items (as indicated in Table 1). These two subscales were found to have high internal consistency and be scalarly invariant across gender and time, indicating that the questions are likely to be equivalently interpreted across those groups [24].

2.2.2. Stress and psychosomatic symptoms

Stress was measured as a subjective evaluation indicating whether participants suffered from stress in the last month (0 – “never” to 5 – “very often”). It was treated as a continuous variable.

Respondents were asked to indicate whether they suffered from psychosomatic symptoms – including sleeping problems, headaches, and weakness or weariness – “not at all”, “somewhat” or “very much” in the last four weeks. These indicators were recoded into binary variables (0 = “not at all” vs 1=”somewhat/very much”).

Table 1

Details about the measure of wellbeing.
Wellbeing domain	Questions	Response options
Positive affect and life satisfaction	Life satisfaction: In general, how satisfied are you with your life?	0 (not at all satisfied) – 10 (completely satisfied)
	Health satisfaction: How satisfied are you with your state of health?	0 (not at all satisfied) – 10 (completely satisfied)
	Relationships satisfaction: How satisfied are you with your personal, social and family relationships?	0 (not at all satisfied) – 10 (completely satisfied)
	Leisure time satisfaction: How satisfied are you with your leisure time activities?	0 (not at all satisfied) – 10 (completely satisfied)
	Energy and optimism: Are you often plenty of strength, energy and optimism?	0 (never) – 10 (always)
	Joy: How frequently do you generally experience the following emotions?	0 (never) – 10 (always)
Negative affect	Anger: How frequently do you generally experience the following emotions?	0 (never) – 10 (always)
	Sadness: How frequently do you generally experience the following emotions?
	Worry: How frequently do you generally experience the following emotions?
	Anxiety and depression: Do you often have negative feelings such as having the blues, being desperate, suffering from anxiety or depression?

Table 2

Details about the measures of stress and psychosomatic symptoms.
Domains	Questions	Response options
Stress	How often have you felt stressed during the last month?	1 (Never) 2 (Almost never) 3 (Sometimes) 4 (Fairly often) 5 (Very often)
Psychosomatic symptoms	Sleeping problems: During the last 4 weeks, have you suffered from any of the following disorders or health problems?	Binarised as: 0 (Not at all) 1 (Somewhat/very much)
	Headaches: During the last 4 weeks, have you suffered from any of the following disorders or health problems?
	Weakness, weariness: During the last 4 weeks, have you suffered from any of the following disorders or health problems?

2.2.3. Sociodemographic characteristics

We included sociodemographic characteristics to further describe wellbeing trajectories across subgroups of young people. These comprised, all collected in 2019: 1) gender (man/woman), 2) being a migrant based on the first nationality (Swiss/non-Swiss), 3) partnership status (married or living with a partner/single, not living with a partner/widowed/divorced/separated), 4) living with parents (vs not), 5) being in education or training/employment/both/neither, 6) equivalised household net income, categorised into quartiles [25]. The age variable represents the age of participants during the pandemic in 2020.

2.4. Analyses

All the analyses were conducted in Stata v.16 [26], code is available online at https://osf.io/vnzcw/?view_only=ffad7d69ae24416692bbdb413363f185.

2.4.1. Population-average trajectories of wellbeing – comparing 14-25-year-old to older adults

The first study objective – to describe population-average trajectories of wellbeing across different age groups in 2017–2022, with a key focus on young people – was addressed using piecewise growth curve analysis. We conducted this analysis within a multilevel modelling framework, whereby wellbeing indicators were treated as continuous outcomes. This approach accounts for the multilevel structure of longitudinal data, where occasion specific measurements are nested within individuals [27]. Hence, it recognises that responses given by the same individual over time are correlated with each other and provides more conservative standard errors [27]. Our model included both fixed effects and random effects. Fixed effects, similar to regression coefficients, represent population average effects of time. Random effects include information about variance around the starting point of the trend (an intercept) and the trend itself (a slope). As a sensitivity check, we tested whether accounting for clustering of individuals within households made any difference to our estimates. This was done by including household as third level clustering variable in the random part of the growth curve analyses. However, as we found no differences in estimates, the results are not reported.

In our study, we conceptualised time as four separate periods (slopes), representing an overall trend between 2017–2022 – for this reason we refer to our analysis as “piecewise” growth curve modelling. The four slopes were 1) pre-pandemic – 2017–2019, 2) into-pandemic – 2019–2020, 3) during pandemic – 2020–2021, 4) out-of-pandemic – 2021–2022. Conceptualising time in this manner allows us to directly compare change in two different periods. For instance, whether a potential decline in wellbeing during pre-pandemic equals a potential improvement in wellbeing in the post-pandemic period. This can be tested using the Wald test, which formulates the null hypothesis that these two slopes equal zero (i.e., slope_{2017 − 2019} - slope_{2021 − 2022} = 0).

We allowed the trajectories (or slopes) to vary by including interaction terms between the age groups and slopes (i.e., age*slope_{2017 − 2019}, age*slope_{2019 − 2020,} age*slope_{2020 − 2021}, age*slope_{2021 − 2022}). We derived a categorical variable representing four age groups: 1) age 14–25, 2) age 26–45, 3) age 46–65, 4) age > 65.

To explicitly test whether there were any differences between the youngest group (14–25) and older groups, we ran a series of Wald tests. First, we conducted an omnibus Wald test to examine whether there were any differences across the entire study period between the age groups (i.e. a test for all interactions simultaneously: age*slope_{2017 − 2019}, age*slope_{2019 − 2020,} age*slope_{2020 − 2021}, age*slope_{2021 − 2022} = 0, at p value < 0.05). We also used Wald tests for the interaction at specific time periods (e.g., age*slope_{2017 − 2019} = 0), as there may be differential time-specific slopes across age groups not detected by the omnibus test. If evidence for an interaction was detected (at p < 0.05), we further investigated where the differences occurred (i.e., between which age groups and during which period) by running pairwise comparisons of estimated marginal means. All analyses controlled for survey mode. As a supplementary analyses, we conducted an analysis of all individual items, which allowed us to examine whether the average trajectories tended to be driven by single items.

2.4.2. Population-average trajectories of stress and psychosomatic symptoms among young people (14-25-year-old)

As the second objective of our paper was to describe population-average trajectories of psychosomatic symptoms and stress among young people (age 14–25) in 2017–2022, subsequent secondary analyses were limited to this age group. Controlling for age (in years) and survey mode, stress was modelled using piecewise growth curve analysis, in a similar manner as with the wellbeing outcomes. Psychosomatic symptoms, due to being binary indicators, were also modelled with piecewise growth curve analysis, but using generalised linear model (GLM), with a log link function and robust standard errors. The changes in intensity of symptoms were expressed as predicted probabilities of “somewhat” or “very much” suffering from a given symptom.

2.4.3. Identifying subgroups of vulnerable young people (14-25-year-old)

Our third objective was to identify subgroups of young people (age 14–25) who experienced disproportionate changes in wellbeing, stress or psychosomatic symptoms. Using a similar approach as described in section 2.4.1., we allowed the slopes to vary across different sociodemographic groups among young people, by including interaction terms (e.g., gender*slope_{2017 − 2019}, gender*slope_{2019 − 2020,} gender*slope_{2020 − 2021}, gender*slope_{2021 − 2022}). More details on the used covariates can be found in “2.2.2. Sociodemographic characteristics”. Potential differences were further explored using the Wald test and marginal effects.

2.4.4. Dealing with missing information

The detailed description of our missing data strategy is provided in the Supplementary Text 1 (including Supplementary Table 1). We used two approaches to account for missing information and to reduce a potential bias in results – multiple imputation and Maximum Likelihood (ML).

ML estimation was used for the analysis of population-average trajectories of wellbeing, psychosomatic symptoms and stress (see sections 2.4.1. and 2.4.2). ML allows for fitting the trajectories including individuals with at least one measure of wellbeing and no missing covariates. Note that the controls age and response mode had full information, which implies no further loss of cases. Using ML in subgroups analysis using sociodemographic covariates (see section 2.4.3.) would result in losing additional cases, as there was greater amount of missing information in these variables. Hence, we used multiple imputation, generating 50 samples, to replace the missing values. Both approaches provide unbiased results under the Missing at Random (MAR) assumption. As a robustness check, we also described the population-average trajectories of wellbeing using the multiply imputed sample. This was done as the MAR assumption may be likely to be met in multiple imputation due to inclusion several variables that predict wellbeing and missingness in the model.

3.1. Descriptive information about the population

Descriptive information about the population can be found in Table 3.

Table 3

Demographic characteristics of the participants.
	N	Missing n (%)	n (%)
Age	11,241	0 (0.0)
14–25			1,394 (12.4)
26–45			2,792 (24.8)
46–65			3,969 (35.3)
>65			3,086 (27.5)
Young people only (14-25-year-old n = 1,394)
Gender	1,394	0 (0.0)
Men			712 (51.1)
Women			682 (48.9)
First nationality	1,394	0 (0.0)
Swiss			1,272 (91.3)
Non-Swiss			122 (8.8)
Partnership status	1,160	234 (16.8)
Married or living with a partner			65 (5.6)
Single, not living with a partner			1,095 (94.4)
Widowed/divorced/separated			0 (0.0)
Living with parents	1,331	63 (4.5)
No			153 (11.5)
Yes			1,178 (88.5)
In education (including training)/employment	1,159	235 (16.9)
Education/training			435 (37.5)
Employment			296 (25.5)
Both			384 (33.1)
Neither			44 (3.8)
Income (quartile)	1,039	355 (25.5)
1st			243 (23.4)
2nd			328 (31.6)
3rd			275 (26.5)
4th			193 (18.6)
	N	Missing n (%)	Mean (SD)
Age	1,394	0 (0.0)	21.15 (2.55)

3.3. Population-average trajectories of wellbeing – comparing 14-25-year-old to older adults

As can be seen from Fig. 1 (panels A-C) young people (age 14–25) experienced a steady wellbeing decline over the entire period, with the greatest change before the pandemic (2017–2019). Their PALS score dropped by between 0.41 (0.22 to 0.59) and 0.52 (0.33 to 0.72) compared to older groups (see Fig. 1 – panel D). Among this age group, we did not detect any evidence for differential trajectories into-pandemic (2019–2020). The youngest group also experienced the greatest decline in PALS during the pandemic (2020–2021) (by 0.56, 0.17 to 0.96 more than age 45–65 and 0.51, 0.10 to 0.92 than age > 65). However, the decline among the youngest was not greater during the pandemic than before, so it appears to be a continuation of a longer trend. Finally, during the out-of-pandemic (2021–2022) period the decline of wellbeing among young people continued. The trajectories were stable among 26–45 and 46-65-year-olds but declined to a greater extent among the oldest group (by -0.43, -0.86 to 0.00), albeit starting from a higher level.

With respect to negative affect, young people experienced an increase during the pre-pandemic period (2017–2019). However, there was a considerable uncertainty around the estimates as reflected by wide confidence intervals (0.13, -0.04 to 0.29 a year) (see Fig. 2 – panels A-C). This increase was greater than among older people, by 0.13 (-0.06 to 0.33) compared with age 45–65 and 0.21 (0.01 to 0.41) compared with age 65 or older.

We did not detect any age differences in the trajectories of negative affect for the into-pandemic period (see Fig. 2 – panel D). During this period (2019–2020) the levels of negative affect declined among the youngest group (age 14–25) (-0.45, -0.78 to -0.12), and midlife individuals (age 46–65) (-0.44, -0.61 to -0.26), whereas for two other age groups the estimates were largely imprecise (age 26–45: -0.16, -0.37 to 0.04; age > 65: -0.16, -0.39 to 0.07). Also, the increase in negative affect during the pandemic (2020–2021) was comparable across all age groups, with the youngest group experiencing a rise of 1.14 (0.77 to 1.50) (see Fig. 2 – panel D). Subsequently (2021–2022), we saw a decrease in negative affect across all age groups, with the 14–25 and 26-45-year-olds having somewhat smaller declines (age 14–25: -0.86, -1.25 to -0.47, age 26–45: -0.77, -0.99 to -0.54 vs age 46–65: -1.18, -1.38 to -0.99, age > 65: -1.19, -1.46 to -0.92). The out-of-pandemic decline largely but not completely compensated for the increase during the pandemic among young people. The out-of-pandemic decline was statistically equivalent for 45–65 and > 65-year-olds.

The age-specific trajectories of individual items can be found in the Supplementary Text 2 and Supplementary Fig. 1). The key findings were that young people experienced the greatest drop in life satisfaction during the entire study period. The decline in satisfaction with leisure activities was most pronounced among young individuals during the pre-pandemic (2017–2019) and the into-pandemic period (2019–2020). Moreover, the youngest cohort also reported a more significant rise in feelings of depression and anxiety both during the pre-pandemic (2017–2019) and pandemic period (2020–2021), along with increased levels of worry before the pandemic.

3.4. Population-average trajectories of psychosomatic symptoms and stress among young people (14-25-year-old)

Figure 3 shows that the frequency of stress increased already before the pandemic (2017–2019) by 0.08 anually (0.05 to 0.12) among young individuals. It remained relatively stable during the initial phase of the pandemic (0.00, -0.06 to 0.08), rose once again during the pandemic (2020–2021) by 0.13 (0.05 to 0.21), and subsequently decreased during the out-of-pandemic period (2021–2022) by -0.13 (-0.22 to -0.05).

As for psychosomatic symptoms, the predicted probability of individuals reporting sleep problems increased throughout the entire period from (34.9%, 31.6 to 38.3) in 2017 to (43.7%, 38.4 to 49.0) in 2022, with the greatest rise before the pandemic (from 34.9%, 31.6 to 38.3 in 2017 to 40.6%, 36.7 to 44.4 in 2019). Likewise, the probability of experiencing weakness and weariness increased pre-pandemic (56.8%, 52.7 to 61.0 in 2017 to 64.2%, 59.5 vs 68.9 in 2019), and during the pandemic 62.0%, 56.7 to 67.3 in 2020 vs 71.7%, 65.6 to 77.8 in 2021). The probability of reporting headaches remained stable pre-pandemic but increased afterwards from 40.7% (36.8 to 44.5) in 2019 to 48.9% (43.3 to 54.4) in 2022 (see Fig. 3).

3.5. Identifying subgroups of vulnerable young people (14-25-year-old)

They key findings were for differential trajectories in negative affect across genders and being in employment or education. The levels of negative affect declined among young women into-pandemic (2019–2020), while they stayed stable among young men (0.06, -0.52 to 0.65 vs -0.76, -1.34 to -0.18, p = 0.03). Subsequently, there was a greater increase among women than men during the pandemic (2020–2021) (1.79, 1.23 to 2.34 vs 0.77, 0.19 to 1.34, p = 0.04). Moreover, during the pandemic (2020–2021), young people who were neither in education nor employment experienced a more substantial rise in negative affect, reporting 3.03 (0.86 to 5.19), compared to other groups such as those in education, which showed an increase of 1.09 (0.31 to 1.86).

4.1. Key findings, previous literature, implications

This is the first study, representative of households in Switzerland, that aimed to compare the population-average trajectories of wellbeing between young people and other age groups, spanning the period of 2017–2022. Positive affect and life satisfaction declined among young people (age 14–25) over the entire study period (2017–2022), with the greatest declines before the pandemic, and steeper declines than in other age groups during the entire observation period. Young people experienced a steady decline in satisfaction with life in general, and with leisure activities before and going into-pandemic. Negative affect had been showing a slight increase among young individuals before the pandemic. However, it experienced a slight decline during the initial phase of the pandemic. During the pandemic, negative affect increased and subsequently declined out-of-pandemic. These declines were more modest among young people compared with the oldest groups, not fully compensating for the prior increase. The youngest also reported a greater increase in the feelings of depression and anxiety pre-pandemic (2017–2019) and during the pandemic (2020–2021) as well as an increase in worry pre-pandemic. Hence, wellbeing during the pandemic decreased in all age groups, but the decline has been observed already pre-pandemic among the youngest. In Switzerland, this has also been observed in a consistent rise in admissions to mental health services among young individuals, especially women, starting as early as 2012 [28]. These findings are consistent with studies from other Western European countries. The strength of our study in this context is that, as opposed to most previous studies, we examined changes within the same individuals [12].

Based on the existing knowledge, we can only speculate on the causes behind the decline in wellbeing and mental health among young individuals in Switzerland and other Western European countries, which began several years prior to the pandemic. It is often suggested that young people have become more open about their mental health problems, due to greater mental health awareness [29]. This might imply that wellbeing measures do not consistently capture the same concept over time. However, like others, we found statistical measurement invariance of the wellbeing measure, indicating that the interpratation of the questions remained consistent over time [30]. Another explanation frequently offered in the literature is about the harmful effect of widespread use of social media. However, the evidence that social media may contribute to poorer wellbeing among young people is merely tentative for the time being [31, 32]. Morever, others speculated that the increasingly challenging economic circumstances faced by young individuals (e.g., housing expenses, inflation) might be an important determinant of declining wellbeing. Nevertheless, in Switzerland, economic indicators have remained relatively stable over the past decade (e.g., concerning youth unemployment, youth poverty, or the growth of the gross domestic product; GDP) [33]. This was not entirely the case during the pandemic, as the GDP experienced a decline of 2.4%, and young individuals (< 25 years) were more affected by unemployment compared to other age groups. However, both the GDP and youth employment swiftly rebounded to pre-pandemic levels [33, 34]. Other potential contributors to decreased wellbeing could be the uncertainty that young people face, in terms of precarious employment, climate change and military conflicts. Young individuals might not have developed adequate coping mechanisms to deal with these challenges. This situation could be intensified by constant exposure to a vast amount of information. Indeed, studies during the pandemic have revealed that the rise in time spent on social media platforms was linked to increased symptoms of anxiety and depression.

Also, largely in line with previous international research, wellbeing declined to a greater extent among young people during the pandemic [1–4]. As shown previously, at least in terms of negative affect, after the initial drop, wellbeing started to improve again during the pandemic at the population level [1–4]. Providing a more holistic picture of how well young people did, particularly during the pandemic, across a comprehensive set of indicators can help to speculate about determinants of wellbeing. As a secondary objective, we examined trajectories among young people in other outcomes, related to wellbeing. We found that stress, sleep problems, weakness and weariness all increased pre-pandemic, while the probability of headaches remained stable. Stress, weakness and weariness, and headaches increased during the pandemic, but only stress declined afterwards. The increase in psychosomatic symptoms during the pandemic was documented previously in other countries [35]. Evidence on pre-pandemic trajectories in these indicators is limited. The increase of psychosomatic symptoms during the pandemic may be due to the pandemic constituting a traumatic event (or stressor) [9, 10]. It has been argued that young people may be susceptible to experience trauma, as the pandemic might be the first exposure to severe disease, potential death and grief for many of the young people in high income countries. This is combined with anxiety and worries about infection of themselves, friends and family as well as feelings of uncertainty, and perception of the world as scary and unsafe. Moreover, young people tended to report loneliness, isolation, concerns about education, breakdown of routines as being particularly stressful [5]. Disruption of daily activities and stress related to pandemic might have led to increased family tensions, particularly affecting young people. Young people could also not rely on their social network during the pandemic, due to limited opportunities for socialising with their friends and extended family [36]. Adolescence and young adulthood are critical developmental stages, characterised by transitions across multiple life domains [11]. These transitions could be especially difficult during the pandemic, translating into a greater decline in wellbeing in this age group. However, as shown by multiple studies of both mass trauma and post-traumatic stress disorders most people are resilient in the mid to long term (around 55–85%) [37]. This has also been seen in our study in the trajectory of negative affect, when after the initial increase it bounced back nearly to pre-pandemic levels.

As the second secondary objective, we aimed to identify subgroups who experienced disproportionate changes in wellbeing, stress or psychosomatic symptoms. We did not find any differences according to pre-pandemic characteristics, such as household income, partnership status, being a migrant, or living with parents. The literature on the changes in wellbeing during the pandemic has been largely inconsistent regarding sociodemographic differences [1–4]. However, women, migrants and socioeconomically disadvantaged individuals have often been identified as particularly vulnerable [1–4]. We only found greater increases in negative affect among women and those in neither in education nor employment/training. Those in NEET may have been at a greater risk of being disconnected from opportunities or social networks typically associated with education or employment. Somewhat suprisingly, we did not find any differences according to the household income. One potential explanation is that the protective social welfare policies were largely effective for wellbeing. Likewise, we did not detect any differential changes in wellbeing according to migration or partnerhsip status and whether the pariticipants lived with their parents. This is not to say that absolute differences in wellbeing do not exist between these groups, but rather, they were not exacerbated or reduced by the pandemic.

4.2. Strengths and limitations

The key strength of our study is that it is based on a representative sample of households in Switzerland, followed by two years pre- and post-pandemic. However, as with all longitudinal studies, attrition and nonresponse may have introduced a survival bias to the findings. Those with high wellbeing could be more likely to remain in the study, leading to an underestimation of the drop in wellbeing during the pandemic. We attempted to correct for this bias by retaining those with at least one observation during the entire study period (2017–2022) and using techniques, such as ML and multiple imputation which allow for missing data. However, this still does not correct for the bias due to not contributing any observation at all (e.g., due to attrition prior to 2017).

The second limitation of our study is that we did not have access to a standardised measure of wellbeing. Instead, we derived it using a range of individual items capturing wellbeing. Prior to this study, we found that the measure has robust psychometric properties (e.g., a clear factorial structure, measurement invariance across time and demographic groups). However, the evaluation of effect sizes or comparisons with other studies were somewhat impeded due to the absence of a widely recognized wellbeing measure.

Wellbeing of young people started to decline at least two years before the pandemic in Switzerland. Negative affect, stress and some psychosomatic symptoms increased during the pandemic and then largely bounced back to pre-pandemic levels, however still maintaining an overall rising trajectory. Hence, there is a need to consider wellbeing of young people through a wider systemic context, beyond the periodic change associated with the pandemic. At the same time, the potential impact of the pandemic should not be underestimated. Even short-lasting effects can have a large social and economic impact at the population level, for instance, by increased vulnerability to future mental health problems. Finally, young women and socioeconomically disadvantaged individuals could be more vulnerable than others and may require more targeted approaches.

Ethics approval and consent to participate

Participants receive information on the Swiss Household Panel, known to them as “Living in Switzerland”, in advance letters, information leaflets and are given information by interviewers if they are taking part in a telephone interview. These communications give participants information about the background and purpose of the study, how they were selected, how the data will be used and about the security and confidentiality of their data. Participants indicate their consent by answering questions.

Consent for publication

Does not apply, as no information on individual persons was used in this research.

(https://www.biomedcentral.com/getpublished/editorial-policies#ethics+and+consent ):

For all manuscripts that include details, images, or videos relating to an individual person, written informed consent for the publication of these details must be obtained from that person

Availability of data and materials

SHP data are freely accessible to the scientific community here: https://www.swissubase.ch/en/catalogue/studies/6097/19347/overview

Competing interests

The authors declare that they have no competing interests.

Funding

This study was conducted within the project The Covid Generation, which is funded by the Swiss National Science Foundation (NRP80, grant number 408040 210152).

Authors' contributions

DG analyzed and interpreted the data and was a major contributor in writing the manuscript. All authors contributed to the conception of the study, read and approved the final manuscript. All authors have agreed both to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature.

Acknowledgements

Not applicable.

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No competing interests reported.

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The COVID-19 pandemic and well-being in Switzerland - worse for young people?

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Abstract

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1. Introduction

1.1. Mental health and wellbeing before and during the Covid-19 pandemic – young people

1.2. Covid-19 pandemic in Switzerland – context

1.3. Study objectives

2. Materials and methods

2.1. Data

2.2. Measures

2.2.1. Wellbeing

2.2.2. Stress and psychosomatic symptoms

2.2.3. Sociodemographic characteristics

2.4. Analyses

2.4.1. Population-average trajectories of wellbeing – comparing 14-25-year-old to older adults

2.4.2. Population-average trajectories of stress and psychosomatic symptoms among young people (14-25-year-old)

2.4.3. Identifying subgroups of vulnerable young people (14-25-year-old)

2.4.4. Dealing with missing information

3. Results

3.1. Descriptive information about the population

3.3. Population-average trajectories of wellbeing – comparing 14-25-year-old to older adults

3.4. Population-average trajectories of psychosomatic symptoms and stress among young people (14-25-year-old)

3.5. Identifying subgroups of vulnerable young people (14-25-year-old)

4. Discussion

4.1. Key findings, previous literature, implications

4.2. Strengths and limitations

5. Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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