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

The COVID-19 pandemic and well-being in Switzerland - worse for young people? | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The COVID-19 pandemic and well-being in Switzerland - worse for young people? D. Gondek, L. Vandecasteele, N. Sánchez-Mira, S. Steinmetz, T. Mehmeti, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3911291/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract 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 Figures Figure 1 Figure 2 Figure 3 1. Introduction 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. Materials and methods 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? 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. Results 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. Discussion 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. 5. Conclusion 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. Declarations 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. References Blendermann M, Ebalu TI, Obisie-Orlu IC, Fried EI, Hallion LS. A narrative systematic review of changes in mental health symptoms from before to during the COVID-19 pandemic. Psychol Med. 2023:1–24. Cénat JM, Farahi SMMM, Dalexis RD, Darius WP, Bekarkhanechi FM, Poisson H, et al. The global evolution of mental health problems during the COVID-19 pandemic: A systematic review and meta-analysis of longitudinal studies. J Affect Disord. 2022;315:70–95. Sun Y, Wu Y, Fan S, Dal Santo T, Li L, Jiang X, et al. Comparison of mental health symptoms before and during the covid-19 pandemic: evidence from a systematic review and meta-analysis of 134 cohorts. BMJ. 2023;380:e074224. Prati G, Mancini AD. The psychological impact of COVID-19 pandemic lockdowns: a review and meta-analysis of longitudinal studies and natural experiments. Psychol Med. 2021;51(2):201–11. Wolf K, Schmitz J. Scoping review: longitudinal effects of the COVID-19 pandemic on child and adolescent mental health. European Child & Adolescent Psychiatry; 2023. Jan-Erik Refle J-E, Voorpostel M, Lebert F, Kuhn U, Klaas HS, Ryser V-A, et al. First results of the Swiss Household Panel – Covid-19 Study. Lausanne, Switzerland: FORS; 2020. Kuhn U, Klaas HS, Antal E, Dasoki N, Lebert F, Lipps O, et al. Who is most affected by the Corona crisis? An analysis of changes in stress and well-being in Switzerland. Eur Soc. 2021;23(sup1):942–S56. Horesh D, Brown AD. Traumatic stress in the age of COVID-19: A call to close critical gaps and adapt to new realities. Psychol Trauma. 2020;12(4):331–5. Watson MF, Bacigalupe G, Daneshpour M, Han W-J, Parra-Cardona R. COVID-19 Interconnectedness: Health Inequity, the Climate Crisis, and Collective Trauma. Fam Process. 2020;59(3):832–46. Erikson EH. Identity: youth and crisis. Norton & Co; 1968. Pierce M, Hope H, Ford T, Hatch S, Hotopf M, John A, et al. Mental health before and during the COVID-19 pandemic: a longitudinal probability sample survey of the UK population. Lancet Psychiatry. 2020;7(10):883–92. swissinfo.ch. Swiss youth seek ways out of mental health crisis. 2023. Organization WH. Mental health of adolescents. 2021. Afari N, Ahumada SM, Wright LJ, Mostoufi S, Golnari G, Reis V, et al. Psychological trauma and functional somatic syndromes: a systematic review and meta-analysis. Psychosom Med. 2014;76(1):2–11. Yue Y, Li L, Liu R, Zhang Y, Zhang S, Sang H, et al. The dynamic changes of psychosomatic symptoms in three waves of COVID-19 outbreak and fatigue caused by enduring pandemic in China. J Affect Disord. 2023;331:17–24. Manchia M, Gathier AW, Yapici-Eser H, Schmidt MV, de Quervain D, van Amelsvoort T, et al. The impact of the prolonged COVID-19 pandemic on stress resilience and mental health: A critical review across waves. Eur Neuropsychopharmacol. 2022;55:22–83. suisse C. Ordonnance 2 sur les mesures destinées à lutter contre le coronavirus (COVID-19). 2020. Swiss State Secretariat for Economic Affairs (SECO). Gross domestic product in the second quarter of 2020: pandemic leads to historic slump. 2020. Eurostat. GDP and main components (output, expenditure and income). European Commission; 2020. Hale T, Angrist N, Goldszmidt R, Kira B, Petherick A, Phillips T, et al. A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker). Nat Hum Behav. 2021;5(4):529–38. SHP Group. Living in Switzerland Waves 1–23 + Covid 19 data. FORS - Swiss Centre of Expertise in the Social Sciences. Financed by the Swiss National Science Foundation, distributed by FORS, Lausanne. 2023. Diener E, Lucas RE, Oishi S. Subjective well-being: The science of happiness and life satisfaction. In: Lopez CRSSJ, editor. Handbook of positive psychology. New York: Oxford University Press; 2002. pp. 63–73. Gondek D, Garzón EG, Sánchez-Mira N, Vandecasteele L, Steinmetz S, Voorpostel M. Going beyond the single item: deriving and evaluating a composite subjective wellbeing measure in the Swiss Household Panel. 2023. Voorpostel M, Tillmann R, Lebert F, Kuhn U, Lipps O, Ryser V-A, et al. Swiss Household Panel User Guide (1999–2020) Wave 22. Lausanne, Switzerland: FORS; 2022. StataCorp. Stata statistical software: release 16. College Station: StataCorp LLC; 2020. Raudenbush SW, Bryk AS. Hierarchical linear models: applications and data analysis methods. Newbury Park: Sage; 2002. Andreani T. Mental disorders: unprecedented rise in hospital admissions for young women aged 10–24. Federal Statistical Office; 2022. Bethune S. Gen Z more likely to report mental health concerns. Monit Psychol. 2019;50(1):20. Gondek D, Bann D, Patalay P, Goodman A, McElroy E, Richards M, et al. Psychological distress from early adulthood to early old age: evidence from the 1946, 1958 and 1970 British birth cohorts. Psychol Med. 2022;52(8):1471–80. Keles B, McCrae N, Grealish A. A systematic review: the influence of social media on depression, anxiety and psychological distress in adolescents. Int J Adolescence Youth. 2020;25(1):79–93. Valkenburg PM, Meier A, Beyens I. Social media use and its impact on adolescent mental health: An umbrella review of the evidence. Curr Opin Psychol. 2022;44:58–68. Office FS. Gross Domestic Product. Neuchâtel, Switzerland: Federal Statistical Office; 2023. Arni P. IZA COVID-19 Crisis Response Monitoring Switzerland (November 2020). Bonn, Germany: IZA - Institute of Labor Economics; 2020. Zidkova R, Malinakova K, van Dijk JP, Tavel P. The Coronavirus Pandemic and the Occurrence of Psychosomatic Symptoms: Are They Related? Int J Environ Res Public Health. 2021;18(7). Vacchiano M. How the First COVID-19 Lockdown Worsened Younger Generations’ Mental Health: Insights from Network Theory. Sociol Res Online. 2023;28(3):884–93. Chen S, Bonanno GA. Psychological adjustment during the global outbreak of COVID-19: A resilience perspective. Psychol Trauma. 2020;12(S1):51–S4. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 20 Mar, 2024 Reviews received at journal 12 Mar, 2024 Reviewers agreed at journal 05 Mar, 2024 Reviewers agreed at journal 28 Feb, 2024 Reviewers invited by journal 11 Feb, 2024 Editor assigned by journal 07 Feb, 2024 Submission checks completed at journal 31 Jan, 2024 First submitted to journal 30 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3911291","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270269769,"identity":"7d20d3ae-49ce-47b7-8d5d-d08e0c445a01","order_by":0,"name":"D. Gondek","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIie3QMWvCQBTA8SeBuhzOyWK+wgsHpsJ9mCsHmSI6iUMrgkMXP0AHP0SmQreTA7MEXDMKgpPFgEtGL8FAh+bqKPT+hJCE+/EeAbDZHjGpL1I/cQcKdvvKJwB4D+l8RA1BM4GGOKQhYCC9NAvkCd76YVcoyjgb+wsy2O8R/LCFeFmMmzWkdLg6ChHzaJhIEqJeLPha/E5QjhJFYPuS5DFVo1IhAhm4mnCULWT33ZDxRT1zhdViZpLXU16rKY4ATfQPNBMvPxebNUqK2ZEGKx4hqqepJm6QtJDeLhLFaTbvYyoObskZ+u/LT6+cMb9tym099ePFqe+u6XzV/K8DNpvN9p+7Arp0Xh04OzvsAAAAAElFTkSuQmCC","orcid":"","institution":"Swiss Centre of Expertise in the Social Sciences","correspondingAuthor":true,"prefix":"","firstName":"D.","middleName":"","lastName":"Gondek","suffix":""},{"id":270269770,"identity":"96db709d-c53b-4008-87ac-57093b6bb1da","order_by":1,"name":"L. Vandecasteele","email":"","orcid":"","institution":"University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"L.","middleName":"","lastName":"Vandecasteele","suffix":""},{"id":270269771,"identity":"57599ccc-e634-494f-9ef3-10c2fd353882","order_by":2,"name":"N. Sánchez-Mira","email":"","orcid":"","institution":"University of Neuchâtel","correspondingAuthor":false,"prefix":"","firstName":"N.","middleName":"","lastName":"Sánchez-Mira","suffix":""},{"id":270269772,"identity":"f8e86095-d7ef-4ee1-a670-a64c44282d79","order_by":3,"name":"S. Steinmetz","email":"","orcid":"","institution":"University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"S.","middleName":"","lastName":"Steinmetz","suffix":""},{"id":270269773,"identity":"1fe7d76f-32df-4096-8c6c-b9a3b7d5248f","order_by":4,"name":"T. Mehmeti","email":"","orcid":"","institution":"University of Neuchâtel","correspondingAuthor":false,"prefix":"","firstName":"T.","middleName":"","lastName":"Mehmeti","suffix":""},{"id":270269774,"identity":"f3a8d645-2984-4624-bbee-5076f059f918","order_by":5,"name":"M. Voorpostel","email":"","orcid":"","institution":"Swiss Centre of Expertise in the Social Sciences","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"","lastName":"Voorpostel","suffix":""}],"badges":[],"createdAt":"2024-01-30 17:14:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3911291/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3911291/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50577768,"identity":"eaf4e2cb-4eee-4912-ac43-2c62f6c721e3","added_by":"auto","created_at":"2024-02-02 18:01:33","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":81801,"visible":true,"origin":"","legend":"\u003cp\u003eAge-specific average trajectories of positive affect and life satisfaction (panels A-C) and comparison of period-specific difference in change across age groups, with young people as a reference group (panel D).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3911291/v1/b66b1ab0522b8648fcdc0be5.jpg"},{"id":50577767,"identity":"d20ad01b-6640-4f26-9def-9d8e80338568","added_by":"auto","created_at":"2024-02-02 18:01:33","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84432,"visible":true,"origin":"","legend":"\u003cp\u003eAge-specific average trajectories of negative affect (panels A-C) and comparison of period-specific difference in change across age groups, with young people as a reference group (panel D).\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3911291/v1/8e4c31e4b7b1bdf04a2360b6.jpg"},{"id":50577769,"identity":"1c3161bd-9611-40a6-a981-43d4a9a827d2","added_by":"auto","created_at":"2024-02-02 18:01:33","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":65469,"visible":true,"origin":"","legend":"\u003cp\u003eAverage trajectories of stress and psychosomatic symptoms among young people (age 14-25).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3911291/v1/99f995d5032e35b7d25b3482.jpg"},{"id":50578650,"identity":"2937d8cf-15aa-4048-8315-bc96ff8f4429","added_by":"auto","created_at":"2024-02-02 18:09:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":770410,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3911291/v1/70a8a694-4c64-489d-9972-c504fdf783af.pdf"},{"id":50577770,"identity":"a37feeb9-0428-4981-b6a3-3aa5e2d71386","added_by":"auto","created_at":"2024-02-02 18:01:34","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":197323,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3911291/v1/fb6799d7ded960f9ae52b955.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The COVID-19 pandemic and well-being in Switzerland - worse for young people?","fulltext":[{"header":"1. Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1. Mental health and wellbeing before and during the Covid-19 pandemic \u0026ndash; young people\u003c/h2\u003e \u003cp\u003eThere 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 [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, these changes are typically described as \u0026ldquo;small\u0026rdquo; in magnitude, with most of the population showing resiliency [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. 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\u0026ndash;2019) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, large inconsistencies persist in the evidence, with studies showing negative, null, or even positive effects of the pandemic [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This may be due to differential timing of measurement, sociodemographic characteristics of the population, definition and measurement of mental health problems [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe 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 [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Young people may not be equipped with skills to deal with stressors of the pandemic, such as severe disease or death, worry about health [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Adolescence and young adulthood are critical developmental stages, characterised by transitions across multiple life domains [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, mental health of young people has been high on the political agenda both internationally and in Switzerland [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Hence, the key objective of our study was to describe population-average trajectories of wellbeing among young people in 2017\u0026ndash;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.\u003c/p\u003e \u003cp\u003eThere 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\u0026ndash;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 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For instance, psychosomatic issues and stress substantially increased during the pandemic in China [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. 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 [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. 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 [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2. Covid-19 pandemic in Switzerland \u0026ndash; context\u003c/h2\u003e \u003cp\u003eTo 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 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In addition, as opposed to some European countries, such as Italy or Spain, no strict confinements were introduced in Switzerland [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Hence, the \u0026ldquo;milder\u0026rdquo; lockdown may have had less of an impact on mental health than in other countries.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3. Study objectives\u003c/h2\u003e \u003cp\u003eTo summarise, the key objective of our study was to describe population-average trajectories of wellbeing among young people in 2017\u0026ndash;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\u0026ndash;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.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data\u003c/h2\u003e \u003cp\u003eThis paper draws on longitudinal data from six waves (2017\u0026ndash;2022) of the Swiss Household Panel [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. 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\u0026thinsp;=\u0026thinsp;11,224; n observations\u0026thinsp;=\u0026thinsp;49,032).\u003c/p\u003e \u003cp\u003eThe individual questionnaire was administered mainly by Computer Assisted Telephone Interviewing (CATI) (\u0026gt;\u0026thinsp;95% for waves 2017\u0026ndash;2019, and about 75% for 2020\u0026ndash;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) (\u0026lt;\u0026thinsp;0.4%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Measures\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Wellbeing\u003c/h2\u003e \u003cp\u003eWellbeing (also referred to as subjective wellbeing) is defined as \u0026ldquo;a person\u0026rsquo;s cognitive and affective evaluations of his or her life\u0026rdquo; [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These selected items were consistently present across all survey waves from 2017 to 2022.\u003c/p\u003e \u003cp\u003eWe selected 10 potentially relevant items, described in more detail in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. 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 \u0026ndash; positive affect and life satisfaction (PALS) and negative affect (NA) \u0026ndash; by summing up the relevant items (as indicated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). 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 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Stress and psychosomatic symptoms\u003c/h2\u003e \u003cp\u003eStress was measured as a subjective evaluation indicating whether participants suffered from stress in the last month (0 \u0026ndash; \u0026ldquo;never\u0026rdquo; to 5 \u0026ndash; \u0026ldquo;very often\u0026rdquo;). It was treated as a continuous variable.\u003c/p\u003e \u003cp\u003eRespondents were asked to indicate whether they suffered from psychosomatic symptoms \u0026ndash; including sleeping problems, headaches, and weakness or weariness \u0026ndash; \u0026ldquo;not at all\u0026rdquo;, \u0026ldquo;somewhat\u0026rdquo; or \u0026ldquo;very much\u0026rdquo; in the last four weeks. These indicators were recoded into binary variables (0 = \u0026ldquo;not at all\u0026rdquo; vs 1=\u0026rdquo;somewhat/very much\u0026rdquo;).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetails about the measure of wellbeing.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWellbeing domain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuestions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResponse options\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003ePositive affect and life satisfaction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLife satisfaction\u003c/b\u003e: In general, how satisfied are you with your life?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (not at all satisfied) \u0026ndash;\u003c/p\u003e \u003cp\u003e10 (completely satisfied)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHealth satisfaction\u003c/b\u003e: How satisfied are you with your state of health?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (not at all satisfied) \u0026ndash;\u003c/p\u003e \u003cp\u003e10 (completely satisfied)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRelationships satisfaction\u003c/b\u003e: How satisfied are you with your personal, social and family relationships?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0 (not at all satisfied) \u0026ndash;\u003c/p\u003e \u003cp\u003e10 (completely satisfied)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLeisure time satisfaction\u003c/b\u003e: How satisfied are you with your leisure time activities?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEnergy and optimism\u003c/b\u003e: Are you often plenty of strength, energy and optimism?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (never) \u0026ndash; 10 (always)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eJoy\u003c/b\u003e: How frequently do you generally experience the following emotions?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (never) \u0026ndash; 10 (always)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eNegative affect\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAnger\u003c/b\u003e: How frequently do you generally experience the following emotions?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0 (never) \u0026ndash; 10 (always)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSadness\u003c/b\u003e: How frequently do you generally experience the following emotions?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWorry\u003c/b\u003e: How frequently do you generally experience the following emotions?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAnxiety and depression\u003c/b\u003e: Do you often have negative feelings such as having the blues, being desperate, suffering from anxiety or depression?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetails about the measures of stress and psychosomatic symptoms.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomains\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuestions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResponse options\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStress\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHow often have you felt stressed during the last month?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (Never)\u003c/p\u003e \u003cp\u003e2 (Almost never)\u003c/p\u003e \u003cp\u003e3 (Sometimes)\u003c/p\u003e \u003cp\u003e4 (Fairly often)\u003c/p\u003e \u003cp\u003e5 (Very often)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003ePsychosomatic symptoms\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSleeping problems\u003c/b\u003e: During the last 4 weeks, have you suffered from any of the following disorders or health problems?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBinarised as:\u003c/p\u003e \u003cp\u003e0 (Not at all)\u003c/p\u003e \u003cp\u003e1 (Somewhat/very much)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHeadaches\u003c/b\u003e: During the last 4 weeks, have you suffered from any of the following disorders or health problems?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWeakness, weariness\u003c/b\u003e: During the last 4 weeks, have you suffered from any of the following disorders or health problems?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. Sociodemographic characteristics\u003c/h2\u003e \u003cp\u003eWe 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 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The age variable represents the age of participants during the pandemic in 2020.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Analyses\u003c/h2\u003e \u003cp\u003eAll the analyses were conducted in Stata v.16 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], code is available online at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/vnzcw/?view_only=ffad7d69ae24416692bbdb413363f185\u003c/span\u003e\u003cspan address=\"https://osf.io/vnzcw/?view_only=ffad7d69ae24416692bbdb413363f185\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Population-average trajectories of wellbeing \u0026ndash; comparing 14-25-year-old to older adults\u003c/h2\u003e \u003cp\u003eThe first study objective \u0026ndash; to describe population-average trajectories of wellbeing across different age groups in 2017\u0026ndash;2022, with a key focus on young people \u0026ndash; 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 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Hence, it recognises that responses given by the same individual over time are correlated with each other and provides more conservative standard errors [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eIn our study, we conceptualised time as four separate periods (slopes), representing an overall trend between 2017\u0026ndash;2022 \u0026ndash; for this reason we refer to our analysis as \u0026ldquo;piecewise\u0026rdquo; growth curve modelling. The four slopes were 1) pre-pandemic \u0026ndash; 2017\u0026ndash;2019, 2) into-pandemic \u0026ndash; 2019\u0026ndash;2020, 3) during pandemic \u0026ndash; 2020\u0026ndash;2021, 4) out-of-pandemic \u0026ndash; 2021\u0026ndash;2022. Conceptualising time in this manner allows us to directly compare change in two different periods. For instance, whether \u003cem\u003ea potential\u003c/em\u003e decline in wellbeing during pre-pandemic equals \u003cem\u003ea potential\u003c/em\u003e 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\u003csub\u003e2017\u0026thinsp;\u0026minus;\u0026thinsp;2019\u003c/sub\u003e - slope\u003csub\u003e2021\u0026thinsp;\u0026minus;\u0026thinsp;2022\u003c/sub\u003e = 0).\u003c/p\u003e \u003cp\u003eWe allowed the trajectories (or slopes) to vary by including interaction terms between the age groups and slopes (i.e., age*slope\u003csub\u003e2017\u0026thinsp;\u0026minus;\u0026thinsp;2019\u003c/sub\u003e, age*slope\u003csub\u003e2019\u0026thinsp;\u0026minus;\u0026thinsp;2020,\u003c/sub\u003e age*slope\u003csub\u003e2020\u0026thinsp;\u0026minus;\u0026thinsp;2021\u003c/sub\u003e, age*slope\u003csub\u003e2021\u0026thinsp;\u0026minus;\u0026thinsp;2022\u003c/sub\u003e). We derived a categorical variable representing four age groups: 1) age 14\u0026ndash;25, 2) age 26\u0026ndash;45, 3) age 46\u0026ndash;65, 4) age\u0026thinsp;\u0026gt;\u0026thinsp;65.\u003c/p\u003e \u003cp\u003eTo explicitly test whether there were any differences between the youngest group (14\u0026ndash;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\u003csub\u003e2017\u0026thinsp;\u0026minus;\u0026thinsp;2019\u003c/sub\u003e, age*slope\u003csub\u003e2019\u0026thinsp;\u0026minus;\u0026thinsp;2020,\u003c/sub\u003e age*slope\u003csub\u003e2020\u0026thinsp;\u0026minus;\u0026thinsp;2021\u003c/sub\u003e, age*slope\u003csub\u003e2021\u0026thinsp;\u0026minus;\u0026thinsp;2022\u003c/sub\u003e = 0, at p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). We also used Wald tests for the interaction at specific time periods (e.g., age*slope\u003csub\u003e2017\u0026thinsp;\u0026minus;\u0026thinsp;2019\u003c/sub\u003e = 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\u0026thinsp;\u0026lt;\u0026thinsp;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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Population-average trajectories of stress and psychosomatic symptoms among young people (14-25-year-old)\u003c/h2\u003e \u003cp\u003eAs the second objective of our paper was to describe population-average trajectories of psychosomatic symptoms and stress among young people (age 14\u0026ndash;25) in 2017\u0026ndash;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 \u0026ldquo;somewhat\u0026rdquo; or \u0026ldquo;very much\u0026rdquo; suffering from a given symptom.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3. Identifying subgroups of vulnerable young people (14-25-year-old)\u003c/h2\u003e \u003cp\u003eOur third objective was to identify subgroups of young people (age 14\u0026ndash;25) who experienced disproportionate changes in wellbeing, stress or psychosomatic symptoms. Using a similar approach as described in section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e2.4.1\u003c/span\u003e., we allowed the slopes to vary across different sociodemographic groups among young people, by including interaction terms (e.g., gender*slope\u003csub\u003e2017\u0026thinsp;\u0026minus;\u0026thinsp;2019\u003c/sub\u003e, gender*slope\u003csub\u003e2019\u0026thinsp;\u0026minus;\u0026thinsp;2020,\u003c/sub\u003e gender*slope\u003csub\u003e2020\u0026thinsp;\u0026minus;\u0026thinsp;2021\u003c/sub\u003e, gender*slope\u003csub\u003e2021\u0026thinsp;\u0026minus;\u0026thinsp;2022\u003c/sub\u003e). More details on the used covariates can be found in \u0026ldquo;2.2.2. Sociodemographic characteristics\u0026rdquo;. Potential differences were further explored using the Wald test and marginal effects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4. Dealing with missing information\u003c/h2\u003e \u003cp\u003eThe detailed description of our missing data strategy is provided in the Supplementary Text 1 (including Supplementary Table\u0026nbsp;1). We used two approaches to account for missing information and to reduce a potential bias in results \u0026ndash; multiple imputation and Maximum Likelihood (ML).\u003c/p\u003e \u003cp\u003eML estimation was used for the analysis of population-average trajectories of wellbeing, psychosomatic symptoms and stress (see sections \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e2.4.1\u003c/span\u003e. 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 \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e2.4.3\u003c/span\u003e.) 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.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Descriptive information about the population\u003c/h2\u003e \u003cp\u003eDescriptive information about the population can be found in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics of the participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMissing n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,394 (12.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,792 (24.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,969 (35.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,086 (27.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYoung people only\u003c/p\u003e \u003cp\u003e(14-25-year-old n\u0026thinsp;=\u0026thinsp;1,394)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e712 (51.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e682 (48.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst nationality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwiss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,272 (91.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Swiss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122 (8.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartnership status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e234 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried or living with a partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (5.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle, not living with a partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,095 (94.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/divorced/separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving with parents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e153 (11.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,178 (88.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn education (including training)/employment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e235 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation/training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e435 (37.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e296 (25.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e384 (33.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeither\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (3.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome (quartile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e355 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e243 (23.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e328 (31.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e275 (26.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e193 (18.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMissing n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.15 (2.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Population-average trajectories of wellbeing \u0026ndash; comparing 14-25-year-old to older adults\u003c/h2\u003e \u003cp\u003eAs can be seen from Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e (panels A-C) young people (age 14\u0026ndash;25) experienced a steady wellbeing decline over the entire period, with the greatest change before the pandemic (2017\u0026ndash;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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026ndash; panel D). Among this age group, we did not detect any evidence for differential trajectories into-pandemic (2019\u0026ndash;2020). The youngest group also experienced the greatest decline in PALS during the pandemic (2020\u0026ndash;2021) (by 0.56, 0.17 to 0.96 more than age 45\u0026ndash;65 and 0.51, 0.10 to 0.92 than age\u0026thinsp;\u0026gt;\u0026thinsp;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\u0026ndash;2022) period the decline of wellbeing among young people continued. The trajectories were stable among 26\u0026ndash;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.\u003c/p\u003e \u003cp\u003eWith respect to negative affect, young people experienced an increase during the pre-pandemic period (2017\u0026ndash;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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026ndash; panels A-C). This increase was greater than among older people, by 0.13 (-0.06 to 0.33) compared with age 45\u0026ndash;65 and 0.21 (0.01 to 0.41) compared with age 65 or older.\u003c/p\u003e \u003cp\u003eWe did not detect any age differences in the trajectories of negative affect for the into-pandemic period (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026ndash; panel D). During this period (2019\u0026ndash;2020) the levels of negative affect declined among the youngest group (age 14\u0026ndash;25) (-0.45, -0.78 to -0.12), and midlife individuals (age 46\u0026ndash;65) (-0.44, -0.61 to -0.26), whereas for two other age groups the estimates were largely imprecise (age 26\u0026ndash;45: -0.16, -0.37 to 0.04; age\u0026thinsp;\u0026gt;\u0026thinsp;65: -0.16, -0.39 to 0.07). Also, the increase in negative affect during the pandemic (2020\u0026ndash;2021) was comparable across all age groups, with the youngest group experiencing a rise of 1.14 (0.77 to 1.50) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026ndash; panel D). Subsequently (2021\u0026ndash;2022), we saw a decrease in negative affect across all age groups, with the 14\u0026ndash;25 and 26-45-year-olds having somewhat smaller declines (age 14\u0026ndash;25: -0.86, -1.25 to -0.47, age 26\u0026ndash;45: -0.77, -0.99 to -0.54 vs age 46\u0026ndash;65: -1.18, -1.38 to -0.99, age\u0026thinsp;\u0026gt;\u0026thinsp;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\u0026ndash;65 and \u0026gt;\u0026thinsp;65-year-olds.\u003c/p\u003e \u003cp\u003eThe age-specific trajectories of individual items can be found in the Supplementary Text 2 and Supplementary Fig.\u0026nbsp;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\u0026ndash;2019) and the into-pandemic period (2019\u0026ndash;2020). Moreover, the youngest cohort also reported a more significant rise in feelings of depression and anxiety both during the pre-pandemic (2017\u0026ndash;2019) and pandemic period (2020\u0026ndash;2021), along with increased levels of worry before the pandemic.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Population-average trajectories of psychosomatic symptoms and stress among young people (14-25-year-old)\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that the frequency of stress increased already before the pandemic (2017\u0026ndash;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\u0026ndash;2021) by 0.13 (0.05 to 0.21), and subsequently decreased during the out-of-pandemic period (2021\u0026ndash;2022) by -0.13 (-0.22 to -0.05).\u003c/p\u003e \u003cp\u003eAs 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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Identifying subgroups of vulnerable young people (14-25-year-old)\u003c/h2\u003e \u003cp\u003eThey 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\u0026ndash;2020), while they stayed stable among young men (0.06, -0.52 to 0.65 vs -0.76, -1.34 to -0.18, p\u0026thinsp;=\u0026thinsp;0.03). Subsequently, there was a greater increase among women than men during the pandemic (2020\u0026ndash;2021) (1.79, 1.23 to 2.34 vs 0.77, 0.19 to 1.34, p\u0026thinsp;=\u0026thinsp;0.04). Moreover, during the pandemic (2020\u0026ndash;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).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Key findings, previous literature, implications\u003c/h2\u003e \u003cp\u003eThis 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\u0026ndash;2022. Positive affect and life satisfaction declined among young people (age 14\u0026ndash;25) over the entire study period (2017\u0026ndash;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\u0026ndash;2019) and during the pandemic (2020\u0026ndash;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 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased 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 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. 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) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This was not entirely the case during the pandemic, as the GDP experienced a decline of 2.4%, and young individuals (\u0026lt;\u0026thinsp;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 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eAlso, largely in line with previous international research, wellbeing declined to a greater extent among young people during the pandemic [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. 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 [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. 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) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Adolescence and young adulthood are critical developmental stages, characterised by transitions across multiple life domains [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. 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\u0026ndash;85%) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eAs 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 [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, women, migrants and socioeconomically disadvantaged individuals have often been identified as particularly vulnerable [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Strengths and limitations\u003c/h2\u003e \u003cp\u003eThe 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\u0026ndash;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).\u003c/p\u003e \u003cp\u003eThe 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.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eWellbeing 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.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants receive information on the Swiss Household Panel, known to them as \u0026ldquo;Living in Switzerland\u0026rdquo;, 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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDoes not apply, as no information on individual persons was used in this research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(https://www.biomedcentral.com/getpublished/editorial-policies#ethics+and+consent ):\u003c/p\u003e\n\u003cp\u003eFor 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\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSHP data are freely accessible to the scientific community here: https://www.swissubase.ch/en/catalogue/studies/6097/19347/overview\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted within the project The Covid Generation, which is funded by the Swiss National Science Foundation (NRP80, grant number 408040 210152).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDG 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\u0026apos;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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBlendermann M, Ebalu TI, Obisie-Orlu IC, Fried EI, Hallion LS. A narrative systematic review of changes in mental health symptoms from before to during the COVID-19 pandemic. Psychol Med. 2023:1\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eC\u0026eacute;nat JM, Farahi SMMM, Dalexis RD, Darius WP, Bekarkhanechi FM, Poisson H, et al. The global evolution of mental health problems during the COVID-19 pandemic: A systematic review and meta-analysis of longitudinal studies. J Affect Disord. 2022;315:70\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun Y, Wu Y, Fan S, Dal Santo T, Li L, Jiang X, et al. Comparison of mental health symptoms before and during the covid-19 pandemic: evidence from a systematic review and meta-analysis of 134 cohorts. BMJ. 2023;380:e074224.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrati G, Mancini AD. The psychological impact of COVID-19 pandemic lockdowns: a review and meta-analysis of longitudinal studies and natural experiments. Psychol Med. 2021;51(2):201\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolf K, Schmitz J. Scoping review: longitudinal effects of the COVID-19 pandemic on child and adolescent mental health. European Child \u0026amp; Adolescent Psychiatry; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJan-Erik Refle J-E, Voorpostel M, Lebert F, Kuhn U, Klaas HS, Ryser V-A, et al. First results of the Swiss Household Panel \u0026ndash; Covid-19 Study. Lausanne, Switzerland: FORS; 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuhn U, Klaas HS, Antal E, Dasoki N, Lebert F, Lipps O, et al. Who is most affected by the Corona crisis? An analysis of changes in stress and well-being in Switzerland. Eur Soc. 2021;23(sup1):942\u0026ndash;S56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoresh D, Brown AD. Traumatic stress in the age of COVID-19: A call to close critical gaps and adapt to new realities. Psychol Trauma. 2020;12(4):331\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatson MF, Bacigalupe G, Daneshpour M, Han W-J, Parra-Cardona R. COVID-19 Interconnectedness: Health Inequity, the Climate Crisis, and Collective Trauma. Fam Process. 2020;59(3):832\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErikson EH. Identity: youth and crisis. Norton \u0026amp; Co; 1968.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePierce M, Hope H, Ford T, Hatch S, Hotopf M, John A, et al. Mental health before and during the COVID-19 pandemic: a longitudinal probability sample survey of the UK population. Lancet Psychiatry. 2020;7(10):883\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eswissinfo.ch. Swiss youth seek ways out of mental health crisis. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganization WH. Mental health of adolescents. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAfari N, Ahumada SM, Wright LJ, Mostoufi S, Golnari G, Reis V, et al. Psychological trauma and functional somatic syndromes: a systematic review and meta-analysis. Psychosom Med. 2014;76(1):2\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYue Y, Li L, Liu R, Zhang Y, Zhang S, Sang H, et al. The dynamic changes of psychosomatic symptoms in three waves of COVID-19 outbreak and fatigue caused by enduring pandemic in China. J Affect Disord. 2023;331:17\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManchia M, Gathier AW, Yapici-Eser H, Schmidt MV, de Quervain D, van Amelsvoort T, et al. The impact of the prolonged COVID-19 pandemic on stress resilience and mental health: A critical review across waves. Eur Neuropsychopharmacol. 2022;55:22\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003esuisse C. Ordonnance 2 sur les mesures destin\u0026eacute;es \u0026agrave; lutter contre le coronavirus (COVID-19). 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSwiss State Secretariat for Economic Affairs (SECO). Gross domestic product in the second quarter of 2020: pandemic leads to historic slump. 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEurostat. GDP and main components (output, expenditure and income). European Commission; 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHale T, Angrist N, Goldszmidt R, Kira B, Petherick A, Phillips T, et al. A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker). Nat Hum Behav. 2021;5(4):529\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSHP Group. Living in Switzerland Waves 1\u0026ndash;23\u0026thinsp;+\u0026thinsp;Covid 19 data. FORS - Swiss Centre of Expertise in the Social Sciences. Financed by the Swiss National Science Foundation, distributed by FORS, Lausanne. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiener E, Lucas RE, Oishi S. Subjective well-being: The science of happiness and life satisfaction. In: Lopez CRSSJ, editor. Handbook of positive psychology. New York: Oxford University Press; 2002. pp. 63\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGondek D, Garz\u0026oacute;n EG, S\u0026aacute;nchez-Mira N, Vandecasteele L, Steinmetz S, Voorpostel M. Going beyond the single item: deriving and evaluating a composite subjective wellbeing measure in the Swiss Household Panel. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVoorpostel M, Tillmann R, Lebert F, Kuhn U, Lipps O, Ryser V-A, et al. Swiss Household Panel User Guide (1999\u0026ndash;2020) Wave 22. Lausanne, Switzerland: FORS; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStataCorp. Stata statistical software: release 16. College Station: StataCorp LLC; 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaudenbush SW, Bryk AS. Hierarchical linear models: applications and data analysis methods. Newbury Park: Sage; 2002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndreani T. Mental disorders: unprecedented rise in hospital admissions for young women aged 10\u0026ndash;24. Federal Statistical Office; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBethune S. Gen Z more likely to report mental health concerns. Monit Psychol. 2019;50(1):20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGondek D, Bann D, Patalay P, Goodman A, McElroy E, Richards M, et al. Psychological distress from early adulthood to early old age: evidence from the 1946, 1958 and 1970 British birth cohorts. Psychol Med. 2022;52(8):1471\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeles B, McCrae N, Grealish A. A systematic review: the influence of social media on depression, anxiety and psychological distress in adolescents. Int J Adolescence Youth. 2020;25(1):79\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValkenburg PM, Meier A, Beyens I. Social media use and its impact on adolescent mental health: An umbrella review of the evidence. Curr Opin Psychol. 2022;44:58\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOffice FS. Gross Domestic Product. Neuch\u0026acirc;tel, Switzerland: Federal Statistical Office; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArni P. IZA COVID-19 Crisis Response Monitoring Switzerland (November 2020). Bonn, Germany: IZA - Institute of Labor Economics; 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZidkova R, Malinakova K, van Dijk JP, Tavel P. The Coronavirus Pandemic and the Occurrence of Psychosomatic Symptoms: Are They Related? Int J Environ Res Public Health. 2021;18(7).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVacchiano M. How the First COVID-19 Lockdown Worsened Younger Generations\u0026rsquo; Mental Health: Insights from Network Theory. Sociol Res Online. 2023;28(3):884\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen S, Bonanno GA. Psychological adjustment during the global outbreak of COVID-19: A resilience perspective. Psychol Trauma. 2020;12(S1):51\u0026ndash;S4.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"child-and-adolescent-psychiatry-and-mental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caph","sideBox":"Learn more about [Child and Adolescent Psychiatry and Mental Health](http://capmh.biomedcentral.com)","snPcode":"13034","submissionUrl":"https://submission.nature.com/new-submission/13034/3","title":"Child and Adolescent Psychiatry and Mental Health","twitterHandle":"@IACAPAP","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Wellbeing, life satisfaction, positive affect, negative affect, young people, trajectories, Covid-19 pandemic","lastPublishedDoi":"10.21203/rs.3.rs-3911291/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3911291/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYoung 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven 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.\u003c/p\u003e","manuscriptTitle":"The COVID-19 pandemic and well-being in Switzerland - worse for young people?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-02 18:01:29","doi":"10.21203/rs.3.rs-3911291/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-20T12:02:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-12T17:33:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"e6eb8d52-2347-406a-90a9-07c241395a6d_SNPRID","date":"2024-03-05T16:23:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"465d72f4-8d0c-4fea-ba8d-ff5a7db33c71","date":"2024-02-28T08:25:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-11T23:02:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-07T10:17:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-31T14:16:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Child and Adolescent Psychiatry and Mental Health","date":"2024-01-30T16:59:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"child-and-adolescent-psychiatry-and-mental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caph","sideBox":"Learn more about [Child and Adolescent Psychiatry and Mental Health](http://capmh.biomedcentral.com)","snPcode":"13034","submissionUrl":"https://submission.nature.com/new-submission/13034/3","title":"Child and Adolescent Psychiatry and Mental Health","twitterHandle":"@IACAPAP","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"43770fbd-e0ac-4e2d-8da4-9c814adfdc21","owner":[],"postedDate":"February 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-05-27T15:52:36+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-02 18:01:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3911291","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3911291","identity":"rs-3911291","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}