Molly L. Scarfe,Kyla Belisario,Jessica Gillard,Jane De Jesus,Benicio N. Frey,Michael Van Ameringen,Margaret C. McKinnon,Mahmood R. Gohari,Jason W. Busse,James MacKillop
Periodicity and severity of changes in depression and anxiety during the COVID-19 pandemic: Ten-wave longitudinal findings from an observational cohort study of community adults
COVID-19,Depression,Anxiety,Observational longitudinal cohort,Minimally important difference
Aims Few multi-wave longitudinal studies have examined mental health changes across the coronavirus 2019 (COVID-19) pandemic. The current study examined: (a) overall changes in depression and anxiety over 10-waves of data collection; (b) subgroup moderators of changes; (c) clinical severity of the changes via minimally important differences (MIDs); and (d) correlates of clinically important changes. Methods Using a longitudinal observational cohort design, 1412 non-clinical adults (Mage=36; 60% female) were assessed for depression and anxiety via the PHQ-9 and GAD-7 from October 2018 to April 2022 (3 pre-pandemic, 7 intra-pandemic waves; M retention = 92%). Results Depression and anxiety exhibited significant intra-pandemic changes, reflecting initial increases, followed by decreases. Pre-pandemic severity moderated changes, with low severity participants exhibiting increases and high severity participants exhibiting non-significant change or decreases. For depression and anxiety, respectively, 10% and 11% exhibited MID increases, while 4% and 6% exhibited MID decreases. Divergent patterns were present by severity subgroup, with the lowest severity exhibiting higher rates of MID increases and the highest severity subgroup exhibiting higher rates of MID decreases. Conclusions These findings illuminate the periodicity of depression and anxiety during the COVID-19 pandemic and reveal an unexpected inverse relationship between increases and decreases based on pre-pandemic severity.
Periodicity and severity of changes in depression and anxiety during the COVID-19 pandemic: Ten-wave longitudinal findings from an observational cohort study of community adults

Few multi-wave longitudinal studies have examined mental health changes across the coronavirus 2019 (COVID-19) pandemic. The current study examined: (a) overall changes in depression and anxiety over 10-waves of data collection; (b) subgroup moderators of changes; (c) clinical severity of the changes via minimally important differences (MIDs); and (d) correlates of clinically important changes.

Using a longitudinal observational cohort design, 1412 non-clinical adults (Mage=36; 60% female) were assessed for depression and anxiety via the PHQ-9 and GAD-7 from October 2018 to April 2022 (3 pre-pandemic, 7 intra-pandemic waves; M retention = 92%).

Depression and anxiety exhibited significant intra-pandemic changes, reflecting initial increases, followed by decreases. Pre-pandemic severity moderated changes, with low severity participants exhibiting increases and high severity participants exhibiting non-significant change or decreases. For depression and anxiety, respectively, 10% and 11% exhibited MID increases, while 4% and 6% exhibited MID decreases. Divergent patterns were present by severity subgroup, with the lowest severity exhibiting higher rates of MID increases and the highest severity subgroup exhibiting higher rates of MID decreases.

These findings illuminate the periodicity of depression and anxiety during the COVID-19 pandemic and reveal an unexpected inverse relationship between increases and decreases based on pre-pandemic severity.

There is considerable interest in changes in mental health over the course of the coronavirus 2019 (COVID-19) pandemic. Most studies have used cross-sectional designs to characterize symptoms and self-attributions about pandemic effects (Beutel et al., 2021; Westrupp et al., 2021) and some studies have tracked symptoms in unlinked individuals (Batterham et al., 2021; Ettman et al., 2021; Hyland et al., 2021). A smaller number of studies have employed longitudinal cohort designs with both pre-COVID-19 and intra-COVID-19 data (Flentje et al., 2020; Racine et al., 2021; Robinson et al., 2022). Despite progress, understanding changes in mental health across the COVID-19 pandemic remains highly incomplete. First, most existing studies focus on high-risk subgroups (Elmer et al., 2020; Flentje et al., 2020; Racine et al., 2021; Shan Wong et al., 2020), rendering general population trends less clear and limiting generalizability to a wider community sample. Second, few studies have examined how key moderators, such as sex, income, age, or pre-existing symptoms, may amplify or attenuate impacts of the pandemic. This leaves an incomplete understanding of how symptom trajectories may differ by important demographic and clinical characteristics. Third, most studies include only one, or a small number of intra-COVID timepoints, providing snapshots of early pandemic impacts on mental health, not overall patterns of changes over longer periods of time. This is particularly important, as COVID-19 has been an unfolding event over approximately three years, and there may be fluctuations in symptoms that are not represented in studies with limited timepoints, or timepoints that only reflect the first wave of COVID-19. The current study sought to address these gaps by examining changes in depression and anxiety severity across ten waves of data collection: three pre-pandemic waves and seven intra-pandemic waves (complete window = October 2018 to April 2022), in an observational longitudinal cohort of general community adults. There were four aims: (1) examine overall changes from pre-COVID to intra-COVID-19; (2) examine differences by subgroups based on demographic factors (i.e., sex, age, income) and pre-COVID clinical severity; (3) characterize the clinical significance of observed changes using minimally important differences [MIDs]; and (4) identify predictors for clinically meaningful symptom changes.

The design was a longitudinal observational cohort design, and this report conforms with the STROBE requirements. Participants were originally recruited from a cross-sectional research registry at St. Joseph's Healthcare Hamilton and a subsequent longitudinal cohort that was created from the registry. The research registry was established in 2015–2017 to identify prospective research participants from the community and the longitudinal cohort was created in September 2018 to track changes in health indicators over the course of legalization of recreational cannabis in Canada. Principal eligibility criteria for the original research registry were: (1) age 18–65; (2) 9th+ grade education (i.e., adequate literacy for assessments); (3) willingness to receive invitations for future studies; and (4) no anticipated barriers for participating in future studies (i.e., current terminal illness). Participants were recruited from the general community in Hamilton, ON, using diverse forms of advertising (e.g., online advertisements, e-mail lists, flyers, etc.). Participants attended a one-time in-person enrollment in the registry, followed by online self-report assessments in the longitudinal cohort via RedCap software (Harris et al., 2019). Participants were provided $40 CAD for their time and effort in each assessment wave in the cohort. The current data are for 10 waves of online data collection. Specifically, the data reflect three pre-pandemic waves taken at six-month intervals (October 2018 (T1), April 2019 (T2) and October 2019 (T3), and seven intra-pandemic waves taken at three- and six-month intervals starting in April 2020 (T4) and ending in April 2022 (T10). More frequent assessments were undertaken for higher-resolution assessment of pandemic effects. Each data collection wave lasted approximately one month (Fig. 1 ). All participants underwent informed consent, the study was reviewed and approved by the Hamilton Integrated Research Ethics Board, and the authors assert that all procedures complied with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975/2008. Of ten waves, participants were required to have at least one pre-COVID observation and two intra-COVID observations. The average number of total completed assessments was 9.6 (SD=0.99), and 78% of participants completed all ten assessments. In addition, in all waves, valid effort/attention was defined as answering at least three of five quality control questions correctly (e.g. “For this item, choose response C”). Of the original cohort (N = 1502), 94% (n = 1412) were eligible and included in the present study (Fig. S1 in Supplemental Materials). Participants who had too few observations or failed quality control were excluded from all analyses or the pertinent wave, respectively. Additionally, Fig. 1 tracks COVID mortality with timing of study assessments. The sample comprised adults (60.3% female) with an average age of 35.86 (SD = 13.97), median income of $60,000-$74,999, median education of a completed bachelor's degree, and who primarily identified racially as White (79.04%). Detailed demographics are in Supplemental Table S3, as are comparisons with provincial and national demographics suggesting the sample was generally representative of Canadian general community adults, albeit with somewhat higher education levels and a greater representation of females.

Depression was assessed using the Patient Health Questionnaire – 9 (PHQ-9; Kroenke et al., 2001), a validated dimensional screening measure for major depressive disorder (depression). Scores < 5 indicate minimal symptoms, scores of 5–9 indicate mild symptoms, and scores of 10+ indicate clinical symptoms of depression (Kroenke et al., 2001). The MID was defined as +/- 5 (Löwe et al., 2004). Anxiety was assessed using the Generalized Anxiety Disorder −7 (GAD-7; Spitzer et al., 2006), a validated dimensional screening measure for anxiety disorders. Scores of < 5 indicate minimal symptoms, scores of 5–9 indicate mild symptoms, and scores of 10+ indicate clinical symptoms of anxiety (Spitzer et al., 2006). The MID was defined as +/- 4 (Toussaint et al., 2020). Impacts of COVID-19 on psychosocial life domains was assessed using a purpose-built instrument that was implemented at all intra-COVID waves. Participants responded to stem questions that asked about how the COVID-19 outbreak impacted various life domains over the previous 6 months. The life domains were: Work, Education, Income, Social Relationships, Romantic Relationships, Mental Health, Physical Health, Sleep, Nutrition, and overall Quality of Life (QoL). Responses were on a 5-point Likert scale: “Very Positive”, “Somewhat Positive”, “Neutral or Not Applicable”, “Somewhat Negative”, and “Very Negative”. Of note, the domains of Mental Health and Quality of Life were secondary indicators because of the content overlap with the primary outcomes for Mental Health, and nonspecific nature of Quality of Life. In addition, a 100-point visual analog scale (VAS) assessed subjective COVID-19-related stress at each timepoint. Participants were also assessed for demographics.

All statistical analyses were carried out using R (R Core Team, 2021). Differential attrition was examined via χ2 tests to examine whether participant characteristics differed for those that were included in the study compared to those that were excluded. Linear mixed-effects modelling was carried out by the restricted maximum likelihood approach using the ‘lme4’ and ‘lmerTest’ packages (Bates et al., 2015; Kuznetsova et al., 2017). Intra-individual variation needs to be accounted for in panel data, and as such, random intercepts for each participant were fit, accounting for the correlation of observations within participants. This allows for the between-person effects of time, as well as the interaction of time with other time-invariant covariates to be estimated (see Cnaan et al. 1997 for overview of methods). Post-hoc testing of timepoint contrasts between the pre-COVID and the intra-COVID waves utilized the ‘emmeans’ package (Lenth, 2021). Outcomes were modelled across 10 time points – to be deemed significant in post-hoc testing, intra-COVID waves needed to be different from the pre-COVID waves. Time was treated as discrete (as opposed to a continuous function of time), which allowed for distinct changes between each time point to be documented and tested. This was important, as the COVID-19 pandemic is a period of changes regarding severity of infection in the community and the degree of provincial lockdown. For the first aim, analyses were conducted to examine the main effect of time on all mental health symptoms. For the second aim, potential moderators were included in the models to examine interaction effects. For the third aim, changes were descriptively characterized in absolute terms to address clinical significance. Specifically, the averaged score across intra-COVID time points for each participant were compared to their averaged pre-COVID scores to identify MIDs in either direction (increases or decreases). For the fourth aim, predictors were examined in both the overall sample and by subgroup. Maximum likelihood logistic regression was used examine predictors of MID increases/decreases in the overall sample. Firth logistic regression was used to examine predictors of MID increases/decreases in subgroups (minimal, mild, clinical) to addresses the smaller sample sizes in the subgroup cells (Wang, 2014). To aid in interpretation of the VAS stress score, scores were standardized so that a one-unit change would represent a one standard deviation change. Predictors of MIDs were analysed as either increases compared to no change/decrease or decreases compared to no change/increase. The alpha level of statistical significance was conservatively set at 0.005 to reduce the possibility of Type I error (i.e., false positive findings) following recent recommendations to increase reproducibility (Benjamin et al., 2018).

Retention was high across waves (92.2%, range = 87.8%−95.2%; Table S1) and attrition analyses revealed no differences in age, sex, or ethnicity (Table S2). Clinical severity subgrouping was based on average pre-pandemic scores. For depression, 19.19% screened in the clinical range, 47.3% scored in the minimal range, and 33.5% screened in the mild category. For anxiety, 13.6% screened in the clinical range, 55.5% minimal, and 30.88% mild. Among those screening in the clinical range, 46% screened positive for both anxiety and depressive symptoms, which is consistent with the measures being validated as screening assessments, and the high comorbidity between depression and anxiety (Kalin, 2020).

Changes in symptom scores across intra-COVID time points were compared to pre-COVID scores separately for both outcomes, revealing significant omnibus main effects of time (Table 1 ), revealing that depression and anxiety symptoms changed pre-COVID to intra-COVID. Post-hoc comparisons revealed that significant increases in depression were present from waves T5-T10, whereas anxiety increases were present at T5, T7, and T8. Other timepoints did not differ from the pre-pandemic period (see Fig. 1). The overall topography of changes indicated that, after minimal change in the first wave, a steep increase took place during the summer of 2020 and declined subsequently.

Significant interactions were present between time (pre-pandemic/intra-pandemic) and clinical severity for both depression and anxiety. Interactions indicated that the pattern of symptom change across time differed depending on clinical category (Table 1 and Fig. 2 ). For depression, participants with minimal and mild symptoms reported significant increases in depression at T5-T10, and T5-T9, respectively. Those in the clinical category reported decreases in depression at T4. For anxiety, participants in the minimal category reported significant increases in anxiety at T5-T10, those in the mild category reported no significant changes, and those in the clinical category reported significant decreases at T7, T9, and T10. No significant interactions were present for sex, age, or income (Table 1).

Participants were categorized by the proportions exhibiting clinically important increases or decreases relative to pre-COVID symptoms based on MIDs. Proportions were examined both by overall sample and by clinical category (Fig. 3 ). In addition, average depression and anxiety scores were examined by MID category to quantitatively characterize the observed changes (Fig. 4 ). Regarding depression, in the overall sample, 10.1% exhibited an important increase and 4.2% exhibited an important decrease. For the clinical subgroups, consistent with the quantitative results, there was an inverse relationship between pre-pandemic severity and clinically important changes. In the minimal severity subgroups, 9.6% exhibited an important increase (no participants reported a decrease). In the mild severity subgroup, 12.7% exhibited clinically important increase and 2.5% exhibited an important decrease. In the subgroup who screened positive (i.e., highest levels of reported symptoms pre-pandemic), 7.0% exhibited a clinically important increase while 17.3% exhibited a clinically important decrease. In each case, the quantitative results supported the MID categorization, with MID-based changes exceeding or falling below the measure cut-offs (Fig. 4). For anxiety, in the overall sample, 11.0% exhibited an important increase and 5.5% exhibited an important decrease. For the clinical subgroups, consistent with the quantitative results, there was an inverse relationship between pre-pandemic severity and clinically important changes. In the minimal severity subgroup, 11.7% exhibited an important increase (no participants reported a decrease). In the mild severity subgroup, 12.6% exhibited a clinically important increase and 6.7% exhibited an important decrease. In the subgroup who screened positive, 4.7% exhibited a clinically important increase and 25.5% exhibited a clinically important decrease. Again, the quantitative results supported the MID categorization (Fig. 4).

Odds ratios for pandemic-related stress and impacts on psychosocial/intrapersonal domains for the overall sample are in Table 2 . As expected, the domains of Mental Health and Quality of Life were highly significant for important increases and decreases in both outcomes. However, these were of secondary interest because of the content overlap with the primary outcomes and nonspecific nature of Quality of Life. For clinically important increases in depression, significant associations were present between subjective stress and adverse impacts in all domains, with the largest associations (ORs>2) for stress and impacts on income, work, physical health, nutrition, and sleep (Table 2). For decreases in depression, significant associations were present with improvements in social relationships, romantic relationships, and sleep (ps<0.005). For clinically important increases in anxiety, significant associations were present between subjective stress and adverse impacts in all domains, with the largest associations (ORs>2) in stress, and negative impacts on work, social relationships, nutrition, and sleep (Table 2). For clinically important decreases in anxiety, significant associations were present with reductions in stress and positive impacts on sleep (ps<0.005). Analyses within each clinical subgroup revealed similar results to the overall sample and are in Tables S4–6 in Supplemental Materials.

The current study examined the longitudinal topography of changes in depression and anxiety during the first 24-months of the COVID-19 pandemic, offering unique temporal specificity about overall and subgroup changes. In terms of overall trends, the results converge with previous evidence of increases (Lee et al., 2020; Novotný et al., 2020; Robinson et al., 2022), but extends those findings by revealing meaningful periodicity over the 24-month period. Specifically, the increases were initially lagged in time, flat during the initial lockdown, then escalating sharply, and subsequently declining over subsequent waves. These patterns followed the level of infection and mortality in Ontario, Canada (Fig. 1), the cohort's catchment area. Critically, changes in depression and anxiety differed substantially by pre-COVID clinical severity, generally exhibiting inverse patterns. For depression, participants at minimal and mild symptoms pre-pandemic exhibited a significant increase, but individuals who were clinically elevated exhibited no change across most intra-COVID timepoints. For anxiety, participants at minimal levels exhibited a significant increase, those at the mild level exhibited no change, and clinically elevated individuals exhibited significant decreases at three timepoints. These moderator results indicate that the pandemic increases were, on average, specific to healthy individuals prior to the pandemic, not exacerbations among individuals who already had clinically meaningful levels of depression and anxiety. While ostensibly counter-intuitive, these results are consistent with several reports (Hamza et al., 2021; Watkins-Martin et al., 2021). Beyond pre-pandemic severity, it is notable that the subgroup analyses did not reveal systematic differences by sex, income, or age. Beyond statistical significance, the results suggested that most changes in the overall sample and in each clinical category were subclinical, although a minority did exhibit clinically meaningful change. Again, an inverse relationship was observed for pre-pandemic severity, with clinically important changes in participants in the minimal and mild clinical categories typically exhibiting MID increases, while individuals who screened positive pre-pandemic more commonly exhibited MID decreases. Some participants (5–7%) who screened positive pre-pandemic did exhibit a MID increases, which is a concerning since these participants were already at high levels of symptoms, but it was comparatively less common. In terms of the factors related to these changes, there were numerous correlates of clinically important change for increases in severity, particularly, pandemic-related stress and negative impacts on income, work, physical health, social relationships, nutrition, and sleep. Fewer factors were related to clinically important decreases in depression and anxiety symptoms, although improvements in sleep were consistently related. This is consistent with other findings linking negative impacts on sleep and clinically important worsening of mental health symptoms (Robillard et al., 2021). Additionally, given the existence of evidence-based sleep interventions (Perlis et al., 2011), these results suggest that developing resources that target sleep improvement may be warranted for future instances of population-level challenges. It is important to note that the life domain indicators were only collected during the intra-pandemic timepoints. Therefore, the conclusions are limited in that perceived pandemic-related changes in these factors are associated with mental health fluctuations, but whether these changes are a cause or consequence to mental health changes cannot be determined. Collectively, this study documents the periodicity and nuances of changes in mental health during the pandemic. These results are compatible with other findings of a decrease in suicide rates in the first year of the pandemic (McIntyre et al., 2021) and comparable levels of intra-pandemic hospitalizations and emergency department visits for mental health and addiction services (Ray et al., 2022; Saunders et al., 2021). These findings by no means minimize the pandemic's mental health impacts, significant adverse impacts have been widely reported (e.g., Friesen et al. 2021). These results show the multifariousness and complexity of the impacts. Furthermore, this study documents the importance of conceptualizing the pandemic as an unfolding event, with effects that are still dynamically emerging, and the need for ongoing surveillance. The current findings should be considered in the context of both strengths and limitations. First, the longitudinal cohort design afforded a high-resolution examination of within-subjects changes in mental health symptoms from pre-COVID to within the pandemic over ten waves of data collection. Additionally, one-year stability in three pre-COVID waves provides a robust platform for examining subsequent changes and suggests limited influence of regression to the mean. Retention was high and the attrition that took place did not appear to confer substantive bias. Finally, the sample was large enough for statistical power to detect relatively small effect size changes. Limitations include that clinical status category was based on screening measures, not formal diagnostic interviews, albeit using psychometrically validated widely-used measures with established cut-off values (Kroenke et al., 2001; Spitzer et al., 2006). Another limitation is that this study cannot address whether these changes in symptoms were caused by the pandemic per se, as it is an observational study. Related, the study cannot empirically address whether the clinically important changes differ from the incidence and recovery rates had the pandemic not happened. Representation of ethnic/racial minorities and both young adults and seniors was modest, limiting generalizability. Further, a statistically larger higher proportion of men were lost to follow-up were excluded from this study, indicating that there was a larger proportion of males who did not complete the required number of assessment waves, which may bias findings. However, even so, with this higher exclusion rate, approximately ∼40% of the sample identified as male, indicating generally similar rates of males and females. Finally, although not a limitation per se, Canada's social infrastructure, including universal healthcare and substantial pandemic economic supports, may have buffered mental health impacts and is a consideration. Nonetheless, this study provides a high-resolution characterization of the dynamic changes in the severity of depression and anxiety over the first 24-months of the pandemic. It documents an initial crest followed by a gradual decline, and that the observed changes differed by pre-pandemic severity level: previously healthy participants, on average, exhibited increases in symptoms, while those with clinically elevated pre-COVID levels exhibited decreases or no change in symptoms. Collateral contributions of these findings are the specific quantitative estimates for clinically important changes and the correlates of these outcomes, particularly the consistent implication of impacts related to sleep. More broadly, these findings underscore the need for an evidence-based understanding of the multifarious impacts of the COVID-19 pandemic on mental health over time and within important population subgroups.

Study data will be made available in response to reasonable requests and pending research ethics board approval.

Molly L. Scarfe: Conceptualization, Investigation, Methodology, Writing – original draft. Kyla Belisario: Conceptualization, Data curation, Formal analysis, Methodology, Validation, Visualization, Writing – review & editing. Jessica Gillard: Project administration, Investigation, Validation. Jane De Jesus: Project administration, Investigation, Validation. Benicio N. Frey: Funding acquisition, Writing – review & editing. Michael Van Ameringen: Funding acquisition, Writing – review & editing. Margaret C. McKinnon: Funding acquisition, Writing – review & editing. Mahmood R. Gohari: Methodology, Validation, Writing – review & editing. Jason W. Busse: Funding acquisition, Methodology, Validation, Visualization, Writing – review & editing. James MacKillop: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Visualization, Supervision, Writing – original draft.

JM is a principal in Beam Diagnostics, Inc and has served as a consultant to Clairvoyant Therapeutics, Inc. No other authors have declarations.