Optimal Well-Being After Psychopathology: Prevalence and Correlates

Abstract

Optimal functioning after psychopathology is understudied. We report the prevalence of optimal well-being (OWB) following recovery after depression, suicidal ideation, generalized anxiety disorder, bipolar disorder, and substance use disorders. Using a national Canadian sample (N = 23,491), we operationalized OWB as absence of 12-month psychopathology, coupled with scoring above the 25th national percentile on psychological well-being and below the 25th percentile on disability measures. Compared with 24.1% of participants without a history of psychopathology, 9.8% of participants with a lifetime history of psychopathology met OWB. Adults with a history of substance use disorders (10.2%) and depression (7.1%) were the most likely to report OWB. Persons with anxiety (5.7%), suicidal ideation (5.0%), bipolar I (3.3%), and bipolar II (3.2%) were less likely to report OWB. Having a lifetime history of just one disorder increased the odds of OWB by a factor of 4.2 relative to having a lifetime history of multiple disorders. Although psychopathology substantially reduces the probability of OWB, many individuals with psychopathology attain OWB.

Keywords

epidemiology depression anxiety bipolar recovery well-being resilience open data open materials preregistered

Clinical psychology and allied mental-health fields have been slow to gather data on the full range of long-term outcomes after psychopathology. Considerable epidemiological research focuses on deleterious outcomes and indicates that mood, anxiety, and substance use disorders are chronic, recurrent conditions that are incompatible with long-term well-being (Bruce et al., 2005; Kessler et al., 2005; Moussavi et al., 2007; Treuer & Tohen, 2010). Studies of deleterious outcomes have largely relied on measures of psychiatric symptoms as the primary end point (i.e., symptom improvement or recovery). Indices of well-being or functioning are seldom incorporated into primary clinical outcomes (McKnight & Kashdan, 2009). Consequently, little is known regarding the prevalence of rigorously defined good outcomes after psychopathology, such as the attainment of “optimal well-being” (OWB; Rottenberg et al., 2018). OWB is defined as full recovery from psychopathology coupled with high levels of psychological well-being and low levels of functional disability. Put otherwise, after major psychopathology, such as a diagnosis of depression, anxiety, or bipolar disorder, how likely is it for someone to recover and live a life characterized by high levels of purpose and meaning, autonomy, self-mastery, healthy relationships, and frequent positive emotions?

After reviewing why the neglect of OWB hampers clinical research, we provide the first comparison of OWB after lifetime history of major depression, bipolar disorders, generalized anxiety disorder, and substance use disorders (i.e., alcohol abuse/dependence, cannabis abuse/dependence, and other drug abuse/dependence).

The Relative Neglect of Well-Being and Functioning in Psychopathology Research

To curb the burden of psychopathology, most clinical research has, understandably, relied on psychiatric symptoms to index the course of psychopathology (Wood & Tarrier, 2010), such as the prevalence and correlates of suicide (May & Klonsky, 2016), self-injury (Bentley et al., 2015), and recurrence of psychopathology (Scholten et al., 2021). Much less research has examined other meaningful outcomes, such as well-being and functioning (Gruber & Moskowitz, 2014; Wood & Tarrier, 2010). Well-being can broadly be conceptualized as “[the] perceived enjoyment and fulfillment with one’s life as a whole” (Goodman et al., 2021, p. 833) and may include elements such as positive emotions (e.g., happiness), life satisfaction, social relationships, and feeling a sense of purpose in life, among others (see Diener & Emmons, 1984; Keyes, 2002; Ryff, 1989). Functioning broadly refers to abilities to fulfill roles in a given life domain (e.g., no disability across home, work, school, and social roles), which can be summarized by the phrase “what people cannot do when they are ill” (Üstün et al., 2010, p. 3).

Extensive work on well-being and functioning (Cooke et al., 2016) has been only slowly assimilated in clinical psychology research. For example, a comprehensive review discovered that 95% of depression-treatment trials neither measured nor reported on healthy functioning as outcomes (McKnight & Kashdan, 2009). Studies that consider good outcomes often have notable design limitations, such as nonrepresentative samples (Hendriks et al., 2019), reliance on self-report of psychopathology (Moreau & Wiebels, 2021), or insufficient measurement of well-being (e.g., using a one-item measure; Flake & Fried, 2020).

There are, however, several important precedents in the study of good mental-health outcomes (e.g., Fava & Tomba, 2009; Keyes, 2005) and physical-health outcomes (e.g., Veenhoven, 2008). Strong meta-analytic evidence has linked elements of well-being, such as positive affect, with desirable outcomes, such as work, health, and social functioning (Lyubomirsky et al., 2005). Furthermore, one important line of work has investigated the concept of flourishing (e.g., Capaldi et al., 2015; Fuller-Thomson et al., 2016), defined as “above-average functioning” on measures of mental health and well-being (Keyes, 2005, p. 543). Building off these precedents, in the current study, we examine the attainment of OWB after psychopathology, which is defined by full symptomatic recovery and good functioning across domains, as indicated by a profile of high psychological well-being and low functional impairment.

The Value of Rigorously Defined Good Outcomes

There are several reasons to incorporate measures of well-being and functioning into studies of clinical outcome rather than solely relying on symptom measures (McKnight & Kashdan, 2009; Rottenberg et al., 2018). First, contrary to assumptions that symptoms are sufficient proxies for functioning, levels of psychiatric symptoms correlate only modestly with well-being and functional impairment (McKnight et al., 2016; McKnight & Kashdan, 2009; Ryff & Keyes, 1995), and although traditional psychotherapy interventions (e.g., cognitive-behavioral therapy) reduce symptoms, they are less effective at repairing well-being (Widnall et al., 2020). Second, symptom reduction is not the only treatment goal for patients. When queried, a substantial number of patients with depression and anxiety have reported goals to live more fulfilling lives, have meaningful relationships, and return to work, which are all elements of well-being (Battle et al., 2010; Holtforth et al., 2009; Zimmerman et al., 2006). A recent content analysis of 3,003 patients, informal caregivers, and health-care professionals found that functioning and well-being were highlighted as valued outcomes in depression treatment—as much or more than the abatement of symptoms (Chevance et al., 2020). Third, well-being and functioning measures may provide incremental prediction of prognosis beyond measures of symptoms (Cloninger, 2006). Preliminary findings indicate that higher levels of well-being may be uniquely protective against future depression and anxiety (Keyes et al., 2010). Among people diagnosed with depression, higher well-being at baseline was associated with a higher probability of achieving higher well-being and symptomatic recovery at a 10-year follow up (Panaite et al., 2021; Rottenberg et al., 2019).

OWB After Psychopathology

The relative neglect of well-being and functioning assessments in psychopathology research motivated our team to consider long-term well-being after psychopathology. Because few studies have considered the prevalence of high functioning after psychopathology, including OWB, we developed rigorous criteria to operationalize OWB (Rottenberg et al., 2018; Tong et al., 2021), informed by prior work on self-determination theory, well-being, and quality of life (Keyes, 2002; Ryan & Deci, 2000; Ryff, 1989). Research from these interrelated literatures suggest that a set of psychological needs must be satisfied for effective functioning and psychological health. Self-determination theory, specifically, outlines three human needs of competence (i.e., environmental mastery), belongingness (i.e., positive relations with others), and autonomy (Ryan & Deci, 2000). These needs represent “psychological nutriments that are essential for ongoing psychological growth, integrity, and well-being” (Deci & Ryan, 2000, p. 229). Once satisfied and supported, these needs provide heightened psychological energy that predicts long-term maintenance of health behaviors (Ng et al., 2012; Ntoumanis et al., 2021). These positive outcomes are highly desirable for people with psychopathology, who generally show high rates of relapse and recurrence (Bruce et al., 2005; Moussavi et al., 2007).

We defined good outcomes after psychopathology with an approach involving population-based norms (for details, see Rottenberg et al., 2018). OWB after a mental-health diagnosis required four elements: (a) a lifetime history of a mental-health diagnosis; (b) absence of a 12-month mental-health diagnosis; (c) high well-being, indicated by exceeding population-based norms on psychological well-being (top quartile on the Mental Health Continuum–Short Form [MHC-SF]; Lamers et al., 2011); and (d) low functional impairment, indicated by population-based norms on disability measures (bottom quartile on World Health Organization Disability Assessment Schedule [WHODAS] 2.0; Üstün et al., 2010). Use of this multipart definition increased the odds that individuals identified as having OWB would unequivocally have high functioning across major domains. Although any cutoff for well-being could be deemed arbitrary, one applied precedent for using the top quartile as a cutoff is intelligence testing, in which people who score at the 75th quartile are qualitatively classified as “high average” (Sattler & Ryan, 2009).

Is OWB Rare After Psychopathology?

Early data indicate that although major psychopathology reduces the likelihood of OWB, many persons with psychopathology achieve OWB. In a nationally representative U.S. adult sample, 10% of persons with study-documented depression satisfied OWB criteria 10 years later, compared with 21% of nondepressed persons meeting the same standard (Rottenberg et al., 2019). In other words, depression reduced the probability of achieving OWB by approximately 50%. Only 6.1% of adults with panic disorder reached OWB status 10 years later in the same U.S. sample, and no adults with a history of generalized anxiety disorder met OWB criteria (Disabato et al., 2021). These findings point to a need for comparative OWB estimates across a range of mental-health conditions to disentangle the unique effects of specific symptom combinations on long-term functioning. For example, there are currently no estimates of OWB after bipolar disorders and substance disorders.

A richer understanding on OWB and how it varies by condition and patient characteristics would help clinicians to communicate reliable, tailored prognostic information to patients (as is customary in other health specialties, e.g., oncology). Documenting how base rates of OWB after psychopathology vary by demographic factors, such as age, gender, and socioeconomic status, could help clinicians implement evidence-based practice (Pendergast et al., 2018; Youngstrom et al., 2017). Likewise, it would be beneficial to know how the number of prior diagnoses influences long-term OWB, in part because comorbidity and co-occurrence of mental-health diagnoses are the norm, rather than exception, in psychopathology (Caspi et al., 2020; Hankin et al., 2016; Krueger & Markon, 2006). For instance, a 4-decade study of the transition to adulthood found that 86% of people will experience some form of psychopathology, and 85% of those people will subsequently accumulate one or more additional mental-health diagnoses (Caspi et al., 2020).

Finally, it will be important to investigate how OWB estimates vary across samples and nations given that cultural and national variables are known to influence mental-health outcomes (Henrich et al., 2010). In this study, we examine rates of OWB in a nationally representative Canadian sample. Canada presents an interesting contrast to the United States because it exhibits geographic, economic, and cultural similarities to the United States but also presents differences, such as greater potential access to mental- and physical-health care. Canada, for example, implements a universal health-care delivery system, whereas the United States implements a largely private, nonuniversal health-care system. Canada also has a much smaller and less racially diverse population compared with the United States; 78% of the Canadian population does not meet the definition of a “visible minority” (Statistics Canada, 2017). That said, Canadians are more likely to be bilingual compared with Americans because French and English are the official languages of Canada and are taught in schools, whereas most schools in the United States teach solely English.

The Current Study

This study extends prior research in two important ways. First, we compared rates of OWB across individuals with various lifetime mental-health diagnoses: major depressive disorder, generalized anxiety disorder, bipolar disorders, and substance use disorders. Major depression, generalized anxiety disorder, and substance use disorders are three of the most prevalent disorders among the general population (Kessler et al., 2005; GBD 2015 Disease and Injury Incidence and Prevalence Collaborators, 2016), and they contribute to substantial health, societal, and economic burdens (Greenberg et al., 2015; Wittchen, 2002). Bipolar disorders, although less prevalent, often result in marked functional impairment and reduced quality of life (Martinez-Aran et al., 2007). For researchers, patients, and clinicians, knowing the proportions of patients who achieve OWB after specific diagnoses would provide valuable prognosis information. Second, we determined the demographic and clinical characteristics that may help or hinder OWB after a lifetime mental-health diagnosis.

Although this is a new area of investigation, we expected that rates of OWB after depression and generalized anxiety disorder in Canada would be similar to those observed in the United States (Disabato et al., 2021; Rottenberg et al., 2019). Given that this was the first study to ascertain OWB rates after bipolar and substance use disorders, we did not have expectations for these disorders. Relatedly, because of the lack of existing data on OWB after psychopathology and the exploratory nature of this study, we did not have specific hypotheses about demographic and clinical correlates. Our OWB study criteria were preregistered, and full information about the preregistration, including information about deviations, can be found at https://osf.io/nskgr/ and in the Supplemental Material available online.

Method

In this study, we conducted secondary analyses of the public-use data set 2012 Canadian Community Health Survey–Mental Health (CCHS-MH; for more information, see Gilmour, 2014; Statistics Canada, 2020), which includes a national sample (N = 25,113) of Canadians ages 15 to 80 and older. The survey targeted Canadian household residents living in any of the 10 provinces, except for people living on reserves and other Aboriginal settlements, full-time members of the Canadian Armed Forces, and residents of institutions. These exclusions amount to about 3% of the national population. Data were collected using computer-assisted interviewing. The overall response rate was 68.9%. Verbal informed consent was received from each participant.

OWB variables

Mental-health diagnoses

Diagnoses were derived from the World Health Organization version of the Composite International Diagnostic Interview 3.0, a structured diagnostic interview that follows the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders and the 10th edition of the International Classification of Disease. Assessments were conducted for lifetime history and 12-month presence of major depressive episode, generalized anxiety disorder, bipolar I disorder, bipolar II disorder, substance abuse with alcohol or drugs, and suicidal ideation. Substance use disorders included alcohol abuse/dependence, cannabis abuse/dependence, and “other” drug abuse/dependence.

Psychological well-being

Psychological well-being was assessed with the MHC-SF (Lamers et al., 2011), a 14-item instrument measuring dimensions of positive mental health, including emotional, social, and psychological well-being. Psychometric properties of the MHC-SF are well established (Lamers et al., 2011). The composite MHC-SF score was used. In the overall sample, internal consistency for the MHC-SF composite score was good (Cronbach’s α = .88).

Disability

The 12-item WHODAS 2.0 assessed functioning and disability status (Üstün et al., 2010). Scoring was based on the “complex scoring” method recommended in the WHODAS 2.0 manual (Üstün et al., 2010). The overall score ranges from 0 to 100 (0 = no disability, 100 = full disability; Statistics Canada, 2013). In the public-use data set, the maximum for this variable was 40.

OWB and mental-health outcomes

A binary OWB variable (1 = OWB, 0 = no OWB) was created, which required three elements: (a) the absence of mental-health conditions in the past 12 months (i.e., depressive disorder, anxiety disorders, bipolar disorders, alcohol or drug abuse, suicidal ideation or attempts); (b) the presence of psychological well-being, defined as exceeding the top quartile of age- and gender-matched norms from the overall sample, as assessed by the MHC-SF (Lamers et al., 2011); and (c) low disability, defined as scoring in the lower quartile of age- and gender-matched norms from the overall sample, as assessed from the WHODAS 2.0 (Bruce et al., 2008). Although OWB was measured dichotomously to aid with the interpretability of our findings, we recognize that well-being, functioning, and mental-health symptoms exist on a continuum. For the age- and gender-matched norms on the MHC-SF and WHODAS 2.0, see Tables S1 through S6 in the Supplemental Material.

To document how rates of OWB compare with symptomatic criteria of mental-health outcomes, we created a variable to measure diagnostic recovery. This variable identified participants who (a) endorsed the presence of a given lifetime history and (b) no longer met criteria for said 12-month diagnosis. Altogether, three possible outcomes existed for people who endorsed a lifetime history of a mental-health diagnosis: 12-month diagnosis, diagnostic recovery, and OWB.

Demographic variables

Age (grouped by decade, except for ages 15–19), household-income, gender, education (postsecondary degree, no postsecondary degree), marital-status, and race (White, non-White) variables were obtained from the public-use data set.

Clinical variables

Number of lifetime mental-health conditions

A continuous variable summed participants’ lifetime mental-health diagnoses.

Persistence of mental-health conditions

The duration of participants’ longest illness episode was computed among participants diagnosed with depression or generalized anxiety disorder. These variables were collapsed and categorized to help with interpretability (< 1 year, 1–2 years, 2–5 years, ≥ 5 years).

Perceived general health

Participants reported their perceived overall health status on a 5-point scale: poor, fair, good, very good, and excellent.

Satisfaction with life

Participants reported their perceived life satisfaction on an 11-point scale.

Distress

Ten items on a 5-point scale assessed participants’ 30-day psychological distress levels using the K-10 (Kessler et al., 2002). Total scores range from 0 to 40; higher scores indicate higher distress. In the overall sample, internal consistency for the K-10 composite score was good (Cronbach’s α = .86).

Perceived mental-health need

Participants were grouped into one of four categories according to whether a participant reported a perceived need for mental-health care (information, medication, counseling, other) in the past 12 months and if so, whether their needs were met, partially met, or unmet.

Sample weights and missing data

Statistics Canada calculated sampling weights to ensure valid inference to the target (household) population. The sampling weights account for design characteristics such as unequal selection probabilities, exclusion of out-of-scope units, nonresponse at the household and personal levels, and extreme values. A set of 500 replicate bootstrap weights allows accurate confidence intervals (CIs) to be calculated by accounting for clustering in the multistage sampling procedure. These weights were applied to all analyses to obtain results representative of the Canadian population.

Missing data for mental-health diagnoses and WHODAS 2.0 ranged from 0% to 3%, whereas missing data for the MHC-SF was 9%. Bivariate correlations indicated that missingness for the MHC-SF was associated with the following variables: age (r = .15), female gender (r = .04), disability (r = .02), education (r = –.06), non-White race (r = –.02), and number of mental-health diagnoses (r = –.05), ps < .001. Given the large remaining sample (N = 23,491), analyses implemented listwise deletion.

Analysis plan

Analyses were conducted using IBM SPSS (Version 26). After ascertaining the prevalence of OWB and other mental-health outcomes, we reported descriptive statistics on characteristics of diagnostic subsamples. We calculated 95% CIs for the difference score in proportions to examine whether subsample groups exhibited reliable differences in OWB estimates (Cumming & Finch, 2005; Franklin, 2007). Consistent with prior research (Disabato et al., 2021), we also conducted sensitivity analyses that explored how variations in OWB criteria and cutoff scores influenced rates. These analyses revealed that our a priori OWB criteria (e.g., quartile cutoffs) resulted in stark differences in the groups selected compared with looser criteria (e.g., tertial cutoffs; Fuller-Thomson, 2016; Keyes, 2002), which suggests that OWB criteria select a different group. For these sensitivity analyses, see Table S7 in the Supplemental Material.

In the total sample, we computed adjusted odds ratios (OR_adjs) to determine whether each lifetime history of a disorder and the total number of disorders influence the likelihood of OWB. A series of logistic regressions examined demographic characteristics (i.e., gender, age, race, income, education) of OWB among each diagnostic subsample. Because interaction analyses in the diagnostic subsample were underpowered, we used the full sample. Note that all analyses with demographic variables were exploratory. For depression and anxiety subsamples, a logistic regression also explored how longest illness duration influenced OWB.

Finally, we described clinical characteristics of people with OWB. χ² tests of independence tested how people with OWB compared with people without OWB on the following variables: perceived health, perceived life satisfaction, and perceived mental-health needs. An analysis of variance compared 30-day psychological distress (i.e., K-10) among people with and without OWB.

A threshold p value of .05 (two-tailed tests) was used to identify significant relationships. Bonferroni corrections for multiple tests were applied to each subset of logistic regression analyses to reduce Type 1 error; however, the pattern of significant findings remained unchanged after this statistical correction. To reduce Type 1 error and provide interpretation of meaningful effects, we used H. Chen et al.’s (2010) recommendations to guide effect size interpretations of ORs in epidemiological data sets. Specifically, H. Chen et al. found that when the base rate is 5%, ORs of 1.52, 2.74, and 4.72 can be interpreted as small, medium, and large effect sizes, respectively. These interpretations are comparable with those for Cohen’s d. For inverse relationships, ORs of 0.66, 0.36, and 0.21 represent small, medium, and large effect sizes, respectively. An OR of 1.52 indicates roughly a 50% increase in the odds of OWB with exposure to a predictor variable (Norton et al., 2018).

Results

The prevalence of OWB after lifetime history of mental-health conditions

Rates of lifetime mental-health conditions were as follows: depression (11.3%), generalized anxiety disorder (8.7%), bipolar I disorder (0.9%), bipolar II disorder (0.6%), substance use disorder (8.7%), suicidal ideation (8.2%), and “any disorder” (33.1%; for standard errors, see Table S7 in the Supplemental Material). For demographic information for these conditions, see Table S8 in the Supplemental Material.

Table 1 provides prevalence estimates of OWB and comparisons across mental-health conditions. The outcome of OWB was starkly less common than outcomes based solely on diagnostic recovery, in which rates ranged from 27.7% (bipolar II disorder) to 77.4% (“other” drug abuse or dependence). Among never-diagnosed participants, 24.1% met criteria for OWB compared with 9.8% of participants with any lifetime disorder. Segmented by lifetime disorder, substance use disorder (10.2%) and depression (7.1%) yielded the highest OWB rates, followed by generalized anxiety (5.7%), suicidal ideation (5.0%), bipolar I (3.3%), and bipolar II (3.2%). Among substance use disorders, rates of OWB were lower after cannabis abuse/dependence and other drug abuse (4.3%) than after alcohol abuse/dependence (10.9%). All differences in OWB proportions between disorders were statistically significant (p < .05), except between bipolar I (3.3%) and bipolar II (3.2%) (difference score = 0.13, 95% CI = [0.02, 0.20], p > .05). For the 95% CIs for the difference scores in OWB, see Table S9 in the Supplemental Material.

Table 1.

Canadian Rates for Optimal Well-Being and Prognostic Course in Subsamples With Different Histories of Mental-Health Diagnoses

		12-month clinical course for subsamples of specific disorders
Sample or subsample	Full sample(N = 23,491)Lifetime prevalence	12-month diagnosis	Diagnostic recovery	OWB
Total sample	—	—	—	18.6 (0.01)
No lifetime history of any disorder	66.9 (0.01)	—	—	23.9 (0.01)
Lifetime history of substance use disorder	8.7 (0.01)	21.3 (0.02)	68.4 (0.02)	10.2 (0.01)
Alcohol abuse or dependence	18.1 (0.01)	18.2 (0.02)	71.0 (0.02)	10.9 (0.01)
Cannabis abuse or dependence	6.8 (0.01)	20.1 (0.03)	73.7 (0.03)	6.3 (0.02)
Drug abuse or dependence (excluding cannabis)	4.0 (0.01)	18.3 (0.04)	77.4 (0.04)	4.3 (0.02)
Lifetime history of any disorder	33.1 (0.01)	32.1 (0.02)	58.4 (0.02)	9.4 (0.01)
Lifetime history of major depressive disorder	11.3 (0.01)	43.3 (0.03)	49.7 (0.03)	7.1 (0.01)
Lifetime history of generalized anxiety disorder	8.7 (0.01)	30.4 (0.03)	63.8 (0.03)	5.7 (0.01)
Lifetime history of suicidal ideation	8.2 (0.01)	23.0 (0.03)	72.7 (0.03)	5.0 (0.01)
Lifetime history of bipolar II disorder	0.6 (< 0.01)	68.9 (0.12)	27.7 (0.11)	3.3 (0.05)
Lifetime history of bipolar I disorder	0.9 (< 0.01)	61.9 (0.10)	34.9 (0.10)	3.2 (0.04)

Note: Values are percentages of individuals with optimal well-being; the values in parentheses are standard errors. Estimates are based on weighted age- and gender-matched norms. The sample size provided is based on the nonweighted sample to help with interpretation. Diagnostic recovery is defined as no longer meeting 12-month criteria for a specific diagnosis. All disorders significantly differed in their rates of OWB, except that OWB was not significantly more common after bipolar II than bipolar I, (difference score = 0.13, 95% confidence interval = [0.02, 0.20], p > .05). OWB = optimal well-being.

Logistic regression revealed that a history of each lifetime mental-health condition was associated with lower observed OWB after controlling for the presence of other conditions (see Table S10 in the Supplemental Material). OWB was more common among participants who did not report a history of bipolar I (OR_adj = 7.47, 95% CI = [7.15, 7.81]), suicidal ideation (OR_adj = 3.74, 95% CI = [3.72, 3.76]), or bipolar II (OR_adj = 3.01, 95% CI = [2.86, 3.16]). Likewise, OWB was more common among participants who did not report a history of generalized anxiety disorder (OR_adj = 2.18, 95% CI = [2.16, 2.19]), depression (OR_adj = 1.62, 95% CI = [1.61, 1.64]), or “any mental disorder” (OR_adj = 1.58, 95% CI = [1.57, 1.59]). Put otherwise, the odds of someone without a history of “any mental disorder” reaching OWB is 0.18:1, whereas the odds of someone with a history of “any mental disorder” reaching OWB is 0.11:1.

The absence of a substance use disorder also predicted OWB, but the effect size did not meet the threshold for clinical significance (OR_adj = 1.34, 95% CI = [1.33, 1.35]). However, when segmenting substance use disorders in the model, we found that the absence of a history of cannabis use abuse/dependence (OR_adj = 1.89, 95% CI = [1.88, 1.90]) and “other” drug abuse/dependence (OR_adj = 1.95, 95% CI = [1.92, 1.98]) significantly predicted OWB with small effect sizes, whereas the absence of alcohol abuse/dependence (OR_adj = 1.05, 95% CI = [1.04, 1.06]) did not meet the threshold for clinical significance.

Demographic characteristics and correlates of OWB

Table 2 provides descriptive variables, subsampled by disorder, for individuals who met OWB. In the full sample including nondisordered participants, a logistic regression indicated that White race (OR_adj = 0.76, 95% CI = [0.76, 0.76]) and not having a postsecondary education (OR_adj = 0.94, 95% CI = [0.94, 0.94]) were associated with lower observed rates of OWB, and male gender (OR_adj = 1.18, 95% CI = [1.18, 1.18]) significantly predicted higher rates of OWB; however, these variables did not meet the threshold for clinical significance. The strongest demographic predictor of higher observed OWB was household income. Compared with participants earning $0 to $20,000, each move upward in income bracket was associated with a small to medium increase in rates of OWB: $20,000–$39,999 (OR_adj = 2.23, 95% CI = [2.20, 2.26]), $40,000–$59,999 (OR_adj = 2.18, 95% CI = [2.15, 2.21]), $60,000–$79,999 (95% CI = [2.81, 2.84]), and $80,000 or more (OR_adj = 2.23, 95% CI = [2.22, 2.24]).

Table 2.

Demographic Breakdown of Individuals With Optimal Well-Being After Lifetime Mental-Health Diagnoses

	Lifetime history
Characteristic	No disorder(n = 733)	MDD(n = 180)	GAD(n = 128)	BP I(n = 8)	BP II(n = 3)	SUD(n = 533)	SUI(n = 109)
Sex
Male	49.9 (0.02)	30.9 (0.10)	40 (0.13)	59.7 (0.55)	70.7 (0.63)	80.6 (0.05)	39.0 (0.15)
Female	50.1 (0.02)	69.1 (0.10)	60 (0.13)	40.3 (0.55)	29.3 (0.63)	19.4 (0.05)	61.0 (0.15)
Race
White	68.1 (0.02)	87.1 (0.07)	90.4 (0.08)	91.5 (0.32)	100	85.7 (0.05)	68.1 (0.14)
Non-White	31.9 (0.02)	12.9 (0.07)	9.6 (0.08)	8.5 (0.32)	0	14.3 (0.05)	31.9 (0.14)
Language used at home
English	71.0 (0.02)	73.4 (0.10)	68.4 (0.13)	93.0 (0.29)	100	77.7 (0.05)	71.4 (0.02)
French	15.7 (0.02)	24.7 (0.09)	31.1 (0.13)	7.0 (0.29)	0	16. 5 (0.05)	16.5 (0.02)
English and French	3.1 (0.01)	1.42 (0.03)	0.45 (0.02)	0	0	2.1 (0.02)	3.0 (0.01)
Neither English nor French	10.1 (0.01)	1.5 (0.03)	0	0	0	3.8 (0.02)	9.1 (0.01)
Education
No postsecondary degree	31.6 (0.02)	19.4 (0.08)	24.1 (0.12)	16 (0.41)	29.8 (0.63)	31.2 (0.06)	28.4 (0.14)
Postsecondary degree	68.4 (0.02)	80.6 (0.08)	75.9 (0.12)	84 (0.41)	70.2 (0.63)	68.8 (0.06)	71.6 (0.14)
Age
15–19 years	8.8 (0.01)	1.6 (0.03)	0	0	29.8 (0.63)	2.5 (0.02)	5.8 (0.07)
20–29 years	17.8 (0.02)	9.9 (0.06)	18.6 (0.11)	21.2 (0.46)	40.9 (0.63)	14.9 (0.05)	14.4 (0.11)
30–39 years	17.5 (0.02)	12.2 (0.07)	14.0 (0.09)	14.5 (0.40)	0	22.2 (0.05)	15.7 (0.11)
40–49 years	19.0 (0.02)	34.5 (0.10)	18.1 (0.10)	15.5 (0.41)	29.3 (63)	23.7 (0.06)	21.1 (0.12)
50–59 years	16.7 (0.02)	26.9 (0.10)	33.9 (0.13)	48.8 (0.56)	0	21.3 (0.05)	22.6 (0.13)
60–69 years	11.8 (0.02)	12.8 (0.07)	13.4 (0.09)	0	0	13.4 (0.04)	16.3 (0.11)
70–79 years	6.3 (0.01)	2.1 (0.03)	2.0 (0.04)	0	0	1.8 (0.02)	4.2 (0.06)
≥ 80 years	2.1 (0.01)	0	0	0	0	0.3 (0.01)	0
Marital status
Single	25.2 (0.02)	16.2 (0.08)	17.0 (0.10)	16.0 (0.41)	70.3 (0.63)	18.0 (0.05)	23.4 (0.13)
Married	56.4 (0.02)	60.3 (0.10)	46.4 (0.11)	0	29.3 (0.63)	58.7 (0.06)	47.4 (0.15)
Common law	8.6 (0.01)	9.5 (0.06)	21.3 (0.05)	54.8 (0.56)	0	16.5 (0.05)	7.2 (0.08)
Widowed	2.9 (0.01)	5.9 (0.05)	4.1 (0.05)	0	0	1.1 (0.01)	1.6 (0.04)
Divorced/separated	6.8 (0.01)	8.1 (0.06)	11.2 (0.09)	29.2 (0.51)	0	5.7 (0.05)	20.5 (0.12)
Household income
No income to < $20,000	2.5 (0.01)	1.8 (0.03)	4.1 (0.05)	0	0	1.7 (0.02)	3.4 (0.06)
$20,000–$39,999	8.8 (0.01)	6.7 (0.05)	5.9 (0.06)	14.5 (0.40)	0	7.2 (0.03)	21.3 (0.12)
$40,000–$59,999	16.2 (0.02)	16.5 (0.08)	13.4 (0.09)	24.7 (0.50)	40.9 (0.68)	10.6 (0.04)	10.9 (0.09)
$60,000–$79,999	20.6 (0.02)	21.1 (0.09)	13.9 (0.09)	37.1 (0.55)	29.3 (0.63)	11.5 (0.04)	18.3 (0.12)
≥ $80,000	51.9 (0.02)	53.7 (0.11)	62.7 (0.13)	23.6 (0.48)	29.4 (0.63)	69.0 (0.06)	46.1 (0.15)

Note: Values are percentages of individuals with optimal well-being; the values in parentheses are standard errors. Sample sizes provided are based on unweighted sample to help with interpretation. MDD = major depressive disorder; GAD = generalized anxiety disorder; BP = bipolar disorder; SUD = substance use disorder; SUI = suicidal ideation.

In the full sample, we also explored interactions among each sociodemographic variable in predicting OWB. We entered each sociodemographic variable and five interaction terms into a logistic regression model (i.e., Sex × Race, Sex × Education, Sex × Income, Race × Education, and Race × Income interactions). The coefficients for the interaction terms yielded an interaction odds ratio (OR_int). To interpret the OR_int (J. J. Chen, 2003), we recalculated it by multiplying the linear term OR for the first variable with the OR_int term of the first and the second variables. Analyses revealed a small but significant interaction of race and education. Examination of the OR_int revealed that White participants without postsecondary education had a greater chance of achieving OWB compared with people who were non-White and had a postsecondary education.

Results also yielded a small Race × Income interaction; whereas higher income, as a main effect, was associated with a greater probability of OWB, inspection of the OR_int indicated that higher income was associated with OWB for people who are non-White; meanwhile, White race was associated with a reduced likelihood of achieving OWB at lower income brackets. Specifically, whereas the odds of OWB decreased for White individuals (compared with non-White individuals), any benefits of one race compared with another tended to decrease in magnitude at increasingly higher income brackets (i.e., $20,000–$39,999; $40,000–$59,999; $60,000–$79,999). However, the effects of this interaction did not meet the threshold of clinical significance at the highest income bracket of $80,000 or more. For details, see Table S12 in the Supplemental Material.

Relationship trends between demographic variables and OWB were generally consistent when segmenting analyses across disorders (Table 3): Most demographic variables were statistically but not clinically significant. However, White race was negatively associated with OWB among people with a history of suicidal ideation (OR_adj = 0.50, 95% CI = [0.49, 0.50]), and lower education was negatively associated with lower rates of observed OWB among people with a history of depression (OR_adj = 0.65, 95% CI = [0.65, 0.66]). Within depression, the odds of OWB also increased significantly across income brackets: $20,000–$39,999 (OR_adj = 2.03, 95% CI = [1.96, 2.10]), $40,000–$59,999 (OR_adj = 3.35, 95% CI = 3.24, 3.45), $60,000–$79,999 (OR_adj = 4.02 = 95% CI = [3.89, 4.16]), and $80,000 or more (OR_adj = 4.74, 95% CI = [4.58, 4.88]); similar effects held for patients with histories of substance use disorder. Cell sizes among the bipolar subsamples were too low for meaningful interpretation and are presented simply for completeness.

Table 3.

Results of Logistic Regression of Demographics as Predictors of Optimal Well-Being After Lifetime Mental-Health Diagnoses

	Lifetime history
Predictor	No disorder(n = 14,495)	MDD(n = 2,966)	GAD(n = 2,360)	BP I(n = 210)	BP II(n = 149)	SUD(n = 5,592)	SUI(n = 2,160)
Constant	0.23	0.02	0.05	0.002	1.36	0.08	0.06
Male	1.18[1.18, 1.89]	0.83[0.83, 0.84]	1.51[1.49, 1.52]	2.24[2.13, 2.35]	2.27[2.13, 2.42]	1.40[1.40, 1.41]	0.88[0.87, 0.89]
White	0.75[0.75, 0.75]	1.24[1.22, 1.26]	1.48[1.45, 1.51]	4.30[3.96, 4.67]		0.80[0.79, 0.80]	0.50[0.49, 0.50]
No postsecondary degree	0.95[0.95, 0.95]	0.65[0.65, 0.66]	0.83[0.82, 0.84]	0.33[0.31, 0.35]	0.19[0.18, 0.20]	1.01[1.00, 1.02]	0.75[0.74, 0.76]
Age	0.96[0.96, 0.96]	1.13[1.13, 1.13]	1.01[1.01, 1.01]	1.29[1.27, 1.31]	0.40[0.39, 0.41]	1.03[1.03, 1.03]	1.13[1.12, 1.13]
Income (reference: income < $20,000)
$20,000–$39,999	1.29[1.28, 1.30]	2.03[1.96, 2.10]	0.58[0.56, 0.60]			1.85[1.81, 1.89]	2.30[2.22, 2.38]
$40,000–$59,999	1.61[1.60, 1.62]	3.35[3.24, 3.45]	0.94[0.91, 0.97]			1.90[1.86, 1.94]	1.02[0.99, .1.06]
$60,000–$79,999	2.19[2.17, 2.20]	4.02[3.89, 4.16]	1.14[1.10, 1.17]			2.10[2.06, 2.15]	1.71[1.65, 1.77]
$80,000 or more	2.07[2.06, 2.09]	4.72[4.58, 4.88]	2.21[2.14, 2.27]			3.73[3.66, 3.81]	1.98[1.91, 2.05]
Number of lifetime mental-health diagnoses	—	0.70[0.70, 0.70]	0.72[0.72, 0.73]	1.33[1.29, 1.36]	0.79[0.75, 0.83]	0.52[0.52, 0.53]	0.39[0.39, 0.40]

Note: Values are adjusted odds ratios with 95% confidence intervals in square brackets. All predictors were significant at a p < .001 value, two-tailed test. Blank cells indicate that convergence could not be reached when these variables were included in the model, which results in unreliable estimates, likely because of the low prevalence of OWB after bipolar disorder (BP). OWB = optimal well-being; MDD = major depressive disorder; GAD = generalized anxiety disorder; SUD = substance use disorder; SUI = suicidal ideation.

Clinical characteristics and correlates of OWB

Logistic regression indicated that more lifetime mental-health conditions were associated with lower observed rates of OWB; the effect size was small to medium (OR_adj = 0.44, 95% CI = [0.44, 0.44]). This relationship held when including demographic covariates. Specifically, participants without lifetime mental-health conditions had 6 times the odds of OWB (OR_adj = 6.04, 95% CI = [6.00, 6.08]) compared with participants with multiple lifetime conditions. Participants with just one lifetime mental-health condition had 4.20 times the odds of OWB (OR_adj = 4.20, 95% CI = [4.16, 4.24]) compared with participants with multiple lifetime conditions; the presence of multiple lifetime mental-health conditions had a large inhibiting effect on OWB.

Table 4 provides clinical characteristics, subsampled by condition, for individuals who met OWB. Having multiple lifetime conditions was common across mental-health subsamples, which indicates that multiple lifetime mental-health conditions does not completely preclude the chance for OWB. Specifically, 43% of individuals with a depression history and OWB experienced at least two lifetime diagnoses, similar to 51% of individuals with a generalized anxiety disorder history and OWB, 80% of individuals with a bipolar I history and OWB, and 100% of individuals with bipolar II and OWB.

Table 4.

Clinical Characteristics of Individuals With Optimal Well-Being After Lifetime Mental-Health Diagnoses

	Lifetime history
Characteristic	No disorder(N = 733)	MDD(n = 180)	GAD(n = 128)	BP I(n = 8)	BP II(n = 3)	SUD(n = 533)	SUI(n = 109)
Comorbid lifetime disorder
MDD	0	100	43.7 (0.13)	80.9 (0.44)	100	8.5 (0.04)	1.2 (0.03)
GAD	0	27.5 (0.10)	100	50.8 (0.56)	70.2 (0.63)	4.5 (0.03)	11.4 (0.10)
BP I	0	2.9 (0.04)	2.9 (0.04)	100	100	0.9 (0.01)	0
BP II	0	2.4 (0.03)	2.7 (0.04)	100	100	0.3 (0.01)	0
SUD	0	23 (0.09)	19.3 (0.11)	67.2 (0.53)	29.8 (0.63)	100	18.5 (0.12)
SUI		99 (0.02)	12.1 (0.10)	0	0	3.5 (0.02)	100
Number of lifetime diagnoses
2	0	31.9 (0.10)	36.3 (0.13)	0	0	6.9 (0.04)	1.7 (0.04)
3	0	9.9 (0.06)	12.9 (0.09)	43.7 (0.56)	100	2.9 (0.03)	0.36 (0.02)
4	0	1.3 (0.02)	2.1 (0.04)	37.1 (0.55)	0	0.5 (0.01)	0
Perceived need for care^a
No needs	96.4 (0.01)	75.5 (0.09)	75.7 (0.12)	71.8 (0.51)	40.9 (0.68)	90.4 (0.04)	81.9 (0.12)
All needs met	3 (0.01)	24.3 (0.09)	23.4 (0.11)	28.2 (0.51)	59.1 (0.68)	8.2 (0.04)	9.5 (0.09)
Needs partially met	0.4 (< 0.001)	0.20 (0.010)	0	0	0	0.6 (0.01)	3.7 (0.06)
Needs not met	0.3 (< 0.001)	0	1 (0.03)	0	0	0.86 (0.01)	4.9 (0.07)
Perceived general health
Poor	0	0	0	0	0	0	0.11 (< 0.01)
Fair	2.35 (< 0.01)	0.65 (0.02)	2.1 (0.04)	0	0	1.3 (0.01)	2.2 (0.01)
Good	16.3 (0.01)	15.9 (0.08)	27.3 (0.12)	35.1 (0.54)	0	23.8 (0.06)	17.0 (0.02)
Very good	39.7 (0.02)	48.9 (0.11)	36.1 (0.13)	43.7 (0.54)	70.2 (0.63)	39.8 (0.06)	39.7 (0.02)
Excellent	41.6 (0.02)	34.6 (0.10)	34.5 (0.13)	21.2 (0.46)	29.8 (0.63)	35.2 (0.06)	40.9 (0.02)
Satisfaction with life
Very dissatisfied	0.1 (< 0.01)	0	0	0	0	0	0.12 (< 0.01)
Dissatisfied	0.1 (< .001)	0	0	0	0	0.04 (< 0.01)	0.13 (< 0.01)
Neither satisfied nor dissatisfied	1.2 (0.01)	0.1 (0.01)	0	19.1 (0.44)	0	0.52 (0.01)	1.1 (< 0.01).
Satisfied	37.7 (0.02)	39.9 (0.1)	43.3 (0.13)	44.1 (0.56)	0	42.1 (0.06)	37.7 (0.02)
Very satisfied	61.6 (0.02)	60 (0.11)	56.7 (0.13)	36.8 (0.54)	100	57.3 (0.06)	61.0 (0.02)

Note: Values are percentages of individuals with optimal well-being; the values in parentheses are standard errors. Sample sizes provided are based on unweighted sample to help with interpretation. OWB = optimal well-being; MDD = major depression; GAD = generalized anxiety disorder; BP = bipolar disorder; SUD = substance use disorder; SUI = suicidal ideation.

Perceived need for care refers to care for problems with emotions, mental health, or use of alcohol and drugs.

Subsampled by depression and anxiety, shorter durations of severe-illness episodes predicted OWB controlling for number of lifetime mental diagnoses. Among participants with a depression history, the odds of OWB increased significantly with shorter depressive episode durations: less than 1 year (OR_adj = 2.77, 95% CI = [2.71, 2.83]), 1 to 2 years (OR_adj = 2.52, 95% CI = [2.46, 2.58]), and 2 to 5 years (OR_adj = 1.34, 95% CI = [1.31, 1.38]).

Among participants with a history of generalized anxiety disorder, there were higher odds of OWB for participants who reported shorter illness durations of less than 1 year (OR_adj = 1.78, 95% CI = [1.75, 1.81]) and 1 to 2 years (OR_adj = 1.57, 95% CI = [1.54, 1.59]) relative to participants who reported durations of more than 5 years, ps < .001. Note that individuals with anxiety episode durations of 2 to 5 years were less likely (OR_adj = 0.64, 95% CI = [0.63, 0.66]) to obtain OWB compared with participants with durations 5 years or more, p < .001.

A positive relationship existed between OWB and perceived health, χ²(4, N = 23,485) = 1,645,282, p < .001; and life satisfaction, χ²(4, N = 23,374) = 1,756,028, p < .001. For OWB individuals, 80% reported their overall health as very good or excellent, whereas for individuals without OWB, just 56% reported their overall health as very good or excellent. Furthermore, individuals with OWB (95%) more commonly reported having “no needs” for mental-health care compared with individuals without OWB (79%), χ²(3, N = 23,374) = 697,321, p < .001. Finally, an analysis of variance (ANOVA) revealed that individuals with OWB (M = 2.14, SD = 2.47) reported significantly lower distress over the previous 30 days compared with individuals without OWB (M = 6.16, SD = 5.71), and the effect size was large, F(1, 26,342,457) = 2,332,063, p < .001, η² = .27. Overall, individuals with OWB reported overall better health, higher life satisfaction, less needs for mental-health care, and less psychological distress compared with individuals without OWB.

Discussion

Incorporating data on long-term positive outcomes after psychopathology will be useful to clinicians, researchers, and patients alike (Chevance et al., 2020). Recent research discovered a substantial percentage of adults diagnosed with depression will subsequently attain high levels of psychological well-being (Disabato et al., 2021; Rottenberg et al., 2018, 2019). The current study extended this work by investigating the prevalence and predictors of OWB after multiple mental-health conditions in a large, nationally representative sample of Canada. Data on the clinical and socioeconomic features associated with OWB are needed to enable clinicians to provide more precise prognostic information to patients.

In this data set, a history of a mental-health condition significantly decreased the likelihood of attaining OWB. However, a substantial group of individuals previously diagnosed with a mental-health condition (10% across disorders) attained OWB at the time of the study. Given that strict criteria were used to define OWB, these findings indicate that high functioning (as indicated by high levels of well-being and low disability) is among the outcomes of mental-health conditions observed in Canada. We also found that OWB as an outcome is associated with lower levels of distress, more positive reports of health status, and lower levels of need for care; 95% of individuals with OWB status reported no need for mental-health care compared with 79% of people without OWB status. Put otherwise, people with a history of psychopathology who reach OWB status may require less mental-health services over time, and thus, OWB may reduce the human and societal cost of psychopathology in the long term.

About 10% of people with a substance use history and 7.1% of people with a depression history met OWB criteria. These rates were notably higher than OWB observed after generalized anxiety disorder (5.7%), suicidal ideation (5.0%), bipolar II (3.3%), and bipolar I (3.2%). Within substance use disorders, a history of alcohol abuse/dependence (18.1%) had smaller effects on OWB status than cannabis abuse/dependence (6.8%) and “other” drug abuse/dependence (4.0%). Levels of observed OWB after depression broadly converged with a previous estimate in a U.S. population sample (9.7%), even with somewhat different ascertainment methods for depression and well-being (Disabato et al., 2021; Rottenberg et al., 2019). Likewise, the current study observed that OWB rates after generalized anxiety disorder were less common than OWB after depression. However, OWB after generalized anxiety disorder (5.7%) in this Canadian sample was more common than a representative U.S. sample (Disabato et al., 2021).

Future studies might examine why generalized anxiety disorder is associated with lower observed OWB. One possibility is that the disorder criteria themselves may contribute to these differences. Generalized anxiety disorder requires excessive worry or anxiety that lasts at least 6 months, whereas a depression diagnosis requires symptoms for only 2 weeks or more (American Psychiatric Association [APA], 2013). However, this rationale is challenged by the comparatively high OWB rates after substance use disorders, in which sustained remission criteria require 12 months of no symptoms (APA, 2013). Conceptual models may elucidate why generalized anxiety disorder affects long-term well-being given that they highlight the role of uncertainty intolerance, a poor understanding of emotions, difficulties effectively managing and harnessing emotions to make progress toward meaningful goals, and a cycle in which avoidance of uncontrollable worries restricts potentially rewarding activities (Behar et al., 2009).

Our results support the broad distinction between unipolar mood disorders and bipolar mood disorders (Judd et al., 2008). Many studies find no obvious differences in course between persons with unipolar and bipolar mood disorders (Cuellar et al., 2005; Scott et al., 2013). At the same time, the majority of studies follow hospitalized patients, who may be unrepresentative of the entire population (Angst, 2008; Rottenberg et al., 2018). For people with bipolar disorders, research has found that changes in depression severity are associated with functional impairment, whereas mania or hypomania symptoms are inconsistently associated with functioning (Hacimusalar & Dog˘an, 2019; Simon et al., 2007). There are also indications that bipolar I produces greater functional impairment relative to other mood disorders (Judd et al., 2008). In our study, the low observed OWB rates after bipolar disorders suggest that the presence of manic or hypomanic episodes detracts from long-term well-being. OWB was 7.5 times more common among people who had no history of bipolar I disorder, whereas an absence of depression history increased odds of OWB by a factor of 1.6. Although these findings suggest that the presence of bipolar I has a particularly deleterious effect on long-term OWB, we did not have data to examine the role of particular clinical features, such as age of onset, illness severity, and number of manic episodes.

Having a greater number of lifetime mental-health conditions inhibited OWB. Compared with people with multiple lifetime conditions, a single lifetime condition increased the odds of OWB by a factor of 4.2 and having no lifetime conditions increased the odds of OWB by a factor of 6. Because 86% of people will experience some form of psychopathology by age 45 and most will experience a subsequent disorder (Caspi et al., 2020), this underscores the importance of interventions that address risk factors linked to comorbidity and recurrence, such as targeting subclinical symptoms (Judd et al., 2008; Treuer & Tohen, 2010), and components of well-being (Cloninger, 2006).

Longer episodes of clinically diagnosed depression and anxiety were negatively associated with OWB. Depressive or anxiety episodes of more than 2 years generally decreased the odds of OWB compared with episodes less than 2 years. These findings highlight the importance of early interventions to help facilitate long-term well-being among people with mental-health diagnoses.

Our study suggests that demographic correlates of OWB vary within specific disorders. Overall, the strongest demographic predictor was household income, in which the odds of OWB increased across income bracket; these relationships were stronger within the depression and substance use disorder subsamples. That a resource variable such as greater income was associated with higher OWB provides clues for understanding mechanisms that may facilitate OWB; this finding converges with literature highlighting the pernicious effects of poverty on depression and anxiety (Ridley et al., 2020). Moving forward, research needs to uncover malleable mechanisms that influence well-being during and after psychopathology, especially those facilitated by pharmacologic and psychotherapy interventions.

There are a few interpretative caveats worth considering. First, our secondary analyses of a nationally representative archival data set relied on retrospective lifetime and 12-month diagnoses. Less severe disorders may not be recalled during retrospective assessments (Moffitt et al., 2010; Streiner et al., 2009), which may underestimate observed OWB rates after lifetime disorders. Lifetime diagnosis of psychopathology could be undercounted with an overrepresentation of severe and personally significant mental-health episodes (Takayanagi et al., 2014). The cross-sectional design precluded analyses of predictors and changes in well-being and psychopathology over time. In these data, a greater number of lifetime diagnoses resulted in less favorable chances for OWB. Future research should clarify the extent to which comorbid psychopathology impairs chances for OWB. Another caveat regards our approach for dealing with interpreting statistically significant findings in this epidemiological data set. To reduce Type 1 errors and identify effects with more practical application, we applied Bonferroni corrections and prioritized interpreting effect sizes of ORs (H. Chen et al., 2010). Other approaches, such as equivalence testing (Da Silva et al., 2009), exist to help researchers identify the smallest effect size of interest. Future studies should also investigate OWB rates in non-WEIRD (Western, educated, industrialized, rich, and democratic) samples, especially because theories of well-being may have limited generalizability to these groups (Henrich et al., 2010).

This study also had notable strengths. The CHSS-MH provided a large representative sample of approximately 25,000 Canadians with “gold-standard” assessments of mental-health diagnoses, well-being, and disability. The richness of this sample allowed us to obtain estimates of less common diagnoses, such as bipolar disorders. We also conceptualized OWB using a theoretically and methodologically rigorous approach that used age- and gender-matched population norms to define OWB. We acknowledge that a focus on a discrete OWB state is just one approach to measuring optimal functioning and that there is also value in analyzing well-being on a continuum. However, given our interest in optimal functioning after mental-health diagnoses (Ryan & Deci, 2000), use of strict cutoffs (i.e., top quartile) enhance the clinical utility of our OWB criteria, and the dichotomous classification approach provides proportional estimates of high functioning after psychopathology—information that can be easily interpreted by patients and clinicians. We invite future researchers to compare different operationalizations of OWB (as we have; see e.g., Disabato et al., 2021). Finally, and most notably, this study is the broadest assessment of OWB after psychopathology yet. Whereas most studies investigate specific mental-health conditions alone, this study provided a comprehensive comparison of OWB across mood disorders, generalized anxiety disorder, and substance use disorders.

Overall, this representative study provides evidence that OWB is a realistic goal for some patients, particularly in the aftermath of alcohol use disorders or depression. These findings challenge public stereotypes that these conditions are overwhelmingly chronic and intractable and preclude long-term well-being (Devendorf et al., 2020; Stacy & Rosenheck, 2019), and thus are in need of replication. On the basis of these data, we find that symptomatic recovery may be a more realistic goal for patients with a history of suicidal ideation or bipolar disorders. Ultimately, we hope this work inspires investigations into understanding why OWB rates differ across disorders, including eating disorders, schizophrenia spectrum disorders, trauma-related disorders, and other anxiety-related disorders (e.g., obsessive compulsive disorder, social anxiety). Studying OWB from a transdiagnostic lens might offers clues about human resilience and recovery across specific symptom presentations and adverse circumstances (Bonanno, 2004). In addition, explorations of longer time trajectories and intensive measurements may help to capture transitions from psychopathology to normal to exceptional functioning, trajectories that, to date, have been neglected by allied health professions.

Supplemental Material

sj-pdf-1-cpx-10.1177_21677026221078872 – Supplemental material for Optimal Well-Being After Psychopathology: Prevalence and Correlates

Supplemental material, sj-pdf-1-cpx-10.1177_21677026221078872 for Optimal Well-Being After Psychopathology: Prevalence and Correlates by Andrew R. Devendorf, Ruba Rum, Todd B. Kashdan and Jonathan Rottenberg in Clinical Psychological Science

Footnotes

Acknowledgements

We thank the University of South Florida for providing funding for this study.

Transparency

Action Editor: Kelly L. Klump

Editor: Kenneth J. Sher

Author Contributions

A. R. Devendorf and J. Rottenberg devised the study concept. A. R. Devendorf and R. Rum conducted the literature review. A. R. Devendorf planned and preregistered all data analysis with input from R. Rum, T. B. Kashdan, and R. Rottenberg. T. B. Kashdan provided guidance on the well-being measures. A. R. Devendorf carried out the data analysis, and R. Rum double-checked analyses and helped with interpretation of interactions. A. R. Devendorf wrote the manuscript, and J. Rottenberg, T. B. Kashdan, and R. Rum provided valuable feedback and editing throughout the process. All of the authors approved the final manuscript for submission.

Open Practices

All data have been made publicly available, upon request, via Statistics Canada and can be accessed at https://www150.statcan.gc.ca/n1/en/catalogue/82M0021X. All materials have been made publicly available via Statistics Canada and can be accessed at https://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&Id=119789. The design and analysis plans for the experiments were preregistered at OSF and are available at https://osf.io/nskgr. This article has received badges for Open Data, Open Materials, and Preregistration. More information about the Open Practices badges can be found at .

ORCID iDs

Andrew R. Devendorf

Jonathan Rottenberg

Supplemental Material

Additional supporting information can be found at

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