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Optimal Well-Being After Psychopathology: Prevalence and Correlates

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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.
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 1
Word Count (including Abstract and References): 9,320
Tables: 4
Figures: 0
Supplement Tables: 13
Optimal well-being after psychopathology: Prevalence and correlates
1Andrew R. Devendorf, M.A., 1Ruba Rum, M.S., 2Todd B. Kashdan, Ph.D., & 1Jonathan
Rottenberg, Ph.D.
1Department of Psychology, University of South Florida, 4202 E. Fowler Ave, Tampa, FL.
33620
2Department of Psychology, MS 3F5, George Mason University, Fairfax, VA 22030
Corresponding Author: Andrew Devendorf, M.A., andrewdevendorf@gmail.com
Acknowledgements: We thank the University of South Florida for providing funding for this
study.
Disclosure Statement: This work was supported by the University of South Florida Creative
Grant awarded to JR (no grant number applicable).
Conflicts of Interest: None
Authorship Contributions: ARD and JR devised the study concept. ARD and RR conducted
the literature review. ARD planned and preregistered all data analysis with input from JR, TK,
and RR. TK provided guidance on the well-being measures. ARD carried out the data analysis,
with RR double-checking analyses and helping with interpretation of interactions. ARD wrote
the manuscript, with JR, TK, and RR providing valuable feedback and editing throughout the
process.
NOTE: Version 1.0, December 20, 2021. This manuscript has been accepted for publication in
Clinical Psychological Science. This version has not been copy-edited and text may change
prior to OnlineFirst publication.
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 2
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 and scoring above
the 25th national percentile on psychological well-being and functioning 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 1 (3.3%), and bipolar 2 (3.2%) were less likely to report
OWB. Having just one lifetime disorder increased the odds of OWB by 4.2 times relative to
multiple lifetime disorders. While psychopathology substantially reduces the probability of
OWB, many individuals with psychopathology attain OWB.
Keywords: Epidemiology, Depression, Anxiety, Bipolar, Recovery, Well-being, Resilience
Public Significance / Lay Friendly Summary:
Research on mental illness has historically focused on adverse outcomes, while overlooking a
potentially important group of people who thrive following recovery after mental illness. We
discovered that a sizable group of people experience long-term thriving in response to common
mental illnesses, including depression, anxiety, and substance use disorders.
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 3
Optimal well-being after psychopathology: Prevalence and correlates
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 (Moussavi et
al., 2007; Bruce et al., 2008; Kessler et al., 2005; Treuer & Tohen, 2010). Studies of deleterious
outcomes largely rely on measures of psychiatric symptoms as the primary endpoint (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, Devendorf, Kashdan, &
Disabato, 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 diagnoses 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
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 4
al., 2015), and recurrence of psychopathology (Scholten et al., 2021). Much less research
examines other meaningful outcomes like 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, Disabato, & Kashdan, 2020, p.
3), 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, 1984; Ryff, 1989;
Keyes, 2002). 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 only been 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 (Kashdan & McKnight, 2009). Studies that consider good outcomes often have notable
design limitations, such as non-representative samples (Hendriks et al., 2019), reliance on self-
report of psychopathology (Moreau & Wiebels, in press), 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 (e.g.,
Fava & Tomba, 2009; Keyes, 2005) and physical health outcomes (e.g., Veenhoven, 2009).
Strong meta-analytic evidence has linked elements of well-being, like positive affect, with
desirable outcomes like work, health, and social functioning (Lyubomirsky et al., 2005). Further,
one important line of work investigates the concept of flourishing (e.g., Capaldie et al, 2015;
Fuller-Thompson et al., 2016), defined as “above-average functioning” on measures of mental
health and well-being (Keyes, 2005, p. 543). Building off these precedents, the current study
examines the attainment of optimal well-being after psychopathology, which is defined by full
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 5
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 (Rottenberg et al.,
2018; McKnight & Kashdan, 2009). First, contrary to assumptions that symptoms are sufficient
proxies for functioning, levels of psychiatric symptoms correlate only modestly with well-being
and functional impairment (Ryff & Keyes, 1995; McKnight & Kashdan, 2009; Mcknight,
Monfort, Kashdan, Blalock, & Calton, 2016), and while 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 report 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, Wyss, Schulte, Trachsel, & Michalak,
2009; Zimmerman et al., 2006). A recent content analysis of 3,003 patients, informal caregivers,
and healthcare 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, Dhingra, & Simoes, 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 ten-year follow up (Rottenberg, Devendorf, Panaite, Disabato,
Kashdan, 2019; Panaite et al., 2020).
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 6
Optimal Well-being (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),
outcomes which 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 a population-based norms
approach (for details see, Rottenberg et al., 2018). OWB after a mental health diagnosis required
four elements: 1) a lifetime history of a mental health diagnosis, 2) absence of a 12-month
mental health diagnosis, 3) high well-being, indicated by exceeding population-based norms on
psychological-well-being (top quartile on MHC-SF) (Lamers, Westerhof, Bohlmeijer, ten
Klooster, & Keyes, 2011), and 4) low functional impairment, indicated by population-based
norms on disability measures (bottom quartile on WHODAS, 2.0) (Üstün, Kostanjsek, Chatterji,
& Rehm, 2010). Use of this multipart definition increased the odds that individuals identified as
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 7
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, where 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 while major psychopathology reduces the likelihood of OWB,
many persons with psychopathology achieve OWB. In a nationally representative United States
adult sample, 10% of persons with study-documented depression satisfied OWB criteria 10-years
later, compared to 21% of non-depressed persons meeting the same standard (Rottenberg et al.,
2019). In other words, depression reduced the probability of achieving OWB by approximately
50 percent. 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, such as oncology). Documenting how base
rates of OWB after psychopathology vary by demographic factors like age, gender, and
socioeconomic status could help clinicians implement evidence-based practice (Pendergast,
Youngstrom, Ruan-lu, & Beysolow, 2018; Youngstrom et al., 2017). Similarly, it would be
beneficial to know how the number of prior lifetime diagnoses influences long-term OWB, in
part because comorbidity and co-occurrence of mental health diagnoses are the norm, rather than
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 8
exception, in psychopathology (Hankin et al., 2016; Krueger & Markon, 2006; Caspi et al.,
2020). For instance, a four-decade study of the transition to adulthood found 86% of people will
experience some form of psychopathology, of which 85% 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, as 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 U.S., in that it exhibits
geographic, economic, and cultural similarities to the U.S., while also presenting differences,
such as greater potential access to mental and physical healthcare. Canada, for example,
implements a universal healthcare delivery system, while the U.S. implements a largely private,
non-universal healthcare system. Canada also has a much smaller and less racially diverse
population compared to the United States, as 78% of the population does not meet the definition
of a “visible minority” (Statistics Canada, 2017). That said, Canadians are more likely to be
bilingual compared to Americans, as French and English are the official languages of Canada
and are taught in schools, while most schools in the United States teach solely English.
The Current Study
This study extended prior research in two important ways. First, we compared rates of
OWB across multiple 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; Vos et al., 2016), and they contribute to substantial
health, societal, and economic burdens (Wittchen, 2002; Greenberg et al., 2015). Bipolar
disorders, while less prevalent, often result in marked functional impairment and reduced quality
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 9
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
U.S. (Rottenberg et al., 2019; Disabato et al., 2021). Given this was the first study to ascertain
OWB rates after bipolar and substance use disorders, we did not have expectations for these
disorders. Relatedly, due to 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 was preregistered and full information about the pre-
registration, including information about deviations can be found at https://osf.io/vpqyx/ and in
supplementary material.
Method
This study conducted secondary analyses of the public use dataset 2012 Canadian
Community Health Survey-Mental Health (CCHS-MH) (see for more information; Gilmour,
2014; Statistics Canada, 2020), which includes a national sample (N = 25,113) of Canadians
aged 15 to 80+. 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
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 10
Mental Health Diagnoses. Diagnoses were derived from the World Health Organization
version of the Composite International Diagnostic interview 3.0 (WHO-CIDI), a structured
diagnostic interview that follows the Diagnostic and Statistical Manual of Mental Disorders,
Fourth Edition (DSM-IV) and the International Classification of Disease (ICD-10). Assessments
were conducted for lifetime and 12-month presence of major depressive episode, generalized
anxiety disorder, bipolar disorder 1, bipolar disorder 2, 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 Mental
Health Continuum – Short Form (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 alpha = 0.88).
Disability. The 12-item WHO Disability Assessment Schedule 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, where 0 means no disability, and 100 means full disability (Statistics Canada, 2013). In
the public use dataset, the maximum for this variable was 40.
Optimal Well-being (OWB) and Mental Health Outcomes. A binary OWB variable (1
= OWB; 0 = No OWB) was created, requiring three elements: 1) 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); 2) the presence of psychological well-
being, defined as exceeding the top quartile of age-gender matched norms from the overall
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 11
sample, as assessed by the MHC-SF; and 3) low disability, defined as scoring in the lower
quartile of age-gender matched norms from the overall sample, as assessed from the WHODAS
2.0 (Bruce et al., 2008). While 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. Supplement Tables 1 through 6 provide age-gender matched
norms on the MHC-SF and WHODAS 2.0.
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 1) endorsed the presence of a given lifetime diagnosis and 2) 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
(post-secondary degree; no post-secondary degree), marital status, and race (white; nonwhite)
variables were obtained from the public use dataset.
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 those diagnosed with depression or generalized anxiety disorder.
These variables were collapsed and categorized to help with interpretability (“less than 1 year,”
“1-2 years,” “2-5 years,” “5+ years”).
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 12
Perceived General Health. Participants reported their perceived overall health status on
a 5-point scale: poor, fair, good, very good, 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 0-40, with higher
scores indicating higher distress. In the overall sample, internal consistency for the K-10
composite score was good (Cronbach’s alpha = 0.86).
Perceived Mental Health Need. Participants were grouped into one of four categories
based on 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 level, and extreme values. A set of 500 replicate bootstrap weights allow accurate
confidence intervals to be calculated by accounting for clustering in the multi-stage 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%, while
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 = 0.15), female gender (r = 0.04),
and disability (r = 0.02), education (r = -0.06), non-white race (r = -0.02), and number of mental
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 13
health diagnoses (r = -0.05), ps < 0.001. Given the large remaining sample (N = 23,491),
analyses implemented listwise deletion.
Analysis Plan
Analyses were conducted using SPSS 26 (IBM Corp., Armonk, N.Y., USA). After
ascertaining the prevalence of OWB and other mental health outcomes, we reported descriptive
statistics on characteristics of diagnostic subsamples. We calculated 95% confidence intervals 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 cut-off scores influenced rates. These analyses revealed that our a
priori OWB criteria (e.g., quartile cut-offs) resulted in stark differences in the groups selected
when compared with looser criteria (e.g., tertial cut-offs; Keyes, 2002; Fuller-Thompson, 2016),
suggesting that OWB criteria select a different group. We present these sensitivity analyses in
Supplement Table 7.
In the total sample, we computed adjusted odds ratios (AOR) to determine whether each
lifetime disorder, and the total number of disorders, influences 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. Since interaction analyses in the
diagnostic subsample were underpowered, we used the full sample. It should be noted 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. Chi square tests of
independence tested how people with OWB compared to people without OWB on the following
variables: perceived health, perceived life satisfaction, and perceived mental health needs. An
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 14
ANOVA compared 30-day psychological distress (i.e., K-10) among people with and without
OWB.
A threshold p-value of 0.05 (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 to Chen et al.’s (2010) recommendations to guide effect size
interpretations of odds ratios (ORs) in epidemiological datasets. Specifically, Chen et al. (2010)
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, which are comparable to Cohen’s D interpretations.
For inverse relationships, ORs of 0.66, 0.36, and 0.21 represent small, medium, and large effect
sizes, respectively. To help contextualize findings, an OR of 1.52 means there is 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 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 2 disorder (0.6%),
substance use disorder (8.7%), suicidal ideation (8.2%), and “any disorder” (33.1%) (see Table 7
in the Supplementary Material for SEs). Table 8 in the Supplementary Material provides
demographic information for these conditions.
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 disorder 2) to 77.4% (“other”
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 15
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 < 0.05), except between bipolar I
(3.3%), and bipolar II (3.2%) (diff = 0.13, 95% CI: 0.02, 0.20, p > 0.05). Table 9 in the
Supplementary Material provides 95% confidence intervals for the difference scores in OWB.
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
Supplement Table 10). OWB was more common among those who did not report a history of
bipolar 1 history (AOR = 7.47, 95% CI: 7.15, 7.81), suicidal ideation (AOR = 3.74, 95% CI:
3.72, 3.76), or bipolar 2 (AOR = 3.01, 95% CI: 2.86, 3.16). Likewise, OWB was more common
among those who did not report a history of generalized anxiety disorder (AOR = 2.18, 95% CI:
2.16, 2.19), depression (AOR = 1.62, 95% CI: 1.61, 1.64), or “any mental disorder” (AOR =
1.58, 95% CI: 1.57, 1.59). Put otherwise, the odds of someone without a history of a “any mental
disorder” reaching OWB is 0.18:1, while the odds of someone with a history of a “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 (AOR = 1.34, 95% CI: 1.33, 1.35). However, when
segmenting substance use disorders in the model, the absence of a history of cannabis use abuse/
dependence (AOR = 1.89; 95% CI: 1.88, 1.90) and “other” drug abuse/dependence (AOR = 1.95;
95% CI: 1.92, 1.98) significantly predicted OWB with small effect sizes, while the absence of
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 16
alcohol abuse/dependence (AOR = 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 non-disordered participants, a logistic regression indicated
that white race (AOR = 0.76, 95% CI: 0.76, 0.76) and not having a post-secondary education
(AOR = 0.94, 95% CI: 0.94, 0.94) were associated with lower observed rates of OWB, and male
gender (AOR = 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 to those earning $0 to
$20,000, each move upward in income bracket was associated with a small to medium increase
in rates of OWB: bracket $20,000 - $39,999 (AOR = 2.23, 95% CI: 2.20, 2.26), bracket $40,000
- $59,999 (AOR = 2.18, 95% CI: 2.15, 2.21), bracket $60,000 - $79,999 (95% CI: 2.81, 2.84),
and bracket $80,000 or more (AOR = 2.23, 95% CI: 2.22, 2.24).
In the full sample, we also explored interactions among each socio-demographic variable
in predicting OWB. We entered each socio-demographic variable and five interaction terms into
a logistic regression model (i.e., sex x race, sex x education, sex x income, race x education, and
race x income). The coefficients for the interaction terms yielded an interaction odds ratio (IOR).
To interpret the IORs (Chen, 2003), we recalculated the IOR by multiplying the linear term OR
for the first variable with the IOR term of the first and the second variable. Analyses revealed
small but significant interaction of race and education. Examination of the IORs revealed that
white participants without post-secondary education had a greater chance of achieving OWB,
compared to people who were non-white and had a post-secondary education.
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 17
Results also yielded a small race x income interaction; whereas higher income, as a main
effect, was associated with a greater probability of OWB, inspection of the IORs 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, while the odds of OWB decreased for white individuals (compared to non-white
individuals), any benefits of one race over 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. See supplement Table 12 for details.
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 (AOR = 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 (AOR = 0.65, 95% CI: 0.65, 0.66). Within depression, the odds of
OWB also increased significantly across income brackets: $20,000 - $39,999 (AOR = 2.03, 95%
CI: 1.96, 2.10), $40,000 - $59,999 (AOR = 3.35, 95% CI: 3.24, 3.45), $60,000 - $79,999 (AOR =
4.02, 95% CI: 3.89, 4.16), and $80,000 or more (AOR = 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 simply presented for
completeness.
Clinical Characteristics and Correlates of OWB
Logistic regression indicated that more lifetime mental health conditions were associated
with lower observed rates of OWB, with a small to medium effect size (AOR = 0.44, 95% CI:
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 18
0.44, 0.44). This relationship held when including demographic covariates. Specifically, those
without lifetime mental health conditions had 6 times the odds of OWB (AOR = 6.04, 95% CI:
6.00, 6.08) compared to those with multiple lifetime conditions. Those with just 1 lifetime
mental health condition had 4.20 times the odds of OWB (AOR = 4.20, 95% CI: 4.16, 4.24)
compared to those 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,
indicating 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 2 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.
Subsampled by depression and anxiety, shorter durations of severe illness episodes
predicted OWB controlling for number of lifetime mental diagnoses. Among those with a
depression history, the odds of OWB increased significantly with shorter depressive episode
durations: “less than 1 year” (AOR = 2.77, 95% CI: 2.71, 2.83), “1-2 years” (AOR = 2.52, 95%
CI: 2.46, 2.58), and “2-5 years” (AOR = 1.34, 95% CI: 1.31, 1.38).
Among those with a history of generalized anxiety disorder, there were higher odds of
OWB (AOR = 1.78, 95% CI: 1.75, 1.81 and AOR = 1.57, 95% CI: 1.54, 1.59) for those reporting
shorter illness durations of “less than 1 year” and “1-2 years,” respectively, relative to those who
reported durations of over 5 years, ps < 0.001. Surprisingly, individuals with anxiety episode
durations of “2-5 years” were less likely (AOR = 0.64, 95% CI: 0.63, 0.66) to obtain OWB
compared to those with durations “5 years or more,” p < 0.001.
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 19
A positive relationship existed between OWB and perceived health (X2 (4, N = 23,485) =
1,645,282, p < 0.001) and life satisfaction (X2 (4, N = 23,374) = 1,756,028, p < 0.001). 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”.
Further, individuals with OWB (95%) more commonly reported having “no needs” for mental
health care compared to individuals without OWB (79%), X2 (3, N = 23,374) = 697,321, p <
0.001. Finally, an ANOVA revealed that OWB individuals (M = 2.14; SD = 2.47) reported
significantly lower distress over the previous 30 days compared to those without OWB (M =
6.16; SD = 5.71), with large effects, F(1, 26,342,457) = 2,332,063, p <0.001, η² = 0.27. Overall,
individuals with OWB reported overall better health, higher life satisfaction, less needs for
mental health care, and less psychological distress compared to 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 (Rottenberg et al., 2018; Rottenberg et al., 2019; Disabato et al.,
2021). 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 dataset, a history of a mental health condition significantly decreased the
likelihood of attaining OWB, reducing the probability by 2.5 to 7 times. 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
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 20
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, with 95% of individuals with OWB status
reporting “no need” for mental health care compared to 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 2 (3.3%), and bipolar 1 (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 (Rottenberg et al., 2019; Disabato et al., 2021). Similarly, the current study
observed that OWB rates after generalized anxiety disorder was 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, while a depression diagnosis only requires symptoms for 2 weeks or more (American
Psychiatric Association, 2013). However, this rationale is challenged by the comparatively high
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 21
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 impacts long-term well-being, as 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 where 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 (Scott et al., 2013; Cuellar, Johnson, &
Winters, 2005). 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, while mania or hypomania symptoms are inconsistently associated with
functioning (Simon et al., 2007; Hacimusalar & Doğan, 2019). 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 1.6 times. While these findings suggest that the
presence of bipolar 1 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
to people with multiple lifetime conditions, a single lifetime condition increased the odds of
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 22
OWB by 4.2 times and having no lifetime conditions increased the odds of OWB by 6 times.
Since 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
sub-clinical symptoms (Treur & Tohen, 2010; Judd et al., 2008), 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 to episodes “less than 2 years.” These findings highlight
the importance of earlier interventions to help facilitate long-term well-being among people with
mental health diagnoses.
Our study suggests demographic correlates of OWB vary within specific disorders.
Overall, the strongest demographic predictor was household income, where the odds of OWB
increased across income bracket; these relationships were stronger within the depression and
substance use disorder subsamples. That a resource variable like 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 dataset relied on retrospective, lifetime and 12-month
diagnoses. Less severe disorders may not be recalled during retrospective assessments (Moffitt et
al., 2010; Streiner, Patten, Anthony, & Cairney, 2009), which may underestimate observed OWB
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 23
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 that co-morbid
psychopathology impairs chances for OWB. Another caveat regards our approach for dealing
with interpreting statistically significant findings in this epidemiological dataset. To reduce Type
1 errors and identify effects with more practical application, we applied Bonferroni corrections
and prioritized interpreting effect sizes of odds ratios (Chen et al., 2010). Other approaches, like
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 since 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 like bipolar disorders. We also conceptualized OWB using a
theoretically and methodologically rigorous approach, that used age-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 cut-offs (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, which, can be easily
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 24
interpreted by patients and clinicians. We invite future researchers to compare different
operationalizations of OWB (as we have, see for example, Disabato et al., 2021). Lastly, 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, if
replicated, challenge public stereotypes that these conditions are overwhelmingly chronic,
intractable, and preclude long-term well-being (Devendorf, Bender, & Rottenberg, 2020; Stacy
& Rosenheck, 2019). Based on these data, 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). Additionally, 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.
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 25
References
Alegría, A. A., Hasin, D. S., Nunes, E. V., Liu, S. M., Davies, C., Grant, B. F., & Blanco, C.
(2010). Comorbidity of generalized anxiety disorder and substance use disorders: Results
from the National Epidemiologic Survey on Alcohol and Related Conditions. The
Journal of Clinical Psychiatry, 71(9), 1187–1195.
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders
(DSM-5®). American Psychiatric Pub.
Angst, J. (2008). Bipolar disorder-methodological problems and future perspectives. Dialogues
in Clinical Neuroscience, 10(2), 129–139.
Bonanno, G. A. (2004). Loss, trauma, and human resilience: have we underestimated the human
capacity to thrive after extremely aversive events?. American Psychologist, 59(1), 20.
Battle, C. L., Uebelacker, L., Friedman, M. A., Cardemil, E. V., Beevers, C. G., & Miller, I. W.
(2010). Treatment goals of depressed outpatients: A qualitative investigation of goals
identified by participants in a depression treatment trial. Journal of Psychiatric
Practice, 16(6), 425–430.
Behar, E., DiMarco, I. D., Hekler, E. B., Mohlman, J., & Staples, A. M. (2009). Current
theoretical models of generalized anxiety disorder (GAD): Conceptual review and
treatment implications. Journal of Anxiety Disorders, 23(8), 1011–1023.
Bentley, K. H., Cassiello-Robbins, C. F., Vittorio, L., Sauer-Zavala, S., & Barlow, D. H. (2015).
The association between nonsuicidal self-injury and the emotional disorders: A meta-
analytic review. Clinical Psychology Review, 37, 72-88.
Bruce, S. E., Yonkers, K. A., Otto, M. W., Eisen, J. L., Weisberg, R. B., Pagano, M., ... & Keller,
M. B. (2005). Influence of psychiatric comorbidity on recovery and recurrence in
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 26
generalized anxiety disorder, social phobia, and panic disorder: A 12-year prospective
study. American Journal of Psychiatry, 162(6), 1179–1187.
Capaldi, C. A., Passmore, H. A., Nisbet, E. K., Zelenski, J. M., & Dopko, R. L. (2015).
Flourishing in nature: A review of the benefits of connecting with nature and its
application as a wellbeing intervention. International Journal of Wellbeing, 5(4).
Caspi, A., Houts, R. M., Ambler, A., Danese, A., Elliott, M. L., Hariri, A., ... & Rasmussen, L. J.
H. (2020). Longitudinal assessment of mental health disorders and comorbidities cross 4
decades among participants in the Dunedin birth cohort study. JAMA Network
Open, 3(4), e203221–e203221.
Chevance, A., Ravaud, P., Tomlinson, A., Le Berre, C., Teufer, B., Touboul, S., ... & Tran, V. T.
(2020). Identifying outcomes for depression that matter to patients, informal caregivers,
and health-care professionals: qualitative content analysis of a large international online
survey. The Lancet Psychiatry, 7(8), 692–702.
Chen, H., Cohen, P., & Chen, S. (2010). How big is a big odds ratio? Interpreting the magnitudes
of odds ratios in epidemiological studies. Communications in Statistics—simulation and
Computation®, 39(4), 860–864.
Chen, J. J. (2003). Communicating complex information: The interpretation of statistical
interaction in multiple logistic regression analysis. American Journal of Public
Health, 93(9), 1376–1377.
Cloninger, C. R. (2006). The science of well-being: An integrated approach to mental health and
its disorders. World Psychiatry, 5(2), 71–76.
Cuellar, A. K., Johnson, S. L., & Winters, R. (2005). Distinctions between bipolar and unipolar
depression. Clinical Psychology Review, 25(3), 307–339.
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 27
Cumming, G., & Finch, S. (2005). Inference by eye: Confidence intervals and how to read
pictures of data. American Psychologist, 60(2), 170–180.
Cooke, P. J., Melchert, T. P., & Connor, K. (2016). Measuring well-being: A review of
instruments. The Counseling Psychologist, 44(5), 730-757.
Da Silva, G. T., Logan, B. R., & Klein, J. P. (2009). Methods for equivalence and noninferiority
testing. Biology of Blood and Marrow Transplantation, 15(1), 120–127.
Devendorf, A., Bender, A., & Rottenberg, J. (2020). Depression presentations, stigma, and
mental health literacy: A critical review and YouTube content analysis. Clinical
Psychology Review, 78, 101843. https://doi.org/10.1016/j.cpr.2020.101843
Diener, E., & Emmons, R. A. (1984). The independence of positive and negative affect. Journal
of Personality and Social Psychology, 47(5), 1105–1117.
Disabato, D. J., Kashdan, T. B., Doorley, J. D., Kelso, K. C., Volgenau, K. M., Devendorf, A. R.,
& Rottenberg, J. (2021). Optimal well-being in the aftermath of anxiety disorders: A 10-
year longitudinal investigation. Journal of Affective Disorders, 291, 110–117.
https://doi.org/10.1016/j.jad.2021.05.009
Fava, G. A., & Tomba, E. (2009). Increasing psychological well‐being and resilience by
psychotherapeutic methods. Journal of Personality, 77(6), 1903-1934.
Franklin, C. H. (2007). The margin of error for differences in polls. See https://abcnews. go.
com/images/PollingUnit/MOEFranklin. pdf.
Flake, J. K., & Fried, E. I. (2020). Measurement schmeasurement: Questionable measurement
practices and how to avoid them. Advances in Methods and Practices in Psychological
Science, 3(4), 456-465.
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 28
Fuller-Thomson, E., Agbeyaka, S., LaFond, D. M., & Bern-Klug, M. (2016). Flourishing after
depression: Factors associated with achieving complete mental health among those with a
history of depression. Psychiatry Research, 242, 111–120.
Gadermann, A. M., Alonso, J., Vilagut, G., Zaslavsky, A. M., & Kessler, R. C. (2012).
Comorbidity and disease burden in the National Comorbidity Survey Replication (NCS‐
R). Depression and Anxiety, 29(9), 797–806.
Gilmour, H. (2014). Positive mental health and mental illness. Statistics Canada.
Goodman, F.R., Disabato, D.J., & Kashdan, T.B. (2020). Reflections on unspoken and potential
solutions for the well-being juggernaut in positive psychology. The Journal of Positive
Psychology.
Greenberg, P. E., Fournier, A. A., Sisitsky, T., Pike, C. T., & Kessler, R. C. (2015). The
economic burden of adults with major depressive disorder in the United States (2005 and
2010). The Journal of Clinical Psychiatry, 76(2), 155–162.
Gruber, J., & Moskowitz, J. T. (2014). Positive emotion: Integrating the light sides and dark
sides. Oxford: Oxford University Press.
Hacimusalar, Y., & Doğan, E. S. (2019). Assessment of the functioning levels and related factors
in patients with bipolar disorder during remission. Archives of Neuropsychiatry, 56(3),
213–218.
Hankin, B. L., Snyder, H. R., Gulley, L. D., Schweizer, T. H., Bijttebier, P., Nelis, S., ... &
Vasey, M. W. (2016). Understanding comorbidity among internalizing problems:
Integrating latent structural models of psychopathology and risk
mechanisms. Development and Psychopathology, 28(4pt1), 987–1012.
Hendriks, T., Warren, M. A., Schotanus-Dijkstra, M., Hassankhan, A., Graafsma, T., Bohlmeijer,
E., & de Jong, J. (2019). How WEIRD are positive psychology interventions? A
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 29
bibliometric analysis of randomized controlled trials on the science of well-being. The
Journal of Positive Psychology, 14(4), 489-501.
Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the
world?. Behavioral and Brain Sciences, 33(2-3), 61–83.
Holtforth, M. G., Wyss, T., Schulte, D., Trachsel, M., & Michalak, J. (2009). Some like it
specific: The difference between treatment goals of anxious and depressed
patients. Psychology and Psychotherapy: Theory, Research and Practice, 82(3), 279–
290.
Judd, L. L., Schettler, P. J., Solomon, D. A., Maser, J. D., Coryell, W., Endicott, J., & Akiskal,
H. S. (2008). Psychosocial disability and work role function compared across the long-
term course of bipolar I, bipolar II and unipolar major depressive disorders. Journal of
Affective Disorders, 108(1–2), 49–58.
Kessler, R. C., Andrews, G., Colpe, L. J., Hiripi, E., Mroczek, D. K., Normand, S. L., ... &
Zaslavsky, A. M. (2002). Short screening scales to monitor population prevalences and
trends in non-specific psychological distress. Psychological Medicine, 32(6), 959–976.
Kessler, R. C., Chiu, W. T., Demler, O., & Walters, E. E. (2005). Prevalence, severity, and
comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey
Replication. Archives of General Psychiatry, 62(6), 617–627.
Keyes, C. L. (2002). The mental health continuum: From languishing to flourishing in
life. Journal of Health and Social Behavior, 207–222.
Keyes, C. L. (2005). Mental illness and/or mental health? Investigating axioms of the complete
state model of health. Journal of consulting and clinical psychology, 73(3), 539.
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 30
Keyes, C. L., Dhingra, S. S., & Simoes, E. J. (2010). Change in level of positive mental health as
a predictor of future risk of mental illness. American Journal of Public Health, 100(12),
2366–2371.
Krueger, R. F., & Markon, K. E. (2006). Reinterpreting comorbidity: A model-based approach to
understanding and classifying psychopathology. Annual Review of Clinical
Psychology., 2, 111–133.
Lamers, S. M., Westerhof, G. J., Bohlmeijer, E. T., ten Klooster, P. M., & Keyes, C. L. (2011).
Evaluating the psychometric properties of the mental health continuum‐short form
(MHC‐SF). Journal of Clinical Psychology, 67(1), 99–110.
Lyubomirsky, S., King, L., & Diener, E. (2005). The benefits of frequent positive affect: Does
happiness lead to success?. Psychological Bulletin, 131(6), 803-855.
May, A. M., & Klonsky, E. D. (2016). What distinguishes suicide attempters from suicide
ideators? A meta‐analysis of potential factors. Clinical Psychology: Science and
Practice, 23(1), 5–20. https://doi.org/10.1037/h0101735
Martinez‐Aran, A., Vieta, E., Torrent, C., Sanchez‐Moreno, J., Goikolea, J. M., Salamero,
M., ...
& Ayuso‐Mateos, J. L. (2007). Functional outcome in bipolar disorder: the role of
clinical and cognitive factors. Bipolar disorders, 9(1–2), 103–113.
McGrath, J. J., Lim, C. C. W., Plana-Ripoll, O., Holtz, Y., Agerbo, E., Momen, N. C., ... & De
Jonge, P. (2020). Comorbidity within mental disorders: A comprehensive analysis based
on 145 990 survey respondents from 27 countries. Epidemiology and Psychiatric
Sciences, 29.
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 31
McKnight, P. E., & Kashdan, T. B. (2009). The importance of functional impairment to mental
health outcomes: a case for reassessing our goals in depression treatment
research. Clinical Psychology Review, 29(3), 243–259.
McKnight, P. E., Monfort, S. S., Kashdan, T. B., Blalock, D. V., & Calton, J. M. (2016). Anxiety
symptoms and functional impairment: A systematic review of the correlation between the
two measures. Clinical Psychology Review, 45, 115–130.
Moffitt, T. E., Caspi, A., Taylor, A., Kokaua, J., Milne, B. J., Polanczyk, G., & Poulton, R.
(2010). How common are common mental disorders? Evidence that lifetime prevalence
rates are doubled by prospective versus retrospective ascertainment. Psychological
Medicine, 40(6), 899–909.
Moreau, D., & Wiebels, K. (in press). Assessing change in intervention research: The benefits of
composite outcomes. Advances in Methods and Practices in Psychological Science, 4(1),
2515245920931930.
Moussavi, S., Chatterji, S., Verdes, E., Tandon, A., Patel, V., & Ustun, B. (2007). Depression,
chronic diseases, and decrements in health: Results from the World Health Surveys. The
Lancet, 370(9590), 851–858.
Ng, J. Y., Ntoumanis, N., Thøgersen-Ntoumani, C., Deci, E. L., Ryan, R. M., Duda, J. L., &
Williams, G. C. (2012). Self-determination theory applied to health contexts: A meta-
analysis. Perspectives on Psychological Science, 7(4), 325–340.
Norton, E. C., Dowd, B. E., & Maciejewski, M. L. (2018). Odds ratios – current best practice
and use. JAMA, 320(1), 84-85.
Ntoumanis, N., Ng, J. Y., Prestwich, A., Quested, E., Hancox, J. E., Thøgersen-Ntoumani, C., ...
& Williams, G. C. (2021). A meta-analysis of self-determination theory-informed
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 32
intervention studies in the health domain: effects on motivation, health behavior,
physical, and psychological health. Health Psychology Review, 15(2), 214–244.
Otto, M. W., Simon, N. M., Wisniewski, S. R., Miklowitz, D. J., Kogan, J. N., Reilly-Harrington,
N. A., ... & Pollack, M. H. (2006). Prospective 12-month course of bipolar disorder in
out-patients with and without comorbid anxiety disorders. The British Journal of
Psychiatry, 189(1), 20–25.
Panaite, V., Devendorf, A. R., Kashdan, T. B., & Rottenberg, J. (2021). Daily Life Positive
Events Predict Well-Being Among Depressed Adults 10 Years Later. Clinical
Psychological Science, 9(2), 222–235. https://doi.org/10.1177/2167702620956967
Pendergast, L. L., Youngstrom, E. A., Ruan-Iu, L., & Beysolow, D. (2018). The nomogram: A
decision making tool for practitioners using multitiered systems of support. School
Psychology Review, 47(4), 345–359.
Penninx, B. W., Nolen, W. A., Lamers, F., Zitman, F. G., Smit, J. H., Spinhoven, P., ... &
Beekman, A. T. (2011). Two-year course of depressive and anxiety disorders: results
from the Netherlands Study of Depression and Anxiety (NESDA). Journal of Affective
Disorders, 133(1–2), 76–85.
Ridley, M., Rao, G., Schilbach, F., & Patel, V. (2020). Poverty, depression, and anxiety: Causal
evidence and mechanisms. Science, 370(6522).
Rottenberg, J., Devendorf, A. R., Kashdan, T. B., & Disabato, D. J. (2018). The curious neglect
of high functioning after psychopathology: The case of depression. Perspectives on
Psychological Science, 13(5), 549–566. https://doi.org/10.1177/1745691618769868
Rottenberg, J., Devendorf, A. R., Panaite, V., Disabato, D. J., & Kashdan, T. B. (2019). Optimal
well-being after major depression. Clinical Psychological Science, 7(3), 621–627. https://
doi.org/10.1177/2167702618812708
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 33
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic
motivation, social development, and well-being. American Psychologist, 55(1), 68–78.
Ryff, C. D. (1989). Happiness is everything, or is it? Explorations on the meaning of
psychological well being. Journal of Personality and Social Psychology, 57(6), 1069.
Ryff, C. D., & Keyes, C. L. M. (1995). The structure of psychological well-being
revisited. Journal of Personality and Social Psychology, 69(4), 719–727.
Sattler, J. M., & Ryan, J. J. (2009). Assessment with the WAIS-IV. Jerome M Sattler Publisher.
Scholten, W., Ten Have, M., van Geel, C., van Balkom, A., de Graaf, R., & Batelaan, N. (2021).
Recurrence of anxiety disorders and its predictors in the general
population. Psychological Medicine, 1-9.
Scott, E. M., Hermens, D. F., Naismith, S. L., Guastella, A. J., De Regt, T., White, D., ... &
Hickie, I. B. (2013). Distinguishing young people with emerging bipolar disorders from
those with unipolar depression. Journal of Affective Disorders, 144(3), 208–215.
Simon, G. E., Bauer, M. S., Ludman, E. J., Operskalski, B. H., & Unützer, J. (2007). Mood
symptoms, functional impairment, and disability in people with bipolar disorder: Specific
effects of mania and depression. The Journal of Clinical Psychiatry, 68(8), 1237–1245.
Stacy, M. A., & Rosenheck, R. (2019). The association of recovery orientation and stigmatizing
beliefs. Journal of Mental Health, 28(3), 276–281.
Statistics Canada (2017, October 25). Census in Brief: Ethnic and cultural origins of
Canadians: Portrait of a rich heritage. https://www12.statcan.gc.ca/census-recensement/
2016/dp-pd/hlt-fst/imm/index-eng.cfm
Statistics Canada. (2013). Canadian Community Health Survey (CCHS) – Mental Health. User
Guide Microdata files. https://www23.statcan.gc.ca/imdb/p2SV.pl?
Function=getSurvVariableList&Id=119789
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 34
Statistics Canada. (2020, July 31) Canada's national statistical agency. Retrieved from
https://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&SDDS=5015#a2
Streiner, D. L., Patten, S. B., Anthony, J. C., & Cairney, J. (2009). Has ‘lifetime
prevalence’reached the end of its life? An examination of the concept. International
Journal of Methods in Psychiatric Research, 18(4), 221–228.
Takayanagi, Y., Spira, A. P., Roth, K. B., Gallo, J. J., Eaton, W. W., & Mojtabai, R. (2014).
Accuracy of reports of lifetime mental and physical disorders: Results from the Baltimore
Epidemiological Catchment Area study. JAMA Psychiatry, 71(3), 273–280.
Treuer, T., & Tohen, M. (2010). Predicting the course and outcome of bipolar disorder: a
review. European Psychiatry, 25(6), 328–333.
Tong, B., Kashdan, T. B., Joiner, T., & Rottenberg, J. (2021). Future well-being among people
who attempt suicide and survive: Research recommendations. Behavior Therapy.
Üstün, T. B., Kostanjsek, N., Chatterji, S., & Rehm, J. (Eds.). (2010). Measuring health and
disability: Manual for WHO disability assessment schedule WHODAS 2.0. World Health
Organization.
Veenhoven, R. (2008). Healthy happiness: Effects of happiness on physical health and the
consequences for preventive health care. Journal of Happiness Studies, 9(3), 449-469.
Vos, T., Allen, C., Arora, M., Barber, R. M., Bhutta, Z. A., Brown, A., ... & Boufous, S. (2016).
Global, regional, and national incidence, prevalence, and years lived with disability for
310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of
Disease Study 2015. The Lancet, 388(10053), 1545–1602.
Widnall, E., Price, A., Trompetter, H., & Dunn, B. D. (2020). Routine cognitive behavioural
therapy for anxiety and depression is more effective at repairing symptoms of
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 35
psychopathology than enhancing wellbeing. Cognitive Therapy and Research, 44(1), 28-
39.
Wittchen, H. U. (2002). Generalized anxiety disorder: Prevalence, burden, and cost to
society. Depression and Anxiety, 16(4), 162–171.
Wood, A. M., & Tarrier, N. (2010). Positive clinical psychology: A new vision and strategy for
integrated research and practice. Clinical Psychology Review, 30(7), 819–829.
Youngstrom, E. A., Van Meter, A., Frazier, T. W., Hunsley, J., Prinstein, M. J., Ong, M. L., &
Youngstrom, J. K. (2017). Evidence‐based assessment as an integrative model for
applying psychological science to guide the voyage of treatment. Clinical Psychology:
Science and Practice, 24(4), 331–363.
Zimmerman, M., McGlinchey, J. B., Posternak, M. A., Friedman, M., Attiullah, N., & Boerescu,
D. (2006). How should remission from depression be defined? The depressed patient’s
perspective. American Journal of Psychiatry, 163(1), 148–150.
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 36
Table 1.
Rates for Optimal Well-being (OWB) and Prognostic Course After Lifetime Mental
Health Diagnoses in Canada
Full Sample
(N = 23,491)
12-month Clinical Course for Subsamples of
Specific Disorders
Lifetime Diagnosis Lifetime
Prevalence
12-month
Diagnosis
Diagnostic
Recovery OWB
% (SE) % (SE) % (SE) % (SE)
Total Sample - - - 18.6 (.01)
No Lifetime Any
Disorder 66.9 (.01) - - 23.9 (.01)
Lifetime Substance
Use Disorders 8.7 (.01) 21.3 (.02) 68.4 (.02) 10.2 (.01)
Alcohol Abuse or
Dependence 18.1 (.01) 18.2 (.02) 71.0 (.02) 10.9 (.01)
Cannabis Abuse
or Dependence 6.8 (.01) 20.1 (.03) 73.7 (.03) 6.3 (.02)
Drug Abuse or
Dependence
(excluding
Cannabis)
4.0 (.01) 18.3 (.04) 77.4 (.04) 4.3 (.02)
Lifetime Any
Disorder 33.1 (.01) 32.1 (.02) 58.4 (.02) 9.4 (.01)
Lifetime MDD 11.3 (.01) 43.3 (.03) 49.7 (.03) 7.1 (.01)
Lifetime GAD 8.7 (.01) 30.4 (.03) 63.8 (.03) 5.7 (.01)
Lifetime Suicidal
Ideation 8.2 (.01) 23.0 (.03) 72.7 (.03) 5.0 (.01)
Lifetime Bipolar II .6 (<.01) 68.9 (.12) 27.7 (.11) 3.3 (.05)
Lifetime Bipolar 1 .9 (<.01) 61.9 (.10) 34.9 (.10) 3.2 (.04)
Note: Estimates are based on weighted, age-gender matched norms. The N provided is based on the non-
weighted 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 OWB was
not significantly more common after BP2 than BP1, (diff = .13, 95% CI: .02, .20, p > .05).
MDD = Major Depressive Disorder; GAD = Generalized anxiety disorder.
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 37
Table 2.
Descriptive Statistics of Individuals with Optimal Well-being (OWB) after Lifetime Mental Health Diagnoses
OWB after Lifetime Disorder
No
Lifetime
Disorder
(N=733)
MDD
(n=180)
GAD
(n=128)
BP1
(n=8)
BP2
(n=3)
SUD
(n=533)
SUI
(n=109)
% (SE) % (SE) % (SE) % (SE) % (SE) % (SE) % (SE)
Sex
Male 49.9 (.02) 30.9 (.10) 40 (.13) 59.7 (.55) 70.7 (.63) 80.6 (.05) 39.0 (.15)
Female 50.1 (.02) 69.1 (.10) 60 (.13) 40.3 (.55) 29.3 (.63) 19.4 (.05) 61.0 (.15)
Race
White 68.1 (.02) 87.1 (.07) 90.4 (.08) 91.5 (.32) 100 85.7 (.05) 68.1 (.14)
Nonwhite 31.9 (.02) 12.9 (.07) 9.6 (.08) 8.5 (.32) 0 14.3 (.05) 31.9 (.14)
Language used at-home
English 71.0 (.02) 73.4 (.10) 68.4 (.13) 93.0 (.29) 100 77.7 (.05) 71.4 (.02)
French 15.7 (.02) 24.7 (.09) 31.1 (.13) 7.0 (.29) 0 16. 5 (.05) 16.5 (.02)
English and French 3.1 (.01) 1.42 (.03) .45 (.02) 0 0 2.1 (.02) 3.0 (.01)
Neither English or French 10.1 (.01) 1.5 (.03) 0 0 0 3.8 (.02) 9.1 (.01)
Education
No Post-secondary degree 31.6 (.02) 19.4 (.08) 24.1 (.12) 16 (.41) 29.8 (.63) 31.2 (.06) 28.4 (.14)
Yes Post-secondary degree 68.4 (.02) 80.6 (.08) 75.9 (.12) 84 (.41) 70.2 (.63) 68.8 (.06) 71.6 (.14)
Age
15-19 years 8.8 (.01) 1.6 (.03) 0 0 29.8 (.63) 2.5 (.02) 5.8 (.07)
20-29 years 17.8 (.02) 9.9 (.06) 18.6 (.11) 21.2 (.46) 40.9 (.63) 14.9 (.05) 14.4 (.11)
30-39 years 17.5 (.02) 12.2 (.07) 14.0 (.09) 14.5 (.40) 0 22.2 (.05) 15.7 (.11)
40-49 years 19.0 (.02) 34.5 (.10) 18.1 (.10) 15.5 (.41) 29.3 (63) 23.7 (.06) 21.1 (.12)
50-59 years 16.7 (.02) 26.9 (.10) 33.9 (.13) 48.8 (.56) 0 21.3 (.05) 22.6 (.13)
60-69 years 11.8 (.02) 12.8 (.07) 13.4 (.09) 0 0 13.4 (.04) 16.3 (.11)
70-79 years 6.3 (.01) 2.1 (.03) 2.0 (.04) 0 0 1.8 (.02) 4.2 (.06)
80+ years 2.1 (.01) 0 0 0 0 .3 (.01) 0
Marital Status
Single 25.2 (.02) 16.2 (.08) 17.0 (.10) 16.0 (.41) 70.3 (.63) 18.0 (.05) 23.4 (.13)
Married 56.4 (.02) 60.3 (.10) 46.4 (.11) 0 29.3 .63) 58.7 (.06) 47.4 (.15)
Common Law 8.6 (.01) 9.5 (.06) 21.3 (.05) 54.8 (.56) 0 16.5 (.05) 7.2 (.08)
Widowed 2.9 (.01) 5.9 (.05) 4.1 (.05) 0 0 1.1 (.01) 1.6 (.04)
Divorced/separated 6.8 (.01) 8.1 (.06) 11.2 (.09) 29.2 (.51) 0 5.7 (.05) 20.5 (.12)
Household Income
No Income to < $20,000 2.5 (.01) 1.8 (.03) 4.1 (.05) 0 0 1.7 (.02) 3.4 (.06)
$20,000 - $39,999 8.8 (.01) 6.7 (.05) 5.9 (.06) 14.5 (.40) 0 7.2 (.03) 21.3 (.12)
$40,000 - $59,999 16.2 (.02) 16.5 (.08) 13.4 (.09) 24.7 (.50) 40.9 (.68) 10.6 (.04) 10.9 (.09)
$60,000 - $79,999 20.6 (.02) 21.1 (.09) 13.9 (.09) 37.1 (.55) 29.3 (.63) 11.5 (.04) 18.3 (.12)
$80,000 - More 51.9 (.02) 53.7 (.11) 62.7 (.13) 23.6 (.48) 29.4 (.63) 69.0 (.06) 46.1 (.15)
Note: Ns 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
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 38
Table 3.
Logistic Regression of Demographics Predicting Optimal Well-being (OWB) after Lifetime Mental Health Diagnoses
No Lifetime
Disorder
(N=14,495)
MDD
(n=2,966)
GAD
(n=2,360)
BP1
(n=210)
BP2
(n=149)
SUD
(n=5,592)
SUI
(n=2,160)
AOR (95%
CI) AOR (95% CI) AOR (95%
CI)
AOR (95%
CI)
AOR (95%
CI)
AOR (95%
CI)
AOR (95%
CI)
Constant .23 .02 .05 .002 1.36 .08 .06
Male (1) 1.18 (1.18-
1.89) .83 (.83-.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) .88 (.87-.89)
White (1) .75 (.75-.75) 1.24 (1.22-1.26) 1.48 (1.45-
1.51)
4.30 (3.96-
4.67) NA .80 (.79-.80) .50 (.49-.50)
No Post-sec (1) .95 (.95-.95) .65 (.65-.66) .83 (.82-.84) .33 (.31-.35) .19 (.18-.20) 1.01 (1.00-
1.02) .75 (.74-.76)
Age .96 (.96-.96) 1.13 (1.13-1.13) 1.01 (1.01-
1.01)
1.29 (1.27-
1.31) .40 (.39-.41) 1.03 (1.03-
1.03)
1.13 (1.12-
1.13)
No Income to <
$20,000(REF)
$20,000 -
$39,999
1.29 (1.28-
1.30) 2.03 (1.96-2.10) .58 (.56-.60) NA NA 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) .94 (.91-.97) NA NA 1.90 (1.86-
1.94)
1.02
(.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) NA NA 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) NA NA 3.73 (3.66-
3.81)
1.98 (1.91-
2.05)
# of Lifetime
Mental Health
Diagnoses
- .70 (.70-.70) .72 (.72-.73) 1.33 (1.29-
1.36) .79 (.75-.83) .52 (.52-.53) .39 (.39-.40)
Note: All predictors were significant at a p < .001 value, two-tailed test.
AOR = Adjusted Odds Ratio; MDD = Major Depressive Disorder; GAD = Generalized Anxiety Disorder; BP = Bipolar Disorder;
SUD = Substance Use Disorder; SUI = Suicidal Ideation; No Post-sec = No Post-secondary degree; NA = convergence could not
be reached when variables were included in the model, which results in unreliable estimates, likely due to the low prevalence of
OWB after BP.
OPTIMAL WELL-BEING AFTER PSYCHOPATHOLOGY 39
Table 4.
Clinical Characteristics of Individuals with Optimal Well-being (OWB) After Lifetime Mental Health Diagnoses
OWB after Lifetime Disorder
No
Lifetime
Disorder
(N=733)
MDD
(n=180)
GAD
(n=128)
BP1
(n=8)
BP2
(n=3)
SUD
(n=533)
SUI
(n=109)
% (SE) % (SE) % (SE) % (SE) % (SE) % (SE) % (SE)
Comorbid Lifetime Disorders
MDD 0 100 43.7 (.13) 80.9 (.44) 100 8.5 (.04) 1.2 (.03)
GAD 0 27.5 (.10) 100 50.8 (.56) 70.2 (.63) 4.5 (.03) 11.4 (.10)
Bipolar 1 0 2.9 (.04) 2.9 (.04) 100 100 .9 (.01) 0
Bipolar 2 0 2.4 (.03) 2.7 (.04) 100 100 .3 (.01) 0
Substance use 0 23 (.09) 19.3 (.11) 67.2 (.53) 29.8 (.63) 100 18.5 (.12)
Suicidal Ideation 99 (.02) 12.1 (.10) 0 0 3.5 (.02) 100
# of Lifetime Diagnoses
2 Diagnoses 0 31.9 (.10) 36.3 (.13) 0 0 6.9 (.04) 1.7 (.04)
3 Diagnoses 0 9.9 (.06) 12.9 (.09) 43.7 (.56) 100 2.9 (.03) .36 (.02)
4 Diagnoses 0 1.3 (.02) 2.1 (.04) 37.1 (.55) 0 .5 (.01) 0
aPerceived Need for Care
No Needs 96.4 (.01) 75.5 (.09) 75.7 (.12) 71.8 (.51) 40.9 (.68) 90.4 (.04) 81.9 (.12)
All Needs Met 3 (.01) 24.3 (.09) 23.4 (.11) 28.2 (.51) 59.1 (.68) 8.2 (.04) 9.5 (.09)
Needs Partially Met .4 (< .001) .20 (.010) 0 0 0 .6 (.01) 3.7 (.06)
Needs Not Met .3 (< .001) 0 1 (.03) 0 0 .86 (.01) 4.9 (.07)
a Perceived General Health
Poor 0 0 0 0 0 0 .11 (<.01)
Fair 2.35 (<.01) .65 (.02) 2.1 (.04) 0 0 1.3 (.01) 2.2 (.01)
Good 16.3 (.01) 15.9 (.08) 27.3 (.12) 35.1 (.54) 0 23.8 (.06) 17.0 (.02)
Very Good 39.7 (.02) 48.9 (.11) 36.1 (.13) 43.7 (.54) 70.2 (.63) 39.8 (.06) 39.7 (.02)
Excellent 41.6 (.02) 34.6 (.10) 34.5 (.13) 21.2 (.46) 29.8 (.63) 35.2 (.06) 40.9 (.02)
Satisfaction with Life
Very Dissatisfied .1 (<.01) 0 0 0 0 0 .12 (<.01)
Dissatisfied .1 (<.01) 0 0 0 0 .04 (<.01) .13 (<.01)
Neither satisfied or
dissatisfied
1.2 (.01) .1 (.01) 0 19.1 (.44) 0 .52 (.01) 1.1 (<.01).
Satisfied 37.7 (.02) 39.9 (.1) 43.3 (.13) 44.1 (.56) 0 42.1 (.06) 37.7 (.02)
Very Satisfied 61.6 (.02) 60 (.11) 56.7 (.13) 36.8 (.54) 100 57.3 (.06) 61.0 (.02)
Note: Ns provided are based on unweighted sample to help with interpretation.
a Perceived need for Problems with Emotions, Mental Health, or Use of Alcohol and Drugs.
MDD = Major Depression; GAD = Generalized Anxiety Disorder; BP = Bipolar Disorder; SUD = Substance Use Disorder;
SUI = Suicidal Ideation
... Effect sizes for t-tests were estimated using Cohen's d. Suicide attempt survivors are at an increased likelihood to report current and past mental health symptoms (e.g., depression, problematic alcohol use, suicidal thoughts; Nichter et al., 2021aNichter et al., , 2021b which are negatively associated with PWB (e.g., Devendorf et al., 2022). Thus, to account for mental health difficulties known to reduce PWB, hierarchical multiple regressions were conducted to examine the associations between suicide attempt status and PWB, controlling for past and current self-reported mental health symptoms (i.e., history of depression, history of problematic alcohol use, history of psychiatric treatment, current depression symptoms, current suicidal ideation). ...
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