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Optimal Well-Being After Psychopathology: Prevalence and Correlates



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.
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.
2Department of Psychology, MS 3F5, George Mason University, Fairfax, VA 22030
Corresponding Author: Andrew Devendorf, M.A.,
Acknowledgements: We thank the University of South Florida for providing funding for this
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
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 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: 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
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
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 (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
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
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
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 and in
supplementary material.
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
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
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”).
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
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
ANOVA compared 30-day psychological distress (i.e., K-10) among people with and without
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).
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”
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
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.
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
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:
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.
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.
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
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
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
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
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
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
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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
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
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.
Table 2.
Descriptive Statistics of Individuals with Optimal Well-being (OWB) after Lifetime Mental Health Diagnoses
OWB after Lifetime Disorder
% (SE) % (SE) % (SE) % (SE) % (SE) % (SE) % (SE)
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)
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)
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)
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
Table 3.
Logistic Regression of Demographics Predicting Optimal Well-being (OWB) after Lifetime Mental Health Diagnoses
No Lifetime
AOR (95%
CI) AOR (95% CI) AOR (95%
AOR (95%
AOR (95%
AOR (95%
AOR (95%
Constant .23 .02 .05 .002 1.36 .08 .06
Male (1) 1.18 (1.18-
1.89) .83 (.83-.84) 1.51 (1.49-
2.24 (2.13-
2.27 (2.13-
1.40 (1.40-
1.41) .88 (.87-.89)
White (1) .75 (.75-.75) 1.24 (1.22-1.26) 1.48 (1.45-
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-
1.29 (1.27-
1.31) .40 (.39-.41) 1.03 (1.03-
1.13 (1.12-
No Income to <
$20,000 -
1.29 (1.28-
1.30) 2.03 (1.96-2.10) .58 (.56-.60) NA NA 1.85 (1.81-
2.30 (2.22-
$40,000 -
1.61 (1.60-
1.62) 3.35 (3.24-3.45) .94 (.91-.97) NA NA 1.90 (1.86-
$60,000 -
2.19 (2.17-
2.20) 4.02 (3.89-4.16) 1.14 (1.10-
1.17) NA NA 2.10 (2.06-
1.71 (1.65-
$80,000 or
2.07 (2.06-
2.09) 4.72 (4.58-4.88) 2.21 (2.14-
2.27) NA NA 3.73 (3.66-
1.98 (1.91-
# of Lifetime
Mental Health
- .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.
Table 4.
Clinical Characteristics of Individuals with Optimal Well-being (OWB) After Lifetime Mental Health Diagnoses
OWB after Lifetime Disorder
% (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
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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Background Most people who survive suicide attempts neither re-attempt suicide nor die by suicide. Research on suicide attempt survivors has primarily focused on negative endpoints (e.g., increased suicide risk) rather than positive outcomes. One important outcome is psychological well-being (PWB), defined as positive functioning across emotional, intrapersonal, and interpersonal domains. We compared PWB among US military veterans with (i.e., attempt survivors) and without (i.e., non-attempters) a history of suicide attempt(s) using data from three nationally representative cohorts. Methods Each US veteran cohort (Cohort1: N = 3148; Cohort2: N = 1474; Cohort3: N = 4042) completed measures of suicidality (e.g., attempt history), character strengths (e.g., curiosity, optimism), psychological symptoms (e.g., depression), and indicators of PWB (e.g., happiness). t-Tests were conducted to examine group differences in PWB; hierarchical regressions were conducted to examine suicide attempt status as a predictor of PWB controlling for symptoms and demographics. Multivariable regressions were conducted to identify predictors of PWB among attempt survivors. Results In each cohort, reported PWB was markedly lower among suicide attempt survivors than non-attempters (ds = 0.9–1.2), even after adjusting for mental health symptoms. Individual differences in PWB were observed, with a subset of suicide attempt survivors reporting higher PWB levels than non-attempters (1.4–7.4 %). Curiosity and optimism were positively associated with PWB among suicide attempt survivors (rs = 0.60–0.78). Limitations Data were cross-sectional, limiting inferences about causation and directionality of associations. Conclusions Findings highlight diminished PWB as an important and understudied concern among veteran attempt survivors. Collectively, our findings underscore the importance of considering PWB in the research, assessment, and treatment of suicidality.
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Background Anxiety disorders frequently recur in clinical populations, but the risk of recurrence of anxiety disorders is largely unknown in the general population. In this study, recurrence of anxiety and its predictors were studied in a large cohort of the adult general population. Methods Baseline, 3-year and 6-year follow-up data were derived from the Netherlands Mental Health Survey and Incidence Study-2 (NEMESIS-2). Respondents ( N = 468) who had been in remission for at least a year prior to baseline were included. Recurrence was assessed at 3 and 6 years after baseline, using the Composite International Diagnostic Interview version 3.0. Cumulative recurrence rates were estimated using the number of years since remission of the last anxiety disorder. Furthermore, Cox regression analyses were conducted to investigate predictors of recurrence, using a broad range of putative predictors. Results The estimated cumulative recurrence rate was 2.1% at 1 year, 6.6% at 5 years, 10.6% at 10 years, and 16.2% at 20 years. Univariate regression analyses predicted a shorter time to recurrence for several variables, of which younger age at interview, parental psychopathology, neuroticism and a current depressive disorder remained significant in the, age and gender-adjusted, multivariable regression analysis. Conclusions Recurrence of anxiety disorders in the general population is common and the risk of recurrence extends over a lengthy period of time. In clinical practice, alertness to recurrence, monitoring of symptoms, and quick access to health care in case of recurrence are needed.
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Intervention research is often time- and resource-intensive, with numerous participants involved over extended periods of time. To maximize the value of intervention studies, multiple outcome measures are often included, either to ensure a diverse set of outcomes is being assessed or to refine assessments of specific outcomes. Here, we advocate for combining assessments, rather than relying on individual measures assessed separately, to better evaluate the effectiveness of interventions. Specifically, we argue that by pooling information from individual measures into a single outcome, composite scores can provide finer estimates of the underlying theoretical construct of interest while retaining important properties more sophisticated methods often forgo, such as transparency and interpretability. We describe different methods to compute, evaluate, and use composites depending on the goals, design, and data. To promote usability, we also provide a preregistration template that includes examples in the context of psychological interventions with supporting R code. Finally, we make a number of recommendations to help ensure that intervention studies are designed in a way that maximizes discoveries. A Shiny app and detailed R code accompany this article and are available at .
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We know relatively little concerning the links between the events and emotions experienced in daily life and long-term outcomes among people diagnosed with depression. Using daily diary data from the Midlife Development in the United States (MIDUS), we examined how positive daily life events and emotions influence long-term (10 years later) depression severity and well-being. Participants met criteria for major depressive disorder (MDD; n=121) or reported no depression (n=839) over the past 12-months. Participants reported positive events, socializing activities, and negative and positive affect (NA, PA) for 8 consecutive days. Relative to non-depressed adults, depressed adults reported fewer positive events (fewer positive interactions, spending less time with others), lower PA, and higher NA. Among initially depressed adults, higher baseline well-being was related to higher daily PA, lower NA, and fewer days of low reported social time; higher daily PA and positive interactions predicted higher well-being 10 years later (N=77). Variations in day-to-day events and emotions among people with depression may presage psychological functioning years later.
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Aims Epidemiological studies indicate that individuals with one type of mental disorder have an increased risk of subsequently developing other types of mental disorders. This study aimed to undertake a comprehensive analysis of pair-wise lifetime comorbidity across a range of common mental disorders based on a diverse range of population-based surveys. Methods The WHO World Mental Health (WMH) surveys assessed 145 990 adult respondents from 27 countries. Based on retrospectively-reported age-of-onset for 24 DSM-IV mental disorders, associations were examined between all 548 logically possible temporally-ordered disorder pairs. Overall and time-dependent hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using Cox proportional hazards models. Absolute risks were estimated using the product-limit method. Estimates were generated separately for men and women. Results Each prior lifetime mental disorder was associated with an increased risk of subsequent first onset of each other disorder. The median HR was 12.1 (mean = 14.4; range 5.2–110.8, interquartile range = 6.0–19.4). The HRs were most prominent between closely-related mental disorder types and in the first 1–2 years after the onset of the prior disorder. Although HRs declined with time since prior disorder, significantly elevated risk of subsequent comorbidity persisted for at least 15 years. Appreciable absolute risks of secondary disorders were found over time for many pairs. Conclusions Survey data from a range of sites confirms that comorbidity between mental disorders is common. Understanding the risks of temporally secondary disorders may help design practical programs for primary prevention of secondary disorders.
Background : Although preliminary research has explored the possibility of optimal well-being after depression, it is unclear how rates compare to anxiety. Using Generalized Anxiety Disorder (GAD) and Panic Disorder (PD) as exemplars of anxiety, we tested the rates of optimal well-being one decade after being diagnosed with an anxiety disorder. Based on reward deficits in depression, we pre-registered our primary hypothesis that optimal well-being would be more prevalent after anxiety than depression as well as tested two exploratory hypotheses. Method : We used data from the Midlife in the United States (MIDUS) study, which contains a nationally representative sample across two waves, 10 years apart. To reach optimal well-being, participants needed to have no symptoms of GAD, PD, or major depressive disorder (MDD) at the 10 year follow-up and exceed cut-offs across nine dimensions of well-being. Results : The results failed to support our primary hypothesis. Follow-up optimal well-being rates were highest for adults previously diagnosed with MDD (8.7%), then PD (6.1%), and finally GAD (0%). Exploratory analyses revealed optimal well-being was approximately twice as prevalent in people without anxiety or depression at baseline and provided partial support for baseline well-being predicting optimal well-being after anxiety. Results were largely replicated across different classifications of optimal well-being. Limitations : Findings are limited by the somewhat unique measurement of anxiety in the MIDUS sample as well as the relatively high rate of missing data. Conclusions : We discuss possible explanations for less prevalent optimal well-being after anxiety vs. depression and the long-term positivity deficits from GAD.
Over 48,000 people died by suicide in 2018 in the United States, and more than 25 times that number attempted suicide. Research on suicide has focused much more on risk factors and adverse outcomes than on protective factors and more healthy functioning. Consequently, little is known regarding relatively positive long-term psychological adaptation among people who attempt suicide and survive. We recommend inquiry into the phenomenon of long-term well-being after non-fatal suicide attempts, and we explain how this inquiry complements traditional risk research by (a) providing a more comprehensive understanding of the sequelae of suicide attempts, (b) identifying protective factors for potential use in interventions and prevention, and (c) contributing to knowledge and public education that reduces the stigma associated with suicide-related behaviors.
In this article, we define questionable measurement practices (QMPs) as decisions researchers make that raise doubts about the validity of the measures, and ultimately the validity of study conclusions. Doubts arise for a host of reasons, including a lack of transparency, ignorance, negligence, or misrepresentation of the evidence. We describe the scope of the problem and focus on how transparency is a part of the solution. A lack of measurement transparency makes it impossible to evaluate potential threats to internal, external, statistical-conclusion, and construct validity. We demonstrate that psychology is plagued by a measurement schmeasurement attitude: QMPs are common, hide a stunning source of researcher degrees of freedom, and pose a serious threat to cumulative psychological science, but are largely ignored. We address these challenges by providing a set of questions that researchers and consumers of scientific research can consider to identify and avoid QMPs. Transparent answers to these measurement questions promote rigorous research, allow for thorough evaluations of a study’s inferences, and are necessary for meaningful replication studies.
Why are people who live in poverty disproportionately affected by mental illness? We review the interdisciplinary evidence of the bidirectional causal relationship between poverty and common mental illnesses—depression and anxiety—and the underlying mechanisms. Research shows that mental illness reduces employment and therefore income, and that psychological interventions generate economic gains. Similarly, negative economic shocks cause mental illness, and antipoverty programs such as cash transfers improve mental health. A crucial step toward the design of effective policies is to better understand the mechanisms underlying these causal effects.
In an earlier paper (Goodman et al., 2018), we found that two models of subjective well-being demonstrated substantial overlap, with correlations between .85-.98. We concluded that these two models do not capture distinct types of well-being – a conclusion consistent with a growing list of studies that have found high correlations between various models of well-being. In response to our work, the developer of one well-being model wrote a commentary offering an alternative conclusion (Seligman, 2018). In this paper, we continue this important discussion by delineating areas of disagreement and common ground. We present our new hierarchical framework of well-being and illustrate how it can resolve long-standing points of contention in well-being measurement.