Introduction

Suicide is a significant public health crisis in the U.S. From 1999 to 2019, suicide rates among adults aged 45–64 in the U.S. increased by 60%, from 6.0 to 9.6 cases per 100 000 population in females and 43.8% from 20.8 to 29.9 in males1. Early intervention and suicide prevention programs are crucial to reduce the risks of suicide2,3. Existing suicide prevention efforts are dominated by risk reduction strategies4,5. However, meta-analyses have found that common risk factors, including sociodemographic characteristics, psychological factors, physical health, and social relationships, yield limited predictive power for fatal and nonfatal suicidal behavior6,7. Besides, suicide risk factors are typically identified through association studies and do not fully account for potential confounding6.

With the increasing availability and acceptance of integrative health practices, recent literature has suggested strength-based positive psychological interventions as a promising new approach to preventing suicide8,9,10,11. These well-being-focused suicide prevention programs can enhance social determinants of health (SDoH) via early intervention among high-risk populations, thus reducing individual and structural burdens of suicidal behaviors.

Flourishing reflects a comprehensive assessment of well-being, with multiple complementary frameworks currently being the subject of research12,13,14,15. In this study, we focus on Keyes’ framework15,16, which combines hedonic and eudaimonic well-being (Table 1). The hedonic framework considers emotional well-being (e.g., happiness, life satisfaction). The eudaimonic framework addresses psychological well-being (e.g., mastery of life, personal growth) and social well-being (e.g., social integration, social cohesion)13,14,15. Flourishing conceptually overlaps with mental well-being and can contribute to a novel approach to reducing suicidal ideation. Recently, human flourishing has been suggested as a conceptual touchstone for prevention-related priorities and objective endpoints in the 2020s17. Previous studies have found flourishing associated with a decreased risk of suicidal ideation18,19. However, we are unaware of any studies that have assessed the causal effect of flourishing on suicidal ideation.

Table 1 Measures of primary exposure (Flourishing Subscales and Domains), primary outcome and instrumental variables.
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This study is the first to examine the causal effect of flourishing on suicidal ideation. Our design is strengthened by the ability to construct a linked, nationally representative, longitudinal dataset where flourishing was measured temporally before suicidal ideation. To estimate the causal association between flourishing and suicidal ideation4,20,21, we conduct instrumental variables (IV) analyses22,23,24. IV is a technique used in health economics, psychiatry, and other areas of medicine to remove parameter bias arising from self-selection, random measurement error, and reverse causation (also called simultaneity bias) when analyzing observational data25,26.

IV analysis requires the identification of instruments: exogenous variables that influence the factor of interest (primary exposures) but have no direct influence on the primary outcome, conditional on included covariates. We use two valid instruments to determine the causal effect of flourishing on suicidal ideation. These instruments are chosen based on theoretical considerations. The stress-diathesis theory implies that suicidal ideation is due to distal factors (diathesis) from early life and proximal factors from recent stressors27. We thus chose two instruments: (1) adverse childhood experiences (ACEs) as a diathesis factor measuring childhood trauma that has been demonstrated to have epigenetic effects28,29,30,31; and (2) daily discrimination as a stressor factor measuring perceptions of discrimination32. Figure 1 shows a directed acyclic graph (DAG) and temporal ordering of primary exposures to outcomes. The key assumption is that both ACEs and daily discrimination are exogenous and should only affect suicidal ideation through their inhibitory impacts on flourishing, conditional on covariates.

Figure 1
figure 1

Directed Acyclic Graph.The relationship between flourishing, X, and suicidal ideation, Y, can be estimated using our set of conditional instruments (ACEs, daily discrimination), Z, that are valid conditional on included covariates, W. Y (suicidal ideation) was measured after X (flourishing). W includes severe psychological distress, depression, anxiety, chronic pain, a set of five inflammatory markers, substance use, binge drinking, health status, personality factors, health insurance, and socioeconomic factors (age, sex, race/ethnicity, education, marital status, household income adjusted for household size). The function of Z, conditional on W, in this model is to remove the confounding effects of U (which is a set of unobserved confounders in the error term of the regression analysis) regarding the effect of X on Y. In other words, all these variables in W are included in closing off backdoor paths (non-causal paths) between X and Y, or in econometric terms, to remove any correlation between the set of instruments and the error term.

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Results

Among 1619 participants, 850 (5.5%) were women, and 769 (47.5%) were men (mean age, 53.4, standard deviation (SD) 12.7 years). Table 2 summarizes the baseline characteristics of our study cohort. 202 participants (12.6%) endorsed suicidal ideation. High-level flourishing (binary) was reported by 486 participants (30%), the mean (SD) of daily discrimination was 12.9 (4.6), and the mean (SD) of ACEs was 3.5 (1.9).

Table 2 Characteristics of the participants Included in the study sample (N = 1619)a.
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Standard overidentification tests were performed using the two-stage limited information maximum likelihood (2SLIML) model. Hansen’s J statistic (a test of the joint null hypothesis that the instruments are uncorrelated with the error term and correctly excluded from the estimated equation, assuming at least one instrument is valid) showed that the null hypothesis could not be rejected in any 2SLIML specification.

The strength of the set of instruments could only be tested using the 2SLIML model. The Montiel-Olea-Pflueger weak instrument test33, a test that is robust to both heteroskedasticity and serial correlation, yielded an effective F-statistic of 15.52, greater than the relevant critical value of 8.38, indicating less than 5% worst-case bias. Hansen’s J \({(\chi }^{2}=0.03, p=0.87)\) failed to reject the hypothesis that no instrument was correlated with the error term, given that the other instrument is valid. Finally, an endogeneity test \({(\chi }^{2}=8.69, p=0.003)\) rejected the hypothesis that flourishing is exogenous. See Table S1 in the Supplementary.

The effect of achieving the standard binary definition of flourishing using the bivariate probit model is a 18.6% reduction (95% CI: 8.0, 29.3) in binary suicidal ideation (Table 3, Table S2 in Supplementary). This is not statistically different from the 44.7 percentage point reduction (95% CI: 11.5, 77.8) in binary suicidal ideation when the 2SLIML model is used.

Table 3 Effect of Flourishing on Suicidal Ideationa.
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Our sensitivity analyses yielded additional findings. We find that the hedonic aspect, while important conceptually, does not make a difference in terms of reducing binary suicidal ideation when using a binary flourishing variable. This may be a special case only relevant to suicidal ideation that may not apply in other applications. The relevant statistics for the 2SLIML model that uses a binary flourishing variable that omits the hedonic scales (these statistics are not available for bivariate probit models) are as follows: Montiel-Olea-Pflueger Effective F 15.46 > 7.7 critical value; Hansen’s J \({\chi }^{2}=0.36,\) p = 0.55; endogeneity \({\chi }^{2}=7.91,\) p = 0.005). See Table S1 in the Supplementary. As shown in Table 3, there is no statistical difference in the results when this is done.

In addition, as shown in Table 3, the continuous version of flourishing yields a similarly-size parameter, whether or not the hedonic aspects are included and whether or not 2SLIML or instrumental variables probit is used as an estimator. The relevant test statistics for the 2SLIML are as follows (this set of statistics is not available for the instrumental probit model): continuous flourishing (hedonic and eudaimonic): Montiel-Olea-Pflueger Effective F 16.83 > 5.14 critical value; Hansen’s J \({\chi }^{2}=0.39,\) p = 0.53; endogeneity \({\chi }^{2}=6.68,\) p = 0.1; continuous flourishing (eudaimonic only): Montiel-Olea-Pflueger Effective F 16.04 > 4.97 critical value; Hansen’s J \({\chi }^{2}=0.60,\) p = 0.44; endogeneity \({\chi }^{2}=6.61,\) p = 0.01.

Further down in Table 3, the binary version of the flourishing combined with the z-score of suicidal ideation yields similarly sized parameters, whether or not the hedonic aspects are included, with suicidal ideation being reduced 0.959 standard deviations (95% CI: 0.079, 1.840) when flourishing is present (Montiel-Olea-Pflueger Effective F 15.53 > 8.38 critical value; Hansen’s J \({\chi }^{2}=0.45,\) p = 0.50; endogeneity \({\chi }^{2}=5.31,\) p = 0.02). The corresponding parameters when only the eudaimonic aspects of flourishing are included is 0.928 (95% CI: 0.089, 1.767) (Montiel-Olea-Pflueger Effective F 15.55 > 7.67 critical value; Hansen’s J \({\chi }^{2}=0.002,\) p = 0.96; endogeneity \({\chi }^{2}=5.44,\) p = 0.02). See Supplementary Table S5.

Finally, in Table 3, the z-score version of flourishing combined with the z-score of suicidal ideation yields similarly sized parameters, whether or not the hedonic aspects are included, with the reduction being approximately half a standard deviation when flourishing increases by one standard deviation: − 0.518 (95% CI: − 0.968, − 0.069) (Montiel-Olea-Pflueger Effective F 16.83 > 5.09 critical value; Hansen’s J \({\chi }^{2}=0.000,\) P = 0.99; endogeneity \({\chi }^{2}=4.05,\) p = 0.04). The corresponding parameter when only the eudaimonic aspects of flourishing are included is − 0.523 (95% CI: − 0.984, − 0.062) (Montiel-Olea-Pflueger Effective F 16.04 > 4.92 critical value; Hansen’s J \({\chi }^{2}=0.01,\) p = 0.92; endogeneity \({\chi }^{2}=4.07,\) p = 0.04). See Table S6 in the Supplementary. All of the above models correct for measurement error and omitted variable bias34,35,36.

Discussion

This is the first study to clarify the nature of the association between flourishing and suicidal ideation. Using the IV approach, we corrected for bias in the estimated parameters of flourishing due to omitted variables or measurement error35. Reverse causation was ruled out by the temporal ordering of the data (suicidal ideation was measured after flourishing). Our findings demonstrated the negative associations of ACEs and discrimination with flourishing37,38,39, as well as the effect of flourishing on reducing suicidal ideation18,19. Results are larger in magnitude but consistent with previous studies, most of which used cross-sectional designs. Flourishing inhibits suicidal ideation both when defined as a threshold to achieve and as a continuous set of measures to improve. Flourishing also need not include hedonic measures, at least in this application. 

The first major strength of our investigation is the use of instrumental variables analysis to account for unmeasured confounding variables40,41. We determined that ACEs and daily discrimination are negatively correlated with flourishing at approximately the same magnitudes (when z-scores are used), suggesting that stress and diathesis factors damage flourishing similarly. Emerging literature, based on the cross-sectional National Survey of Children’s Health, has revealed the negative impact of ACEs on childhood flourishing42,43. Similarly, others have shown an association between daily discrimination and flourishing17. We extended such dose–response relationships in the context of the U.S. midlife population. Existing positive psychology and human flourishing theories can explain our findings. People who are flourishing will thrive amid adversity by maximizing their potential by changing abilities and limitations14, which reduces the risks of suicidal ideation. By contrast, those who lack purpose in life (psychological aspect of flourishing) are more likely to consider suicide when exposed to childhood trauma or daily discrimination14.

The causal effect of flourishing on reducing suicidal ideation suggests flourishing can serve as a target of suicide prevention, shifting the paradigm from the traditional notion of risk reduction towards a more holistic approach—focusing on wellbeing and social determinants of health (SDoH) including flourishing, especially by enhancing social networks and social connectedness17. Our second major contribution is to parse the key constructs of flourishing to those that are modifiable and can reduce suicidal ideation via clinical or population-level interventions. Using multiple flourishing measures, we found that the hedonic aspect of flourishing does not add additional magnitude to the measured relationship compared with the situation when we only included the eudaimonic aspects of flourishing. While this may not be the case for other outcomes, this is nevertheless an important issue, as the eudaimonic aspects of flourishing are more modifiable and can be taught through direct interventions (aimed at improving psychological and social well-being measured in our study). Improving the eudaimonic aspects of flourishing may improve the hedonic aspects of flourishing44. Evidence-based positive psychology interventions (focusing on systematically promoting mindfulness, patient-caregiver dyadic interpersonal interactions, and coping) have been associated with increased positive affect and reduced depression and mortality in medical populations10,12.

Flourishing-based suicide prevention can be useful and effectively implemented in both clinical care and population-based settings12,12. Within psychiatry, screening for flourishing may be a promising way to detect individuals susceptible to childhood trauma or stress that could end in suicide. Treating patients using the lens of flourishing may help clinicians provide better patient-centered care that is more holistic and more acceptable to patients42,45. Flourishing-centered suicide prevention programs may be especially relevant to suicide attempts encountered in the emergency department setting given the high suicide rates of 178 per 100 000 person-years in the first three months after discharge and the lack of care coordination with the warm handoff46,47. Family-focused interventions, as promoted by the Institute of Medicine, may be pivotal to promoting flourishing by supporting family connections37,48. Equally important is to promote flourishing among physicians and healthcare workers who may encounter patients with suicidal ideation, as improving the personal growth and environmental mastery of physicians and healthcare workers may reduce their risks of burnout49.

At the population level, flourishing-based suicide prevention could be achieved effectively with evidence-based programs and policy efforts across the sectors of health care, education, and human services17. On the one hand, it is key to implement a collaborative and holistic approach to address structural factors (e.g., policies that improve education, income, reduce racism and other forms of discrimination, etc.), create safe and connected families, and build connected communities as a foundation for intergenerational flourishing. On the other hand, our findings highlight that the broad dissemination of individual-oriented “positive psychology” interventions (e.g., strength-based asset development, emotional regulations, positive interpersonal skills)12,13 can reduce suicide via a more effective public health approach. These individually-oriented interventions can amplify the positive effects of other policies focused on structural change. Our findings emphasize the importance of flourishing in reducing suicidal ideation as an urgent consideration with potential short-term and longer-term benefits.

Limitations

Our study had limitations. First, studies of attrition and retention in MIDUS have revealed that White, female, married people with higher education and better health were more likely to stay in subsequent waves50. Given the possible disproportionate exposure to ACEs and discrimination among vulnerable populations (e.g., racial/ethnic minorities, males), this attrition bias suggests that our findings may be understated. However, the recently NIA-funded MIDAS Retention Early Warning project reinstated a substantial portion of dropouts and provided valuable opportunities to investigate the extent to which flourishing among those who dropped out would cause reduced suicidal ideation43. Second, the measure of suicidal ideation was self-reported, which may underestimate suicide risks. In addition, we only used a single question to measure suicidal ideation. Suicidal ideation is a multifaceted issue, and there are full instruments developed for this single issue, but to our knowledge, such instruments are not available in data that also contains measures of flourishing. Third, our operationalization of flourishing is relatively narrow, whereas alternative measures of flourishing cover a broader array of life domains. Future studies are encouraged to validate the findings in this study using a more diverse sample and other measures of flourishing. Lastly, although we included all common suicide risks in our data, results may be biased if some risk factors for suicide that correlate with our instruments are unmeasured.

In sum, risk-reduction suicide interventions have focused on teaching people how not to die, whereas flourishing-focused suicide prevention teaches us how to live. Our evidence showing the possible causal effect of promoting flourishing on reducing suicidal ideation provides a promising future direction for a more effective and scalable approach.

Methods

Data and participants

We obtained data from four samples of the National Survey of Midlife Development in the United States (MIDUS) study that allowed us to construct two longitudinal cohorts, which we then combined51. The first is the MIDUS 2 Project (2004–2006)52, which contains a 10-year follow-up of participants from the original MIDUS study. The second is the MIDUS 2 Biomarker Project (2004–2009)53, a longitudinal follow-up of MIDUS 2, containing biological assessments and information on suicidal ideation. The third is the MIDUS Refresher Study (2011–2014)54, designed to replenish the MIDUS cohort. The fourth is from the MIDUS Refresher Biomarker study (2012–2016)55, which paralleled the MIDUS 2 Biomarker Project and was a longitudinal follow-up of the MIDUS Refresher Study, containing biological assessments and information on suicidal ideation. All participants gave informed consent56. The response rates for the four samples were 81.0%, 39.3%, 73.0%, and 41.5%, respectively. Following previous research40,57, the current study linked these four samples (n = 1619). We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines. This study was not considered human subjects research by the Committee for the Protection of Human Subjects at the University of California, as defined by federal regulations at 45 CFR 46.102 (DHHS) and/or 21 CFR 50.3 (FDA).

Measures

The primary outcome, primary exposure, and instrumental variable described below have a clear temporal ordering (Fig. 1). Suicidal ideation is temporally preceded by flourishing, which is temporally preceded by the set of instrumental variables (all of which look back to exogenous events from the past).

Primary outcome: suicidal ideation

Suicidal ideation is a single question from the Mood and Anxiety Symptoms Questionnaire (MASQ) available in both the MIDUS 2 Biomarker and Refresher Biomarker projects58, asking the respondent, “during the past week, how much you have felt or experienced thoughts about death or suicide?” Because we want to capture any amount of suicidal ideation, we transformed this question into a binary variable (having “a little bit” up to “extreme” thoughts of suicide) vs. non-suicidal ideation cases (having “no” thoughts of suicide). Binary measures of suicidal ideation are commonly employed when measuring self-report suicidal ideation using similar measures of MASQ like PHQ-959 (e.g., dichotomizing the 4-point scale responses60,61) and have been found to be valid for clinical assessment41. For sensitivity analyses, following a recent review regarding the effectiveness of different suicide screening measures62, we transformed the original suicidal ideation question into a z-score, which is expressed in standard deviations and avoids any loss of data.

Primary exposure: flourishing

Flourishing is based on a 13-scale index measuring emotional well-being (hedonic aspect), psychological well-being, and social well-being (eudaimonic aspects) (Table 1)15.

Emotional well-being contains two subscales. These include a 6-item measure of positive affect, with each item being measured on a 5-point scale (1 = none of the time, 2 = a little of the time, 3 = some of the time, 4 = most of the time, 5 = all of the time), and a single life satisfaction item measured on a 10-point scale (1 = worst possible life overall these days to 10 = best possible life overall these days).

Psychological well-being includes 6 subscales. Each item is coded on a 7-point scale (1 = strongly disagree to 7 = strongly agree). Items are reverse coded as indicated in Table 1.

Social well-being includes 5 subscales, one with 2 items and the remaining each having 3 items. Each item is scored on a 7-point scale and coded in the same way as the items for psychological well-being. Items are reverse coded as indicated in Table 1.

Keyes’ binary measure of flourishing (high-level flourishing) is constructed using the following algorithm: for each of the 13 scales described above, we computed binary measures, where cut-points were applied to code the upper third of the distribution of each scale as one, and the lower two-thirds as zero. Flourishing was then indicated when six of the 11 psychological and social well-being scales were equal to one, and at least one of the two emotional well-being scales was equal to one16.

To facilitate sensitivity analyses, we also constructed additional flourishing measures. These alternative measures are designed to determine whether outcomes are sensitive to the inclusion/exclusion of the hedonic aspect of flourishing and whether flourishing may be not only be considered as a state (whether an individual is in the upper third of emotional, psychological, and social well-being), but also as a process (improvements in emotional, psychological, and social well-being are valuable and desirable as one moves toward the upper one-third threshold goal). We thus constructed an alternative binary measure that omitted the two emotional well-being (hedonic) scales. We also constructed two continuous measures of flourishing (one that included both the hedonic and eudaimonic scale sets, and one that only included the eudaimonic scale set) by summing all relevant scales and converting the sum to a z-score15,16,17. The distribution of each continuous measure is approximately normal. See Fig. 2. These scales are described in Table 1, and the relevant scoring algorithms are described in the footnotes to the table.

Figure 2
figure 2

Distribution of Flourishing (Continuous Measures).

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Instrumental variables: ACEs and daily discrimination

Exposure to ACEs is a summed score of 8 binary (yes/no) categories of adverse experiences (physical abuse, physical neglect, emotional abuse, emotional neglect, sexual abuse, parental divorce, parental depression, and alcohol or drug abuse of any parent) occurring before age 18 (range 0–8, Table 1). This measure has been validated and is consistent with empirical applications29,37,63 and theory28. We transformed this scale into a z-score.

Daily discrimination is measured using the validated daily perceived discrimination scale (Table 1)32,64. Responses to each question are coded 0–3 (never, rarely, sometimes, often) and then summed. We transformed this scale into a z-score.

Covariates

We controlled for potential pathways between the instrumental variables and suicidal ideation to ensure that the instruments only affect suicidal ideation through their impact on flourishing. We thus include the following known risk factors for suicidal ideation: (1) socioeconomic characteristics, including participants’ age, sex, race/ethnicity, marital status, educational level, household income (adjusted for household size), and health insurance status; (2) physical health, including diminished health status (poor health or fair health), binge drinking (number of days with more than five drinks per day in the past 30 days), substance use (ever used the following in the past 12 months either without a doctor's prescription, in larger amounts than prescribed, or for a longer period than prescribed: sedatives, tranquilizers, stimulants, painkillers, antidepressants, inhalants, marijuana/hashish, cocaine/crack, LSD/hallucinogens, heroin), any chronic pain (yes/no), and a set of 5 inflammatory markers, which have been found to be affected by ACEs and could impact suicidal ideation40,65, including C-reactive protein, interleukin-6, fibrinogen, E-selectin, and intercellular adhesion molecule; (3) psychological factors, including the Kessler K666, generalized anxiety disorder67, depressed affect67; and 4) two personality traits measured by the Big Five (neuroticism, conscientiousness) that are known determinants of suicidal ideation68,69,70.

Statistical analysis

Main analysis

Our main analysis includes three instrumental variables models: a two-stage limited information maximum likelihood (2SLIML) model, a bivariate probit model that is a recursive probit model71,72,73 and an IV probit model that is a control function35. All instrumental variables models must satisfy three criteria: instruments must be exogenous, strongly correlate with the endogenous variable of interest, and not correlate with the error term24.

Both instruments were theoretically exogenous: ACEs occurred in childhood, and daily discrimination occurred due to the actions of third parties. We evaluated whether or not the correlation between the endogenous variable of interests (flourishing) and instruments (ACEs and daily discrimination) is sufficiently strong using the Montiel-Olea-Pfluegar weak instrument test33. To avoid any potential correlations between the instruments and the error term, we included the known risk factors for suicidal ideation, including socioeconomic characteristics (e.g., marital status, household income), physical health (e.g., chronic pain, inflammation), psychological factors, and social relationships, which are listed in detail above6,47,74,75. We additionally evaluated whether the instruments were exogenous using an overidentification test and whether the flourishing parameter was significantly different after being corrected (endogeneity test). Figure 1 presents a directed acyclic graph (DAG) of the model76,77. All models are estimated using Stata 1633,78.

Sensitivity analysis

We performed three sets of sensitivity analyses. We used binary measures of flourishing that omitted emotional well-being (measures included eudaimonic aspects only). We used continuous versions of flourishing (z-scores) for both the eudaimonic-hedonic measure of flourishing and the eudaimonic-only measure of flourishing. Finally, we estimated every model substituting the z-score of suicidal ideation for the binary version. Thus, we estimated all combinations of measures of suicidal outcomes (binary, z-score) and measures of flourishing (binary eudaimonic-hedonic, binary eudaimonic only, continuous eudaimonic-hedonic, continuous eudaimonic only). Binary suicidal ideation-binary flourishing models were estimated using 2SLIML and bivariate probit; binary suicidal ideation-continuous flourishing models were estimated using 2SLIML and IV probit (control function probit); continuous suicidal ideation-binary flourishing models were estimated using 2SLIML, and continuous suicidal ideation-continuous flourishing models were estimated using 2SLIML.

Ethical approval

This study was not required to obtain ethics approval since it uses publicly available data that contains no identifiable private information and is therefore not considered human subjects research by the Committee for the Protection of Human Subjects at the University of California. The authors did not have access to any personally identifiable information or information that would link the data to individuals’ identities. All data are reported in aggregate to eliminate the possibility of deductive identification of individuals.