Prospective trajectories of depression predict mortality in cancer patients.

An ever-growing body of empirical evidence has demonstrated the relationship between depression and cancer. The objective of this study was to examine whether depression trajectories predict mortality risk above and beyond demographics and other general health-related factors. Participants (n = 2,345) were a part of the Health and Retirement Study. The sample consisted of patients who were assessed once before their cancer diagnosis and thrice after. Depressive symptoms and general health-related factors were based on self-reports. Mortality risk was determined based on whether the patient was alive or not at respective time points. Latent Growth Mixture Modeling was performed to map trajectories of depression, assess differences in trajectories based on demographics and general health-related factors, and predict mortality risk. Four trajectories of depression symptoms emerged: resilient (69.7%), emerging (13.5%), recovery (9.5%), and chronic (7.2%). Overall, females, fewer years of education, higher functional impairment at baseline, and high mortality risk characterized the emerging, recovery, and chronic trajectories. In comparison to the resilient trajectory, mortality risk was highest for the emerging trajectory and accounted for more than half of the deaths recorded for the participants in emerging trajectory. Mortality risk was also significantly elevated, although to a lesser degree, for the recovery and chronic trajectories. The data highlights clinically relevant information about the depression-cancer association that can have useful implications towards cancer treatment, recovery, and public health.

Associations of polygenic risk scores with posttraumatic stress symptom trajectories following combat deployment.

BACKGROUND: Identification of genetic risk factors may inform the prevention and treatment of posttraumatic stress disorder (PTSD). This study evaluates the associations of polygenic risk scores (PRS) with patterns of posttraumatic stress symptoms following combat deployment. METHOD: US Army soldiers of European ancestry (n = 4900) provided genomic data and ratings of posttraumatic stress symptoms before and after deployment to Afghanistan in 2012. Latent growth mixture modeling was used to model posttraumatic stress symptom trajectories among participants who provided post-deployment data (n = 4353). Multinomial logistic regression models tested independent associations between trajectory membership and PRS for PTSD, major depressive disorder (MDD), schizophrenia, neuroticism, alcohol use disorder, and suicide attempt, controlling for age, sex, ancestry, and exposure to potentially traumatic events, and weighted to account for uncertainty in trajectory classification and missing data. RESULTS: Participants were classified into low-severity (77.2%), increasing-severity (10.5%), decreasing-severity (8.0%), and high-severity (4.3%) posttraumatic stress symptom trajectories. Standardized PTSD-PRS and MDD-PRS were associated with greater odds of membership in the high-severity v. low-severity trajectory [adjusted odds ratios and 95% confidence intervals, 1.23 (1.06-1.43) and 1.18 (1.02-1.37), respectively] and the increasing-severity v. low-severity trajectory [1.12 (1.01-1.25) and 1.16 (1.04-1.28), respectively]. Additionally, MDD-PRS was associated with greater odds of membership in the decreasing-severity v. low-severity trajectory [1.16 (1.03-1.31)]. No other associations were statistically significant. CONCLUSIONS: Higher polygenic risk for PTSD or MDD is associated with more severe posttraumatic stress symptom trajectories following combat deployment. PRS may help stratify at-risk individuals, enabling more precise targeting of treatment and prevention programs.

Influence of systolic blood pressure trajectory on in-hospital mortality in patients with sepsis.

BACKGROUND: Numerous studies have investigated the mean arterial pressure in patients with sepsis, and many meaningful results have been obtained. However, few studies have measured the systolic blood pressure (SBP) multiple times and established trajectory models for patients with sepsis with different SBP trajectories. METHODS: Data from patients with sepsis were extracted from the Medical Information Mart for Intensive Care-III database for inclusion in a retrospective cohort study. Ten SBP values within 10 h after hospitalization were extracted, and the interval between each SBP value was 1 h. The SBP measured ten times after admission was analyzed using latent growth mixture modeling to construct a trajectory model. The outcome was in-hospital mortality. The survival probability of different trajectory groups was investigated using Kaplan-Meier (K-M) analysis, and the relationship between different SBP trajectories and in-hospital mortality risk was investigated using Cox proportional-hazards regression model. RESULTS: This study included 3034 patients with sepsis. The median survival time was 67 years (interquartile range: 56-77 years). Seven different SBP trajectories were identified based on model-fit criteria. The in-hospital mortality rates of the patients in trajectory classes 1-7 were 25.5%, 40.5%, 11.8%, 18.3%, 23.5%, 13.8%, and 10.5%, respectively. The K-M analysis indicated that patients in class 2 had the lowest probability of survival. Univariate and multivariate Cox regression analysis indicated that, with class 1 as a reference, patients in class 2 had the highest in-hospital mortality risk (P < 0.001). Subgroup analysis indicated that a nominal interaction occurred between age group and blood pressure trajectory in the in-hospital mortality (P < 0.05). CONCLUSION: Maintaining a systolic blood pressure of approximately 140 mmHg in patients with sepsis within 10 h of admission was associated with a lower risk of in-hospital mortality. Analyzing data from multiple measurements and identifying different categories of patient populations with sepsis will help identify the risks among these categories.

Influence of the trajectory of the urine output for 24 h on the occurrence of AKI in patients with sepsis in intensive care unit.

BACKGROUND: Sepsis-associated acute kidney injury (S-AKI) is a common and life-threatening complication in hospitalized and critically ill patients. This condition is an independent cause of death. This study was performed to investigate the correlation between the trajectory of urine output within 24 h and S-AKI. METHODS: Patients with sepsis were studied retrospectively based on the Medical Information Mart for Intensive Care IV. Latent growth mixture modeling was used to classify the trajectory of urine output changes within 24 h of sepsis diagnosis. The outcome of this study is AKI that occurs 24 h after sepsis. Cox proportional hazard model, Fine-Gray subdistribution proportional hazard model, and doubly robust estimation method were used to explore the risk of AKI in patients with different trajectory classes. RESULTS: A total of 9869 sepsis patients were included in this study, and their 24-h urine output trajectories were divided into five classes. The Cox proportional hazard model showed that compared with class 1, the HR (95% CI) values for classes 3, 4, and 5 were 1.460 (1.137-1.875), 1.532 (1.197-1.961), and 2.232 (1.795-2.774), respectively. Competing risk model and doubly robust estimation methods reached similar results. CONCLUSIONS: The trajectory of urine output within 24 h of sepsis patients has a certain impact on the occurrence of AKI. Therefore, in the early treatment of sepsis, close attention should be paid to changes in the patient's urine output to prevent the occurrence of S-AKI.

Trajectories of Substance Use among Child Welfare-Involved Youth: Longitudinal Associations with Child Maltreatment History and Emotional/Behavior Problems.

BACKGROUND: Maltreated children experience a variety of adverse outcomes including substance use problems. Although previous research indicated that there may be distinct trajectories of substance use among these youth, studies have examined them as if they were a single homogeneous group. OBJECTIVES: The goals of this study were to explore substance use trajectories among child welfare-involved youth and to identify characteristics that distinguish substance use trajectories. METHODS: Data from the National Survey of Child and Adolescent Well-Being (NSCAW II) were used. Multilevel latent growth mixture modeling (MLGMM) was performed using a subsample of 625 youth from ages 11-17 years investigated for maltreatment in 2008-2009. Measures included self-reported use of substance use during the previous 30 days, demographic characteristics, maltreatment history, placement in out-of-home care, and behavioral health problems. RESULTS: MLGMM identified two distinct substance use trajectory classes including High Stable Substance Use and Rapid Progression Use. Findings suggest that the experience of physical abuse is the key factor that distinguishes the two groups. When the effects of class-specific covariates were examined, results suggest that involvement in substance use behavior and its escalation vary between groups and are affected by youth's different previous experiences. Conclusions/Importance: The results have important implications for understanding individual differences in substance use behavior over time and how these differences were shaped by youth's experiences of family adversity. Study findings may be helpful for developing and enhancing the effectiveness of interventions targeted at decreasing substance use behaviors in child welfare-involved youth.

Implementing continuous non-normal skewed distributions in latent growth mixture modeling: An assessment of specification errors and class enumeration.

Recent advances have allowed for modeling mixture components within latent growth modeling using robust, skewed mixture distributions rather than normal distributions. This feature adds flexibility in handling non-normality in longitudinal data, through manifest or latent variables, by directly modeling skewed or heavy-tailed latent classes rather than assuming a mixture of normal distributions. The aim of this study was to assess through simulation the potential under- or over-extraction of latent classes in a growth mixture model when underlying data follow either normal, skewed-normal, or skewed-t distributions. In order to assess this, we implement skewed-t, skewed-normal, and conventional normal (i.e., not skewed) forms of the growth mixture model. The skewed-t and skewed-normal versions of this model have only recently been implemented, and relatively little is known about their performance. Model comparison, fit, and classification of correctly specified and mis-specified models were assessed through various indices. Findings suggest that the accuracy of model comparison and fit measures are dependent on the type of (mis)specification, as well as the amount of class separation between the latent classes. A secondary simulation exposed computation and accuracy difficulties under some skewed modeling contexts. Implications of findings, recommendations for applied researchers, and future directions are discussed; a motivating example is presented using education data.

Trajectories of Suicidal Ideation in People Seeking Web-Based Help for Suicidality: Secondary Analysis of a Dutch Randomized Controlled Trial.

BACKGROUND: Suicidal ideation (SI) is a common mental health problem. Variability in intensity of SI over time has been linked to suicidal behavior, yet little is known about the temporal course of SI. OBJECTIVE: The primary aim was to identify prototypical trajectories of SI in the general population and, secondarily, to examine whether receiving Web-based self-help for SI, psychiatric symptoms, or sociodemographics predicted membership in the identified SI trajectories. METHODS: We enrolled 236 people, from the general Dutch population seeking Web-based help for SI, in a randomized controlled trial comparing a Web-based self-help for SI group with a control group. We assessed participants at inclusion and at 2, 4, and 6 weeks. The Beck Scale for Suicide Ideation was applied at all assessments and was included in latent growth mixture modeling analysis to empirically identify trajectories. RESULTS: We identified 4 SI trajectories. The high stable trajectory represented 51.7% (122/236) of participants and was characterized by constant high level of SI. The high decreasing trajectory (50/236, 21.2%) consisted of people with a high baseline SI score followed by a gradual decrease to a very low score. The third trajectory, high increasing (12/236, 5.1%), also had high initial SI score, followed by an increase to the highest level of SI at 6 weeks. The fourth trajectory, low stable (52/236, 22.0%) had a constant low level of SI. Previous attempted suicide and having received Web-based self-help for SI predicted membership in the high decreasing trajectory. CONCLUSIONS: Many adults experience high persisting levels of SI, though results encouragingly indicate that receiving Web-based self-help for SI increased membership in a decreasing trajectory of SI.

Optimism and death: predicting the course and consequences of depression trajectories in response to heart attack.

The course of depression in relation to myocardial infarction (MI), commonly known as heart attack, and the consequences for mortality are not well characterized. Further, optimism may predict both the effects of MI on depression as well as mortality secondary to MI. In the current study, we utilized a large population-based prospective sample of older adults (N=2,147) to identify heterogeneous trajectories of depression from 6 years prior to their first-reported MI to 4 years after. Findings indicated that individuals were at significantly increased risk for mortality when depression emerged after their first-reported MI, compared with resilient individuals who had no significant post-MI elevation in depression symptomatology. Individuals with chronic depression and those demonstrating pre-event depression followed by recovery after MI were not at increased risk. Further, optimism, measured before MI, prospectively differentiated all depressed individuals from participants who were resilient.

Trajectories of loneliness during childhood and adolescence: predictors and health outcomes.

The present study employed latent growth mixture modeling to discern distinct trajectories of loneliness using data collected at 2-year intervals from age 7-17 years (N = 586) and examine whether measures taken at age 5 years were good predictors of group membership. Four loneliness trajectory classes were identified: (1) low stable (37% of the sample), (2) moderate decliners (23%), (3) moderate increasers (18%), and (4) relatively high stable (22%). Predictors at age 5 years for the high stable trajectory were low trust beliefs, low trusting, low peer acceptance, parent reported negative reactivity, an internalizing attribution style, low self-worth, and passivity during observed play. The model also included outcome variables. We found that both the high stable and moderate increasing trajectories were associated with depressive symptoms, a higher frequency of visits to the doctor, and lower perceived general health at age 17. We discuss implications of findings for future empirical work.

How does change in leisure-time physical activity influence the growth trajectory of depressive symptoms in college students?

Objectives: This longitudinal study was designed to examine the growth trajectory of depressive symptoms among early-stage college students and how the development of vigorous, moderate, and light leisure-time physical activity (LTPA) was related to the growth trajectory. Participants: Four hundred and eighty-eight first- and second-year undergraduate students completed measures of depressive symptoms and LTPA at the beginning, middle, and end of a semester. Methods: Latent growth mixture modeling (LGMM) was conducted. Results: On average, students reported mild levels of depressive symptoms with significant variability at the semester start, but the symptoms elevated over time. LGMM identified two trajectories: low/gradual (75.8%) and high/increasing (24.2%). For both groups, neither vigorous nor moderate LTPA development predicted the growth trajectory of depressive symptoms. However, the change of light LTPA was negatively and significantly associated with the growth trajectory. Even when controlling for covariances, increased light LTPA still had a unique effect on buffering depressive symptoms. Conclusion: There is great potential in targeting comprehensive LTPA strategies to improve college students' mental health and promote an active lifestyle.

How do teacher support trajectories influence primary and lower-secondary school students' study well-being.

Effective long term teacher support is key to promoting and sustaining students' study well-being at school. However, little is known about individual variations in the development of perceived teacher support and how such variations are associated with study engagement and study-related burnout. Also, understanding of the differences between age cohorts across school levels is still limited. To address this limitation, we used latent growth mixture (LGM) modeling to study whether teacher support trajectories influenced study engagement and study-related burnout among Finnish primary and lower-secondary school students. Two cohorts of students, namely primary school students from the 4th to 6th grades (N = 2,204) and lower-secondary school students from the 7th to 9th grades (N = 1,411), were followed for three years. LGM revealed four latent trajectories for teacher support, which were labeled high stable (72%), low stable (12%), decreasing (11%) and increasing (5%). The teacher support trajectories were strongly associated with students' study engagement and study burnout. Moreover, heightened study-related burnout symptoms and decreased study engagement were associated with a decline in perceived teacher support, while higher levels of study engagement and low levels of study burnout symptoms were associated with a continuum of positive teacher support experience. Primary school students were more likely to employ stable and high levels of teacher support, compared with lower-secondary school students, highlighting the importance of improving conditions in lower-secondary school so that the teacher support will better reach all their students.

Trajectories of disability in low back pain.

INTRODUCTION: Low back pain (LBP) is the leading course of years lived with disability. Unfortunately, not much knowledge exists about distinct trajectories of recovery from disability after LBP and their potential psychological predictors. OBJECTIVES: Hence, the aim of the present study was to identify trajectories of functional disability in LBP and their potential baseline psychological predictors. METHODS: A 1-year consecutive cohort (N = 1048) of patients with LBP referred to the Spine Centre if they have not improved satisfactorily from a course of treatment in primary care after 1 to 2 months were assessed by self-report questionnaires at their first visit and at 6- and 12-month follow-up. Data from patients who responded to the Roland Morris Disability Questionnaire at least twice (N = 747) were used to assess trajectories of functional disability by Latent Growth Mixture Modeling. The following measures were used as baseline predictors of the trajectories: Pain Intensity Numerical Rating Scales, Pain Catastrophizing Scale, Tampa Scale for Kinesiophobia, and Hospital Anxiety and Depression Scale. RESULTS: Four distinct trajectories were identified: high-stable (22.0%), high-decreasing (20.4%), medium-stable (29.7%), and low-decreasing (27.9%). Using the low-decreasing trajectory as reference, baseline pain intensity, depressive symptoms, and pain-catastrophizing predicted membership of all 3 symptomatic trajectories. However, using the high-decreasing trajectory as reference, age, baseline pain intensity, and depression were predictors of the high-stable trajectory. CONCLUSION: In particular, the finding of a high-stable trajectory characterized by high levels of baseline psychological distress is of potential clinical importance because psychological distress may be targeted by cognitive behavioral therapeutic approaches.

Effects of growth trajectory of shock index within 24 h on the prognosis of patients with sepsis.

Background: Sepsis is a serious disease with high clinical morbidity and mortality. Despite the tremendous advances in medicine and nursing, treatment of sepsis remains a huge challenge. Our purpose was to explore the effects of shock index (SI) trajectory changes on the prognosis of patients within 24 h after the diagnosis of sepsis. Methods: This study was based on Medical Information Mart for Intensive Care IV (MIMIC- IV). The effects of SI on the prognosis of patients with sepsis were investigated using C-index and restricted cubic spline (RCS). The trajectory of SI in 24 h after sepsis diagnosis was classified by latent growth mixture modeling (LGMM). Cox proportional hazard model, double robust analysis, and subgroup analysis were conducted to investigate the influence of SI trajectory on in-hospital death and secondary outcomes. Results: A total of 19,869 patients were eventually enrolled in this study. C-index showed that SI had a prognostic value independent of Sequential Organ Failure Assessment for patients with sepsis. Moreover, the results of RCS showed that SI was a prognostic risk factor. LGMM divided SI trajectory into seven classes, and patients with sepsis in different classes had notable differences in prognosis. Compared with the SI continuously at a low level of 0.6, the SI continued to be at a level higher than 1.0, and the patients in the class whose initial SI was at a high level of 1.2 and then declined had a worse prognosis. Furthermore, the trajectory of SI had a higher prognostic value than the initial SI. Conclusion: Both initial SI and trajectory of SI were found to be independent factors that affect the prognosis of patients with sepsis. Therefore, in clinical treatment, we should closely monitor the basic vital signs of patients and arrive at appropriate clinical decisions on basis of their change trajectory.

Smartphone Addiction, Social Support, and Cybercrime Victimization: A Discrete Survival and Growth Mixture Model.

In recent decades, criminological theories have identified a set of vulnerabilities in potential victims that seek to explain their victimization. When it comes to explaining cybercrime victimization, however, the important role that addiction to the vulnerabilities associated with technological devices can play has tended to be overlooked. In this paper we empirically link smartphone addiction, social support, and cyberfraud victimization in a nationally representative sample of 716 smartphone users followed for three years. The results of discrete survival and growth mixture models suggest that the probability of cyberfraud victimization is lower among users with a decrease in smartphone addiction and an increase in social support over the three years. These results allow us to suggest new avenues in the study of cybercrime victimization, with special emphasis on the psychosocial consequences that the deregulated use of these technological devices may entail.

En las últimas décadas, las teorías criminológicas han identificado una serie de vulnerabilidades en las víctimas potenciales que tratan de explicar su victimización. Sin embargo, cuando se trata de explicar la victimización por ciberdelincuencia, se ha tendido a pasar por alto el importante papel que puede desempeñar la adicción a los dispositivos tecnológicos y sus consecuencias psicosociales. En este trabajo relacionamos empíricamente la adicción a los smartphones, el apoyo social y la victimización por ciberdelincuencia en una muestra representativa a nivel nacional de 716 usuarios a los que se siguió durante tres años. Los resultados de los modelos de curvas de supervivencia para tiempo discreto y mixtura de crecimiento latente sugieren que la probabilidad de victimización por ciberfraude es menor entre los usuarios con una disminución de la adicción a los teléfonos inteligentes y un aumento del apoyo social a lo largo de los tres años. Estos resultados nos permiten sugerir nuevas vías en el estudio de la victimización por ciberdelincuencia, con especial énfasis en las consecuencias psicosociales que puede conllevar el uso desregulado de estos dispositivos tecnológicos.

Impacts of Low Self-Control and Delinquent Peer Associations on Bullying Growth Trajectories Among Korean Youth: A Latent Growth Mixture Modeling Approach.

The emergence of studies applying Gottfredson and Hirschi's (1990) self-control theory to offending behaviors has produced empirical support confirming the position that individuals with low self-control are more likely to engage in deviant behaviors. However, few have examined its effects with opportunity factors. The present study examines the time-invariant effect of low self-control, as well as the time-concurrent and lagged effects of opportunity factors (parental attachment and delinquent peer associations), on bullying growth trajectories. The findings in the latent growth curve analysis demonstrate that low self-control is significantly related to both the initial levels and change in bullying over time, even after controlling for delinquent peer associations in homogeneous populations. The new approach described within the latent class growth modeling framework (i.e., growth mixture) incorporates a categorical latent trajectory variable representing latent classes (i.e., distinct subgroups), having similar patterns of bullying growth trajectories. Three groups of students emerged from the student-reported data at five time points from ages 11 to 15, decreasers (90%), moderate late peakers (7%), and high late peakers(3%), defined by different predictors and sequelae. Low self-control was rendered insignificant for both moderate late peakers and high late peakers relative to decreasers; delinquent peer associations had a time-concurrent effect for moderate late peakers than decreasers; and high late peakers had a time-lagged effect relative to moderate late peakers.

Association between trajectories of personal growth initiative and post-traumatic stress after Hurricane Harvey: A latent growth mixture modeling approach.

Studies examining the association between personal growth initiative (PGI) and post-traumatic stress symptoms (PTSS) have often utilized cross-sectional research designs, and as a result, the changes in the levels of PGI and its association with the trajectory of PTSS remain unclear. The current study aimed to (1) explore the different trajectories in both PGI and PTSS and (2) examine the associations of the identified trajectories between PGI and PTSS among individuals. The final sample were 419 adults who were physically residing in the area when Hurricane Harvey made landfall on 26 August 2017. The initial data collection occurred approximately 16 months after the Hurricane, and participants were asked to participate again after 1- and 3-month later. A result from the latent growth mixture modeling revealed that for PGI, the 4-class model was the best-fitting model, and for PTSS, the 3-class model was the best-fitting model. When examining the association between the trajectories of PGI and PSS, individuals classified to higher PGI subgroups were more likely to be associated with the Recovery PTSS subgroup. The current study suggests that disaster survivors with higher PGI were more likely to recover from PTSS, raising an importance of incorporating PGI to alleviate future PTSS.

Positive Affect Over Time and Emotion Regulation Strategies: Exploring Trajectories With Latent Growth Mixture Model Analysis.

The influence of Positive Affect (PA) on people's well-being and happiness and the related positive consequences on everyday life have been extensively described by positive psychology in the past decades. This study shows an application of Latent Growth Mixture Modeling (LGMM) to explore the existence of different trajectories of variation of PA over time, corresponding to different groups of people, and to observe the effect of emotion regulation strategies on these trajectories. We involved 108 undergraduates in a 1-week daily on-line survey, assessing their PA. We also measured their emotion regulation strategies before the survey. We identified three trajectories of PA over time: a constantly high PA profile, an increasing PA profile, and a decreasing PA profile. Considering emotion regulation strategies as covariates, reappraisal showed an effect on trajectories and class membership, whereas suppression regulation strategy did not.

Identifying subgroups: Part 2: Trajectories of change over time.

Methods to identify multiple trajectories of change over time are of great interest in nursing and in related health research. Latent growth mixture modeling is a data-centered analytic strategy that allows us to study questions about distinct trajectories of change in key measures or outcomes of interest. In this article, a worked example of latent growth mixture modeling is presented to help expose researchers to the use and appeal of this analytic strategy.

Trajectories of resilience and dysfunction following potential trauma: A review and statistical evaluation.

Given the rapid proliferation of trajectory-based approaches to study clinical consequences to stress and potentially traumatic events (PTEs), there is a need to evaluate emerging findings. This review examined convergence/divergences across 54 studies in the nature and prevalence of response trajectories, and determined potential sources of bias to improve future research. Of the 67 cases that emerged from the 54 studies, the most consistently observed trajectories following PTEs were resilience (observed in: n = 63 cases), recovery (n = 49), chronic (n = 47), and delayed onset (n = 22). The resilience trajectory was the modal response across studies (average of 65.7% across populations, 95% CI [0.616, 0.698]), followed in prevalence by recovery (20.8% [0.162, 0.258]), chronicity (10.6%, [0.086, 0.127]), and delayed onset (8.9% [0.053, 0.133]). Sources of heterogeneity in estimates primarily resulted from substantive population differences rather than bias, which was observed when prospective data is lacking. Overall, prototypical trajectories have been identified across independent studies in relatively consistent proportions, with resilience being the modal response to adversity. Thus, trajectory models robustly identify clinically relevant patterns of response to potential trauma, and are important for studying determinants, consequences, and modifiers of course following potential trauma.

Emergence of depression following job loss prospectively predicts lower rates of reemployment.

Job loss has been associated with the emergence of depression and subsequent long-term diminished labor market participation. In a sample of 500 adults who lost their jobs, trajectories of depression severity from four years before to four years after job loss were identified using Latent Growth Mixture Modeling. Rates of unemployment by trajectory were compared at two and four years following job loss. Four trajectories demonstrated optimal model fit including resilience (72%), chronic pre-to-post job loss depression (9%), emergent depression (10%), and remitting depression (9%). Logistic regression comparing reemployment status by class while controlling for age, gender, and education at two-years post job loss revealed no significant differences by class. An identical logistic regression on four-year reemployment revealed significant differences by class with post-hoc analyses revealing emergent depression resulting in a 33.3% reemployment rate compared to resilient individuals (60.4%) together indicating that depression affects reemployment rather than lack of reemployment causing the emergence of depression. The emergence of depression following job loss significantly increases the risk of continued unemployment. However, observed high rates of resilience with resulting downstream benefits in reemployment mitigates significant concern about the effects of wide spread unemployment on ongoing global economic recovery following the Great Recession.