From Loss to Loneliness: The Relationship Between Bereavement and Depressive Symptoms Eiko Fried KU Leuven

Current research practices Depression understood as a latent variable We can measure this latent variable by assessing its observable indicators We assess symptoms such as sad mood, fatigue, and insomnia to indicate the presence of the underlying disorder We can so because depression is the common cause for its symptoms Symptom sum-scores used to provide information about people's position on the latent variable Cutoffs on sum-scores used to distinguish between healthy and depressed Introduction

Current research practices Consequences: Depression is studied as homogeneous, discrete diagnostic category ("genes for depression", "risk factors for depression") Symptoms modeled as passive and interchangeable indicators Reciprocal interactions among symptoms (emotion dynamics) are considered irrelevant These results encouraged us to study bereavement on the level of symptoms Introduction

Current research practices Consequences: Depression is studied as homogeneous, discrete diagnostic category ("genes for depression", "risk factors for depression") Symptoms modeled as passive and interchangeable indicators Reciprocal interactions among symptoms (emotion dynamics) are considered irrelevant This contrasts with evidence: 1,030 unique depression symptom profiles identified in 3,703 depressed patients These results encouraged us to study bereavement on the level of symptoms Introduction

Current research practices Consequences: Depression is studied as homogeneous, discrete diagnostic category ("genes for depression", "risk factors for depression") Symptoms modeled as passive and interchangeable indicators Reciprocal interactions among symptoms (emotion dynamics) are considered irrelevant This contrasts with evidence: 1,030 unique depression symptom profiles identified in 3,703 depressed patients MD symptoms differ in their risk factors, impact on impairment of functioning, and biological markers These results encouraged us to study bereavement on the level of symptoms Introduction

Current research practices Consequences: Depression is studied as homogeneous, discrete diagnostic category ("genes for depression", "risk factors for depression") Symptoms modeled as passive and interchangeable indicators Reciprocal interactions among symptoms (emotion dynamics) are considered irrelevant This contrasts with evidence: 1,030 unique depression symptom profiles identified in 3,703 depressed patients MD symptoms differ in their risk factors, impact on impairment of functioning, and biological markers MD symptoms organized in dynamic networks of causal influences These results encouraged us to study bereavement on the level of symptoms Introduction

From Loss to Loneliness: The Relationship Between Bereavement and Depressive Symptoms  Fried, E. I., Bockting, C., Arjadi, R., Borsboom, D., Tuerlinckx, F., Cramer, A., Epskamp, S., Amshoff, M., Carr, D., & Stroebe, M. (2015). From Loss to Loneliness: The Relationship Between Bereavement and Depressive Symptoms. Journal of Abnormal Psychology, 1–10. doi:10.1037/abn0000028

From Loss to Loneliness Research question Does the stressful life event spousal loss affect all or only some depression symptoms? (Keller & Nesse 2005, 2006; Keller et al. 2007) From Loss to Loneliness

From Loss to Loneliness Research question Does the stressful life event spousal loss affect all or only some depression symptoms? (Keller & Nesse 2005, 2006; Keller et al. 2007) Can the effect be better explained by … H1: the common cause framework, indirect effect of partner loss on depressive symptoms that goes through a latent variable s1 s2 D B s3 s4 s5 From Loss to Loneliness

From Loss to Loneliness Research question Does the stressful life event spousal loss affect all or only some depression symptoms? (Keller & Nesse 2005, 2006; Keller et al. 2007) Can the effect be better explained by … H2: a network, direct effect of loss on symptoms s2 B s1 s3 s4 From Loss to Loneliness

From Loss to Loneliness Methods Lives of Older Couples (CLOC) study Baseline: married couples enrolled (60+ years) Bereaved: N=241 Controls: N=274 (still-married) CES-D11, dichotomized Baseline Death Follow-up … 6 months … t From Loss to Loneliness

From Loss to Loneliness Demographics N=515 85.4% female Mean age during enrollment: 73.3 Bereaved participants experienced spousal loss on average 31 months after enrollment Most frequent causes of death: heart attacks (29.5%) cancer (25.3%) arteriosclerosis and related conditions (12.4%) strokes (8.7%) From Loss to Loneliness

From Loss to Loneliness Results Lives of Older Couples (CLOC) study Baseline: married couples, 65 years or older Bereaved: N=241 Controls: N=274 (still-married) Baseline: no differences between bereaved and control participants (age, sex, depressive symptoms) Baseline Death Follow-up … 31 months … … 6 months … t From Loss to Loneliness

From Loss to Loneliness Results Lives of Older Couples (CLOC) study Baseline: married couples, 65 years or older Bereaved: N=241 Controls: N=274 (still-married) Baseline: no differences between bereaved and control participants (age, sex, depressive symptoms) Death Follow-up … 31 months … … 6 months … t From Loss to Loneliness

From Loss to Loneliness Results I are specific symptoms increased in the context of bereavement? From Loss to Loneliness

Results II: common cause model Model fit: ²= 288.7, df = 54, p < .001 RMSEA = .09, CFI = .90 From Loss to Loneliness

Results II: alternative model From Loss to Loneliness

Results II: alternative model Model fit: ²= 171.4, df = 58, p < .001 RMSEA = .07, CFI = .95 Model comparison: ²diff = 124.69, dfdiff = 6, p < .001 From Loss to Loneliness

Results III: Network model Ising Model (binary data) "Partial correlations" Conservative estimation of edges due to penalization (lasso based on EBIC) Fruchterman-Reingold algorithm for visualization The symptom network is cross-sectional, so we have to be careful with a causal interpretation; however, the main finding can likeyl be interpreted causally: loss triggers loneliness, and not the other way around; from there, sypmtom activatoin spreads through the NW. From Loss to Loneliness

Results III: Network model Ising Model (binary data) "Partial correlations" Conservative estimation of edges due to penalization (lasso based on EBIC) Fruchterman-Reingold algorithm for visualization From Loss to Loneliness

From Loss to Loneliness Conclusion Bereavement differentially impacts on depression symptoms; common cause explanation problematic In line with other research documenting "situation-symptom-congruence" Sum-scores obfuscate important (dynamic) insights Loneliness as a gateway symptom; implications for intervention and prevention DSM-3 and DSM-4 bereavement exclusion criterion From Loss to Loneliness

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