1. From Loss to Loneliness: The Relationship Between Bereavement and Depressive Symptoms Eiko Fried KU Leuven ICPS Amsterdam March 2015

2. 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 2Introduction

3. Current research practices Consequences: 1.Depression is studied as homogeneous, discrete diagnostic category ("genes for depression", "risk factors for depression") 2.Symptoms modeled as passive and interchangeable indicators 3.Reciprocal interactions among symptoms (emotion dynamics) are considered irrelevant 3Introduction

4. Current research practices Consequences: 1.Depression is studied as homogeneous, discrete diagnostic category ("genes for depression", "risk factors for depression") 2.Symptoms modeled as passive and interchangeable indicators 3.Reciprocal interactions among symptoms (emotion dynamics) are considered irrelevant This contrasts with evidence: 1.1,030 unique depression symptom profiles identified in 3,703 depressed patients 4Introduction

5. Current research practices Consequences: 1.Depression is studied as homogeneous, discrete diagnostic category ("genes for depression", "risk factors for depression") 2.Symptoms modeled as passive and interchangeable indicators 3.Reciprocal interactions among symptoms (emotion dynamics) are considered irrelevant This contrasts with evidence: 1.1,030 unique depression symptom profiles identified in 3,703 depressed patients 2.MD symptoms differ in their risk factors, impact on impairment of functioning, and biological markers 5Introduction

6. Current research practices Consequences: 1.Depression is studied as homogeneous, discrete diagnostic category ("genes for depression", "risk factors for depression") 2.Symptoms modeled as passive and interchangeable indicators 3.Reciprocal interactions among symptoms (emotion dynamics) are considered irrelevant This contrasts with evidence: 1.1,030 unique depression symptom profiles identified in 3,703 depressed patients 2.MD symptoms differ in their risk factors, impact on impairment of functioning, and biological markers 3.MD symptoms organized in dynamic networks of causal influences 6Introduction

7. From Loss to Loneliness: The Relationship Between Bereavement and Depressive Symptoms 7 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

8. Research question 1.Does the stressful life event spousal loss affect all or only some depression symptoms? (Keller & Nesse 2005, 2006; Keller et al. 2007) 8From Loss to Loneliness

9. Research question 1.Does the stressful life event spousal loss affect all or only some depression symptoms? (Keller & Nesse 2005, 2006; Keller et al. 2007) 2.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 9From Loss to Loneliness s1 s2 s3 s4 s5 D D B B

10. Research question 1.Does the stressful life event spousal loss affect all or only some depression symptoms? (Keller & Nesse 2005, 2006; Keller et al. 2007) 2.Can the effect be better explained by … – H2: a network, direct effect of loss on symptoms 10From Loss to Loneliness s1 s2 s3 s4 B B

11. Methods Lives of Older Couples (CLOC) study Baseline: married couples enrolled (60+ years) Bereaved: N=241 Controls: N=274 (still-married) CES-D 11, dichotomized 11From Loss to Loneliness t Baseline DeathFollow-up … 6 months …

12. Demographics 12From Loss to Loneliness 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%)

13. 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) 13From Loss to Loneliness t Baseline DeathFollow-up … 6 months … … 31 months …

14. 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) 14From Loss to Loneliness t Death Follow-up … 6 months … … 31 months …

15. Results I 15From Loss to Loneliness

16. Results II: common cause model 16From Loss to Loneliness Model fit:  ²= 288.7, df = 54, p <.001 RMSEA =.09, CFI =.90

17. Results II: alternative model 17From Loss to Loneliness

18. Results II: alternative model 18From Loss to Loneliness Model comparison:  ² diff = 124.69, df diff = 6, p <.001 Model fit:  ²= 171.4, df = 58, p <.001 RMSEA =.07, CFI =.95

19. Results III: Network model 19From Loss to Loneliness -Ising Model (binary data) -"Partial correlations" -Conservative estimation of edges due to penalization (lasso based on EBIC) -Fruchterman-Reingold algorithm for visualization

20. Results III: Network model 20From Loss to Loneliness -Ising Model (binary data) -"Partial correlations" -Conservative estimation of edges due to penalization (lasso based on EBIC) -Fruchterman-Reingold algorithm for visualization

21. 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 21From Loss to Loneliness

22. Open questions How well does the network model describe the data? Does the latent variable or the network model describe the data better? How to use network model with highly skewed polytomous items? 22From Loss to Loneliness

23. Thank you