Background: Recent network models of schizophrenia propose it is the consequence of mutual interaction between its symptoms. While cross-sectional associations between negative and positive symptoms are consistent with this idea, they may merely reflect their involvement in the diagnostic process. Longitudinal analyses however may allow the identification of possible causal relationships. The European Schizophrenia Cohort (EuroSC) provides data suitable for this purpose.
Methods: EuroSC includes 1208 patients randomly sampled from outpatient services in France, Germany and the UK. Initial measures were repeated after 12 and 24 months. Latent variable structural equation modelling was used to investigate the direction of effect between positive and negative symptoms assessed with the Positive and Negative Syndrome Scale, controlling for the effects of depressed mood and antipsychotic medication.
Results: The structural model provided acceptable overall fit [χ2 (953) = 2444.32, P < 0.001; CFI = 0.909; RMSEA = 0.046 (90% CI: 0.043, 0.048); SRMR = 0.052]. Both positive and negative symptoms were persistent, and strongly auto-correlated. There were also persistent cross-sectional associations between positive and negative symptoms. While the path from latent positive to negative symptoms from T1 to T2 approached conventional levels of statistical significance (P = 0.051), that from T2 to T3 did not (P = 0.546). Pathways in the reverse direction were uniformly non-significant.
Conclusions: There was no evidence that negative symptoms predict later positive symptoms. The prediction of negative symptoms by positive symptoms was ambiguous. We discuss implications for conceptualization of schizophrenic processes.
Keywords: Diathesis models; Longitudinal studies; Negative symptoms; Network models; Positive symptoms; Schizophrenia.
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