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What is the Best Latent Structure of Negative Symptoms in Schizophrenia? A Systematic Review
Bernardo Haguiara1, Gabriela Koga1, Elton Diniz1,2
1Laboratory of Integrative Neurosciences, Department of Psychiatry, Escola Paulista de Medicina/Universidade Federal de São Paulo, SP, Brazil.
Background:
Determining the best latent structure of negative symptoms in schizophrenia could benefit assessment tools, neurobiological research, and targeted interventions. However, no review systematically evaluated studies that assessed and validated latent models of negative symptoms.
Objective:
To identify and evaluate existing latent structure models in the literature of negative symptoms and to determine the best model.
Method:
Systematic search of MEDLINE, EMBASE, and Scopus on July 19, 2020, for confirmatory factor analysis models of negative symptoms in patients with schizophrenia. The available evidence was assessed through 2 sets of criteria: (1) study design quality-based on negative symptoms assessment and modeling strategy and (2) psychometric quality and model fit-based on fit indices and factor definition quality.
Results:
In total, 22 studies (n = 17 086) from 9 countries were included. Studies differed greatly regarding symptom scales, setting, and sample size (range = 86-6889). Dimensional models included 2-6 factors (median = 4). Twelve studies evaluated competing models and adopted appropriate instruments to assess the latent structure of negative symptoms. The 5-factor and hierarchical models outperformed unitary, 2-factor, and 3-factor models on all direct comparisons, and most of the analyses derived from the Brief Negative Symptom Scale. Considering the quality criteria proposed, 5-factor and hierarchical models achieved excellent fit in just one study.
Conclusions:
Our review points out that the 5-factor and hierarchical models represent the best latent structure of negative symptoms, but the immaturity of the relevant current literature may affect the robustness of this conclusion. Future studies should address current limitations regarding psychometric properties and also address biological and clinical validity to refine available models.
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