Predictors of within-individual variability in cognitive performance in schizophrenia in a South African case-control study

Acta Neuropsychiatr. 2023 Jun 21:1-7. doi: 10.1017/neu.2023.28. Online ahead of print.

Abstract

Introduction: Cognitive dysfunction in schizophrenia may be assessed by measuring within-individual variability (WIV) in performance across a range of cognitive tests. Previous studies have found increased WIV in people with schizophrenia, but no studies have been conducted in low- to middle-income countries where the different sociocultural context may affect WIV. We sought to address this gap by exploring the relationship between WIV and a range of clinical and demographic variables in a large study of people with schizophrenia and matched controls in South Africa.

Methods: 544 people with schizophrenia and 861 matched controls completed an adapted version of The University of Pennsylvania Computerized Neurocognitive Battery (PennCNB). Demographic and clinical information was collected using the Structured Clinical Interview for DSM-IV Diagnoses. Across-task WIV for performance speed and accuracy on the PennCNB was calculated. Multivariate linear regression was used to assess the relationship between WIV and a diagnosis of schizophrenia in the whole sample, and WIV and selected demographic and clinical variables in people with schizophrenia.

Results: Increased WIV of performance speed across cognitive tests was significantly associated with a diagnosis of schizophrenia. In people with schizophrenia, increased speed WIV was associated with older age, a lower level of education and a lower score on the Global Assessment of Functioning scale. Increased accuracy WIV was significantly associated with a younger age in people with schizophrenia.

Conclusions: Measurements of WIV of performance speed can add to the knowledge gained from studies of cognitive dysfunction in schizophrenia in resource-limited settings.

Keywords: Cognition disorders; Humans; Neuropsychological tests; Schizophrenia; Within-individual variability.