Schizophrenia research · 2025
Neighborhood environmental deprivation predicts the generalized neurocognitive deficit in schizophrenia.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Zhang Luyu, Zhang Zhixin, Strauss Gregory P
Аннотация
BACKGROUND: The generalized neurocognitive deficit is a core feature of schizophrenia (SZ). However, no current treatments have demonstrated satisfactory efficacy in remediating cognitive impairment in this population. This lack of treatment progress might be due in part to the failure to consider broader environmental factors and social determinants of health. The current paper explored the contributing role of these factors to the generalized neurocognitive deficit in SZ.
METHODS: Participants included 114 outpatients with SZ and 117 matched healthy controls (CN) who completed a cross-sectional study. Cognitive impairment was assessed by the MATRICS Consensus Cognitive Battery (MCCB). Environmental deprivation was measured by Area Deprivation Index (ADI). Rurality was measured by the rural urban continuum code (RUCC). A linear regression model was fitted to predict MCCB total score with ADI, RUCC, and the interaction between ADI and RUCC as predictors.
RESULTS: The overall model was significant, F = 8.06, p < .001, R2 = 0.14. The effect of ADI score was also significant, β = -0.36, t = -2.30, p < .05; while the effect of RUCC, β = 0.06, t = 0.34, p = .73, and the interaction between ADI and RUCC, β = -0.001, t = 0.27, p = .99, were nonsignificant.
CONCLUSIONS: Neighborhood-level environmental deprivation predicted cognitive impairment in SZ. This highlights the need to incorporate environment-focused interventions when treating cognitive impairment in SZ.
METHODS: Participants included 114 outpatients with SZ and 117 matched healthy controls (CN) who completed a cross-sectional study. Cognitive impairment was assessed by the MATRICS Consensus Cognitive Battery (MCCB). Environmental deprivation was measured by Area Deprivation Index (ADI). Rurality was measured by the rural urban continuum code (RUCC). A linear regression model was fitted to predict MCCB total score with ADI, RUCC, and the interaction between ADI and RUCC as predictors.
RESULTS: The overall model was significant, F = 8.06, p < .001, R2 = 0.14. The effect of ADI score was also significant, β = -0.36, t = -2.30, p < .05; while the effect of RUCC, β = 0.06, t = 0.34, p = .73, and the interaction between ADI and RUCC, β = -0.001, t = 0.27, p = .99, were nonsignificant.
CONCLUSIONS: Neighborhood-level environmental deprivation predicted cognitive impairment in SZ. This highlights the need to incorporate environment-focused interventions when treating cognitive impairment in SZ.