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Published on: January 24, 2020
Predicting occupational outcomes from neuropsychological test performance in older people with HIV
Marie-Josée Brouillette1,2,3,4, Lisa Koski5, Laurence Forcellino6
1Department of Psychiatry, McGill University.
Objective:
The ability to work is amongst the top concerns of people living with well treated HIV. Cognitive impairment has been reported in many otherwise asymptomatic persons living with HIV and even mild impairment is associated with higher rates of occupational difficulties. There are several classification algorithms for HIV-associated neurocognitive disorder (HAND) as well as overall scoring methods available to summarize neuropsychological performance. We asked which method best explained work status and productivity.
Design:
Participants (N = 263) drawn from a longitudinal Canadian cohort underwent neuropsychological testing.
Methods:
: Several classification algorithms were applied to establish a HAND diagnosis and two summary measures (NPZ and Global Deficit Score) were computed. Self-reported work status and productivity was assessed at each study visit (four visits, 9 months apart). The association of work status with each diagnostic classification and summary measure was estimated using logistic regression. For those working, the value on the productivity scale was regressed within individuals over time, and the slopes were regressed on each neuropsychological outcome.
Results:
The application of different classification algorithms to the neuropsychological data resulted in rates of impairment that ranged from 28.5 to 78.7%. Being classified as impaired by any method was associated with a higher rate of unemployment. None of the diagnostic classifications or summary methods predicted productivity, at time of testing or over the following 36 months.
Conclusion:
Neuropsychological diagnostic classifications and summary scores identified participants who were more likely to be unemployed, but none explained productivity. New methods of assessing cognition are required to inform optimal workforce engagement.
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