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Updated: Nov 22, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Evaluation of a screening tool for the identification of neurological disorders in rural Uganda
Andy Tran1, Kiran T Thakur2, Noeline Nakasujja3
1University of Iowa Carver College of Medicine, Iowa City, IA, USA.
Background:
Neurological disorders are common in sub-Saharan African, but accurate neuroepidemiologic data are lacking from the region. We assessed a neuroepidemiological screening tool in a rural Ugandan cohort with high HIV prevalence.
Methods:
Participants were recruited from the Rakai Neurology Study in rural Rakai District, Uganda. A nurse administered the tool and a sociodemographic survey. 100 participants returned for validation examinations by a neurologist (validation cohort). The diagnostic utility and validity of the instrument were calculated and characteristics of those with and without neurological disorders compared.
Results:
The tool was administered to 392 participants, 48% female, 33% people with HIV, average age 35.1 ± 8.5 years. 33% of the study cohort screened positive for neurologic disorders. These participants were older [mean (SD): 38.3 (9.7) vs. 33.5 (7.1) years, p < 0.001], had a lower Karnofsky score [89.8 (8.4) vs. 93.9 (7.5), p < 0.001] and had a lower body mass index [21.8 (3.3) vs. 22.8 (3.7), p = 0.007] than those who screened negative. Amongst the validation cohort, 54% had a neurological abnormality of which 46% were symptomatic. The tool was 57% sensitive and 74% specific for detecting any neurological abnormality and 80% sensitive and 69% specific for symptomatic abnormalities.
Conclusions:
We found a lower sensitivity and similar specificity for the screening tool compared with two previous studies. The lower validity in this study was likely due in part to the high percentage of asymptomatic neurological abnormalities detected. This screening tool will require further refinement and cultural contextualization before it can be widely implemented across new populations.
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