Screening Questionnaires to Detect Neurological Disorders in Developing Countries: A Systematic Review.

Neurological disorders are an important cause of disability and death worldwide. The distribution of these disorders differs significantly in developing countries. Screening questionnaires have been used as an important tool to detect neurological illnesses. This systematic literature review aimed to report the validity of screening questionnaires for neurological disorders in developing countries. The PubMed/MEDLINE, Scopus, Science-Direct, and PASCAL databases were searched. All published studies performed in developing countries were eligible. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies version 2 tool. Summary measures of validity were reported (sensitivity and specificity). Eight hundred and thirty-five records were identified, and 49 articles that met eligibility criteria were selected. The most frequently neurological disorders detected with a screening tool were epilepsy, stroke, and neuropathies (77, 53, and 40%, respectively). Ten screening questionnaires were accessible. Two questionnaires were mainly used to detect neurological disorders: the World Health Organization Protocol for Epidemiologic Studies of Neurologic Disorders and the Ten Questions Questionnaire. The sensitivity of the questionnaires was ranged from 84 to 100% and 56 to 100%, respectively. This systematic review presents evidence that screening questionnaires are valid tools to detect neurological disorders in developing countries. Disease detection provides epidemiological data and the opportunity to implement secondary and tertiary prevention strategies that will contribute to reduce the global burden of neurological disorders. © 2019 S. Karger AG, Basel.

Citation

Jaime Luna, Charline Leroi, Pierre-Marie Preux, Edgard Brice Ngoungou, Benoît Marin, Farid Boumediene. Screening Questionnaires to Detect Neurological Disorders in Developing Countries: A Systematic Review. Neuroepidemiology. 2020;54(1):24-32

Mesh Tags

PMID: 31812966

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