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A Prognostic Tool to Identify Youth at Risk of at Least Weekly Cannabis Use

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AI for Disease Surveillance and Pandemic Intelligence (W3PHAI 2021)

Abstract

We developed and validated an 8-item prognostic tool to identify youth at risk of initiating frequent (i.e., at least weekly) cannabis use in the next year. The tool, which aims to identify youth who would benefit most from clinician intervention, can be completed by the patient or clinician using a computer or smart phone application prior to or during a clinic visit. Methodological challenges in developing the tool included selecting a parsimonious model from a set of correlated predictors with missing data. We implemented Bach’s bolasso algorithm which combines lasso with bootstrap and investigated the performance of the prognostic tool in new data collected in a different time period (temporal validation) and in another location (geographic validation). The tool showed adequate discrimination abilities, as reflected by a c-statistic above 0.8, in both validation samples. Most predictors selected into the tool pertained to substance use including use of cigarettes, e-cigarettes, alcohol and energy drinks mixed with alcohol, but not to mental or physical health.

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Notes

  1. 1.

    The language class variable refers to an English class in the Ontario sample and to a French class in the Québec sample.

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Acknowledgements

The COMPASS study was supported by a bridge grant from the Canadian Institutes of Health Research (CIHR) Institute of Nutrition, Metabolism and Diabetes (OOP-110788; awarded to SL) and an operating grant from the CIHR Institute of Population and Public Health (MOP-114875; awarded to SL). The COMPASS-Quebec project benefits from funding from the Ministre de la Sant et des Services sociaux of the province of Qubec, and the Direction rgionale de sant publique du CIUSSS de la Capitale-Nationale. MPS is supported by a salary award from Fonds de recherche du Qubec. JOL holds a Canada Research Chair in the Early Determinants of Adult Chronic Disease.

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Correspondence to Marie-Pierre Sylvestre .

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Sylvestre, MP. et al. (2022). A Prognostic Tool to Identify Youth at Risk of at Least Weekly Cannabis Use. In: Shaban-Nejad, A., Michalowski, M., Bianco, S. (eds) AI for Disease Surveillance and Pandemic Intelligence. W3PHAI 2021. Studies in Computational Intelligence, vol 1013. Springer, Cham. https://doi.org/10.1007/978-3-030-93080-6_4

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