Predicting opioid misuse at the population level is different from identifying opioid misuse in individual patients
Copyright © 2019 Elsevier Inc. All rights reserved..
Tumin and Bhalla mentioned challenges associated with the use of population-based survey and machine learning (ML) results on adolescent opioid misuse to clinical settings. In a clinical setting, medical providers do know patient's identity. So, it is not surprising that drug misuse is rarely identified through patient's self-report especially if it involves illicit drug. Even though self-report is susceptible to bias, it is a valid and affordable tool to gather data on illicit drug use at the population level. Use of audio computer-assisted self-interviewing (ACASI) and computer-assisted personal interviewing (CAPI) in NSDUH provides the respondent with a highly private and confidential mode for responding to questions, which helps increase the level of honest reporting of illicit drug use and other sensitive behaviors. As acknowledged in the paper, opioid misuse should not be inferred at the individual level from our ML models. Such interpretations may lead to ecological fallacy. Predicting opioid misuse at the population level is different from identifying opioid misuse in individual patients. Nonetheless, we believe that coordinated multisectoral collaborations that leverage the expertise and resources of both public health and clinical sectors would offer a promising model for addressing the opioid crisis.
| Errataetall: | |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:131 |
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| Enthalten in: |
Preventive medicine - 131(2020) vom: 23. Feb., Seite 105969 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Seo, Dong-Chul [VerfasserIn] |
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| Links: |
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| Themen: |
Adolescents |
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| Anmerkungen: |
Date Completed 21.04.2020 Date Revised 21.04.2020 published: Print CommentOn: Prev Med. 2020 Jan;130:105886. - PMID 31705938 Citation Status MEDLINE |
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| doi: |
10.1016/j.ypmed.2019.105969 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM305306065 |
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| 520 | |a Copyright © 2019 Elsevier Inc. All rights reserved. | ||
| 520 | |a Tumin and Bhalla mentioned challenges associated with the use of population-based survey and machine learning (ML) results on adolescent opioid misuse to clinical settings. In a clinical setting, medical providers do know patient's identity. So, it is not surprising that drug misuse is rarely identified through patient's self-report especially if it involves illicit drug. Even though self-report is susceptible to bias, it is a valid and affordable tool to gather data on illicit drug use at the population level. Use of audio computer-assisted self-interviewing (ACASI) and computer-assisted personal interviewing (CAPI) in NSDUH provides the respondent with a highly private and confidential mode for responding to questions, which helps increase the level of honest reporting of illicit drug use and other sensitive behaviors. As acknowledged in the paper, opioid misuse should not be inferred at the individual level from our ML models. Such interpretations may lead to ecological fallacy. Predicting opioid misuse at the population level is different from identifying opioid misuse in individual patients. Nonetheless, we believe that coordinated multisectoral collaborations that leverage the expertise and resources of both public health and clinical sectors would offer a promising model for addressing the opioid crisis | ||
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