Has the Increase in Disability Insurance Participation Contributed to Increased Opioid-Related Mortality?
Notice bibliographique
Résumé
Ideas and Opinions15 November 2016Has the Increase in Disability Insurance Participation Contributed to Increased Opioid-Related Mortality?Nicholas B. King, PhD, Erin Strumpf, PhD, and Sam Harper, PhDNicholas B. King, PhDFrom McGill University, Montreal, Quebec, Canada.Search for more papers by this author, Erin Strumpf, PhDFrom McGill University, Montreal, Quebec, Canada.Search for more papers by this author, and Sam Harper, PhDFrom McGill University, Montreal, Quebec, Canada.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/M16-0918 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Mortality from unintentional prescription drug overdose has increased sharply in the United States since the mid-1990s. A large proportion of these deaths have involved benzodiazepines and other sedatives, antidepressants, sleep aids, and particularly opioid analgesics. In 2013, a total of 43 982 deaths were attributed to drug poisoning; 16 235 of these involved prescription opioids, a nearly 4-fold increase since 1999 (1).Researchers have identified a broad range of possible “supply-side” determinants of increasing opioid-related mortality, including increased prescription of opioid analgesics; prescription of stronger formulations and higher dosages; and the introduction and aggressive marketing of new pharmaceuticals, particularly OxyContin (Purdue Pharma), ...References1. Chen LH, Hedegaard H, Warner M. Rates of deaths from drug poisoning and drug poisoning involving opioid analgesics—United States, 1999–2013. MMWR Morb Mortal Wkly Rep. 2015;64:32. Google Scholar2. King NB, Fraser V, Boikos C, Richardson R, Harper S. Determinants of increased opioid-related mortality in the United States and Canada, 1990–2013: a systematic review. Am J Public Health. 2014;104:e32-42. [PMID: 24922138] doi:10.2105/AJPH.2014.301966 CrossrefMedlineGoogle Scholar3. Social Security Administration. Annual Statistical Report on the Social Security Disability Insurance Program, 2014. Washington, DC: Social Security Administration, Office of Retirement and Disability Policy, Office of Research Evaluation and Statistics; 2015. Google Scholar4. Liebman JB. Understanding the increase in disability insurance benefit receipt in the United States. J Econ Perspect. 2015;29:123-50. CrossrefMedlineGoogle Scholar5. Autor DH, Duggan MG. The rise in the disability rolls and the decline in unemployment. Q J Econ. 2006;118:157-206. CrossrefGoogle Scholar6. Wamhoff S, Wiseman M. The TANF/SSI connection. Soc Secur Bull. 2005;66:21-36. [PMID: 17590982] MedlineGoogle Scholar7. Hansen H, Bourgois P, Drucker E. Pathologizing poverty: new forms of diagnosis, disability, and structural stigma under welfare reform. Soc Sci Med. 2014;103:76-83. [PMID: 24507913] doi:10.1016/j.socscimed.2013.06.033 CrossrefMedlineGoogle Scholar8. Lakdawalla DN, Bhattacharya J, Goldman DP. Are the young becoming more disabled? Health Aff (Millwood). 2004;23:168-76. [PMID: 15002639] CrossrefMedlineGoogle Scholar9. Meara E, Horwitz JR, Powell W, McClelland L, Zhou W, O'Malley AJ, et al. State legal restrictions and prescription-opioid use among disabled adults. N Engl J Med. 2016. [PMID: 27332619] CrossrefMedlineGoogle Scholar10. Pacula RL, Powell D, Taylor E. Does Prescription Drug Coverage Increase Opioid Abuse? Evidence From Medicare Part D. NBER Working Paper no. 21072. Cambridge, MA: National Bureau of Economic Research; 2015. Google Scholar Author, Article, and Disclosure InformationAffiliations: From McGill University, Montreal, Quebec, Canada.Financial Support: Drs. Strumpf and Harper were each supported by a Chercheur boursier Junior 2 from the Fonds de la Recherche en Santé du Québec.Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M16-0918.Corresponding Author: Nicholas B. King, McGill University, Biomedical Ethics Unit, 3647 Peel Street, Montreal, Quebec H3A 1X1, Canada.Current Author Addresses: Dr. King: McGill University, Biomedical Ethics Unit, 3647 Peel Street, Montreal, Quebec H3A 1X1, Canada.Dr. Strumpf: McGill University, Department of Economics, Leacock 418, 855 Sherbrooke Street West, Montreal, Quebec H3A 2T7, Canada.Dr. Harper: McGill University, Department of Epidemiology, Biostatistics, and Occupational Health, Purvis Hall, 1020 Pine Avenue West, Montreal, Quebec H3A 1A2, Canada.Author Contributions: Conception and design: N.B. King, E. Strumpf, S. Harper.Analysis and interpretation of the data: N.B. King, E. Strumpf, S. Harper.Drafting of the article: N.B. King, E. Strumpf, S. Harper.Critical revision of the article for important intellectual content: N.B. King, E. Strumpf, S. Harper.Final approval of the article: N.B. King, E. Strumpf, S. Harper.Statistical expertise: E. Strumpf, S. Harper.Collection and assembly of data: E. Strumpf, S. Harper.This article was published at www.annals.org on 30 August 2016. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics Cited byThe effect of awarding disability benefits on opioid consumptionTrends in medical conditions and functioning in the U.S. population, 1997–2017Declining Life Expectancy in the United States: Missing the Trees for the ForestReceipt of Disability Benefits and Prescription Opioid PrevalenceEarly High-Risk Opioid Prescribing Practices and Long-Term Disability Among Injured Workers in Washington State, 2002 to 2013Increased overall and cause‐specific mortality associated with disability among workers’ compensation claimants with low back injuriesAssessment of Opioid Prescribing Practices Before and After Implementation of a Health System Intervention to Reduce Opioid Overprescribing 15 November 2016Volume 165, Issue 10Page: 729-730KeywordsAddictionAnalgesicsDisabilitiesDrugsLower back painMedicareMortalityOpioidsOxycodonePublic policy ePublished: 30 August 2016 Issue Published: 15 November 2016 Copyright & PermissionsCopyright © 2016 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,056 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,005 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,005 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,068 | 0,010 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».