The role of race and place in drug use and mortality in the United States
Notice bibliographique
Résumé
Background: Much of the research on the emergence and recent trends in the opioid epidemic in the United States examines differences by either race or place.Yet, the few studies which assess the intersection of these dimensions reveal unexpected findings, which challenge initial assumptions about the uniformity of the epidemic's impact.As such, this thesis aims to better describe differences in trends of drug overdose mortality and drug-use outcomes for non-Hispanic Blacks and Whites across three metropolitan categories: large metro, small metro, and nonmetro.Objectives: This thesis aims to 1) present trends in all-drug and opioid-related mortality rates between 2003-2018; 2) highlight differences in changes in drug-related mortality before and after the peak of the epidemic in 2015; 3) delineate time trends in illicit drug use and prescription pain reliever misuse between 2003-2018; and 4) describe recent patterns in access to and source of drugs, for Blacks and Whites based on their metropolitan status.Methods: Drug mortality data was obtained from the Centers for Disease Control and Prevention (CDC) WONDER database for national and population level data.Drug use data was obtained from the National Survey for Drug Use and Health (NSDUH), a nationally representative annual survey.Results: For Blacks, drug-related mortality rates between 2003 to 2018 were consistently higher in large and small metro areas than in non-metro areas; such disparities by metro status did not emerge among Whites until 2011.In 2018, opioid-related deaths continued to rise for Blacks in large and small metro areas, but declined for Blacks in nonmetro areas, and for Whites in all metro categories.In contrast to drug-related mortality trends, self-reported drug use trends did not vary greatly between 2003-2018.Despite similar mortality rates prior to 2011, metro-based differences in illicit drug use and prescription pain reliever misuse among Whites were apparent over the entire time period.Among Blacks, illicit drug use was always higher in large and small metro areas; however, there were no metro-based differences in trends in prescription pain reliever misuse.Although reported drug use rates are generally lower, Blacks in all metro categories were more likely than Whites to report being approached by someone selling drugs, and this likelihood was highest among Blacks in large and small metro areas. Conclusions:The findings demonstrate that many of the common narratives in drug use and mortality trends cannot be applied across racial and metropolitan groups.In contrast to common narratives, in recent years the opioid drug overdose epidemic is worsening for Blacks in both large and small metro areas, while it is declining for Whites.Thus, recent intervention efforts may be overlooking a particularly vulnerable subpopulation.Moreover, efforts to address drug use and its outcomes among Blacks should not be limited to large-urban areas, as patterns of drug use and mortality between small and large urban areas are consistently similar.Among rural Blacks, drug-related mortality and illicit drug use were consistently lowest, in spite of heightened risks for drug use for this population.The persistence of this trend is significant and not explained by just barriers in access to prescription opioids.Empirical research is needed to better understand why rates are escalating among more urban Blacks, while remaining low for rural Blacks and declining for Whites in all metro categories.Overall, studying drug-related mortality
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».