Hospital and physician-based mental healthcare during 12 months of opioid agonist treatment for opioid use disorder: Exploring costs and factors associated with acute care
Notice bibliographique
Résumé
BACKGROUND: Individuals with opioid use disorder (OUD) have a high prevalence of co-occurring mental health disorders; however, there exists little information on mental health service use for this population. We aimed to determine the prevalence of non-substance use-related mental health emergency department (ED) visits, hospitalizations, and outpatient physician visits for individuals receiving treatment for OUD over one year. We also explored individual-level characteristics associated with mental health care service use and estimated the costs of this care. METHODS: We linked observational cohort data collected from 3,430 individuals receiving treatment for OUD in Ontario, Canada, with health administrative records available for all individuals enrolled in Ontario's public health insurance program. Eligible participants were receiving medication treatment for OUD and were recruited between 2011 and 2021 Starting on the day of cohort enrolment, we included health service data for up to 12 months. We identified ED visits and hospitalizations for non-substance use-related mental health disorders using ICD-10-CA diagnostic codes. Outpatient mental health visits to primary care providers and psychiatrists were ascertained by examining the diagnostic codes of physician billing claims. We used logistic regression to explore the association between demographic and clinical factors of interest and mental health-related ED visits or hospitalizations. Mean one-year mental healthcare costs, calculated in 2022 Canadian dollars, were estimated. We fit a two-part zero-inflated negative binomial model to explore the association between factors of interest and healthcare costs. FINDINGS: Altogether, 14.9% of individuals had mental health-related acute care ED visits or hospitalizations and 37.3% had outpatient mental health visits during the follow up period. For participants with at least one visit, we determined the mean number of ED visits (1.93, standard deviation [SD] = 2.15), hospitalizations (1.46, SD = 1.05), primary care visits (3.51, SD = 4.31), and psychiatry visits (4.04, SD = 4.73). Lower odds of ED use and hospitalization were associated with older age (46+ compared to less than 25 years: odds ratio [OR] 0.43, 95% confidence interval [CI]: 0.29, 0.63) and being employed (OR 0.48, 95% CI 0.37, 0.61). Higher odds of ED use and hospitalization was associated with positive opioid urine drug screens (50% positive urine drug screens compared to 0%: OR 1.45, 95% CI 1.05, 2.01), having more comorbid conditions (7+ health conditions compared to 0-2 health conditions: OR 3.76, 95% CI 2.60, 5.44), and receipt of outpatient mental healthcare (OR 2.38, 95% CI 1.95, 2.92) were associated with higher odds of ED visits or hospitalizations. Mean one-year mental healthcare costs for individuals receiving ED visits or hospitalizations totaled $9,117.80 (95% CI 7,372.90, 10,862.70) per person. Mean one-year costs for individuals with outpatient mental healthcare alone totaled $382.30 (95% CI 343.20, 421.30) per person. CONCLUSIONS: Individuals receiving treatment for OUD receive care in EDs, inpatient units, and outpatient clinics for mental health conditions other than substance use-related diagnoses. Healthcare costs were considerably higher for those receiving acute care treatment for mental health conditions. Studying integrated mental health and substance use disorder treatment in the outpatient setting should be a priority to bolster care for this population.
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 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 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 ».