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Enregistrement W1591593808

Insurance coverage and the treatment of mental illness: effect on medication and provider use.

2008· article· en· W1591593808 sur OpenAlexaffabout
Gillian Mulvale, Jeremiah Hurley

Notice bibliographique

RevuePubMed · 2008
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealthcare Systems and Reforms
Établissements canadiensMental Health Commission of Canada
Organismes subventionnairesnon disponible
Mots-clésMental healthMedical prescriptionMental illnessMedicineMoodAnxietyPsychiatryFamily medicineNursing
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Canada's public health insurance system fully covers medically necessary hospital and physician services, but does not cover community-based non-physician mental health provider services or prescription drugs. Almost 2/3 of Canadians have private supplemental insurance for extended health benefits, typically through their employer, so its distribution is skewed to higher-income, employed Canadians, and typically features substantial cost-sharing and coverage limits. A recent national survey suggests only one-third of Canadians with selected mental disorders talked to a health professional during the previous 12 months and only a minority (19.3%) receive drug treatment. Financial barriers to care constitute a potentially important contributor to this under-use of mental health treatments. AIMS OF THE STUDY: The objective is to understand how private supplemental insurance status affects the utilization of prescription medication and four types of community-based providers for mental health problems in Canada. METHODS: The data derive from a special mental health supplement to the nationally representative Canadian Community Health Survey. Utilization of five types of prescribed medications (sleep, anxiety, mood stabilizers, anti-depressants and anti-psychotics) is measured dichotomously as use/no-use in the previous 12 months. Utilization of community-based provider services (family physician, psychiatrist, psychologist and social worker) is measured as (i) use/no-use and (ii) conditional on use, number of contacts in the previous 12 months. We employ multivariate regression methods appropriate to the binary and count nature of the dependent variable to measure the impact of supplemental private insurance status on utilization, controlling for health, demographic and socio-economic characteristics. We test for endogeneity of insurance status using instrumental variable techniques. RESULTS: Having private supplemental insurance significantly increases the odds of using medications for mental illness, with particularly large increases for anti-psychotic and mood-stabilizer medications. Private supplemental insurance coverage does not increase use of provider services. We find little evidence of endogeneity of private insurance. DISCUSSION: Lack of supplemental insurance for prescription medication is a potentially important financial barrier to mental health treatment in Canada. The estimated effect is likely understated because the utilization measure does not capture quantity of medication use. It is not surprising that no significant relationship between private insurance status and utilization of provider services is found for publicly-covered family physician and psychiatry services, where the link between supplemental insurance and use is indirect, through the need to visit a physician to obtain a prescription. The result is surprising for psychologists and social workers, and may reflect limits to private coverage which are not fully captured here. IMPLICATIONS FOR HEALTH CARE PROVISION AND USE: Insurance coverage has an important relative impact on the likelihood of drug use for mental illness. IMPLICATIONS FOR HEALTH POLICIES: A program that offers insurance coverage for anti-psychotic and mood-stabilizing medication could reduce the high personal and societal burden associated with serious mental illness, without a large overall budgetary impact. IMPLICATIONS FOR FUTURE RESEARCH: Future research should incorporate insurance measures which capture details of coverage among all survey respondents. Linking survey to utilization data will help to overcome issues of recall bias.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,377
Score d'incertitude au seuil0,214

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,029
Tête enseignante GPT0,210
Écart entre enseignants0,181 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations17
Publié2008
Routes d'admission2
Résumé présentoui

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