Pharmaceutical policies: effects of regulating drug insurance schemes
Notice bibliographique
Résumé
BACKGROUND: Drug insurance schemes are systems that provide access to medicines on a prepaid basis and could potentially improve access to essential medicines and reduce out-of-pocket payments for vulnerable populations. OBJECTIVES: To assess the effects on drug use, drug expenditure, healthcare utilisation and healthcare outcomes of alternative policies for regulating drug insurance schemes. SEARCH METHODS: We searched CENTRAL, MEDLINE, Embase, nine other databases, and two trials registers between November 2014 and September 2020, including a citation search for included studies on 15 September 2021 using Web of Science. We screened reference lists of all the relevant reports that we retrieved and reports from the Background section. Authors of relevant papers, relevant organisations, and discussion lists were contacted to identify additional studies, including unpublished and ongoing studies. SELECTION CRITERIA: We planned to include randomised trials, non-randomised trials, interrupted time-series studies (including controlled ITS [CITS] and repeated measures [RM] studies), and controlled before-after (CBA) studies. Two review authors independently assessed the search results and reference lists of relevant reports, retrieved the full text of potentially relevant references and independently applied the inclusion criteria to those studies. We resolved disagreements by discussion, and when necessary by including a third review author. We excluded studies of the following pharmaceutical policies covered in other Cochrane Reviews: those that determined how decisions were made about which conditions or drugs were covered; those that placed restrictions on reimbursement for drugs that were covered; and those that regulated out-of-pocket payments for drugs. DATA COLLECTION AND ANALYSIS: Two review authors independently extracted data from the included studies and assessed risk of bias for each study, with disagreements being resolved by consensus. We used the criteria suggested by Cochrane Effective Practice and Organisation of Care (EPOC) to assess the risk of bias of included studies. For randomised trials, non-randomised trials and controlled before-after studies, we planned to report relative effects. For dichotomous outcomes, we reported the risk ratio (RR) when possible and adjusted for baseline differences in the outcome measures. For interrupted time series and controlled interrupted time-series studies, we computed changes along two dimensions: change in level; and change in slope. We undertook a structured synthesis following the EPOC guidance on this topic, describing the range of effects found in the studies for each category of outcomes. MAIN RESULTS: We identified 58 studies that met the inclusion criteria (25 interrupted time-series studies and 33 controlled before-after studies). Most of the studies (54) assessed a single policy implemented in the United States (US) healthcare system: Medicare Part D. The other four assessed other drug insurance schemes from Canada and the US, but only one of them provided analysable data for inclusion in the quantitative synthesis. The introduction of drug insurance schemes may increase prescription drug use (low-certainty evidence). On the other hand, Medicare Part D may decrease drug expenditure measured as both out-of-pocket spending and total drug spending (low-certainty evidence). Regarding healthcare utilisation, drug insurance policies (such as Medicare Part D) may lead to a small increase in visits to the emergency department. However, it is uncertain whether this type of policy increases or decreases hospital admissions or outpatient visits by beneficiaries of the scheme because the certainty of the evidence was very low. Likewise, it is uncertain if the policy increases or reduces health outcomes such as mortality because the certainty of the evidence was very low. AUTHORS' CONCLUSIONS: The introduction of drug insurance schemes such as Medicare Part D in the US health system may increase prescription drug use and may decrease out-of-pocket payments by the beneficiaries of the scheme and total drug expenditures. It may also lead to a small increase in visits to the emergency department by the beneficiaries of the policy. Its effects on other healthcare utilisation outcomes and on health outcomes are uncertain because of the very low certainty of the evidence. The applicability of this evidence to settings outside US healthcare is limited.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,012 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».