The Relief Effect of Copayment Decreasing Policy on Unmet Needs in Targeted Diseases
Notice bibliographique
Résumé
Background: Bankrupted households have recently been increased due to excessive medical expenditure in Korea. They have not been protected from economic risk when household's member has severe diseases that need a lot of money for treatment. Purpose of this study examines policy effect by comparing unmet needs' change of policy object households and non-object groups. Methods: We used Korea Health panel 2nd 4th data collected by Korea Institute for Health and Social Affairs and National Health Insurance Service. Analysis subjects were 381 households (pre-policy) and 393 households (post-policy) that had cancer and cardiovascular and cerebrovascular diseases. Since it was major concern that estimates benefit strengthening policy started by certain time, we setup comparing households which had diabetes, hypertension disease. Comparison subjects were 393,247 households, respectively and we evaluated policy effect using difference in difference (DID) model. Results: Although unmet needs of policy object households were higher than non-object groups, policy execution variable affected negative direction. But interaction-term which shows pure effect of policy was not statistically significant. We utilized multi-DID model to examine factors affecting unmet needs causes. Copayment assistance policy did not significantly affect households that responded to 'economic reason,' and 'no have time to visit' for unmet needs causes. Conclusion: The second copayment assistance policy did not significantly give positive effect to beneficiary households than non-beneficiary groups. When we consider that primary purpose of public insurance guarantee high medical expenditure occurred by unexpected events, it needs to deliberate on switch of benefit strengthening policy that can assist vulnerable people. Also, we suggest that government forward a policy covering non-reimbursable medical expenses as well as switch of benefit strengthening direction because benefit policy do not affect non-covered medical cost which accounts for quarter of total health expenditure.
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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.
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