High-cost users still came to hospitals during the COVID-19 pandemic during first wave data in Thailand: secondary data analysis
Notice bibliographique
Résumé
BACKGROUND: The phenomenon of high-cost users (HCUs) in health care occurs when a small proportion of patients account for a large proportion of health care expenditures. By understanding this phenomenon during the COVID-19 pandemic, tailored interventions can be provided to ensure that patients receive the care they need and reduce the burden on the health system. OBJECTIVES: This study aimed to determine (1) whether the HCUs phenomenon occurred during the pandemic in Thailand by exploring the pattern of inpatient health expenditures over time from 2016 to 2021; (2) the patient characteristics of HCUs; (3) the top 5 primary diagnoses of HCUs; and (4) the potential predictors associated with being an HCU. METHODS: The secondary data analysis was conducted via inpatient department (IPD) e-Claim data from the National Health Security Office for the Universal Coverage Scheme, which provides health care to ~ 80% of the Thai population. Health care expenditure over time was calculated, and the characteristics of the population were examined via descriptive analysis. Multinomial logistic regression was applied to explore the potential predictors associated with being an HCU. RESULTS: The characteristics of HCUs remained relatively the same from 2016 to 2021. In terms of the proportion of male (55%) to female patients (45%), the age ranged from 55 to 57 years, with an estimated 8-day length of hospital stay and 7 admissions per year, and the average health care cost per patient was ≥ USD 2,860 (100,000 THB). The low-cost users (LCUs) group (the bottom 50% of the population), had more female patients (55%), a younger age ranging from 27 to 33 years, a 3-day length of stay, 1‒2 admissions per year, and a lower average health care cost per patient, which was less than USD 315 (≤ 11,000 THB). CONCLUSION: The HCUs phenomenon still existed even with limited health care accessibility or lockdown measures implemented during the COVID-19 pandemic. This finding could indicate the uniqueness of the need for health services by HCUs, which differ from those of other population groups. By understanding the trends of health care utilization and expenditure, along with potential predictors associated with being an HCU, policies can be introduced to ensure the appropriate allocation of health resources to the right people in need of the right care during future pandemics.
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 | 0,008 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».