Monetary Valuation of Congenital Heart Disease in Indonesia: Economic Modeling Study
Notice bibliographique
Résumé
BACKGROUND: Congenital heart disease (CHD) constitutes a significant health and economic burden in low- and middle-income countries, including Indonesia. However, its macroeconomic impact across provinces remains poorly quantified. OBJECTIVE: This study aims to estimate the economic burden associated with premature death and disability due to CHD in Indonesia, with a focus on regional and gender disparities. METHODS: Using data from the Global Burden of Disease 2019 study, we assessed the value of lost welfare (VLW) attributable to CHD across all 34 Indonesian provinces. Economic valuation was conducted using 3 approaches: the US Department of Transportation model, a method based on the Organisation for Economic Co-operation and Development, and a national wage-based estimate. Analyses were stratified by sex and derived from both disability-adjusted life years (DALYs) and years of life lost. We examined disparities using two approaches: (1) gender disparity was measured as the relative difference in VLW between males and females, and (2) geographical disparity was quantified using both location quotients (for raw VLW) and a disparity index (for VLW-to-gross domestic product ratios). RESULTS: The national CHD-related VLW derived from DALYs was estimated at US $16.83 billion (US Department of Transportation), US $11.41 billion (Organisation for Economic Co-operation and Development), and US $9.03 billion (wage-based). West Java recorded the highest provincial VLW (US $1.60 billion), followed by East Java (US $0.83 billion), North Sumatra (US $0.80 billion), and Central Java (US $0.79 billion), indicating a concentration of burden in populous provinces. In contrast, Yogyakarta (US $0.04 billion), North Kalimantan (US $0.04 billion), and West Papua (US $0.06 billion) had the lowest estimates. In several provinces, male-attributed VLW was more than 150% higher than female VLW, with extreme gaps observed in Riau Islands (281.25%), Aceh (166.10%), and Banten (145.86%). These patterns were consistent across both DALY- and years-of-life-lost-based estimates. Based on the location quotients, provinces such as Papua (2.42), West Sulawesi (2.37), Maluku (1.78), East Nusa Tenggara (1.75), and Central Sulawesi (1.66) bore VLW burdens far greater than their population share. The burden was disproportionately high in several eastern provinces, including East Nusa Tenggara (3.35%), Maluku (2.61%), and West Sulawesi (2.66%). CONCLUSIONS: CHD is a macroeconomically manageable burden across most of Indonesia. However, the presence of deep gender disparities and geographically concentrated burdens in eastern and underserved provinces calls for targeted pediatric cardiac health investments.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».