Download PDFOpen PDF in browserComparative Analysis of Community Rating and Dynamic Pricing on Enhancing Healthcare Accessibility in Low-Income Countries: a Case Study of TanzaniaEasyChair Preprint 1492718 pages•Date: September 18, 2024AbstractOver the last two decades out-of-pocket (OOP) payments and external donations have contributed over 60% of current health expenditure (CHE) in low-income countries (LICs), in which OOP account for more than 40%, indicating a heavy reliance on individual contributions. Current studies on pricing models for healthcare largely focuses on achieving computational efficiency, but not the pricing effect on healthcare accessibility that influence healthcare improvement. Analysis of healthcare pricing models based on cost affordability can bridge this gap. Commonly used pricing models such as community rating (CR), dynamic pricing (DP) and OOP are considered. DP is a machine learning (ML) model based on Tanzania’s National Panel Survey (NPS) data, while the CR model is based on rates from the Tanzania’s Act Supplement for the mandatory public health insurance scheme of 2023, to the Ministry of Health (MoH). A pure premium approach for the DP model and current rates for the CR model is employed for comparative purpose. The results showed that CR does not significantly improve healthcare costs compared to DP ("p-value = 0" ), conversely, DP significantly outperforms CR with p-value "3.49×" 〖"10" 〗^"-08" . Moreover, DP model remains superior to CR until loading factor range of 5.7 and 6.4, where no significant difference, beyond which DP increases healthcare costs. Likewise, DP outperforms OOP until the loading factor range of 0.1 and 0.2, where costs are insignificantly different, above which DP increases costs. Load factor analysis confirm DP to significantly enhance healthcare accessibility by reducing cost compared to CR and OOP pricing models. Keyphrases: Community rating, Healthcare Accessibility, Individual contribution, Pricing affordability, dynamic pricing, machine learning
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