Riewpaiboon Arthorn, Pornlertwadee Penkae, Pongsawat Kwanjai
Department of Pharmacy, Mahidol University, Bangkok, Thailand.
Value Health. 2007 Jul-Aug;10(4):223-30. doi: 10.1111/j.1524-4733.2007.00172.x.
This study aims to formulate a cost model from a provider perspective regarding the direct medical costs for diabetic patients who received care in a 30-bed public hospital in Thailand during the fiscal year of 2001.
This study is a retrospective prevalence-based cost of illness study. Data were collected by reviewing the medical record of each patient for the whole year. The statistical analysis employed was the stepwise multiple regression method.
The study covered 186 diabetic patients. It was found that the average cost of caring for a diabetic patient per year was 6331 Thai baht (THB) at 2001 prices (approximately 40 THB = US 1 dollar). A major portion of this cost was spent for pharmacy services, which accounted for 45% of the whole cost, followed by outpatient services (24%), inpatient services (16%), and laboratory investigation (11%). Regarding the model for forecasting the cost, the type of diabetes and its accompanying complications, i.e., hyperlipidemia, cardiovascular accident, hypertension, hyperglycemia, hypoglycemia, gangrene, and diabetic foot, were considered as significant predictor variables (adjusted R(2) = 0.48). The quantitative effects in monetary term of these significant predictors were also demonstrated.
The results could be beneficial in forecasting the economic burden of diabetes mellitus in Thailand. Furthermore, the results could be used as a financial tool for cost control and disease management at the community hospital level.
本研究旨在从提供者的角度，针对2001财年在泰国一家拥有30张床位的公立医院接受治疗的糖尿病患者的直接医疗费用，制定一个成本模型。
本研究是一项基于患病率的回顾性疾病成本研究。通过查阅每位患者一整年的病历收集数据。采用的统计分析方法是逐步多元回归法。
该研究涵盖了186名糖尿病患者。研究发现，以2001年的价格计算，每年照顾一名糖尿病患者的平均费用为6331泰铢（约40泰铢 = 1美元）。这笔费用的大部分用于药房服务，占总费用的45%，其次是门诊服务（24%）、住院服务（16%）和实验室检查（11%）。关于成本预测模型，糖尿病类型及其伴随的并发症，即高脂血症、心血管意外、高血压、高血糖、低血糖、坏疽和糖尿病足，被视为显著的预测变量（调整后R(2) = 0.48）。还展示了这些显著预测变量在货币方面的量化影响。
这些结果可能有助于预测泰国糖尿病的经济负担。此外，这些结果可作为社区医院层面成本控制和疾病管理的财务工具。
