題名: Disease Patterns and Socioeconomic Status Associated with Utilization of Computed Tomgraphy in Taiwan, 1997-2003.
作者: Kung PT;Tsai WC;Hu HY
貢獻者: 健康產業管理學系
關鍵詞: computed tomography;CT;disease patterns;high-tech equipment;socioeconomic status
日期: 2008-02
上傳時間: 2009-10-28T05:58:45Z
摘要: Background/Purpose: The utilization of computed tomography (CT) has rapidly grown year after year. Yet, literature that explores the factors related to CT
utilization is limited. We attempted to determine the profiles of populations with high rates of CT usage, and to understand the association of disease patterns and socioeconomic status with CT-involved treatments. Methods: National Health Insurance medical claim data from the cohort of 200,000 samples representing 23 million insured people during 1997–2003 were used for analysis. Multiple logistic regression analysis was performed to identify factors associated with CT usage.
Results: Annual growth rate in CT usage during 1997–2003 was 7.35% and average use of CT was 28.69 per 1000 people. The diseases associated with the highest CT usage rates were neoplasm (169 per 1000 people), diseases of the circulatory system (33 per 1000 people), and congenital malformations (20 per 1000 people). Disease patterns with high annual growth rate of CT usage were morbidities originating in the perinatal period (29.85%), mental disorders (15.47%), and other disease patterns without clear symptoms and diagnosis (13.33%).
Individuals with lower salary used CT more frequently than those with higher salary. Multiple logistic regression analysis showed that cancer patients had significantly higher likelihood (odds ratio [OR], 7.71) of CT use than those with other diseases. Males (OR, 1.64) and elderly (OR, 1.96–7.05) had higher likelihoods of CT use and those with higher salaries had lower likelihood (OR, 0.50–0.89) of CT use. Conclusion: Neoplasm, diseases of the circulatory system, congenital malformations, and poor socioeconomic status were significantly associated with a higher rate of CT utilization. The distribution of disease patterns varied with gender, age groups, salary levels, and health care region’s household income levels. Further study is needed to better understand the nature of the findings.
