癌症已成為國人主要死因之一，其可究因於癌症轉移的高死亡率；同樣地，癌症的醫療費用已成為沉重的社會負擔。為評估風險與經濟相關議題，本研究發展半馬可夫模型於肺癌、腦癌、肝癌、淋巴癌的轉移研究，資料母體與成本來源為台灣的一個全國性醫療資料庫；並以共變量分析研究性別、年齡、醫療費用對於狀態間的轉移風險，及以蒙地卡羅模擬考慮癌症轉移條件與醫療費用的癌症轉移時間。從共變量分析結果得知，增加疾病治療費用可顯著降低癌症轉移發生的風險，若以中位數為20年的存活時間為基準，可觀察得醫療成本範圍從肺癌腦轉移的$399,397新台幣到腦癌肝轉移的$2,145,650新台幣。模擬結果同樣顯示了醫療費用與存活時間的正相關性。細分其成本組成結構，藥物治療佔了總成本的最大部分，平均約達35%；此外也觀察到大部分的成本支出在診斷後逐漸下降。本研究透過敏感度分析，更進一步提供了在當前狀況下，決策者檢閱或預測不同變化影響的機制。總而言之，本研究提出的模型提供了醫療保健政策的決策參考機制，對了解癌症轉移所造成的負擔提供更深度的研究探討。

Cancer has become one of the leading causes of death. One major cause of its high mortality is attributed to the metastasized cancer. Analogously, the medical expenses for the cancer treatment have generated a socioeconomic burden. In this study, a semi-Markov model was developed to perform risk and economic evaluation associated to several types of cancer metastasis for lung, brain, liver and lymphoma cancer. A nationwide medical database in Taiwan was used to derive baseline study population and costs data. Covariates analysis investigated the influence of gender, age and medical expenses generated to the transition risk among states. Then, the aggregate time until development of metastasis, survivability from metastasis condition and lifetime medical costs were estimated through Monte Carlo simulation. From covariates analysis, we found that the risks of metastatic cancer occurrence is significantly reduced by high medical expenses spent to treat the disease. The 20 years median lifetime cost ranged from 399,397 NTD for lung cancer-brain metastasis case to 2,145,650 NTD for brain cancer-liver metastasis case. Simulation results also revealed a positive correlation between the expensed cost and patient’s lifetime. Breaking down the cost into its components, it is revealed that drugs made up the largest portion of all expenses, accounting for 35% in average. Moreover, it was also observable that most expenses decreased gradually after diagnosis. Moreover, sensitivity analysis provides a mechanism for policy makers to review or predict the impact of several change in current condition. In conclusion, this model provides a healthcare policy decision-making mechanism to understand the disease burden of cancer metastasis cases.

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