研究背景：工作為社經地位指標之一，可了解社會結構如何廣泛的影響健康狀態。近年臺灣口腔癌發生率趨緩，但這樣的現象是否在不同工作群族間具一致性未可知，除此之外也無實證資料比較不同類別工作口腔癌患者的診斷與治療、存活情形，以及罹癌後的工作變化。難以完整說明口腔癌對不同類別工作者所造成的衝擊。
研究目的：為完整描述口腔癌對不同類別工作者所造成的影響，研究目的依序了解不同類別工作者在口腔癌發生差異、診斷與治療情形、存活，以及工作衝擊，也將各別討論相關影響因子。
研究方法：本研究採回溯性世代追蹤研究。依照不同研究目的使用不同個案，包含口腔癌個案(OC)、母群體(POP)、口腔癌勞動個案(OC workers)、非口腔癌勞動個案(non-OC workers)。口腔癌個案(OC)來自癌症登記檔長表(LF) 2011年至2020年30-59歲之新發口腔癌患者，且經串聯全民健保資料庫承保檔(ID)後篩選診斷時有健保投保者。母群體(POP)則來自2011年至2020年30-59歲全民健保資料庫承保檔(ID)，再串聯同期間口腔癌個案資訊後刪除歷年舊口腔癌個案後而得。口腔癌勞動個案(OC workers)則是口腔癌個案(OC)中2011年至2017年診斷時有工作男性，將其診斷日定義為指標日(T0)，並從全民健保資料庫承保檔(ID)配對具可比性樣本成為非口腔癌勞動個案(non-OC workers)。首先以口腔癌個案(OC)與母群體(POP)計算不同類別工作者之口腔癌標準化發生率，並透過卜瓦松迴歸模型比較各類別工作之發生風險。第二，以口腔癌個案(OC)使用多變項邏輯斯迴歸模型了解各類別工作者的晚期癌症、首次治療與診斷間隔>30天的風險。第三，同樣以口腔癌個案(OC)使用Kaplan-Meier存活分析、COX等比例風險模型比較各類別工作者的存活情形。最後，比較口腔癌勞動個案(OC workers)與非口腔癌勞動個案(non-OC workers)在指標日(T0)後各時間的工作變化並計算整體工作損失、存活者工作損失，即因口腔癌在整體及倖存者減少的工作率。同時，針對口腔癌勞動個案(OC workers)也使用多元邏輯斯回歸模型分析造成指標日(T0)後第48(T48)個月無業、死亡之影響因子。
研究結果：臺灣2011年至2020年30-59歲不同類別工作者之新發口腔癌年齡標準化發生率最高為農民(52.4)、漁民(50.3)，且經過校正潛在干擾因子後，漁民、無業者仍維持極高風險。診斷情形部分，無業、農民晚期癌症風險較高，而各類別工作者首次治療與診斷間隔時間>30天的風險均不顯著。各類別工作者的存活具顯著差異，其中癌症期別、治療方式與治療間隔時間是造成死亡風險差距的重要因子。在罹癌後勞動衝擊方面，口腔癌患者在指標日後第48個月的整體工作損失、存活者工作損失分為27.79%、10.74%，其中存活者工作損失隨年代增加。不同類別工作存活者罹癌後工作率趨勢不同，其他勞工最快反映因癌症減少工作，但長期而言公務人員因癌症減少的工作率最高。指標日後第48個月造成死亡的影響因子包含診斷年較早、年齡較大、健保投保金額較低、癌症期別較晚、指標日前一年共病指數較高。而指標日後第48個月造成無業的影響因子則包含公務人員、勞工(職業工會投保)、其他勞工、診斷年較晚、年齡較大、健保投保金額較低、癌症期別較晚。
結論：口腔癌在發生、罹癌情形、存活與癌後工作等不同面向的衝擊在不同類別工作者中不盡相同，背後成因包含社經地位、健康適能與健康生活型態的差距。從不同工作的角度出發，除了可完整說明闡述口腔癌在臺灣勞動族群的現況，釐清口腔癌本身的影響，本篇研究結果也期盼能與口腔癌防治政策對話。在未來將相關策略融入工作場域與脈絡，改善不同工作者罹癌前後情形，消弭不同類別工作者的健康不平等。未來仍需深入了解不同類別工作者的罹癌歷程以及後續影響，例如醫療資源使用、癌後薪資變動、停工到返回勞動市場的時間等，更全面描繪口腔癌患者面臨的困境。

Occupation, as a proxy for social status, may influence health through resource accessibility, health literacy, healthy lifestyle, and other factors. The disparities in these aspects can perpetuate health inequalities among different types of workers. Therefore, comparing oral cancer incidence, diagnosis, treatment, survival, and post-cancer employment rates by occupation can identify high-risk populations and illustrate the impact of oral cancer on different types of workers.
This was a population-based retrospective cohort study using data from the Health and Welfare Data Science Center (HWDC). This research aimed to investigate the differences and covariates of incidence, diagnosis and treatment, survival, and post-cancer work changes using regression models. Each research aim used different subjects but mainly focused on new oral cancer cases aged 30-59 from January 1, 2011, to December 31, 2020.
The fishermen and the unemployed had the highest risk of being diagnosed with oral cancer, while the unemployed and farmers had higher risks of advanced cancer. Survival also differed significantly among different types of workers, with cancer stage, treatment, and time-to-treatment being important factors contributing to these differences. The reductions in employment rate overall and among survivors due to oral cancer were 27.79% and 10.74%, respectively, at the 48th month after diagnosis.
Not only did this study highlight the difficulties that Taiwanese workers faced with oral cancer, but it also aimed to promote reforms in oral cancer prevention policies and workplace settings in the future, in order to reduce health inequalities across different types of workers.

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