Time-Space Analysis of Suicide Risk among Cancer Patients in Taiwan

Cancer patients'' suicide rate in Taiwan is much more than other European countries. This research aims to understand the whole picture of cancer patients’ suicide by using time-space analysis. Space analysis can locate health events which are unknown. It''s important to disease prevention, health services and resources distribution.
The dataset used in this study taken from the 1995-2007 Cancer Registry linking Death Certificate File published by Taiwan’s Department of Health, which include gender, age, cancer type, time since diagnosis, death month and suicide methods. We also use 2001 and 2005 National Health Interview Survey published by Taiwan’s Department of Health to calculate rates of betel quid chewing and cigarette smoking by township. The dataset about terminal care services were taken from the website of Bureau of National Health Insurance. The statistical methods used in this study are logistic regression by SAS9.2, and Scan Statistics by SaTScan.
We found that if cancer patient is male, 40-64 years old or his/her cancer type would change appearance will have higher risk to commit suicide, and the risk is rising by time. Cancer patients with good prognosis have lower suicide rate in short time interval since diagnosis. Suicide behaviors among cancer patients have spatial clusters. The most likely clusters are in Yunlin, Chiayi, Nantou, Tainan, Kaohsiung, Pingtung, Taitung and Hualien, those townships have higher rate of smoking and chewing betel nuts besides. Time-space analysis also found clusters in those townships, in 1999-2003. There are two subclusters founded after we lessen the scan radius (cluster size<10%), the primary subcluster locate in Tainan and Kaohsiung in 2002; the secondary subcluster locate in Yunlin, Chiayi and Nantou between 1999-2003.
On the whole, risk factors such gender, age, cancer type and time since diagnosis have significant influences to suicide behavior among cancer patients. We found out that there have time-space clusters in 1995-2007, and those clusters have different characteristics. We have to focus on the environment of higher risk clusters and those patients who live in there to make the policy of suicide prevention.