[Developing the predictive model for the group at high risk for colon cancer]

J Prev Med Public Health. 2006 Sep;39(5):438-46.
[Article in Korean]

Abstract

Objectives: We developed the predictive model for the incidence of colon cancer by utilizing the health screening data of the National Health Insurance in Korea. We also explored the characteristics of the high risk group for colon cancer.

Methods: The predictive model was used to determine those people who have a high risk for colon cancer within 2 years of their NHI health screening, and we excluded the people who had already been treated for cancer or who were cancer patient. The study population is the insured of the NHI, aged 40 or over and they had undergone health screening from the year 2000 to 2004, according to NHI health screening formula. We performed logistic regression analysis and used SAS Enterprise Miner 4.1.

Results: This study shows that there exists a higher rate of colon cancer in males than females. Also, for the population in their 60s, the incidence rate of colon cancer is much higher by 5.36 times than that for those people in their 40s. Amongst the behavioral factors, heavy drinking is the most important determinant of the colon cancer incidence (7.39 times in males and 21.51 times in females).

Conclusions: Our study confirms that the major influencing factors for the incidence of colon cancer are drinking, lack of exercise, a medical history of colon polypus and a family history of colon cancer. As a result, we can choose the group that is at a high risk for colon cancer and provide customized medical information and selective management services according to their characteristics.

Publication types

  • English Abstract

MeSH terms

  • Adult
  • Age Factors
  • Colonic Neoplasms / diagnosis
  • Colonic Neoplasms / epidemiology*
  • Health Behavior
  • Humans
  • Incidence
  • Korea / epidemiology
  • Mass Screening / statistics & numerical data*
  • Middle Aged
  • National Health Programs / statistics & numerical data*
  • Predictive Value of Tests
  • Risk Assessment
  • Sex Factors