Related Experiment Video
Updated: Jun 14, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Insurance Types and All-Cause Mortality in Korean Cancer Patients: A Nationwide Population-Based Cohort Study
Jinyoung Shin1, Yoon-Jong Bae2, Hee-Taik Kang3
1Department of Family Medicine, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul 05030, Republic of Korea.
Background:
Economic deprivation is expected to influence cancer mortality due to its impact on screening and treatment options, as well as healthy lifestyle. However, the relationship between insurance type, premiums, and mortality rates remains unclear. This study investigated the relationship between insurance type and mortality in patients with newly diagnosed cancer using data from the Korean National Health Insurance Database.
Methods:
this retrospective cohort study included 111,941 cancer patients diagnosed between 1 January 2007 and 31 December 2008, with a median follow-up period of 13.41 years. The insurance types were categorized as regional and workplace subscribers and income-based insurance premiums were divided into tertiles (T1, T2, and T3).
Results:
Cox proportional hazards regression analysis adjusted for age, lifestyle factors, health metrics, and comorbidities showed workplace subscribers (n = 76,944) had a lower all-cause mortality hazard ratio (HR) (95% confidence interval [CI]: 0.940 [0.919-0.961]) compared to regional subscribers (n = 34,997). Higher income tertiles (T2, T3) were associated with lower mortality compared to the T1 group, notably in male regional and workplace subscribers, and female regional subscribers.
Conclusion:
The study identified that insurance types and premiums significantly influence mortality in cancer patients, highlighting the necessity for individualized insurance policies for cancer patients.
More Related Videos
06:46Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
06:55Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Related Concept Videos
Cancer Survival Analysis
Actuarial Approach
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Cancer Prevention
Some...
Kaplan-Meier Approach
Statistical Methods for Analyzing Epidemiological Data
Comparing the Survival Analysis of Two or More Groups