Multistate models for comparing trends in hospitalizations among young adult survivors of colorectal cancer and matched controls

• Main Authors: Sutradhar Rinku, Forbes Shawn, Urbach David R, Paszat Lawrence, Rabeneck Linda, Baxter Nancy N
• Format: Article
• Language: English
• Published: BMC 2012-10-01
• Series: BMC Health Services Research
• Subjects: Multistate model; Counting process; Random effects; Young adult survivors; Proportional rate regression model; Baseline rate function
• Online Access: http://www.biomedcentral.com/1472-6963/12/353

<p>Abstract</p> <p>Background</p> <p>Over the past years, the incidence of colorectal cancer has been increasing among young adults. A large percentage of these patients live at least 5 years after diagnosis, but it is unknown whether their rate of hospitalizations after this 5-year mark is comparable to the general population.</p> <p>Methods</p> <p>This is a population-based cohort consisting of 917 young adult survivors diagnosed with colorectal cancer in Ontario from 1992–1999 and 4585 matched cancer-free controls. A multistate model is presented to reflect and compare trends in the hospitalization process among survivors and their matched controls.</p> <p>Results</p> <p>Analyses under a multistate model indicate that the risk of a subsequent hospital admission increases as the number of prior hospitalizations increases. Among patients who are yet to experience a hospitalization, the rate of admission is 3.47 times higher for YAS than controls (95% CI (2.79, 4.31)). However, among patients that have experienced one and two hospitalizations, the relative rate of a subsequent admission decreases to 3.03 (95% CI (2.01, 4.56)) and 1.90 (95% CI (1.19, 3.03)), respectively.</p> <p>Conclusions</p> <p>Young adult survivors of colorectal cancer have an increased risk of experiencing hospitalizations compared to cancer-free controls. However this relative risk decreases as the number of prior hospitalizations increases. The multistate approach is able to use information on the timing of hospitalizations and answer questions that standard Poisson and Negative Binomial models are unable to address.</p>