Multivariable prediction of returning to work among early-onset colorectal cancer survivors in China: A two-year follow-up
Year: 2024
Type: article
Study design: Observational Study
Abstract: Objective: The number of early-onset colorectal cancer survivors (EOCRCs) is increasing. The primary aim of rehabilitation after battling cancer is to enable patients to return to work, as they constitute a significant contributor to societal productivity. A predictive model was developed to identify priority populations requiring intervention and refine responses to increase their capacity to return to work after undergoing treatment for EOCRC. Methods: The baseline information was collected before patients were discharged at the end of their treatment course. The data of patients who returned to work were collected at 1 and 2 years after discharge. A predictive variable model was developed via binary logistic regression. The TRIPOD checklist was used. Results: At 1 year, 64.7% of the EOCRC survivors had returned to work. Male sex, education, return to work self efficacy, re-entry readiness and social support increased the possibility of returning to work; higher levels of self-perceived fatigue and lower levels of family care decreased the possibility of returning to work within the 1-year model. At 2 years, 72.8% of the EOCRC survivors had returned to work. In the 2-year model, education, self-transcendence, return to work self efficacy, re-entry readiness and occupational environment increased the possibility of returning to work; self-perceived fatigue and psychosocial adjustment decreased the possibility of returning to work. Conclusions: The results of this study can guide early assessment and intervention for EOCRC survivors, to facilitate their return to work.
Authors: Xiaojun Chen, Mengjiao Zhong, Chunyan Chen, Lingyao Huang, Kun Zhang, Xiaodan Wu
Language: English
Topic: Cancer survivorship and care
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,980
OpenAlex ID: W4405234722
