PATTERNS AND PREDICTORS OF LUNG CANCER CARE TRAJECTORIES: A SEQUENCE ANALYSIS OF INSURANCE CLAIMS
Author(s)
Yue Xu, PhD Student, Xiaozhu An, Master student, Yixin Liu, Undergraduate, MIN HU, PhD, Wen Chen, PhD;
School of Public Health, Fudan University, Shanghai, China
School of Public Health, Fudan University, Shanghai, China
OBJECTIVES: This study aimed to identify distinct care trajectories among lung cancer patients using insurance claims data and to examine associated demographic, clinical, and socioeconomic factors.
METHODS: We analyzed claims data from 11,495 incident lung cancer patients in a major metropolitan area of Eastern China (2022-2024). Patient-level care sequences were constructed from quarterly records over a two-year follow-up. Trajectory classes were identified using sequence analysis and the CLARA algorithm. Multinomial logistic regression modeled associations with sex, age, insurance type (employee vs. resident basic medical insurance), and comorbidity burden (Charlson Comorbidity Index).
RESULTS: Three distinct trajectory classes were identified, suggesting a gradient of disease severity from Class 1 (curative-intent surgery with follow-up, least severe) through Class 2 (continuous systemic treatment) to Class 3 (rapid progression to death, most severe). With Class 1 as the reference, males had significantly higher relative risk ratios (RRRs) for belonging to Class 2 and Class 3 (RRR=1.74 and 3.81, respectively; both p<0.001). Older age (per 10 years) was associated with increased risks for Class 2 and Class 3 (RRR=1.34 and 2.11; both p<0.001). Enrollees in resident basic medical insurance (vs. employee insurance) had higher risks for both Class 2 and Class 3 (RRR=1.24 and 1.27; both p<0.05). Compared with non-cancer CCI=0, a non-cancer CCI of 1 was associated with increased risks of Class 2 and Class 3 (RRR=1.68 and 1.91; both p<0.001), and non-cancer CCI ≥2 was associated with increased risks of Class 2 and particularly Class 3 (RRR=1.23, p=0.035; and RRR=2.82, p<0.001).
CONCLUSIONS: Claims-based trajectory phenotyping supports the early identification of high-risk patients, enabling timely interventions. It informs targeted clinical strategies—such as optimized diagnostic work-ups, timely definitive treatment, and structured follow-up—to reduce avoidable care delays. At the system level, this approach enhances care continuity and resource allocation efficiency, especially for clinically or socioeconomically vulnerable populations.
METHODS: We analyzed claims data from 11,495 incident lung cancer patients in a major metropolitan area of Eastern China (2022-2024). Patient-level care sequences were constructed from quarterly records over a two-year follow-up. Trajectory classes were identified using sequence analysis and the CLARA algorithm. Multinomial logistic regression modeled associations with sex, age, insurance type (employee vs. resident basic medical insurance), and comorbidity burden (Charlson Comorbidity Index).
RESULTS: Three distinct trajectory classes were identified, suggesting a gradient of disease severity from Class 1 (curative-intent surgery with follow-up, least severe) through Class 2 (continuous systemic treatment) to Class 3 (rapid progression to death, most severe). With Class 1 as the reference, males had significantly higher relative risk ratios (RRRs) for belonging to Class 2 and Class 3 (RRR=1.74 and 3.81, respectively; both p<0.001). Older age (per 10 years) was associated with increased risks for Class 2 and Class 3 (RRR=1.34 and 2.11; both p<0.001). Enrollees in resident basic medical insurance (vs. employee insurance) had higher risks for both Class 2 and Class 3 (RRR=1.24 and 1.27; both p<0.05). Compared with non-cancer CCI=0, a non-cancer CCI of 1 was associated with increased risks of Class 2 and Class 3 (RRR=1.68 and 1.91; both p<0.001), and non-cancer CCI ≥2 was associated with increased risks of Class 2 and particularly Class 3 (RRR=1.23, p=0.035; and RRR=2.82, p<0.001).
CONCLUSIONS: Claims-based trajectory phenotyping supports the early identification of high-risk patients, enabling timely interventions. It informs targeted clinical strategies—such as optimized diagnostic work-ups, timely definitive treatment, and structured follow-up—to reduce avoidable care delays. At the system level, this approach enhances care continuity and resource allocation efficiency, especially for clinically or socioeconomically vulnerable populations.
Conference/Value in Health Info
2026-05, ISPOR 2026, Philadelphia, PA, USA
Value in Health, Volume 29, Issue S6
Code
HSD64
Topic
Health Service Delivery & Process of Care
Disease
SDC: Oncology