Risk stratification of cancer survivors: a quantitative method using clinical attendance patterns for colorectal, Hodgkins disease and multiple myeloma.
Session type: Poster / e-Poster / Silent Theatre session
As cancer cases continue to rise and deaths fall, the population of survivors grows. The requirements of cancer survivors vary. Cancer survivorship may be examined by considering the post-treatment clinical attendance patterns(NHS footprints) for entire populations of survivors, for specific cancer types.
NHS footprint is a concept that is not directly measurable. It may be, however, described by a combination of features that are observed; such as the number, duration and interval between hospital episodes.
Structural equation modelling will be applied to data that has been sourced from the NCIN hosted National Cancer Data Repository, linking cancer registry data with Hospital Episode Statistics data, for patients diagnosed in the Northern and Yorkshire region with colorectal cancer (C) in Q1 2001 and myeloma (M) and Hodgkins (H) in 2001. Differences in NHS footprint due to stage at diagnosis, treatment and co-morbidities will be investigated; and related to survival outcome for each cancer site.
The total duration of inpatient hospital stays (median days) for those who survive (24C; 82M; 24H) varies significantly from those who die (44C; 63M; 93H), respectively (p<0.01). The total number of episodes (median days) of episodes also varies, by cancer site, for survivors (10C; 32M; 16H) and non-survivors (8C; 11M; 25H) (p<0.01). Five year survival rates for these groups are: 41%C; 22%M and 79%H.
Structural equation modelling will identify types of survivors for each cancer site. It is expected patients will range from those who are completely cured with no further impact on the health service; to frequent users due to necessary follow ups, late effects, and co-morbidities; and probably a range in between.
This study contributes to risk stratification of cancer survivorship, illustrating differences cancer sites NHS footprints. This work will facilitate identification of different support requirements for cancer patients, plus associated co-morbidities.