422P - Comparison of diagnostic and treatment delays of adolescent and young adult brain tumor patients with older adult brain tumor patients
|Date||30 September 2012|
|Event||ESMO Congress 2012|
|Session||Poster presentation II|
|Topics|| Central Nervous System Malignancies
Y. Xu1, M. Cardoso2, M.O. Palumbo3, O. Ishibashi3, H. Oltean3, K. Kumari3, P. Kavan3
Background: Adolescent and young adult (AYA) cancer patients are confronted with obstacles and challenges related to their diagnosis and treatment compared to children and older adults. The aim of this study is to compare patient-related and health care system-related delays, from cancer symptom onset to diagnosis and treatment, between the AYA brain tumor patients and older adult brain tumor patients.
Material and Methods: This study is based on a questionnaire conducted in 2010-2012 completed by AYA brain tumor patients diagnosed at the ages between 16 and 39 years and older adult brain tumor patients diagnosed at an average age of 59 years. Total delay (time from cancer symptom onset to treatment start) was calculated and divided into three stages (1) Patient delay: time from patient symptom onset until first health care contact date; (2) Health care system delay: time from first health care contact until diagnosis date; (3) Treatment delay: time from diagnosis date until first treatment. Median delay in days with interquartile interval (IQI) is the main outcome measure.
Results: For AYA brain tumor patients, we identify a median total delay of 169 (IQI 72-395) days, a median patient delay of 1 (0-78) days, a median health care system delay of 42 (5-203) days and a median treatment delay of 60 (35-92) days. For older adult brain tumor patients, we identify a median total delay of 76 (58-175) days, a median patient delay of 0 (0-14) days, a median health care system delay of 25 (14-42) days and a median treatment delay of 35 (21-63) days. Delays of all stages are longer in AYA brain tumor patients as compared to older adult brain tumor patients, and the findings of total delay and treatment delay are statistically significant (p=0.013 and p=0.048, respectively).
Conclusions: Health care system delay and treatment delay account for much of the delay from symptom onset to first treatment in both groups which indicates professional characteristics of frontline medical personnel may contribute to delay. Also, we have longer delays in AYA brain tumor patients. This suggests that AYA cancer patients as underserved patient population need to get more attention from healthcare professionals and the general community.