Screening for distress can predict loss of follow-up and treatment in cancer patients: results of development and validation of the Distress Inventory for Cancer Version 2.
BACKGROUND
The evidence suggests that in most instances distress from cancer goes unrecognised. There has been substantial work in screening for distress leading to development in practice guidelines recommending screening for distress. There is a need to identify distress early in the continuum, where suitable and timely interventions will benefit the patient. The Distress Inventory for Cancer (DIC) was designed for this.


METHODS
The present study reports the improved psychometric properties of the second version of the DIC (DIC 2). A standardisation sample of 520 patients was administered the 73-item draft questionnaire as well as the Functional Assessment of Cancer Therapy-General (FACT-G). Additional 240 patients were administered Hospital Anxiety and Depression Scale (HADS) besides the final DIC 2 and FACT-G. Internal consistency was measured with the Cronbach's alpha. Chi-square, odds ratio, and binary logistic regression were used to compare the results of three tools and to establish validity.


RESULTS
A principal factor analysis of the 73-item draft questionnaire with varimax rotation in a six-factor structure resulted in 33 items. Activity of daily living emerged as a single item domain. The FACT-G scores negatively correlated with DIC 2 scores, while emotional distress of DIC 2 overlapped with anxiety in HADS. DIC 2 significantly predicted patient non-compliance to treatment and follow-up.


CONCLUSIONS
The results show a suitable internal consistency, construct, and convergent/divergent validity of the global distress measure (DIC 2). The DIC 2 also demonstrates a predictive function for future negative clinical behaviour, the knowledge of which may facilitate better intervention triage.
Thomas
B C
BC
Department of Psychosocial Oncology, Tom Baker Cancer Centre, Calgary, Alberta, Canada.
Thomas
I
I
Nandamohan
V
V
Nair
M K
MK
Pandey
M
M
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003863:Depression / Q000175:diagnosis / Q000453:epidemiology* / Q000209:etiology; D005163:Factor Analysis, Statistical; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008403:Mass Screening / Q000379:methods*; D008875:Middle Aged; D009369:Neoplasms / Q000453:epidemiology / Q000523:psychology / Q000628:therapy; D011446:Prospective Studies; D011594:Psychometrics; D011788:Quality of Life / Q000523:psychology; D015203:Reproducibility of Results; D011795:Surveys and Questionnaires; D055815:Young Adult