10.6084/M9.FIGSHARE.C.5119613.V1
A. Sheriffdeen
J. L. Millar
C. Martin
M. Evans
G. Tikellis
S. M. Evans
0000-0003-2962-8400
(Dis)concordance of comorbidity data and cancer status across administrative datasets, medical charts, and self-reports
Abstract Background Benchmarking outcomes across settings commonly requires risk-adjustment for co-morbidities that must be derived from extant sources that were designed for other purposes. A question arises as to the extent to which differing available sources for health data will be concordant when inferring the type and severity of co-morbidities, how close are these to the “truth”. We studied the level of concordance for same-patient comorbidity data extracted from administrative data (coded from International Classification of Diseases, Australian modification,10th edition [ICD-10 AM]), from the medical chart audit, and data self-reported by men with prostate cancer who had undergone a radical prostatectomy. Methods We included six hospitals (5 public and 1 private) contributing to the Prostate Cancer Outcomes Registry-Victoria (PCOR-Vic) in the study. Eligible patients from the PCOR-Vic underwent a radical prostatectomy between January 2017 and April 2018.Health Information Manager’s in each hospital, provided each patient’s associated administrative ICD-10 AM comorbidity codes. Medical charts were reviewed to extract comorbidity data. The self-reported comorbidity questionnaire (SCQ) was distributed through PCOR-Vic to eligible men. Results The percentage agreement between the administrative data, medical charts and self-reports ranged from 92 to 99% in the 122 patients from the 217 eligible participants who responded to the questionnaire. The presence of comorbidities showed a poor level of agreement between data sources. Conclusion Relying on a single data source to generate comorbidity indices for risk-modelling purposes may fail to capture the reality of a patient’s disease profile. There does not appear to be a ‘gold-standard’ data source for the collection of data on comorbidities.
Medicine
Biotechnology
Immunology
69999 Biological Sciences not elsewhere classified
80699 Information Systems not elsewhere classified
19999 Mathematical Sciences not elsewhere classified
Cancer
figshare
2020
2020-09-12
2020-09-12
Collection
10.1186/s12913-020-05713-5
10.6084/m9.figshare.c.5119613
CC BY 4.0