(Dis)concordance of comorbidity data and cancer status across administrative datasets, medical charts, and self-reports
Related Experiment Video
Updated: Dec 9, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
A Sheriffdeen1, J L Millar1,2, C Martin1
1Department of Epidemiology & Preventive Medicine, Monash University, Melbourne, Australia.
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.
Related Concept Videos
Cancer Survival Analysis
Contingency Table
Confounding in Epidemiological Studies
Methods of Documentation IV: Focus Charting
It typically involves three columns for recording information:
Comparing the Survival Analysis of Two or More Groups
Chi-square Analysis
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...

