The comorbidity variables that constitute the Charlson index are widely used in health care research using administrative data. However, little is known about the validity of administrative data in these comorbidities. The agreement between administrative hospital discharge data and chart data for the recording of information on comorbidity was evaluated. The predictive ability of comorbidity information in the two data sets for predicting in-hospital mortality was also compared.Methods.
One thousand two hundred administrative hospital discharge records were randomly selected in the region of Calgary, Alberta, Canada in 1996 and used a published coding algorithm to define the 17 comorbidities that constitute the Charlson index. Corresponding patient charts for the selected records were reviewed as the “criterion standard” against which validity of the administrative data were judged.Results.
Compared with the chart data, administrative data had a lower prevalence in 10 comorbidities, a higher prevalence in 3 and a similar prevalence in 4. The κ values ranged from a high of 0.87 to a low of 0.34; agreement was therefore near perfect for one variable, substantial for six, moderate for nine, and only fair for one variable. For the Charlson index score ranging from 0 to 5 to 6 or higher, agreement was moderate to substantial (κ = 0.56, weighted κ = 0.71). When 16 Charlson comorbidities from administrative data were used to predict in-hospital mortality, 10 comorbidities and the index scores defined using administrative data yielded odds ratios that were similar to those derived from chart data. The remaining six comorbidities yielded odds ratios that were quite different from those derived from chart data.Conclusions.
Administrative data generally agree with patient chart data for recording of comorbidities although comorbidities tend to be under-reported in administrative data. The ability to predict in-hospital mortality is less reliable for some of the individual comorbidities than it is for the summarized Charlson index scores in administrative data.