Comparing Health Administrative and Clinical Registry Data: Trends in Incidence and Prevalence of Pediatric Inflammatory Bowel Disease in British Columbia.

PURPOSE: Canada maintains robust health administrative databases and British Columbia Children's Hospital (BCCH), as the only tertiary care pediatric hospital in British Columbia (BC), maintains a comprehensive clinical inflammatory bowel disease (IBD) registry. To evaluate the strengths and weaknesses of utilizing health administrative and clinical registry data to study the epidemiology of IBD in BC, we conducted a population-based retrospective cohort study of all children <18 years of age who were diagnosed with IBD between 1996 and 2008 in BC. METHODS: IBD cases from health administrative data were identified using a combination of IBD-coded physician encounters and hospitalizations while a separate IBD cohort was identified from the BCCH clinical registry data. Age and gender standardized incidence and prevalence rates were fitted to Poisson regression models. RESULTS: The overall incidence of pediatric IBD identified in health administrative data increased from 7.1 (95% CI 5.5-9.2) in 1996 to 10.3 (95% CI 8.2-12.7) per 100,000 children in 2008. Similarly, the incidence of the BCCH cohort increased from 4.3 (95% CI 3.0-6.0) to 9.7 (95% CI 7.6-12.1) per 100,000. Children aged 10-17 had the highest rise in incidence in both data sources; however, the administrative data identified significantly more 10-17-year-olds and significantly less 6-9-year-olds (p<0.05) compared to clinical registry data. CONCLUSION: While the application of both health administrative and clinical registry data demonstrates that the incidence of IBD is increasing in BC, we identify strengths and limitations to both and suggest that the utilization of either data source requires unique considerations that mitigate misclassification biases.

Risk adjustment performance of Charlson and Elixhauser comorbidities in ICD-9 and ICD-10 administrative databases.

BACKGROUND: The performance of the Charlson and Elixhauser comorbidity measures in predicting patient outcomes have been well validated with ICD-9 data but not with ICD-10 data, especially in disease specific patient cohorts. The objective of this study was to assess the performance of these two comorbidity measures in the prediction of in-hospital and 1 year mortality among patients with congestive heart failure (CHF), diabetes, chronic renal failure (CRF), stroke and patients undergoing coronary artery bypass grafting (CABG). METHODS: A Canadian provincial hospital discharge administrative database was used to define 17 Charlson comorbidities and 30 Elixhauser comorbidities. C-statistic values were calculated to evaluate the performance of two measures. One year mortality information was obtained from the provincial Vital Statistics Department. RESULTS: The absolute difference between ICD-9 and ICD-10 data in C-statistics ranged from 0 to 0.04 across five cohorts for the Charlson and Elixhauser comorbidity measures predicting in-hospital or 1 year mortality. In the models predicting in-hospital mortality using ICD-10 data, the C-statistics ranged from 0.62 (for stroke) - 0.82 (for diabetes) for Charlson measure and 0.62 (for stroke) to 0.83 (for CABG) for Elixhauser measure. CONCLUSION: The change in coding algorithms did not influence the performance of either the Charlson or Elixhauser comorbidity measures in the prediction of outcome. Both comorbidity measures were still valid prognostic indicators in the ICD-10 data and had a similar performance in predicting short and long term mortality in the ICD-9 and ICD-10 data.

ICD-10 coding algorithms for defining comorbidities of acute myocardial infarction.

BACKGROUND: With the introduction of ICD-10 throughout Canada, it is important to ensure that Acute Myocardial Infarction (AMI) comorbidities employed in risk adjustment methods remain valid and robust. Therefore, we developed ICD-10 coding algorithms for nine AMI comorbidities, examined the validity of the ICD-10 and ICD-9 coding algorithms in detection of these comorbidities, and assessed their performance in predicting mortality. The nine comorbidities that we examined were shock, diabetes with complications, congestive heart failure, cancer, cerebrovascular disease, pulmonary edema, acute renal failure, chronic renal failure, and cardiac dysrhythmias. METHODS: Coders generated a comprehensive list of ICD-10 codes corresponding to each AMI comorbidity. Physicians independently reviewed and determined the clinical relevance of each item on the list. To ensure that the newly developed ICD-10 coding algorithms were valid in recording comorbidities, medical charts were reviewed. After assessing ICD-10 algorithms' validity, both ICD-10 and ICD-9 algorithms were applied to a Canadian provincial hospital discharge database to predict in-hospital, 30-day, and 1-year mortality. RESULTS: Compared to chart review data as a 'criterion standard', ICD-9 and ICD-10 data had similar sensitivities (ranging from 7.1-100%), and specificities (above 93.6%) for each of the nine AMI comorbidities studied. The frequencies for the comorbidities were similar between ICD-9 and ICD-10 coding algorithms for 49,861 AMI patients in a Canadian province during 1994-2004. The C-statistics for predicting 30-day and 1 year mortality were the same for ICD-9 (0.82) and for ICD-10 data (0.81). CONCLUSION: The ICD-10 coding algorithms developed in this study to define AMI comorbidities performed similarly as past ICD-9 coding algorithms in detecting conditions and risk-adjustment in our sample. However, the ICD-10 coding algorithms should be further validated in external databases.