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Abstract Background Observational outcome studies of patients with obstructive sleep apnea (OSA) require adjustment for co-morbidity to produce valid results. The aim of this study was to evaluate whether the combination of administrative data and self-reported data provided a more complete estimate of co-morbidity among patients referred for sleep diagnostic testing. Methods A retrospective observational study of 2149 patients referred for sleep diagnostic testing in Calgary, Canada. Self-reported co-morbidity was obtained with a questionnaire; administrative data and validated algorithms (when available) were also used to define the presence of these co-morbid conditions within a two-year period prior to sleep testing. Results Patient self-report of co-morbid conditions had varying levels of agreement with those derived from administrative data, ranging from substantial agreement for diabetes (κ = 0.79) to poor agreement for cardiac arrhythmia (κ = 0.14). The enhanced measure of co-morbidity using either self-report or administrative data had face validity, and provided clinically meaningful trends in the prevalence of co-morbidity among this population. Conclusion An enhanced measure of co-morbidity using self-report and administrative data can provide a more complete measure of the co-morbidity among patients with OSA when agreement between the two sources is poor. This methodology will aid in the adjustment of these coexisting conditions in observational studies in this area.

Abstract Background Kappa is commonly used when assessing the agreement of conditions with reference standard, but has been criticized for being highly dependent on the prevalence. To overcome this limitation, a prevalence-adjusted and bias-adjusted kappa (PABAK) has been developed. The purpose of this study is to demonstrate the performance of Kappa and PABAK, and assess the agreement between hospital discharge administrative data and chart review data conditions. Methods The agreement was compared for random sampling, restricted sampling by conditions, and case-control sampling from the four teaching hospitals in Alberta, Canada from ICD10 administrative data during January 1, 2003 and June 30, 2003. A total of 4,008 hospital discharge records and chart view, linked for personal unique identifier and admission date, for 32 conditions of random sampling were analyzed. The restricted sample for hypertension, myocardial infarction and congestive heart failure, and case-control sample for those three conditions were extracted from random sample. The prevalence, kappa, PABAK, positive agreement, negative agreement for the condition was compared for each of three samples. Results The prevalence of each condition was highly dependent on the sampling method, and this variation in prevalence had a significant effect on both kappa and PABAK. PABAK values were obviously high for certain conditions with low kappa values. The gap between these two statistical values for the same condition narrowed as the prevalence of the condition approached 50%. Conclusion Kappa values varied more widely than PABAK values across the 32 conditions. PABAK values should usually not be interpreted as measuring the same agreement as kappa in administrative data, particular for the condition with low prevalence. There is no single statistic measuring agreement that captures the desired information for validity of administrative data. Researchers should report kappa, the prevalence, positive agreement, negative agreement, and the relative frequency in each cell (i.e. a, b, c and d) to enable the reader to judge the validity of administrative data from multiple aspects.

Abstract Background We have previously described a method for dealing with missing data in a prospective cardiac registry initiative. The method involves merging registry data to corresponding ICD-9-CM administrative data to fill in missing data 'holes'. Here, we describe the process of translating our data merging solution to ICD-10, and then validating its performance. Methods A multi-step translation process was undertaken to produce an ICD-10 algorithm, and merging was then implemented to produce complete datasets for 1995–2001 based on the ICD-9-CM coding algorithm, and for 2002–2005 based on the ICD-10 algorithm. We used cardiac registry data for patients undergoing cardiac catheterization in fiscal years 1995–2005. The corresponding administrative data records were coded in ICD-9-CM for 1995–2001 and in ICD-10 for 2002–2005. The resulting datasets were then evaluated for their ability to predict death at one year. Results The prevalence of the individual clinical risk factors increased gradually across years. There was, however, no evidence of either an abrupt drop or rise in prevalence of any of the risk factors. The performance of the new data merging model was comparable to that of our previously reported methodology: c-statistic = 0.788 (95% CI 0.775, 0.802) for the ICD-10 model versus c-statistic = 0.784 (95% CI 0.780, 0.790) for the ICD-9-CM model. The two models also exhibited similar goodness-of-fit. Conclusion The ICD-10 implementation of our data merging method performs as well as the previously-validated ICD-9-CM method. Such methodological research is an essential prerequisite for research with administrative data now that most health systems are transitioning to ICD-10.

Abstract Background Missing data present a challenge to many research projects. The problem is often pronounced in studies utilizing self-report scales, and literature addressing different strategies for dealing with missing data in such circumstances is scarce. The objective of this study was to compare six different imputation techniques for dealing with missing data in the Zung Self-reported Depression scale (SDS). Methods 1580 participants from a surgical outcomes study completed the SDS. The SDS is a 20 question scale that respondents complete by circling a value of 1 to 4 for each question. The sum of the responses is calculated and respondents are classified as exhibiting depressive symptoms when their total score is over 40. Missing values were simulated by randomly selecting questions whose values were then deleted (a missing completely at random simulation). Additionally, a missing at random and missing not at random simulation were completed. Six imputation methods were then considered; 1) multiple imputation, 2) single regression, 3) individual mean, 4) overall mean, 5) participant's preceding response, and 6) random selection of a value from 1 to 4. For each method, the imputed mean SDS score and standard deviation were compared to the population statistics. The Spearman correlation coefficient, percent misclassified and the Kappa statistic were also calculated. Results When 10% of values are missing, all the imputation methods except random selection produce Kappa statistics greater than 0.80 indicating 'near perfect' agreement. MI produces the most valid imputed values with a high Kappa statistic (0.89), although both single regression and individual mean imputation also produced favorable results. As the percent of missing information increased to 30%, or when unbalanced missing data were introduced, MI maintained a high Kappa statistic. The individual mean and single regression method produced Kappas in the 'substantial agreement' range (0.76 and 0.74 respectively). Conclusion Multiple imputation is the most accurate method for dealing with missing data in most of the missind data scenarios we assessed for the SDS. Imputing the individual's mean is also an appropriate and simple method for dealing with missing data that may be more interpretable to the majority of medical readers. Researchers should consider conducting methodological assessments such as this one when confronted with missing data. The optimal method should balance validity, ease of interpretability for readers, and analysis expertise of the research team.

Background: We have previously described a method for dealing with missing data in a prospective cardiac registry initiative. The method involves merging registry data to corresponding ICD-9-CM administrative data to fill in missing data 'holes'. Here, we describe the process of translating our data merging solution to ICD-10, and then validating its performance. Methods: A multi-step translation process was undertaken to produce an ICD-10 algorithm, and merging was then implemented to produce complete datasets for 1995-2001 based on the ICD-9-CM coding algorithm, and for 2002-2005 based on the ICD-10 algorithm. We used cardiac registry data for patients undergoing cardiac catheterization in fiscal years 1995-2005. The corresponding administrative data records were coded in ICD-9-CM for 1995-2001 and in ICD-10 for 2002-2005. The resulting datasets were then evaluated for their ability to predict death at one year. Results: The prevalence of the individual clinical risk factors increased gradually across years. There was, however, no evidence of either an abrupt drop or rise in prevalence of any of the risk factors. The performance of the new data merging model was comparable to that of our previously reported methodology: c-statistic = 0.788 (95% CI 0.775, 0.802) for the ICD-10 model versus c-statistic = 0.784 (95% CI 0.780, 0.790) for the ICD-9-CM model. The two models also exhibited similar goodness-of-fit. Conclusion: The ICD-10 implementation of our data merging method performs as well as the previously-validated ICD-9-CM method. Such methodological research is an essential prerequisite for research with administrative data now that most health systems are transitioning to ICD-10. [ABSTRACT FROM AUTHOR]