Clifford R Marshall | PubMed Health

PubMed2004

Why ethnic designation matters for stroke rehabilitation: comparing VA administrative data and clinical records.

Using existing administrative data to look at issues of ethnic disparities in rehabilitation-related outcomes may lead to misleading results. Problems can emerge from apparently small issues of reliability that are magnified by reclassification of ethnic designation and missing data in complete-subject analyses. We compared the reliability of ethnic assignment in Department of Veterans Affairs (VA) medical rehabilitation records for stroke patients with administrative records; reclassified the racial identifier from the administrative data in two ways; and examined the different sources of ethnic information in relation to severity, length-of-stay, disability assessment, and discharge disposition. Our results show how small changes increase the potential for Type II error when describing ethnic differences in outcomes or using ethnicity as a predictor with dichotomous response variables. We discuss our results with reference to the literature on ethnic classification and underline the importance of initiatives for improved data collection on ethnicity in VA data sources and in rehabilitation research.

Databases, Factual↗

PubMed2002

The hazards of stroke case selection using administrative data.

BACKGROUND: Administrative data and ICD-9-CM diagnostic codes are frequently used in research efforts to evaluate risk adjusted patient outcomes, particularly mortality. Varying ICD-9-CM sampling algorithms have been used to identify stroke patients. OBJECTIVES: This study evaluates the effects of different sampling strategies (one high sensitivity and one high specificity) on modeling stroke mortality as a performance indicator. RESEARCH DESIGN: Risk adjustment models were developed for two stroke cohorts identified using differing ICD-9-CM algorithms. Standard mortality ratios were calculated in a validation sample as network performance measures and compared across the two stroke samples. SUBJECTS: VHA inpatients with stroke during years 1997 (model development) and 1998 (model validation) were selected from the Patient Treatment File based on cerebrovascular diagnostic codes. MEASURES: Patient mortality within 30 days of admission. RESULTS: The model development and validation for each stroke sampling method produced consistent results: c-statistics 0.74 to 0.75, R2 0.07 to 0.09, concordance 73% to 74%. However, ranking differences in network performance varied by 5 or more positions for 7 of the 22 patient networks. CONCLUSIONS: These findings highlight a potential problem when using administrative data to assess stroke mortality. In the absence of an agreed upon definition of stroke patients, results of provider profiling will vary depending on the ICD-9 algorithm used.

Algorithms↗