Variation in the recording of common health conditions in routine hospital data: study using linked survey and administrative data in New South Wales, Australia. Issue 9 (3rd September 2014)

• Record Type: Journal Article
• Authors: Lujic, Sanja; Watson, Diane E; Randall, Deborah A; Simpson, Judy M; Jorm, Louisa R
• Is Part Of: BMJ open. Volume 4:Issue 9(2014)
• Publication Date: 2014-09-03
• Subjects: Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2014-005768
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Objectives: To investigate the nature and potential implications of under-reporting of morbidity information in administrative hospital data. Setting and participants: Retrospective analysis of linked self-report and administrative hospital data for 32 832 participants in the large-scale cohort study (45 and Up Study), who joined the study from 2006 to 2009 and who were admitted to 313 hospitals in New South Wales, Australia, for at least an overnight stay, up to a year prior to study entry. Outcome measures: Agreement between self-report and recording of six morbidities in administrative hospital data, and between-hospital variation and predictors of positive agreement between the two data sources. Results: Agreement between data sources was good for diabetes (κ=0.79); moderate for smoking (κ=0.59); fair for heart disease, stroke and hypertension (κ=0.40, κ=0.30 and κ =0.24, respectively); and poor for obesity (κ=0.09), indicating that a large number of individuals with self-reported morbidities did not have a corresponding diagnosis coded in their hospital records. Significant between-hospital variation was found (ranging from 8% of unexplained variation for diabetes to 22% for heart disease), with higher agreement in public and large hospitals, and hospitals with greater depth of coding. Conclusions: The recording of six common health conditions in administrative hospital data is highly variable, and for some conditions, very poor. To support more valid performance comparisons, it is important to stratify or control for factors that predict the completeness of recording, including hospital depth of coding and hospital type (public/private), and to increase efforts to standardise recording across hospitals. Studies using these conditions for risk adjustment should also be cautious of their use in smaller hospitals.