Concordance between administrative health data and medical records for diabetes status in coronary heart disease patients: a retrospective linked data study

Abstract

BackgroundAdministrative data are a valuable source of estimates of diabetes prevalence for groups such as coronary heart disease (CHD) patients. The primary aim of this study was to measure concordance between medical records and linked administrative health data for recording diabetes in CHD patients, and to assess temporal differences in concordance. Secondary aims were to determine the optimal lookback period for identifying diabetes in this patient group, whether concordance differed for Indigenous people, and to identify predictors of false positives and negatives in administrative data.MethodsA population representative sample of 3943 CHD patients hospitalized in Western Australia in 1998 and 2002–04 were selected, and designated according to the International Classification of Diseases (ICD) version in use at the time (ICD-9 and ICD-10 respectively). Crude prevalence and concordance were compared for the two samples. Concordance measures were estimated from administrative data comparing diabetes status recorded on the selected CHD admission (‘index admission’) and on any hospitalization in the previous 1, 2, 5, 10 or 15 years, against hospital medical records. Potential modifiers of agreement were determined using chi-square tests and multivariable logistic regression models.ResultsIdentification of diabetes on the index CHD admission was underestimated more in the ICD-10 than ICD-9 sample (sensitivity 81.5% versus 91.1%, underestimation 15.1% versus 4.4% respectively). Sensitivity increased to 89.6% in the ICD-10 period using at least 10 years of hospitalization history. Sensitivity was higher and specificity lower in Indigenous patients, and followed a similar pattern of improving concordance with increasing lookback period. Characteristics associated with false negatives for diabetes on the index CHD hospital admission were elective admission, in-hospital death, principal diagnosis, and in the ICD-10 period only, fewer recorded comorbidities.ConclusionsThe accuracy of identifying diabetes status in CHD patients is improved in linked administrative health data by using at least 10 years of hospitalization history. Use of this method would reduce bias when measuring temporal trends in diabetes prevalence in this patient group. Concordance measures are as reliable in Indigenous as non-Indigenous patients.

Comparative Trends in the Incidence of Hospitalized Myocardial Infarction and Coronary Heart Disease in Adults With and Without Diabetes Mellitus in Western Australia From 1998 to 2010

“…We have shown that trends would not be affected by the small degree of misclassification of DM status because of the use of an extended lookback period to determine DM status. 21 However, there may still be a degree of undiagnosed DM even after presentation to hospital, because of reluctance by doctors to label a patient in the acute setting with DM. This inclusion of patients with MI and DM in the non-DM group may overestimate rates in people without DM, but would only affect trends if there were changes in this practice over time.…”

Accuracy of inter-hospital transfer information in Australian hospital administrative databases

“…26,[48][49][50][51] Recently, the observed very low false-positive rates in administrative datasets 24,26,49,52 prompted the use of internal data linkage and temporal accumulation of information as methods to measure and reduce discrepancies resulting from different data recording practices and rules. 25,36,53,54 Similarly, we combined patient-specific admission and ED presentation records from both sending and receiving hospitals to capture all transfers regardless of their pathways and local strategies in documentation. This design enabled us to directly estimate inaccuracy in transfer data and associated variation, in particular for in-transfers, which can be eliminated through patient-level data linkage across datasets and hospitals.…”

Hospitalisations up to adulthood for children born with orofacial clefts

“…Diagnosis and procedure coding changed twice during the study period, potentially affecting accuracy . Although coding became more detailed in later versions, we were limited to the broader categories defined by the earlier coding systems.…”

“…20 Diagnosis and procedure coding changed twice during the study period, potentially affecting accuracy. 21 Although coding became more detailed in later versions, we were limited to the broader categories defined by the earlier coding systems. There are no validation studies evaluating the accuracy of diagnoses and procedures for admissions related to cleft management, but in the WA hospital morbidity data, procedures are more accurately recorded than diagnoses, and major procedures and diagnoses are likely to be identified.…”