Examination of validity of identifying congenital heart disease from hospital discharge data without a gold standard: Using a data linkage approach

Background: Administrative health data has been used extensively to examine congenital heart disease (CHD). However, the accuracy and completeness of these data must be assessed.Objectives: To use data linkage of multiple administrative data sources to examine the validity of identifying CHD cases recorded in hospital discharge data.Methods: We identified all liveborn infants born 2013-2017 in New South Wales, Australia with a CHD diagnosis up to age one, recorded in hospital discharge data. Using record linkage to multiple data sources, the diagnosis of CHD was compared with five reference standards: (i) multiple hospital admissions containing CHD diagnosis; (ii) receiving a cardiac procedure; (iii) CHD diagnosis in the Register of Congenital Conditions; (iv) cardiac-related outpatient health service recorded; and/or (v) cardiac-related cause of death. Positive predictive values (PPV) comparing CHD diagnosis with the reference standards were estimated by CHD severity and for specific phenotypes.Results: Of 485,239 liveborn infants, there were 4043 infants with a CHD diagnosis identified in hospital discharge data (8.3 per 1000 live births). The PPV for any CHD identified in any of the five methods was 62.8% (95% confidence interval [CI] 60.9, 64.8), with PPV higher for severe CHD at 94.1% (95% CI 88.2, 100). Infant characteristics associated with higher PPVs included lower birthweight, presence of a syndrome or non-cardiac congenital anomaly, born to mothers agedUsing data linkage of multiple datasets is a novel and cost-effective method to examine the validity of CHD diagnoses recorded in one dataset. These results can be incorporated into bias analyses in future studies of CHD.

Automated summary

This study utilized data linkage of multiple administrative data sources to assess the accuracy and completeness of congenital heart disease (CHD) diagnoses recorded in hospital discharge data. The positive predictive value for any CHD diagnosis was found to be 62.8%. Using data linkage of multiple datasets is a novel and cost-effective method to examine the validity of CHD diagnoses.