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

Abstract

AbstractBackgroundAdministrative health data has been used extensively to examine congenital heart disease (CHD). However, the accuracy and completeness of these data must be assessed.ObjectivesTo use data linkage of multiple administrative data sources to examine the validity of identifying CHD cases recorded in hospital discharge data.MethodsWe 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.ResultsOf 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 aged <20 years and residing in disadvantaged areas.ConclusionUsing 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.