Measuring misclassification and sample bias in passive surveillance systems: Improving prevalence estimates of critical congenital heart defects in state-based passive surveillance systems

Abstract

Objectives

We assessed reporting misclassification for 12 critical congenital heart defects (CCHDs) identified through administrative diagnosis codes within a passive surveillance system. We measured the effect of misclassification on prevalence estimation. Lastly, we investigated a sample-based review strategy to estimate surveillance misclassification resulting from administrative diagnosis codes for case detection.

Methods

We received 419 reports of CCHDs between 2007 and 2018; 414 were clinically reviewed. We calculated confirmation probabilities to assess misclassification and adjust prevalence estimates. Random samples of reported cases were taken at proportions between 20% and 90% for each condition to assess sample bias. Sampling was repeated 1000 times to measure sample-estimate variability.

Results

Misclassification ranged from a low of 19% (n = 4/21) to a high of 84% (n = 21/25). Unconfirmed prevalence rates ranged between one and six cases per 10,000 live births, with some conditions significantly higher than national estimates. However, confirmed rates were either lower or comparable to national estimates.

Conclusion

Passive birth defect surveillance programs that rely on administrative diagnosis codes for case identification of CCHDs are subject to misclassification that bias prevalence estimates. We showed that a sample-based review could improve the prevalence estimates of 12 cardiovascular conditions relative to their unconfirmed prevalence rates.