Identifying Algorithms to Improve the Accuracy of Unverified Diagnosis Codes for Birth Defects

Objective: We identified algorithms to improve the accuracy of passive surveillance programs for birth defects that rely on administrative diagnosis codes for case ascertainment and in situations where case confirmation via medical record review is not possible or is resource prohibitive. Methods: We linked data from the 2009-2011 Florida Birth Defects Registry, a statewide, multisource, passive surveillance program, to an enhanced surveillance database with selected cases confirmed through medical record review. For each of 13 birth defects, we calculated the positive predictive value (PPV) to compare the accuracy of 4 algorithms that varied case definitions based on the number of diagnoses, medical encounters, and data sources in which the birth defect was identified. We also assessed the degree to which accuracy-improving algorithms would affect the Florida Birth Defects Registry’s completeness of ascertainment. Results: The PPV generated by using the original Florida Birth Defects Registry case definition (ie, suspected cases confirmed by medical record review) was 94.2%. More restrictive case definition algorithms increased the PPV to between 97.5% (identified by 1 or more codes/encounters in 1 data source) and 99.2% (identified in \u3e1 data source). Although PPVs varied by birth defect, alternative algorithms increased accuracy for all birth defects; however, alternative algorithms also resulted in failing to ascertain 58.3% to 81.9% of cases. Conclusions: We found that surveillance programs that rely on unverified diagnosis codes can use algorithms to dramatically increase the accuracy of case finding, without having to review medical records. This can be important for etiologic studies. However, the use of increasingly restrictive case definition algorithms led to a decrease in completeness and the disproportionate exclusion of less severe cases, which could limit the widespread use of these approaches