Development and validation of claims-based algorithms to identify pregnancy based on data from a university hospital in Japan.

OBJECTIVE

When using administrative data, validation is essential since these data are not collected for research purposes and misclassification can occur. Thus, this study aimed to develop algorithms identifying pregnancy and to evaluate the validity of administrative claims data in Japan.

METHODS

All females who visited the Tohoku University Hospital Department of Obstetrics in 2018 were included. The diagnosis, medical procedure, medication, and medical service addition fee data were utilized to identify pregnancy, with the electronic medical records set as the gold standard. Combination algorithms were developed using predefined pregnancy-related claims data with a positive predictive value (PPV) ≥80%. Sensitivity (SE), specificity (SP), PPV, and negative predictive value (NPV) with their corresponding 95% confidence intervals (CIs) were calculated for these combination algorithms.

RESULTS

This study included 1757 females with a mean age of 32.8 (standard deviation: 5.9) years. In general, the individual claims data were able to identify pregnancy with a PPV ≥80%; however, the number of pregnancies identified using a single claims data was limited. Based on the combination algorithm with all of the categories, including diagnosis, medical procedure, medication, and medical service addition, the calculated SE, SP, PPV, and NPV were 73.4% (95% CI: 71.2%-75.4%), 96.9% (95% CI: 89.3%-99.6%), 99.8%,(95% CI: 99.4%-100.0%), and 12.3% (95% CI: 9.6%-15.4%), respectively.

CONCLUSIONS

The combination algorithm to identify pregnancy demonstrated a high PPV and moderate SE. The algorithm validated in this study is expected to accelerate future studies that aim to identify pregnancies and evaluate pregnancy outcome.