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

Abstract

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.

Keywords:

• Administrative claims data
• validation
• positive predictive value
• sensitivity
• pregnancy
• Japan

Transparency

Declaration of funding

This study was supported in part by the Kurokawa Cancer Research Foundation under grant (grant number: 1332).

Declaration of financial/other relationships

The authors declare no conflicts of interest directly relevant to this research. KT and TI are employees of Pfizer. The other authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Author contributions

All authors of this research have significantly contributed to the manuscript and have approved this submission. KT, TI, and TO designed this study, performed the data analyses, and drafted the initial manuscript. AN, FM, and KM acquired the data and conducted the review of medical record. RI, NI, HN, JS, MS, and NM contributed to the interpretation of the results and supervised the drafting of the manuscript.

Acknowledgements

The authors would like to thank Mr. Tatsuro Ishikawa of Tohoku University Hospital for his assistance in preparing the administrative data.

Ethics statement

This study was approved by the Institutional Review Board of Tohoku University School of Medicine on 27 January 2020, with a waiver of informed consent (receipt number: 2019-1-753).

Data availability statement

Due to the nature of this research, data cannot be shared.