Skip to main content
SpringerLink
Log in

Validation of an ICD-9-Based Algorithm to Identify Stillbirth Episodes from Medicaid Claims Data

  • Original Research Article
  • Published:
Drug Safety Aims and scope Submit manuscript

Abstract

Introduction

In administrative data, accurate timing of exposure relative to gestation is critical for determining the effect of potential teratogen exposure on pregnancy outcomes.

Objective

To develop an algorithm for identifying stillbirth episodes in the ICD-9-CM era using national Medicaid claims data (1999–2014).

Methods

Unique stillbirth episodes were identified from clusters of medical claims using a hierarchy that identified the encounter with the highest potential of including the actual stillbirth delivery and that delineated subsequent pregnancy episodes. Each episode was validated using clinical detail on retrieved medical records as the gold standard.

Results

Among 220 retrieved records, 197 were usable for validation of 1417 stillbirth episodes identified by the algorithm. The positive predictive value (PPV) was 64.0% (57.3–70.7%) overall, 80.4% (73.8–87.1%) for inpatient episodes, 28.2% (14.1–42.3%) for outpatient-only episodes, and 20.0% (2.5–37.5%) for outpatient episodes with overlapping hospitalizations. The absolute difference between the dates of the algorithm-specified stillbirth delivery and the medical record-based event was 4.2 ± 24.3 days overall, 1.7 ± 7.7 days for inpatient episodes, 14.3 ± 51.4 days for outpatient-only episodes, and 1.0 ± 2.0 days for outpatient episodes that overlapped with a hospitalization. Excluding all outpatient episodes, as well as pregnancies involving multiple births, the PPV increased to 82.7% (76.8–89.8%).

Conclusions

Our algorithm to identify stillbirths from administrative claims data had a moderately high PPV. Positive predictive value was substantially increased by restricting the setting to inpatient episodes and using only input diagnostic codes for singleton stillbirths.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Hornbrook MC, Hurtado AV, Johnson RE. Health care episodes: definition, measurement and use. Med Care Rev. 1985;42(2):163–218.

    Article  CAS  PubMed  Google Scholar 

  2. Zhu Y, Hampp C, Wang X, Albogami Y, Wei YJ, Brumback BA, et al. Validation of algorithms to estimate gestational age at birth in the Medicaid Analytic eXtract-Quantifying the misclassification of maternal drug exposure during pregnancy. Pharmacoepidemiol Drug Saf. 2020;29(11):1414–22.

    Article  CAS  PubMed  Google Scholar 

  3. Margulis AV, Setoguchi S, Mittleman MA, Glynn RJ, Dormuth CR, Hernández-Díaz S. Algorithms to estimate the beginning of pregnancy in administrative databases. Pharmacoepidemiol Drug Saf. 2013;22(1):16–24.

    Article  PubMed  Google Scholar 

  4. Li Q, Andrade SE, Cooper WO, Davis RL, Dublin S, Hammad TA, et al. Validation of an algorithm to estimate gestational age in electronic health plan databases. Pharmacoepidemiol Drug Saf. 2013;22(5):524–32.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Eworuke E, Hampp C, Saidi A, Winterstein AG. An algorithm to identify preterm infants in administrative claims data. Pharmacoepidemiol Drug Saf. 2012;21(6):640–50.

    Article  PubMed  Google Scholar 

  6. Fitzpatrick T, Wilton AS, Guttmann A. Development and validation of a simple algorithm to estimate common gestational age categories using standard administrative birth record data in Ontario, Canada. J Obstet Gynaecol. 2021;41(2):207–11.

    Article  PubMed  Google Scholar 

  7. Hornbrook MC, Whitlock EP, Berg CJ, Callaghan WM, Bachman DJ, Gold R, et al. Development of an algorithm to identify pregnancy episodes in an integrated health care delivery system. Health Serv Res. 2007;42(2):908–27.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Manson JM, McFarland B, Weiss S. Use of an automated database to evaluate markers for early detection of pregnancy. Am J Epidemiol. 2001;154(2):180–7.

    Article  CAS  PubMed  Google Scholar 

  9. Matcho A, Ryan P, Fife D, Gifkins D, Knoll C, Friedman A. Inferring pregnancy episodes and outcomes within a network of observational databases. PLoS ONE. 2018;13(2): e0192033.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Bertoia ML, Phiri K, Clifford CR, Doherty M, Zhou L, Wang LT, et al. Identification of pregnancies and infants within a US commercial healthcare administrative claims database. Pharmacoepidemiol Drug Saf. 2022;31(8):863–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Moll K, Wong HL, Fingar K, Hobbi S, Sheng M, Burrell TA, et al. Validating claims-based algorithms determining pregnancy outcomes and gestational age using a linked claims-electronic medical record database. Drug Saf. 2021;44(11):1151–64.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Minassian C, Williams R, Meeraus WH, Smeeth L, Campbell OMR, Thomas SL. Methods to generate and validate a Pregnancy Register in the UK Clinical Practice Research Datalink primary care database. Pharmacoepidemiol Drug Saf. 2019;28(7):923–33.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Andrade SE, Shinde M, Moore Simas TA, Bird ST, Bohn J, Haynes K, et al. Validation of an ICD-10-based algorithm to identify stillbirth in the Sentinel system. Pharmacoepidemiol Drug Saf. 2021;30(9):1175–83.

    Article  PubMed  Google Scholar 

  14. Martin JA, Hamilton BE, Osterman MJK, Driscoll AK. Births: final data for 2019. Natl Vital Stat Rep. 2021;70(2):1–51.

    PubMed  Google Scholar 

  15. Winterstein AG, Thai TN, Nduaguba S, Smolinski NE, Wang X, Sahin L, et al. Risk of fetal or neonatal death or neonatal intensive care unit admission associated with gadolinium magnetic resonance imaging exposure during pregnancy. Am J Obstet Gynecol. 2022. https://doi.org/10.1016/j.ajog.2022.10.005.

    Article  PubMed  Google Scholar 

  16. Li Y, Zhu Y, Chen C, Wang X, Choi Y, Henriksen C, et al. Internal validation of Medicaid Analytic eXtract (MAX) data capture for comprehensive managed care plan enrollees from 2007 to 2010. Pharmacoepidemiol Drug Saf. 2018;27(10):1067–76.

    Article  PubMed  Google Scholar 

  17. Likis FE, Sathe NA, Carnahan R, McPheeters ML. A systematic review of validated methods to capture stillbirth and spontaneous abortion using administrative or claims data. Vaccine. 2013;31(Suppl 10):K74-82.

    Article  PubMed  Google Scholar 

  18. Jones RK, Jerman J. Abortion incidence and service availability in the United States, 2014. Perspect Sex Reprod Health. 2017;49(1):17–27.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Duke CW, Correa A, Romitti PA, Martin J, Kirby RS. Challenges and priorities for surveillance of stillbirths: a report on two workshops. Public Health Rep. 2009;124(5):652–9.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Hoyert DL, Gregory ECW. Cause-of-death data from the fetal death file, 2015–2017. Natl Vital Stat Rep. 2020;69(4):1–20.

    PubMed  Google Scholar 

  21. Thai TN, Rasmussen SA, Smolinski NE, Nduaguba S, Zhu Y, Bateman BT, et al. Implication of maternal continuous enrollment on stillbirth gestational age distributions and maternal characteristics in Medicaid enrollees. Am J Epidemiol. 2022. https://doi.org/10.1093/aje/kwac206.

    Article  Google Scholar 

  22. Palmsten K, Huybrechts KF, Mogun H, Kowal MK, Williams PL, Michels KB, et al. Harnessing the Medicaid Analytic eXtract (MAX) to evaluate medications in pregnancy: design considerations. PLoS ONE. 2013;8(6): e67405.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Adam MP, Polifka JE, Friedman JM. Evolving knowledge of the teratogenicity of medications in human pregnancy. Am J Med Genet C Semin Med Genet. 2011;157C(3):175–82.

    Article  PubMed  Google Scholar 

  24. Suarez EA, Bateman BT, Hernández-Díaz S, Straub L, Wisner KL, Gray KJ, et al. Association of antidepressant use during pregnancy with risk of neurodevelopmental disorders in children. JAMA Intern Med. 2022;182(11):1149–60.

    Article  PubMed  Google Scholar 

  25. Bateman BT, Hernandez-Diaz S, Straub L, Zhu Y, Gray KJ, Desai RJ, et al. Association of first trimester prescription opioid use with congenital malformations in the offspring: population based cohort study. BMJ. 2021;372: n102.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Thai TN, Sarayani A, Wang X, Albogami Y, Rasmussen SA, Winterstein AG. Risk of pregnancy loss in patients exposed to mycophenolate compared to azathioprine: a retrospective cohort study. Pharmacoepidemiol Drug Saf. 2020;29(6):716–24.

    Article  CAS  PubMed  Google Scholar 

  27. Huybrechts KF, Bateman BT, Zhu Y, Straub L, Mogun H, Kim SC, et al. Hydroxychloroquine early in pregnancy and risk of birth defects. Am J Obstet Gynecol. 2021;224(3):290.e1-290.e22.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank the Florida Department of Health for provision of birth and fetal death certificates. This study represents the opinions of the authors and not necessarily those of the Food and Drug Administration or the Florida Department of Health.

Author information

Authors and Affiliations

  1. Department of Pharmaceutical Systems and Policy, College of Pharmacy, West Virginia University, Morgantown, WV, USA

    Sabina O. Nduaguba

  2. West Virginia University Cancer Institute, Morgantown, WV, USA

    Sabina O. Nduaguba

  3. Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, 1225 Center Drive, PO Box 100496, Gainesville, FL, 32611, USA

    Sabina O. Nduaguba, Nicole E. Smolinski, Thuy N. Thai & Almut G. Winterstein

  4. Faculty of Pharmacy, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Vietnam

    Thuy N. Thai

  5. Division of Epidemiology, Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA

    Steven T. Bird

  6. Center for Drug Evaluation and Safety (CoDES), University of Florida, Gainesville, FL, USA

    Sonja A. Rasmussen & Almut G. Winterstein

  7. Department of Epidemiology, College of Public Health and Health Professionals and College of Medicine, University of Florida, Gainesville, FL, USA

    Sonja A. Rasmussen & Almut G. Winterstein

  8. Department of Pediatrics and Obstetrics and Gynecology, College of Medicine, University of Florida, Gainesville, FL, USA

    Sonja A. Rasmussen

Authors
  1. Sabina O. Nduaguba
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nicole E. Smolinski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thuy N. Thai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Steven T. Bird
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sonja A. Rasmussen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Almut G. Winterstein
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Almut G. Winterstein.

Ethics declarations

Funding

This work was supported by the Food and Drug Administration (contract HHSF223201810083C).

Conflict of interest

SON, NES, and TNT have no conflict of interest to declare. STB works for the US Food and Drug Administration and have no conflicts of interest to disclose. SAR has served on an advisory committee for the Teva Pregnancy Registry; has consulted for F. Hoffmann-La Roche AG as a litigation consultant; and receives grant support from the National Institutes of Health, and the Centers for Disease Control and Prevention, and Health Services Research Administration. AGW has received funding for research studies unrelated to this work from NIH, AHRQ, PCORI, FDA, the Bill and Melinda Gates Foundation, CDC, Merck Sharpe and Dohme, and the state of Florida. She has received consulting fees from Arbor Pharmaceuticals, Bayer, Ipsen, and Genentech Inc., likewise unrelated to this work.

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

The data that support the findings of this study are available from the Centers for Medicaid and Medicare Services but restrictions apply to the availability of these data, which were used under a data user agreement for the current study and are not publicly available.

Code availability

Codes will be made available upon request.

Author contributions

SON made substantial contribution to the design, analysis, and interpretation of data; as well as drafting the manuscript. NES made substantial contribution to the design, analysis, and interpretation of data and also contributed to the critical revision of the manuscript. TNT made substantial contribution to the design and interpretation of data and also contributed to the critical revision of the manuscript. STB made substantial contribution to the interpretation of data and also contributed to the critical revision of the manuscript. SAR made substantial contribution to the design and interpretation of data and also contributed to the critical revision of the manuscript. AGW made substantial contribution to the conception and design, data acquisition, analysis, and interpretation of data; as well as the critical revision of the manuscript. All authors read and approved the final version.

FDA disclaimer

This work represents the opinions of the authors and not necessarily those of the Food and Drug Administration.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 44 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nduaguba, S.O., Smolinski, N.E., Thai, T.N. et al. Validation of an ICD-9-Based Algorithm to Identify Stillbirth Episodes from Medicaid Claims Data. Drug Saf 46, 457–465 (2023). https://doi.org/10.1007/s40264-023-01287-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40264-023-01287-3

Search

Navigation