Maternal and Child Health Journal

, Volume 13, Issue 2, pp 250–259 | Cite as

Issues and Biases in Matching Medicaid Pregnancy Episodes to Vital Records Data: The Arkansas Experience

  • Janet M. Bronstein
  • Charles T. Lomatsch
  • David Fletcher
  • Terri Wooten
  • Tsai Mei Lin
  • Richard Nugent
  • Curtis L. Lowery
Article

Abstract

Objectives This study examines the extent of selection biases identified in the process of linking Medicaid claims with evidence of pregnancy to vital records. Methods Two years of Medicaid claims were scanned to identify pregnancy-related diagnoses and procedures. Information on 55,764 Medicaid recipients was provided to the Division of Health Statistics, which linked the information to vital records data on a range of identifying characteristics. Claims were then clustered by date and then into episodes of care surrounding the birth date of the infant. We identified 38,222 pregnancy episodes matched to vital records; 8,474 episodes unmatched to vital records that appeared to terminate before a delivery; and 5,278 episodes that appeared to include a delivery but did not match to vital records. The characteristics of matched episodes and unmatched episodes and the characteristics of matched episodes with and without delivery claims are compared. Results Unmatched episodes spanned fewer weeks than matched episodes, included more diagnostic indicators of elevated risk, and occurred more frequently in more impoverished populations. Among the matched records, 13% did not include claims for delivery services. These episodes occurred more frequently among Hispanic women, women delivering out of hospitals and women with preterm births and infant deaths. Conclusions The results provide evidence, as other studies have demonstrated, that matching Medicaid claims and vital records data is feasible. However, the matched analytic data set does tend to under-represent the outcomes of high-risk pregnancies. An additional source of selection bias can be avoided by using evidence of pregnancy as the Medicaid index for matching against vital records, rather than using only index cases with evidence of delivery.

Keywords

Medicaid Vital statistics Linked files High-risk pregnancies 

References

  1. 1.
    Piper, J. M., Ray, W. A., & Griffin, M. R., et al. (1990). Methodological issues in evaluating expanded medicaid coverage for pregnant women. American Journal of Epidemiology, 132(3), 561–571.PubMedGoogle Scholar
  2. 2.
    Bell, R. M., Keesey, J., & Richards, T. (1994). The urge to merge: Linking vital statistics records and medicaid claims. Medical Care, 32(10), 1004–1018. doi:10.1097/00005650-199410000-00003.PubMedCrossRefGoogle Scholar
  3. 3.
    Ray, W. A., Mitchel, E. F., & Piper J. M. (1997). Effects of medicaid expansions on preterm birth. American Journal of Preventive Medicine, 13(4), 292–297.PubMedGoogle Scholar
  4. 4.
    Howell, E. M., Heiser, N., Cherlow, A., et al. (2000). Identifying pregnant substance abusers and studying their treatment using birth certificates, medicaid claims, and state substance abuse treatment data. Journal of Drug Issues, 20(1), 205–224.Google Scholar
  5. 5.
    Howell, E. M. (2001). The impact of the medicaid expansions for pregnant women: A synthesis of the evidence. Medical Care Research and Review, 58(1), 3–30.PubMedGoogle Scholar
  6. 6.
    Rogowski, J. (1998). Cost-effectiveness of care for very low birth weight infants. Pediatrics, 102(1), 35–43. doi:10.1542/peds.102.1.35.PubMedCrossRefGoogle Scholar
  7. 7.
    HRSA Maternal and Child Health Bureau. The Maternal and Child Health Servicestitle V Block Grant to States Program, Application/Annual Report/Guidance. Fourth Edition, Section F, Health System Capacity Indicators. ftp://ftp.hrsa.gov/guidance06/mchblock2006.doc. Accessed 28 April 2008.
  8. 8.
    Gyllstrom, M. E., Jensen, J. L., & Vaughan, J. N., et al. (2002). Linking birth certificates with medicaid data to enhance population health assessment: methodological issues addressed. Journal of Public Health Management Practice, 8(4), 38–44.Google Scholar
  9. 9.
    Lowery, C., Bronstein, J. M., & McGhee, J., et al. (2007). ANGELS & University of Arkansas for medical sciences paradigm for distant obstetrical care delivery. American Journal of Obstetrics and Gynecology, 196(6), 534.e1–534.e9.CrossRefGoogle Scholar
  10. 10.
    Hornbrook, M. C., Hurtado, A. V., & Johnson, R. E. (1985). Health care episodes: Definition, measurement and use. Medical Care Review, 42(2), 163. doi:10.1177/107755878504200202.PubMedCrossRefGoogle Scholar
  11. 11.
    Wingert, T. D., Kralewski, J. E., Lindquist, T. J., et al. (1995) Constructing episodes of care from encounter and claims data: Some methodological issues. Inquiry, 32, 430–443.PubMedGoogle Scholar
  12. 12.
    Shulman, K. A., Yabroff, R., Kong, J., et al. (1999). A claims data approach to defining an episode of care. Health Services Research, 34(2), 603–621.Google Scholar
  13. 13.
    Silver, R. M. (2007). Fetal death. Obstetrics and Gynecology, 109(1), 153–167.PubMedGoogle Scholar
  14. 14.
    Grisso, J. A., Carson, J. L., Feldman, H. I., et al. (1997). Epidemiological pitfalls using medicaid data in reproductive health research. Journal of Maternal-Fetal Medicine, 6, 230–236. doi:10.1002/(SICI)1520-6661(199707/08)6:4≤230::AID-MFM9≥3.0.CO;2-K.PubMedCrossRefGoogle Scholar
  15. 15.
    Paulson, J., Ramsini, W., Conrey, E., et al. (2007). Unregistered deaths among extremely low birthweight infants—Ohio, 2006. MMWR Weekly, October 26, 56(42), 1101–1103.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Janet M. Bronstein
    • 1
  • Charles T. Lomatsch
    • 2
  • David Fletcher
    • 3
  • Terri Wooten
    • 4
  • Tsai Mei Lin
    • 5
  • Richard Nugent
    • 4
  • Curtis L. Lowery
    • 6
  1. 1.School of Public HealthUniversity of Alabama at BirminghamBirminghamUSA
  2. 2.APS Health CareMadisonUSA
  3. 3.Arkansas Division of Medical AssistanceLittle RockUSA
  4. 4.Arkansas Department of HealthLittle RockUSA
  5. 5.Arkansas Department of HealthLittle RockUSA
  6. 6.Department of Obstetrics and GynecologyUniversity of Arkansas for Medical SciencesLittle RockUSA

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