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Over-utilization of cesarean sections and misclassification error

  • Alejandro ArrietaEmail author
Article

Abstract

This paper develops a model for defining Cesarean sections’ over- and under-utilization as deviations from clinically appropriate treatment due to non-clinical factors. Physician decisions can be affected by both monetary and non-monetary incentives, and the perception of the patient’s medical information and preferences. This structural model elucidates the physician decision-making process and identifies and tests for the degree of deviation from appropriate treatment (over- and under-utilization). The model is applied to estimate over-utilization of Cesarean section deliveries performed in the state of New Jersey during the 1999–2002 period. Non-clinically appropriate Cesarean sections occur at a moderate but growing rate of roughly 3.2 %. The growth of the Cesarean section rate in New Jersey over these years was explained mainly by non-clinical factors.

Keywords

Healthcare utilization Misclassification model Cesarean section Physician incentives 

References

  1. Abowd, J.M., Farber, H.S.: Job queues and the union status of workers. Ind. Labor Relat. Rev. 35(3), 354–367 (1982)CrossRefGoogle Scholar
  2. Aron, D., Gordon, H., DiGiuseppe, D., Harper, D., Rosenthal, G.: Variations in risk-adjusted cesarean delivery rates according to race and health insurance. Med. Care 38(1), 35–44 (2000)CrossRefPubMedGoogle Scholar
  3. Aron, D., Harper, D., Shepardson, L., Rosenthal, G.: Impact of risk-adjusting cesarean delivery rates when reporting hospital performance. JAMA 279(24), 1968 (1998)CrossRefPubMedGoogle Scholar
  4. Betrán, A.P., Merialdi, M., Lauer, J.A., Bing-Shun, W., Thomas, J., Van Look, P., Wagner, M.: Rates of caesarean section: analysis of global, regional and national estimates. Paediatr. Perinat. Epidemiol. 21(2), 98–113 (2007)CrossRefPubMedGoogle Scholar
  5. Das, M.: Is there evidence against the induced demand hypothesis? Explaining the large reduction in cesarean rates. Department of Economics Discussion Paper Series. Columbia University, Department of Economics, Columbia (2002)Google Scholar
  6. De Jaegher, K., Jegers, M.: The physician–patient relationship as a game of strategic information transmission. Health Econ. 10(7), 651–668 (2001)CrossRefPubMedGoogle Scholar
  7. DiGiuseppe, D., Aron, D., Payne, S., Snow, R., Dierker, L., Rosenthal, G.: Risk adjusting cesarean delivery rates: a comparison of hospital profiles based on medical record and birth certificate data. Health Serv. Res. 36(5), 959 (2001)PubMedCentralPubMedGoogle Scholar
  8. Dranove, D.: Demand inducement and the physician/patient relationship. Econ. Inq. 26(2), 281–298 (1988)CrossRefPubMedGoogle Scholar
  9. Ford, E.S., Cooper, R.S.: Racial/ethnic differences in health care utilization of cardiovascular procedures: a review of the evidence. Health Serv Res 30(1), 237–252 (1995)PubMedCentralPubMedGoogle Scholar
  10. Fuchs, V.R.: The supply of surgeons and the demand for operations. J Human Resour 13(Supplement), 35–56 (1978)CrossRefGoogle Scholar
  11. Fuchs, V. R., McClellan, M., Skinner, J.: Area differences in utilization of medical care and mortality among US elderly. NBER Working Paper No. 8628, (2001)Google Scholar
  12. Gruber, J., Owings, M.: Physician financial incentives and cesarean section delivery. Rand J Econ 27(1), 99–123 (1996)CrossRefPubMedGoogle Scholar
  13. Hausman, J.A., Abrevaya, J., Scott-Morton, F.M.: Misclassification of the dependent variable in a discrete-response setting. J Econom 87(2), 239–269 (1998)CrossRefGoogle Scholar
  14. Keeler, E., Park, R., Bell, R., Gifford, D., Keesey, J.: Adjusting cesarean delivery rates for case mix. Health Serv. Res. 32(4), 511 (1997)PubMedCentralPubMedGoogle Scholar
  15. Kenkel, D.S., Lillard, D.R.M., Alan, D.: Accounting for misclassification error in retrospective smoking data. Health Econ. 13(10), 1031–1044 (2004)CrossRefPubMedGoogle Scholar
  16. Kressin, N.R., Petersen, L.A.: Racial Differences in the use of invasive cardiovascular procedures: review of the literature and prescription for future research. Ann. Intern. Med. 135(5), 352–366 (2001)CrossRefPubMedGoogle Scholar
  17. Li, T., Rhoads, G.G., Smulian, J., Demissie, K., Wartenberg, D., Kruse, L.: Physician cesarean delivery rates and risk-adjusted perinatal outcomes. Obstet. Gynecol. 101(6), 1204–1212 (2003)CrossRefPubMedGoogle Scholar
  18. Magder, L.S., Hughes, J.P.: Logistic regression when the outcome is measured with uncertainty. Am. J. Epidemiol. 146(2), 195–203 (1997)CrossRefPubMedGoogle Scholar
  19. Meng, C.-L., Schmidt, P.: On the Cost of Partial Observability in the Bivariate Probit Model. Int. Econom. Rev. 26(1), 71–85 (1985)CrossRefGoogle Scholar
  20. Pauly, M.: Doctors and their workshops: Economic models of physician behaviour. University of Chicago Press, Chicago (1980)CrossRefGoogle Scholar
  21. Poirier, D.J.: Partial observability in bivariate probit models. J. Econom. 12(2), 209–217 (1980)CrossRefGoogle Scholar
  22. Schneider, E.C., Leape, L.L., Weissman, J.S., Piana, R.N., Gatsonis, C., Epstein, A.M.: Racial differences in cardiac revascularization rates: does “overuse” explain higher rates among white patients? Ann. Intern. Med. 135(5), 328–337 (2001)CrossRefPubMedGoogle Scholar
  23. Tussing, A., Wojtowycz, M.: The cesarean decision in New York State, 1986: economic and noneconomic aspects. Med. Care 30(6), 529–540 (1992)CrossRefPubMedGoogle Scholar
  24. Van Ryn, M., Burke, J.: The effect of patient race and socio-economic status on physicians’ perceptions of patients. Soc. Sci. Med. 50(6), 813–828 (2000)CrossRefPubMedGoogle Scholar
  25. Vella, F.: Estimating models with sample selection bias: a survey. J. Human Resour. 33(1), 127–169 (1998)CrossRefGoogle Scholar
  26. Wennberg, J.E.: Unwarranted variations in healthcare delivery: implications for academic medical centres. BMJ 325(7370), 961–964 (2002)CrossRefPubMedCentralPubMedGoogle Scholar
  27. Xie, B., Dilts, D.M., Shor, M.: The physician–patient relationship: the impact of patient-obtained medical information. Health Econ. 15(8), 813–833 (2006)CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Health Policy and ManagementFlorida International UniversityMiamiUSA

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