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Measuring inappropriate medical diagnosis and treatment in survey data: The case of ADHD among school-age children

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Author Info

  • Evans, William N.
  • Morrill, Melinda S.
  • Parente, Stephen T.

Abstract

We exploit the discontinuity in age when children start kindergarten generated by state eligibility laws to examine whether relative age is a significant determinant of ADHD diagnosis and treatment. Using a regression discontinuity model and exact dates of birth, we find that children born just after the cutoff, who are relatively old-for-grade, have a significantly lower incidence of ADHD diagnosis and treatment compared with similar children born just before the cutoff date, who are relatively young-for-grade. Since ADHD is an underlying neurological problem where incidence rates should not change dramatically from one birth date to the next, these results suggest that age relative to peers in class, and the resulting differences in behavior, directly affects a child's probability of being diagnosed with and treated for ADHD.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Health Economics.

Volume (Year): 29 (2010)
Issue (Month): 5 (September)
Pages: 657-673

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Handle: RePEc:eee:jhecon:v:29:y:2010:i:5:p:657-673

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Web page: http://www.elsevier.com/locate/inca/505560

Related research

Keywords: ADHD Stimulants Regression discontinuity design;

References

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  1. Dobkin, Carlos & Ferreira, Fernando, 2010. "Do school entry laws affect educational attainment and labor market outcomes?," Economics of Education Review, Elsevier, vol. 29(1), pages 40-54, February.
  2. Currie, Janet & Stabile, Mark, 2006. "Child mental health and human capital accumulation: The case of ADHD," Journal of Health Economics, Elsevier, vol. 25(6), pages 1094-1118, November.
  3. Anna Aizer, 2008. "Peer Effects and Human Capital Accumulation: the Externalities of ADD," NBER Working Papers 14354, National Bureau of Economic Research, Inc.
  4. Datar, Ashlesha, 2006. "Does delaying kindergarten entrance give children a head start?," Economics of Education Review, Elsevier, vol. 25(1), pages 43-62, February.
  5. Anthony T. LoSasso & Thomas C. Buchmueller, 2002. "The Effect of the State Children's Health Insurance Program on Health Insurance Coverage," NBER Working Papers 9405, National Bureau of Economic Research, Inc.
  6. Michael Fertig & Jochen Kluve, 2005. "The Effect of Age at School Entry on Educational Attainment in Germany," RWI Discussion Papers 0027, Rheinisch-Westfälisches Institut für Wirtschaftsforschung.
  7. Patrick J. McEwan & Joseph S. Shapiro, 2008. "The Benefits of Delayed Primary School Enrollment: Discontinuity Estimates Using Exact Birth Dates," Journal of Human Resources, University of Wisconsin Press, vol. 43(1).
  8. Jason Fletcher, 2010. "Spillover effects of inclusion of classmates with emotional problems on test scores in early elementary school," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 29(1), pages 69-83.
  9. Kelly Bedard & Elizabeth Dhuey, 2006. "The Persistence of Early Childhood Maturity: International Evidence of Long-Run Age Effects," The Quarterly Journal of Economics, MIT Press, vol. 121(4), pages 1437-1472, November.
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Citations

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Cited by:
  1. Mühlenweg, Andrea & Blomeyer, Dorothea & Stichnoth, Holger & Laucht, Manfred, 2012. "Effects of age at school entry (ASE) on the development of non-cognitive skills: Evidence from psychometric data," Economics of Education Review, Elsevier, vol. 31(3), pages 68-76.
  2. Elder, Todd E., 2010. "The importance of relative standards in ADHD diagnoses: Evidence based on exact birth dates," Journal of Health Economics, Elsevier, vol. 29(5), pages 641-656, September.
  3. Daniel Muller & Lionel Page, 2013. "Political Selection and the Relative Age Effect," QuBE Working Papers 009, QUT Business School.
  4. Morrill, Melinda Sandler, 2011. "The effects of maternal employment on the health of school-age children," Journal of Health Economics, Elsevier, vol. 30(2), pages 240-257, March.

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