Variation in mental illness and provision of public mental health services

  • William C. Johnson
  • Michael LaForest
  • Brett Lissenden
  • Steven Stern
Article

Abstract

By providing affordable healthcare to many Americans for the first time, the Affordable Care Act increases demand for public mental health services. It is, however, unclear if states’ provision standards for supply of mental health services will be able to accommodate this demand increase. Both the demand and supply of public mental health services vary within states; it is necessary to measure both locally. In this paper, we estimate the prevalence of mental illness within 30 geographical regions in the state of Virginia, a representative example of how many states organize their public mental health systems and how mental illness prevalence can be measured locally. Our methodology extends the analysis in Stern (Health Serv. Outcomes Res. Methods 14:109–155, 2014) by covering an entire state and accounting for peoples’ insurance status. The latter allows us to compare estimates of demand for public mental health services among those 30 geographical regions. We find that over 66,000 uninsured and Medicaid-insured individuals in Virginia are not provided with public mental health services. The deficit varies locally, with several regions having no deficit and others having 5000 or more untreated people. We also estimate that a large portion of the unserved people with mental illness are uninsured but would be insured for mental health services through Medicaid if Virginia were to accept the Medicaid expansion associated with the Affordable Care Act. These results provide evidence that there is significant variation in the demand for and public health systems’ ability to supply mental health services within states. This implies states can better serve populations relying on mental health care by allocating scarce public mental health dollars to localities reflecting their need.

Keywords

Mental health Affordable Care Act Health insurance 

JEL Classification

H75 I13 I18 I38 

References

  1. Alegria, M., Takeuchi, D., Canino, G., Duan, N., Shrout, P., Meng, X.-L., Vega, W., Zane, N., Vila, D., Woo, M., Vera, M., Guarnaccia, P., Aguilar-gaxiola, S., Sue, S., Escobar, J., Lin, K.-m., Gong, F.: Considering context, place and culture: the national latino and asian american study. Int. J. Methods Psychiatr. Res. 13(4), 208–220 (2006a)CrossRefGoogle Scholar
  2. Alegria, M., Vila, D., Woo, M., Canino, G., Takeuchi, D., Vera, M., Febo, V., Guarnaccia, P., Aguilar-Gaxiola, S., Shrout, P.: Cultural relevance and equivalence in the NLAAS instrument: integrating etic and emic in the development of cross-cultural measures for a psychiatric epidemiology and services study of Latinos. Int. J. Methods Psychiatr. Res. 13(4), 270–288 (2006b)CrossRefGoogle Scholar
  3. Baldwin, M., Marcus, S.: Labor market outcomes of persons with mental disorders. Ind. Relat. 46(3), 481–510 (2007)CrossRefGoogle Scholar
  4. Banerjee, S., Wall, M., Carlin, B.: Frailty modeling for spatially correlated survival data, with application to infant mortality in Minnesota. Biostatistics 4(1), 123–142 (2003)CrossRefPubMedGoogle Scholar
  5. Barker, P., Epstein, J., Hourani, L., Gfroerer, J., Monique Clinton-Sherrod, A., West, N., Shi, W.: Patterns of mental health service utilization and substance use among adults, 2000 and 2001. DHHS Publication No. SMA 04-3901, Analytic Series A-22. Substance Abuse and Mental Health Services Administration, Office of Applied Studies, Rockville, MD (2004)Google Scholar
  6. Brown, C., Guo, D., Stern, S.: Cost and service variation across community service boards for mental health services in Virginia. Unpublished manuscript (2015)Google Scholar
  7. Burke, B., Miller, B., Proser, M., Petterson, S., Bazemore, A., Goplerud, E., Phillips, R.: A needs-based method for estimating the behavioral health staff needs of community health centers. BMC Health Serv. Res. 13(1), 245 (2013)CrossRefPubMedPubMedCentralGoogle Scholar
  8. Chihara, T.: An Introduction to Orthogonal Polynomials. Gordon and Breach, New York (1978)Google Scholar
  9. Centers for Medicare & Medicaid Services: Medicaid C CHIP: October monthly applications and eligibility determinations report. Baltimore (2013)Google Scholar
  10. Congdon, P.: Estimating population prevalence of psychiatric conditions by small area with applications to analysing outcome and referral variations. Health Place 12, 465–478 (2006)CrossRefPubMedGoogle Scholar
  11. Cressie, N.: Statistics for Spatial Data. Wiley, New York (1993)Google Scholar
  12. Cunningham, P.: Health policy brief. Unpublished manuscript (2014)Google Scholar
  13. Dickson, V.: Medicaid plans struggle to provide mental health services. Modern Healthcare (2015). http://www.modernhealthcare.com/article/20150704/MAGAZINE/307049979
  14. Elbers, C., Fujii, T., Lanjouw, P., Ozler, B., Yin, W.: Poverty alleviation through geographic targeting: how much does disaggregation help? J. Dev. Econ. 83, 198–213 (2007)CrossRefGoogle Scholar
  15. Elbers, C., Lanjouw, J., Lanjouw, P.: Micro-level estimation of poverty and inequality. Econometrica 71(1), 355–364 (2003)CrossRefGoogle Scholar
  16. Ellis, A., Konrad, T., Thomas, K., Morrissey, J.: County-level estimates of mental health professional supply in the United States. Psychiatr. Serv. 60(10), 1315–1322 (2009)CrossRefPubMedGoogle Scholar
  17. Frank, R., Goldman, H., Hogan, M.: Medicaid and mental health: be careful what you ask for. Health Aff. 22(1), 101–113 (2003)CrossRefGoogle Scholar
  18. Frean, M., Gruber, J., Sommers, B.: Premium subsidies, the mandate, and medicaid expansion: coverage effects of the Affordable Care Act. Unpublished manuscript (2016)Google Scholar
  19. Geweke, J.: Antithetic acceleration of Monte Carlo integration in Bayesian inference. J. Econom. 38(1/2), 73–89 (1988)CrossRefGoogle Scholar
  20. Heeringa, S., Wagner, J., Torres, M., Duan, N., Adams, T., Berglund, P.: Sample designs and sampling methods for the collaborative psychiatric epidemiology studies (CPES). Int. J. Methods Psychiatr. Res. 13(4), 221–240 (2006)CrossRefGoogle Scholar
  21. Hudson, C.: Validation of a model for estimating state and local prevalence of serious mental illness. Int. J. Methods Psychiatr. Res. 18(4), 251–264 (2009)PubMedGoogle Scholar
  22. Jackson, J., Neighbors, H., Nesse, R., Trierweiler, S., Torres, M.: Methodological innovations in the national survey of American life. Int. J. Methods Psychiatr. Res. 13(4), 289–298 (2006a)CrossRefGoogle Scholar
  23. Jackson, J., Torres, M., Caldwell, C., Neighbors, H., Nesse, R., Taylor, R., Trierweiler, S., Williams, D.: The national survey of American Life: a study of racial, ethnic and cultural influences on mental disorders and mental health. Int. J. Methods Psychiatr. Res. 13(4), 196–207 (2006b)CrossRefGoogle Scholar
  24. Kessler, R., Berglund, P., Walters, E., Leaf, P., Iouzis, A., Bruce, M., Friedman, R., Grosser, R., Kennedy, C., Kuehnel, T., Laska, E., Manderscheid, R., Narrow, W., Rosenheck, R., Schneier, M.: A methodology for estimating the 12-month prevalence of serious mental illness. Mental Health, United States, 1998. US Department of Health and Human Services, SAMHSA (1998)Google Scholar
  25. Kessler, R., Chiu, W.T., Demier, O., Walters, E.: Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the national comorbidity survey replication. Arch. Gen. Psychiatry 62, 617–627 (2005)CrossRefPubMedPubMedCentralGoogle Scholar
  26. Konrad, T., Ellis, A., Thomas, K., Holzer, C., Morrissey, J.: County-level estimates of need for mental health professionals in the United States. Psychiatr. Serv. 60(10), 1307–1314 (2009)CrossRefPubMedGoogle Scholar
  27. LaForest, M., Stern, S.: Translation of correlation in latent variables into correlation in observed variables. Unpublished manuscript (2016a)Google Scholar
  28. LaForest, M., Stern, S.: Wald test for common mean probabilities across communities. Unpublished manuscript (2016b)Google Scholar
  29. Lavy, V., Palumbo, M., Stern, S.: Simulation of multinomial probit probabilities and imputation. In: Fomby, T., Carter Hill, R. (eds.) Advances in Econometrics. JAI Press, Stamford (1998)Google Scholar
  30. Leroux, B., Lei, X., Breslow, N.: Estimation of disease rates in small areas: a new mixed model for spatial dependence. In: Elizabeth Halloran, M., Berry, D. (eds.) Statistical Models in Epidemiology, the Environment, and Clinical Trials. Springer, New York (1999)Google Scholar
  31. Malec, D., Müller, P.: Pushing the limits of contemporary statistics: contributions in honor of Jayanta K. Ghosh. In: A Bayesian Semi-parametric Model for Small Area Estimation, vol. 3, pp. 223–236 (2008)Google Scholar
  32. McAlpine, D., Mechanic, D.: Utilization of specialty mental health care among persons with severe mental illness: the roles of demographics, need, insurance, and risk. Health Serv. Res. 35(1), 277–292 (2000)PubMedPubMedCentralGoogle Scholar
  33. Mechanic, D.: Is the prevalence of mental disorders a good measure of the need for services? Health Aff. 22(5), 8–20 (2003)CrossRefGoogle Scholar
  34. Merwin, E., Hinton, I., Dembling, B., Stern, S.: Shortages of rural mental health professionals. Arch. Psychiatr. Nurs. 27(1), 42–51 (2003)CrossRefGoogle Scholar
  35. Miller, J., Maududi N.: NASMHPD Resource Management Guide: Impacts of Affordable Care Act on Coverage for Uninsured People with Behavioral Health Conditions. National Association of State Mental Health Program Directors (2013)Google Scholar
  36. Morrissey, J., Domino, M., Cuddeback, G.: Expedited medicaid enrollment, mental health service use, and criminal recidivism among released prisoners with severe mental illness. Psychiatr. Serv. 67, 842–849 (2016)CrossRefPubMedGoogle Scholar
  37. Muntaner, C., Eaton, W., Diala, C., Kessler, R., Sorlie, P.: Social class, assets, organizational control and the prevalence of common groups of psychiatric disorders. Soc. Sci. Med. 47(12), 2043–2053 (1998)CrossRefPubMedGoogle Scholar
  38. Narrow, W., Rae, D., Robins, L., Regier, D.: Revised prevalence estimates of mental disorders in the united states: using a clinical significance criterion to reconcile 2 surveys’ estimates. Arch. Gen. Psychiatry 59, 115–123 (2002)CrossRefPubMedGoogle Scholar
  39. Opsomer, J., Claeskens, G., Ranalli, M., Kauermann, G., Breidt, F.: Non-parametric small area estimation using penalized spline regression. J. R. Stat. Soc. Ser. B Stat. Methodol. 70(1), 265–286 (2008)CrossRefGoogle Scholar
  40. Pearlman, S.: The patient protection and Affordable Care Act: impact on mental health services demand and provider availability. J. Am. Psychiatr. Nurses Assoc. 19(6), 327–334 (2013)CrossRefPubMedGoogle Scholar
  41. Pennell, B.-E., Bowers, A., Carr, D., Chardoul, S., Cheung, G.-q., Dinkelmann, K., Gebler, N., Hansen, S.E., Pennell, S., Torres, M.: The development and implementation of the national comorbidity survey replication, the national survey of American life, and the national Latino and Asian American survey. Int. J. Methods Psychiatr. Res. 13(4), 241–269 (2006)CrossRefGoogle Scholar
  42. Perloff, J., Kletke, P., Fossett, J., Banks, S.: Medicaid participation among urban primary care physicians. Med. Care 35(2), 142–157 (1997)CrossRefPubMedGoogle Scholar
  43. Prasad, N., Rao, J.: On robust small area estimation using a simple random effects model. Surv. Methodol. 25, 67–72 (1999)Google Scholar
  44. Robins, L., Regier, D.: Psychiatric Disorders in America: The Epidemiologic Catchment Area Study. The Free Press, New York (1991)Google Scholar
  45. Sommers, B., Rick, K., Kenneth, F., Rosa, P., Karyn, S., Sherry, G.: Understanding participation rates in medicaid: implications for the Affordable Care Act. ASPE Issue Brief (2012)Google Scholar
  46. Stern, S.: Semiparametric estimates of the supply and demand effects of disability on labor force participation. J. Econom. 71(1–2), 49–70 (1992a)Google Scholar
  47. Stern, S.: A method for smoothing simulated moments of discrete probabilities in multinomial probit models. Econometrica 60(4), 943–952 (1992b)CrossRefGoogle Scholar
  48. Stern, S.: Estimating local prevalence of mental health problems. Health Serv. Outcomes Res. Methods 14, 109–155 (2014)CrossRefGoogle Scholar
  49. Stern, S., Merwin, E., Hauenstein, E., Hinton, I., Rovnyak, V., Wilson, M., Williams, I., Mahone, I.: The effects of rurality on mental and physical health. Health Serv. Outcomes Res. Methods 10(1), 33–66 (2010)CrossRefGoogle Scholar
  50. Tarozzi, A., Deaton, A.: using census and survey data to estimate poverty and inequality for small areas. Rev. Econ. Stat. 91(4), 773–792 (2009)CrossRefGoogle Scholar
  51. Thomas, K., Ellis, A., Konrad, T., Holzer, C., Morrissey, J.: County-level estimates of mental health professional shortage in the united states. Psychiatr. Serv. 60(10), 1323–1328 (2009)CrossRefPubMedGoogle Scholar
  52. Virginia Department of Medical Assistance Services: Bridging the mental health coverage gap in Virginia. http://www.dmas.virginia.gov/Content_atchs/gap/FINALFact Sheet.pdf (2014)
  53. Wanchek, T., Mcgarvey, E., Leon-Verdin, M., Bonnie, R.: The Effect of community mental health services on hospitalization rates in Virginia. Psychiatr. Serv. 62(2), 194–199 (2011)CrossRefPubMedGoogle Scholar
  54. Wang, P., Lane, M., Olfson, M., Pincus, H., Wells, K., Kessler, R.: Twelve-month use of mental health services in the United States: results from the national comorbidity survey replication. Arch. Gen. Psychiatry 62(6), 629–640 (2005)CrossRefPubMedGoogle Scholar
  55. Wells, K., Sturm, R., Burnam, A.: National Survey of Alcohol, Drug, and Mental Health Problems [Healthcare for Communities], 2000–2001 [Computer file]. ICPSR version. University of California, Los Angeles, Health Services Research Center [producer], 2004, Los Angeles, CA. Inter-university Consortium for Political and Social Research [distributor], Ann Arbor, MI (2005)Google Scholar
  56. Wooldridge, J.: Econometric Analysis of Cross Section and Panel Data. MIT Press, Cambridge (2002)Google Scholar
  57. Zuvekas, S.: National estimates of health insurance coverage, mental health utilization, and spending for low-income individuals. Agency for Healthcare Research and Quality, Rockville, MD (2009). http://www.ahrq.gov/policymakers/health-initiatives/meps/lowinc/lowinc.html

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Stony Brook UniversityStony BrookUSA
  2. 2.University of VirginiaCharlottesvilleUSA

Personalised recommendations