Advertisement

Health Services and Outcomes Research Methodology

, Volume 14, Issue 3, pp 109–155 | Cite as

Estimating local prevalence of mental health problems

  • Steven SternEmail author
Article

Abstract

I present an improved methodology for estimating local prevalence rates using classical econometric methods. I provide information on the variation within national mental health surveys associated with ICD versus DSM coding. Conditional on the validity of national survey responses, I estimate precise and statistically significant models associated with binary measures of mental health diagnoses. I also present estimates from polychotomous discrete choice allowing for covariance in errors. Focusing on binary discrete measures, empirical results from NCS-R and NSADMHP are qualitatively similar though very different from NHIS. I speculate that, to a significant degree, this occurs because both NCS-R and NSADMHP rely on popular screening tools to mechanically diagnosis sample participants, while NHIS relies on self-diagnosis. I also discuss the effects on local prevalence estimates caused by unobserved community-specific effects. Finally, I use the results to make policy statements about the provision of public mental health services in central Virginia; the results document a severe shortage of services for people who are unlikely to be able to afford services in the private market.

Keyword

Mental health prevalence Small area estimation Mental health policy 

Notes

Acknowledgments

I would like to thank Dori Stern and Yiyi Zhou for excellent research assistance, Ivora Hinton, Caruso Brown, Ted Lutterman, and Summer Durrant for help with data, Deborah Stanford for help with manuscript preparation, the Bankard Fund at the University of Virginia for financial support, and Beth Merwin and seminar participants at UVA, NYU, the Region Ten Community Services Board, and AHRQ for valuable comments. All remaining errors are mine.

References

  1. Alderman, H., Babita, H.M., Demombynes, G., Makhatha, N., Ozler, B.: How low can you go? Combining census and survey data for mapping poverty in South Africa. J. Afr. Econ. 11, 169–200 (2002)CrossRefGoogle Scholar
  2. Alegria, M., Jackson, J., Kessler, R., Takeuchi, D.: Collaborative Psychiatric Epidemiology: Surveys (CPES), 2001–2003 [United States] [Computer file]. ICPSR20240-v4. Institute for Social Research, Survey Research Center [producer], Ann Arbor (2007). Inter-university Consortium for Political and Social Research [distributor], Ann Arbor (2008)Google Scholar
  3. Alegria, M., Takeuchi, D., Canino, G., Duan, N., Shrout, P., Meng, X.-L., Vega, W., Zane, N., Vila, D., Woo, M., Vera, M., Guarnaccia, Peter, Aguilar-gaxiola, Sergio, Sue, S., Escobar, J., Lin, K., 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
  4. Alegria, M., Vila, D., Woo, M., Canino, G., Takeuchi, David, Vera, Mildred, Febo, Vivian, Guarnaccia, Peter, Aguilar-Gaxiola, Sergio, Shrout, Patrick: Cultural relevance and equivalence in the NLAAS instrument: integrating etic and mmic 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
  5. Alexandre, P., French, M.: Labor supply of poor residents in metropolitan Miami, Florida: the role of depression and the co-morbid effects of substance use. J. Ment. Health Policy Econ. 4, 161–173 (2001)PubMedGoogle Scholar
  6. American Psychological Association: ICD vs. DSM. Monit. Psychol. 40(9), 63 (2009)Google Scholar
  7. Andrews, G., Slade, T.: The classification of anxiety disorders in ICD-10 and DSM-IV: a concordance analysis. Psychopathology 35, 100–106 (2002)PubMedCrossRefGoogle Scholar
  8. Andrews, Gavin, Slade, Tim: Classification in psychiatry: ICD-10 vs DSM-IV. Br. J. Psychiatry. 174, 3–5 (1999)PubMedCrossRefGoogle Scholar
  9. Anthony, J., Helzer, J.: Syndromes of drug abuse and dependence. In: Robins, L., Regier, D. (eds.) Psychiatric Disorders in America. The Free Press, New York (1991)Google Scholar
  10. Aoun, S., Pennebaker, D., Wood, C.: Assessing population need for mental health care: a review of approaches and predictors. Ment. Health Serv. Res. 6(1), 33–46 (2004)PubMedCrossRefGoogle Scholar
  11. Baldwin, M.: Explaining the differences in employment outcomes between persons with and without mental disorders. Unpublished manuscript. (2005)Google Scholar
  12. 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)PubMedCrossRefGoogle Scholar
  13. Blazer, D., Hughes, D., George, L., Swartz, M., Boyer, R.: Generalized anxiety disorder. In: Robins, L., Regier, D. (eds.) Psychiatric Disorders in America. The Free Press, New York (1991)Google Scholar
  14. Borsch-Supan, A., Hajivassiliou, V.: Smooth unbiased multivariate probability simulators for maximum likelihood estimation of limited dependent variable models. J. Econ. 58(3), 347–368 (1993)CrossRefGoogle Scholar
  15. Brown, C., Guo, D., Stern, S.: Analysis of the potential cost savings in medicaid for mental health services in Virginia. Unpublished manuscript (2013)Google Scholar
  16. Chamberlain, G.: Analysis of covariance with qualitative data. Rev. Econ. Stud. 47, 225–238 (1980)CrossRefGoogle Scholar
  17. Chihara, T.: An Introduction to Orthogonal Polynomials. Gordon and Breach, New York (1978)Google Scholar
  18. Choy, M., Switzer, P., Parsonnet, J.: Estimating disease prevalence using census data. Epidemiol. Infect. 136, 1253–1260 (2008)PubMedCentralPubMedCrossRefGoogle Scholar
  19. Citro, C., Kalton, G.: Small Area Income and Poverty Estimates: Priorities for 2000 and Beyond. National Academy Press, Washington, DC (2000)Google Scholar
  20. 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)PubMedCrossRefGoogle Scholar
  21. Congdon, P.: A multilevel model for cardiovascular disease prevalence in the US and its application to micro area prevalence estimates. Int. J. Health Geogr. 8(1), 6 (2009)PubMedCentralPubMedCrossRefGoogle Scholar
  22. Cox, D.: The Analysis of Binary Data. Methuen, London (1970)Google Scholar
  23. Eaton, W., Dryman, A., Weissman, M.: Panic and phobia. In: Robins, L., Regier, D. (eds.) Psychiatric Disorders in America. The Free Press, New York (1991)Google Scholar
  24. 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
  25. Elbers, C., Lanjouw, J., Lanjouw, P.: Micro-level estimation of poverty and inequality. Econometrica 71(1), 355–364 (2003)CrossRefGoogle Scholar
  26. 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)PubMedCrossRefGoogle Scholar
  27. Fleishman, J., Lawrence, W.: Demographic variation in SF-12 scores: true differences or differential item functioning? Med. Care 41(7), 75–86 (2003)CrossRefGoogle Scholar
  28. Ford, D., Mead, L., Chang, P., Cooper-Patrick, L., Wang, N., Klag, M.: Depression is a risk factor for coronary artery disease in men: the precursors study. Arch. Intern. Med. 158, 1422–1426 (1998)PubMedCrossRefGoogle Scholar
  29. Frasure-Smith, N., Lesperance, F.: Depression and coronary heart disease: complex synergism of mind, body, and environment. Curr. Dir. Psychol. Sci. 14(1), 39–43 (2005)CrossRefGoogle Scholar
  30. Geweke, J.: Efficient simulation from the multivariate normal and Student-t distributions subject to linear constraints, computer science and statistics. In: Proceedings of the Twenty-Third Symposium on the Interface, pp. 571–578 (1991)Google Scholar
  31. Ghosh, M., Natarajan, K., Stroud, T., Carlin, B.: Generalized linear models for small-area estimation. J. Am. Stat. Assoc. 93, 273–282 (1998)CrossRefGoogle Scholar
  32. Ghosh, M., Rao, J.: Small area estimation: an appraisal. Stat. Sci. 9, 55–93 (1994)CrossRefGoogle Scholar
  33. Gill, S., Butterworth, P., Rodgers, B., Mackinnon, A.: Validity of the mental health component scale of the 12-item Short-Form Health Survey (MCS-12) as measure of common mental disorders in the general population. Psychiatry Res. 152(1), 63–71 (2007)PubMedCrossRefGoogle Scholar
  34. Glassman, A., Shapiro, P.: Depression and the course of coronary artery disease. Am. J. Psychiatry 155, 4–11 (1998)PubMedGoogle Scholar
  35. Grant, B., Compton, W., Crowley, T., Hasin, D., Helzer, J., Li, T.-K., Rounsaville, B., Volkow, N., Woody, G.: Errors in assessing DSM-IV substance use disorders. Arch. Gen. Psychiatry 64(3), 1–5 (2007)CrossRefGoogle Scholar
  36. Grella, C.: Older Adults and Co-occuring Disorders. Alcohol and Drug Policy Institute, Sacramento (2009)Google Scholar
  37. Hauenstein, E., Petterson, S., Merwin, E., Rovnyak, V., Heise, B., Wagner, D.: Rurality, gender and mental health treatment. Fam. Community Health 29(3), 169–185 (2006)PubMedCrossRefGoogle Scholar
  38. 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
  39. Helzer, J., Burnam, A., McEvoy, L.: Alcohol abuse and dependence. In: Robins, L., Regier, D. (eds.) Psychiatric Disorders in America. The Free Press, New York (1991)Google Scholar
  40. Hiller, W., Dichtl, G., Hecht, H., Hundt, W., von Zerssen, D.: An empirical comparison of diagnoses and reliabilities in ICD-10 and DSM-III-R. Eur. Arch. Psychiatry Clin. Neurosci. 242, 209–217 (1993)PubMedCrossRefGoogle Scholar
  41. 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
  42. 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 eultural influences on mental disorders and mental health. Int. J. Methods Psychiatr. Res. 13(4), 196–207 (2006b)CrossRefGoogle Scholar
  43. Jarjoura, D., McCord, G., Holzer, C., Champney, T.: Implementing indirect needs-assessment synthetic estimation of the distribution of mentally disabled adults for allocations to Ohio’s Mental Health Board areas. Eval. Program Plan. 16(4), 305–313 (1993)CrossRefGoogle Scholar
  44. Katon, W., Sullivan, M.: Depression and chronic medical illness. J. Clin. Psychol. 51, 3–11 (1990)Google Scholar
  45. Keith, S., Regier, D., Rae, D.: Schizophrenic disorders. In: Robins, L., Regier, D. (eds.) Psychiatric Disorders in America. The Free Press, New York (1991)Google Scholar
  46. Kessler, R., Abelson, J., Demier, O., Escobar, J., Gibbon, M., Guyer, M., Howes, M., Jin, R., Vega, W., Walters, E., Wnag, P., Zaslavsky, A., Zheng, H.: Clinical calibration of DSM-IV diagnoses in the World Mental Health (WMH) version of the World Health Organization (WHO) Composite International Diagnostic Interview (WMH-CIDI). Int. J. Methods Psychiatr. Res. 13(2), 122–139 (2004)PubMedCrossRefGoogle Scholar
  47. Kessler, R., Andrews, G., Colpe, L., Hiripi, E., Mroczek, D., Normand, S., Walters, E., Zaslavsky, A.: Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol. Med. 32(6), 959–976 (2002)PubMedCrossRefGoogle Scholar
  48. Kessler, R., Berglund, P., Bruce, M., Koch, J., Laska, E., Leaf, P., Manderscheid, R., Rosenheck, R., Walters, E., Wang, P.: The prevalence and correlates of untreated serious mental illness. Health Serv. Res. 36(6 Pt 1), 987–1007 (2001)PubMedCentralPubMedGoogle Scholar
  49. Kessler, R., Berglund, P., Walters, E., Leaf, P., Louzis, 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. In: Mental Health, United States, 1998. US Department of Health and Human Services, SAMHSA (1998)Google Scholar
  50. Kessler, R., Demler, O., Frank, R., Olfson, M., Pincus, H., Walters, E., Wang, P., Wells, K., Zaslavsky, A.: Prevalence and treatment of mental disorders, 1990 to 2003. N. Engl. J. Med. 352, 2515–2523 (2005)PubMedCentralPubMedCrossRefGoogle Scholar
  51. Kessler, R., Berglund, P., Zhao, S., Leaf, P., Kouzis, A., Bruce, M., Friedman, R., Grosser, R., Kennedy, C., Kuehnel, T., Laska, E., Manderscheid, R., Narrow, W., Rosenheck, R., Santoni, T., Schneier, M.: The 12-month prevalence and correlates of serious mental illness. In: Manderscheid, R., Sonnenschein, M. (eds.) Mental Health, United States 1996, pp. 59–70. US Government Printing Office, Washington, DC (1996)Google Scholar
  52. Kessler, R., Chiu, W.T., 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)PubMedCentralPubMedCrossRefGoogle Scholar
  53. 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)PubMedCrossRefGoogle Scholar
  54. 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, Greenwich (1998)Google Scholar
  55. Legler, J., Breen, N., Meissner, H., Malec, D., Coyne, Cathy: Predicting patterns of mammography use: a geographic perspective on national needs for intervention research. Health Serv. Res. 37(4), 929–947 (2002)PubMedCentralPubMedCrossRefGoogle Scholar
  56. Leroux, B., Lei, X., Breslow, N.: Estimation of disease rates in small areas: a new mixed model for spatial dependence. In: Halloran, M., Berry, D. (eds.) Statistical Models in Epidemiology, the Environment and Clinical Trials, pp. 135–178. Springer-Verlag, New York (1999)Google Scholar
  57. Little, R.J.A.: Inference with survey weights. J. Off. Stat. 7, 405–424 (1991)Google Scholar
  58. Lutterman, T.: The impact of the state fiscal crisis on state mental health systems (2011). http://www.nri-inc.org/reports_pubs/2011/ImpactOfStateFiscalCrisisOnMentalHealthSytems_Updated_12Feb11_NRI_Study.pdf. Accessed 29 June 2014
  59. Maddala, G.: Limited-Dependent and Qualitative Variables in Econometrics. Cambridge University Press, Cambridge (1983)CrossRefGoogle Scholar
  60. Malec, D., Müller, P.: A Bayesian semi-parametric model for small area estimation. In: Pushing the Limits of Contemporary Statistics: Contributions in Honor of Jayanta K. Ghosh., vol. 3, pp. 223–236 (2008)Google Scholar
  61. Malec, D., Sedransk, J., Moriarity, C., LeClere, F.: Small area inference for binary variables in the National Health Interview Survey. J. Am. Stat. Assoc. 92, 815–826 (1997)CrossRefGoogle Scholar
  62. Mark, T., Buck, J., Dilonardo, J., Coffey, R., Chalk, M.: Medicaid expenditures on behavioral health care. Psychiatr. Serv. 54, 188–194 (2003)PubMedGoogle Scholar
  63. 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
  64. Mendez-Luck, C., Hongjian, Y.: Estimating health conditions for small areas: asthma symptom prevalence for state legislative districts. Health Serv. Res. 42(6), 2389–2409 (2007)PubMedCentralPubMedCrossRefGoogle Scholar
  65. Messer, S., Liu, X., Hoge, C., Cowan, D., Engel, C.: Projecting mental disorder prevalence from national surveys to populations-of-interest: an illustration using ECA data and the US Army. Soc. Psychiatry Psychiatr. Epidemiol. 39, 419–426 (2004)PubMedCrossRefGoogle Scholar
  66. Moriarty, D., Zack, M., Holt, J., Chapman, D., Safran, M.: Geographic patterns of frequent mental distress: U.S. Adults, 1993/2003. Am. J. Prev. Med. 36(6), 497–505 (2009)PubMedCrossRefGoogle Scholar
  67. Musselman, D., Evans, D., Nemeroff, C.: The relationship of depression to cardiovascular disease: epidemiology, biology, and treatment. Arch. Gen. Psychiatry 55, 580–592 (1998)PubMedCrossRefGoogle Scholar
  68. 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)PubMedCrossRefGoogle Scholar
  69. Nelsen, R.: An Introduction to Copulas. Springer-Verlag, New York (1999)CrossRefGoogle Scholar
  70. NASMHPD Research Institute: How do SMHAs organize and fund community mental health services: 2010. State Mental Health Agency Profiles System (2010)Google Scholar
  71. Opsomer, J., Claeskens, G., Ranalli, M., Kauermann, G., Breidt, F.: Non-parametric small area estimation using penalized spline regression. J. R. Stat. Soc. B 70(1), 265–286 (2008)CrossRefGoogle Scholar
  72. Pennell, B.-E., Bowers, A., Carr, D., Chardoul, S., Cheung, G., 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
  73. Prasad, N., Rao, J.: On robust small area estimation using a simple random effects model. Surv. Methodol. 25, 67–72 (1999)Google Scholar
  74. Rao, J.: Some recent advances in model-based small area estimation. Surv. Methodol. 25, 175–186 (1999)Google Scholar
  75. Regier, D., Kaelber, C., Rae, D., Farmer, A., Knauper, B., Kessler, R., Norquist, G.: Limitations of diagnostic criteria and assessment instruments for mental disorders: implications for research and policy. Arch. Gen. Psychiatry 55, 109–115 (1998)PubMedCrossRefGoogle Scholar
  76. Robins, L., Regier, D.: Psychiatric Disorders in America: The Epidemioligic Catchment Area Study. The Free Press, New York (1991)Google Scholar
  77. Rounsaville, B.: Experience with ICD-10/DSM-IV substance use disorders. Psychopathology 35, 82–88 (2002)PubMedCrossRefGoogle Scholar
  78. Stern, S.: Semiparametric estimates of the supply and demand effects of disability on labor force participation. J. Econ. 71(1–2), 49–70 (1996)CrossRefGoogle Scholar
  79. Stern, S.: Simulation-based estimation. J. Econ. Lit. 35(4), 2006–2039 (1997)Google Scholar
  80. 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. Methodol. 10(1), 33–66 (2010)CrossRefGoogle Scholar
  81. Substance Abuse and Mental Health Services Administration: Results from the 2002 National Survey on Drug Use and Health: National Findings. NHSDA Series H-22, DHHS Publication No. SMA 03–3836. Office of Applied Studies, Rockville (2003)Google Scholar
  82. Tarozzi, A., Deaton, A.: Using census and survey data to estimate poverty and inequality for small areas. Rev. Econ. Stat. 91, 773–792 (2009)CrossRefGoogle Scholar
  83. Twigg, L., Moon, G., Jones, K.: Predicting small-area health-related behaviour: a comparison of smoking and drinking indicators. Soc. Sci. Med. 50, 1109–1120 (2000)PubMedCrossRefGoogle Scholar
  84. Virginia Department of Behavioral Health and Developmental Services (2010). http://www.dbhds.virginia.gov/SVC-CSBs.asp. Accessed 29 June 2014
  85. Virginia Department of Medical Assistance Services: Statistical Record of the Virginia Medicaid Program and Other Indigent Health Care Programs, FY 2006 Edition (2006). http://www.dmas.virginia.gov/downloads/Stats_06/OVEXPEND-06.pdf. Accessed 29 June 2014
  86. Wang, P., Demler, O., Kessler, R.: Adequacy of treatment for serious mental illness in the United States. Am. J. Public Health 92(1), 92–98 (2002)PubMedCentralPubMedCrossRefGoogle Scholar
  87. 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)PubMedCrossRefGoogle Scholar
  88. Watanabe, N., Stewart, R., Jenkins, R., Bhugra, D., Furukawa, T.: The epidemiology of chronic fatigue, physical illness, and symptoms of common mental disorders: a cross-sectional survey from the second British National Survey of Psychiatric Morbidity. J. Psychosom. Res. 64(4), 357–362 (2008)PubMedCrossRefGoogle Scholar
  89. Weissman, M., Bruce, M., Leaf, P., Florio, L., Holzer, C.: Affective disorders. In: Robins, L., Regier, D. (eds.) Psychiatric Disorders in America. The Free Press, New York (1991)Google Scholar
  90. Wells, K., Stewart, A., Hays, R., Burnam, A., Rogers, W., Daniels, M., Berry, S., Greenfield, S., Ware, J.: The functioning and well-being of depressed patients: results from the medical outcomes study. J. Am. Stat. Assoc. 262(7), 914–919 (1989)CrossRefGoogle Scholar
  91. 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], Los Angeles (2004). Inter-university Consortium for Political and Social Research [distributor], Ann Arbor (2005)Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.University of VirginiaCharlottesvilleUSA

Personalised recommendations