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Estimating local prevalence of mental health problems

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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.

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Notes

  1. Later, in Sect. 5.4, I perform an empirical exercise using data from central Virginia.

  2. Lutterman (2011) shows similar results across the United States.

  3. Regier et al. (1998) argues that diagnostic counts by themselves are not that useful for mental health planning purposes.

  4. Note that there is some overlap (e.g., New York).

  5. Konrad et al. (2009) goes a few steps further in constructing estimates of discrepancies between supply and demand for mental health services. In particular, it estimates individual demand for mental health services conditional on observed demographic and mental health characteristics to complete the task of estimating demand for services. Then, Ellis et al. (2009) estimates supply of mental health providers disaggregated by type of provider and geography.

  6. Later, we include a Strata/PSU-specific error term causing correlation across observations.

  7. Throughout the paper, I assume that errors are normally distributed. For the single outcome case in Eqs. (1) and (2), one might consider replacing a normality assumption with a logit assumption. In a different context, Stern (1996) provides evidence that estimation results are insensitive to the distributional assumption. This result is consistent with, for example, Cox (1970) and Maddala (1983). However, the logit does not generalize as nicely as probit for multivariate outcomes.

  8. See Borsch-Supan and Hajivassiliou (1993) or Stern (1997) for a discussion.

  9. Kessler et al. (1998) does not need to simulate any missing values because they limit the set of explanatory variables to a small set available in Census data.

  10. There may be some individual observations in the local data set with missing values. For such observations, one can treat the individual missing variables as part of \(Z_{2i}\).

  11. LPS allows for cases (1), (2), and (3) but not (4) and (5).

  12. See, for example, Maddala (1983) for a discussion of each of these cases.

  13. Other potential sources of data are (a) the National Survey on Drug Use and Health (SAMHSA 2003), emphasizing substance abuse; and (b) the Behavioral Risk Factor Surveillance System (See Moriarty et al. 2009).

  14. Weissman et al. (1991) finds that lifetime prevalence rates for affective disorders decline with age. Helzer et al. (1991) finds similar results for alcohol abuse. This age effect on lifetime prevalence is mechanically impossible as a true age effect; however it could be a cohort effect. See, for example, Grella (2009) for a thoughtful discussion of cohort effects with respect to substance abuse.

  15. The variables used are listed in Sect. 8.1.

  16. This may occur because the question used to identify anxiety is “Frequently Depressed or Anxious,” thus probably including some respondents with depression as suffering from anxiety.

  17. Variable names are written in a different font to distinguish them from the English usage of the same word.

  18. Health better than fair aggregates health good and health excellent.

  19. I classify someone as having a functional limitiation if they answer affirmatively to being limited in any one of the following ways: (a) lifting; (b) climbing steps; (c) walking; (d) standing; (e) bending; (f) reaching; (g) using fingers; or (h) writing.

  20. The NCS-R is part of a consortium of surveys, the Collaborative Psychiatric Epidemiology Surveys (CPES). The CPES merges data from the NCS-R, the National Survey of American Life, and the National Latino and Asian American Study. See Alegria et al. (2006a, b), Heeringa et al. (2006), Jackson et al. (2006a, b), and Pennell et al. (2006) for more detail.

  21. The ICD is published by the WHO and used internationally to classify diseases including mental health disorders. The DSM is published by the American Psychiatric Association and prefered over the ICD by psychiatrists, especially in the U.S., in order to diagnose patients with mental disorders. See Andrews and Slade (1999) or American Psychological Association (2009) for more information.

  22. I classify someone as having a functional limitiation if they answer affirmatively to any one of the three questions: (a) Have you been limited in past 3 months due to a health problem (Variable SC10_1H); (b) Do you have a physical disability (Variable SC10_4E); or (c) Do you have a condition that substantially limits physical activity (Variable SC10_4F).

  23. See, for example, Alexandre and French (2001) for a discussion of the relationship between religiosity and mental health.

  24. The college variable is constructed as the union of four separate questions of highest grade attained. While there are a few cases where there are inconsistencies across these four measures, they are not the cause of the unusually high proportion of college graduates.

  25. See Sect. 8.3 for a list of the NSADMHP question #s that define having a mental health problem.

  26. Kessler et al. (2002) suggests other similar measures based on DSM.

  27. Gill et al. (2007) finds that MCS-12 has good predictive power using Australian survey data. Fleishman and Lawrence (2003) suggests that, to some degree, MCS-12, scores may be contaminated by variation in demographic variables.

  28. The equivalent curve conditioning on MCS-12 would be relatively flat given the small difference in MCS-12 distributions.

  29. Also, there is a small number of observations for which income is missing.

  30. A community is typically a collection of counties in close geographic proximity.

  31. The correlation is across individuals within the same family.

  32. Community characteristics come from merging with the Area Resources File and are county specific.

  33. Prasad and Rao (1999) and Opsomer et al. (2008) treat the community effects as random effects. Stern et al. (2010) treats them as fixed effects because (a) it has a large number of observations per community and (b) it does not have to assume the community effects are independent of observed community characteristics by treating them as fixed effects. Congdon (2009) looks at the effect of community characteristics on the prevalence of heart disease and finds that community characteristics are important in explaining some of the geographical variation in prevalence. However, Stern et al. (2010) finds important community-specific fixed effects even after controlling for a larger set of community characteristics than were found in Congdon (2009).

  34. The NCR and NSADMHP have similar types of information, although it is somewhat harder to use in NSADMHP.

  35. In theory, alternatively one could treat the effects as fixed. However, given the nonlinearity of the model, the fixed effects can not be differenced out except in very special cases (Chamberlain 1980)..

  36. Kessler et al. (2005) reports prevalence rates for NCS-R different than in Table 12. They are different partially because of sample selection rules and partially because I aggregate diagnoses differently.

  37. Anxiety: Blazer et al. (1991), Table 8-3; Panic Attacks: Eaton et al. (1991), Table 7-2a; Depression: Weissman et al. (1991), Table 6-12; Schizophrenia: Keith et al. (1991), Table 3-2; Alcohol Abuse: Helzer et al. (1991), Table 5.1; Drug Problem: Anthony and Helzer (1991), Table 6-4.

  38. Dysthymia is reported in the ECA but not by Robins and Regier (1991) in a way that is comparable to the other numbers in the table.

  39. The NHIS reports separate rates for drug and alcohol abuse, but I aggregate them for this analysis.

  40. Anthony and Helzer (1991, Table 6-17) finds important interactions between age and gender for substance abuse. I do not include such an interaction in my analysis.

  41. Keith et al. (1991) finds similar insignificant results for the effect of education on schizophrenia. It argues that the correlation between age and education explains the lack of effect. However, I control for age and still get statistically insignificant results.

  42. For example, Wells et al. (1989), Katon and Sullivan (1990), and Watanabe et al. (2008) document high comorbidity rates between physical and mental health problems. Among others, Ford et al. (1998), Glassman and Shapiro (1998), Musselman et al. (1998), and Frasure-Smith and Lesperance (2005) provide support for causation going in both directions.

  43. There are only 9 degrees of freedom because the last interval had no observations.

  44. While the figure might lead one to believe that one cannot reject \(H_{0}:Q_{1}=Q_{2}=Q_{3}\) (where \(Q_{j}\) is the \(j\)th quantile), such a conclusion would be incorrect because it ignores the positive correlation among the three quantile estimates. In fact, almost all of the movement across realizations of the quantile estimates are just vertical shifts of all three together.

  45. Figures for diagnoses other than anxiety and depression are available from the author.

  46. The coefficients associated with each polynomial depend upon the sample density of age in the NCS-R, and they are listed in the notes to Table 13. See, for example, Chihara (1978) for a discussion of orthonormal polynomials.

  47. Also, since the sample density of age is not very different, the coefficients associated with the orthonormal polynomials are pretty similar (see notes in Tables  15 and 17).

  48. In a typical multivariate probit model, one would have to restrict all of the diagonal terms of the covariance matrix to be one for identification purposes. Here, the identifying restrictions are that the first diagonal term is one and the psu/strata standard deviation is common across all diagnoses.

  49. Kessler et al. (1996) does not control for the other variables included in Table 16.

  50. Also, in Fig. 10, it is not obvious that the curves stochastically dominate each other in the way one would expect. However, integration of the displayed density functions results in three curves that do stochastically dominate each other in the expected order.

  51. I do not use a Strata/PSU standard deviation for the NSADMHP because the sample already provides for some important geographic dispersion.

  52. The confidence region for health poor looks like a line segment because the NSADMHP estimate is estimated very precisely.

  53. Confidence regions are ellipses because the two data sets provide independent estimates with different standard errors.

  54. In fact, since MHI-5 is a function of underlying answers about the existence of mental health problems, it is very unlikely that an MHI-5 score would be reported without the inputs to the MHI-5 score also being reported.

  55. See Virginia Department of Behavioral Health and Developmental Services (2010) or Brown et al. (2013) for more information about CSBs.

  56. In the interest of fair advertising, I should point out that I was on the board of directors of the Region Ten CSB when I wrote this paper.

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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.

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    Steven Stern

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Appendices

Appendix 1: NHIS (1995) mental health questions

See Table 23.

Table 23 National Health Interview Survey mental health variable construction
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Appendix 2: NCS-R mental health questions

See Table 24.

Table 24 Mental health definitions for NCS-R
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Appendix 3: NSADMHP mental health questions

See Table 25.

Table 25 NSADMHP mental health variable construction
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Stern, S. Estimating local prevalence of mental health problems. Health Serv Outcomes Res Method 14, 109–155 (2014). https://doi.org/10.1007/s10742-014-0120-2

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