Samples
Data were drawn from the National Comorbidity Survey (NCS) and the National Comorbidity Survey-Replication (NCS-R) (
14 ). Briefly, NCS and NCS-R were nationally representative cross-sectional surveys of households in the 48 coterminous U.S. states. NCS was administered between September 1990 and February 1992 to 8,089 individuals between the ages of 15 and 54 and had a response rate of 82.4%. NCS-R was administered between February 2001 and April 2003 to 9,282 individuals aged 18 and older and had a response rate of 70.9%.
The NCS and NCS-R interviews each included two parts. Part 1 comprised a core diagnostic assessment module administered to all participants. Part 2 included further diagnostic assessments as well as questions about treatment-seeking attitudes and beliefs and was administered to all part 1 participants who met lifetime criteria for any core disorder and to probability subsamples of other participants. Part 2 samples comprised 5,877 participants in NCS and 5,692 in NCS-R. To make the age composition of samples comparable for the main analyses reported here, samples were further limited to 5,388 participants in NCS and 4,319 in NCS-R who were between the ages of 18 and 54.
To examine the generation effect on attitudes, further analyses were conducted in which age groups in NCS were combined with age groups in NCS-R that were, on average, 11 years older. The sample for examining the generation effect included all part 2 NCS participants (aged 15 to 54) and 4,116 part 2 NCS-R participants who were in the age range of 26 to 65 years.
Interviews were conducted in person. Informed consent was obtained before interviews. The human subjects committees of Harvard Medical School and the University of Michigan approved these recruitment and consent procedures. Secondary analyses of the data presented in this report were exempted from full review by the Institutional Review Board of the Beth Israel Medical Center, New York.
Assessments
Attitudes toward mental health treatment seeking were assessed by three questions. The first assessed willingness: "If you had a serious emotional problem, would you definitely go for professional help, probably go, probably not go, or definitely not go for professional help?" The second assessed comfort level: "How comfortable would you feel talking about personal problems with a professional—very comfortable, somewhat, not very, or not at all comfortable?" The third assessed perceived stigma: "How embarrassed would you be if your friends knew you were getting professional help for an emotional problem—very embarrassed, somewhat, not very, or not at all embarrassed?" For this study, the responses were coded from 0 to 3 so that a higher score indicates more willingness, more comfort, and less perceived stigma.
Beliefs about the effectiveness of mental health treatment and the likelihood of recovery without it were assessed by asking two questions: "Of the people who see a professional for serious emotional problems, what percent do you think are helped?" and "Of those who do not get professional help, what percent do you think get better even without it?" For this study, responses to each question were categorized into four mutually exclusive categories: 0%–24%, 25%–49%, 50%–74%, and 75%–100%.
Lifetime mental health treatment seeking was assessed by asking participants to identify from a list the types of professionals that they had ever seen for problems with their "emotions or nerves" or their "use of alcohol or drugs." The list included psychiatrists, psychologists, social workers, counselors, general practitioners or family physicians, other physicians, nurses, occupational therapists, other health professionals, ministers or priests, spiritualists, herbalists, and other professionals. Contact with any of these professionals was recorded as a positive history of lifetime mental health treatment seeking.
Psychological distress was assessed by 14 questions, mostly drawn from the Hopkins Symptom Checklist (
15 ). The questions covered specific depressive and anxiety symptoms, such as depressed mood, loss of interest, hopelessness, trouble concentrating, inappropriate guilt or feelings of worthlessness, lack of energy, worrying too much, and feeling tense, and nonspecific symptoms of distress, such as feeling "trapped" or lonely. The participants were asked to rate the frequency of each symptom over the past 30 days on a scale ranging from never to often. For the purpose of this study, these ratings were given values ranging from 0 to 3, respectively. The possible scores on this scale, therefore, ranged from 0 to 42. The internal consistency of this symptom scale was quite high (Cronbach's
α =.92), and the items were moderately correlated (average interitem correlation=.45). Principal components analysis of the items produced only one component with an eigenvalue greater than or equal to 1, indicating that one latent factor underlies all items (
15 ). In the NCS-R sample, scores on this questionnaire correlated strongly (r=.73) with scores on K6 (
16 )—another measure of distress commonly used in general population surveys.
To identify participants with significant distress on the symptom scale, a cutoff point was chosen that would identify the 5% of individuals in the general population who were most distressed (≥27 compared with <27). In the NCS-R sample, this cutoff score was strongly associated with the presence of common 12-month
DSM-IV (
17 ) disorders (major depression, dysthymia, generalized anxiety disorder, posttraumatic stress disorder, panic disorder with or without agoraphobia, and social anxiety) as ascertained by the Composite International Diagnostic Interview (
18 ) (odds ratio [OR]=15.47, 95% confidence interval [CI]=9.42–25.42; t=11.13, df=42, p<.001). Of participants with a symptom score of 27 or higher, 77.4% met criteria for one or more of these
DSM-IV conditions, compared with 18.1% of those with a symptom score of less than 27.
NCS and NCS-R used different versions of the Composite International Diagnostic Interview, based on different diagnostic criteria—
DSM-III-R (
19 ) in NCS and
DSM-IV (
17 ) in NCS-R. For this reason, diagnoses were not included in this study (
20 ).
Impairment in functioning was assessed by three similarly structured sets of questions about the number of days in the past 30 days in which the participant was "totally unable to work or carry out normal activities" or had to "cut down" on what he or she did or "did not get as much done as usual" or in which it took the participant "an extreme effort" to perform up to his or her "usual level" (
15 ). After each question about the number of days, the participant was asked to estimate the number of days of impairment that was attributable to "emotions, nerves, mental health, or use of alcohol or drugs." For this study, impairment was operationalized as having one or more impaired days in the past 30 days according to any of the above definitions.
Sociodemographic variables included sex, age (18–24, 25–34, 35–44, and 45–54 years), race-ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, other), years of education (0–11, 12, 13–15, 16 or more), family income as a percentage of the federal poverty level for 1990 in the NCS and for 2001 in the NCS-R (low income, less than 150%; low to average, 150% to 299%; high to average, 300% to 599%; and high, 600% or greater) (
20 ). For the analysis of generation effect, four mutually exclusive generations were constructed which combined each NCS age group with an NCS-R age group that was on average 11 years older (for example, the NCS participants who were from 15 to 24 years old were combined with NCS-R participants who were 26 to 35 years old).
Data analysis
Data were analyzed in four stages. First, attitudes and beliefs about mental health treatment seeking were compared across NCS and NCS-R by using bivariate proportional-odds ordinal logistic regression models. The independent variable in these analyses was the dummy-coded variable of time (1990–1992 [NCS]=0, and 2001–2003 [NCS-R]=1). To adjust for multiple testing, a cutoff p value of .01 was used for determining statistical significance in these analyses. Because only attitudes showed significant variation across time, further analyses were limited to attitudes.
Second, sociodemographic and clinical correlates of attitudes were examined by using a series of bivariate proportional-odds ordinal logistic regression analyses. For variables with multiple categories, such as race-ethnicity, overall adjusted Wald tests were computed. To limit the number of regression models fitted and thus minimize type I error, the three attitude variables were combined into one multivariate proportional-odds ordinal logistic regression model by using the methodology proposed by Horton and Fitzmaurice (
21 ) for analysis of multiple variables from the same individuals in complex survey designs. This method is conceptually similar to multivariate analysis of variance and allows for efficient modeling of multiple ratings for the same individuals in one regression model while adjusting for lack of independence among ratings. By entering an independent dummy variable as an identifier for different ratings, variations among these different ratings can be modeled. Thus, to model variations among the three attitude ratings, two dummy variables were entered into the model as independent variables.
When exponentiated, ordinal logistic regression coefficients can be interpreted as odds ratios (ORs), representing the ratio of odds of having a higher (more positive) attitude rating compared with the odds in the reference group. For example, an OR of 1.41 associated with female gender compared with male gender indicates that females have 41% greater odds of having a more positive attitude rating than males.
Third, to further evaluate the magnitude of variations in attitudes across different sociodemographic and clinical subgroups, a set of bivariate proportional-odds ordinal logistic regression analyses were conducted that stratified the sample on the basis of population subgroups. The attitude rating was the dependent variable in these analyses, and the time variable (1990–1992 [NCS]=0, and 2001–2003 [NCS-R]=1) was the independent variable of interest. These analyses were repeated within each stratum.
Fourth, a multivariate proportional-odds ordinal logistic regression analysis was conducted to examine the characteristics associated with attitudes as well the interaction terms of different characteristics with the variable of time. As recommended by Hosmer and Lemeshow (
22 ), variables that were associated with attitude ratings at a p level of less than .25 in the bivariate analyses in the second stage above were entered in the multivariate model. In addition to main effects, two-way interaction terms of each variable with the time variable (1990–1992 [NCS]=0, and 2001–2003 [NCS-R]=1) were also tested, and those associated with attitude ratings at a p level of less than .25 were entered into the multivariate regression model. These interaction terms tested whether changes in attitudes across time varied in different subgroups of the population (
23 ). For example, a significant age-by-time interaction would indicate that attitudes of individuals from different age groups varied differently across time. Main effects and interaction terms were maintained in the final multivariate model if they were associated with attitude ratings at the predefined significance level of less than .01 (based on adjusted Wald tests for multicategory variables).
Age variations in attitudes can be attributed to either period effect or generation effect. However, because of collinearity, these effects cannot be simultaneously modeled. By comparing participants in the same age groups across time, the main analyses focused on age-period effect. To examine the generation-period effect, further analyses were conducted in which NCS participants were compared with NCS-R participants who were 11 years older, on average. These analyses were limited to all NCS part 2 participants and NCS-R part 2 participants between the ages of 26 and 65 years, as noted above.
NCS and NCS-R used complex stratified sampling designs. Survey weights, stratification, and clustering (primary sampling units) were included in the analyses to adjust for their effects and make samples representative of the U.S. population. Clustering at the level of primary sampling units also adjusted for lack of independence in multiple attitude ratings from the same individuals. Stata 9.2 software was used for all analyses (
24 ). All percentages presented here are weighted.