0
Get Alert
Please Wait... Processing your request... Please Wait.
You must sign in to sign-up for alerts.

Please confirm that your email address is correct, so you can successfully receive this alert.

1
Article   |    
Poverty and Response to Treatment Among Panic Disorder Patients in Primary Care
Peter Roy-Byrne, M.D.; Cathy Sherbourne, M.D.; Jeanne Miranda, Ph.D.; Murray Stein, M.D., M.P.H.; Michelle Craske, Ph.D.; Daniela Golinelli, Ph.D.; Greer Sullivan, M.D., M.S.Ph.
Am J Psychiatry 2006;163:1419-1425. doi:10.1176/appi.ajp.163.8.1419
Abstract

Objective: Despite well-established links between poverty and poor mental illness outcome as well as recent reports exploring racial and ethnic health disparities, little is known about the outcomes of evidence-based psychiatric treatment for poor individuals. Method: Primary care patients with panic disorder (N=232) who were participating in a randomized controlled trial comparing a cognitive behavior therapy (CBT) and pharmacotherapy intervention to usual care were divided into those patients above (N=152) and below (N=80) the poverty line. Telephone assessments at 3, 6, 9, and 12 months were used to compare the amount of evidence-based care received as well as clinical and functional outcomes. Results: Poor subjects were more severely ill at baseline, with more medical and psychiatric comorbidity. The increases in the amount of evidence-based care and reductions in clinical symptoms and disability were comparable in the two groups such that poorer individuals, although responding equivalently, continued to be more ill and disabled at 12 months. Conclusions: The comparable response of poor individuals in this study suggests that standard CBT and pharmacotherapy treatments for panic disorder do not need to be “tailored” to be effective in poor populations. However, the more severe illness both at baseline and follow-up in these poor individuals suggests that treatment programs may need to be extended in order to treat residual symptoms and disability in these patients so that they might achieve comparable levels of remission.

Abstract Teaser
Figures in this Article

A great deal of attention is being given to racial and ethnic disparities in health care. At the same time, the wide differences in health between the haves and the have-nots are largely ignored. We contend that increased attention should be given to the reality of class and its effect on the nation’s health.

New England Journal of Medicine editorial, Sept. 9, 2004 (1)

While a great deal of attention has been devoted to racial and ethnic health disparities, health disparities in individuals disadvantaged by low income, lack of education, or employment (i.e., lower class) have been relatively neglected (1). Depression and anxiety disorders are known to be more prevalent in economically disadvantaged populations (2–5), and impoverished depressed and anxious persons have demonstrated poor clinical outcomes in observational studies (2, 4). Despite these associations, little is known about outcomes of evidence-based care with poor and low-income individuals. Miranda and colleagues (6) showed that both cognitive behavior therapy (CBT) and antidepressant medications (paroxetine or bupropion) were effective for treating poor minority women with depression who received extensive outreach and supportive services for attending care. A study of impoverished Chilean primary care patients with depression demonstrated that a collaborative care program designed to increase adherence to antidepressant pharmacotherapy improved outcomes (7). While both studies document the effectiveness of evidence-based interventions relative to usual care in poor populations, neither study included a comparison group of subjects with greater socioeconomic resources. In a secondary analysis of a large (43 primary care clinics) quality improvement study for depression (Partners in Care), the quality improvement interventions were found to produce significantly greater effects in Latino and African American patients than in Caucasian patients (8). Although the Latino patients tended to be poorer than the Caucasian patients, the impact of poverty per se on outcome was not examined.

We previously conducted a three-site primary care study (the Collaborative Care for Anxiety and Panic study) that demonstrated superior clinical effectiveness of a combined CBT and pharmacotherapy intervention relative to usual care for panic disorder patients in six university-based primary care clinics in three West Coast cities (9, 10). Because approximately one-third of this study group was at or below the federal poverty line and included significant numbers of impoverished Caucasian patients, this study provides an opportunity to examine the association between income and clinical outcomes without substantial confounding by ethnicity. Furthermore, because disadvantaged patients are more likely to seek care in medical as opposed to mental health specialty settings (11), this study group and setting is especially appropriate for examining the effects of poverty. The purpose of this secondary post hoc analysis is to determine whether or not, and to what extent, economic disadvantage is associated with clinical outcomes when an evidence-based intervention for panic disorder is compared with treatment as usual. If poor subjects show less improvement than those who are not disadvantaged, it is likely that interventions tailored to the needs of this population will be required to optimize the effectiveness of these interventions. If poor subjects demonstrate a better or equal response, then specially tailored interventions are unlikely to be required.

+

Setting and Subjects

Patients between the ages of 18 and 70 who met DSM-IV criteria for panic disorder—with no comorbid psychiatric or medical conditions that were life threatening (i.e., suicidal ideation, terminal medical illness) or likely to interfere with participation (psychosis, dementia, severe substance abuse)—were recruited from six university-affiliated primary care clinics in Seattle, San Diego, and Los Angeles. Consistent with effectiveness design (12), all subjects meeting inclusion criteria were enrolled regardless of prior treatment history or current use of medication or psychotherapy. The study was approved by the institutional review boards of all three universities (University of Washington, UCLA, and UC San Diego).

+

Procedure

More detailed descriptions of the assessments and interventions for the Collaborative Care for Anxiety and Panic study have been previously published (9, 10, 13). After baseline assessment, subjects were randomly assigned to the intervention or usual care condition. For the intervention, a master’s-level behavioral health specialist delivered, free of charge, six sessions of CBT for panic disorder and related depression and anxiety symptoms (14, 15) and also coordinated the patient’s care, which included medication managed by the primary care physician using an algorithm (16) and paid for by the patient. Specific medication recommendations for individual subjects were relayed as needed from a consulting psychiatrist to the primary care physician via the behavioral health specialist, who informed the psychiatrist about subject clinical status and received feedback at weekly meetings. Subjects had to complete the six CBT sessions within the first 3 months of the study. Six follow-up telephone booster sessions, each lasting from 15 to 30 minutes, were scheduled through the rest of the year at 6- to 12-week intervals to monitor clinical status, reinforce proper medication use and cognitive behavior skills, and make further medication recommendations if necessary. Subjects in the usual care condition received pharmacotherapy from their primary care physician without psychiatric consultation, with referral to specialty mental health providers possible. Hence, in this effectiveness study, treatments were not matched in the two study arms but allowed to vary according to subject adherence and choice. Patients assigned to the usual care condition could conceivably receive CBT from specialty referral sources.

Assessments were conducted by telephone with interviewers blind to subject intervention status administering questionnaires at baseline and every 3 months during the course of the study. This secondary analysis focused on dimensional measures of panic/anxiety, depression, and functional disability as determined with 1) the Anxiety Sensitivity Index (17), a scale that measures the cognitions that underlie panic-related somatization but is also sensitive to the frequency of recent panic attacks (18); 2) the CES-D (19), a scale measuring depression severity (19); and 3) five items selected from the larger WHO Disability Scale (20). The Anxiety Sensitivity Index was chosen as a simple global measure of panic on the basis of previous consensus that panic frequency as a single measure of panic correlated poorly with global burden of illness and outcome (21). We also measured intervention effects on subject-reported quality of pharmacotherapy (guideline-concordant antipanic medication at a sufficient dose for at least 6 weeks [16]) and psychotherapy (attending a minimum of three sessions that contained a minimum of four of seven key components considered characteristic of CBT [22]). Our measure of income was derived from self-report of annual income and family size at baseline telephone assessment.

+

Statistical Analysis

The overall study group was divided into those patients with income at or below the poverty line (N=80) and those with income above the poverty line (N=152). We used 2001 Federal Poverty Guidelines (23) and indexed the categorization to family size reported by subjects. Baseline demographic and clinical characteristics of patients above and below the poverty line were compared using group t tests. For the longitudinal analysis of intervention effects, we specified a (2-level) hierarchical model with random effects, using all patients randomly assigned to a condition. The repeated observations model (or level 1 model) was a piecewise linear growth model (24) that specified a linear segment between baseline and the first follow-up evaluation at 3 months (at which point the six CBT sessions ended) and then another linear segment for the subsequent follow-up evaluations at 6, 9, and 12 months. This model was intended to reflect an observation from previous effectiveness studies in which the greatest effect occurs during the acute study phase, mirroring the greater intensity of early intervention, and then effects remain stable, fall off, or increase at a much diminished rate. Time trends for patients within group were allowed to vary around the group-specific mean by the inclusion of patient-specific random effects for the intercept (i.e., baseline), the first slope (i.e., baseline to 3 months), and the difference between the second slope (i.e., 3 months to 12 months) and the first slope. We adopted a Bayesian approach (25, 26) to fit this model to both continuous and dichotomous longitudinal responses using baseline, 3, 6, 9, and 12-month follow-up data. We used previously described methods (27, 28) and WinBUGS software (29) to implement the model. The model provided 95% posterior probability intervals for main effects of intervention, income, and their interaction.

There were significant and substantial differences between subjects above (N=152) and below (N=80) the poverty line at baseline time of study entry (Table 1). Impoverished subjects were somewhat older, had less education, more self-reported chronic medical illnesses, more frequent full-symptom panic attacks, higher Fear Questionnaire scores, greater anticipatory anxiety and anxiety sensitivity, higher CES–D scores, higher rates of current PTSD and comorbid agoraphobia, poorer physical functioning and emotional well-being, and a higher rate of receiving any mental health specialty care in the preceding 3 months. However, differences in ethnicity (fewer impoverished patients were Caucasian) and receipt of recent treatment with medication or psychotherapy (somewhat higher in impoverished patients) were not statistically significant.

+

Effects of Intervention on Guideline-Concordant Care

We previously reported that for the entire population included in this study, our intervention did not result in different rates of guideline-concordant pharmacotherapy relative to usual care but did result in increased rates of CBT treatment at the 3-month time point relative to usual care (10). The results of new analyses that focused on the slope or rate of change between baseline and 3 months and between 3 months and 12 months demonstrate again an intervention effect for CBT (between 0 and 3 months) but not medication (Table 2). More important, however, the analysis shows that there is no income effect and no interaction between income and the intervention. That is, the effects or lack of effects of the intervention on rates of guideline-concordant care hold true for subjects both above and below the poverty line. Thus, we can conclude that income is not confounded in this study with the receipt of care that was more likely to be guideline-concordant or more clinically appropriate.

+

Outcome Effects

Effects on the various outcome measures are included in Table 2 and Figure 1, Figure 2, and Figure 3. Table 2 again depicts regression parameters for the baseline, 0–3-month, and 3–12-month slopes for each outcome. As documented in our previous study (10), there were significant intervention effects on dimensional measures of anxiety sensitivity, depression severity, and disability. Consistent with the baseline data, the two income groups significantly differed on all three measures at baseline (intercept), with impoverished patients doing more poorly. Overall, impoverished patients, despite showing greater improvements in intervention compared with usual care, continued to show greater symptom severity over the yearlong study than patients above the poverty line. Significant differences were observed between the two income groups for all three measures for the acute time trend segment (baseline to 3 months) and significant differences for anxiety sensitivity and depression severity but not disability for the more extended time trend segment (3–12 months). There was no differential effect of the intervention in the two income groups (i.e., no income-intervention interaction) at either of these two time-trend segments (i.e., the acute 3 month phase or the longer 3–12 month phase).

This analysis confirms prior findings that low-income psychiatric patients with depression and anxiety have more severe symptoms and functional disability associated with their illness (2, 3, 5, 30). Even after they receive clinically effective, carefully executed, evidence-based interventions their outcomes, because they start out more severely ill, lag behind those without economic disadvantage. At baseline, our panic disorder patients with incomes below the poverty line had more severe symptoms of panic, anxiety, and depression and also more severe chronic medical illness and associated physical disability. This may be related to the increased rate of stressful life events (31, 32) and exposure to trauma (33) in the poor, along with a relative lack of adequate resources to cope with these stressors (34, 35). This is also consistent with a previous study contrasting primary care panic patients from a public sector setting with panic disorder patients from more typical primary care settings (30). In that study, the low income and minority status of patients seeking care in the public sector setting accounted for the majority of clinical and functional differences (i.e., these differences disappeared when these variables were included as covariates) except for anxiety symptoms, which may have been affected by additional unmeasured factors related to the disadvantaged states of patients served in this setting (e.g., the chronic stress associated with dangerous neighborhoods and diminished social and material resources).

There were no differences in the effect of our intervention on quality of care in the two income groups. In both groups, the intervention successfully improved exposure to quality CBT during the first 3 months, when CBT sessions were offered to intervention patients. In contrast, the intervention failed to improve the proportion of patients receiving high-quality pharmacotherapy, with both intervention and usual care patients in both income groups increasing their exposure to quality pharmacotherapy during the study. It is possible that the nonmedical background of the behavioral health specialist—or the competing demands of both delivering CBT and trying to maximize medication use—may have led to less than optimal focus on or achievement of quality medication. It is also possible that patients were less motivated to pay for and maximize their use of medications when CBT was already improving their symptoms and was being provided free of charge.

Finally, the intervention had equivalent effects on the various measures of clinical and functional outcome in the two income groups. Low-income patients started off with more severe symptoms and on average remained more symptomatic than patients above the poverty line, with both income groups showing greater improvement with the intervention relative to usual care. It is of interest that low income patients receiving the intervention were able to achieve a level of anxiety symptom reduction on the Anxiety Sensitivity Index comparable to higher income patients in the usual care group, suggesting that this intervention was able to eradicate the differences in illness severity that existed at baseline and also persisted across the combined group (intervention plus usual care) at 12 months.

Previous analysis from this dataset documented that more patients in the intervention group received CBT than in the usual care group, but that delivery of high-quality pharmacotherapy was not different in the two groups (10). CBT appeared to be the active ingredient in the intervention. The current analysis, demonstrating similar intervention response in subjects above and below the poverty line, suggests that CBT as an intervention for panic disorder works equally well in poor people. Thus, we do not think that this intervention needs to be modified or specially “tailored” for this population, even if these individuals may be less well educated. This is consistent with recent studies showing that CBT can be effectively delivered to anxiety disorder patients in low income settings (36) and to low income panic disorder patients of African American ethnicity (37), although none of these studies included an inactive treatment control group so that magnitude of treatment response could be compared. It is also consistent with studies documenting an expected level of response to evidence-based mental health treatments in disadvantaged depressed patients (6, 7). Unlike the Partners in Care study, where depressed minority patients (many of them quite poor) improved more than depressed Caucasian patients (most of whom were not poor), our poor patients appeared to respond equally to our more economically advantaged patients. This may be because, unlike that study, our poor patients’ rate of receiving care at baseline was comparable to, rather than lower than, the comparison group. It should be noted, however, that our power to detect an income-intervention interaction, which is estimated in this study to be quite small, is extremely low and would require a much larger sample size. It would not appear, however, that this estimated small effect would likely be clinically significant.

In conclusion, this analysis showed that impoverished patients with panic disorder responded equally well to an intervention designed to increase rates of quality antipanic pharmacotherapy and CBT. Because these patients began with more severe symptoms and disability, they ended up after treatment with less complete responses (i.e., a higher level of residual symptoms) than patients above the poverty line who started off with less severe symptoms. Their greater rate of comorbid psychiatric and medical conditions also likely contributed to this. This suggests that although CBT worked equally well in this impoverished population, helping these initially more ill patients achieve more complete resolution of their symptoms and functional deficits will require more intensive treatment, characterized either by greater amounts and frequency of the same treatment or perhaps additional “stepped-care,” which would deliver either adjunctive medications or behavioral treatments targeted toward both residual symptoms and additional comorbid conditions that may be contributing to disability. This hypothesis should be tested in future controlled studies.

+Received Nov. 15, 2004; revision received Feb. 8, 2005; accepted March 29, 2005. From the Department of Psychiatry & Behavioral Science, University of Washington School of Medicine at Harborview Medical Center; the RAND Corporation, Santa Monica, Calif.; the UCLA Department of Psychiatry and Department of Psychology, Los Angeles; the UC San Diego Department of Psychiatry and Department of Family & Preventive Medicine, La Jolla, Calif.; the VA South Central Mental Illness Research Education and Clinical Center (MIRECC), Los Angeles; and the University of Arkansas for Medical Science, Little Rock. Address correspondence and reprint requests to Dr. Roy-Byrne, Department of Psychiatry & Behavioral Science, University of Washington School of Medicine at Harborview Medical Center, 326 9th Ave., Seattle, WA 98104; roybyrne@u.washington.edu (e-mail).Supported by NIMH grants MH-57835 and MH-64122 (Dr. Stein), MH-57858 and MH-065324 (Dr. Roy-Byrne), and MH-58915-03 (Dr. Craske).

1.Isaacs SL, Schroeder SA: Class: the ignored determinant of the nation’s health. N Engl J Med 2004; 351:1137–1142
 
2.Weich S, Lewis G: Poverty, unemployment, and common mental disorders: population based cohort study. BMJ 1998; 317:115–119
 
3.Kessler RC, McGonagle KA, Zhao S, Nelson CB, Hughes M, Eshleman S, Wittchen HU, Kendler KS: Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States: results from the National Comorbidity Survey. Arch Gen Psychiatry 1994; 51:8–19
 
4.Murphy JM, Olivier DC, Monson RR, Sobol AM, Federman EB, Leighton AH: Depression and anxiety in relation to social status: a prospective epidemiologic study. Arch Gen Psychiatry 1991; 48:223–239
 
5.Bruce ML, Takeuchi DT, Leaf PJ: Poverty and psychiatric status: longitudinal evidence from the New Haven Epidemiologic Catchment Area study. Arch Gen Psychiatry 1991; 48:470–474
 
6.Miranda J, Chung JY, Green BL, Krupnick J, Siddique J, Revicki DA, Belin T: Treating depression in predominantly low-income young minority women: a randomized controlled trial. JAMA 2003; 290:57–65
 
7.Araya R, Rojas G, Fritsch R, Gaete J, Rojas M, Simon G, Peters TJ: Treating depression in primary care in low-income women in Santiago, Chile: a randomised controlled trial. Lancet 2003; 361:995–1000
 
8.Miranda J, Duan N, Sherbourne C, Schoenbaum M, Lagomasino I, Jackson-Triche M, Wells KB: Improving care for minorities: can quality improvement interventions improve care and outcomes for depressed minorities? results of a randomized, controlled trial. Health Serv Res 2003; 38:613–630
 
9.Craske MG, Roy-Byrne PP, Stein MB, Donald-Sherbourne C, Bystritsky A, Katon WJ, Sullivan G: Treating panic disorder in primary care: a collaborative care intervention. Gen Hosp Psychiatry 2002; 24:148–155
 
10.Roy-Byrne PP, Craske MG, Stein MB, Sullivan G, Bystritsky A, Katon W, Golinelli D, Sherbourne CD: A randomized effectiveness trial of cognitive-behavioral therapy and medication for primary care panic disorder. Arch Gen Psychiatry 2005; 62:290–298
 
11.US Department of Health and Human Services: Mental Health: Culture, Race, and Ethnicity: A Supplement to Mental Health: A Report of the Surgeon General. Rockville, Md, US Department of Health and Human Services, Public Health Service, Office of the Surgeon General, 2001
 
12.Wells KB: Treatment research at the crossroads: the scientific interface of clinical trials and effectiveness research. Am J Psychiatry 1999; 156:5–10
 
13.Roy-Byrne P, Sherbourne CD, Craske MG, Stein MB, Katon W, Sullivan G, Means-Christensen A, Bystritsky A: Moving treatment research from clinical trials to the real world: the design of a first-generation effectiveness study for panic disorder. Psychiatr Services 2003; 54:288–300
 
14.Barlow DH, Craske MG: Mastery of Your Anxiety and Panic, 3rd ed. Boulder, Colo, Graywind Publications, 2000
 
15.Barlow DH, Craske MG: Agoraphobia Supplement, 3rd ed. Boulder, Colo, Graywind Publications, 2000
 
16.Roy-Byrne PP, Stein MS, Bystrisky A, Katon W: Pharmacotherapy of panic disorder: proposed guidelines for the family physician. J Am Board Family Pract 1998; 11:282–290
 
17.Otto MW, Reilly-Harrington N: The impact of treatment on anxiety sensitivity, in Anxiety Sensitivity: Theory, Research, and Treatment of the Fear of Anxiety. Edited by Taylor S. Mahwah, NJ, Lawrence Erlbaum, 1999, pp 321–336
 
18.Schmidt NB, Lerew DR, Joiner TE Jr: Prospective evaluation of the etiology of anxiety sensitivity: test of a scar model. Behav Res Ther 2000; 38:1083–1095
 
19.Radloff LS: The CES-D scale: a self-report depression scale for research in the general population. Applied Psychol Measurements 1977; 1:385–401
 
20.Epping-Jordan J, Usten T: The WHODAS II: level the playing field for all disorders. World Health Organization Bull Ment Health 2000; 6:5–6
 
21.Shear MK, Maser JD: Standardized assessment for panic disorder research: a conference report. Arch Gen Psychiatry 1994; 51:346–354
 
22.Steketee G, Perry JC, Goisman RM, Warshaw MG, Massion AO, Peterson LG, Langford L, Weinshenker N, Farreras IG, Keller MB: The psychosocial treatments interview for anxiety disorders: a method for assessing psychotherapeutic procedures in anxiety disorders. J Psychother Pract Res 1997; 6:194–210
 
23.US Department of Health and Human Services: The 2001 HHS Poverty Guidelines. Federal Register 2001; 66(Feb 16):10695-10697
 
24.Shi M, Weiss RE, Taylor JMG: An analysis of pediatric AIDS CD4 counts using flexible random curves. Applied Statistics 1996; 45:151–163
 
25.Gelman A, Carlin J, Stern H, Rubin DB: Bayesian Data Analysis. London, Chapman & Hall, 1995
 
26.Balk EM, Bonis PA, Moskowitz H, Schmid CH, Ioannidis JP, Wang C, Lau J: Correlation of quality measures with estimates of treatment effect in meta-analyses of randomized controlled trials. JAMA 2002; 287:2973–2982
 
27.Carlin B, Louis T: Bayes and Empirical Bayes Methods for Data Analysis (2nd ed). London, Chapman & Hall, 2000
 
28.Gilks W, Richardson S, Spiegelhalter D: Markhov chain Monte Carlo methods in practice. London, Chapman & Hall, 1996
 
29.WinBUGS User Manual Version 1.4. London, 2003
 
30.Roy-Byrne PP, Russo J, Cowley DS, Katon WJ: Panic disorder in public sector primary care: clinical characteristics and illness severity compared with “mainstream” primary care panic disorder. Depress Anxiety 2003; 17:51–57
 
31.Boyce P, Harris M, Silove D, Morgan A, Wilhelm K, Hadzi-Pavlovic D: Psychosocial factors associated with depression: a study of socially disadvantaged women with young children. J Nerv Ment Dis 1998; 186:3–11
 
32.Ross CE: Neighborhood disadvantage and adult depression. J Health and Soc Behavior 2000; 41:177–187
 
33.Breslau N, Davis GC, Andreski P, Peterson E: Traumatic events and posttraumatic stress disorder in an urban population of young adults. Arch Gen Psychiatry 1991; 48:216–222
 
34.Taylor SE, Seeman TE: Psychosocial resources and the SES-health relationship. Ann N Y Acad Sci 1999; 896:210–225
 
35.Wilkinson RG: Health, hierarchy, and social anxiety. Ann N Y Acad Sci 1999; 896:48–63
 
36.Wade WA, Treat TA, Stuart GL: Transporting an empirically supported treatment for panic disorder to a service clinic setting: a benchmarking strategy. J Consult Clin Psychol 1998; 66:231–239
 
37.Chambless DL, Williams KE: A preliminary study of the effects of exposure in vivo for African Americans with agoraphobia. Behavior Therapy 1995; 26:501–515
 
 
Figure 1. Change in Anxiety Sensitivity Index Scores Among Panic Disorder Patients in Primary Care, by Treatment Condition and Income Level
 
Figure 2. Change in World Health Organization Disability Scale Scores Among Panic Disorder Patients in Primary Care, by Treatment Condition and Income Level
 
Figure 3. Change in CES-D Scale Scores Among Panic Disorder Patients in Primary Care, by Treatment Condition and Income Level

Figure 1. Change in Anxiety Sensitivity Index Scores Among Panic Disorder Patients in Primary Care, by Treatment Condition and Income Level

Figure 2. Change in World Health Organization Disability Scale Scores Among Panic Disorder Patients in Primary Care, by Treatment Condition and Income Level

Figure 3. Change in CES-D Scale Scores Among Panic Disorder Patients in Primary Care, by Treatment Condition and Income Level
+

References

1.Isaacs SL, Schroeder SA: Class: the ignored determinant of the nation’s health. N Engl J Med 2004; 351:1137–1142
 
2.Weich S, Lewis G: Poverty, unemployment, and common mental disorders: population based cohort study. BMJ 1998; 317:115–119
 
3.Kessler RC, McGonagle KA, Zhao S, Nelson CB, Hughes M, Eshleman S, Wittchen HU, Kendler KS: Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States: results from the National Comorbidity Survey. Arch Gen Psychiatry 1994; 51:8–19
 
4.Murphy JM, Olivier DC, Monson RR, Sobol AM, Federman EB, Leighton AH: Depression and anxiety in relation to social status: a prospective epidemiologic study. Arch Gen Psychiatry 1991; 48:223–239
 
5.Bruce ML, Takeuchi DT, Leaf PJ: Poverty and psychiatric status: longitudinal evidence from the New Haven Epidemiologic Catchment Area study. Arch Gen Psychiatry 1991; 48:470–474
 
6.Miranda J, Chung JY, Green BL, Krupnick J, Siddique J, Revicki DA, Belin T: Treating depression in predominantly low-income young minority women: a randomized controlled trial. JAMA 2003; 290:57–65
 
7.Araya R, Rojas G, Fritsch R, Gaete J, Rojas M, Simon G, Peters TJ: Treating depression in primary care in low-income women in Santiago, Chile: a randomised controlled trial. Lancet 2003; 361:995–1000
 
8.Miranda J, Duan N, Sherbourne C, Schoenbaum M, Lagomasino I, Jackson-Triche M, Wells KB: Improving care for minorities: can quality improvement interventions improve care and outcomes for depressed minorities? results of a randomized, controlled trial. Health Serv Res 2003; 38:613–630
 
9.Craske MG, Roy-Byrne PP, Stein MB, Donald-Sherbourne C, Bystritsky A, Katon WJ, Sullivan G: Treating panic disorder in primary care: a collaborative care intervention. Gen Hosp Psychiatry 2002; 24:148–155
 
10.Roy-Byrne PP, Craske MG, Stein MB, Sullivan G, Bystritsky A, Katon W, Golinelli D, Sherbourne CD: A randomized effectiveness trial of cognitive-behavioral therapy and medication for primary care panic disorder. Arch Gen Psychiatry 2005; 62:290–298
 
11.US Department of Health and Human Services: Mental Health: Culture, Race, and Ethnicity: A Supplement to Mental Health: A Report of the Surgeon General. Rockville, Md, US Department of Health and Human Services, Public Health Service, Office of the Surgeon General, 2001
 
12.Wells KB: Treatment research at the crossroads: the scientific interface of clinical trials and effectiveness research. Am J Psychiatry 1999; 156:5–10
 
13.Roy-Byrne P, Sherbourne CD, Craske MG, Stein MB, Katon W, Sullivan G, Means-Christensen A, Bystritsky A: Moving treatment research from clinical trials to the real world: the design of a first-generation effectiveness study for panic disorder. Psychiatr Services 2003; 54:288–300
 
14.Barlow DH, Craske MG: Mastery of Your Anxiety and Panic, 3rd ed. Boulder, Colo, Graywind Publications, 2000
 
15.Barlow DH, Craske MG: Agoraphobia Supplement, 3rd ed. Boulder, Colo, Graywind Publications, 2000
 
16.Roy-Byrne PP, Stein MS, Bystrisky A, Katon W: Pharmacotherapy of panic disorder: proposed guidelines for the family physician. J Am Board Family Pract 1998; 11:282–290
 
17.Otto MW, Reilly-Harrington N: The impact of treatment on anxiety sensitivity, in Anxiety Sensitivity: Theory, Research, and Treatment of the Fear of Anxiety. Edited by Taylor S. Mahwah, NJ, Lawrence Erlbaum, 1999, pp 321–336
 
18.Schmidt NB, Lerew DR, Joiner TE Jr: Prospective evaluation of the etiology of anxiety sensitivity: test of a scar model. Behav Res Ther 2000; 38:1083–1095
 
19.Radloff LS: The CES-D scale: a self-report depression scale for research in the general population. Applied Psychol Measurements 1977; 1:385–401
 
20.Epping-Jordan J, Usten T: The WHODAS II: level the playing field for all disorders. World Health Organization Bull Ment Health 2000; 6:5–6
 
21.Shear MK, Maser JD: Standardized assessment for panic disorder research: a conference report. Arch Gen Psychiatry 1994; 51:346–354
 
22.Steketee G, Perry JC, Goisman RM, Warshaw MG, Massion AO, Peterson LG, Langford L, Weinshenker N, Farreras IG, Keller MB: The psychosocial treatments interview for anxiety disorders: a method for assessing psychotherapeutic procedures in anxiety disorders. J Psychother Pract Res 1997; 6:194–210
 
23.US Department of Health and Human Services: The 2001 HHS Poverty Guidelines. Federal Register 2001; 66(Feb 16):10695-10697
 
24.Shi M, Weiss RE, Taylor JMG: An analysis of pediatric AIDS CD4 counts using flexible random curves. Applied Statistics 1996; 45:151–163
 
25.Gelman A, Carlin J, Stern H, Rubin DB: Bayesian Data Analysis. London, Chapman & Hall, 1995
 
26.Balk EM, Bonis PA, Moskowitz H, Schmid CH, Ioannidis JP, Wang C, Lau J: Correlation of quality measures with estimates of treatment effect in meta-analyses of randomized controlled trials. JAMA 2002; 287:2973–2982
 
27.Carlin B, Louis T: Bayes and Empirical Bayes Methods for Data Analysis (2nd ed). London, Chapman & Hall, 2000
 
28.Gilks W, Richardson S, Spiegelhalter D: Markhov chain Monte Carlo methods in practice. London, Chapman & Hall, 1996
 
29.WinBUGS User Manual Version 1.4. London, 2003
 
30.Roy-Byrne PP, Russo J, Cowley DS, Katon WJ: Panic disorder in public sector primary care: clinical characteristics and illness severity compared with “mainstream” primary care panic disorder. Depress Anxiety 2003; 17:51–57
 
31.Boyce P, Harris M, Silove D, Morgan A, Wilhelm K, Hadzi-Pavlovic D: Psychosocial factors associated with depression: a study of socially disadvantaged women with young children. J Nerv Ment Dis 1998; 186:3–11
 
32.Ross CE: Neighborhood disadvantage and adult depression. J Health and Soc Behavior 2000; 41:177–187
 
33.Breslau N, Davis GC, Andreski P, Peterson E: Traumatic events and posttraumatic stress disorder in an urban population of young adults. Arch Gen Psychiatry 1991; 48:216–222
 
34.Taylor SE, Seeman TE: Psychosocial resources and the SES-health relationship. Ann N Y Acad Sci 1999; 896:210–225
 
35.Wilkinson RG: Health, hierarchy, and social anxiety. Ann N Y Acad Sci 1999; 896:48–63
 
36.Wade WA, Treat TA, Stuart GL: Transporting an empirically supported treatment for panic disorder to a service clinic setting: a benchmarking strategy. J Consult Clin Psychol 1998; 66:231–239
 
37.Chambless DL, Williams KE: A preliminary study of the effects of exposure in vivo for African Americans with agoraphobia. Behavior Therapy 1995; 26:501–515
 
+
+

CME Activity

There is currently no quiz available for this resource. Please click here to go to the CME page to find another.
Submit a Comments
Please read the other comments before you post yours. Contributors must reveal any conflict of interest.
Comments are moderated and will appear on the site at the discertion of APA editorial staff.

* = Required Field
(if multiple authors, separate names by comma)
Example: John Doe



Web of Science® Times Cited: 13

Related Content
Articles
Books
Gabbard's Treatments of Psychiatric Disorders, 4th Edition > Chapter 29.  >
The American Psychiatric Publishing Textbook of Psychiatry, 5th Edition > Chapter 12.  >
The American Psychiatric Publishing Textbook of Substance Abuse Treatment, 4th Edition > Chapter 33.  >
Textbook of Psychotherapeutic Treatments > Chapter 8.  >
Textbook of Psychotherapeutic Treatments > Chapter 8.  >
Topic Collections
Psychiatric News
APA Guidelines
PubMed Articles