Evidence-based Synthesis Program Department of Veterans Affairs

Health Services Research & Development Service

August 2011

Prepared for:

Department of Veterans Affairs

Veterans Health Administration

Health Services Research & Development Service

Washington, DC 20420

Prepared by:

Evidence-based Synthesis Program (ESP) Center 

Durham Veterans Affairs Healthcare System

Durham, N.C.

John W. Williams Jr., M.D., M.H.Sc, Director

Performance Characteristics

of Self-report Instruments

for Diagnosing Generalized Anxiety 

and Panic Disorders in Primary Care:

A Systematic Review 

Investigators:

Principal Investigator:

Sophiya Benjamin, M.D.

Co-Investigators:

Nathaniel R. Herr, Ph.D.

Jennifer McDuffie, Ph.D.

John W. Williams Jr., M.D., M.H.Sc

Research Associate:

Avishek Nagi, M.S.

Medical Editor:

Liz Wing, M.A.
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Performance Characteristics of Self-report Instruments for Diagnosing 

Generalized Anxiety and Panic Disorders in Primary Care Evidence-based Synthesis Program

This report is based on research conducted by the Evidence-based Synthesis Program 

(ESP) Center located at the Durham VA Medical Center, Durham, NC, funded by the 

Department of Veterans Affairs, Veterans Health Administration, Office of Research and 

Development, Health Services Research and Development. The findings and conclusions in this document are those of the author(s) who are responsible for its contents; 

the findings and conclusions do not necessarily represent the views of the Department 

of Veterans Affairs or the United States government. Therefore, no statement in this 

article should be construed as an official position of the Department of Veterans Affairs. 

No investigators have any affiliations or financial involvement (e.g., employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents 

received or pending, or royalties) that conflict with material presented in the report.

PREFACE

Health Services Research & Development Service’s (HSR&D’s) Evidence-based Synthesis 

Program (ESP) was established to provide timely and accurate syntheses of targeted healthcare 

topics of particular importance to Veterans Affairs (VA) managers and policymakers, as they 

work to improve the health and healthcare of Veterans. The ESP disseminates these reports 

throughout VA.

HSR&D provides funding for four ESP Centers and each Center has an active VA affiliation. The 

ESP Centers generate evidence syntheses on important clinical practice topics, and these reports 

help:

• develop clinical policies informed by evidence,

• guide the implementation of effective services to improve patient outcomes and to 

support VA clinical practice guidelines and performance measures, and 

• set the direction for future research to address gaps in clinical knowledge.

In 2009, the ESP Coordinating Center was created to expand the capacity of HSR&D Central 

Office and the four ESP sites by developing and maintaining program processes. In addition, 

the Center established a Steering Committee comprised of HSR&D field-based investigators, 

VA Patient Care Services, Office of Quality and Performance, and Veterans Integrated Service 

Networks (VISN) Clinical Management Officers. The Steering Committee provides program 

oversight, guides strategic planning, coordinates dissemination activities, and develops 

collaborations with VA leadership to identify new ESP topics of importance to Veterans and the 

VA healthcare system.

Comments on this evidence report are welcome and can be sent to Nicole Floyd, ESP 

Coordinating Center Program Manager, at nicole.floyd@va.gov.

Recommended citation: Benjamin S, Herr NR, McDuffie J, Nagi A, Williams JW Jr. Performance Characteristics of Self-report Instruments for Diagnosing Generalized Anxiety and Panic 

Disorders in Primary Care: A Systematic Review. VA-ESP Project #09-010; 2011
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Performance Characteristics of Self-report Instruments for Diagnosing 

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TABLE OF CONTENTS

EXECUTIVE SUMMARY

Background.................................................................................................................................................... 1

Methods......................................................................................................................................................... 1

Data Synthesis............................................................................................................................................... 2

Peer Review................................................................................................................................................... 2

Results ........................................................................................................................................................... 2

Future Research............................................................................................................................................. 3

Abbreviations Table....................................................................................................................................... 4

INTRODUCTION

Background.................................................................................................................................................... 5

METHODS

Topic Development........................................................................................................................................ 7

Analytic Framework...................................................................................................................................... 7

Search Strategy.............................................................................................................................................. 7

Study Selection.............................................................................................................................................. 8

Data Abstraction ............................................................................................................................................ 9

Quality Assessment ....................................................................................................................................... 9

Data Synthesis............................................................................................................................................. 10

Rating the Body of Evidence....................................................................................................................... 11

Peer Review................................................................................................................................................. 11

RESULTS

Literature Flow ............................................................................................................................................ 12

KQ 1: In general medical patients with somatic symptoms, what are the performance characteristics 

(e.g., sensitivity, specificity) of self-report questionnaires for diagnosing generalized anxiety 

disorder or panic disorder?.............................................................................................................. 13

KQ 2: For questionnaires evaluated in KQ 1, which measures are most feasible to use in primary care 

settings? Specifically, what is the reading comprehension level, time required to complete, 

response format, and compatibility with telephone administration? .............................................. 22

KQ 3: For questionnaires evaluated in KQ 1, do the performance characteristics vary by gender, race, 

age group, or setting? ...................................................................................................................... 24

SUMMARY AND DISCUSSION

Summary of Evidence by Key Question ..................................................................................................... 25

Strengths and Limitations............................................................................................................................ 27

Recommendations for Future Research....................................................................................................... 28

Conclusions ................................................................................................................................................. 29

REFERENCES ................................................................................................................................................... 30

APPENDIX A. SEARCH STRATEGY..................................................................................................... 34
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Performance Characteristics of Self-report Instruments for Diagnosing 

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APPENDIX B. STUDY SELECTION FORM....................................................................................... 35

APPENDIX C. EXCLUDED STUDIES................................................................................................... 36

APPENDIX D. DATA EXTRACTION FORM ..................................................................................... 41

APPENDIX E. CRITERIA USED IN QUALITY ASSESSMENT................................................ 45

APPENDIX F. PEER REVIEW COMMENTS/AUTHOR RESPONSES................................. 46

FIGURES

Figure 1. Analytic framework........................................................................................................................ 7

Figure 2. Literature flow diagram................................................................................................................ 12

Figure 3. SROC curve for GAD measures.................................................................................................. 19

Figure 4. SROC curve for PD measures...................................................................................................... 20

Figure 5. Forest plot of pooled sensitivity for studies that tested the SDDS-PC for PD............................. 21

Figure 6. QUADAS summaries for KQ 1 ................................................................................................... 22

TABLES

Table 1. Summary of inclusion and exclusion criteria .................................................................................. 8

Table 2. Characteristics of 8 self-report measures for GAD and PD .......................................................... 15

Table 3. Characteristics of 9 included studies ............................................................................................. 16

Table 4. Performance characteristics of self-report measures in primary care settings .............................. 17

Table 5. Summary of strength of evidence by KQ ...................................................................................... 25
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Performance Characteristics of Self-report Instruments for Diagnosing 

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EVIDENCE REPORT

INTRODUCTION

Anxiety disorders are a major public health concern associated with functional impairment 

and increased use of the health care system.1,2 Two of the more common anxiety disorders are 

generalized anxiety disorder (GAD) and panic disorder (PD), which if identified, can be treated 

successfully with medications or psychotherapy. GAD is characterized by at least 6 months of 

persistent and excessive anxiety and worry that is difficult to control and is accompanied by three 

of six additional symptoms: restlessness, fatigue, decreased concentration, irritability, muscle 

tension, and sleep disturbance.3

 PD, on the other hand, is episodic in nature and characterized 

by recurrent and unexpected panic attacks—periods of intense fear or terror associated with 

autonomic arousal such as breathlessness, chest pain, or fear of losing control—that become a 

cause of persistent concern in the patient.3

In community samples, GAD has a lifetime prevalence of 5.1 percent in the U.S. population aged 

15 to 54 years.4

 The prevalence of PD in the National Comorbidity Survey was 2.3 percent in 

the 12-month period prior to the interview and 3.5 percent over one’s lifetime.5 The same study 

found that in the 12-month period preceding the interview, anxiety disorders (17%) were more 

likely to occur when compared to other common mental illnesses such as substance abuse (11%) 

and affective disorders (11%), indicating that anxiety disorders are more chronic in nature.5

In the sample of participants aged 55 years or older, the 12-month prevalence of GAD was 2 

percent, and PD was found to be 1.3 percent.6

 The prevalence of these disorders in primary care 

populations is higher than in community samples, with rates for GAD at 7.6 percent and PD at 

6.8 percent.7

Among Operation Enduring Freedom and Operation Iraqi Freedom (OEF/OIF) Veterans referred 

for further behavioral health assessment based on preliminary screening, the incidence for GAD 

was 4.2 percent and for PD, 8 percent. Further, both disorders were significantly associated with 

limitations on work performance across multiple domains, thus affecting a Veteran’s ability to 

maintain gainful employment and successfully reintegrate into civilian life.8 The main barrier 

to receiving mental health treatment among Veterans is stigma: 65 percent of Veterans who met 

screening criteria for a mental disorder stated, “I would be seen as weak.”9

 Besides stigmarelated reasons, more pragmatic concerns, such as difficulty scheduling appointments or getting 

time off from work, were high on the list.9

BACKGROUND 

Many patients with anxiety disorders present to their primary care doctors.7 However, almost 

half of the patients with an anxiety disorder are not diagnosed or treated. One obstacle in the 

diagnosis of anxiety disorders is the somatic presentation.10,11 In addition to anxiety and worry, 

the DSM-IV TR (text revision) includes symptoms such as restlessness, being easily fatigued, 

muscle tension, and sleep disturbance as diagnostic criteria for GAD—symptoms that might be 

associated with other common physical illnesses.3

 Similarly, the DSM-IV TR defines a panic 

attack as a discrete period of discomfort where 4 out of 13 symptoms are present; however, many 

of these symptoms—palpitations, sweating, trembling or shaking, shortness of breath, feeling 
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Performance Characteristics of Self-report Instruments for Diagnosing 

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of choking, chest pain or discomfort, nausea or abdominal distress, feeling dizzy, unsteady, 

lightheaded or faint, paresthesias, chills or hot flushes—could be considered symptoms of other 

medical illnesses. In the Veteran population, symptoms of GAD and PD might also be present 

in patients with other mental illnesses such as substance abuse, PTSD, and depression. As one 

author describes, anxiety is a relatively easy symptom to elicit in medical settings; the diagnostic 

difficulty lies in the interpretation.12 In addition to identifying anxiety disorders that may 

present with physical symptoms, screening and treatment have the potential to improve patient 

outcomes.

Many of the barriers to the recognition, diagnosis, and effective treatment of GAD and PD 

parallel the quality gaps identified in the management of patients with depressive disorders.13,14

Over the past decade, models of care that included screening and integration between primary 

care and mental health professionals have been shown to improve outcomes for patients with 

depression or anxiety disorders.15-17 The VA has embraced these integrated-care models and 

employs systematic screening for depression, posttraumatic stress disorder, and alcohol-related 

disorders. Patients referred to the integrated-care programs are also screened for comorbid 

conditions, including anxiety disorders. Patients with comorbid conditions (e.g., major 

depressive disorder and GAD) have a more chronic course of illness and may require more 

intensive treatment to achieve remission.

While there exist standard screening instruments for depression (e.g., the Patient Health 

Questionnaire [PHQ-9]18) and alcohol consumption (e.g., AUDIT-C19), there is no one instrument 

recommended to screen for common anxiety disorders. A uniform approach to screening 

for anxiety could facilitate, at a national level, the performance measurement, cross-facility 

comparison, and quality improvement efforts of the VA health system. Selection of a single 

instrument should consider evidence on performance characteristics, ease of use, and ability to 

monitor treatment response.16,20-22 Instruments easiest to use in busy primary care settings will be 

in a self-report format and will not require specialized equipment or trained personnel, making 

them more feasible to implement. To inform decisionmaking regarding a standard screening 

instrument for anxiety disorders, we conducted a systematic review of the literature to evaluate 

the performance of self-report instruments used to diagnose GAD and PD in primary care 

settings.
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Performance Characteristics of Self-report Instruments for Diagnosing 

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METHODS

TOPIC DEVELOPMENT

This review was commissioned by the VA’s Evidence-based Synthesis Program. The topic 

was nominated after a formal topic nomination and prioritization process that included 

representatives from the VA Office of Mental Health Services, Health Services Research and 

Development, Mental Health QUERI, and Primary Care–Mental Health Integration Program. 

We further developed and refined the key questions (KQs) for this review based on a preliminary 

review of published peer-reviewed literature in consultation with VA and non-VA experts to 

select patients, measures, outcomes, and settings addressed in this review.

The final KQs were:

KQ 1. In general medical patients with somatic symptoms, what are the performance 

characteristics (e.g., sensitivity, specificity) of self-report questionnaires for diagnosing 

generalized anxiety disorder or panic disorder?

KQ 2. For questionnaires evaluated in KQ 1, which measures are most feasible to use in primary 

care settings? Specifically, what is the reading comprehension level, time required to complete, 

response format, and compatibility with telephone administration?

KQ 3. For questionnaires evaluated in KQ 1, do the performance characteristics vary by gender, 

race, age group, or setting?

ANALYTIC FRAMEWORK

We developed and followed a standard protocol for all steps of this review. Our approach was 

guided by the analytic framework shown in Figure 1.

Figure 1. Analytic framework

Screening Treatment

Adults with

somatic symptoms

Detection of GAD

and PD

Anxiety symptoms; health 

related quality of life; 

patient satisfaction with 

treatment; change in health 

status

Reduced 

mortality and or 

morbidity

Adverse 

effects of 

screening

Adverse 

effects of 

treatment

1 2 3 

SEARCH STRATEGY

We searched MEDLINE® (via PubMed®), PsycINFO®, and the Cochrane Library for studies 

comparing self-report measures of GAD and PD with an acceptable criterion standard from 

January 1980 through December 2010. We chose 1980 to coincide with the publication of DSMIII so that the criterion standards used would be consistent with those used in current clinical 
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Performance Characteristics of Self-report Instruments for Diagnosing 

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practice. We limited the search to articles involving human subjects 18 years of age and older 

and published in the English language. We combined terms for the disorders with a validated 

search filter for retrieving articles on the diagnosis of health disorders.23,24 Our final search terms 

included generalized anxiety disorder, panic disorder, questionnaires, screening, sensitivity, 

specificity, likelihood, medical history taking, and accuracy as well as terms for individual 

screening tools that were identified during our initial review of the search results (Appendix A).

We developed our search strategy in consultation with a master librarian. We supplemented 

electronic searching by examining the bibliographies of review articles, systematic reviews, and 

included studies. We also consulted experts in the field.

STUDY SELECTION

Using prespecified inclusion and exclusion criteria, two reviewers assessed titles and abstracts for 

relevance. Full-text articles, identified by either reviewer as potentially relevant, were retrieved for 

further review. Each article retrieved was examined by two reviewers against the eligibility criteria 

in Appendix B; disagreements were resolved by discussion or by a third reviewer. Studies excluded 

at the full-text review stage are listed with the reasons for exclusion in Appendix C. 

Although KQ 1 was developed to focus on general medical patients with somatic symptoms, 

we chose to include studies with unselected patients presenting to general medical settings. 

We broadened the population criteria because few studies addressed only patients with specific 

somatic symptoms, and we reasoned that somatic symptoms were common (even if not 

specifically documented) in unselected general medical patients.25 To identify studies most 

applicable to the Veteran population in the U.S., we included studies that examined measures 

in English or Spanish and were conducted in North America, Western Europe, New Zealand, or 

Australia. Detailed eligibility criteria are described in Table 1. 

Table 1. Summary of inclusion and exclusion criteria

Study characteristic Inclusion criteria Exclusion criteria

Study design Prospective comparison of an anxiety questionnaire 

to a reference standard

Case control studies that select 

patients with known disease and 

compare to healthy controls

Population Adults ≥18 in general medical clinics with or without 

somatic symptoms. 

Study populations of patients with 

preexisting mental health diagnosis

Instrument

Self-report instrument used to measure GAD or PD 

that is feasible to use in a medical setting without 

need for specialized equipment

Instruments requiring interviewer 

administration

Reference standard Criteria based on DSM III or later; ICD 9 or later Studies without an independent 

criterion standard

Outcome

Study reports a measure of sensitivity/specificity 

or data to derive an n*k table to validate operating 

characteristics. 

Data to calculate sensitivity or 

specificity not provided

Setting

Primary care clinics including general internal 

medicine, family medicine and geriatrics; general 

medical and selected specialty medical settings (e.g., 

emergency departments, cardiology, gastrointestinal 

clinics, rheumatology and women’s health)

Highly specialized clinics (e.g., 

memory disorder clinic) and 

psychiatric or mental health clinics
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Performance Characteristics of Self-report Instruments for Diagnosing 

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DATA ABSTRACTION 

We abstracted data pertinent to the KQs into a data abstraction form (Appendix D). Key data 

included: 

• study design

• setting

• population characteristics

• subject eligibility and exclusion criteria

• number of subjects

• details about the index test and criterion standard and their application

• psychiatric and medical comorbidities of the sample and outcomes

Data elements selected for abstraction were informed by the principles outlined by the Standards 

for Reporting of Diagnostic Accuracy (STARD).26 These elements included descriptors to assess 

applicability (e.g., setting, sample characteristics, anxiety disorder prevalence) and quality 

elements (e.g., recruitment method, blinding, reference standard, sample size). When provided, 

raw data for the n*k table was abstracted, and when not provided, data were derived from other 

performance characteristics such as sensitivity and specificity. 

We abstracted data for all relevant thresholds when data for more than one threshold of the index 

test were reported. If data were analyzed for subgroups based on race or sex, we abstracted 

them in separate n*k tables to address KQ 3. Performance characteristics such as sensitivity and 

specificity were recalculated by each investigator performing the abstraction to validate the data. 

When results were adjusted for the sampling design (e.g., partial verification of the criterion 

based diagnosis), we use the adjusted results.

All data abstractions were confirmed by a second reviewer. Disagreements were resolved by 

consensus or by obtaining a third reviewer’s opinion when consensus could not be reached. 

Further, one author independently abstracted and validated all the n*k tables to check for 

accuracy. 

QUALITY ASSESSMENT

We adopted a two-step approach to rate the quality of the evidence. First, we applied the Quality 

Assessment of Diagnostic Accuracy Studies (QUADAS) criteria for each study. QUADAS is 

a 14-item tool that was developed to assess the quality of diagnostic accuracy studies included 

in systematic reviews so that appropriate conclusions can be drawn in the context of potential 

biases.27,28 Each item has three possible answers: Yes, No, and Unclear. The QUADAS criteria 

were applied for each study by the reviewer abstracting the article; this initial assessment was 

then overread by a second reviewer. Disagreements were resolved between the two reviewers, or 

when needed, by arbitration from a third reviewer. 

To make QUADAS most relevant to studies of self-report tests for anxiety disorders, we 

modified question number 12 of the tool to ask: “Was the cutoff point for the test chosen a 

priori?” The rationale for this is many psychiatric scales intended for diagnostic purposes can 

have markedly different performance characteristics based on the cutoff used, with sensitivity 

and specificity changing in opposite directions. Not choosing the cutoff a priori, ideally based 
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Performance Characteristics of Self-report Instruments for Diagnosing 

Generalized Anxiety and Panic Disorders in Primary Care Evidence-based Synthesis Program

on prior research, can introduce bias and result in poor replicability in subsequent samples. The 

QUADAS criteria with our modification are detailed in Appendix E.

We then assigned a level of evidence for each study based on the elements covered in the 

QUADAS criteria. The levels of evidence ranged from I to V, with I being the highest level of 

evidence with a low risk of bias and V being the lowest level of evidence with a high risk for 

overestimating the accuracy of the test. The above assessment of quality is recommended by the 

Rational Clinical Exam series in the Journal of the American Medical Association (http://jama.

ama-assn.org/cgi/collection/rational_clinical_exam). We also assessed studies for applicability to 

U.S. Veterans. 

DATA SYNTHESIS

We critically analyzed studies to compare their characteristics, methods, and findings. Data 

derived from the studies for n*k tables were used to calculate performance measures (e.g., 

sensitivity, specificity) for each individual study. We then explored heterogeneity among the 

studies using Cochran’s Q and the I2

, which describes the percentage of total variation across 

studies that is due to heterogeneity rather than to chance.29 Heterogeneity was categorized as 

low, moderate, or high based on I

2

 values of 25 percent, 50 percent, and 75 percent respectively. 

We also plotted study outcomes on a graph and visually evaluated the variation in study results. 

To further explore heterogeneity, we examined whether a threshold effect was present. A strong 

positive correlation between the sensitivities (and 1-specificities) is suggestive of a threshold 

effect. This is calculated as a Spearman correlation coefficient between the logit sensitivity and 

logit of 1-specificity. In the absence of a threshold effect, data were pooled, when statistically 

appropriate, using a random effects model. When a threshold effect was present, we analyzed the 

data using a summary receiver operating characteristic (ROC) curve. 

Because GAD and PD are two distinct clinical entities, we did not attempt to combine the studies 

reporting on instruments that were specific to one or the other. Instead, we grouped them into two 

categories, studies on instruments specific for GAD and studies on instruments specific for PD, 

for analyses of heterogeneity. When an instrument was nonspecific and screened for both GAD 

and PD, we included it in both groups. When studies reported data to derive n*k tables by race or 

ethnicity, we calculated performance characteristics for each group for comparison.

Outliers were defined as studies that were markedly different from other included studies, 

due to either methodological or sample characteristics, or if they were significant outliers on 

visual inspection of forest plots of the operating characteristics. When we observed significant 

heterogeneity in operating characteristics, we recomputed summary estimates and test statistics 

after outliers were removed. Analysis was conducted using the open source software MetaDisc, version 1.4 (Unit of Clinical Biostatistics, Ramón y Cajal Hospital, Madrid, Spain)30for all 

analyses except when we statistically combined data using a random effects model for which we 

used MetaAnalyst version Beta 3.13 (Tufts Medical Center).31

Because reading levels were not reported, we determined the reading level for each questionnaire 

using the Flesch-Kincaid Grade level readability formula through the corresponding Microsoft 

Word application.32
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Performance Characteristics of Self-report Instruments for Diagnosing 

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RATING THE BODY OF EVIDENCE

In addition to rating the quality of individual studies, we evaluated the overall quality of the 

evidence for each KQ as proposed by the GRADE Working Group.33 In brief, the GRADE 

approach requires assessment of four domains: risk of bias, consistency, directness, and 

precision. Additional domains are to be used when appropriate. These domains were considered 

qualitatively, and two reviewers assigned a summary rating after discussion as High, Moderate, 

Low, or Insufficient strength of evidence according to the following criteria:

• High—Further research is very unlikely to change our confidence on the estimate of 

effect.

• Moderate—Further research is likely to have an important impact on our confidence in 

the estimate of effect and may change the estimate.

• Low—Further research is very likely to have an important impact on our confidence in 

the estimate of effect and is likely to change the estimate.

• Insufficient—Evidence on an outcome is absent or too weak, sparse, or inconsistent to 

estimate an effect.

In some cases, high, moderate, or low ratings were not possible or imprudent to make. In these 

situations, a grade of Insufficient was assigned.33

PEER REVIEW

A draft version of the report was reviewed by technical experts and clinical leadership, and their 

comments are provided in Appendix F.
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Performance Characteristics of Self-report Instruments for Diagnosing 

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RESULTS

LITERATURE FLOW

We identified 2890 unique citations from a combined electronic search of MEDLINE® via 

PubMed® (n = 982), PsycINFO® (n = 1472), and Cochrane Library (n = 413) and a manual 

examination of references (n = 23); zero additional articles were identified from an updated 

search of relevant systematic reviews. After applying inclusion and exclusion criteria, 2824 

articles were excluded at the title-and-abstract level. We retrieved 66 articles for full-text review 

and excluded 54. For data abstraction and evidence synthesis, we retained a total of 12 articles 

representing 9 unique studies. Figure 2 illustrates each step of our literature search process.

Figure 2. Literature flow diagram

Search results = 2890 articles

Full-text review = 66 articles

KQ 3 

9 studies + 3 

companion studies

Excluded = 54 articles 

Population not of interest = 17

Setting not PC/clinic/ER = 19

No self-reported index test at screening = 2

Reference standard not acceptable = 5

No instrument characteristic data = 2

Design not prospective = 1

Reference standard not applied correctly = 4

Publication not English = 3

Screening tool not English/Spanish = 1

Excluded = 2824 articles

Excluded at the title/abstract level

KQ 2 

9 studies + 3 

companion studies

Included for data abstraction = 12 

articles (9 unique studies plus 3 

companion studies*)

KQ 1 

9 studies + 3 

companion studies

*Article reference list includes additional references cited for background and methods plus Web sites relevant to 

key questions. Companion studies are articles containing additional data pertinent to the unique, primary studies that 

were abstracted.
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Performance Characteristics of Self-report Instruments for Diagnosing 

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KQ 1. In general medical patients with somatic symptoms, what are 

the performance characteristics (e.g., sensitivity, specificity) of selfreport questionnaires for diagnosing generalized anxiety disorder or 

panic disorder?

Self-report Measures

We identified nine studies that validated eight self-report measures of GAD and PD in primary 

care and general medical settings. Of these, two measures were GAD specific: Anxiety Disorder 

Scale–Generalized Anxiety and the Generalized Anxiety Disorder-7.34,35 Three measures were PD 

specific: an unnamed 10-item questionnaire,36 the Patient Health Questionnaire module for panic 

disorder,18 and the Brief Panic Disorder Screen.37 Two measures were multicomponent: Primary 

Care Evaluation of Mental Disorders38 and Symptom Driven Diagnostic System–Primary 

Care.39,40 One measure addressed both GAD and PD: Beck Anxiety Inventory–Primary Care.41

All of the questionnaires were self-administered and did not require specialized equipment or 

trained personnel, making them suitable for patients to complete in a variety of settings. 

Symptom Driven Diagnostic System-Primary Care (SDDS-PC)

The SDDS-PC is a two-stage instrument developed to diagnose mental disorders in primary care 

clinics. The first stage is a brief self-administered screen for six disorders including GAD and 

PD. Each positive screen requires further administration of a detailed diagnostic module. Among 

the instruments included in this review, the SDDS-PC is the only one that has been validated 

in more than one study; however, the versions tested in the two articles are slightly different. 

Leon and colleagues tested a 26-item scale with 4 items for GAD and 5 items for PD,39 whereas 

Broadhead and colleagues tested an earlier version with 3 items for anxiety and 2 for GAD.40

Primary Care Evaluation of Mental Disorders (PRIME-MD)

The PRIME-MD was developed in the early 1990s to diagnose common mental illnesses, 

including anxiety disorders, in medical populations. It is a multicomponent questionnaire with 

two stages, the first of which is a brief screen consisting of 26 items, of which 3 address anxiety. 

If the patient answers yes to any of these questions, the physician is then prompted to use a 

diagnostic module for that disorder.38 The two-step process takes 5 to 6 minutes in patients 

without a mental illness versus 11 to 12 minutes in those with a diagnosis. This was considered 

a barrier to use in routine clinical settings and subsequently led to the development of the Patient 

Health Questionnaire and Generalized Anxiety Disorder-7.

Patient Health Questionnaire (PHQ)

The PHQ is a three-page instrument that addresses depression, PD, other anxiety disorders, 

eating disorders, and alcohol abuse. After completion of the self-report form by the patient, 

the clinician can apply the abbreviated algorithm at the end of each page to make a diagnostic 

decision.18 Though the PHQ specifically addresses PD, it is not specific to GAD.

Generalized Anxiety Disorder-7 (GAD-7)

The GAD-7 is a seven-item questionnaire specific to the diagnosis of GAD. It was developed to 

be a short instrument that can be used in primary care settings. An affirmative answer on any of 

the seven primary questions requires the patient to answer one more question about the effect 
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Performance Characteristics of Self-report Instruments for Diagnosing 

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of symptoms on functioning at work, home, and relationships. The authors suggested that, in 

addition to diagnosing GAD, this instrument could be used for tracking the severity of symptoms 

since cut points of 5, 10, and 15 could be interpreted as mild, moderate, and severe level of 

anxiety.35

Beck Anxiety Inventory-Primary Care (BAI-PC)

The BAI-PC is an abbreviated version of the more commonly known Beck Anxiety Inventory.42

The seven items included in the BAI-PC are said to represent a “subjective dimension” of 

anxiety and include “unable to relax,” “fear of the worst happening,” “terrified,” “nervous,” 

“fear of losing control,” “fear of dying,” and “scared.” The authors proposed that not including 

somatic symptoms might help screen out medical patients without anxiety and thereby increase 

the specificity of the instrument. 

Anxiety Disorder Scale-Generalized Anxiety (ADS-GA)

The ADS-GA was developed as an instrument to detect anxiety disorders in individuals 65 years 

of age and older. The scale is an 11-item questionnaire in a dichotomous Yes/No format. Early 

validation studies determined the optimum cutoff to be 4 or 5 positive responses out of a possible 

11 items.43 Krasucki and colleagues validated this instrument in an outpatient geriatric population 

and developed a shortened version called the FEAR (frequency of anxiety; enduring nature of 

anxiety; alcohol or sedative use; restlessness or fidgeting). However, the shortened version has 

not been evaluated in an independent sample.34

Brief Panic Disorder Scale (BPDS)

The BPDS is a subscale of the 16-item Anxiety Sensitivity Inventory (ASI), a self-report 

measure used to assess sensitivity to the physical sensations associated with panic, such as 

heart racing and dizziness.44 The subscale was derived in a sample of patients from an anxiety 

disorders clinic.45 It consists of four items, all of which start with “It scares me when I ...” and 

are followed by symptoms that include “feel shaky,” “feel faint,” “become short of breath,” 

and “heart beats rapidly.” Johnson and colleagues then validated this in a biracial primary care 

population.37

Unnamed 10-item questionnaire for PD

The 10-item instrument for the diagnosis of PD developed by Barsky and colleagues is 

unique in that it was derived in a sample of patients who were referred to an ambulatory 

electrocardiogram laboratory for the evaluation of palpitations. All of the other instruments were 

studied in unselected patients presenting to a general medical setting. The 10-item instrument 

was then validated in an independent sample of 124 patients referred for Holter monitoring of 

palpitations.36 This scale has not been studied in other populations, and so the performance might 

vary based on patient characteristics.

Table 2 summarizes the characteristics of the 8 self-report measures.
15

 Self-report Instruments for Diagnosing 

Disorders in Primary Care Evidence-based Synthesis Program

eport measures for GAD and PD

cope Response format Timeframe Score range Usual cut point Literacy levelsa Completion time Tracking of symptoms

and PD 4 items of symptom severity Past 2 weeks to today 0-21 ≥ 5 Easy ≈ 1 minute46 Unknown

e 

onents 

AD and 

Yes or No Past month 0-3 ≥ 1 Easy <1 minute No

Yes or No Past month 0-5 Unclear Easy <2 minutes Yes (scale has 

a separate longitudinal tracking module)

Yes or No 6 months 0-5 Unclear Easy <2 minutes

4 frequency ratings:“Not at all,” “several days,” “more than half the days,” “nearly every day”

2 weeks 0-21

 5 = mild10 = moderate15 = severe

Average Unknown Unknown

Yes or No Unknown 0-11 4–5 Easy Unknown Unknown

Yes or No 4 weeks 0-5

Yes on first 4 questions with yes on 4 of the 11 subitems for question 5

Easy

< 1 for 42%1 to 2 for 43%3 to 5 for 13%> 5 for 3%

No

Symptom severity: “Very Little,” “A Little,” “Some,” “Much,”“Very Much”

None 0-16 ≥ 11 Average Unknown Unknown

Symptom severity: “not at all,” “a little bit,” “moderately,” “quite a bit,” “a great deal”

Unknown 0-50 > 21 Average Unknown Unknown

ding level; average, sixth- to ninth-grade reading level.47
16

Performance Characteristics of Self-report Instruments for Diagnosing 

Generalized Anxiety and Panic Disorders in Primary Care Evidence-based Synthesis Program

Characteristics of Included Studies

Table 3 summarizes the characteristics of the nine studies included in this evidence synthesis. 

The sample sizes among the studies varied widely ranging from 56 to 3000. Other than one 

study that was conducted on patients attending a referral clinic for Holter monitoring,36 all 

other studies were conducted in general medical clinics. One study selected patients presenting 

with palpitations;36 all other studies recruited patients without regard to the presenting medical 

problem. None of the studies were conducted in VA hospitals or clinics; however, all but one of 

the studies were conducted in the U.S. 

All of the studies had a high proportion of women (≥ 60%), which might make the samples 

somewhat different from a predominantly male Veteran population. All but one study 34 reported 

the distribution of race, which was predominantly white but varied widely between studies (35 

to 98%). In most of the studies, the screening instrument was self-administered. In one study in 

which 22 percent of the participants had not completed high school, a research assistant verbally 

administered the test to those who had difficulty reading.48 Seven studies reported educational 

achievement among the participants, and the proportion of patients who had completed high 

school ranged from 77.8 to 94 percent.

Table 3. Characteristics of 9 included studies

Study

Setting and

patient 

selection

Sample 

characteristicsa Prevalence

Instrument 

and 

threshold

Criterion 

standard

Quality 

rating

Beck et al., 199741 Primary care

Unselected

N: 56

Age: 48.54 (15.52)

Female: 73%

GAD or PD:

23%

BAI-PC

5 DSM IIIR III

Spitzer et al., 

199438

Primary care

Unselected

N: 431

Age:55 (16.5)

Female: 60%

GAD or PD: 

17.9%

PRIME-MD

≥ 1 DSM IIIR I

Broadhead et 

al.,1995(a)40

Primary care

Unselected

N: 388

Age: 39.4 (12.4)

Female: 72%

GAD: 3.09%

PD: 6.9% SDDS-PC DSM IIIR I

Broadhead et 

al.,1995(b)40

Primary care

Unselected

N:257

Age: 40.3 (13.2)

Female: 70%

GAD: 5.44%

PD: 6.2% SDDC-PC DSM IIIR I

Leon et al., 1996 39 Primary care

Unselected

N: 501

Age: 49.4

Female: 66%

GAD: 15.96%

PD: 7.9% SDDC-PC DSM IV I

Spitzer et al., 

199918

Primary care

Unselected

N: 585

Age: 46 (17.2)

Female: 66%

PD: 7.0% PHQ DSM IIIR I

Krasucki et al., 

199934

Primary care

Unselected

N: 40

Age: 72.6 (NR)

Female: 64%

GAD: 15.0% ADS-GA ICD 10 III

Barsky et al., 

199736

Specialty clinic

Palpitations

N: 124

Age: 47.36 (NR)

Female : 57%

PD: 25.8% Un-named 

instrument DSM IIIR II

Spitzer et al., 

200635

Primary care

Unselected

N: 965

Age: 47.4 (15.5)

Female: 65%

GAD: 7.6% GAD-7 DSM IV I

Johnson et al., 

200748

Primary care

Unselected

N: 295

Age: 54.1 (11.2)

Female: 66%

PD: 13.9% BPDS DSM IV I

a

Reported Ns were calculated based on the number of patients who completed the criterion standard and not based on number 

enrolled in study.
17

Performance Characteristics of Self-report Instruments for Diagnosing 

Generalized Anxiety and Panic Disorders in Primary Care Evidence-based Synthesis Program

Performance Characteristics of Self-report Measures

Table 4 shows performance characteristics of the self-report measures in the primary care setting 

(along with 95% confidence intervals). An ideal self-report measure has a high sensitivity (a 

high proportion of patients with an anxiety disorder have a positive self-report) and a high 

specificity (a high proportion of patients without an anxiety disorder have a negative selfreport). Likelihood ratios are a measure of how useful the self-report is based on sensitivity and 

specificity. Values may range from 0 to infinity, with values above 1 increasing and values below 

1 decreasing the likelihood of an anxiety disorder. The likelihood ratio positive indicates how 

much more likely a positive self-report comes from a patient with, rather than without, an anxiety 

disorder. The likelihood ratio negative indicates how much more likely a negative self-report 

comes from a patient with, rather than without, an anxiety disorder. When coupled with the 

pretest probability of disease, the likelihood ratio allows ready calculation of posttest probability 

of disease. In general, diagnostic tests with likelihood ratios ≥ 5 or ≤ 0.2 have large effects on the 

posttest probability of disease. 

Table 4. Performance characteristics of self-report measures in primary care settings

Study Instrument Sensitivity Specificity Likelihood ratio 

positive

Likelihood ratio 

negative

GAD and PD

Spitzer et al., 

199438

PRIME-MD 0.93

(0.85 to 0.98)

0.53 

(0.48 to 0.58)

1.99

(1.76 to 2.26)

0.12

(0.05 to 0.29)

Beck et al, 199741 BAI-PC 0.85

(0.55 to 0.98)

0.81 

(0.67 to 0.92)

4.55

(2.33 to 8.86)

0.19

(0.05 to 0.68)

GAD specific

Broadhead et al., 

1995(a)40

SDDS-PC 0.92

(0.61 to 1.00)

0.54

(0.49 to 0.59)

1.99

(1.63 to 2.44)

0.15

(0.02 to 1.01)

Broadhead et al., 

1995(b)40

SDDS-PC 0.86

(0.57 to 0.98)

0.60

(0.53 to 0.66)

2.12

(1.63 to 2.76)

1.12

(1.63 to 2.76)

Leon et al., 199639 SDDS-PC 0.74

(0.63 to 0.83)

0.82

(0.78 to 0.85)

4.08

(3.21 to 5.20)

0.32

(0.22 to 0.46)

Krasucki et al., 

199934

ADS-GA 0.40 

(0.05 to 0.85)

 0.86 

(0.70-0.95)

 2.88 

(0.75 to 11.07)

 0.70 

(0.34 to 0.46)

Spitzer et al., 

200635

GAD-7 0.89

(0.79 to 0.95)

0.82

(0.79 to 0.84)

4.93

(4.20 to 5.80)

0.13

(0.07 to 0.26)

PD specific

Spitzer et al., 

199918

PHQ 0.80 

(0.65 to 0.91)

0.99 

(0.98 to 1.00)

87.41 

(36.06 to 211.88)

0.20 

(0.11 to 0.37)

Broadhead et al., 

1995(a)40

SDDS-PC 0.78

(0.58 to 0.91)

0.80

(0.76 to 0.84)

3.90

(2.92 to 5.20)

0.28

(0.14 to 0.56)

Broadhead et al., 

1995(b)40

SDDS-PC 0.62

(0.35 to 0.85)

0.83

( 0.78 to 0.88)

3.77

( 2.34 to 6.05)

0.45

(0.24 to 0.85)

Leon et al., 199639 SDDS-PC 0.70

( 0.53 to 0.83)

0.91

(0.88 to 0.93)

7.87

(5.51 to 11.23)

0.33

(0.20 to 0.53)

Barsky et al., 

199736

10-item scale 0.72

(0.53 to 0.86)

0.71

(0.60 to 0.80)

2.45

(1.67 to 3.60)

0.40

(0.22 to 0.70)

Johnson et al., 

200748

BPDS 0.61

(0.44 to 0.76)

0.29

(0.23 to 0.35)

0.86

(0.66 to 1.11)

1.36

(0.88 to 2.08)
18

Performance Characteristics of Self-report Instruments for Diagnosing 

Generalized Anxiety and Panic Disorders in Primary Care Evidence-based Synthesis Program

The PRIME-MD had a relatively high sensitivity compared to its specificity, which is not 

surprising given that the screen has three broad questions intended to prompt further physician 

interview. Among the instruments that screen for GAD, the GAD-7 had good sensitivity and 

specificity with comparable likelihood ratios. However, this instrument has been validated in 

only one population. 

The SDDS-PC has been validated in more than one population; however, the versions in the two 

studies are slightly different in that the study by Broadhead and colleagues40 tested the 16-item 

SDDS-PC with 2 questions for GAD and 3 for PD corresponding to the DSM-IIIR, whereas 

the study by Leon and colleagues39 tested a 26-item instrument under the same name with 4 

questions for GAD and 5 for PD made to correspond to DSM-IV. Not surprisingly, the SDDS-PC 

showed variable performance characteristics depending on the version and the sample. The two 

samples (initial validation and cross validation) in the study by Broadhead and colleagues40 were 

significantly different in race, education, and marital status.

The ADS-GA was unique in that it was the only study conducted outside the U.S., and the 

population in the study was also substantially older, with a mean age of 72.6 years. The ADS-GA 

had low sensitivity (40%) for GAD, making its performance and study population an outlier in 

this group of studies.

Among the studies that examined instruments specific for panic disorder, the PHQ had the 

highest sensitivity and specificity with a very high positive predictive value and a moderately 

good negative predictive value. Other instruments, the BAI-PC, the 10-item scale, and the 

BPDS are not discussed further due to very small sample sizes and/or unimpressive performance 

characteristics.

Examination of Heterogeneity and Statistical Pooling of Data

Because GAD and PD are two distinct illnesses with unique courses and differences in response 

to treatment, we did not attempt to statistically combine the studies; instead, we analyzed them 

in two groups: GAD-specific instruments and PD-specific instruments. However, for analysis 

purposes, we did combine the two studies that screened for both GAD and PD38,41 with both 

groups. We also conducted a sensitivity analysis without the above two studies to determine if 

they contributed significantly to heterogeneity.

For GAD, the test for heterogeneity (Cochran’s Q) was statistically significant (p<0.001) for 

all 4 performance characteristics (sensitivity, specificity, positive predictive value, and negative 

predictive value). The I

2 ranged from 78.3 to 97.2 percent, which indicated a high level of 

heterogeneity. Sensitivity analysis without the two nonspecific instruments did not significantly 

decrease the heterogeneity. Neither did repeating the analysis without the study by Krasucki and 

colleagues,34 which, on visual inspection of plots, appeared to be an outlier. 

To further explore heterogeneity, we examined if a threshold effect was present. The Spearman 

correlation coefficient between the logit sensitivity and logit of 1-specificity was 0.786 (p = 

0.036), which suggests a threshold effect. A threshold effect can occur when different studies 

examining an instrument use different thresholds for diagnosis, resulting in varying sensitivities 

and specificities. However, it is unlikely here since the studies included were examining different 

instruments, which in itself can contribute to heterogeneity. Further, partial verification bias 
19

Performance Characteristics of Self-report Instruments for Diagnosing 

Generalized Anxiety and Panic Disorders in Primary Care Evidence-based Synthesis Program

and differences in samples and settings can also contribute to heterogeneity. In this analysis, 

all studies were conducted with unselected patients from primary care settings, but diagnostic 

standards and assessment methods varied across studies, which could have contributed to a 

threshold effect.

For PD, the test for heterogeneity (Cochran’s Q) was statistically significant (p<0.001) and I

2 

ranged from 71.4 to 99.0 percent, again indicating a high level of heterogeneity. Sensitivity 

analyses without the nonspecific instruments did not significantly reduce heterogeneity. We 

again explored the presence of a threshold effect, but in this case, the Spearman correlation 

coefficient was –0.071 (p = 0.867), indicating that a threshold effect is unlikely to contribute to 

heterogeneity. Because the patients in the study by Barsky and colleagues36 were significantly 

different in that they were all referred for Holter monitoring of palpitations and had a high 

prevalence of PD, we conducted a sensitivity analysis without this study but did not find a 

change in the heterogeneity. Given the high degree of heterogeneity, we did not statistically 

combine the results from the all studies to yield summary estimates of sensitivity and specificity. 

Instead, we plotted the results on a summary receiver operating characteristic curve (SROC) 

for each disorder (Figures 3 and 4). A summary ROC displays the trade-off between sensitivity 

and specificity. Several useful statistical properties are reported in the figures, including the 

area under the curve (AUC) and the Q*. The AUC is a measure of the questionnaire’s ability to 

discriminate between those with and without an anxiety disorder. A value of 0.5 is no better than 

chance, while a value of 1.0 represents perfect diagnostic test discrimination; tests with values 

greater than 0.80 are generally considered to have good discriminate properties. The Q* statistic 

is the point on the SROC where sensitivity equals specificity. As can be seen in Figures 3 and 4, 

the questionnaires collectively have good overall test performance.

Figure 3. SROC curve for GAD measures

Sensitivity

1

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0 0 0.2 0.4 0.6 0.8 1

SROC Curve

Symmetric SROC

AUC = 0.8584

SE(AUC) = 0.0238

Q* = 0.7892

SE(Q*) = 0.0229

1-specificity
20

Performance Characteristics of Self-report Instruments for Diagnosing 

Generalized Anxiety and Panic Disorders in Primary Care Evidence-based Synthesis Program

Figure 4. SROC curve for PD measures

Sensitivity

1

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0 0 0.2 0.4 0.6 0.8 1

SROC Curve

Symmetric SROC

AUC = 0.8435

SE(AUC) = 0.0492

Q* = 0.7751

SE(Q*) = 0.0460

1-specificity

We did an exploratory analysis of the three studies that validated the SDDS-PC. Heterogeneity 

for GAD was moderate to high, and there was a high threshold effect with a Spearman 

correlation coefficient of 1 (p < 0.001). However, for PD, the heterogeneity was minimal for 

sensitivity (Cochran’s Q = 1.19, df = 2, p = 0.55; I

2 of 0). Similarly, the heterogeneity for the 

negative likelihood ratio was also low (Cochran’s Q = 1.09, df=2, p = 0.58; I2 = 0). Heterogeneity 

for other operating characteristics was high. Overall threshold effect was small, with the 

Spearman correlation coefficient of 0.5 (p = 0.67). Therefore, we statistically combined the 

results for sensitivity and negative likelihood ratio of the SDDC-PC for PD using a random 

effects model. The pooled sensitivity for the SDDS-PC was 0.71 (95% CI 0.60 to 0.80) and the 

pooled negative likelihood ratio was 0.35 (95% CI 0.25 to 0.48) (Figure 5).
21

Performance Characteristics of Self-report Instruments for Diagnosing 

Generalized Anxiety and Panic Disorders in Primary Care Evidence-based Synthesis Program

Figure 5. Forest plot of pooled sensitivity for studies that tested the SDDS-PC for PD

 Study Name N Confidence Interval

Broadhead-a (350) 388 0778 (0.574. 0.907

Broadhead-b (380) 257 0.625 (0.360, 0.838)

Lean (792) 501 0.700 (0.534, 0.830

Overall 0.706 (0.300, 0.796)

0.0 0.2 0.4 0.6 0.8 1.0

Quality of Evidence for KQ 1 

The quality of evidence for each of the studies was evaluated using the modified QUADAS. A 

summary graph of the quality is provided in Figure 6. All included studies gave clearly described 

inclusion criteria for patients, used accurate reference standards, and conducted the reference 

standard within a reasonable time. The study authors decided that administering the reference 

standard within a month of the index test would not affect the validity given that both PD and 

GAD exist for longer periods of time. 

Most studies did not choose a cutoff a priori since many studies combined both the development 

and the validation of the scale—thus there was no established cutoff the investigators could use. 

This is a weakness because many of these validation studies have not been replicated. Also, 

many of the studies did not confirm the diagnosis using the reference standard in all, or in a 

random sample, of the patients, which could have contributed to partial verification bias. 
22

Performance Characteristics of Self-report Instruments for Diagnosing 

Generalized Anxiety and Panic Disorders in Primary Care Evidence-based Synthesis Program

Figure 6. QUADAS summaries for KQ 1

Withdrawals from studies explained

Missing data reported

Cut off chosen a priori

Reference test interpretation blinded

Index test interpretation blinded

Description of reference test adequate

Description of index test adequate

Independent reference standard

Same reference standard irrespective of index test

Verification of all or random sample

Approriate time between index and reference test

Accurate reference standard

Clear selection criteria

Spectrum of patients representative

Yes

No

Unclear

0% 20% 40% 60% 80% 100%

KQ 2. For questionnaires evaluated in KQ 1, which measures are most 

feasible to use in primary care settings? Specifically, what is the reading comprehension level, time required to complete, response format, 

and compatibility with telephone administration?

Studies of Efficacy 

Based on considerations of feasibility, there is limited evidence to recommend one screening 

tool over another. Instrument characteristics regarding feasibility are summarized in Table 2. 

Most of the instruments are brief, with the number of questions ranging from 3 to 11. The GAD7, BPDS, and 10-item PD instrument are at an average reading level (sixth to ninth grade), and 

the rest are at an easy reading level (third to fifth grade). The PRIME-MD has been validated 

for telephone administration and includes screening and diagnostic assessments for multiple 

common mental illnesses; the 3-item screening measure for anxiety disorders takes less than 

1 minute to complete.49 The average time taken to complete all sections of the PRIME-MD 
23

Performance Characteristics of Self-report Instruments for Diagnosing 

Generalized Anxiety and Panic Disorders in Primary Care Evidence-based Synthesis Program

diagnostic interview, triggered by positive responses on the associated screening component, 

was 8.4 minutes, which in a busy clinic was found to be too high to be practical.38 The following 

observations are about selected instruments that may be promising based on our qualitative 

assessment of their feasibility. 

For PD, the panic module of the PHQ is a short, self-report instrument with reading and 

comprehension at the third- to fifth-grade level and a simple yes/no response format. In the initial 

validation study, 42 percent of patients took less than 1 minute to complete the questionnaire, 43 

percent took 2 to 3 minutes, and 13 percent took more than 5 minutes. We did not identify any 

studies where the feasibility of telephone administration of the PHQ was studied. However, both 

the yes/no and the 4-item Likert response formats of the PHQ for depression have been used 

successfully with telephone administration.50 Considering feasibility as a function of performance 

of the instrument, in an average primary care clinic with a 7 percent prevalence of PD, a clinician 

seeing 100 patients per week can expect 6 to 7 patients to screen positive for PD, while 1 patient 

might not be identified by the instrument. Of patients who screen positive, almost all will have 

PD. One disadvantage is that the PHQ panic module, unlike the PHQ-9 for depression, has been 

studied rigorously in only one large sample.18

For generalized anxiety disorder, the GAD-7 is a short, well-performing instrument that assesses 

the severity of DSM -IV criteria symptoms over the past 2 weeks. Unlike the dichotomized 

response format of the panic module of the PHQ, the GAD-7 has four response options reflecting 

increased levels of severity. Both the 2-week time period and the graded response format might 

be useful in tracking response to treatment; however, responsiveness has not been studied 

formally. In a primary care clinic with GAD prevalence of 7.6 percent, 23 out of 100 patients will 

screen positive for GAD, while 1 patient with GAD might not be identified by the instrument. 

Of patients who screen positive, 16 to 17 will not have GAD after a diagnostic evaluation (false 

positives) and 6 to 7 will be true positives. Similar to the PHQ for panic, the GAD-7 has been 

validated with a diagnostic standard in only one sample.35

The SDDS-PC has the advantage of being a single instrument for both GAD and PD along 

with other mental disorders, which can be useful in a busy primary care setting. However, the 

combined sensitivity of the instrument is lower than the GAD-7 and PHQ. Further, the SDDS 

then requires confirmation through a nurse-administered computer module that is entered using 

the SDDS-PC software. No recent studies of the SDDS-PC have been published, and it is 

uncertain if the needed software has been updated for modern computer operating systems. We 

did not identify any studies that examined its compatibility with telephone administration. 

Quality of Evidence for KQ 2

We did not find studies that directly addressed the feasibility of the administration of the various 

instruments included in this review. Our assessment is based on the primary validation studies 

for KQ 1, discussed previously. Further, the assessment of reading level was not provided in the 

primary studies but was assessed by the authors using computer software. 
24

Performance Characteristics of Self-report Instruments for Diagnosing 

Generalized Anxiety and Panic Disorders in Primary Care Evidence-based Synthesis Program

KQ 3. For questionnaires evaluated in KQ 1, do the performance characteristics vary by gender, race, age group, or setting? 

Among the self-report measures evaluated, none of the GAD studies and only one PD study 

reported a subgroup analysis. Johnson and colleagues48 compared Caucasian to AfricanAmerican patients, while no studies reported subgroup analyses by gender, age group, or setting 

for PD. The absence of such data suggests that additional research is needed to determine if the 

various screening measures perform similarly among different subpopulations.

The report by Johnson and colleagues48 was good quality and differed from other studies only 

in the fact that the screening instrument was administered verbally to an unspecified number 

of participants who had difficulty reading. In the subgroup analysis, the study by Johnson and 

colleagues48 compared the performance characteristics of the BPDS for 216 Caucasian versus 

79 African-American primary care patients. Sensitivity data indicated that the questionnaire 

performed similarly in the two groups (Caucasian = .60, African American = .63, χ2

[1, N = 295] 

= 0.03, p = .87). The questionnaire’s specificity, however, significantly differed between the two 

groups (Caucasian = .25, African American = .60, χ2

[1, N = 295] = 0.75, p = .03). These results 

indicate that the BPDS performed better among Caucasians because at the optimal cutoff of 

10, there were more false positives among African Americans. Table 4 displays the sensitivity, 

specificity, positive likelihood ratio, and negative likelihood ratio from this study.

Qualitatively, there were differences in sensitivity and specificity among the two samples in the 

initial validation and cross-validation samples in the study by Broadhead and colleagues, which 

had different ethnic distributions.40 The instrument, methods and quality of the two studies were 

the same. The initial validation sample was 97.9 percent white and 0.3 percent black, while the 

cross-validation sample had 71.9 percent white and 26 percent black (p < 0.001). The sensitivity 

and specificity were 0.92, 0.54 and 0.86, 0.60 respectively. However, the two samples also varied 

significantly in terms of education and marital status; therefore, one cannot conclude that the 

differences are exclusively due to the racial distribution. 
25

Performance Characteristics of Self-report Instruments for Diagnosing 

Generalized Anxiety and Panic Disorders in Primary Care Evidence-based Synthesis Program

SUMMARY AND DISCUSSION

Most of the instruments included in this review have moderate to good operating characteristics 

for the detection of GAD and PD in unselected primary care settings. Only one study 

specifically addressed test performance in a sample presenting with somatic symptoms, while 

the others assessed test performance on all patients presenting to primary care settings. Most 

of the instruments included in this review appear feasible for use in primary care based on 

comprehension level. However, it is uncertain if these instruments are responsive to change 

in symptom status and the specific values associated with a clinically important response are 

unknown. There is preliminary evidence from one study that test performance can vary based on 

race. There is a current lack of evidence about differences in test performance based on gender 

and setting. 

SUMMARY OF EVIDENCE BY KEY QUESTION

A summary of the strength of evidence (SOE) for each KQ is in Table 5.

Table 5. Summary of strength of evidence by KQ

Number 

of studies 

(subjects)

Study quality Consistency Directness Precision SOE key finding

KQ 1: Performance characteristics in unselected primary care patients

GAD

6 (2638) Good Inconsistent (high 

heterogeneity) Indirect Imprecise Moderate

PD

7 (2513) Good Inconsistent (high 

heterogeneity) Indirect Imprecise Moderate

KQ 2: Feasibility for use in primary care settings SOE not formally 

assessed

9(3518) Good Not applicable Not applicable Not applicable

KQ 3: Variable performance by race Low

1 (295) Good Not applicable Direct

Sensitivity 

imprecise;

specificity 

precise

Equivalent sensitivity 

but lower specificity in 

African Americans

KQ 3: Variable performance by gender, age, and setting Insufficient

None Not applicable Not applicable Not applicable Not applicable No studies

Our search identified a large number of anxiety measures, but few measures had been studied in 

primary care populations. These measures had moderate to good operating characteristics, but 

unlike instruments used in the detection of other common mental illnesses such as depression or 

dementia, the operating characteristics have not been replicated in multiple samples. Even for 

the SDDS-PC—the only instrument evaluated in multiple studies—the versions studied were 

different, which might change the test performance. Based on operating characteristics, we did 

not find sufficient evidence to recommend a single validated self-report measure in the screening 
26

Performance Characteristics of Self-report Instruments for Diagnosing 

Generalized Anxiety and Panic Disorders in Primary Care Evidence-based Synthesis Program

of GAD or PD in primary care. Our results are congruent with the recent guidelines from 

the National Institute for Health and Clinical Excellence on GAD and PD.51 However, based 

on study quality, operating characteristics, precision of the estimates, potential for assessing 

response to change, and other feasibility issues, the most promising instruments are: the panic 

module of the PHQ, the GAD-7 and the SDDS-PC.

It is important to note that all of the instruments included in this review are for screening or 

case-finding purposes and do not by themselves make a diagnosis of GAD and PD. A diagnosis 

must be established through further evaluation by a primary care physician or by a mental 

health professional to whom a patient is referred. All but one of the instruments were validated 

by screening unselected primary care patients. Therefore, it is possible that they might perform 

differently if the instrument is used for case-finding in a high-risk population. For example, 

patients who present with unexplained GI symptoms might have a higher incidence or severity of 

anxiety disorders and, therefore, change the performance of the instrument. We did not identify 

any studies that validated instruments in a case-finding situation except for the unnamed 10-item 

instrument by Barsky and colleagues.36

While the studies included in the review provide information about the performance of various 

screening instruments, the impact of screening for GAD and PD on direct, patient-related 

outcomes is not known. In the absence of direct evidence on the impact of screening for anxiety, 

we considered a number of theoretical implications. These included patients who receive a true 

negative test result might be reassured that they do not have a mental illness, and if they present 

with a physical symptom, medical causes for this might be investigated further. For those who 

receive a false positive test result, there might be distress from an incorrect diagnosis, a greater 

likelihood of receiving ineffective treatment, or a delay in diagnosis of the true cause of their 

symptoms. Patients who receive a false negative test result might continue to remain anxious 

due to a delay in effective treatment and might undergo unnecessary diagnostic testing in a futile 

search for other medical causes for the anxiety symptoms. 

Though many of the above considerations are inferred rather than proven in studies, there is 

good evidence that anxiety disorders are underrecognized and that there are effective treatments 

for anxiety disorders, and thus screening has the potential to improve patient outcomes. One 

review of antidepressant medications found that the number needed to treat for GAD was 5.15 

and that antidepressants were significantly better that placebo.21 Similarly, various types of 

psychotherapy, including cognitive behavioral therapy and supportive therapy, have been shown 

to be better than treatment as usual for GAD.52 Both tricyclic antidepressants and selective 

serotonin reuptake inhibitors (SSRIs) have been shown to be superior to placebo in the treatment 

of PD.53 The mean effect size for acute treatment outcome for SSRIs relative to placebo was 0.55 

(a moderate effect) in one analysis of twelve randomized controlled trials.54 Psychotherapy as 

well as Internet-based therapy have been shown to be effective in treating PD.55,56 Although not 

a test of anxiety screening per se, at least two care management trials have shown that screening, 

coupled with effective primary care treatment, improves clinical outcomes for patients with a 

variety of anxiety disorders.16,20

When evaluating screening instruments for anxiety, it is important to consider that the criteria 

for GAD will change with the new DSM-V. While still not final, one proposed change is to 
27

Performance Characteristics of Self-report Instruments for Diagnosing 

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reduce the duration of symptoms of GAD from 6 to 3 months.57 In the primary validation study 

for the GAD-7, 67 percent of patients with scores greater than 10 (the proposed cut point) had 

symptoms for more than 6 months, and 96 percent had symptoms more than 1 month.35 With 

changes such as those mentioned above, the number of false positive results could decrease—but 

the number of false negatives would likely increase as the spectrum of disease is shifted to less 

chronic and potentially milder disease. Another proposed change is the addition of criterion D, 

which requires the presence of at least one of four avoidance behaviors that are not included in 

screening instruments based on DSM-III and DSM-IV editions. Thus, the implications on the 

performance characteristics of the current instruments are yet unknown. 

In summary, the U.S. Preventive Services Task Force has not issued a recommendation on 

screening for anxiety disorders. Our review shows several promising self-report instruments that 

could be used in VA for case-finding or evaluated in studies to determine the impact of systematic 

screening. Currently, the best clinical use of these measures in primary care would be for casefinding in patients with somatic symptoms or other factors that heighten the suspicion of an anxiety 

disorder. The reorganization of VA primary care services into Patient Aligned Care Teams (PACTs) 

that better integrate mental health services may present an opportunity to test the utility of anxiety 

screening measures that are coupled with high-quality diagnostic evaluation and treatment.

STRENGTHS AND LIMITATIONS

An important limitation of the current review is the lack of studies reporting on patient outcomes 

and societal impact. This has been recognized as a challenge in systematic reviews of diagnostic 

tests.58 We were unable to assess for publication bias because there is currently no reliable way 

to make this assessment for studies of diagnostic tests. Unlike studies of interventions, studies 

of diagnostic accuracy do not have a database such as ClinicalTrials.gov, where one can identify 

studies that were started but not published or which are ongoing—making the assessment of 

publication bias challenging. 

The studies included in this report were heterogeneous and thus prevented the statistical pooling 

of data. Further, the number of studies identified was small and therefore limited our ability to do 

subgroup analyses or meta-regressions to further explain observed heterogeneity. 

Our eligibility criteria were designed to exclude poor-quality studies (e.g., studies where the 

same person conducted the screening and criterion standard were excluded). As a result, all 

studies were of at least moderate quality. This means that some poor-quality studies that could 

provide low-level evidence on the topic might have been excluded from the review. However, we 

think that the solution to this would be to encourage high-quality validation studies. 

We also excluded studies of instruments in languages other than English and Spanish as well 

as articles that were published in languages other than English. While this might have excluded 

studies that were otherwise rigorous in methodology, we thought that these studies would not be 

directly applicable to the U.S. Veteran population and therefore not relevant for this report.59

Despite these limitations, this report was a highly structured and systematic review of the 

extant evidence. Our evidence synthesis was guided by a carefully designed standardized 

protocol, including a systematic search of research databases and relevant bibliographies, double 
28

Performance Characteristics of Self-report Instruments for Diagnosing 

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data abstraction, and use of validated criteria to assess the quality of identified studies. Our 

multidisciplinary team included expertise in internal medicine, primary care, psychiatry, and 

psychology. 

RECOMMENDATIONS FOR FUTURE RESEARCH

Replication of the results for instruments with promising characteristics is needed to verify initial 

findings. Specifically, rigorous validation of these instruments in the VHA is needed to ascertain 

applicability to the Veteran population. Given the limited number of measures evaluated in 

primary care populations, some investigators might reasonably develop and test novel anxiety 

instruments. An important consideration for such an undertaking is whether to develop general 

screens for significant anxiety symptoms, or more specific measures that are disease specific. 

Disease-specific measures that screen for a range of disorders would likely require more items 

and take longer to complete than general measures but could facilitate diagnostic evaluation 

and better treatment matching. General and disease-specific measures may also differ in 

responsiveness to change. Whether general or disease specific, these instruments would be tested 

ideally in unselected primary care populations, in patients presenting with symptoms commonly 

associated with anxiety disorders (e.g., chest pain or insomnia), and in patients with common 

mental illnesses (e.g., depressive disorder) as a screen for a co-occurring anxiety disorder. 

Further, given the preliminary data suggesting a possible difference by race, these studies should 

be powered for subgroup differences. The presence of a higher proportion of older adults in 

the Veteran population is another reason for validation of these instruments in the VHA since 

detection of anxiety disorders among older adults is known to be especially challenging.60

To evaluate the effects of screening for anxiety disorders, RCTs would be needed that include 

important patient outcomes such as effects on symptom status and patient functioning. Given the 

lack of benefit from screening for depression as a single intervention, and the high likelihood of 

similar findings for anxiety disorders, these trials would need to include a structured treatment 

component. There are no current recommendations for routine screening of primary care 

patients with anxiety disorders though prevalence is high and there are demonstrable impacts 

on functioning. Establishing patient outcomes, such as the percentage of patients who go 

on to receive treatment, their quality of life, and treatment side effects, would be important 

in determining if screening for anxiety in primary care has an overall positive impact to the 

individual patient and to the health system and society at large. Alternatively, if RCTs are 

considered not practical, a formal process—such as one suggested by the U.S. Preventive 

Services Task Force that evaluates and links evidence from screening and treatment studies—

could be used to evaluate the potential benefits of systematic screening. 

Another consideration for future studies is the inclusion of feasibility questions in the study 

design that assess the feasibility of the instrument in an average clinic. Specifically, patient 

receptiveness to completing screening, time taken, incompletion rates, and validity of 

instruments when administrated by telephone, handheld device, or Web compared with in-person 

screening are all critical to the comprehensive assessment of the effectiveness of an instrument. 

Responsiveness to change is another desirable characteristic and has been a key feature in 

the adoption of the PHQ-9 for depression. A similar evaluation of this property for anxiety 

instruments could promote uptake of these instruments into routine practice.
29

Performance Characteristics of Self-report Instruments for Diagnosing 

Generalized Anxiety and Panic Disorders in Primary Care Evidence-based Synthesis Program

Summary of Recommendations

• Though none of the included scales have sufficient evidence to be recommended as the 

single best option, the PHQ, GAD-7, and SDDS-PC are the most promising based on 

performance and applicability. Future research should focus on replicating early findings.

• These scales should be considered for incorporation into the VA Mental Health Assistant 

to facilitate use by providers. 

• The Primary Care–Mental Health Integration Program and PACT should consider anxiety 

measures that have the most evidence based on this review. 

• VA Research and Development should consider supporting studies to evaluate the 

performance of these instruments in the Veteran population since performance may differ 

in older adults with high rates of medical and psychiatric comorbidities. 

• The instruments should be evaluated for sensitivity to change to enable monitoring of 

illness and response to treatment. 

• Studies should include assessments of the feasibility and validity of different modes 

of administration and should be powered to detect differences in the performance of 

instruments based on age, race, setting or ethnicity.

CONCLUSIONS

In summary, there are several promising case-finding instruments with good performance for 

GAD and PD in primary care populations. However, there has been little replication of initial 

validation studies. There is also a lack of evidence about the feasibility of these instruments for 

telephone administration and their sensitivity to change. Though there is preliminary evidence 

that test performance can vary by race, this has not been addressed by any of the major validation 

studies, and there have been no followup studies on this question. Studies are needed that 

replicate initial findings and systematically study feasibility and variations in performance based 

on race, gender, and setting.
30

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