Skip to main content
Download PDF

Standardization of health outcomes assessment for depression and anxiety: recommendations from the ICHOM Depression and Anxiety Working Group

Quality of Life Research volume 26, pages 3211–3225 (2017)Cite this article

Abstract

Purpose

National initiatives, such as the UK Improving Access to Psychological Therapies program (IAPT), demonstrate the feasibility of conducting empirical mental health assessments on a large scale, and similar initiatives exist in other countries. However, there is a lack of international consensus on which outcome domains are most salient to monitor treatment progress and how they should be measured. The aim of this project was to propose (1) an essential set of outcome domains relevant across countries and cultures, (2) a set of easily accessible patient-reported instruments, and (3) a psychometric approach to make scores from different instruments comparable.

Methods

Twenty-four experts, including ten health outcomes researchers, ten clinical experts from all continents, two patient advocates, and two ICHOM coordinators worked for seven months in a consensus building exercise to develop recommendations based on existing evidence using a structured consensus-driven modified Delphi technique.

Results

The group proposes to combine an assessment of potential outcome predictors at baseline (47 items: demographics, functional, clinical status, etc.), with repeated assessments of disease-specific symptoms during the treatment process (19 items: symptoms, side effects, etc.), and a comprehensive annual assessment of broader treatment outcomes (45 items: remission, absenteeism, etc.). Further, it is suggested reporting disease-specific symptoms for depression and anxiety on a standardized metric to increase comparability with other legacy instruments. All recommended instruments are provided online (www.ichom.org).

Conclusion

An international standard of health outcomes assessment has the potential to improve clinical decision making, enhance health care for the benefit of patients, and facilitate scientific knowledge.

Introduction

Treatment of depression and anxiety disorders remains one of today’s most important health challenges. Combined, these two conditions represent the most years lived with disability of any disease [1]. Their direct treatment and indirect impact on other conditions contributes to a substantial portion of health care spending [2]. According to the most recent data available, depression in the United States alone costs society $210 billion per year, including direct medical costs (45%), suicide-related mortality costs (5%), and workplace costs (50%) [3].

A variety of treatment options have been proven effective in reducing symptom burden and improving functioning for patients with depression or anxiety [4]. These include several types and combinations of psychological interventions and antidepressant medications [5]. Although the general effectiveness of these treatments has been established, the questions of what works for whom and how to sequence and combine treatments remain to be addressed [6].

There are many well-validated health outcome assessments available to monitor the treatment process of mental health conditions [7]. However, utilization of empirical evidence in clinical practice to inform clinical decision making is still a rare occurrence.

There are a number of reasons why the empirical assessment of mental health domains is less common compared to the assessment of biomedical markers. Several methodological issues have been discussed, including insufficient measurement precision, limited measurement range, high respondent burden, inadequate physician reports, and the impracticality of using paper-and-pencil assessments within daily clinical routine [8]. Another important issue is that for many of the most relevant mental health domains there are several competing tools, and even if the same constructs are measured results from different instruments are difficult to compare [9]. Like in many other fields, lack of standardization seriously hinders communication among patients, practitioners, and scientists.

To date, the most comprehensive effort to initiate standardized outcome assessments for the treatment of mental health disorders has come from the United Kingdom (“Improving Access to Psychological Therapies” (IAPT)) [10]. Routine collection of patient-reported outcomes (PRO) was coupled to a new program of expanding access to psychotherapists [11]. The success of the program (63.7% achieved reliable improvement or recovery) was celebrated, and has supported the case for its funding and led to similar initiatives in other health systems [12,13,14]. Unfortunately, as outcome monitoring initiatives proliferate, no consensus exists as to which measures to include in such programs and many are now developing without awareness of existing global practices. Lack of data standards between programs hinders comparisons of program effectiveness or opportunities for data aggregation and research.

To address this need for a consolidated recommendation on what outcomes are essential to track for patients with depression and anxiety, we convened an international, multi-disciplinary Working Group under the leadership of the International Consortium for Health Outcomes Measurement (ICHOM).

Method

The Working Group

A Working Group was organized by ICHOM (www.ichom.org), a non-profit organization focused on the development of standardized datasets of outcomes and case-mix factors for use in clinical practice. Working Group members were selected by purposive sampling [15] based on their expertise with the aim of representing a wide clinical, scientific and cultural background. Members (n = 24) included patient representatives (LL, DS), measurement experts (EdB, EH, SN, PP, CS, MR), clinical (LB, TF, DM, RR, GR, KWB), social and public health researchers (AC, DC, PE, MK, AL, VP), clinicians (AO, LB, TF, MK, AL, DM, HP, RR, GR, MR) and coordinators (LvM, CSt). The final group included members from twelve countries: Australia, Brazil, Canada, Chile, Germany, India, Japan, the Netherlands, Sweden, Uganda, the United Kingdom, and the United States. Patient representatives participated in the development process of the standard set in every step, with equal voting rights, and contributed actively to the discussion.

Development of the standard set

A structured consensus-driven modified Delphi technique was used to develop the ICHOM standard set. The Delphi approach is an iterative, multistage process with the aim of transforming opinion into group consensus [16]. The technique was developed by ICHOM and successfully applied to create outcome standards for a growing number of health conditions (www.ichom.org) [17,18,19,20,21,22,23]. Over a period of eleven months, the Working Group met monthly by teleconference. Preparation of the meetings and surveys, guided by the ICHOM framework, followed a pre-defined set of activities: (1) prioritizing outcome domains, (2) selecting outcome measures, (3) prioritizing case-mix domains, and (4) selecting case-mix definitions. Prioritization of outcome domains and case-mix variables was carried out by allocating all variables to the outcome measures hierarchy developed by Porter [24]. In preparation of the teleconference calls, a comprehensive literature search using common databases (PubMed, EMBASE, Medline, PsycINFO) and a specific database for clinical outcome assessments (www.proqolid.org) was conducted for each outcome domain or case-mix factor, augmented by interviews with the patient representatives in the Working Group and selected experts (see Fig. 1 for a detailed search strategy, see Online Appendix 1 for a list of all instruments found, see “Results” section for a summary). During the teleconferences, the collated evidence was presented. Following each teleconference, the discussion content (qualitative data) was collated into online surveys (quantitative data). Working Group members were then asked to submit their feedback; final votes were carried out via an anonymous web questionnaire. Content was included if a two-third majority vote (66%) was reached, items rated below 50% were excluded, results between 50 and 66% were subject to further discussion in subsequent teleconferences and re-voted upon until consensus for in- or exclusion was reached. Results were fed back to the group in summarized form. Within eleven months of the duration of the project, seven surveys were conducted with response rates between 70 and 100%. Online surveys were compiled using Qualtrics® online survey platform (www.qualtrics.com). The final standard set was approved by all members of the Working Group. Explanation of the consensus process (Online Appendix 2) and voting results (Online Appendix 3–7) are provided as online supplements.

Fig. 1
figure 1

Search strategy and selection process for instruments considered for the D+A standard set (modified PRISMA flow diagram). Stepwise selection based on literature review, monthly teleconference calls, and subsequent online surveys. Initial search term for scientific databases: “(depress* [TITLE] OR anxiety [TITLE] OR PTSD [TITLE] OR post-traumatic stress disorder [TITLE] OR dysthymia [TITLE] OR GAD [TITLE] OR SAD [TITLE] OR agoraphobia [TITLE] OR panic [TITLE] OR obsessive compulsive [TITLE] OR OCD [TITLE]) AND (instrument [TITLE] OR patient-reported outcome [TITLE] OR questionnaire [TITLE])”. IAPT UK = Improving Access to Psychological Therapies program by the National Institute of Health in the United Kingdom; PHQ-9 = Patient Health Questionnaire 9-item version; GAD-7 = PHQ module for assessment of General anxiety disorder, 7-items; WHODAS = WHO Disability Assessment Schedule

Full size image

In selecting measures for prioritized domains, available measurement tools covering the selected domains were reviewed. If there were no validated tools for prioritized case-mix variables, ad hoc items were generated based on existing instruments (IAPT UK [10], Canadian Community Health Survey [25]) modified to be appropriate for low health literacy levels [26]. This was not the case for outcome instruments (i.e., scales) but only for 13 single items included for case-mix adjustment collecting information about the patients’ medical history, such as the duration of symptoms or prior episodes of their disease (Table 1). Pre-defined inclusion criteria for instruments or single items comprised the following criteria: (1) extent of domain coverage (extent to which the instrument or item covers the a priori defined domains, for example, whether a questionnaire or set of questionnaires measuring functioning completely covered physical, social and occupational functioning or only partially), (2) psychometric properties (validity, reliability, responsiveness), (3) clinical interpretability (if instrument was validated with clinical and non-clinical samples), (4) feasibility (scale length, complexity, health literacy level), (5) number of available translations (including existence of cross-cultural validation), and (6) absence of license fees. In addition, instruments had to be available at least in English, and instruments and items had to be applicable for patients from age 14 and above. The selection process for the outcome measures is further illustrated in Fig. 1.

Table 1 Adapted or newly developed items in the standard set
Full size table

Results

Scope

Given the aim to recommend standard assessments for depression and anxiety, we first defined the target disorders. The group decided to not limit the recommendations to a single disorder but to consider the following spectrum of diseases: Major Depressive Disorder, Depressive Disorder—Not Otherwise Specified, Adjustment Disorder/Depressive Adaptive Disorder, Dysthymia, General Anxiety Disorder, Social Anxiety Disorder, Agoraphobia, Panic Disorder, Post-Traumatic-Stress Disorder, and Obsessive Compulsive Disorder. The aim was that the suggested outcome variables should be responsive to therapy effects from established interventions. Recommendations were limited to adults including adolescents above the age of 14 years as there was agreement across working group members that onset of depression in younger people often occurs before the age of 18 years. Evidence suggests good validity for common adult measures for adolescents (see Online Appendix 3) [27].

Outcome domains

Following the ICHOM framework [24], the Working Group agreed on four general treatment outcomes: (a) symptom burden, (b) functioning, (c) disease progression and treatment sustainability, and (d) potential side effects of treatments (Tables 2, 3, Online Appendix 4, 6, reference guide at www.ichom.org).

Table 2 General outcome measures in the depression and anxiety standard set
Full size table
Table 3 Assessment sets, domains, number of items, and estimated time for completion of the depression and anxiety standard set
Full size table

Prioritization of treatment outcomes (teleconference #1, see Online Appendix 4) based on Porter’s outcome measures hierarchy [24, 28] resulted in the exclusion of the domains “survival” and “long-term consequences of therapy” as they were felt to be less relevant for depression or anxiety. “Degree of health achieved or maintained,” “Time to recovery,” “disutility of care or treatment process,” and “sustainability of health” were included resulting in 13 final outcomes (i.e., symptoms of depression/anxiety, social functioning, medication side effects, etc.; see Online Appendix 4).

A comprehensive literature review to find potential instruments measuring these outcomes was carried out between July 2nd and 21st, 2014 (Fig. 1). After removing duplicates, instruments for children, instruments in other languages than English, and instruments assessing depression or anxiety as a comorbidity of other disorders (e.g., depression following a stroke), a total of 80 instruments were retained which assess depressive, general or specific anxiety symptoms, or disease-related functioning. These instruments were reduced further based on aforementioned inclusion criteria resulting in 23 instruments (see Fig. 1 and “Methods” section).

Symptom burden

Fifteen scales were analyzed in detail based on aforementioned considerations (Fig. 1, Online Appendix 4, 6), discussed within the working group (teleconference #2) and voted on. The depression subscale of the Patient Health Questionnaire [29], the 9-item PHQ-9, was selected to measure depressive symptoms for patients with depressive disorders, and its anxiety subscale Generalized Anxiety Disorder 7-Item scale (GAD-7) [30] was chosen to measure anxiety symptoms in patients with anxiety spectrum disorders. These scales were selected due to their excellent psychometric properties, the large amount of translations available, the availability of population norms, cross-cultural validation for a large number of languages (www.phqscreeners.com), and their acceptance in the scientific community [30].

In making this recommendation, we recognized that the GAD-7 is a generic measure of anxiety developed primarily to assess generalized anxiety disorders (GAD) and may fail to properly measure the impact of treatment on more specific anxiety disorders (e.g., in cases where avoidance reduces the anxious affect as in housebound agoraphobia or in cases with intrusive memories, compulsions or avoidance). For this reason, institutions desiring a more comprehensive assessment of specific anxiety disorders may wish to complement the GAD-7 with additional instruments. In Online Appendix 8 and 9 (online supplements), the instruments used in the IAPT program are listed for reference purposes. We did not include these measures as part of the formal standard set as most remain research instruments and would benefit from additional optimization (e.g. reduction of item burden) before use in clinical practice.

Recent studies and initiatives have emerged using item response theory methods to develop large item banks allowing to score different instruments measuring the same construct on one common—instrument-independent—metric [9, 31, 32]. These item banks provide an opportunity to move away from instrument defined measurements to construct defined measurements; similar to the assessment of biomedical markers, where for example, measurement of an HbA1c level is independent from the manufacture of the laboratory device. There are several efforts in this respect (www.common-metrics.org), the one receiving the most public support today is the development of the Patient-Reported Outcomes Measurement Information System (PROMIS®, www.nihpromis.org) [33], cross-funded by all National Institutes of Health the U.S.. Thus, to facilitate comparisons of our current recommendations with other existing instruments and to ensure its forward-compatibility we propose that raw scores of the PHQ-9, GAD-7 should be converted to the common-metric provided by the PROMIS initiative (referred to as “PROMIS metric” throughout the text). This can be easily achieved using look-up tables (Table A2 in [31, 32], also included in the reference guide) or freely available software (www.common-metrics.org).

Functioning

Given the large body of evidence suggesting that depression and anxiety disorders are associated with impaired functioning, the Working Group recommended its inclusion in the standard set [34]. However, functioning is a broad domain with often lengthy assessments, which reduces feasibility in clinical practice, particularly in community-based, frontline care settings. To counterbalance these considerations, the Working Group recommended a more comprehensive assessment at baseline and annual follow-up and a shorter one-item measure during treatment. Due to its availability in many languages and general population reference data, we selected the World Health Organization Disability Schedule 2.0 (WHODAS 2.0) 12-item self-rating version to measure physical, social, and occupational functioning (Tables 2, 3, Online Appendix 8, 9) at baseline and at annual follow-ups [35, 36]. For ongoing treatment, a single item from the PHQ-9 (additional item) that assesses the difficulties of daily life functioning patients attribute to their symptoms, which has been found to correlate highly with other longer functioning scales was selected [29, 34].

Disease progression and treatment sustainability

Depression and anxiety are remitting and relapsing in nature, prompting the Working Group’s desire to capture the time to recovery and sustainability of recovery over time. We recommend capturing time to recovery using the reliable change index (RCI) on symptom burden assessments that are collected throughout the process of care. The RCI helps determine whether changes in instrument score indicate a clinically meaningful (reliable) alteration of symptoms rather than an artifact of measurement error (Table 2, Online Appendix 8) [37, 38]. To assess sustainability of recovery, in addition to the annual assessment of symptom burden and functioning, we developed a single item regarding patients’ self-report of depressive episodes during the past year (Table 1, #6, and #12). Workplace absenteeism, a primary driver of overall economic costs was also prioritized for inclusion, defined as the number of days missed during the last month due to illness (Table 1, #11). Finally, we prioritized patients’ perceived success of treatment as this appraisal is one of the best indicators for good treatment outcome (Table 1, #9) [39].

Treatment side effects

Primarily informed by the experience of patients in the Working Group, treatment side effects were included in the Standard Set. Mild side effects with intake of antidepressants are very common and we recognize that clinicians often accept these side effect profiles, but the awareness of side effects and the improved ability to project which side effects a patient was most likely to experience, was considered of sufficient importance to warrant their inclusion. As no short but well-validated instrument was found for assessing treatment side effects, we developed a simple assessment for proposed use and future validation (Table 1, #10).

Baseline characteristics

A primary goal of this effort is to ensure comparisons of treatment outcomes across providers. As such, we sought to identify a minimum set of baseline characteristics to allow for future case-mix adjustments. In identifying case-mix factors, the Working Group agreed on the four following areas: demographics, baseline functional status, baseline clinical status, and prior treatments.

Demographic factors

Age, sex, socioeconomic status (SES), and living situation were included as key demographic variables and defined in line with other ICHOM Standard Sets (Table 1, #1–4). Patient-reported highest level of education can be collected as a surrogate measurement of SES [40], as patients generally feel comfortable reporting this information and it can be compared across countries using the International Standard Classification of Education [41]. Individual countries may elect to complement education level with additional measures of SES if available, such as income-based or postal-code based measures. Although influence of living situation on outcome has not yet been systematically investigated, clinical experience indicates that it influences treatment effect. We recommend collecting living situation using a simple assessment routinely collected across the National Health Service PROMs program [42].

Baseline functional status

We recommend collecting all outcome measures at baseline, including the WHODAS 2.0 to allow for changes in status to be calculated over time. We also recommend collecting work (Table 1, #5) status and social support at baseline as these factors are predictors of treatment success [43]. As with other ICHOM Standard Sets, a single item was used to assess work status. To capture social support, we recommend using four items of the “Medical Outcomes Study—Social Support Survey” (MOS-SSS) [44]. This instrument yielded a stable factor structure even with a reduced number of items and within assessments in low and middle income countries [45].

Baseline clinical status

To allow segmentation of patients for analyses, we recommend recording clinical diagnoses using established classification systems (i.e., ICD or DSM). In addition, we recommend capturing mental and general medical comorbidities, as they have been shown to influence treatment outcomes [46]. We recommend using the Self-administered Comorbidity Questionnaire (SCQ) extended by a list of mental comorbidities to capture these factors [47, 48]. Although relatively unknown, the SCQ has shown to predict functional outcome with equivalence to medical record based Charlson Comorbidity Index [49]. Patients’ expectation regarding success of their treatment is also strongly related to treatment outcome and we elected to include an adapted single item from the credibility/expectancy questionnaire: “How successful do you think will the therapy be in reducing your symptoms?” [39]. Adaptation of this questionnaire to a single item in previous studies has shown good applicability [50].

Prior treatment and course of disease

Prior treatments and duration of disease have also been shown to influence treatment outcomes [51]. We developed a single item to capture the use of mental health treatments (i.e., medication, psychotherapy, or other) during the last year (Table 1, #8) as well as single items on prior episodes of depression and duration (in months) of the current episode (Table 1, #6–7).

Assessment time points

Throughout the consensus process, the assessment scheme was revised twice. Initial online voting supported the recommendation of monthly assessments during treatment, assessments on every third month during the first year after completion of treatment, and a two-year follow-up period. Our final recommendation arose from the thinking that beginning and duration of treatments may vary significantly across patients and that completeness of pre-post treatment data could be improved by frequent assessments during active treatment. In addition, some fixed (annual) assessments would allow for better comparability between patient groups.

Finally, we designed the standardized set with a baseline assessment and two follow-up modules, one focused on capturing changes in symptom burden during active treatment (treatment monitoring (TM)), and a more comprehensive annual outcome assessment (annual assessment (AA)) to allow for research and benchmarking activities with data collected at the same time points. The IAPT program has shown that regular data collection during a course of treatment helps guide therapy and ensures very high (up to 97%) pre-post data completeness [52]. In order to be more helpful in clinical practice, we designed the short TM as a set of variables that are very succinct and focused to inform clinical decision making (Fig. 2). Although it is usually recommended the AA be collected at least annually, we do encourage institutions that wish to conduct more frequent follow-up to do so. As some baseline characteristics may change over the course of the treatment process (living status, work status, comorbidities, prior and current treatment, social support, and outcome expectancy), we also recommend they be updated annually.

Fig. 2
figure 2

Follow-up timeline for the depression and anxiety standard set. Proposed and optional assessment time points for subsets included in the ICHOM depression and anxiety standard set. BL baseline assessment, TM treatment monitoring, AA annual assessment

Full size image

Reference guide

A freely available reference guide that further describes each instrument and provides detailed information about how to calculate scale scores including PROMIS conversion tables and RCIs is accessible online (www.ichom.org).

Discussion

As health systems around the world shift their attention to measuring the value (outcomes relative to cost) of the services they fund and deliver, there is a need to align on what constitutes the outcomes most relevant to patients. An internationally standardized dataset would satisfy the diverse needs of patients, clinicians, researchers, and policy makers, including: (1) improved communication and decision making between patients and their providers on what treatment plans would best suit their needs, (2) improved monitoring of the impact of treatments across populations of patients, including informing comparative effectiveness studies, and (3) consumer-facing comparisons of the relative outcome performance of different care facilities.

For patients with mental health disorders, such a dataset will mainly rely on the patients’ self-assessment. However, although patient-reported outcomes have been receiving more attention over the last years, the measurement of PROs is still not as established as the measurement of biomedical markers. One reason for this is a lack of standardization. The goal of the present initiative was to recommend a basic set of outcome assessments for depression and anxiety disorders that can align existing and newly developing initiatives and facilitate more global collaboration in the move towards outcome- or value-based systems.

In contrast to other consensus efforts, patient representatives (LL, DS) were full members of the working group; hence, they were involved in the formal development process of the standard set as well as preparation of the manuscript. They participated in every teleconference and the online surveys and had the same voting rights as any other member of the group. For example, inclusion of medication side effects for follow-up assessments was regarded as important by patient representatives and finally included in the standard set. Thus, patient representatives were heavily involved in the decision-making process.

Limitations

Group of experts

Our intention was to include a wide spectrum of patient representatives, clinicians, and researchers from all continents in our group, resulting in a group of 24 members to discuss the different steps of this proposal; however, there are many other well-known scientists, clinicians or patient representatives that could have provided their valuable expertise, and different opinions might have been expressed. By extensive literature reviews, and applying a Delphi technique to document our decision-making process, we strived to achieve a high level of transparency. Nevertheless, like any similar efforts, a different group of participants could have agreed on different recommendations.

Outcome measures

The main challenge during the entire project was to propose a set of domains and variables comprehensive enough to be meaningful but short enough to be implementable on a large scale and in a variety of settings. With a focus on feasibility of implementation, we focused on measures considered to be most helpful in the clinical setting. From a scientific perspective, a larger set of domains would be of interest, and we consider this set a foundation upon which other measures might be added for specific research questions.

Another limitation is the inclusion of adhoc items, primarily assessing parts of the patients’ medical history to allow for case-mix adjustment across patient groups. Including these types of items (i.e., to assess prior episodes of the disorder or current work status) was essential for the current value-based approach. Prospectively collected data over the next years will facilitate validation of the proposed standard set, which will deliver crucial information on whether the chosen variables work or whether potential adjustments are necessary.

During the consensus process, there were extensive discussions within the working group as to whether the different depressive disorders such as major depression, dysthymia, or double depression can be assessed with one single tool to measure depressive symptoms (i.e., the PHQ-9), and in particular whether anxiety symptoms from different anxiety spectrum disorders, like General Anxiety Disorder, Social Anxiety Disorder, Agoraphobia, Panic Disorder, Post-Traumatic-Stress Disorder, or Obsessive Compulsive Disorder, can be assessed with another single tool either (i.e., the GAD-7). To prioritize simplicity and standardization we recommended, nevertheless, this as the rather parsimonious approach. The measures proposed to assess symptom burden, i.e., the PHQ-9 and GAD-7, have been widely used to assess different disease states and manifestations of depression and anxiety, respectively [53]. However, we are aware that in particular for patients with phobic disorders additional questionnaires may be required to appropriately document the symptom burden of the individual patient (Online Appendix 8 and 9; online supplements).

Another important consideration was the accessibility of the tools worldwide. This criterion excluded many alternative tools. However, the suggestion to report the raw scores of the PHQ-9 and GAD-7 on a common metric based on modern IRT-methods should allow continued adoption of future instruments that are compatible with such an approach. Among several methods to make scores from different instruments comparable which have been described in the literature [54, 55], we decided to recommend an IRT-approach as this promises to provide an instrument-independent metric for many tools at one time [9], and not just a method to compare one score to another, like an regression approach. Among the few IRT-based metrics which are available today, we decided to use the PROMIS metric, as it received the most public support over the last decade, and is in our opinion the most likely to be widely accepted in the future [31, 32]. However, we are aware that in particular for the more heterogeneous anxiety construct there are still several scientific issues which are currently discussed from those applying these methods [56].

Stakeholders

We recognized from the beginning that a single standardized set cannot meet the expectations of all potential stakeholders. Our focus was first on meeting the needs of practicing clinicians to better communicate with their patients and assess the impact of their care. Other stakeholders, such as administrators and economists, may have preferred metrics that are used across diseases for utility measurement (e.g., EQ-5D) [57]. For clinicians, utility-based instruments are insufficiently sensitive to change and unrelated to the disease construct, making interpretability and actionability in the clinical context more difficult. Certainly, research programs wishing to compare utilities alongside disease-focused impact would be welcome to add such measures to their battery of assessments.

Evaluation

Measures and items included in the standard set have been carefully chosen with regard to their psychometric properties and availability of validation studies. Some new items had to be adapted or developed to allow for case-mix adjustment (Table 1), evaluation of these items is pending. In addition, as the standard set had just been translated into other languages, future cross-cultural validation studies of the entire set are warranted. For the main outcome instruments such as PHQ-9, GAD-7, and WHODAS 2.0 available evidence has already shown cross-cultural validity [58,59,60,61].

Implementation

The Working Group recognized that many implementation challenges remain to achieve the anticipated impact of this set. A number of pilot institutions, including selected members of this Working Group, are currently implementing the set with the intention of sharing their experience on the cost and quality impact. In many health systems, the collection of outcomes data is becoming more routine through the use of health information technology, which should facilitate the adoption of these recommendations. Moreover, in health systems with low adoption of such technologies, paper and pencil still provides a cheap and effective mode of data capture. The recommendation of license-free measures further supports adoption.

Conclusion

Through the efforts reported in this paper, we defined a parsimonious set of patient-reported outcome measures recommended to be applied in patients with depression and anxiety disorders. We hope this can become an important step towards improving the quality and value of care for persons living with depression and anxiety around the world.

References

  1. Vos, T., Flaxman, A. D., Naghavi, M., Lozano, R., Michaud, C., Ezzati, M., et al. (2012). Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet, 380(9859), 2163–2196. doi:10.1016/S0140-6736(12)61729-2.

    Article  PubMed  Google Scholar 

  2. Ferrari, A. J., Charlson, F. J., Norman, R. E., Patten, S. B., Freedman, G., Murray, C. J., et al. (2013). Burden of depressive disorders by country, sex, age, and year: Findings from the global burden of disease study 2010. PLoS Medicine, 10(11), e1001547. doi:10.1371/journal.pmed.1001547.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Greenberg, P. E., Fournier, A. A., Sisitsky, T., Pike, C. T., & Kessler, R. C. (2015). The economic burden of adults with major depressive disorder in the United States (2005 and 2010). Journal of Clinical Psychiatry, 76(2), 155–162. doi:10.4088/JCP.14m09298.

    Article  PubMed  Google Scholar 

  4. Cuijpers, P., Sijbrandij, M., Koole, S. L., Andersson, G., Beekman, A. T., & Reynolds, C. F., 3rd. (2014). Adding psychotherapy to antidepressant medication in depression and anxiety disorders: A meta-analysis. World Psychiatry, 13(1), 56–67. doi:10.1002/wps.20089.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Middleton, H., Shaw, I., Hull, S., & Feder, G. (2005). NICE guidelines for the management of depression. BMJ, 330(7486), 267–268. doi:10.1136/bmj.330.7486.267.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Fonagy, P. (2010). Psychotherapy research: do we know what works for whom? British Journal of Psychiatry, 197(2), 83–85. doi:10.1192/bjp.bp.110.079657.

    Article  PubMed  Google Scholar 

  7. Pilkonis, P. A., Choi, S. W., Reise, S. P., Stover, A. M., Riley, W. T., Cella, D., et al. (2011). Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS(R)): Depression, anxiety, and anger. Assessment, 18(3), 263–283. doi:10.1177/1073191111411667.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Rose, M., & Bezjak, A. (2009). Logistics of collecting patient-reported outcomes (PROs) in clinical practice: An overview and practical examples. Quality of Life Research, 18(1), 125–136. doi:10.1007/s11136-008-9436-0.

    Article  PubMed  Google Scholar 

  9. Wahl, I., Lowe, B., Bjorner, J. B., Fischer, F., Langs, G., Voderholzer, U., et al. (2014). Standardization of depression measurement: A common metric was developed for 11 self-report depression measures. Journal of Clinical Epidemiology, 67(1), 73–86. doi:10.1016/j.jclinepi.2013.04.019.

    Article  PubMed  Google Scholar 

  10. Clark, D. M. (2011). Implementing NICE guidelines for the psychological treatment of depression and anxiety disorders: The IAPT experience. International Review of Psychiatry, 23(4), 318–327. doi:10.3109/09540261.2011.606803.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Gyani, A., Shafran, R., Layard, R., & Clark, D. M. (2013). Enhancing recovery rates: Lessons from year one of IAPT. Behaviour Research and Therapy, 51(9), 597–606. doi:10.1016/j.brat.2013.06.004.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Sveus – Swedish National collaboration for value-based reimbursement and monitoring of health care (2014). Retrieved November 26, 2014, from http://www.sveus.se/

  13. Boswell, J. F., Kraus, D. R., Miller, S. D., & Lambert, M. J. (2015). Implementing routine outcome monitoring in clinical practice: Benefits, challenges, and solutions. Psychotherapy Research, 25(1), 6–19. doi:10.1080/10503307.2013.817696.

    Article  PubMed  Google Scholar 

  14. Kramer, T. L., Evans, R. B., Landes, R., Mancino, M., Booth, B. M., & Smith, G. R. (2001). Comparing outcomes of routine care for depression: the dilemma of case-mix adjustment. Journal of Behavioral Health Services and Research, 28(3), 287–300.

    CAS  Article  PubMed  Google Scholar 

  15. Polit-O’Hara, D., & Hungler, B. P. (1997). Essentials of nursing research: Methods, appraisal, and utilization. New York: Lippincott.

    Google Scholar 

  16. Hasson, F., Keeney, S., & McKenna, H. (2000). Research guidelines for the Delphi survey technique. Journal of Advanced Nursing, 32(4), 1008–1015.

    CAS  PubMed  Google Scholar 

  17. Clement, R. C., Welander, A., Stowell, C., Cha, T. D., Chen, J. L., Davies, M., et al. (2015). A proposed set of metrics for standardized outcome reporting in the management of low back pain. Acta Orthopaedica, 86(5), 523–533. doi:10.3109/17453674.2015.1036696.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Mahmud, I., Kelley, T., Stowell, C., Haripriya, A., Boman, A., Kossler, I., et al. (2015). A proposed minimum standard set of outcome measures for cataract surgery. JAMA Ophthalmology, 133(11), 1247–1252. doi:10.1001/jamaophthalmol.2015.2810.

    Article  PubMed  Google Scholar 

  19. Mak, K. S., van Bommel, A. C., Stowell, C., Abrahm, J. L., Baker, M., Baldotto, C. S., et al. (2016). Defining a standard set of patient-centred outcomes for lung cancer. European Respiratory Journal. doi:10.1183/13993003.02049-2015.

    PubMed  PubMed Central  Google Scholar 

  20. Martin, N. E., Massey, L., Stowell, C., Bangma, C., Briganti, A., Bill-Axelson, A., et al. (2015). Defining a standard set of patient-centered outcomes for men with localized prostate cancer. European Urology, 67(3), 460–467. doi:10.1016/j.eururo.2014.08.075.

    Article  PubMed  Google Scholar 

  21. McNamara, R. L., Spatz, E. S., Kelley, T. A., Stowell, C. J., Beltrame, J., Heidenreich, P., et al. (2015). Standardized outcome measurement for patients with coronary artery disease: Consensus from the International Consortium for Health Outcomes Measurement (ICHOM). Journal of the American Heart Association. doi:10.1161/JAHA.115.001767.

    PubMed  PubMed Central  Google Scholar 

  22. Morgans, A. K., van Bommel, A. C., Stowell, C., Abrahm, J. L., Basch, E., Bekelman, J. E., et al. (2015). Development of a standardized set of patient-centered outcomes for advanced Prostate cancer: An international effort for a unified approach. European Urology, 68(5), 891–898. doi:10.1016/j.eururo.2015.06.007.

    Article  PubMed  Google Scholar 

  23. Rodrigues, I. A., Sprinkhuizen, S. M., Barthelmes, D., Blumenkranz, M., Cheung, G., Haller, J., et al. (2016). Defining a minimum set of standardized patient-centered outcome measures for macular degeneration. American Journal of Ophthalmology, 168, 1–12. doi:10.1016/j.ajo.2016.04.012.

    Article  PubMed  Google Scholar 

  24. Porter, M. E. (2010). What is value in health care? New England Journal of Medicine, 363(26), 2477–2481. doi:10.1056/NEJMp1011024.

    CAS  Article  PubMed  Google Scholar 

  25. Statistics Canada: Canadian Community Health Survey - Annual Component (CCHS) (2014). Retrieved November 11, 2014 from http://www.statcan.gc.ca/concepts/index-eng.htm

  26. Kickbusch, I. S. (2001). Health literacy: Addressing the health and education divide. Health Promotion International, 16(3), 289–297.

    CAS  Article  PubMed  Google Scholar 

  27. Allgaier, A. K., Pietsch, K., Fruhe, B., Sigl-Glockner, J., & Schulte-Korne, G. (2012). Screening for depression in adolescents: Validity of the patient health questionnaire in pediatric care. Depress Anxiety, 29(10), 906–913. doi:10.1002/da.21971.

    Article  PubMed  Google Scholar 

  28. Porter, M. E., & Teisberg, E. O. (2006). Redefining health care: Creating value-based competition on results. Boston: Harvard Business School Press.

    Google Scholar 

  29. Spitzer, R. L., Kroenke, K., & Williams, J. B. (1999). Validation and utility of a self-report version of PRIME-MD: The PHQ primary care study. Primary Care Evaluation of Mental Disorders. Patient Health Questionnaire. JAMA, 282(18), 1737–1744.

    CAS  Article  PubMed  Google Scholar 

  30. Spitzer, R. L., Kroenke, K., Williams, J. B., & Lowe, B. (2006). A brief measure for assessing generalized anxiety disorder: The GAD-7. Archives of Internal Medicine, 166(10), 1092–1097. doi:10.1001/archinte.166.10.1092.

    Article  PubMed  Google Scholar 

  31. Choi, S. W., Schalet, B., Cook, K. F., & Cella, D. (2014). Establishing a common metric for depressive symptoms: Linking the BDI-II, CES-D, and PHQ-9 to PROMIS depression. Psychological Assessment, 26(2), 513–527. doi:10.1037/a0035768.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Schalet, B. D., Cook, K. F., Choi, S. W., & Cella, D. (2014). Establishing a common metric for self-reported anxiety: Linking the MASQ, PANAS, and GAD-7 to PROMIS Anxiety. Journal of Anxiety Disorders, 28(1), 88–96. doi:10.1016/j.janxdis.2013.11.006.

    Article  PubMed  Google Scholar 

  33. Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., et al. (2010). The patient-reported outcomes measurement information system (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008. Journal of Clinical Epidemiology, 63(11), 1179–1194. doi:10.1016/j.jclinepi.2010.04.011.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Wells, K. B., Stewart, A., Hays, R. D., Burnam, M. A., Rogers, W., Daniels, M., et al. (1989). The functioning and well-being of depressed patients. Results from the Medical Outcomes Study. JAMA, 262(7), 914–919.

    CAS  Article  PubMed  Google Scholar 

  35. Andrews, G., Kemp, A., Sunderland, M., Von Korff, M., & Ustun, T. B. (2009). Normative data for the 12 item WHO Disability Assessment Schedule 2.0. PLoS ONE, 4(12), e8343. doi:10.1371/journal.pone.0008343.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Ustun, T. B., Chatterji, S., Kostanjsek, N., Rehm, J., Kennedy, C., Epping-Jordan, J., et al. (2010). Developing the World Health Organization disability assessment schedule 2.0. Bulletin of the World Health Organization, 88(11), 815–823. doi:10.2471/BLT.09.067231.

    Article  PubMed  PubMed Central  Google Scholar 

  37. de Beurs, E., Barendregt, M., de Heer, A., et al. (2016). Comparing methods to denote treatment outcome in clinical research and benchmarking mental health care. Clinical Psychology and Psychotherapy, 23, 308–318.

    Article  PubMed  Google Scholar 

  38. Jacobson, N. S., & Truax, P. (1991). Clinical significance: a statistical approach to defining meaningful change in psychotherapy research. Journal of Consulting and Clinical Psychology, 59(1), 12–19.

    CAS  Article  PubMed  Google Scholar 

  39. Devilly, G. J., & Borkovec, T. D. (2000). Psychometric properties of the credibility/expectancy questionnaire. Journal of Behavior Therapy and Experimental Psychiatry, 31(2), 73–86.

    CAS  Article  PubMed  Google Scholar 

  40. Braveman, P. A., Cubbin, C., Egerter, S., Chideya, S., Marchi, K. S., Metzler, M., et al. (2005). Socioeconomic status in health research: One size does not fit all. JAMA, 294(22), 2879–2888. doi:10.1001/jama.294.22.2879.

    CAS  Article  PubMed  Google Scholar 

  41. United Nations Educational, Scientific and Cultural Organization. International Standard Classification of Education: ISCED 2011. Retrieved November 10, 2015 from http://www.uis.unesco.org/Education/Documents/isced-2011-en.pdf

  42. Coles, J. (2010). PROMs risk adjustment methodology guide for general surgery and orthopaedic procedures. Retrieved November 10, 2015 from http://www.england.nhs.uk/statistics/wp-content/uploads/sites/2/2013/07/proms-ris-adj-meth-sur-orth.pdf

  43. van der Lem, R., Stamsnieder, P. M., van der Wee, N. J., van Veen, T., & Zitman, F. G. (2013). Influence of sociodemographic and socioeconomic features on treatment outcome in RCTs versus daily psychiatric practice. Social Psychiatry and Psychiatric Epidemiology, 48(6), 975–984. doi:10.1007/s00127-012-0624-4.

    Article  PubMed  Google Scholar 

  44. Holden, L., Lee, C., Hockey, R., Ware, R. S., & Dobson, A. J. (2014). Validation of the MOS Social Support Survey 6-item (MOS-SSS-6) measure with two large population-based samples of Australian women. Quality of Life Research, 23(10), 2849–2853. doi:10.1007/s11136-014-0741-5.

    Article  PubMed  Google Scholar 

  45. Guan, N. C., Sulaiman, A. R., Seng, L. H., Ann, A. Y., Wahab, S., & Pillai, S. K. (2013). Factorial validity and reliability of the Tamil version of multidimensional scale of perceived social support among a group of participants in university malaya medical centre, Malaysia. Indian Journal of Psychological Medicine, 35(4), 385–388. doi:10.4103/0253-7176.122234.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Carter, J. D., Crowe, M. T., Jordan, J., McIntosh, V. V., Frampton, C., & Joyce, P. R. (2015). Predictors of response to CBT and IPT for depression; The contribution of therapy process. Behaviour Research and Therapy, 74, 72–79. doi:10.1016/j.brat.2015.09.003.

    Article  PubMed  Google Scholar 

  47. Kupfer, D. J., & Frank, E. (2003). Comorbidity in depression. Acta Psychiatrica Scandinavica, 108, 57–60.

    Article  Google Scholar 

  48. Sangha, O., Stucki, G., Liang, M. H., Fossel, A. H., & Katz, J. N. (2003). The Self-Administered Comorbidity Questionnaire: a new method to assess comorbidity for clinical and health services research. Arthritis and Rheumatism, 49(2), 156–163. doi:10.1002/art.10993.

    Article  PubMed  Google Scholar 

  49. Olomu, A. B., Corser, W. D., Stommel, M., Xie, Y., & Holmes-Rovner, M. (2012). Do self-report and medical record comorbidity data predict longitudinal functional capacity and quality of life health outcomes similarly? BMC Health Services Research, 12, 398. doi:10.1186/1472-6963-12-398.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Williams, A. D., Blackwell, S. E., Mackenzie, A., Holmes, E. A., & Andrews, G. (2013). Combining imagination and reason in the treatment of depression: a randomized controlled trial of internet-based cognitive-bias modification and internet-CBT for depression. Journal of Consulting and Clinical Psychology, 81(5), 793–799. doi:10.1037/a0033247.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Crits-Christoph, P., Baranackie, K., Kurcias, J., Beck, A., Carroll, K., Perry, K., et al. (1991). Meta-analysis of therapist effects in psychotherapy outcome studies. Psychotherapy Research, 1(2), 81–91.

    Article  Google Scholar 

  52. Crawford, M. J., Robotham, D., Thana, L., Patterson, S., Weaver, T., Barber, R., et al. (2011). Selecting outcome measures in mental health: The views of service users. Journal of Mental Health, 20(4), 336–346. doi:10.3109/09638237.2011.577114.

    Article  PubMed  Google Scholar 

  53. Gilbody, S., Richards, D., Brealey, S., & Hewitt, C. (2007). Screening for depression in medical settings with the Patient Health Questionnaire (PHQ): a diagnostic meta-analysis. Journal of General Internal Medicine, 22(11), 1596–1602. doi:10.1007/s11606-007-0333-y.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Reise, S. P., & Rodriguez, A. (2016). Item response theory and the measurement of psychiatric constructs: some empirical and conceptual issues and challenges. Psychological Medicine, 46(10), 2025–2039. doi:10.1017/S0033291716000520.

    CAS  Article  PubMed  Google Scholar 

  55. Kolen, M. J., & Brennan, R. L. (2014). Test equating, scaling, and linking. New York: Springer.

    Book  Google Scholar 

  56. Fischer, H. F., Klug, C., Roeper, K., Blozik, E., Edelmann, F., Eisele, M., et al. (2014). Screening for mental disorders in heart failure patients using computer-adaptive tests. Quality of Life Research, 23(5), 1609–1618. doi:10.1007/s11136-013-0599-y.

    Article  PubMed  Google Scholar 

  57. Knapp, M. (2013). Making an economic case for better mental health services. In G. Thornicroft, M. Ruggeri, & D. Goldberg (Eds.), Improving mental health care, the global challenge (pp. 193–200). Oxford: Wiley-Blackwell.

    Chapter  Google Scholar 

  58. Garcia-Campayo, J., Zamorano, E., Ruiz, M. A., Pardo, A., Perez-Paramo, M., Lopez-Gomez, V., et al. (2010). Cultural adaptation into Spanish of the generalized anxiety disorder-7 (GAD-7) scale as a screening tool. Health Qual Life Outcomes, 8, 8. doi:10.1186/1477-7525-8-8.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Gelaye, B., Williams, M. A., Lemma, S., Deyessa, N., Bahretibeb, Y., Shibre, T., et al. (2013). Validity of the Patient Health Questionnaire-9 for depression screening and diagnosis in East Africa. Psychiatry Research, 210(2), 653–661. doi:10.1016/j.psychres.2013.07.015.

    Article  PubMed  Google Scholar 

  60. Zhong, Q., Gelaye, B., Fann, J. R., Sanchez, S. E., & Williams, M. A. (2014). Cross-cultural validity of the Spanish version of PHQ-9 among pregnant Peruvian women: A Rasch item response theory analysis. Journal of Affective Disorders, 158, 148–153. doi:10.1016/j.jad.2014.02.012.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Silveira, C., Parpinelli, M. A., Pacagnella, R. C., Camargo, R. S., Costa, M. L., Zanardi, D. M., et al. (2013). Cross-cultural adaptation of the World Health Organization Disability Assessment Schedule (WHODAS 2.0) into Portuguese. Revista Da Associacao Medica Brasileira, 59(3), 234–240. doi:10.1016/j.ramb.2012.11.005.

    Article  PubMed  Google Scholar 

  62. Kroenke, K., Spitzer, R. L., & Williams, J. B. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of general internal medicine., 16(9), 606–613.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  63. Chwastiak, L. A., & Von Korff, M. (2003). Disability in depression and back pain: Evaluation of the World Health Organization Disability Assessment Schedule (WHO DAS II) in a primary care setting. Journal of clinical epidemiology., 56(6), 507–514.

    Article  PubMed  Google Scholar 

Download references

Funding

As the International Consortium for Health Outcomes Measurement (ICHOM) is a non-profit organization, several sponsors supported the development of the Depression and Anxiety Standard Set. In addition to the general sponsors of ICHOM (Harvard Business School, Karolinska Institutet, Boston Consulting Group), the Stichting Benchmark GGZ (Leiden, The Netherlands) and the Douglas Mental Health University Institute (Montréal, Canada) supported the present project financially. Further, the research fellow was funded by Charité - Universitätsmedizin Berlin. Five Working Group members are currently working for these institutions (EdB, AC, AO, SN, MR, see affiliations); all were involved in the preparation, review and approval of the manuscript. As this effort was a consensus study and was carried out on the basis of a structured Delphi protocol, interpretation and selection of results was only done by majority decision of the whole Working Group.

Author information

Authors and Affiliations

  1. Department of Psychosomatic Medicine, Center for Internal Medicine and Dermatology, Charité – Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany

    Alexander Obbarius, Sandra Nolte & Matthias Rose

  2. International Consortium for Health Outcomes Measurement (ICHOM), 14 Arrow St., Ste. #11, Cambridge, MA, 02138, USA

    Lisa van Maasakkers & Caleb Stowell

  3. Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA

    Lee Baer

  4. Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA

    Lee Baer

  5. Department of Experimental Psychology, University of Oxford, Tinbergen Building, South Parks Road, Oxford, OX1 3UD, UK

    David M. Clark

  6. Institut Philippe-Pinel de Montréal, 10905 Henri-Bourassa Est, Montréal, QC, H1C 1H1, Canada

    Anne G. Crocker

  7. Department of Psychiatry, University of Montréal, Pavillon Roger-Gaudry, C.P., 6128 succursale Centre-ville, Montréal, QC, H3C 3J, Canada

    Anne G. Crocker & Alain Lesage

  8. Stichting Benchmark GGZ, Rembrandtlaan 46, 3723 BK, Bilthoven, The Netherlands

    Edwin de Beurs

  9. Department of Clinical Psychology, Leiden University, Rapenburg 70, 2311 EZ, Leiden, The Netherlands

    Edwin de Beurs

  10. Netherlands Institute for Advanced Study, Meijboomlaan 1, 2242 PR, Wassenaar, The Netherlands

    Paul M. G. Emmelkamp

  11. The Center for Social and Humanities Research, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Paul M. G. Emmelkamp

  12. School of Public Health, Kyoto University Graduate School of Medicine, Yoshida Konoecho, Sakyo-ku, Kyoto, 606-8501, Japan

    Toshi A. Furukawa

  13. Department of Clinical Neuroscience, Karolinska Institutet, Nobels väg 9, 17177, Stockholm, Sweden

    Erik Hedman-Lagerlöf

  14. Gustavsberg Primary Care Clinic, Stockholm County Council, Odelbergs väg 19, 13440, Gustavsberg, Sweden

    Erik Hedman-Lagerlöf

  15. Centre for Emotional Health, Department of Psychology, Macquarie University, Building C3A, Level 7, Sydney, NSW, 2109, Australia

    Maria Kangas

  16. Ottawa, ON, Canada

    Lucie Langford

  17. Research centre, Institut universitaire en santé mentale de Montréal, 7401 rue Hochelaga, Montréal, QC, H1N 3M5, Canada

    Alain Lesage

  18. Makerere College of Health Sciences, Makerere University, P.O. Box 7072, Kampala, Uganda

    Doris M. Mwesigire

  19. Population Health Strategic Research Centre, School of Health and Social Development, Deakin University, 221 Burwood Highway, Burwood, VIC, 3125, Australia

    Sandra Nolte

  20. Department of Global Health and Social Medicine, Harvard Medical School, 641 Huntington Avenue, Boston, 02115, MA, USA

    Vikram Patel

  21. Department of Psychiatry, University of Pittsburgh School of Medicine, Thomas Detre Hall, 3811 O’Hara Street, Pittsburgh, PA, 15213, USA

    Paul A. Pilkonis

  22. Department of Psychiatry, Columbia University, New York Presbyterian Hospital, 630 West 168th Street, New York, NY, 10032, USA

    Harold A. Pincus

  23. The RAND Corporation, 1776 Main Street, Santa Monica, CA, 90407-2138, USA

    Harold A. Pincus & Cathy Sherbourne

  24. Federal University of Rio Grande do Sul, Grupo CNPq - GPEMSA - USP/São Paulo, Av. Paulo Gama, 110 - Bairro Farroupilha, Porto Alegre, Rio Grande do Sul, Brazil

    Roberta A. Reis

  25. Clinical Hospital, Department of Psychiatry and Mental Health, University of Chile, Av. Libertador Bernardo O’Higgins 1058, Santiago de Chile, Chile

    Graciela Rojas

  26. Anxiety UK, Zion Community Centre, 339 Stretford Road, Hulme, Manchester, M15 4ZY, UK

    Dave Smithson

  27. Office of the U.S. Army Surgeon General, 7700 Arlington Blvd., Room 3SW116A, Falls Church, VA, 22042, USA

    Kelly Woolaway-Bickel

  28. Department of Quantitative Health Sciences, Medical School, University of Massachusetts, 368 Plantation Street, Worcester, MA, 01605, USA

    Matthias Rose

Authors
  1. Alexander Obbarius
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lisa van Maasakkers
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lee Baer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David M. Clark
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Anne G. Crocker
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Edwin de Beurs
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Paul M. G. Emmelkamp
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Toshi A. Furukawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Erik Hedman-Lagerlöf
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Maria Kangas
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Lucie Langford
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Alain Lesage
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Doris M. Mwesigire
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Sandra Nolte
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Vikram Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Paul A. Pilkonis
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Harold A. Pincus
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Roberta A. Reis
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Graciela Rojas
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Cathy Sherbourne
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Dave Smithson
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Caleb Stowell
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Kelly Woolaway-Bickel
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Matthias Rose
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

There are no persons who contributed to the work reported in the manuscript and do not fulfill authorship criteria.

Corresponding author

Correspondence to Alexander Obbarius.

Ethics declarations

Conflict of interest

Edwin de Beurs and Anne Crocker are currently working for institutions that financially contributed to the present project. And as they were both members of the Working Group, they were also involved in analysis and interpretation of data and revision of the manuscript. Paul A. Pilkonis holds “founders’ shares” in Psychiatric Assessment, Inc. (PAI); however, no measures developed by PAI were considered for inclusion in the ICHOM Standard Set. Toshi A. Furukawa has received lecture fees from Eli Lilly, Meiji, Mochida, MSD, Otsuka, Pfizer and Tanabe-Mitsubishi, and consultancy fees from Sekisui Chemicals and Takeda Science Foundation. He has received royalties from Igaku-Shoin, Seiwa-Shoten and Nihon Bunka Kagaku-sha publishers. He has received grant or research support from the Japanese Ministry of Education, Science, and Technology, the Japanese Ministry of Health, Labour and Welfare, the Japan Society for the Promotion of Science, the Japan Foundation for Neuroscience and Mental Health, Mochida and Tanabe-Mitsubishi. He is diplomate of the Academy of Cognitive Therapy. Erik Hedman-Lagerlöf is a shareholder of Dahlia, a company specializing in psychiatric symptom assessment. Lucie Langford is currently employed by the Canadian Institutes of Health Research (CIHR) as a student. Sandra Nolte has no commercial or financial interest in promoting any of the instruments mentioned in this paper. She has received several research grants from the German Research Foundation and the European Organisation for Research and Treatment of Cancer (EORTC) and is Co-Chair of the PROMIS International Initiative. Harold A. Pincus has no commercial relationships or funding. He has received grants from the Commonwealth Fund and National Institutes of Health/National Center for Advancing Translational Sciences,/National Institute of Mental Health/National Institute on Drug Abuse/National Institute on Alcohol Abuse and Alcoholism/National Institute on Nursing Research, the US Department of Health and Human Services/Substance Abuse and Mental Health Services Administration/Assistant Secretary for Planning and Evaluation, Atlantic Philanthropies, John A. Hartford Foundation, the governments of Australia, Canada, England, Germany, Ireland, Netherlands, New Zealand, Norway, and Scotland, New York State Health Foundation, Patient-Centered Outcomes Research Institute, Department of Veterans Affairs, and the Department of Defense. He is employed by Columbia University/New York Presbyterian and RAND. He has consulted for Mathematica Policy Research and Manila Consulting. He serves on committees for (and received travel support but no compensation) from the National Quality Forum, National Committee for Quality Assurance, and American Psychiatric Association. Matthias Rose has no commercial, or financial interest promoting any of the instruments mentioned in this paper. He has received several grants from the U.S. National Institutes of Health, including one RO1 grant where he has worked together with Kurt Kroenke, one of the developers of the PHQ questionnaire. He was also one of the Co-Investigators of the NIH cross-funded initiative to develop a comprehensive Patient-Reported Measurement Information System (PROMIS), and is one of the Co-Chairs of the PROMIS International Initiative. The other authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Electronic supplementary material

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Obbarius, A., van Maasakkers, L., Baer, L. et al. Standardization of health outcomes assessment for depression and anxiety: recommendations from the ICHOM Depression and Anxiety Working Group. Qual Life Res 26, 3211–3225 (2017). https://doi.org/10.1007/s11136-017-1659-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11136-017-1659-5

Keywords

  • Depression
  • Anxiety
  • Patient-reported outcomes
  • Health-related quality of life
  • Standardization
  • Outcome Set