Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-26T10:13:31.702Z Has data issue: false hasContentIssue false

Patient-reported outcome measures in community mental health teams: pragmatic evaluation of PHQ-9, GAD-7 and SWEMWBS

Published online by Cambridge University Press:  22 March 2019

Paul Blenkiron*
Affiliation:
Tees, Esk and Wear Valleys NHS Foundation Trust, UK
Lucy Goldsmith
Affiliation:
St George's, University of London, UK
*
Correspondence to Paul Blenkiron ([email protected])
Rights & Permissions [Opens in a new window]

Abstract

Aims and method

We evaluated routine use, acceptability and response rates for the Patient Health Questionnaire (PHQ-9), Generalised Anxiety Disorder Scale (GAD-7) and Short Warwick-Edinburgh Mental Well-Being Scale (SWEMWBS) within adult community mental health teams. Measures were repeated 3 months later. Professionals recorded the setting, refusal rates and cluster diagnosis.

Results

A total of 245 patients completed 674 measures, demonstrating good initial return rates (81%), excellent scale completion (98–99%) and infrequent refusal/unsuitability (11%). Only 32 (13%) returned follow-up measures. Significant improvements occurred in functioning (P = 0.01), PHQ-9 (P = 0.02) and GAD-7 (P = 0.003) scores (Cohen's d = 0.52–0.77) but not in SWEMWBS (P = 0.91) scores. Supercluster A had higher initial PHQ-9 and GAD-7 scores (P < 0.001) and lower SWEMWBS scores (P = 0.003) than supercluster B. Supercluster C showed the greatest functional impairment (P = 0.003).

Clinical implications

PHQ-9 and GAD-7 appear acceptable as patient-reported outcome measures in community mental health team. SWEMWBS seems insensitive to change. National outcome programmes should ensure good follow-up rates.

Type
Original Papers
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Authors 2019

Reliable, valid and practical outcome measures are a priority for mental health services.1 It is now essential for clinical teams to report outcomes in order to evaluate their work, demonstrate effectiveness and support future commissioning decisions.Reference Tadros2 However, few pragmatic studies exist to inform delivery of mental health outcomes programmes,3,4 including current initiatives within the UK National Health Service (NHS).

The NHS quality agenda promotes three central themes: effective services, safety and a positive patient experience.5 Arguably, it is the users of services who are best placed to judge how they feel.Reference Devlin and Appleby6 Patient-reported outcome measures (PROMs) are standardised questionnaires that elicit subjective reports of health and illness. They aim to assess the personal effects of symptoms, functioning, problems, risks and general well-being on an individual's quality of life. However, no single PROM has evidence of validity across all areas of mental health.Reference Coulter7 Community mental health teams (CMHTs) are a key component of specialist mental healthcare, yet front-line use of PROMs has not been systematically evaluated in this setting. In addition, it remains unclear how outcomes in secondary care vary across mental healthcare clusters.Reference Trevithick, Painter and Keown8 ‘Clustering’ is an important tool within the National Tariff Payment System (‘Payment by Results’) and is recommended by NHS England in order to deliver its Five Year Forward View for Mental Health.9

Aims of this study

This study evaluated routine use of three PROMs within adult CMHTs: the Patient Health Questionnaire (PHQ-9), the Generalised Anxiety Disorder Scale (GAD-7) and the Short Warwick-Edinburgh Mental Well-Being Scale (SWEMWBS). We aimed to:

  1. (a) assess completion rates and patient acceptability;

  2. (b) evaluate responsiveness – comparing measures at initial assessment and at review/discharge across mental health superclusters

Method

This project was registered as a service evaluation by the Department of Research and Development at Leeds and York Partnership NHS Foundation Trust and granted NHS research governance approval (R&D no: LYPFT 2014/498/L).

Setting and participants

Secondary care mental health services in York and Selby are provided to a population of 280 000 by a specialised mental health trust. The population is predominantly White British (95%), with those of Asian ethnicity (2.2%) representing the largest single ethnic minority.10 We collected data from May to October 2014 from the two large ‘ageless’ adult CMHTs. NHS data from trust informatics during the study showed that 38% of contacts were new referrals, with 31% of patients classified as being under the Care Programme Approach (CPA). A mean of 78% of the total caseload were being seen each month.

Data collection

We included patients aged 18 years and over attending CMHT appointments. Individuals were receiving care from one or more professionals at a psychiatric clinic, at a community mental health team base, at home or in another setting.

Patients were invited to complete the SWEMWBS, PHQ-9 and GAD-7 scales together. Measures were posted with the appointment letter to new referrals to CMHTs, with a request to hand them to the professional they saw. Staff also offered the measures at the initial appointment to individuals who had not completed them. Patients were asked to complete the measures again at follow-up 3 months later, or at discharge if sooner. Follow-up questionnaires were offered in person at the appointment by reception staff or the professional the individual saw. We introduced the study at a team business meeting and obtained staff agreement to participate before the start. In addition to verbal reminders at team meetings during the 6 month study period, we contacted staff individually by e-mail on two occasions (at 3 and 5 months) to remind them to collect follow-up questionnaires.

Patients could choose to complete the measures before, during or immediately after their appointment. Forms explicitly stated that if an individual did not feel like completing the questionnaires, they could decline and this would not affect their care. The questionnaires also informed patients that they could choose to receive this information in audio format (for example, as a CD) or in other languages, including via an interpreter.

Using a standardised pro forma attached to the measures, we asked staff to record details about the clinical setting, the reason for seeing the patient, and the main mental health problem (care cluster and diagnosis). To assess return rates accurately, at both initial and follow-up time points we specifically asked staff to return the pro forma even if an individual was unable (or declined) to complete measures. Staff also entered the responses into the computerised clinical record. Missing data were later accessed from this record.

Outcome measures

PHQ-9

PHQ-9 is a nine-item measure of depressive symptoms.Reference Kroenke, Spitzer and Williams11 Each item is rated using four ordinal response options (0, not at all; 3, nearly every day), giving a severity score between 0 and 27. PHQ-9 also rates difficulty in functioning. A score greater than 9 indicates clinically significant depression. The PHQ-9 is well validated against standard criteria, demonstrates sensitivity to change and is used in a variety of clinical settings.Reference Horton and Perry12,Reference McMillan, Gilbody and Richards13

GAD-7

GAD-7 is a seven-item measure of anxiety symptoms.Reference Kroenke, Spitzer, Williams, Monahan and Lowe14 Each item is rated on the same four ordinal responses as the PHQ-9, giving a severity score between 0 and 21. A score above 7 is recommended to identify a likely anxiety disorder.

PHQ-9 and GAD-7 form part of the UK Department of Health's National Minimum Data Set.3 Their use is supported by the National Institute for Health and Care Excellence for assessing clinical progress in mental health services.1

SWEMWBS

SWEMWBS is a short version of a measure originally developed to monitor well-being in the general population and to evaluate policies addressing well-being.Reference Tennant, Hiller, Fishwick, Stephen, Joseph and Weich15,Reference Stewart-Brown, Tennant, Tennant, Platt, Parkinson and Weich16 There are seven items, each with five response categories (1, none of the time; 5, all of the time). The score range is 7–35 and higher scores indicate greater mental well-being. At the time of this study, the local NHS adopted SWEWWBS within the Regional Care Pathways and Packages Project, designed to implement Mental Health Payment by Results. SWEMWBS has been reported to have adequate internal consistency and reliability.Reference Haver, Akerjordet, Caputi, Furunes and Magee17 It has not been systematically evaluated in mental health populations. The developers recommended that sensitivity to change be demonstrated before its introduction into clinical settings.

Prospectively, we also aimed to analyse responses to the following three key questions separately.

  • Self-harm risk (PHQ-9 question 9): ‘How often have you been bothered by thoughts of being better off dead or of hurting yourself in some way?’ This question is of particular interest in clinical risk assessments.

  • Functional impairment (additional tenth PHQ-9 question): ‘How difficult have these problems made it for you to do your work, take care of things at home, or get along with other people?’ This question is of practical importance and is independent of symptom scoring.

  • Problem-solving ability (SWEMWBS question 4): ‘How often over the past 2 weeks would you agree that “I've been dealing with problems well?”’ An inability to solve problems is significantly associated with hopelessness and suicidal intent.Reference Milnes, Owens and Blenkiron18

Data analysis

Data were anonymised and analysed using IBM Statistical Package for Social Sciences for Windows, version 22.19 We adjusted total PHQ-9, GAD-7 and SWEMWBS scores for individuals who omitted some replies, using syntax coding with the following formula:

$$\hbox{Corrected score} = \left( {\displaystyle{{\hbox{Total score}} \over {\hbox{No of questions answered}}}} \right) \times \hbox{Total no. of questions}. $$

This is a recommended way of handling potential bias in the analysis due to missing items in questionnaires with no subscales.Reference Bell, Fairclough, Fiero and Butow20,Reference Fairclough and Cella21 Results are quoted as percentages to the nearest whole number, with totals based on valid known responses. Non-parametric tests were applied to ordinal and continuous variables. We used Spearman's correlation coefficient for independent samples at baseline, Wilcoxon's signed rank (z) test for paired data (initial versus final outcomes), and the Kruskal–Wallis H-test for differences between superclusters.Reference Altman22 Clinical effect sizes (Cohen's d) were calculated for reported changes in measures.Reference Cohen23

Results

Response rate

Individuals returned 674 out of 831 questionnaires – a completion rate of 81%. These comprised 277 sets of forms (Table 1). Initial forms were completed by 245 patients, with similar response rates (78–81%) for each PROM. Follow-up forms were received from 32 (13%) individuals. The mean time period between initial and follow-up forms was 74 (s.d. 58) days. There were high rates of scale completion – most respondents answered all questions on each form. Professionals completed their part of the initial forms in 55% (134) cases. Staff recorded that nine (7%) patients declined to fill in the initial forms, and judged it inappropriate to offer forms in another five (4%) cases. No patient was reported to have declined or been judged unsuitable to complete follow-up questionnaires.

Table 1 Completion rates for outcome measures

Patients and setting

The mean age of patients was 47 years (range 18–93), including 127 (60%) women. Initial forms were handed in at a clinic or CMHT base in 99 (74%) cases, and at home or another place in 34 (26%) cases. Most patients (96, 72%) completed forms alone, 16 (12%) received assistance from carers or relatives, and seven (5%) had assistance from staff. No patient asked to receive the questionnaires in an audio format or in another language.

Professionals

A mean of 27 sets of forms (range 1–50) were returned by 25 professionals. These included five community psychiatric nurses, five social workers, two psychologists, one occupational therapist and 12 psychiatrists (five working age adult consultants, two older age consultants, four core trainees and one higher trainee). Psychiatrists returned twice as many measures per professional (mean 36, 438 forms, 65% of total) as other staff (mean 18, 236 forms, 35%), general z-test P < 0.0001, 95% confidence interval 61–68%.Reference Bell, Fairclough, Fiero and Butow20 The reason recorded for completing initial forms was assessment in 77 (63%) cases, review (including CPA review) in 39 (32%) cases and discharge in three (2%) cases.

Diagnosis

The main diagnosis according to the 10th edition of the International Classification of Diseases24 was available for 211 (86%) of patients. Most common was depressive disorder (acute, recurrent or chronic) in 71 (34%) cases, psychosis (including schizophrenia) in 28 (13%) cases and bipolar disorder (including mania) in 26 (13%) cases. Personality disorder comprised 16 (8%) cases, anxiety disorder 14 (7%), dementia 13 (6%), adjustment disorder 12 (6%), alcohol or drug dependence nine (4%), post-traumatic stress disorder nine (4%), obsessive–compulsive disorder five (2%) and other diagnoses eight (3%).

Correlations between outcomes

Table 2 describes the associations between measures (construct validity). PHQ-9 and GAD-7 scores were strongly correlated with each other at initial and final appointments. There was a moderate correlation between initial and final PHQ-9 scores, and between initial and final GAD-7 scores. SWEMWBS also showed a moderately strong correlation with concurrent PHQ-9 and GAD-7 scores. However, we found no significant correlation between final SWEMWBS and initial scores on any of the measures.

Table 2 Correlations between measures for initial and follow-up appointments

Values are Spearman's r. *P < 0.05, **P < 0.01, ***P < 0.001.

Initial versus final scores

Table 3 shows initial and final outcome scores for the paired data (n = 32). Applying Wilcoxon's signed rank test, PHQ-9 and GAD-7 scores were significantly lower at review, whereas SWEMWBS showed no significant change. For specific questions, patients' median ratings for thoughts of self-harm and also for their ability to function day to day improved significantly. Respondents' perceived ability to solve problems did not change significantly.

Table 3 Initial and final scores for outcome measures

IQR, interquartile range. *P < 0.05, **P < 0.01.

Text shows wording of median response.

The mean initial PHQ-9 score of 16.8 (s.d. 7.6) decreased on review to 12.6 (s.d. 8.6), representing a moderate effect size (Cohen's d = 0.52) across the total sample. The mean initial GAD-7 score of 12.9 (s.d. 6.2) also improved at follow-up to 8.1 (s.d. 6.1), indicating a large effect size (d = 0.77).

To examine whether there was any selection bias in follow-up responses, we compared initial median scores for those who did (n = 32) and did not (n = 213) complete final measures. The Mann–Whitney U-test for independent samples showed no significant difference on the SWEMWBS (P = 0.91), PHQ-9 (P = 0.42) or GAD-7 (P = 0.78).

Age, gender and time interval

For both initial and final measures, older patients answered fewer questions on the PHQ-9 (r = −0.52, P = 0.002) and GAD-7 (r = −0.31, P < 0.001). Age was correlated positively with initial SWEMWBS score (Spearman's r = 0.36, P < 0.001) and negatively with initial PHQ-9 (r = −0.15, P = 0.04) and ability to function (r = −0.17, P = 0.03). There was no significant association between age and any final outcome (SWEMWBS, PHQ-9, GAD-7 or functioning). We found no significant association between gender and initial or final measures. The time period between completion of initial and final forms also showed no significant correlation with any initial or final outcome.

Superclusters

Tables 4 and 5 describe patients and their outcomes across the three supercluster categories.Reference Trevithick, Painter and Keown8 There were significant differences between PHQ-9, GAD-7 and SWEMWBS scores at initial but not final review. Individuals with non-psychotic disorders (supercluster A) had lower initial SWEMWBS scores (P < 0.001), and high levels of anxiety and depressive symptoms that improved at review. Respondents with psychosis (supercluster B) had the lowest PHQ-9 and GAD-7 scores (P = 0.003). Those with organic disorders (supercluster C, mainly dementia or cognitive impairment) had the greatest difficulty in functioning (P = 0.003) based on the PHQ-9 functioning question). They also reported significant depression, anxiety and self-harm thoughts. Insufficient responses were received to calculate reliable final median outcome scores for supercluster C.

Table 4 Mental health superclusters: age, risk, functioning and problem solving, initial responses

Figures are corrected median scores.

P-values from Kruskal–Wallis H-test for differences between superclusters. *P < 0.05, **P < 0.01, ***P < 0.001.

Table 5 Mental health superclusters: initial and final outcome scores

Figures are corrected initial median scores.

P-values from Kruskal–Wallis H-test for differences between superclusters. **P < 0.01, ***P < 0.001.

Discussion

This is the first study to examine the pragmatic integration of the PHQ-9, GAD-7 and SWEMWBS within routine CMHT practice. For these three PROMs, we found good initial return rates (80%), excellent rates of scale completion (98–99%) and low rates (11%) of patient refusal or unsuitability. After 3 months, patients reported significant improvements in symptoms of depression and anxiety, self-harm thoughts and functioning, but not in subjective well-being or perceived ability to handle problems.

It is important that outcomes are validated for the population in which they are used. Decreasing anxiety scores were observed across superclusters A and B. Building on research in other settings,Reference Löwe, Decker, Müller, Brähler, Schellberg and Wolfgang25,Reference Kertz, Bigda-Peyton and Bjorgvinsson26 our study provides new evidence that the GAD-7, like PHQ-9, is responsive to change in a community mental health population. For depressive symptoms, a drop of more than five PHQ-9 points is reported to indicate a significant and reliable clinical improvement.Reference Richards and Borglin27 We found an eight-point reduction in PHQ-9 scores in supercluster A, which includes those diagnosed with depressive disorder. This effect is similar in size to those observed in large randomised treatment trials for depression.Reference Gilbody, Littlewood, Hewitt, Brierley, Tharmanathan and Araya28 These findings suggest that both PHQ-9 and GAD-7 might be adopted as PROMs within secondary mental healthcare in functional (non-dementia) populations.

This study has several limitations. Patients and professionals were not asked about their views on the usefulness of collecting these PROMS, or about possible harms. It is also uncertain whether professionals used the responses during their meetings with patients to improve the quality of care (rather than simply to measure it). Furthermore, we do not know the extent to which the improvements observed were due to professional interventions (including medication and psychosocial approaches) rather than the passage of time or regression to the mean.

An important finding is the low collection rate (n = 32, 13%) for follow-up measures in ‘real world’ clinical practice. Other mental health outcome studies have also recorded follow-up rates as low as 10–25%, even after (as in our study) professionals are prompted.Reference Timimi29,Reference Keetharuth, Brazier, Connell, Bjorner, Carlton and Buck Taylor30 The difference between initial and final response rates might in part be linked to the number of requests to complete measures. For completion of initial measures, patients were asked both in writing (posted with the appointment letter) and again in person at the appointment. By contrast, collection of follow-up measures relied on staff remembering to ask patients to complete forms at face-to-face clinical review alone.

While the low final response rate limits some conclusions drawn, the outcome score changes observed may be generalisable to the wider patient population for several reasons. First, our analysis comparing initial median scores for completers versus non-completers showed no significant difference in either PHQ-9, GAD-7 or SWEMWBS. Second, response rates by gender ratio were similar at initial and final follow-up, and the time interval between initial and final measures showed no significant relation with any outcome. We have no evidence to support the idea that individuals who improved the most were more, or less, likely to complete final measures. This suggests that attrition bias at follow-up – due to variations between patients in symptoms, functioning or well-being – is less likely. Third, we observed a large (50-fold) variation in the collection of PROMS between professionals. For example, psychiatrists returned twice as many initial and follow-up questionnaires as other team members. In conclusion, it appears more likely that differences in staff engagement with the study, and inconsistent prompting of patients to complete measures (rather than patient characteristics) may account for the variations in return rates.

However, a good response rate remains central to the future success of PROMs.3,Reference Devlin and Appleby6,9 This may be improved in busy CMHTs by providing clinicians with adequate administrative time and support, and by implementing robust electronic collection systems.Reference Tadros2,3 For example, patients could complete forms directly on an electronic tablet linked to their clinical record.

Consistent with previous research, individuals with psychosis rated their well-being on SWEMWBS higher than those with affective disorders.Reference Blenkiron and Hammill31 However, overall SWEMWBS scores did not change in this study, and there was no significant correlation between initial and follow-up SWEMWBS ratings. There are several possible explanations for this. First, subjective well-being could lag behind improvements in symptoms and functioning. Second, SWEMWBS includes questions about areas such as feeling useful and close to people,Reference Stewart-Brown, Tennant, Tennant, Platt, Parkinson and Weich16 which could be measuring something different from other outcomes. Third, the psychometric properties of SWEMWBS may include lower internal reliability and less sensitivity to clinical change than other PROMs. Future research in this population could evaluate the responsiveness of SWEMWBS using methods such as the standardised response mean,Reference Van Sonderen and Middel32 which allows for improvement or worsening over time. Alternative well-being measures are currently being developed. Recovering Quality of Life (http://www.reqol.org.uk) is a new national well-being PROM commissioned by the UK Department of Health. Specifically designed to assess quality of life and recovery outcomes in adults with different mental health conditions, it has been tested in 6000 mental health patients.Reference Keetharuth, Brazier, Connell, Bjorner, Carlton and Buck Taylor30 The brief version (ReQoL-10) is now freely available for clinical and research use in the UK NHS.

Acknowledgements

We thank Victoria Allgar, Senior Statistician at the Department of Health Sciences, York University, for her advice, and all NHS staff who participated in this study.

About the authors

Paul Blenkiron, MMedSc, FRCPsych, is a Consultant Psychiatrist in Adult Mental Health at Tees, Esk and Wear Valleys NHS Foundation Trust, and Honorary Professor at Hull York Medical School; Lucy Goldsmith, PhD, is a Research Assistant at the Population Health Research Institute, St George's, University of London, UK.

Footnotes

Declaration of interest: None.

References

1National Institute for Health and Care Excellence. Commissioning Stepped Care for People with Common Mental Health Disorders: Guide & Bench Marking Tool CMG41. NICE, 2011 (http://www.nice.org.uk/usingguidance/commissioningguides/commonmentalhealthdisorderservices/commonmentalhealthdisorderservices.jsp).Google Scholar
2Tadros, G. Intelligent outcome measures in liaison psychiatry: essential even if not desirable. BJPsych Bull 2016; 40: 195–8.Google Scholar
3Department of Health. The IAPT Data Handbook Version 2.0: Guidance on Recording and Monitoring Outcomes to Support Local Evidence-Based Practice. Department of Health, 2011 (http://www.iapt.nhs.uk/).Google Scholar
4Department of Health. NHS Outcomes Framework: List of Outcomes and Indicators in the NHS Outcomes Framework for 2016-17. Department of Health, 2016 (https://www.gov.uk/government/publications/nhs-outcomes-framework-2016-to-2017).Google Scholar
5Department of Health. The Operating Framework for the NHS in England 2012/2013. Department of Health, 2011.Google Scholar
6Devlin, NJ, Appleby, J. Getting the Most Out of PROMS: Putting Health Outcomes at the Heart of NHS Decision Making. The King's Fund, 2010 (https://www.kingsfund.org.uk/sites/files/kf/Getting-the-most-out-of-PROMs-Nancy-Devlin-John-Appleby-Kings–Fund-March-2010.pdf).Google Scholar
7Coulter, A. Measuring what matters to patients. BMJ 2017; 356: j816.Google Scholar
8Trevithick, L, Painter, J, Keown, P. Mental health clustering and diagnosis in psychiatric in-patients. BJPsych Bull 2015; 39(3): 119–23.Google Scholar
9NHS England. The Five Year Forward View for Mental Health. NHS England, 2016 (https://www.england.nhs.uk/wp-content/uploads/2016/07/fyfv-mh.pdf).Google Scholar
10Vale of York Clinical Commissioning Group. Vale of York Public Health Report: Equality Strategy – Population and Health Inequalities Data. Vale of York Clinical Commissioning Group, 2013 (http://www.valeofyorkccg.nhs.uk/data/uploads/governing-body-papers/4-july-2013/item-6-public-health-report.pdf).Google Scholar
11Kroenke, K, Spitzer, RL, Williams, JB. The PHQ-9: validity of a brief depression severity measure. J Gen Int Med 2001; 16: 606–13.Google Scholar
12Horton, M, Perry, AE. Screening for depression in primary care: a Rasch analysis of the PHQ-9. BJPsych Bull 2016; 40: 237–43.Google Scholar
13McMillan, D, Gilbody, S, Richards, D. Defining successful treatment outcome in depression using the PHQ-9: a comparison of methods. J Affect Disord 2010; 127: 122–9.Google Scholar
14Kroenke, K, Spitzer, RL, Williams, JB, Monahan, PO, Lowe, B. Anxiety disorders in primary care: prevalence, impairment, comorbidity and detection. Ann Int Med 2007; 146: 317–25.Google Scholar
15Tennant, R, Hiller, L, Fishwick, R, Stephen, P, Joseph, S, Weich, S, et al. The Warwick-Edinburgh Mental Well-being Scale (WEMWBS): development and UK validation. Health Qual Life Outcomes 2007; 5: 63.Google Scholar
16Stewart-Brown, S, Tennant, A, Tennant, R, Platt, S, Parkinson, J, Weich, S. Internal construct validity of the Warwick-Edinburgh Mental Well-being Scale (WEMWBS): a Rasch analysis using data from the Scottish Health Education Population Survey. Health Qual Life Outcomes 2009; 7: 1522.Google Scholar
17Haver, A, Akerjordet, K, Caputi, P, Furunes, T, Magee, C. Measuring mental well-being: A validation of the Short Warwick–Edinburgh Mental Well-Being Scale in Norwegian and Swedish. Scand J Public Health 2015; 43(7): 721–7.Google Scholar
18Milnes, D, Owens, D, Blenkiron, P. Problems reported by deliberate self-harm patients: perception, hopelessness and suicidal intent. J Psychosom Res 2002; 53(3): 819–22.Google Scholar
19IBM Corp. IBM SPSS Statistics for Windows, Version 22.0. IBM released, 2013.Google Scholar
20Bell, MA, Fairclough, DL, Fiero, MH, Butow, PN. Handling missing items in the Hospital Anxiety and Depression Scale (HADS): a simulation study. BMC Res Notes 2016; 9(1): 479.Google Scholar
21Fairclough, DL, Cella, DF. Functional Assessment of Cancer Therapy (FACT-G): non-response to individual questions. Qual Life Res 1996; 5(3): 321–9.Google Scholar
22Altman, DG. Practical Statistics for Medical Research. Chapman and Hall, 1991.Google Scholar
23Cohen, J. Statistical Power Analysis for the Behavioral Sciences (2nd edn). Lawrence Earlbaum Associates, 1988.Google Scholar
24World Health Organization. International Classification of Diseases (Vol. 10). WHO, 1992.Google Scholar
25Löwe, B, Decker, O, Müller, S, Brähler, E, Schellberg, D, Wolfgang, H, et al. Validation and standardization of the Generalized Anxiety Disorder Screener (GAD-7) in the general population. Med Care 2008; 46(3): 266–74.Google Scholar
26Kertz, S, Bigda-Peyton, J, Bjorgvinsson, T. Validity of the Generalised Anxiety Disorder-7 scale in an acute psychiatric sample. Clin Psychol Psychother 2013; 20(5): 456–64.Google Scholar
27Richards, DA, Borglin, G. Implementation of psychological therapies for anxiety and depression in routine practice: two year prospective cohort study. J Affect Disord 2011; 133: 5160.Google Scholar
28Gilbody, S, Littlewood, E, Hewitt, C, Brierley, G, Tharmanathan, P, Araya, R. Computerised cognitive behaviour therapy as treatment for depression in primary care (REEACT trial): large scale pragmatic randomised controlled trial. BMJ 2015; 351: h5627.Google Scholar
29Timimi, S. Children and young people's improving access to psychological therapies: inspiring innovation or more of the same? BJPsych Bull 2015; 39: 5760.Google Scholar
30Keetharuth, AD, Brazier, J, Connell, J, Bjorner, JB, Carlton, C, Buck Taylor, E, et al. Recovering Quality of Life (ReQoL): a new generic self-reported outcome measure for use with people experiencing mental health difficulties. Br J Psychiatry 2018; 212(1): 42–9.Google Scholar
31Blenkiron, P, Hammill, CA. What determines patients’ satisfaction with their mental health care and quality of life? Postgrad Med J 2003; 79(932): 337–40.Google Scholar
32Van Sonderen, E, Middel, B. Statistical significant change versus relevant or important change in (quasi) experimental design: some conceptual and methodological problems in estimating magnitude of intervention-related change in health services research. Int J Integr Care 2002; 2: e15.Google Scholar
Figure 0

Table 1 Completion rates for outcome measures

Figure 1

Table 2 Correlations between measures for initial and follow-up appointments

Figure 2

Table 3 Initial and final scores for outcome measures

Figure 3

Table 4 Mental health superclusters: age, risk, functioning and problem solving, initial responses

Figure 4

Table 5 Mental health superclusters: initial and final outcome scores

Submit a response

eLetters

No eLetters have been published for this article.