CG123 Common mental health disorders - National Institute for ...

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Further assessment of risk and need for treatment, and routine outcome monitoringsymptoms to have a longer period of impaired occupational functioning when theyhad reached remission.Factors predicting non-adherence to treatmentOne of the key factors associated with treatment response is rate of adherence to treatment.POMPILI2009 conducted a systematic review of the factors that can influencelikelihood of treatment non-adherence. Reported rates of non-adherence increaseweekly, starting at 16% in week one and increasing to 41% in week two, 59% in weekthree and 68% in week four. Approximately 30% of people on antidepressants willstop taking them within 1 month of commencing treatment, with as many as 60%stopping by 3 months.Many factors predict non-adherence. These include medication-specific issues,service user factors and physician factors. Looking first at medication-specificissues, problems of non-adherence have been associated with treatment-relatedadverse events, delayed onset of action, complicated dosage, titration schedule andsub-therapeutic dosing, hyperstimulation due to antidepressants, non-cooperationfrom the service users’ family, fear of the drug and a lack of an adequate long-termtreatment regimen (POMPILI2009).Looking next at patient factors, POMPILI2009 also identified a number of variablesthat could influence adherence. Poor motivation, failure to perceive a benefit,education, age of first episode, presence of melancholia or anxiety, psychiatricmorbidity or clinical setting and concerns about the cost have all been found to influencenon-adherence.Finally, with regard to physician factors, it has been found that the patient–doctorrelationship is vital in determining adherence. According to POMPILI2009, having agood alliance can enhance long-term treatment adherence, reduce drop-out rates toless than 10% and increase adherence rates to more than 85%. The alliance has beenfound to be especially important during the early stages of treatment, particularlywhen the short-term benefits are often outweighed by adverse events.6.3.6 Clinical evidence summaryResponse to antidepressant treatment is influenced by an array of factors. Initially,more severe depression, reactive depression and comorbid depression may all beassociated with reduced response to treatment. The GDG considered these factors andfelt many were already taken into account in the existing guidelines and recommendations.Specific personality factors have also been identified in the reviews as havinga potentially important role, with passive/aggressive personality, antisocial personalitytraits, mild depression with personality dysfunction, high harm avoidance and highreward dependence, high harm avoidance and low reward dependence, and low harmavoidance and high reward dependence all being identified as possible causes of areduced likelihood of recovery with antidepressant treatment. Similarly, rumination,poor occupational functioning, psychosocial vulnerabilities and length of intervalbetween the onset of depressive symptoms and commencement of treatment may also165
Further assessment of risk and need for treatment, and routine outcome monitoringTable 34: Summary of findings for systematic reviews of factors that predicttreatment response/recurrence of depression/non-adherenceStudy ID Outcome Summary of findingsDODD2004 Treatment The following non-biological factorsresponse predicted better response:• moderate depression (compared with severedepression)• endogenous depression (compared withsituational/reactive depression)• high autonomy and low sociotropy• high co-operativeness andself-directedness• high reward dependence and novelty seekingand low harm avoidance• greater non-verbal attunement between patientand interviewer• psychiatrist’s initial optimism• strong alliance between therapist and service user• strongly held religious beliefs and activities.The following non-biological factors predictedpoor response:• comorbidity of GAD• comorbidity of panic disorder• bipolarity• alcohol misuse and dependance• poor occupational functioning.Biological predictive factors included neuroendocrinefactors, platelet markers, electroencephalographicmarkers and magnetic resonancemarkers, but the review authors suggest thatwhile these are useful in research they are of onlylimited use to the treating clinican.FEKADU2009 Depression The following factors predicted good outcomerecovery and recovery:• initial responsiveness to lithium• absence of previous history of admission• shorter duration of illness at intake• less severe illness during follow-up.The following factors predicted poorer outcomeand readmission:• prior history of treatment with lithium• presence of delusions and agitation.Age, sex and history of dysthymia were notpredictive of recovery.HARDEVELD2010 Recurrence The following factors predictedof MDD recurrence of MDD:• the number of previous episodes• subclinical residual symptoms after recoveryfor the last episode.166
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Commonmental healthdisordersTHE NIC
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© The British Psychological Societ
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Contents6 FURTHER ASSESSMENT OF RIS
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Guideline development group members
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Preface1 PREFACEThis guideline has
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Prefacecollaborative manner using t
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PrefaceThe experience of people wit
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Common mental health disorders2.2 T
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Common mental health disordersto ho
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Common mental health disordersManua
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Common mental health disordershighe
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Common mental health disordersNegat
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Common mental health disordersover
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Common mental health disorders2.2.6
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Common mental health disordersTable
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Common mental health disorders1996)
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Common mental health disordersSumma
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Common mental health disordersTable
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Common mental health disordersappli
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Methods used to develop this guidel
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Access to healthcare4. ACCESS TO HE
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Access to healthcareunder the Menta
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Access to healthcare4.2.3 Studies c
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Access to healthcareIndividual-leve
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Access to healthcareJUNG2003 review
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Access to healthcareSCHEPPERS2006).
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Access to healthcare70Table 8: Clin
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Access to healthcareTable 9: Study
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Access to healthcareCHAPMAN2004 inc
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Access to healthcare4.4 SERVICE DEV
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Access to healthcareBEACH2006 [Beac
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Access to healthcareTable 12: Summa
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Access to healthcareAll studies wer
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Access to healthcarepractitioners h
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Access to healthcareIndividual-leve
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Access to healthcarefrom such popul
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Access to healthcarestructure and d
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Summary of recommendations8.1.1.6 S
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Summary of recommendationsFigure 12
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Summary of recommendations●Genera
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Summary of recommendations8.4 STEPS
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Summary of recommendations●●a g
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Summary of recommendationsDiscuss w
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Summary of recommendationsinclude p
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Appendices9 APPENDICESAppendix 1: S
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Appendix 1of a depressive disorder
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Appendix 1b) Assessment of anxiety
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Appendix 2APPENDIX 2:DECLARATIONS O
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Appendix 2Mr Mike BessantEmployment
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Appendix 2Personal non-pecuniary in
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Appendix 2Actions takenMr Rupert Su
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Appendix 2Personal family interestN
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Appendix 3APPENDIX 3:STAKEHOLDERS A
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Appendix 4ClinicalpopulationPeople
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Appendix 4Case identificationNo. Pr
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Appendix 5APPENDIX 5:REVIEW PROTOCO
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Appendix 7APPENDIX 7:METHODOLOGY CH
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Appendix 9APPENDIX 9:METHODOLOGY CH
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Appendix 92.10 Are all important pa
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Appendix 9case also excludes costs
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Appendix 9For consistency, the EQ-5
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Appendix 9Answer ‘yes’ if the a
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Appendix 9Answer ‘yes’ if the e
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Appendix 9Answer ‘yes’ if appro
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Appendix 10APPENDIX 10: EVIDENCE TA
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Appendix 11APPENDIX 11:HIGH PRIORIT
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Appendix 11discussion of options bu
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Appendix 13APPENDIX 13:GENERALIZED
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ReferencesAndlin-Sobocki, P., Jöns
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ReferencesBower, P., Gilbody, S., R
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ReferencesCooper, S., Smiley, E., M
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ReferencesEhlers A., Gene-Cos N. &
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ReferencesGiles, D. E., Jarrett, R.
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ReferencesHorowitz, M. J., Wilner,
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ReferencesKnapp, M. & Ilson, S. (20
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ReferencesMarks, J., Goldberg, D. P
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ReferencesNCCMH (forthcoming) Self-
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ReferencesOzer, E. J., Best, S. R.,
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ReferencesSartorius, N. (2002) Eine
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ReferencesTiemens, B. G., Ormel, J.
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ReferencesWilkinson, M. J. B. & Bar
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Glossarybeliefs and interpretations
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Glossaryto learn to become more awa
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Abbreviations12 ABBREVIATIONSADDADS
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AbbreviationsOASISOCDONSORPHQ (-A,
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