Opinion ❚ Personalised care
Personalised care in severe mental
illness – there’s no need to wait
Andrew Molodynski MBChB, MRCPsych, Rob Bale MBBS, FRCPsych, MD
R
epeated promises over several decades from
leading academics in imaging and genetics1–3 that
biologically personalised treatments were close and
would be available in ‘a few years’ have not
translated into routine clinical practice. Expansive
claims are still made by some, though most contemporary views are more realistic about the likely contribution of such approaches in the near future.4
Such ‘biological’ approaches have substantially and
importantly improved our knowledge and understanding of mental health disorders and should be
recognised as having done so. It is disappointing,
however, that these advances in understanding have
not led to significant advances in routine clinical
practice or outcomes.
Similar promises are now being made for ‘big
data’ generated by electronic records systems and its
ability to identify characteristics that will transform
healthcare.5,6 Smart-phone technology is also being
strongly promoted as the cornerstone of a ‘new era’
of healthcare, as demonstrated by a bewildering variety of apps now available.7 It is early days and some
are already urging caution in interpreting the results
of studies using metadata for methodological reasons.8 While there is no doubt there will be significant gains, the largely unfulfilled previous claims of
the imaging specialists and geneticists would suggest
we should not expect miracles.
During this prolonged period of ultimately unfulfilled claims from different disciplines, high quality
evidence has steadily accumulated that does support
personalised care, albeit in a different way. Numerous
randomised controlled trials (RCTs) and systematic
reviews have been reported that strongly support the
use of specific interventions for people with specific
deficits. While we may not be able to put our patients
in a scanner or run a blood test to select a specially
designed medication, we can sit and discuss with
them a raft of evidence-based interventions that
should improve well-being and functioning. This
more holistic view of individualised treatment could
include many elements, encompassing both whole
service approaches and individual interventions targeted at specific difficulties faced by those with severe
mental illness (SMI). Below we outline a few examples but the list is by no means exhaustive.
4
We have known for many years that patients with
psychosis have poor physical health and die much
younger than the general population, even after
allowing for suicide. One recent study9 estimated a
reduction of 18.7 years and 16.3 years in men and
women with schizophrenia, respectively – a shocking
health inequality. The causes for this are complex,
but we have the evidence-based interventions to ameliorate or even eliminate some. Smoking cessation
therapy has strong evidence in mental health services10 and can significantly reduce morbidity and
mortality while conferring other benefits such as
reduced financial pressures and improved self-­
esteem. However, we know that many patients under
our care are not offered it, or are not assertively
assisted to take it up. We also know that metabolic
syndrome causes significant morbidity and reduces
functioning and that intervening early with lifestyle
support and a mindful choice of antipsychotic can
minimise the damage.11 Again, we do not always do
this, despite the literature clearly supporting individualised choice of medication in psychosis that takes
into account side-effect profiles, efficacy, and patient
characteristics.12 This evidence extends to the preferential use of long-acting injections (LAI) in those
who have had repeated episodes of illness,13 another
area where there is substantial variation in practice in
UK psychiatry.
The evidence base for individualised placement
and support is unquestionable now, with numerous
RCTs and systematic reviews14 showing meaningful
benefit in terms of return to functionality and
employment. Contingency management is an
approach in which reductions in substance misuse or
improved adherence to medication may be rewarded
by money or vouchers. It remains controversial and
little used. This appears to be a missed opportunity
given that recent empirical data, such as those from
the FIAT study,15 show clear evidence of benefit.
The availability of talking therapies for people
with SMI remains patchy and inadequate, with local
investments in increased availability frequently
appearing to be swallowed up by less unwell and more
vocal groups in primary care, backed by national drivers and targets. Family therapy is a good example of
a treatment that has strong evidence dating back over
Progress in Neurology and Psychiatry November/December 2016
www.progressnp.com
Personalised care ❚
three decades,16 but how many patients and families
receive it? Though the evidence for ‘traditional’ CBT
in psychosis is questionable, it is still considered by
many as a mainstay in psychosis management and studies of newer variants show substantial promise.17 There
is stronger evidence for its use in affective disorders so
perhaps we should be more selective referrers and try
to end the practice of just saying ‘here’s one for psychology’ in our multidisciplinary team (MDT) meetings without thinking of the evidence and how precious
resources can be used to create most benefit? Perhaps
we should be stronger advocates for the adoption of
evidence-based recovery and psychosocial interventions, as there can be barriers due to training needs or
other resource implications?18,19 This increased flexibility might serve our patients better than the current
rigidity of the stepped care approach and divided and/
or separate psychological services.
Not only do we have this evidence of effectiveness
for a variety of interventions, we have good quality evidence to inform the way in which we deliver them.
Accumulated evidence supports multidisciplinary
working, outreach, and home visiting by dedicated
teams with direct medical input.20 These key ingredients may be more important than overall service
design and are often overlooked in our preoccupation
with complete service models such as crisis teams, early
intervention services, assertive community treatment
teams and now Functional Assertive Community Treatment Teams (FACT). The increased fragmentation of
services and introduction of novel service arrangements that are not adequately based on practice or
research findings during times of austerity21 is concerning and likely to exacerbate this discrepancy
between evidence and practice.
The list above is by no means exhaustive but, intelligently used within systems that support such thinking, its contents would allow for genuinely personalised
care of patients with evidence-based interventions that
improve functioning, reduce symptoms, and/or
reduce admissions. So why do we not succeed in this
aim more often? The answers to this are undoubtedly
complex, but one key factor is the failure to rigorously
assess and clarify strengths, weaknesses, and wider environmental and social factors at initial contact. Such
clarity could be followed by an individually tailored
care plan to address these needs. There is a sound
rationale for assessing social, occupational and relationship functioning22 and several specific measures
with good reliability and validity to do so.23,24 They do
not take long to use. It is disappointing that despite
this such measures are rarely used in clinical practice.
Even when they are they often appear to be in a ‘tick
box’ fashion’ and not genuinely to direct patient care.
www.progressnp.com
Perhaps this is because it is compulsory in UK services
(and many elsewhere) to use Health of the Nation
Outcome Scales HONOS, or the ‘care clustering tool’
as it is now commonly known. The way it is often used
to meet administrative demands does not generally
enhance clinical practice and certainly does not lead
to tailored and personalised care packages that are
evidence based. It would undoubtedly be possible to
use clustering and its associated assessment better,
however, and certainly if clustering were rigorously
applied it could lead to the greater use of evidence-­
based interventions.
We believe we could do much better, with no
increase in resources, if services were allowed to choose
on a rational basis which evidence-based outcome
measures to use and were allowed to continue to ‘follow the evidence’ as to which interventions to use and
shape the services to deliver them. By empowering staff
to take responsibility for identifying desired outcomes
with patients and ensuring the delivery of evidence-based interventions we can improve patient satisfaction with care, reduce inefficiency, and put the
‘smile’ back into services that have all too frequently
become formulaic. Most importantly, we believe we
can improve outcomes in their widest sense and we can
do it now, dependent on no breakthroughs in science.
These will undoubtedly come but perhaps not for
some time – our patients need not wait. Andrew Molodynski is Consultant Psychiatrist at Oxford
Health NHS Foundation Trust and Honorary Senior
Clinical Lecturer at Oxford University and Rob Bale is
Consultant Psychiatrist and Clinical Director of Adult
Services at Oxford Health NHS Foundation Trust.
Declaration of interest
Andrew Molodynski has been involved in the development and publication of the MINI ICF social functioning measure. Rob Bale has no declaration of
interest to make.
References
1. Owen MJ, McGuffin P. Genetics and psychiatry. Br J Psychiatry
1997;171(3):201–2.
2. Rutter MR, Plomin R. Opportunities for psychiatry from genetic
findings. Br J Psychiatry 1997;171(3):209–19.
3. Price BH, Adams RD, Coyle JT. Neurology and psychiatry: closing the
great divide. Neurology 2000;54(1):8–14.
4. Linden DEJ, Fallgatter AJ. Neuroimaging in psychiatry: from bench
to bedside. Front Hum Neurosci 2009;3:49.
5. Murdoch TB, Detsky AS. The inevitable application of big data to
health care. JAMA 2013;309(13):1351–2.
6. Monteith S, Glenn T, Geddes J, et al. Big data are coming to
psychiatry: a general introduction. Int J Bipolar Disord 2015;3(1):21.
7. Torous J, Staples P, Onnela J-P. Realizing the potential of mobile mental
health: new methods for new data in psychiatry. Curr Psychiatry Rep
2015;17(8):1–7.
Progress in Neurology and Psychiatry November/December 2016
5
Opinion
Opinion ❚ Personalised care
8. Duncan LE, Keller MC. A critical review of the first 10 years of candidate
gene-by-environment interaction research in psychiatry. Am J Psychiatry
2011;168(10):1041–9.
9. Laursen TM. Life expectancy among persons with schizophrenia
or bipolar affective disorder. Schizophr Res 2011;131(1–3):101–4.
10. El-Guebaly N, Cathcart J, Currie S, et al. Smoking cessation
approaches for persons with mental illness or addictive disorders.
Psychiatr Serv 2002;53(9):1166–70.
11. Meyer JM, Stahl SM. The metabolic syndrome and schizophrenia.
Acta Psychiatr Scand 2009;119(1):4–14.
12. Leucht S, Corves C, Arbter D, et al. Second-generation versus
first-generation antipsychotic drugs for schizophrenia: a metaanalysis. Lancet 2009;373(9657):31–41.
13 Leucht C, Heres S, Kane JM, et al. Oral versus depot antipsychotic
drugs for schizophrenia – a critical systematic review and metaanalysis of randomised long-term trials. Schizophr Res 2011;127(1–
3):83–92.
14. Crowther RE, Marshall M, Bond GR, et al. Helping people
with severe mental illness to obtain work: systematic review. BMJ
2001;322(7280):204–8.
15. Priebe S, Yeeles K, Bremner S, et al. Effectiveness of financial
incentives to improve adherence to maintenance treatment
with antipsychotics: cluster randomised controlled trial. BMJ
2013;347:f5847.
16. Vaughn CE, Leff JP. The influence of family and social factors on
the course of psychiatric illness. A comparison of schizophrenic and
depressed neurotic patients. Br J Psychiatry 1976;129:125–37.
17. Freeman D, Dunn G, Startup H, et al. Effects of cognitive
behaviour therapy for worry on persecutory delusions in patients
with psychosis (WIT): a parallel, single-blind, randomised controlled
trial with a mediation analysis. Lancet Psychiatry 2015;2(4):305–13.
18. van der Krieke L, Bird V, Leamy M, et al. The feasibility of
implementing recovery, psychosocial and pharmacological inter­
ventions for psychosis: comparison study. Implement Sci 2015;
10:73.
19. Bucci S, Berry K, Barrowclough C, et al. Family interventions in
psychosis: A review of the evidence and barriers to implementation.
Australian Psychologist 2016;51:62–8.
20. Williams C, Firn M, Wharne S, et al., eds. Assertive outreach
in mental healthcare: current perspectives. Oxford: WileyBlackwell, 2011.
21. The King’s Fund. Briefing: mental health under pressure (www.
kingsfund.org.uk/sites/files/kf/field/field_publication_file/mentalhealth-under-pressure-nov15_0.pdf; accessed November 2016).
22. Brissos S, Molodynski A, Dias VV, et al. The importance of
measuring psychosocial functioning in schizophrenia. Ann Gen
Psychiatry 2011; 10:18.
23. Morosini, PL, Magliano L, Brambilla L, et al. Development,
reliability and acceptability of a new version of the DSM‐IV Social
and Occupational Functioning Assessment Scale (SOFAS) to assess
routine social functioning. Acta Psychiatr Scand 2000;101(4):323–9.
24. Molodynski A, Linden M, Juckel G, et al. The reliability, validity,
and applicability of an English language version of the Mini-ICFAPP. Soc Psychiatry Psychiatr Epidemiol 2013;48(8):1347–54.
POEMs
The use of SSRIs in pregnancy associated with preterm birth
Clinical question
Is the use of selective serotonin reuptake inhibitors in pregnancy associated with a risk of preterm
birth?
Reference
Eke AC, Saccone G, Berghella V. Selective serotonin reuptake inhibitor (SSRI) use during pregnancy and risk of preterm birth: a systematic
review and meta-analysis. BJOG 2016;123(12):
1900-1907.
Study design Meta-analysis (other) Funding
source: Self-funded or unfunded Setting: Various
(meta-analysis)
Synopsis
This study was a meta-analysis of 8 observational
studies (N = 1,237,669 women) regarding the
association of taking SSRIs during pregnancy
and preterm birth. Studies were included if they
reported the rate of preterm birth among
women exposed to SSRIs during pregnancy (n =
93,982) compared with untreated control
patients. The untreated control groups
6
included either all women without depression,
or women with depression but no exposure to
SSRIs (treated with psychotherapy alone). Two
studies were of fluoxetine, 2 were of paroxetine,
and the remainder were of any SSRI. Exposure
to SSRIs in pregnancy was associated with preterm birth compared with no exposure (11.6%
vs 5.2%; odds ratio [OR] 1.45; 95% CI 1.24 1.68). After adjusting for known potential confounding variables such as maternal age,
smoking status, and parity the association was
still statistically significant (adjusted odds ratio
1.24; 1.09 - 1.41). The analysis did not control
for the severity of depression, which could be
an important confounding factor. Neonates of
women exposed to SSRIs also showed significant
associations with respiratory distress syndrome
and lower birth weight. In the comparison of
SSRI-exposed women to the group of depressed
women who were not prescribed antidepressants the association was smaller, but still statistically significant (6.8% vs 5.8%; OR 1.17; 1.10
- 1.25). A future re-analysis should exclude paroxetine, since it is now classified as category D
in pregnancy.
Progress in Neurology and Psychiatry November/December 2016
www.progressnp.com