

24th European Congress of Psychiatry / European Psychiatry 33S (2016) S116–S348
S223
Disclosure of interest
The authors have not supplied their decla-
ration of competing interest.
http://dx.doi.org/10.1016/j.eurpsy.2016.01.431Guidelines/guidance
EW314
Including mental health in emergency
response: Lessons learnt from the
Ebola virus disease outbreak in West
Africa
A. Mohammed
1 ,∗
, T. Sheikh
1, P. Nguku
2, A. Olayinka
2,
C. Ohuabunwo
2, G. Pogenssee
2, J. Eaton
31
Federal Neuro-psychiatric hospital, Clinical services, Kaduna,
Nigeria
2
Nigerian Field Epidemiology and Laboratory Training Program,
Resident Advisor, Abuja, Nigeria
3
London School of Hygiene and Tropical Medicine, Lecturer, London,
United Kingdom
∗
Corresponding author.
Ebola virus disease (EVD) outbreaks create widespread panic, fear
and anxiety. Psychological disorder and distress has been demon-
strated among survivors and contacts of EVD and their relations,
potentially having a negative effect on contact tracing. In the
recently controlled outbreak inNigeria, mental healthprofessionals
played active roles in case management, contact tracing, opera-
tional research and development of an emergency response plan.
At-risk countries need to have a proactive intervention strategy
that involves mental health professionals in response to disease
outbreaks. This ensures comprehensive support for people during
outbreaks that address mental health as well as physical needs of
the community.
Disclosure of interest
The authors have not supplied their decla-
ration of competing interest.
http://dx.doi.org/10.1016/j.eurpsy.2016.01.432EW315
Effectiveness and cost-effectiveness of
a cardiovascular risk prediction
algorithm for people with severe
mental illness
E. Zomer
1 ,∗
, D. Osborn
2 , 3, I. Nazareth
1, R. Blackburn
2,
A. Burton
2, S. Hardoon
1, R.I.G. Holt
4, M. King
2, L. Marston
1,
S. Morris
5, R. Omar
6, I. Petersen
1, K. Walters
1, R.M. Hunter
11
University College London, Department of Primary Care and
Population Health, London, United Kingdom
2
University College London, Division of Psychiatry, London, United
Kingdom
3
National Health Service, Camden and Islington National Health
Service Foundation Trust, London, United Kingdom
4
University of Southampton, Human Development and Health
Academic Unit, Southampton, United Kingdom
5
University College London, Department of Applied Health Research,
London, United Kingdom
6
University College London, Department of Statistical Science,
London, United Kingdom
∗
Corresponding author.
Introduction
Cardiovascular risk prediction tools are important
for cardiovascular disease (CVD) prevention, however, which algo-
rithms are appropriate for people with severe mental illness (SMI)
is unclear.
Objectives/aims
To determine the cost-effectiveness using the
net monetary benefit (NMB) approach of two bespoke SMI-specific
risk algorithms compared to standard risk algorithms for primary
CVD prevention in those with SMI, from an NHS perspective.
Methods
A microsimulation model was populated with 1000
individuals with SMI from The Health Improvement Network
Database, aged 30–74 years without CVD. Four cardiovascular risk
algorithms were assessed; (1) general population lipid, (2) gen-
eral population BMI, (3) SMI-specific lipid and (4) SMI-specific
BMI, compared against no algorithm. At baseline, each cardiovas-
cular risk algorithm was applied and those high-risk (> 10%) were
assumed to be prescribed statin therapy, others received usual care.
Individuals entered the model in a ‘healthy’ free of CVD health state
and with each year could retain their current health state, have
cardiovascular events (non-fatal/fatal) or die from other causes
according to transition probabilities.
Results
The SMI-specific BMI and general population lipid algo-
rithms had the highest NMB of the four algorithms resulting in 12
additional QALYs and a cost saving of approximately
£
37,000 (US$
58,000) per 1000 patients with SMI over 10 years.
Conclusions
The general population lipid and SMI-specific BMI
algorithms performed equally well. The ease and acceptability of
use of a SMI-specific BMI algorithm (blood tests not required)
makes it an attractive algorithm to implement in clinical settings.
Disclosure of interest
The authors have not supplied their decla-
ration of competing interest.
http://dx.doi.org/10.1016/j.eurpsy.2016.01.433Intellectual disability
EW316
Intellectual disability among
delusional disorder: A case series
register
C.M. Carrillo de Albornoz Calahorro
∗
, M. Guerrero Jiménez ,
A. Porras Segovia , J. Cervilla Ballesteros
Hospital Universitario San Cecilio, Unidad de Salud Mental, Granada,
Spain
∗
Corresponding author.
Introduction
The quoted prevalence of intellectual disability (ID)
among adults with psychiatric illness varies widely. Some believe
that these people are protected from certain intellectual and psy-
chological stress by having ID, and therefore, are less prone to
develop psychiatric illness. However, in the past decades, the more
prevailing view is that people with ID are more vulnerable to psy-
chosocial stress than people without ID, and therefore, are more
likely to develop psychiatric symptomatology. According to various
population surveys the probability of suffering a mental disability
increase with age. Delusional disorder is as well a disease related
to advanced stages of life.
Objectives/aims
The aims of the present study is to establish the
prevalence of functional intellectual disability among adults who
fulfil DSM 5 delusional disorder criteria.
Methods
Our data come from a case register study of delusional
disorder in Andalucia (Spanish largest region). By accessing digital
health data, we selected 1927 cases, which meet criteria DSM 5
for delusional disorder collecting whether in its history intellectual
disability was registered by the referent psychiatrist.
Results
Of our sample, 2.6% had reflected some kind of intellec-
tual disability in their digital clinical record.
Conclusion
These percentage has been found to concur with
other epidemiological studies linking mental retardation and psy-
chotic spectrumdisease although there are no epidemiological data