Showing posts with label Nonsuidical Self-Injury. Show all posts
Showing posts with label Nonsuidical Self-Injury. Show all posts

Thursday, February 25, 2016

Young People Who Self-Harm: A Prospective 1-Year Follow-Up Study

Purpose
To explore repetition, service provision and service engagement following presentation of young people to emergency services with self-harm.

Methods
969 patients who presented to accident and emergency services after self-harm were followed up prospectively for a period of 1 year. Data on rates, method, clinical history, initial service provision, engagement and repetition (defined as re-presenting to emergency services with further self-harm) were gathered from comprehensive electronic records.

Results
Young people were less likely to repeat self-harm compared to those aged 25 and above. A psychiatric history and a history of childhood trauma were significant predictors of repetition. Young people were more likely to receive self-help as their initial service provision, and less likely to receive acute psychiatric care or a hospital admission. There were no differences in engagement with services between young people and those aged 25 and above.

Conclusion
Younger individuals may be less vulnerable to repetition, and are less likely to represent to services with repeated self-harm. All young people who present with self-harm should be screened for mental illness and asked about childhood trauma. Whilst young people are less likely to be referred to psychiatric services, they do attend when referred. This may indicate missed opportunity for intervention.

Method of self-harm and precipitating factors by age group
VariablesaAll ages16–24 years25+ years
n (%)n (%)n (%)
Method of SH (969)
 Self-poisoning704 (72.7)236 (76.4)468 (70.9)
 Self-injury214 (22.0)59 (19.1)155 (23.5)
 Both self-injury and self-poisoning51 (5.3)14 (4.5)37 (5.6)
Drugs in overdose
 Single drug in overdose (931)418 (44.9)144 (48.2)274 (43.4)
 Paracetamol127 (13.6)59 (20.3)68 (11.0)
 Opioid analgesic55 (5.9)18 (6.2)37 (6.0)
 Antidepressant51 (5.5)16 (5.5)35 (5.6)
 Multiple drugs in overdose (931)297 (31.9)94 (31.4)203 (32.1)
Self-injury (969)
 Self-cutting156 (16.1)48 (15.5)108 (16.4)
 Other self-injury109 (11.2)26 (8.4)83 (12.6)
Alcohol with SH (966)334 (34.5)69 (22.3)265 (40.1)
Precipitating factors to SH
 Alcohol misuse (957)289 (30.2)50 (16.4)239 (36.6)
 Drug misuse (956)145 (15.2)47 (15.5)98 (15.0)
 Child abuse (sexual/physical/emotional) (952)204 (21.4)80 (26.4)124 (19.1)
 Adult abuse (sexual/physical/emotional) (943)109 (11.4)33 (10.9)76 (11.7)
 Bereavement (955)145(15.2)30 (9.9)115 (17.6)
 Financial problems (956)113 (11.8)20 (6.6)93 (14.3)
 Housing problems (957)75 (7.8)16 (5.2)59 (9.0)
 Legal problems (959)29 (3.0)7 (2.3)22 (3.4)
 Relationship problems (958)445 (46.5)165 (54.1)280 (42.9)
 Physical health problems (960)278 (29.0)55 (18.0)223 (34.1)
 Self-harm in response to symptoms (961)36 (3.7)11 (3.6)25 (3.8)
aNumber of patient cases with available information varied between variables

Below:  Kaplan–Meier curve showing cumulative probability of self-harm repetition by age groups


Full article at:   http://goo.gl/uydTsf

College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK
Birmingham and Solihull Mental Health Foundation Trust, Birmingham, UK
Warwick University, Coventry, UK
Department of Psychiatry, University of Oxford, Oxford, UK
Oxford Health NHS Foundation Trust, Oxford, UK
Rachel Upthegrove,  ku.ca.mahb@evorgehtpu.r.
*Corresponding author.




Thursday, December 24, 2015

Psychotic-Like Experiences and Nonsuidical Self-Injury in England: Results from a National Survey

Background
Little is known about the association between psychotic-like experiences (PLEs) and nonsuicidal self-injury (NSSI) in the general adult population. Thus, the aim of this study was to examine the association using nationally-representative data from England.

Methods
Data from the 2007 Adult Psychiatric Morbidity Survey was analyzed. The sample consisted of 7403 adults aged ≥16 years. Five forms of PLEs (mania/hypomania, thought control, paranoia, strange experience, auditory hallucination) were assessed with the Psychosis Screening Questionnaire. The association between PLEs and NSSI was assessed by multivariable logistic regression. Hierarchical models were constructed to evaluate the influence of alcohol and drug dependence, common mental disorders, and borderline personality disorder symptoms on this association.

Results
The prevalence of NSSI was 4.7% (female 5.2% and male 4.2%), while the figures among those with and without any PLEs were 19.2% and 3.9% respectively. In a regression model adjusted for sociodemographic factors and stressful life events, most types of PLE were significantly associated with NSSI: paranoia (OR 3.57; 95%CI 1.96–6.52), thought control (OR 2.45; 95%CI 1.05–5.74), strange experience (OR 3.13; 95%CI 1.99–4.93), auditory hallucination (OR 4.03; 95%CI 1.56–10.42), and any PLE (OR 2.78; 95%CI 1.88–4.11). The inclusion of borderline personality disorder symptoms in the models had a strong influence on the association between PLEs and NSSI as evidenced by a large attenuation in the ORs for PLEs, with only paranoia continuing to be significantly associated with NSSI. Substance dependence and common mental disorders had little influence on the association between PLEs and NSSI.

Conclusions
Borderline personality disorder symptoms may be an important factor in the link between PLEs and NSSI. Future studies on PLEs and NSSI should take these symptoms into account.

Below:  Prevalence of nonsuicidal self-injury by the presence of psychotic-like experience. Abbreviation: PLE Psychotic-like experience. Bars denote 95% confidence intervals. Prevalence figures are based on weighted sample.



Full article at:   http://goo.gl/w0BF2U

By:   
Ai Koyanagi, Josep Maria Haro
Parc Sanitari Sant Joan de Déu, Universitat de Barcelona, Fundació Sant Joan de Déu, Sant Boi de Llobregat, Barcelona, Spain

Ai Koyanagi, Josep Maria Haro
Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental, (CIBERSAM), Madrid, Spain

Andrew Stickley
The Stockholm Centre for Health and Social Change (SCOHOST), Södertörn University, Huddinge, Sweden

Andrew Stickley
Department of Human Ecology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan