| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Psychiatric Bulletin | Advances in Psychiatric Treatment | All RCPsych Journals |
|
|
|
||||||||||
|
||||||||||||
Centre for Suicide Prevention, University of Manchester, UK
Centre for Women’s Mental Health, University of Manchester, UK
Centre for Suicide Prevention, University of Manchester, UK
Correspondence: Dr N. Kapur, Centre for Suicide Prevention, Williamson Building, University of Manchester, Oxford Road, Manchester M13 9PL, UK. Tel: +44 (0)161 275 0733; fax: +44 (0)161 275 0716; email: nav.kapur{at}manchester.ac.uk
Declaration of interest None. Funding detailed in Acknowledgements.
 |
ABSTRACT |
|---|
 TOP ABSTRACT INTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
|---|
Aims To investigate the association between area-level factors and incidence and repetition of self-harm, and to identify which area-level factors are independently associated with repetition after adjustment for individual factors.
Method Prospective cohort study using the Manchester Self-Harm database. Adults who were resident in Manchester and presented to an emergency department following self-harm between 1997 and 2002 were included (n = 4743). The main outcome measure was repeat self-harm within 6 months of the index episode.
Results Four individual factors (previous self-harm, previous psychiatric treatment, employment status, marital status) and one area-based factor (proportion of individuals who were of White ethnicity) were independently associated with repetition.
Conclusions Repetition of self-harm may be more strongly related to individual factors than to area characteristics. We need to better understand the processes underlying ecological associations with suicidal behaviour before embarking on area-based interventions.
|
|
INTRODUCTION |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
|---|
|
|
METHOD |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
|---|
The electoral ward of residence was established by linking individuals’ postcodes to ward codes for boundaries using a geography conversion table provided by the Updated UK Area Master-files project (Simpson & Yu, 1999). There were no ward boundary changes during the period under study (Office for National Statistics, 2002). Patients with no fixed abode (n=128, 0.03%) and those for whom ward of residence could not be established (n=84, 0.02%) were excluded from analysis.
Outcome measures
For each electoral ward in the Metropolitan Borough (n=33), the
self-harm incidence rate (per 100 000 persons per year) was estimated using
the resident population estimate from the 2001 census as a denominator.
Individuals who repeated self-harm within 6 months of their first episode during the study period (index episode) were identified by linking episodes to individuals on the MASH database. The cut-off point chosen was 6 months because the majority (over 75%) of those who repeat within a year do so within this time period (Gilbody et al, 1997).
For each electoral ward in Manchester, the 6-month repetition rate was estimated using the number of patients (per ward of residence) with an index self-harm episode as the denominator.
Individual and ward characteristics
Individual-level socio-demographic and clinical factors for analyses were
obtained from the MASH database, which contains a large number of variables.
The individual factors considered in this study were specified a
priori on the basis of their clinical importance and their association
with repeat self-harm in previous studies
(NHS Centre for Reviews and Dissemination,
1998; Sakinofsky,
2000).
Ward-level socio-demographic variables were selected from various sources to provide indicators for a wide range of area characteristics, including those found to be associated with area rates of self-harm in the general population. Measures for ward levels of unemployment, economic inactivity due to permanent sickness or disability, population turnover, single-person households, White ethnicity and concentrated advantage (the proportion of households where the head of the household is in a professional, managerial or technical job) were derived from Census 2001 tables provided by the Office for National Statistics on DVD in Supertable format. The Townsend Index is a widely used composite deprivation measure derived using four census variables: the proportion of non-owner-occupied households; the proportion of households without access to a car; the proportion of overcrowded households; and the proportion of individuals who are unemployed (Townsend et al, 1986). The measure for social fragmentation is another composite measure based on four census variables: population turnover; the proportion of single-person households; the proportion of unmarried adults; and the proportion of households living in private rented accommodation (Congdon, 1996).
Several indicators of deprivation were included to enable comparison with the Townsend Index, which has previously been found to be independently associated with ward rates of self-harm (Congdon, 1996; Hawton et al, 2001). These included the Index of Multiple Deprivation (IMD) 2000 (Office for National Statistics, 2003), the separate IMD 2000 domains and concentrated advantage. The IMD 2000 is based on both census and administrative data sources, and provides an overall measure and six separate domains (income, employment, health, education, housing and access to services) which reflect different aspects of deprivation. A seventh domain is also available (child poverty), but this does not contribute to the overall IMD score. The IMD was commissioned by the Department of Transport, Local Government and the Regions, and was obtained by download from the neighbourhood statistics website (Office for National Statistics, 2003).
Data for the proportion of school leavers entering continuing education were collated by Career Partnership and were downloaded from the Community Health Information Profile (CHIP) for Manchester website (Manchester Geomatics Limited, 2003). The measure of population density was provided by Manchester City Council. All these measures were complete for the 33 wards under study.
Statistical analyses
Analyses were conducted using Stata software, release 8.0. The degree of
association between area-level explanatory variables and ward-level self-harm
incidence rates was first assessed using the non-parametric Spearman rank
correlation coefficient (Bland,
2000). Logistic regression models were then fitted to identify the
predictors of self-harm repetition within 6 months (the individual-level
outcome). Initially, univariate models were fitted. A multivariate
individual-level model was then created using backwards elimination procedures
to enable mutual adjustment for individual characteristics. The degree of
association between the area-level explanatory variables and self-harm
repetition rates was then assessed using the nonparametric Spearman rank
correlation coefficient. Finally, the area-level explanatory variables that
were statistically significant in the Spearman rank correlation analyses were
iteratively added to the multivariate individual-level model in a
forwards-stepwise fashion. As the final model was multi-level in nature, with
variables at both individual and area level, a survey variance estimator that
corrected for potential area-level clustering effects was involved. The
adjusted population attributable fraction (PAF) was used to calculate the
proportion of repetitions that were attributable to the risk factors in the
multivariate model (assuming a causal relationship between risk factors and
outcome). The PAF takes into account both the prevalence of a risk factor and
its relative risk (Benichou,
2001).
|
|
RESULTS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
|---|
Incidence of self-harm
There was considerable variation between the wards in the rates of
self-harm (Table 1). The
associations between the area-level explanatory variables and the ward-level
self-harm incidence rates (per 100 000 persons per year) are presented in
Table 2. The Spearman rank
correlation analyses indicated strong associations for most of the explanatory
variables, many of which were measures of material or social deprivation.
|
|
Individual-level predictors of self-harm repetition
A number of individual-level variables were significantly associated with
repetition. These included both socio-demographic variables (for example, age,
employment, marital status, living circumstances and White ethnicity) and
clinical variables (for example, previous self-harm, psychiatric treatment,
alcohol misuse and hopelessness at the index attempt).
Area-level predictors of self-harm repetition
As with incidence rates, there was considerable variation between the wards
in rates of repetition of self-harm (Table
1). The associations between the area-level explanatory variables
and ward-level self-harm repetition within 6 months are presented in
Table 3. In contrast to the
association between area measures and self-harm incidence rates
(Table 2), there was little
evidence that area-level measures predicted repetition at ward level. The only
variable significantly associated with poor outcome was the proportion of
individuals in a ward who were from a White ethnic group. For this variable,
the negative Spearman correlation coefficient (–0.39, P=0.02)
indicated that repetition rates were lower in wards with a predominantly White
ethnic profile.
|
Individual and area-level multivariate model
The final multivariate model, combining variables at both individual and
area levels is presented in Table
4. Variance inflation factors indicated that the model was not
subject to collinearity problems. Four individual-level predictors (previous
self-harm, previous psychiatric treatment, employment status and marital
status) were found to be independently associated with repetition, together
with one area-level variable (proportion of individuals with White ethnicity).
This variable was recategorised into tertiles in order to enhance
interpretability. People living in wards with a lower proportion of White
residents had a higher risk of repetition which was independent of the
individual-level covariates. A post-hoc analysis showed that this
relationship did not vary according to the ethnicity of the individual
(P value for interaction =0.98). The ecological association was in
the opposite direction to that observed for the individual-level ethnicity
variable; the univariate model indicated that White individuals were at higher
risk of repetition (OR=1.70, 95% CI 1.19–2.42), although this variable
was dropped from the multivariate model owing to non-significance.
|
The adjusted PAF estimates for the co-variates in the final model are also presented in Table 4. These were large, ranging from 15.9% for White ethnicity (area-level) to 44.4% for previous self-harm (individual-level), which partly reflects the high prevalence of the risk factors in this high-risk sample. The combined adjusted PAF for all variables in the model indicated that 78.8% of all self-harm repetitions were attributable to these independent predictors. However, this estimate should be treated cautiously, as the explanatory variables in general are not modifiable and the associations are unlikely to be causal.
|
|
DISCUSSION |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
|---|
Methodological issues
We used a wide variety of area-based measures from a number of sources, and
our multivariate analysis took account of ward-level clustering effects. Our
study is the first, to our knowledge, to attempt to quantify the association
between individual and area-level factors and repetition of self-harm.
However, as with all studies of this type, we were restricted in the amount of
data we could analyse – we did not measure all possible confounding and
explanatory variables.
The findings of the current study need to be interpreted in the context of its specific methodological shortcomings. First, this study investigated individuals who presented to teaching hospitals serving a relatively deprived inner-city area, and the results may not be generalisable to other settings. Equally, our findings may not be applicable to those who do not present for treatment following self-harm or those who choose not to wait after presenting to hospital. Second, although the response rate for the MASH project is good and we have no evidence that the response rate varied systematically by ward, males and those who use cutting as a method of harm may be under-represented in our sample. Third, it is possible that, for wards on the periphery of the study area, a proportion of patients attended hospitals which were not included in the MASH project. We do not have a direct estimate of the size of this effect for index episodes, but data from within the Manchester district suggest that repeat episodes are followed by presentation to the same hospitals as the index episodes in 80–90% of cases. Fourth, the number of repeat self-harm attempts in some wards was relatively small and we did not use statistical techniques such as Bayesian modelling (Richardson et al, 2004) to smooth the underlying risk estimates. However, the sensitivity of Bayesian disease-mapping models is low when (as in this study) the raised risks are moderate and the expected counts are less than 50 (Richardson et al, 2004). Fifth, we did not adjust for the fact that wards in close proximity to one another were likely to have similar exposure prevalence (spatial autocorrelation), but spatial autocorrelation may not be a major issue with ecological studies of suicidal behaviour (Wasserman & Stack, 1995). Sixth, we only considered two levels in this study: individual and small-area. There are suggestions that exposures which occur at other levels (for example, at the level of household) may also be important determinants of mental health (Weich et al, 2005).
We could have elected to analyse the data using survival methods and we have used this approach in previous individual-level studies (Cooper et al, 2005). Using survival analysis would have allowed us to make full use of the data by including varying lengths of follow-up. However, the influence of area on repetition risk may vary according to the length of time since the index episode. Longer periods of follow-up would also have made it more likely that an individual would have moved between areas. We therefore decided a priori to investigate repetition within a fixed period of 6 months.
Interpretation of findings
It was striking that only one area-based variable was associated with
repetition of self-harm. Why might this be? It is possible that the lower
number of repeat episodes of self-harm (when compared with index episodes)
limited the power of this study to detect significant associations. However,
the coefficients reported in Table
4 are less than 0.3 in either direction (with the exception of the
significant variable – White ethnicity), and type II error is therefore
unlikely to be the explanation. It is also possible that our unit of analysis
for area effects (electoral ward) was too large and heterogeneous to detect
contextual influences on repetition. However, the self-harm event data would
have become too sparse if we had used a smaller area-level unit than the ward.
Of course, it may be that our findings are correct and that area-based
influences were much more important for index cases of self-harm than for
repeat episodes. Our failure to find area-based predictors of repetition in
this comparatively large study could reflect the fact that such influences are
not clinically important. Only very few studies to date have found an
association between area-based factors and mental health after adjustment for
individual factors (Skapinakis et
al, 2005).
Individual-level risk factors appeared to be more important determinants of repeat self-harm than area characteristics. However, our final model did suggest that both individual- and area-based factors were independently associated with repetition. The finding, that the risk of self-harm increased as the proportion of individuals who were from a White ethnic background decreased, appears counter-intuitive. Being of White ethnicity (on an individual level) was associated with increased risk of repetition. This is an example of how area-based and individual-level exposures may affect risk differently. There are several possible explanations for this finding. First, the association may be spurious. A ward’s ethnic composition may simply be a proxy indicator of other exposures, for example relative deprivation or degree of social cohesion or other factors which we did not measure. A second explanation relates to the distribution of people who repeat self-harm within the borough. It is plausible that, given their characteristics, more of these individuals live in hostels, temporary accommodation and supported housing and that these types of accommodation may be concentrated in wards with more ethnically mixed populations. Third, the finding could reflect the underlying characteristics of the individuals who live in these ethnically diverse areas. Fourth, it could be a true effect, with the individual’s risk being modified by the prevalence of the exposure (in this case ethnicity) at a ward level. Neeleman et al (2001) found that the risk of self-harm behaviour associated with individual ethnicity was mediated by the local size of the individual’s ethnic group (as the size of the local ethnic population increased, the risk associated with Black and minority ethnicity on an individual level decreased). It could be that the degree to which an individual fits with the social environment influences the risk of adverse outcomes.
Clinical implications
If our findings are correct, then the repetition of self-harm may be more
strongly related to individual factors than to the characteristics of the
areas in which people live. This might suggest that the most productive
strategy to reduce repetition would be to focus on individual-level
interventions. However, area-based risk factors might also warrant
consideration – in our study, such factors accounted for approximately
16% of repeat episodes in the population. It is possible that area-based
interventions which, for example, seek to address issues related to social and
material deprivation, might be more effective in preventing the incidence of
self-harm rather than its repetition. We need to better understand the
processes underlying ecological associations with suicidal behaviour before
embarking on area-based interventions.
|
|
ACKNOWLEDGMENTS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
|---|
This study was funded by the Manchester Mental Health and Social Care Trust.
|
|
REFERENCES |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
|---|
Bland, M. (2000) An Introduction to Medical Statistics, 3rd edn. Oxford: Oxford University Press.
Congdon, P. (1996) Suicide and parasuicide in
London: a small area study. Urban Studies,
33, 137
–158.
Cooper, J., Kapur, N.,Webb, R., et al
(2005) Suicide after deliberate self-harm: a 4-year cohort
study. American Journal of Psychiatry,
162, 297
–303.
Department of Health (2002) National Suicide Prevention Strategy for England. London: Department of Health.
Gilbody, S., House, A. & Owens, D. (1997) The early repetition of deliberate self harm. Journal of the Royal College of Physicians, 31, 171 –172.
Gunnell, D., Shepherd, M. & Evans, M. (2000) Are recent increases in deliberate self-harm associated with changes in socio-economic conditions? An ecological analysis of patterns of deliberate self-harm in Bristol 1972–3 and 1995–6. Psychological Medicine, 30, 1197 –1203.[CrossRef][Medline]
Hawton, K., Harris, L., Hodder, K., et al (2001) The influence of the economic and social environment on deliberate self-harm and suicide: an ecological and person-based study. Psychological Medicine, 31, 827 –836.[CrossRef][Medline]
Kapur, N., House, A., Creed, F., et al
(1998) Management of deliberate self-poisoning in adults in
four teaching hospitals: descriptive study. BMJ,
316, 831
–832.
Kelly, J., Cooper, J., Johnston, A., et al (2004) Manchester Self-Harm Project, 5th Year Report. Manchester: University of Manchester.
Kessler, R. C., Berghund, P., Borges, G., et al
(2005) Trends in suicide ideation, plans, gestures and
attempts in the United States, 1990–1992 to 2001–2003.
JAMA, 293, 2487
–2495.
Manchester Geomatics Limited (2003) Community health information profile (CHIP) for Manchester, Salford and Trafford, 2003. http://www.healthprofile.org.uk (accessed 4 August 2003).
National Collaborating Centre for Mental Health (2004) Self-Harm. The Short-Term Physical and Psychological Management and Secondary Prevention of Self-Harm in Primary and Secondary Care. Leicester & London: British Psychological Society & Gaskell.
Neeleman, J., Wilson-Jones, C. & Wessely, S.
(2001) Ethnic density and deliberate self-harm: small area
study in south-east London. Journal of Epidemiology and Community
Health, 55, 85
–90.
NHS Centre for Reviews and Dissemination (1998) Deliberate self-harm. Effective Health Care Bulletin, 4, 1 –12.
Office for National Statistics (2002) Ward History Database. http://www.stistics.gov.uk (accessed 21 August 2003).
Office for National Statistics (2003) Index of Multiple Deprivation 2000. http://neighbourhood.statistics.gov.uk (accessed 4 May 2003).
Owens, D., Horrocks, J. & House, A. (2002)
Fatal and non-fatal repetition of self-harm. Systematic review.
British Journal of Psychiatry,
181, 193
–199.
Richardson, S., Thomson, A., Best, N., et al (2004) Interpreting posterior relative risk estimates in disease-mapping studies. Environmental Health Perspectives, 112, 1016 –1025.[Medline]
Sakinofsky, I. (2000) Repetition of suicidal behaviour. In Suicide and Attempted Suicide (eds K. Hawton & K. Van Heeringen), pp. 385–404. Chichester: Wiley.
Schmidtke, A., Bille-Brahe, U., DeLeo, D., et al (1996) Attempted suicide in Europe: rates, trends and sociodemographic characteristics of suicide attempters during the period 1989–1992. Results of the WHO/EURO Multicentre Study on Parasuicide. Acta Psychiatrica Scandinavica, 93, 327 –338.[Medline]
Simpson, L. & Yu, A. (1999) Updated UK Area Master-files. http://convert.mimas.ac.uk (accessed 22 February 2005).
Skapinakis, P., Lewis, G., Araya, R., et al
(2005) Mental health inequalities in Wales, UK: multi-level
investigation of the effect of area deprivation. British Journal of
Psychiatry, 186, 417
–422.
Townsend, P., Phillimore, P. & Beattie, A. (1986) Health and Deprivation: Inequality and the North. London: Croom Helm.
Wasserman, I. M. & Stack, S. (1995) Geographical spatial autocorrelation and United States suicide patterns. Archives of Suicide Research, 1, 121 –129.
Weich, S., Twigg, L., Lewis, G., et al
(2005) Geographical variation in rates of common mental
disorders in Britain: prospective cohort study. British Journal of
Psychiatry, 187, 29
–34.
Received for publication October 7, 2005. Revision received May 22, 2006. Accepted for publication July 4, 2006.
This article has been cited by other articles:
|
|
 |
|
  D. O'Reilly, M. Rosato, S. Connolly, and C. Cardwell Area factors and suicide: 5-year follow-up of the Northern Ireland population The British Journal of Psychiatry, February 1, 2008; 192(2): 106 - 111. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Psychiatric Bulletin | Advances in Psychiatric Treatment | All RCPsych Journals |
