233557 Suicide Rates in US Counties and the Relevance of Area Socioeconomic Structure

Wednesday, November 10, 2010

Peter Congdon, Professor , Department of Geography, Health Research Group, Queen Mary College, University of London, London, United Kingdom
Analysis of geographic variation in suicide and other psychiatric outcomes has demonstrated the impact of latent area scales (e.g. deprivation, fragmentation, rurality). For example, recent work on suicide and attempted suicide in the UK and Ireland has demonstrated an independent effect of social fragmentation (meaning absence of community ties linked to residential turnover, many one person households, etc) after allowing for deprivation. Variables such as fragmentation and deprivation are not observed directly, but may be proxied by collections of observed indicators (e.g. census data on social and demographic structure). Using such observed data, the latent variables may be derived by conventional multivariate techniques (e.g. by principal components), by composite variable methods, or by methods which explicitly consider the spatial framework of areas, and in particular the spatial clustering of latent risks and outcomes. This talk considers a latent random variable approach to explaining geographic contrasts in suicide in the US, and develops a spatial structural equation model incorporating deprivation, social fragmentation and rurality. The approach allows for latent spatial constructs to be correlated both within and between areas. For example, whereas UK studies show some positive correlation (within areas) between deprivation and fragmentation, this may not be true of the US, where poverty tends to be higher in rural areas. Potential effects of area ethnic mix on suicide are also included, in particular the higher suicide levels among white non-Hispanics and native Americans as compared to black non-Hispanic and Hispanic groups. The model is applied to male and female suicide deaths over 2002-2006 in 3142 US counties.

Learning Areas:
Biostatistics, economics
Chronic disease management and prevention
Social and behavioral sciences

Learning Objectives:
Identify the major dimensions of area socio-economic structure that affect suicide variation in 3142 US counties (during 2002-06) Explain how to derive the major latent dimensions of area social structure from observed indicators Describe the role of three major area dimensions (deprivation, fragmentation, rurality) in explaining ecological suicide variation Demonstrate the utility of Bayesian methods in area ecological studies and in deriving common spatial factors from observed indicators

Keywords: Suicide, Psychiatric Epidemiology

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I have considerable experience in statistical modelling as applied to area health variations
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.