peter congdon, department of geography, queen mary university of london. p.congdon@qmul.ac.uk 1

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The Spatial Pattern of Suicide in the US in relation to Deprivation, Fragmentation and Rurality

Peter Congdon, Department of Geography, Queen Mary University of London.

p.congdon@qmul.ac.uk

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Ecological studies of suicide & latent area constructs

Analysis of geographic patterns of suicide & psychiatric morbidity shows impact of latent ecological variables (e.g. deprivation, rurality).

Latent variables (aka “constructs”, “factors”) such as rurality not observed directly, but proxied (“measured”) by collections of observed indicators (e.g. census socio-demographic indices).

Existing work on area suicide variation is mainly in GB and Ireland (but wider relevance…?)

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Spatial FactorsThis talk outlines a spatial latent random

variable (“spatial factors”) approach to geographic contrasts in suicide

Latent constructs involved: deprivation, social fragmentation & rurality.

Effects of area ethnic mix are also included. Model applied to male and female suicide

deaths over 2002-2006 in 3142 US counties.Data from CDC Wonder

(http://wonder.cdc.gov/)

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Deriving Latent VariablesLatent variables may be derived by conventional

multivariate techniques (e.g. principal components), or by composite variable methods (e.g. sum of z scores),

These methods neglect spatial correlation. Benefit in explicitly considering spatial framework of areas & spatial clustering in outcome & risk factors (albeit such factors not directly observed).

Provides evidence-based mechanism for deriving smoothed area rates of rare mortality outcome, & parametric measure of spatial correlation in latent risk

In fact, we allow for latent spatial constructs to be correlated within as well as between areas.

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MCMC & Bayesian MethodsAnalysis uses MCMC methods, Bayesian inference and

random effects (“pooling strength”) methodsBenefits include: A) Obtain smoothed (stabilized) mortality rate

estimates for rare (suicide) outcome for each small area

B) Spatial factor can be used to impute (predict) missing mortality (e.g. suicide deaths not reported for some counties because populations too small)

C) Facilitates inferences not possible (or considerably more difficult) under classical approach, e.g. may monitor male-female suicide rate ratio across all counties, test whether this ratio exceeds a threshold, etc

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Relevant ConstructsSeveral UK/Ireland studies show area constructs

(deprivation, rurality, fragmentation) relevant to explaining area variations in suicide, e.g. Whitley et al. Ecological study of social fragmentation, poverty, and suicide. BMJ 1999

Multilevel studies (with both individual & area variables) show mixed findings on whether area variables are significant contextual influences. Suitable datasets limited, response event rare (large samples needed for power).

E.g. O'Reilly et al Br. J Psych (2008); Stafford et al, 2008, Eur J Pub Health

Anyway effects of area constructs remain relevant risk factors in ecological studies even if they are primarily summarizing compositional effects

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Deprivation & RuralityFamiliar latent variables with several

underlying aspects, e.g. relevant to area socioeconomic status (or area deprivation) are education, income, employment status, wealth, car/home ownership, etc

To choose just one observed indicator (e.g. area income) as proxy for area SES means effect of latent variable may be understated

To include several as separate regression predictors introduces multicollinearity

So better to include contributing dimensions in single latent variable

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Social fragmentation: what this construct represents

Originally conceived as inverse measure of familism, representing area household structure with many one person and non-family households, high residential turnover, etc. Area level proxy for higher levels of social isolation, lower family support, etc. Usually higher in central cities

Broader connotations: Fagg et al (2008) Soc Sci Med : “Social fragmentation is conceptualised here in terms of lack of social integration or social cohesion and implies that aspects of social capital such as reinforcement of social norms, trust, and reciprocity may be more difficult to maintain. Social integration at community level may for example, be weak when large proportions of the population are socially isolated because they live alone or without a partner”

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Form of Model for US SuicideSeek (inter alia) to pool strength over areas

(stabilize estimates of relative mortality risk, often based on small death totals).

Standard demographic techniques to estimate mortality risk unreliable. Rate for each area-age treated as fixed effect in isolation of any other information

Instead smooth estimates using spatially correlated latent variables (“local smoothing”)

Both health outcomes (Y) and observed socioeconomic indices (Z) relevant in derivation of latent constructs (C)

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Formal model statement

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Observed Risk FactorsSome suicide risk factors may be observed

(denoted X), not latent constructs. Example is race mix: main contrast between

relatively high rate for white non-Hispanics (WNH), and lower rates for black non-Hispanics (BNH), Hispanics and Asian Americans.

Rates for native Americans (NTVAM) are intermediate between WNH and BNH/Hispanic.

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US StudyQ=3 Latent Constructs C1= Deprivation,

C2=Fragmentation, C3=RuralityK=13 Socioeconomic Indices, ZJ=2 Health Outcomes, Y (male suicide, female

suicide)P=2 Observed predictors, X. Race

differentials summarised by taking X1=log(%WNH+1) and X2=log(%NTVAM+1).

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Z to C linkages

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Expected vs actual effects of postulated risk constructs on suicide

All constructs C, and X variables, expected to be positive risk factors for county suicide rates.

Confirmed in US study except that rurality not significant risk factor for female suicide

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Model parameter estimates

Standardised coefficients (l(s),b(s)) Mean 2.5% 97.5%

Impacts on male suicide

Deprivation 0.58 0.49 0.67

Fragmentation 0.30 0.20 0.40

Rurality 0.34 0.26 0.40

White non-hispanic 0.22 0.17 0.26

Native American 0.40 0.35 0.45

Impacts on female suicide

Deprivation 0.23 0.11 0.35

Fragmentation 0.18 0.04 0.32

Rurality 0.01 -0.09 0.11

White non-hispanic 0.24 0.17 0.31

Native American 0.49 0.42 0.55

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Correlations between constructsSometimes asserted that fragmentation is expected to be

positively correlated with deprivation. Originally (Congdon, 1996, Urban Studies) what is now termed

“fragmentation” intended to measure demographic/household structure, not intrinsically linked to area SES

Maybe expectation of a +ve correlation based on implicit assumption that both constructs will be higher in inner city areas, or based on the wider connotations for “fragmentation”?

But sociologists remind us (Portes, Am Soc Rev, 2000) of the “myth” that “Poor urban areas are socially disorganized”

Also in US, poverty higher in rural areas (esp. in South East of US), whereas fragmentation (non-family structure) tends to be high in central cities

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Construct Correlations in US Study

Mean 2.5% 97.5%

Correlations between constructs

Deprivation-fragmentation -0.46 -0.50 -0.43

Deprivation-rurality 0.56 0.53 0.59

Fragmentation-rurality -0.58 -0.61 -0.56

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Deprivation scores, C1i

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Fragmentation scores, C2i

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Rurality Scores, C3i

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Smoothed male suicide risk, 1i

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Smoothed female suicide risk, 2i

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M-F suicide ratio, 1i/2i