Download The Spatial Pattern of Suicide in the US in relation to Deprivation

Peter Congdon, Department of Geography, Queen
Mary University of London.
[email protected]
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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 Factors
 This 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 Variables
 Latent 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 Methods
 Analysis uses MCMC methods, Bayesian inference and
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random effects (“pooling strength”) methods
Benefits 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 Constructs
 Several 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 & Rurality
 Familiar 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 Suicide
 Seek (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 Factors
 Some 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 Study
 Q=3 Latent Constructs C1= Deprivation,
C2=Fragmentation, C3=Rurality
 K=13 Socioeconomic Indices, Z
 J=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
Impacts on male suicide
Impacts on female
suicide
Standardised coefficients (l(s),b(s))
Mean
2.5%
97.5%
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
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 constructs
 Sometimes asserted that fragmentation is expected to be
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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
Correlations between
constructs
Mean
2.5%
97.5%
Deprivationfragmentation
-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
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