Transcript Spatial SEM methods for representing the impact

representing the impact
deprivation and
fragmentation constructs
on suicide and psychiatric
outcomes
Peter Congdon, Geography,
QMUL [email protected]
Ecology/Context etc
My talk will concern ecological
(geographical) variations. Effects of area
constructs on area health outcomes may be
taken to represent combined impact of
compositional & contextual influences on
psychiatric & suicide outcomes.
 Caution of ecological fallacy; but also
atomistic fallacy
 Work on fragmentation/deprivation &
impacts on area mental health outcomes
contributes to wider literature on now well
established links between context and
health

Spatial Correlation in Outcomes &
Risk Factors
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Mortality or disease outcomes in areas that are
geographically close typically display spatial
dependence. Geographically defined risk factors
also typically spatially correlated
Such dependence should be acknowledged in
developing constructs (e.g. deprivation,
fragmentation, mental illness needs) or in spatial
regression context
Conventional statistical analysis techniques taking
areas as independent are inappropriate for
spatially correlated data.
Spatially correlated may be errors due to omitted
risk factors that vary smoothly over space
Spatial SEMs
Describe two applications involving
modelling of spatially defined social
constructs on health outcomes using SEM
approach.
 SEM has measurement model (defining
constructs from measured or ‘manifest’
indicators), & structural model which uses
constructs in explanatory model
 Here the measurement model uses census
variables as indicators of latent
constructs, which may be spatially
correlated, while structural model relates
area health outcomes to latent constructs.
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Case Studies
1st application considers impact of two
latent constructs (social deprivation and
social fragmentation) on two suicide
outcomes: suicide deaths &
hospitalizations for deliberate self-harm in
32 London boroughs.
 Structural model allows both linear and
nonlinear impacts of the constructs on
suicide relative risks
 2nd application considers impact of
deprivation and fragmentation on
hospitalisations for schizophrenia & BPD
for 354 English local authorities (over
2002-3 to 2004-5).
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Ecological Suicide Variations
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Work on geographical suicide variations has
highlighted impact of factors associated with elevated
psychiatric morbidity in general, esp. social
deprivation (Gunnell et al, 1995).
Analysis of area suicide data has shown excess risk
associated with social fragmentation; fragmentation
higher in areas characterised by non-family
households, high population turnover, extensive
private renting in ‘bedsitters’.
An index summarising such factors is used by Whitley
et al (1999) and Congdon (1996) to analyse suicide
variations.
Social fragmentation may occur in affluent areas (e.g.
central London) as well as deprived areas, and
deprivation and fragmentation are not necessarily
highly correlated. Fragmentation scores tend to be
high in inner city areas and in coastal resorts with
transient workforces.
Influences on DSH
 Analysis
of ecological DSH variations
(hospitalisations) shows deprivation
to be important influence. Gunnell et
al (2000, Psych Med) find deprivation
effects on DSH stronger than
fragmentation effects, though
Hawton et al (2001) find associations
between DSH rates and social
fragmentation scores were similar to
those pattern observed for socioeconomic deprivation
Influences on psych admissions
 Such
admissions concentrated in
psychosis diagnoses (schizophrenia,
BPD)
 Some indices oriented to steering
resources
 Fragmentation as distinct influence
recognised in work of Allardyce &
Boydell (Schizophr Bull. 2006)
Previous Scores
 Congdon's
(1996) `anomie' score
derived as sum of z scores from four
census variables: (i) population
turnover (ii) proportion of single
person households; (iii) proportion of
non-married adults; and (iv)
proportion of persons in privately
rented accommodation.
 Townsend deprivation score.
Overcrowding dubious as component
Scores in SEM
 Deprivation
& Fragmentation Scores
determined both by measurement
model and structural model
 Construct Scores not just based on
PCA/factor analysis/Z score sum
using census indices
 OR construct scores not just based
on regression of service use on
census indices (York indices)
Spatial SEM for Suicide & DSH in
London
 Four
responses SUIM, SUIF, DSHM,
DSHF over areas i=1,32. Denote
outcomes j=1,4. Counts yij of
hospitalisation (so Poisson).
Expected deaths/referrals Eij
 Yij ~ Poisson(Eijij)
 ij are relative risks of mortality/self
harm over London Boroughs
Measurement Model
There are P=6 indicators of Q=2 latent
social area constructs: Fragmentation z1 &
Deprivation z2
 Indicators of (i.e. measured proxies for)
social fragmentation are 2001 Census one
person households, the rate of residential
turnover & people over 15 not married.
 Indicators of deprivation are 2001 Census
low skill workers, renting from social
landlords, and % unemployment among
economically active.
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Features of Measurement Model
 Allow
constructs to be spatially
correlated (in fact allow for data to
pick appropriate level of spatial
pooling)
 Allow for correlation between
Deprivation & Fragmentation
 So ‘correlated across space and over
outcomes’
Structural Model
 Relate
area relative risks ij for
suicide and DSH to area social
constructs ziq
 Also allow unstructured influences uij
on ij (esp relevant for DSH because
of procedural variations between
trusts in DSH admission procedures)
 Use Bayesian methods/WINBUGS
FLOW CHART FOR SUICIDE SEM
LINEAR EFFECTS OF
CONSTRUCTS
 Correlation
between deprivation
and fragmentation around 0.7, but
distinct spatial pattern shows in
maps of scores
 Deprivation has strongest effects
on DSH, Fragmentation has
strongest effects on Suicide
 Female suicide most affected by
fragmentation
NONLINEAR CONSTRUCT
EFFECTS
 Use
Spline Regression to Model
Nonlinear construct effects (e.g. see
plots of Fragmentation Impacts)
 Relative Risk Effects similar to Linear
Model
Nonlinear Impacts on Suicide RRs
Female suicide & Fragmentation
Male Suicide & Fragmentation
-0.80
-0.60
-0.40
-0.20
0.40
0.80
0.30
0.60
0.20
0.40
0.10
0.20
0.00
0.00
-0.10
0.20
0.40
0.60
0.80
-0.80
-0.60
-0.40
0.00
-0.20 -0.200.00
-0.20
-0.40
-0.30
-0.60
0.20
0.10
0.05
-0.80
0.00
-0.20 -0.050.00
-0.10
0.20
0.40
0.60
0.80
-0.60
-0.40
-0.20
0.00
0.00
-0.05
-0.10
-0.15
-0.20
0.80
0.05
0.15
-0.40
0.60
0.10
0.25
-0.60
0.40
Female self-harm & fragmentation
Male Self Harm & Fragmentation
-0.80
0.20
-0.15
0.20
0.40
0.60
0.80
Influences on SMI Hospitalisations,
354 English LAs
 Fragmentation
Score (Z1) based on
One person hhlds, private renting,
residential turnover, SWD adults
 Deprivation/ethnicity (Z2) based on
unemployment, social housing, nonwhite, low skill
 J=2 responses (SMI=schizophrenia &
BPD combined) for males (Y1) and
females (Y2)
 Fragmentation
has stronger effect on
female SMI admissions than male
SMI admissions
 Deprivation effect stronger for male
SMI admissions than female SMI
admissions
 Spatial pattern for two scores differs
Final Remarks
Construct Overlaps: interesting to see
how far social capital (however
defined) and social fragmentation are
related. Social capital often assessed
in health surveys. Social capital
sometimes distinguished from social
cohesion
Practical Constructs: For resourcing a
single index is often required, so
compromising underlying concepts