Spatial SEM methods for representing the impact deprivation and fragmentation constructs on suicide and psychiatric outc

1. Spatial SEM methods for representing the impact deprivation and fragmentation constructs on suicide and psychiatric outcomes Peter Congdon, Geography, QMUL p.congdon@qmul.ac.uk

2. 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 Caveat: ideal framework is multilevel Ecology/Context etc

3. 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 Correlation in Outcomes & Risk Factors

4. 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. Spatial SEMs

5. 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).

6. Ecological Suicide Variations • 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.

7. 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 socio-economic deprivation Influences on DSH

8. 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)

9. 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

10. 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)

11. 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

12. 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.

13. 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’

14. 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

15. FLOW CHART FOR SUICIDE SEM

16. 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 LINEAR EFFECTS OF CONSTRUCTS

20. 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

22. Nonlinear Impacts on Suicide RRs

23. 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, non-white, low skill • J=2 responses (SMI=schizophrenia & BPD combined) for males (Y1) and females (Y2)

24. 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

27. 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