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

1. 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

2. 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…?)

3. 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/)

4. 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.

5. MCMC & Bayesian Methods • Analysis uses MCMC methods, Bayesian inference and 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

6. 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

7. 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

8. 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”

9. 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)

10. Formal model statement

11. 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.

13. 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).

14. Z to C linkages

15. 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

17. Correlations between constructs • Sometimes 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

18. Construct Correlations in US Study

19. Deprivation scores, C1i

20. Fragmentation scores, C2i

21. Rurality Scores, C3i

22. Smoothed male suicide risk, 1i

23. Smoothed female suicide risk, 2i

24. M-F suicide ratio, 1i/2i