A spatial structural equation modelling framework for health count responses.
A structural equation model is proposed for the impact on area health referral counts of spatially correlated latent constructs. One type of construct is indicator based and represents the underlying morbidity or health need; such constructs are derived in a normal errors measurement model involving a set of observed socio-economic indicators. Another set of residual constructs represents particularities of service configuration or spatially correlated risks that cannot be proxied by observed indicators. The structural model relates the referral outcomes to both types of construct in a Poisson regression. While multiple spatial factors based on measured socio-economic indicators have already been proposed, the extension to include both indicator-based and residual factors is novel. Further novel features are to represent spatially structured heterogeneity and nonlinearity in the impact of the indicator-based constructs. A case study considers the modelling of a single indicator-based morbidity construct (taken to represent a need for care) in 62 New York counties, with responses being psychiatric referrals to ambulatory and hospital care.
Congdon
Peter
P
Department of Geography, Queen Mary University of London, Mile End Rd, London E1 4NS, UK. p.congdon@qmul.ac.uk
Almog
Michael
M
Curtis
Sarah
S
Ellerman
Raymond
R
D001499:Bayes Theorem; D005163:Factor Analysis, Statistical; D006301:Health Services Needs and Demand / Q000706:statistics & numerical data*; D006801:Humans; D001523:Mental Disorders / Q000453:epidemiology; D017146:Models, Organizational; D015999:Multivariate Analysis; D009518:New York; D011567:Psychiatric Department, Hospital; D019984:Quality Indicators, Health Care / Q000706:statistics & numerical data; D012017:Referral and Consultation / Q000706:statistics & numerical data; D012044:Regression Analysis; D012111:Residence Characteristics / Q000706:statistics & numerical data*; D017062:Small-Area Analysis