A multilevel model for cardiovascular disease prevalence in the US and its application to micro area prevalence estimates.
BACKGROUND
Estimates of disease prevalence for small areas are increasingly required for the allocation of health funds according to local need. Both individual level and geographic risk factors are likely to be relevant to explaining prevalence variations, and in turn relevant to the procedure for small area prevalence estimation. Prevalence estimates are of particular importance for major chronic illnesses such as cardiovascular disease.


METHODS
A multilevel prevalence model for cardiovascular outcomes is proposed that incorporates both survey information on patient risk factors and the effects of geographic location. The model is applied to derive micro area prevalence estimates, specifically estimates of cardiovascular disease for Zip Code Tabulation Areas in the USA. The model incorporates prevalence differentials by age, sex, ethnicity and educational attainment from the 2005 Behavioral Risk Factor Surveillance System survey. Influences of geographic context are modelled at both county and state level, with the county effects relating to poverty and urbanity. State level influences are modelled using a random effects approach that allows both for spatial correlation and spatial isolates.


RESULTS
To assess the importance of geographic variables, three types of model are compared: a model with person level variables only; a model with geographic effects that do not interact with person attributes; and a full model, allowing for state level random effects that differ by ethnicity. There is clear evidence that geographic effects improve statistical fit.


CONCLUSIONS
Geographic variations in disease prevalence partly reflect the demographic composition of area populations. However, prevalence variations may also show distinct geographic 'contextual' effects. The present study demonstrates by formal modelling methods that improved explanation is obtained by allowing for distinct geographic effects (for counties and states) and for interaction between geographic and person variables. Thus an appropriate methodology to estimate prevalence at small area level should include geographic effects as well as person level demographic variables.
Congdon
Peter
P
Department of Geography and Center for Statistics, Queen Mary University of London, London, UK. p.congdon@qmul.ac.uk
D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000368:Aged; D002318:Cardiovascular Diseases / Q000453:epidemiology*; D005260:Female; D040362:Geographic Information Systems / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D015995:Prevalence; D012044:Regression Analysis; D012307:Risk Factors; D012424:Rural Population / Q000706:statistics & numerical data*; D017678:Sex Distribution; D014481:United States / Q000453:epidemiology; D014505:Urban Population / Q000706:statistics & numerical data*; D055815:Young Adult