A multilevel model for cardiovascular disease prevalence in the US and its application to micro area prevalence estimates
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Volume
8
Pagination
? - ?
Publisher
DOI
10.1186/1476-072X-8-6
Journal
INT J HEALTH GEOGR
ISSN
1476-072X
Metadata
Show full item recordAbstract
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.
Conclusion: 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.
Authors
Congdon, PCollections
- Geography [278]
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