ArticleInternational journal of health geographics2009
A multilevel model for cardiovascular disease prevalence in the US and its application to micro area prevalence estimates.
Article in International journal of health geographics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- US county-level estimation for maternal and infant health-related behavior indicators using pregnancy risk assessment monitoring system data, 2016-2018.Population health metrics · 2022Article
- Constructing Statistical Intervals for Small Area Estimates Based on Generalized Linear Mixed Model in Health Surveys.Open journal of statistics · 2022Article
- Schistosomiasis mansoni incidence data in Rwanda can improve prevalence assessments, by providing high-resolution hotspot and risk factors identification.BMC public health · 2017Article
- Comparison of Methods for Estimating Prevalence of Chronic Diseases and Health Behaviors for Small Geographic Areas: Boston Validation Study, 2013.Preventing chronic disease · 2017Article
- Validation of multilevel regression and poststratification methodology for small area estimation of health indicators from the behavioral risk factor surveillance system.American journal of epidemiology · 2015Article
- The geography of diabetes by census tract in a large sample of insured adults in King County, Washington, 2005-2006.Preventing chronic disease · 2014Article
- Contribution of individual risk factor changes to reductions in population absolute cardiovascular risk.BioMed research international · 2014Article
- A multilevel approach to estimating small area childhood obesity prevalence at the census block-group level.Preventing chronic disease · 2013Article
- County-level estimates of human papillomavirus vaccine coverage among young adult women in Texas, 2008.Texas public health journal · 2013Article
- Overcoming data challenges examining oral health disparities in appalachia.Online journal of public health informatics · 2012Article
- Spatial clustering of non-transported cardiac decedents: the results of a point pattern analysis and an inquiry into social environmental correlates.International journal of health geographics · 2011Article
- The effects of rurality on mental and physical health.Health services & outcomes research methodology · 2010Article
- Human papillomavirus vaccine coverage among females aged 11 to 17 in Texas counties: an application of multilevel, small area estimation.Women's health issues : official publication of the Jacobs Institute of Women's HealthArticle
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Abstract
backgroundEstimates 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.
methodsA 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.
resultsTo 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.
conclusionGeographic 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.
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