title: Predicting geographical variations in behavioural risk factors: an analysis of physical and mental healthy days.
abstract: OBJECTIVE
  To determine the validity of physical and mental unhealthy days as summary measures for county health status and to forward a method for examining county level health trends using a single year of data from the Behavioral Risk Factor Surveillance System (BRFSS).
  
  
  METHODS
  The study analysed geographical variation in physical and mental unhealthy days at the state and county level using the 2000 BRFSS. Whereas state level analyses used individual level data, this research conducted multilevel regression analysis using county level data as independent variables and individual level reports of physical and mental unhealthy days as dependent variables.
  
  
  METHODS
  Population based samples of non-institutionalised civilian adult residents from each of the 50 states and the District of Columbia in the United States.
  
  
  RESULTS
  Socioeconomic variables predicted similar mean numbers of physical and mental unhealthy days at both the state and county level, validating the county level analyses. County level disability rates were strongly associated with county mean unhealthy days. Using the regression method we forward, it is possible to analyse county level trends using a single year of BRFSS data.
  
  
  CONCLUSIONS
  Physical and mental unhealthy days may be used as valid summary measures of county health status. Regression models may be used to assist local decision makers in assessing the needs of their communities and may be used to improve health resource allocation within states.
authors.affiliation: Department of Community Medicine, Mercer University School of Medicine, Macon, GA, USA. haomia@yahoo.com
authors.forename: H
authors.initials: H
authors.lastname: Jia
authors.forename: P
authors.initials: P
authors.lastname: Muennig
authors.forename: E I
authors.initials: EI
authors.lastname: Lubetkin
authors.forename: M R
authors.initials: MR
authors.lastname: Gold
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001294:Attitude to Health; D040343:Behavioral Risk Factor Surveillance System; D003659:Decision Making, Organizational; D005260:Female; D005843:Geography; D006295:Health Resources / Q000600:supply & distribution*; D006305:Health Status Indicators; D006801:Humans; D008297:Male; D008875:Middle Aged; D012044:Regression Analysis; D012111:Residence Characteristics; D017062:Small-Area Analysis; D014481:United States / Q000453:epidemiology