Predicting geographical variations in behavioural risk factors: an analysis of physical and mental healthy days. Issue 2 (16th January 2004)

• Record Type: Journal Article
• Authors: Jia, H; Muennig, P; Lubetkin, E I; Gold, M R
• Is Part Of: Journal of epidemiology and community health. Volume 58:Issue 2(2004)
• Page Start: 150
• Page End: 155
• Publication Date: 2004-01-16
• Subjects: health related quality of life -- Behavioral Risk Factor Surveillance System; BRFSS, Behavioral Risk Factor Surveillance System -- HRQOL, health related quality of life -- CDC, The Centers for Disease Control and Prevention; Public health -- Periodicals; Epidemiology -- Periodicals; 614.4
• DOI: 10.1136/jech.58.2.150
• Languages: English
• ISSNs: 0143-005X

Abstract

Abstract : Study objectives: 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). Design: 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. Setting: Population based samples of non-institutionalised civilian adult residents from each of the 50 states and the District of Columbia in the United States. Main 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.