Predicting geographical variations in behavioural risk factors: an analysis of physical and mental healthy days

• Published in: Journal of epidemiology and community health (1979) 2004-02, Vol.58 (2), p.150-155
• Main Authors: Jia, H, Muennig, P, Lubetkin, E I, Gold, M R
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Age; Aged; Attitude to Health; Behavioral Risk Factor Surveillance System; Bias; Biological and medical sciences; BRFSS; CDC; Decision making; Decision Making, Organizational; Disabilities; Disability rates; Disease control; Employment; Female; Geography; health related quality of life; Health Resources - supply & distribution; Health status; Health Status Indicators; HRQOL; Humans; Institutionalization; Linear regression; Male; Medical sciences; Mental health; Methods; Middle Aged; Miscellaneous; Mortality; Parameter estimation; Physical disabilities; Population; Poverty; Predisposing factors; Preventive medicine; Public health. Hygiene; Public health. Hygiene-occupational medicine; Regression Analysis; Residence Characteristics; Resource allocation; Risk factors; Risk taking; Small-Area Analysis; Sociodemographics; Socioeconomic factors; The Centers for Disease Control and Prevention; Theory and Methods; Trends; Unemployment; United States - epidemiology
• ISSN: 0143-005X; 1470-2738
• DOI: 10.1136/jech.58.2.150

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.