1. Race and Socioeconomic Differences in Health Behavior Trajectories Across the Adult Life Course ACKNOWLEDGEMENTS This research was supported by the grant R01 AG031109-02 (Benjamin Shaw, PI) from the National Institute on Aging. Benjamin A. Shaw 1,2 and Kelly McGeever 2,3 University at Albany, State University of New York 1 School of Public Health, 2 Center for Social and Demographic Analysis, and 3 Department of Sociology BACKGROUND Physical Activity Males, whites, highly educated, and young adults were more active, on average Gender and education differences were larger, and race differences smaller, among older adults On average, levels of physical activity decreased within individuals during the follow-up period However, significant age differences indicated that younger adults increased, while older adults decreased Elevated levels of activity among whites diminished over time, especially among older adults METHODS Data source Americans’ Changing Lives study (4 waves: 1986-2002) 3,497 individuals; 10,197 observations Mean age at baseline: 53.94 (SD = 17.59) Key measures Physical activity: “How often do you work in the garden or yard?” “Engage in active sports or exercise?” “Take walks?” Overweight: 1=Highest 15% BMI; 0=Other Smoking: 1=Current smoker; 0=Other Drinking: Drinks per month Data analysis Hierarchical Linear Modeling, with occasions of measurement nested within individuals Level 1 Model: Behavior ij = π 0i + π 1i Time +  ij Level 2 Model: π 0i =  00 +  01 Baseline Age i +  02 X i +  03 X i *Age i + u 0i π 1i =  10 +  11 Baseline Age i +  12 X i +  13 X i *Age i + u 1i Where X equals race (1=black; 0=white), gender, (1=male; 0=female), or education level. RESULTS Concerns about periods of expanded morbidity and increasing health care costs associated with population aging are leading to a renewed appreciation for the protective and damaging roles of individual behaviors. Many believe that health behavior improvements offer the most promising means for offsetting, or “compressing”, the impending illness burden associated with the aging of the population (Fries, 2003). However, we currently know little about the health behaviors of older adults, or how these behaviors change within individuals during later life. Dynamic facets of behavior, such as the patterns with which individuals change various behaviors over time, may be important determinants of health outcomes which can only be examined by measuring behaviors over extended time periods Using longitudinal data from a nationwide sample of American adults age 25 and older, we sought to examine both within- person and between-persons variation in key health behaviors, including physical activity, weight management, smoking, and drinking. These findings suggest the presence of substantial and evolving race-, education-, and gender-based stratification in health behaviors throughout the life course. Race disparities were particularly apparent in physical activity and weight management. However, both within- and between-persons analyses showed a convergence of these disparities with increasing age. Education disparities were consistent across behaviors, and mostly persisted over time. However, our between-persons analyses showed older adults exhibiting wider disparities in physical activity and drinking, and smaller disparities in weight management and smoking. Significant gender disparities were also apparent, with males showing advantages with respect to physical activity and weight management, but disadvantages with respect to smoking and drinking Over time and with increasing age, the health behaviors of males tended to improve relative to females, particularly with respect to physical activity and drinking. CONCLUSIONS Overweight Females, blacks, low education, and young adults were more likely to be overweight Education differences were smaller among older adults On average, the predicted odds of being overweight increased within individuals in successive months since baseline (OR = 1.08 for every 5 years of follow-up) Significant age differences were evident, with younger adults’ odds increasing (OR = 1.42) and older adults’ odds decreasing (OR =.97) Age differences in odds ratios for being overweight across time were greater among blacks (OR’s between 1.53 for young adults and.91 for older adults) compared to whites (OR’s between 1.35 and 1.01) Drinking Males, whites, highly educated, and young adults drank more Gender differences were smaller and education differences larger among older adults Over time, levels of drinking declined within individuals, with a decelerating rate of decline Slower rates of decline were evident among older adults; faster rates of decline were evident among men Smoking Males, low education, and young adults were more likely to smoke Gender differences were larger, and education differences smaller among older adults The predicted odds of smoking decreased over time (OR =.70 for every 5 years of follow-up) Odds declined by about 24% every 5 years for young adults (OR =.76) and by about 33% among older adults (OR =.67) Age differences in odds ratios for smoking over time were slightly greater among highly educated (OR’s between.80 and.66) compared to low education adults (OR’s between.74 and.68) Parameter estimates for multilevel linear and logistic regression models, 1986-2002 Physical activityOverweight Current smokingDrinks/mo.^ For Intercept Intercept-0.069***-1.229***-1.086***7.727*** Baseline Age-0.115***-0.219***-0.764***-1.938*** Race0.069***-0.265***-0.0400.744* Education0.145***-0.298***-0.264***0.874* Gender0.161***-0.069*0.118***4.276*** Age*Race-0.033*0.006-0.052-0.239 Age*Education0.035*0.174***0.327***0.781* Age*Gender0.065***-0.0270.076**-1.072** For Linear Slope Intercept-0.051***0.073***-0.359***-2.674*** Baseline Age-0.039***-0.169***-0.052**0.801** Race-0.056***0.020-0.0180.310 Education0.0010.029-0.003-0.183 Gender0.019*0.0140.009-1.664*** Age*Race-0.037***0.049**-0.029-0.087 Age*Education0.008-0.038*-0.041*-0.184 Age*Gender0.019*0.0080.010-0.061 Models also adjusted for mortality and attrition; *p<.05, **p<.01, ***p<.001. ^A quadratic model was estimated, but only the linear portion is shown