Suchergebnisse

Investigates the clustering of immigrants & recent migrants from Puerto Rico into non-English-language communities in NJ, using aggregate data from the 1990 US census. Results indicate that approximately 20% of NJ residents speak a language other than English at home. A majority of this population reside in northeastern counties in tightly knit neighborhoods that allow them to use their native languages in many public contexts as well. Although Spanish-language communities also experience socioeconomic disadvantage relative to English speakers, this is not the case for Asian- or Pacific Island-language communities. Implications are discussed: (1) Continuing migration may be required for non-English-language communities to continue to survive. (2) The link between non-English language & socioeconomic disadvantage reveals that more recent immigrants may be prevented from adapting easily to their new homeland. (3) A complex set of relationships exists between language, socioeconomic attainment, & race & ethnicity. More investigation is needed to discern whether findings are generalizable to other US regions. 7 Tables, 4 Figures, 31 References. D. M. Smith

Purpose. Describe the relationship among modifiable health behaviors and short-term medical costs. Design. Prospective study linking cross-sectional survey data that assessed modifiable risk behaviors with insurance claims. Setting. A large health plan in Minnesota. Subjects. A stratified, random sample of 10,000 yielded an analytic data set for 7983 members. Measures. The dependent variable was per-member-per-month insurance payment plus subscriber liability. Eighteen months of medical costs were analyzed. Control variables included subscriber age, sex, type of insurance plan, days of enrollment, chronic disease status, education, and marital status. Independent variables included self-reported health behaviors of smoking, heavy drinking, nutrition, and physical activity. Analysis. Linear regression was performed on the natural log of the cost variable, followed by a retransformation to dollars. Results. Physical inactivity and smoking were significant predictors of higher medical costs. Each day a member did not exercise there was a 2.9% difference in cost. Compared with never smokers, current smokers had 16% higher costs. Former smokers who had quit more than 1 year before taking the survey had 15% higher costs than never smokers. Recent former smokers cost 32% more than never smokers and more frequently experienced smoking-related medical conditions before they quit. Alcohol consumption was nonsignificant. Nutrition also was not significant but was narrowly measured by only fruit and vegetable consumption. Conclusion. Physical inactivity and smoking were associated with higher short-term medical costs among health plan members.

Purpose. The purpose of this study was to examine the relationship between stage of change for smoking cessation and stage of change for (1) fruit and vegetable consumption and (2) physical activity. Design. The data come from a cross-sectional telephone survey administered to a stratified random sample of health plan members (n = 9675). Setting. This study was conducted at a mixed-model health plan with approximately 1 million adult members. Subjects. Respondents were adults age 18 and older, who were randomly selected from five health plan product groups: commercial fully insured, commercial self-insured, two publicly subsidized plans, and Medicare supplemental insurance. Response rates ranged from 74.7% to 90.1% across these groups. Measures. The assessment included demographics and stage of change for smoking cessation, physical activity, and fruit and vegetable intake. Bivariate relationships among variables were analyzed with the use of contingency tables. Ordered logistic regression was used to examine the effects of stage of change for fruit and vegetable consumption and physical activity on stage of change for smoking while controlling for other factors. Results. Stage of change for smoking is more clearly related to stage of change for fruit and vegetable consumption (χ2 = 161.3, p < .001; Cramer's V = .11, p < .001) than to stage of change for physical activity (χ2 = 89.7, p < .001; Cramer's V = .08, p < .001). However, stage of change for fruit and vegetable consumption and physical activity are not strong predictors of stage of change for smoking. Conclusions. This study indicates that stage of change for both fruit and vegetable consumption and physical activity are independent constructs from stage of change for smoking cessation.