An exploratory analysis of Latino risk and protective health factors in a community sample Julie Gast, PhD, MSCHES, Terry Peak, MSW, PhD, & Jason J. Leiker, PhD, Utah State University Julie Gast, PhD, MCHES Utah State University Department of HPER Latinos constitute an increasingly large and diverse segment of the U.S. population and are the largest minority group at about 17% of the U.S. population. The impact of cultural factors on this diverse Latino population is still unclear, however, and more research might help clarify whether cultural factors promote health or hinder it. For example, machismo has typically been measured as a negative factor in Latino men’s health but studies have found positive aspects of machismo, such as dedication to providing for one’s family. The primary purpose of this paper was to examine cultural and demographic factors that might influence risk and protective factors in a community sample of Latinos. These include fatalism, machismo, Cabalarismo, marital status, gender, health insurance, health satisfaction and self-rated health, religiosity, education, language preference, and age. In addition we looked at risk and protective factors of diet, anger- stress, medical compliance, preventive care, as well as overall health behaviors. I.Introduction Design, setting, and sample Participants were recruited from health fairs, an English language center, and other community events targeting Latinos in northern Utah. Due to missing demographic data a static sample was used in all analyses for a total of 144 participants; 37% male (n=53) and 63% female (n=91). The mean age of the sample was years old (SD=10.6) with a range of 19 to 71. A majority of the sample reported a preference for speaking Spanish (65%; n=94), only 10% indicated English as their preferred language (n=12); 26% preferred both English and Spanish (n=38). Procedures and analysis Multicollinearity was evaluated through correlation analysis of all potential independent variables to eliminate redundancy. Normality testing using the Shapiro-Wilk Test of outcome variables indicated normal distribution for the HBI-20, diet, preventive care, and anger and stress subscales, with highly skewed results for the medical compliance subscale. General linear models were used in analyzing normally distributed outcomes and generalized linear models for the medical compliance subscale to model its exponential form. II. Methods HBI-20 Total score: significantly related to health satisfaction, health insurance status, health status, and preferred language. Diet Subscale: significantly related to self-reported health, language preference, and religious importance. Anger/Stress Subscale: significantly associated with health satisfaction and self- reported health status. Preventive Care Subscale: significantly associate with health satisfaction, health insurance, gender, religious importance, and age Medical Compliance Subscale: significantly related to health satisfaction, health insurance status, gender and religious importance. Note: All results are from the parsimonious model. III. Results Cultural variables did not play a role in the risk and protective behaviors of Latinos in this study. Marital status has been linked to health protective behaviors but not in this study. A small sample size may have played a role in these non- significant findings. Preferred language proved to be significant with HBI-20 score and diet; we assume Spanish language serves as a proxy measure for acculturation. Women engaged in more protective behaviors overall, as shown in total HBI-20, preventive care, and medical compliance results. Health satisfaction showed a significant relationship with total health, anger/stress, preventive care, and medical compliance but not the diet subscales. Self- reported health status, an important predictor of health protective factors, indicated that those self-reporting excellent health engaged in more healthful behaviors. As expected, health insurance status was a significant finding in connection with total HBI-20 score, preventive care, and medical compliance. IV. Conclusions Table One: Parsimonious Model