Statistical analysis Categorical variables are described with percentages and quantitative variables with mean (SD) or, for data that did not have normal distribution, median. Additionally, a multiple logistic regression model fit to the combined demographic data and responses to job stress questions/health behaviors was used to analyze responses. Race, marital status, work hours, and clinic employment were included as control variables. Because medical diagnoses were specified as dichotomous variables and positioned as outcomes in our hypothesized model, these parameters were estimated using logistic regression with the log odds for these two outcomes representing linear combinations of the predictor variables. Methods for examining mediation based on bootstrap resampling are viewed as most accurate when dichotomous or categorical variables serve as mediating variables, so we followed recommendations by Preacher and colleagues and used a 10,000 bootstrap resampling approach with 95 percent bias-corrected confidence intervals (CIs) to test for the significance of indirect effects reflecting our proposed simple and serial mediation. Indirect effects are significant (p < 0.05) when the 95 percent CIs do not include zero.14-16 Results Demographics are described in Table 2. Job demands (p < 0.05), abusive supervision (p < 0.05), and workfamily conflict (p < 0.01) were all related to burnout. In turn, burnout was positively related to having a chronic illness diagnosis (p < 0.01; OR 1.85) or having a chronic pain diagnosis (p < 0.05; OR 1.75). Thus, a one unit increase in burnout increased the odds of being diagnosed with a chronic illness over the next 12 months by a factor of 1.85. As expected, a chronic illness diagnosis was related to both chronic illness medications (p < 0.01) and visits to a medical professional (p < 0.01). Similarly, a chronic pain diagnosis was related to both chronic pain medications (p < 0.01) and visits to a medical professional (p < 0.01). Having an abusive supervisor was also directly related to the number of visits to a medical professional (p < 0.05). Each of the indirect effects from job demands (estimate = 0.032, 95 percent CIs = 0.003, 0.135), abusive supervision (estimate = 0.054, 95 percent CIs = 0.006, 0.220), and work-family conflict (estimate = 0.064, 95 percent CIs = 0.011, 0.203) to chronic pain medications via burnout and chronic pain diagnosis were significant. None of the direct effects from job demands, abusive supervision, or work-family conflict to chronic pain medications were statistically significant. Finally, each of the indirect effects from job demands (estimate = 0.154, 95 percent CIs = 0.011, .544), abusive supervision (estimate = 0.257, 95 percent CIs = 0.024, 0.924), and work-family conflict (estimate = 0.305, 95 percent CIs = 0.056, 0.888) to visits to a medical professional via burnout and chronic pain diagnosis were significant. Respondents reporting burnout smoked more (p < 0.01), exercised less (p < 0.038), and had more positive answers to alcohol CAGE questions (p < 0.009) than those without burnout. People with abusive supervisors were less likely to get a flu vaccine (p < 0.05), more likely to get drug testing (p < 0.05), and more likely to be hospitalized (p < 0.01). Discussion Psychosocial work stressors (job demands, abusive supervision, and work-family conflict) do indeed have an impact on employees’ health care utilization. Work stressors and burnout contributed to diagnoses of both chronic illness and chronic pain. The indirect effect (through burnout and diagnosis) of each of the work stressors predicted each form of health care utilization: chronic pain medications, chronic illness medications, and visits to a medical professional. These results contribute to the advancement of research on stressors in relation to health care utilization. Our findings demonstrate that work-tofamily conflict goes beyond the workplace to impact health care utilization of the employee, emphasizing the need for researchers to more strongly consider the cost of work-family conflict. Additionally, while abusive supervision has been linked with a variety of detrimental work outcomes, previous research has not shown its impact on health care utilization.17 Interestingly, we also found a main effect of abusive supervision on number of visits to a medical professional. This suggests a strong relationship between abusive supervision and diminished health that would benefit from future research. The research using valid health care data is limited and the present research is the first to our knowledge that links specific work stressors with actual health outcome data obtained from EHRs.8 A significant contribution of this research is that we are no longer relying on self-reporting to make the work-to-health connection but now have empirical evidence of a relationship. Thus, while we have known that the psychosocial stressors influence work life (through things like reduced performance and turnover), and family life (through things like family functioning and relationship tension), we now extend that knowledge to show how it also impacts an individual’s health. The study has several limitations. First, a primarily “underserved” population was surveyed. This group of people might be different from the general population with respect to resilience in their response to life stressors such as increasing job demands and dealing with an abusive supervisor. Second, we have just begun to examine the employee utilization of health care but do not have direct costs associated with this use. While certainly costs can be inferred, future research could benefit from directly relating medical costs (i.e., those paid by insurance, by the government, or by the individual) in www.tafp.org 27