EXPOSURE TO EXTREME HEAT EVENTS AND CHRONIC RESPIRATORY DISEASES AMONG A NATIONALLY REPRESENTATIVE SAMPLE OF THE UNITED STATES POPULATION

Previous studies have shown that extreme weather events are on the rise in response to our changing climate. Such events are projected to become more frequent, more intense, and longer lasting. A consistent exposure metric for measuring these extreme events as well as information regarding how these events lead to ill health are needed to inform meaningful adaptation strategies that are specific to the needs of local communities. Using federal meteorological data corresponding to 17 years (1997-2013) of the National Health Interview Survey, this research: 1) developed a location-specific exposure metric that captures individuals’ “exposure” at a spatial scale that is consistent with publicly available county-level health outcome data; 2) characterized the United States’ population in counties that have experienced higher numbers of extreme heat events and thus identified population groups likely to experience future events; and 3) developed an empirical model describing the association between exposure to extreme heat events and hay fever. This research confirmed that the natural modes of forcing (e.g., El Niño-Southern Oscillation), seasonality, urban-rural classification, and division of country have an impact on the number extreme heat events recorded. Also, many of the areas affected by extreme heat events are shown to have a variety of vulnerable populations including women of childbearing age, people who are poor, and older adults. Lastly, this research showed that adults in the highest quartile of exposure to extreme heat events had a 7% increased odds of hay fever compared to those in the lowest quartile, suggesting that exposure to extreme heat events increases risk of hay fever among US adults.

Predictors of Patient Activation Among Underserved Patients in a Nurse-managed Health Center: A Pilot Study

The concept of patient activation has gained traction as the term referring to patients who understand their role in the care process and have “the knowledge, skills and confidence” necessary to manage their illness over time (Hibbard & Mahoney, 2010). Improving health outcomes for vulnerable and underserved populations who bear a disproportionate burden of health disparities presents unique challenges for nurse practitioners who provide primary care in nurse-managed health centers. Evidence that activation improves patient self-management is prompting the search for theory-based self-management support interventions to activate patients for self-management, improve health outcomes, and sustain long-term gains. Yet, no previous studies investigated the relationship between Self-determination Theory (SDT; Deci & Ryan, 2000) and activation. The major purpose of this study, guided by the Triple Aim (Berwick, Nolan, & Whittington, 2008) and nested in the Chronic Care Model (Wagner et al., 2001), was to examine the degree to which two constructs– Autonomy Support and Autonomous Motivation– independently predicted Patient Activation, controlling for covariates. For this study, 130 nurse-managed health center patients completed an on-line 38-item survey onsite. The two independent measures were the 6-item Modified Health Care Climate Questionnaire (mHCCQ; Williams, McGregor, King, Nelson, & Glasgow, 2005; Cronbach’s alpha =0.89) and the 8-item adapted Treatment Self-Regulation Questionnaire (TSRQ; Williams, Freedman, & Deci, 1998; Cronbach’s alpha = 0.80). The Patient Activation Measure (PAM-13; Hibbard, Mahoney, Stock, & Tusler, 2005; Cronbach’s alpha = 0.89) was the dependent measure. Autonomy Support was the only significant predictor, explaining 19.1% of the variance in patient activation. Five of six autonomy support survey items regressed on activation were significant, illustrating autonomy supportive communication styles contributing to activation. These results suggest theory-based patient, provider, and system level interventions to enhance self-management in primary care and educational and professional development curricula. Future investigations should examine additional sources of autonomy support and different measurements of autonomous motivation to improve the predictive power of the model. Longitudinal analyses should be conducted to further understand the relationship between autonomy support and autonomous motivation with patient activation, based on the premise that patient activation will sustain behavior change.