Faculty Bibliography

Despite the availability of the human papillomavirus (HPV) vaccine for males, uptake of the vaccine has been low, particularly among young adult males. This study aimed to investigate the levels of HPV vaccination and predictors of HPV vaccine completion in college men ages 18-26. We analyzed data from the 2015 College Student Health Survey, which was administered at 17 post-secondary institutions in Midwest areas. We included only responses from male participants who were ages 18-26 years old, resulting in a sample size of 2516. We used Andersen's Behavioral Model of Health Services Utilization to guide our study design. Multivariate logistic regression was used to examine predictors of HPV vaccine receipt. College-aged males in our sample had a HPV vaccine completion rate of 50.0%. Male students who were younger, had at least one parent who held a graduate degree, had initiated sex, and were enrolled at a private 4-year institution were more likely to have been vaccinated. These findings suggest that HPV vaccination in college-aged men are low. Efforts are needed to increase HPV vaccination in male students who are older, from lower socioeconomic statuses, have not initiated sex, and enrolled at public institutions. Findings also indicate important gender disparities in vaccine uptake that must be addressed in order to achieve optimal vaccine uptake in college-aged males.

INTRODUCTION:Although breast cancer deaths have declined, the mortality rate among women from medically underserved communities is disproportionally high. Screening mammography is the most effective tool for detecting breast cancer in its early stages, yet many women from medically underserved communities do not have adequate access to screening mammograms. Mobile mammography may be able to bridge this gap by providing screening mammograms at no cost or low cost and delivering services to women in their own neighborhoods, thus eliminating cost and transportation barriers. The objective of this systematic review was to describe the scope and impact of mobile mammography programs in promoting mammographic screening participation among medically underserved women. METHODS:We searched electronic databases for English-language articles published in the United States from January 2010 through March 2018 by using the terms "mobile health unit," "mammogram," "mammography," and "breast cancer screening." Of the 93 articles initially identified, we screened 55; 16 were eligible to be assessed and 10 qualified for full text review and data extraction. Each study was coded for study purpose, research design, data collection, population targeted, location, sample size, outcomes, predictors, analytical methods, and findings. RESULTS:Of the 10 studies that qualified for review, 4 compared mobile mammography users with users of fixed units, and the other 6 characterized mobile mammography users only. All the mobile mammography units included reached underserved women. Most of the women screened in mobile units were African American or Latina, low income, and/or uninsured. Mobile mammography users reported low adherence to 1-year (12%-34%) and 2-year (40%-48%) screening guidelines. Some difficulties faced by mobile clinics were patient retention, patient follow-up of abnormal or inconclusive findings, and women inaccurately perceiving their breast cancer risk. CONCLUSION:Mobile mammography clinics may be effective at reaching medically underserved women. Adding patient navigation to mobile mammography programs may promote attendance at mobile sites and increase follow-up adherence. Efforts to promote mammographic screening should target women from racial/ethnic minority groups, women from low-income households, and uninsured women. Future research is needed to understand how to best improve visits to mobile mammography clinics.

Chronic stress is a risk factor for incident cardiovascular (CV) disease. Emotion regulation is the ability to modulate one's state or behavior in response to a given situation or stressor, and may mitigate the effect of chronic stress on CV disease risk. Data from a cohort of 754 community-dwelling young to middle-aged adults who were assessed between 2007 and 2012 on stress, emotion regulation, and CV risk measures were used to test the hypothesis that emotion regulation mitigates the effect of chronic stress on CV risk. Emotion regulation was measured using the Difficulties in Emotion Regulation Scale (DERS). We created a composite stress score using data from the Cumulative Adversity Interview and the Perceived Stress Scale. Our outcomes included blood pressure, body mass index, and insulin resistance separately and combined into a composite CV risk score. Covariates included age, sex, race, years of education, and smoking status. We used multivariable logistic regression to evaluate associations between stress measures and CV risk among participants and the impact of emotion regulation (DERS scores) on this association. We found that composite stress interacted significantly with the DERS score to affect CV risk (p = .007). A median split of the DERS scores indicated that CV risk was associated with the composite stress score in the fully adjusted model (ß = 0.206; p = .005) among participants with low emotion regulation, but not among those with high emotion regulation (ß = 0.048; p = .59). Chronic stress was associated with CV risk only among participants with poor emotion regulation. Emotion regulation is a teachable skill, and may play a role in preventing CV disease.Lay summaryEmotion regulation is the ability to modify one's reaction to a negative or stressful event, and is a teachable skill. Effective emotion regulation dampens the negative effect of chronic stress on the body, which may reduce risk for cardiovascular disease.

OBJECTIVE:Even though safe and effective treatments for depression are available, many individuals with a diagnosis of depression do not obtain treatment. This study aimed to develop a tool to identify persons who might not initiate treatment among those who acknowledge a need. METHODS:Data were aggregated from the 2008-2014 U.S. National Survey on Drug Use and Health (N=391,753), including 20,785 adults given a diagnosis of depression by a health care provider in the 12 months before the survey. Machine learning was applied to self-report survey items to develop strategies for identifying individuals who might not get needed treatment. RESULTS:A derivation cohort aggregated between 2008 and 2013 was used to develop a model that identified the 30.6% of individuals with depression who reported needing but not getting treatment. When applied to independent responses from the 2014 cohort, the model identified 72% of those who did not initiate treatment (p<.01), with a balanced accuracy that was also significantly above chance (71%, p<.01). For individuals who did not get treatment, the model predicted 10 (out of 15) reasons that they endorsed as barriers to treatment, with balanced accuracies between 53% and 65% (p<.05 for all). CONCLUSIONS:Considerable work is needed to improve follow-up and retention rates after the critical initial meeting in which a patient is given a diagnosis of depression. Routinely collected information about patients with depression could identify those at risk of not obtaining needed treatment, which may inform the development and implementation of interventions to reduce the prevalence of untreated depression.

BACKGROUND:Well-being is a positively-framed, holistic assessment of health and quality of life that is associated with longevity and better health outcomes. We aimed to identify county attributes that are independently associated with a comprehensive, multi-dimensional assessment of individual well-being. METHODS:We performed a cross-sectional study examining associations between 77 pre-specified county attributes and a multi-dimensional assessment of individual US residents' well-being, captured by the Gallup-Sharecare Well-Being Index. Our cohort included 338,846 survey participants, randomly sampled from 3,118 US counties or county equivalents. FINDINGS:We identified twelve county-level factors that were independently associated with individual well-being scores. Together, these twelve factors explained 91% of the variance in individual well-being scores, and they represent four conceptually distinct categories: demographic (% black); social and economic (child poverty, education level [<high school, high school diploma/equivalent, college degree], household income, % divorced); clinical care (% eligible women obtaining mammography, preventable hospital stays per 100,000, number of federally qualified health centers); and physical environment (% commuting by bicycle and by public transit). CONCLUSIONS:Twelve factors across social and economic, clinical care, and physical environmental county-level factors explained the majority of variation in resident well-being.