Faculty Bibliography

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.

OBJECTIVES:To propose collective well-being as a holistic measure of the overall "health" of a community. To define collective well-being as a group-level construct measured across 5 domains (vitality, opportunity, connectedness, contribution, and inspiration) and introduce an actionable model that demonstrates how community characteristics affect collective well-being. To review the literature describing each domain's association with health outcomes and community characteristics' associations with collective well-being. METHODS:We came to consensus on topics describing each component of our conceptual model. Because "well-being" is not indexed in MEDLINE, we performed topic-specific database searches and examined bibliographies of papers retrieved. We excluded articles that were limited to narrow subtopics or studies within small subpopulations. Preference was given to quasi-experimental or randomized studies, systematic reviews, or meta-analyses. Consensus was reached on inclusion or exclusion of all articles. RESULTS:Reviewed literature supported each of the proposed domains as important aspects of collective well-being and as determinants of individual or community health. Evidence suggests a broad range of community characteristics support collective well-being. CONCLUSIONS:The health and quality of life of a community may be improved by focusing efforts on community characteristics that support key aspects of well-being. Future work should develop a unified measure of collective well-being to evaluate the relative impact of specific efforts on the collective well-being of communities.

Importance:New US health care payment models have increasingly incentivized health care systems to promote health and reduce health care spending at the population level, with Medicare beneficiaries representing one of the largest populations affected by new payment models. Identifying novel strategies to promote health and reduce health care spending is necessary. Objective:To assess whether the overall well-being of a population is associated with health care spending for people 65 years of age or older. Design, Setting, and Participants:This US national, population-based cross-sectional study examined the association between county well-being and Medicare fee-for-service (FFS) spending. Population well-being, a holistic assessment of the overall health of the population comprising interrelated domains, including physical, mental, and social health, as measured by the Gallup-Sharecare Well-Being Index (2010), was linked to the mean spending per Medicare FFS beneficiary (2010) and county characteristics data for all US counties assessed. The data were adjusted for prevalence of 4 low-variation conditions (hip fracture, stroke, colorectal cancer, and acute myocardial infarction) and regional penetration of Medicare Advantage. Data analyses were conducted October 13, 2016, to October 31, 2017. Main Outcomes and Measures:Mean spending per Medicare FFS beneficiary per county. Results:In total, 2998 counties were assessed using county-level mean values, with 4 to 7317 participants (mean [SD] number of participants, 755 [1220]) per county. The mean (SD) values of the demographic characteristics of the participants were 50.8% (1.3%) female, 74.9% (16.5%) white, 12.1% (13.0%) black, 4.0% (5.3%) Asian, and 13.7% (14.8%) Hispanic with a mean (SD) of the median county age of 38.2 (4.4) years. Medicare spent a mean (SE) of $992 ($110) less per Medicare FFS beneficiary in counties in the highest quintile of well-being compared with counties in the lowest well-being quintile. This inverse association persisted after accounting for key population characteristics such as median household income and contextual factors such as urbanicity and health care system capacity. Medicare spent a mean (SE) of $1233 ($104) less per Medicare FFS beneficiary in counties with the greatest access to basic needs than in those with the lowest access. Conclusions and Relevance:In this US national study, the overall well-being of a geographically defined population was inversely associated with its health care spending for people 65 years and older. Identifying this association between well-being and health care spending at the population level may help to lay the foundation for further study to first illuminate the mechanisms underlying the association and to second study interventions aimed at creating greater well-being and lower health care spending at the population level.

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.

Population wellbeing, an aggregate measure of positive mental, physical, and emotional health, has previously been used as a marker of community thriving. We examined whether several community measures of wellbeing, and their change since 2012, could be used to understand electoral changes that led to the outcome of the 2016 United States presidential election. We found that areas of the US which had the largest shifts away from the incumbent party had both lower wellbeing and greater drops in wellbeing when compared with areas that did not shift. In comparison, changes in income were not related to voting shifts. Well-being may be more useful in predicting and understanding electoral outcomes than some more conventional voting determinants.

Living in communities with persistent gun violence is associated with negative social, behavioral, and health outcomes, analogous to those of a natural disaster. Taking a disaster-preparedness approach may identify targets for community-based action to respond to on-going gun violence. We assessed the relevance of adapting a disaster-preparedness approach to gun violence and, specifically, the relationship between perceived collective efficacy, its subscales of social cohesion and informal social control, and exposure to gun violence. In 2014, we conducted a cross-sectional study using a community-based participatory research approach in two neighborhoods in New Haven, CT, with high violent crime rates. Participants were ≥18 years of age and English speaking. We measured exposure to gun violence by adapting the Project on Human Development in Chicago Neighborhoods Exposure to Violence Scale. We examined the association between perceived collective efficacy, measured by the Sampson Collective Efficacy Scale, and exposure to gun violence using multivariate modeling. We obtained 153 surveys (51% response rate, 14% refusal rate, and 35% non-response rate). Ninety-five percent reported hearing gunfire, 58% had friend or family member killed by gun violence, and 33% were physically present during a shooting. In the fully adjusted model, one standard deviation higher perceived collective efficacy was associated with lower reported exposure to gun violence (β = -0.91, p < 0.001). We demonstrated that it is possible to activate community members and local officials to engage in gun violence research. A novel, community-based approach adapted from disaster-preparedness literature may be an effective framework for mitigating exposure to gun violence in communities with persistent gun violence.

INTRODUCTION/BACKGROUND:' (RheLaunCh) will be a cross-Atlantic comparative study of the mechanisms by which healthcare and social service delivery may impact patient health with chronic conditions. Insight into these mechanisms is needed to better and cost-effectively organise healthcare and social services. METHODS:We designed a mixed methods study to compare the socioeconomic background, needs of and service delivery to patients with congestive heart failure and chronic obstructive pulmonary disease in the USA and the Netherlands. We will conduct: (1) a literature scan to compare national and regional healthcare and social service systems; (2) a retrospective database study to compare patient's socioeconomic and clinical characteristics and the service use and spending at the national, regional and hospital level; (3) a survey to compare patient perceived quality of life, receipt and experience of service delivery and ability of these services to meet patient needs; and (4) multiple case studies to understand what patients need to better govern their quality of life and how needs are met by services. ETHICS AND DISSEMINATION/BACKGROUND:Ethics approval was granted by the ethics committee of the Radboud University Medical Center (2016-2423) in the Netherlands and by the Human Subjects Research Committee of the Hennepin Health Care System, Inc. (HSR #16-4230) in the USA. Multiple approaches will be used for dissemination of results, including (inter)national research presentations and peer-reviewed publications. A website will be established to support the development of a community of practice.