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Background: Electronic health records (EHRs) provide researchers with abundant sample sizes, detailed clinical data, and other advantages for performing high-quality observational health research on diverse populations. We review and demonstrate strategies for the design and analysis of cohort studies on neighborhood diversity and health, including evaluation of the effects of race, ethnicity, and neighborhood socioeconomic position on disease prevalence and health outcomes, using localized EHR data. Methods: Design strategies include integrating and harmonizing EHR data across multiple local health systems and defining the population(s) of interest and cohort extraction procedures for a given analysis based on the goal(s) of the study. Analysis strategies address inferential goals, including the mechanistic study of social risks, statistical adjustment for differences in distributions of social and neighborhood-level characteristics between available EHR data and the underlying local population, and inference on individual neighborhoods. We provide analyses of local variation in mortality rates within Cuyahoga County, Ohio. Results: When the goal of the analysis is to adjust EHR samples to be more representative of local populations, sampling and weighting are effective. Causal mediation analysis can inform effects of racism (through racial residential segregation) on health outcomes. Spatial analysis is appealing for large-scale EHR data as a means for studying heterogeneity among neighborhoods even at a given level of overall neighborhood disadvantage. Conclusions: The methods described are a starting point for robust EHR-derived cohort analysis of diverse populations. The methods offer opportunities for researchers to pursue detailed analyses of current and historical underlying circumstances of social policy and inequality. Investigators can employ combinations of these methods to achieve greater robustness of results. Highlights: EHR data are an abundant resource for studying neighborhood diversity and health. When using EHR data for these studies, careful consideration of the goals of the study should be considered in determining cohort specifications and analytic approaches. Causal mediation analysis, stratification, and spatial analysis are effective methods for characterizing social mechanisms and heterogeneity across localized populations. [ABSTRACT FROM AUTHOR]

Electronic health records (EHRs) offer the potential to study large numbers of patients but are designed for clinical practice, not research. Despite the increasing availability of EHR data, their use in research comes with its own set of challenges. In this article, we describe some important considerations and potential solutions for commonly encountered problems when working with large-scale, EHR-derived data for health services and community-relevant health research. Specifically, using EHR data requires the researcher to define the relevant patient subpopulation, reliably identify the primary care provider, recognize the EHR as containing episodic (i.e., unstructured longitudinal) data, account for changes in health system composition and treatment options over time, understand that the EHR is not always well-organized and accurate, design methods to identify the same patient across multiple health systems, account for the enormous size of the EHR, and consider barriers to data access. Associations found in the EHR may be nonrepresentative of associations in the general population, but a clear understanding of the EHR-based associations can be enormously valuable to the process of improving outcomes for patients in learning health care systems. In the context of building 2 large-scale EHR-derived data sets for health services research, we describe the potential pitfalls of EHR data and propose some solutions for those planning to use EHR data in their research. As ever greater amounts of clinical data are amassed in the EHR, use of these data for research will become increasingly common and important. Attention to the intricacies of EHR data will allow for more informed analysis and interpretation of results from EHR-based data sets. [ABSTRACT FROM AUTHOR]

Introduction. Recommendations for colorectal cancer screening encourage patients to choose among various screening methods based on individual preferences for benefits, risks, screening frequency, and discomfort. We devised a model to illustrate how individuals with varying tolerance for screening complications risk might decide on their preferred screening strategy. Methods. We developed a discrete-time Markov mathematical model that allowed hypothetical individuals to maximize expected lifetime utility by selecting screening method, start age, stop age, and frequency. Individuals could choose from stool-based testing every 1 to 3 years, flexible sigmoidoscopy every 1 to 20 years with annual stool-based testing, colonoscopy every 1 to 20 years, or no screening. We compared the life expectancy gained from the chosen strategy with the life expectancy available from a benchmark strategy of decennial colonoscopy. Results. For an individual at average risk of colorectal cancer who was risk neutral with respect to screening complications (and therefore was willing to undergo screening if it would actuarially increase life expectancy), the model predicted that he or she would choose colonoscopy every 10 years, from age 53 to 73 years, consistent with national guidelines. For a similar individual who was moderately averse to screening complications risk (and therefore required a greater increase in life expectancy to accept potential risks of colonoscopy), the model predicted that he or she would prefer flexible sigmoidoscopy every 12 years with annual stool-based testing, with 93% of the life expectancy benefit of decennial colonoscopy. For an individual with higher risk aversion, the model predicted that he or she would prefer 2 lifetime flexible sigmoidoscopies, 20 years apart, with 70% of the life expectancy benefit of decennial colonoscopy. Conclusion. Mathematical models may formalize how individuals with different risk attitudes choose between various guideline-recommended colorectal cancer screening strategies. [ABSTRACT FROM AUTHOR]

Objective While previous work has demonstrated elevation of both comorbid anxiety disorders and diabetes mellitus type II in individuals with serious mental illness, little is known regarding the impact of comorbid anxiety on diabetes mellitus type II outcomes in serious mental illness populations. We analyzed baseline data from patients with serious mental illness and diabetes mellitus type II to examine relationships between comorbid anxiety, glucose control as measured by hemoglobin A1c score, and overall illness burden. Methods Using baseline data from an ongoing prospective treatment study involving 157 individuals with serious mental illness and diabetes mellitus type II, we compared individuals with and without a comorbid anxiety disorder and compared hemoglobin A1c levels between these groups to assess the relationship between anxiety and management of diabetes mellitus type II. We conducted a similar analysis using cumulative number of anxiety diagnoses as a proxy for anxiety load. Finally, we searched for associations between anxiety and overall medical illness burden as measured by Charlson score. Results Anxiety disorders were seen in 33.1% (N = 52) of individuals with serious mental illness and diabetes mellitus type II and were associated with increased severity of depressive symptoms and decreased function. Hemoglobin A1c levels were not significantly different in those with or without anxiety, and having multiple anxiety disorders was not associated with differences in diabetes mellitus type II control. However, depressive symptoms were significantly associated with higher hemoglobin A1c levels. Neither comorbid anxiety nor anxiety load was significantly associated with overall medical burden. Conclusion One in three people with serious mental illness and diabetes mellitus type II had anxiety. Depressive symptoms were significantly associated with Hb1Ac levels while anxiety symptoms had no relation to hemoglobin A1c; this is consistent with previously published work. More studies are needed to better understand the relationship between depression, anxiety, and health management in people with serious mental illness and diabetes mellitus type II. [ABSTRACT FROM AUTHOR]

Most studies of decisions to curtail alcohol consumption reflect experiences of abusing drinkers. An exploratory sequential research design is used to explore the applicability of this research to the experience of nonabusing drinkers advised to curtail alcohol consumption after a hepatitis C diagnosis. A qualitative component identified 17 new decision factors not reflected in an inventory of factors based on synthesis of existing scales. Qualitative data were triangulated by supplementing semistructured interviews with Internet postings. A quantitative component estimated prevalence and association with current drinking of these new decision factors. Patients who quit drinking tended to attribute postdiagnosis drinking to occasional triggers, whereas patients who were still drinking were more likely to endorse rationales not tied to specific triggers. [ABSTRACT FROM PUBLISHER]

Presents a study which examined the decision process that led to retirement migration. Review of literature on retirement migration; Method; Results and discussion.

Purpose: This study compared a novel self-management (TargetEd MAnageMent Intervention [TEAM]) versus treatment as usual (TAU) to reduce stroke risk in African American (AA) men.Design: Six-month prospective randomized controlled trial with outcomes evaluated at baseline, 3 months, and 6 months.Setting: Academic health center.Participants: Thirty-eight (age < 65) AA men who had a stroke or transient ischemic attack and a Barthel index score of >60 were randomly assigned to TEAM (n = 19) or TAU (n = 19).Intervention: Self-management training, delivered in 1 individual and 4 group sessions (over 3 months).Measures: Blood pressure, glycosylated hemoglobin (HbA1c), lipids, medication adherence, weight, and standardized measures of health behaviors (diet, exercise, smoking, substances), depression, and quality of life. Qualitative assessments evaluated the perspectives of TEAM participants.Analysis: T tests for paired differences and nonparametric tests. Thematic content qualitative analysis.Results: Mean age was 52.1 (standard deviation [SD] = 7.4) and mean body mass index was 31.4 (SD = 7.4). Compared to TAU, TEAM participants had significantly lower mean systolic blood pressure by 24 weeks, and there was also improvement in HbA1c and high-density lipoprotein cholesterol ( P = .03). Other biomarker and health behaviors were similar between groups. Qualitative results suggested improved awareness of risk factors as well as positive effects of group support. [ABSTRACT FROM AUTHOR]