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We used a collective impact model to form a statewide diabetes quality improvement collaborative to improve diabetes outcomes and advance diabetes health equity. Between 2020 and 2022, in collaboration with the Ohio Department of Medicaid, Medicaid Managed Care Plans, and Ohio's seven medical schools, we recruited 20 primary care practices across the state. The percentage of patients with hemoglobin A1c greater than 9% improved from 25% to 20% over two years. Applying our model more broadly could accelerate improvement in diabetes outcomes. (Am J Public Health. 2023;113(12):1254–1257. https://doi.org/10.2105/AJPH.2023.307410) [ABSTRACT FROM AUTHOR]

Background: Evidence on the effects of neighborhood socioeconomic disadvantage on dementia risk in racially and ethically diverse populations is limited. Our objective was to evaluate the relative extent to which neighborhood disadvantage accounts for racial/ethnic variation in dementia incidence rates. Secondarily, we evaluated the spatial relationship between neighborhood disadvantage and dementia risk. Methods: In this retrospective study using electronic health records (EHR) at two regional health systems in Northeast Ohio, participants included 253,421 patients aged >60 years who had an outpatient primary care visit between January 1, 2005 and December 31, 2015. The date of the first qualifying visit served as the study baseline. Cumulative incidence of composite dementia outcome, defined as EHR‐documented dementia diagnosis or dementia‐related death, stratified by neighborhood socioeconomic deprivation (as measured by Area Deprivation Index) was determined by competing‐risk regression analysis, with non‐dementia‐related death as the competing risk. Fine‐Gray sub‐distribution hazard ratios were determined for neighborhood socioeconomic deprivation, race/ethnicity, and clinical risk factors. The degree to which neighborhood socioeconomic position accounted for racial/ethnic disparities in the incidence of composite dementia outcome was evaluated via mediation analysis with Poisson rate models. Results: Increasing neighborhood disadvantage was associated with increased risk of EHR‐documented dementia diagnosis or dementia‐related death (most vs. least disadvantaged ADI quintile HR = 1.76, 95% confidence interval = 1.69–1.84) after adjusting for age and sex. The effect of neighborhood disadvantage on this composite dementia outcome remained after accounting for known medical risk factors of dementia. Mediation analysis indicated that neighborhood disadvantage accounted for 34% and 29% of the elevated risk for composite dementia outcome in Hispanic and Black patients compared to White patients, respectively. Conclusion: Neighborhood disadvantage is related to the risk of EHR‐documented dementia diagnosis or dementia‐related death and accounts for a portion of racial/ethnic differences in dementia burden, even after adjustment for clinically important confounders. [ABSTRACT FROM AUTHOR]

Healthcare systems are increasingly investing in approaches to address social determinants of health and health disparities. Such initiatives dovetail with certain approaches to neighborhood development, such as the EcoDistrict standard for community development, that prioritize both ecologically and socially sustainable neighborhoods. However, healthcare system and community development initiatives can be untethered from the preferences and lived realities of residents in the very neighborhoods upon which they focus. Utilizing the go‐along approach to collecting qualitative data in situ, we interviewed 19 adults to delineate residents' community health perspectives and priorities. Findings reveal health priorities distinct from clinical outcomes, with residents emphasizing social connectedness, competing intra‐ and interneighborhood perceptions that potentially thwart social connectedness, and a neighborhood emplacement of agency, dignity, and self‐worth. Priorities of healthcare systems and community members alike must be accounted for to optimize efforts that promote health and social well‐being by being valid and meaningful to the community of focus. Highlights: Health system investments in social determinants are often untethered from neighborhood preferences.Go‐along interviews efficiently ascertain community priorities for neighborhood transformation.Neighborhood residents articulate social connectedness as a key meaning of health.Participants described neighborhood emplacement of self‐worth from internalizing outsiders' views.Community development interventions should include resident voices in planning and evaluation. [ABSTRACT FROM AUTHOR]

Despite having a higher prevalence and severity of obstructive sleep apnea (OSA), African Americans have lower adherence to continuous positive airway pressure (CPAP) compared to other groups. Information regarding challenges faced by African Americans prescribed CPAP are lacking. To determine the barriers and facilitators to optimal management of OSA with CPAP among African Americans and to understand the role bed partners may play. We conducted semi-structured in-depth interviews via video conferencing with African American patients of an urban safety-net health care system with OSA prescribed CPAP and their bed partners. Recruitment continued until theoretical saturation was achieved. Verbatim transcripts were analyzed using the principles of thematic analysis. 15 patients (12 women) diagnosed with OSA and prescribed CPAP a mean 2.6 years prior along with 15 bed partners (3 women) were individually interviewed. Four themes emerged regarding impediments to CPAP use: 1) inadequate education and support, 2) CPAP maintenance and hygiene, 3) inconvenient design of CPAP interfaces, and 4) impediment to intimacy. Four themes emerged as facilitators to CPAP use: 1) provider and technical support, 2) properly fitted CPAP masks, 3) active support from partner and family, and 4) experiencing positive results from CPAP. African American patients with OSA and their bed partners identified several unique barriers and facilitators to CPAP use. Active involvement by bed partners was considered by both patients and partners as helpful in improving CPAP adherence. Interventions to improve OSA outcomes in this population should focus on patients and their bed partners. [ABSTRACT FROM AUTHOR]

Patients experiencing homelessness and mental illness face conditions and circumstances that deserve focused ethical and clinical attention. The first commentary on the case applies insights from qualitative research about social determinants of health to these patients' care and dignity. The second commentary describes 3 kinds of power wielded by physicians--charismatic, social, and Aesculapian--each of which is considered in terms of whether and to what extent physicians' power should be owned, aimed, or shared. [ABSTRACT FROM AUTHOR]

Objective: Problem behavior theory hypothesizes that adolescent attitudinal tolerance of deviance (ATD) precedes health risk behaviors. We examined ATD patterns among early adolescents with prenatal drug exposure and associated the patterns with subsequent risk behaviors. The ATD patterns were further characterized by concurrent developmental contexts. Method: Participants were 306 urban adolescents (51% girls), primarily African American and of low socioeconomic status, from a prospective birth-cohort study on the effects of prenatal cocaine exposure. ATD was assessed at age 12. Substance use, via self-report and biologic assays, and sexual intercourse before age 15 were assessed at age 15. Adolescents reported on measures assessing their family, peer, and neighborhood environments at age 12. Results: Latent class analysis identified three patterns: intolerance (G1; 59%), tolerance of relational deviance (G2; 30%), and tolerance of all deviance (G3; 11%). The patterns with tolerant attitudes (G2, G3) and the intolerance pattern (G1) were differentially related to subsequent alcohol use and sexual risk behaviors. Adolescents' current developmental contexts were distinctively associated with the ATD patterns, demonstrating the validity of the extracted patterns. Conclusions: Identifying ATD patterns in early adolescence may facilitate etiology research and prevention strategies among at-risk urban adolescents with prenatal drug exposure. [ABSTRACT FROM AUTHOR]

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]

Department of Sociology, Case Western Reserve University, Cleveland, Ohio. Objectives. We examine anticipated preferences for nursing home placement as a strategy for meeting possible future long-term care needs among a sample of community-dwelling elderly European Americans who migrated to Florida after retirement. We synthesize prior research on ethnicity in late life, retirement migration patterns, and informal networks of retired migrants. Methods. We gathered data through structured personal interviews with 578 retired migrants identified through screening from telephone listings, supplemented with snowball sampling techniques. Results. Lifetime migration patterns and current ethnic involvement are significant predictors of mentioning nursing home placement as a strategy for possible long-term care needs. Discussion. We interpret these results within the framework of Rowles's emphasis on the permeability of community-nursing home boundaries.

The authors discuss a study by J. B. Lusk and colleagues, published in the issue, which provided evidence for the connection between neighborhood conditions and health. Topics include powerful determinants of pulmonary health outcomes, evidence suggesting a relationship between neighborhood factors and individual health and disease, and research finding on differences in major health outcomes and survival.

Background: Social and ecological differences in early SARS-CoV-2 pandemic screening and outcomes have been documented, but the means by which these differences have arisen are not well understood. Objective: To characterize socioeconomic and chronic disease-related mechanisms underlying these differences. Design: Observational cohort study. Setting: Outpatient and emergency care. Patients: 12900 Cleveland Clinic Health System patients referred for SARS-CoV-2 testing between March 17 and April 15, 2020. Interventions: Nasopharyngeal PCR test for SARS-CoV-2 infection. Measurements: Test location (emergency department, ED, vs. outpatient care), COVID-19 symptoms, test positivity and hospitalization among positive cases. Results: We identified six classes of symptoms, ranging in test positivity from 3.4% to 23%. Non-Hispanic Black race/ethnicity was disproportionately represented in the group with highest positivity rates. Non-Hispanic Black patients ranged from 1.81 [95% confidence interval: 0.91–3.59] times (at age 20) to 2.37 [1.54–3.65] times (at age 80) more likely to test positive for the SARS-CoV-2 virus than non-Hispanic White patients, while test positivity was not significantly different across the neighborhood income spectrum. Testing in the emergency department (OR: 5.4 [3.9, 7.5]) and cardiovascular disease (OR: 2.5 [1.7, 3.8]) were related to increased risk of hospitalization among the 1247 patients who tested positive. Limitations: Constraints on availability of test kits forced providers to selectively test for SARS-Cov-2. Conclusion: Non-Hispanic Black patients and patients from low-income neighborhoods tended toward more severe and prolonged symptom profiles and increased comorbidity burden. These factors were associated with higher rates of testing in the ED. Non-Hispanic Black patients also had higher test positivity rates. [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]

BACKGROUND: Accurate assessment of atherosclerotic cardiovascular disease (ASCVD) risk across heterogeneous populations is needed for effective primary prevention. Little is known about the performance of standard cardiovascular risk factors in older adults. OBJECTIVE: To evaluate the performance of the American College of Cardiology/American Heart Association Pooled Cohort Equations (PCE) risk model, as well as the underlying cardiovascular risk factors, among adults older than 65 years. DESIGN AND SETTING: Retrospective cohort derived from a regional referral system's electronic medical records. PARTICIPANTS: A total of 25 349 patients who were 65 years or older at study baseline (date of the first outpatient lipid panel taken between 2007 and 2010). MEASUREMENTS: Exposures of interest were traditional cardiovascular risk factors, as defined by inclusion in the PCE model. The primary outcome was major ASCVD events, defined as a composite of myocardial infarctions, stroke, and cardiovascular death. RESULTS: The PCE and internally estimated models produced similar risk distributions for white men aged 65 to 74 years. For all other groups, PCE predictions were generally lower than those of the internal models, particularly for African Americans. Discrimination of the PCE was poor for all age groups, with concordance index (95% confidence interval) estimates of 0.62 (0.60‐0.64), 0.56 (0.54‐0.57), and 0.52 (0.49‐0.54) among patients aged 65 to 74, 75 to 84, and 85 years and older, respectively. Reestimating relationships within these age groups resulted in better calibration but negligible improvements in discrimination. Blood pressure, total cholesterol, and diabetes either were not associated at all or had inverse associations in the older age groups. CONCLUSION: Traditional clinical risk factors for cardiovascular disease failed to accurately characterize risk in a contemporary population of Medicare‐aged patients. Among those aged 85 years and older, some traditional risk factors were not associated with ASCVD events. Better risk models are needed to appropriately inform treatment decision making for the growing population of older adults. J Am Geriatr Soc 68:754–761, 2020 [ABSTRACT FROM AUTHOR]

The contemporary scientific community places a growing emphasis on the reproducibility of research. The projects R package is a free, open-source package created in the interest of facilitating reproducible research workflows. It adds to existing software tools for reproducible research and introduces several practical features that are helpful for scientists and their collaborative research teams. For each individual project, it supplies a framework for storing raw and cleaned study data sets, and it provides script templates for protocol creation, data cleaning, data analysis and manuscript development. Internal databases of project and author information are generated and displayed, and manuscript title pages containing author lists and their affiliations are automatically generated from the internal database. File management tools allow teams to organize multiple projects. When used on a shared file system, multiple researchers can harmoniously contribute to the same project in a less punctuated manner, reducing the frequency of misunderstandings and the need for status updates. [ABSTRACT FROM AUTHOR]

In epidemiology, gerontology, human development and the social sciences, age-period-cohort (APC) models are used to study the variability in trajectories of change over time. A well-known issue exists in simultaneously identifying age, period and birth cohort effects, namely that the three characteristics comprise a perfectly collinear system. That is, since age = period−cohort, only two of these effects are estimable at a time. In this paper, we introduce an alternative framework for considering effects relating to age, period and birth cohort. In particular, instead of directly modeling age in the presence of period and cohort effects, we propose a risk modeling approach to characterize age-related risk (i.e., a hybrid of multiple biological and sociological influences to evaluate phenomena associated with growing older). The properties of this approach, termed risk-period-cohort (RPC), are described in this paper and studied by simulations. We show that, except for pathological circumstances where risk is uniquely determined by age, using such risk indices obviates the problem of collinearity. We also show that the size of the chronological age effect in the risk prediction model associates with the correlation between a risk index and chronological age and that the RPC approach can satisfactorily recover cohort and period effects in most cases. We illustrate the advantages of RPC compared to traditional APC analysis on 27496 individuals from NHANES survey data (2005–2016) to study the longitudinal variability in depression screening over time. Our RPC method has broad implications for examining processes of change over time in longitudinal studies. [ABSTRACT FROM AUTHOR]

Objectives: A 60-week randomized controlled trial assessed the effects of targeted training in illness management (TTIM) versus treatment as usual among 200 individuals with serious mental illness and diabetes mellitus.Methods: The study used the Clinical Global Impression (CGI), the Montgomery-Asberg Depression Rating Scale (MADRS), and the Brief Psychiatric Rating Scale (BPRS) to assess psychiatric symptoms; the Global Assessment of Functioning (GAF) and the Sheehan Disability Scale (SDS) to assess functioning; the 36-Item Short-Form Health Survey (SF-36) to assess general health, and serum glycosylated hemoglobin (HbA1c) to assess diabetes control.Results: Participants' mean±SD age was 52.7±9.5 years, and 54% were African American. They were diagnosed as having depression (48%), schizophrenia (25%), and bipolar disorder (28%). At baseline, depression severity was substantial but psychosis severity was modest. At 60 weeks, there was greater improvement among TTIM participants versus treatment-as-usual recipients on the CGI (p<.001), the MADRS (p=.016), and the GAF (p=.003). Diabetes knowledge was significantly improved among TTIM participants but not in the treatment-as-usual group. In post hoc analyses among participants whose HbA1c levels at baseline met recommendations set by the American Diabetes Association for persons with high comorbidity (53%), TTIM participants had minimal change in HbA1c over the 60-week follow-up, whereas HbA1c levels worsened in the treatment-as-usual group.Conclusions: TTIM was associated with improved psychiatric symptoms, functioning, and diabetes knowledge compared with treatment as usual. Among participants with better diabetes control at baseline, TTIM participants had better diabetes control at 60 weeks compared with recipients of treatment as usual. [ABSTRACT FROM AUTHOR]

Objectives This exploratory study identifies barriers and facilitators to self-management to inform future epilepsy self-management interventions for persons who have epilepsy complicated by co-morbid mental health conditions and serious medical events. Methods Focus group methods were used in a series of community advisory board meetings. Analysis was conducted using a thematic, constant comparative approach aiming to describe the range of barriers and facilitators salient to participants. There were a total of 22 participants, including 8 health professionals, 9 patients with epilepsy, and 5 care partners. Mean age was 49.1 (SD = 11.0, range 32–69), 11 (50%) were female, and 11 (50%) were male. For those with epilepsy, mean years having epilepsy was 24.7 (SD = 19.9, range 1–58 years). Results Individual psychological barriers (mental illness, fatigue, and psychological distress) prominently interfered with health behaviors. Community and family barriers included stigma, lack of epilepsy knowledge, and poor social support. Facilitators included planning for seizures, learning about medications, stress management, socializing with others, and talking with other epilepsy patients. Discussion Qualitative evidence in this study suggests a linkage between social integration and positive health behaviors. Future efforts to embed patients with epilepsy and their caregivers into clinical care processes could offset barriers and enhance facilitators. [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]

Background: Self-management of bipolar disorder (BD) is challenging for many individuals. Material: Interviews were used to assess perceived barriers to disease self-management among 21 high-risk patients with BD. Content analysis, with an emphasis on dominant themes, was used to analyze the data. Results: Three major domains of barriers emerged: individual barriers (psychological, knowledge, behavioral and physical health); family/community-level barriers (lack of support and resources); and provider/healthcare system (inadequate communication and access to care). Conclusion: Care approaches providing social and peer support, optimizing communication with providers and integrating medical and psychiatric care may improve self-management of BD in this vulnerable population. [ABSTRACT FROM AUTHOR]

The article presents a study on the obstacles to self-management of serious mental illness (SMI) and diabetes mellitus (DM). It discusses findings on the domains of barriers to such self-management including personal level barriers such as stress, isolation and stigma, family and community level barriers such as lack of support from family and friends and provider and health care system level barriers such as poor relationships and communication with providers as well as fragmentation of care.

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AimTo better understand the type and range of health issues initiated by patients and providers in ‘high-quality’ primary-care for adults with diabetes and low socio-economic status (SES).BackgroundAlthough quality of care guidelines are straightforward, diabetes visits in primary care are often more complex than adhering to guidelines, especially in adults with low SES who experience many financial and environmental barriers to good care.MethodsWe conducted a qualitative study using direct observation of primary-care diabetes visits at an exemplar safety net practice in 2009–2010.FindingsIn a mainly African American (93%) low-income population with fair cardiovascular control (mean A1c 7.5%, BP 134/81 mmHg, and low-density lipoprotein cholesterol 100 mg/dL), visits addressed a variety of bio-psychosocial health issues [median: 25 problems/visit (range 13–32)]. Physicians most frequently initiated discussions about chronic diseases, prevention, and health behavior. Patients most frequently initiated discussions about social environment and acute symptoms followed by prevention and health behavior.ConclusionsPrimary-care visits by diabetes patients with low SES address a surprising number and diversity of problems. Emerging new models of primary-care delivery and quality measurement should allow adequate time and resources to address the range of tasks necessary for integrating biomedical and psychosocial concerns to improve the health of socio-economically disadvantaged patients. [ABSTRACT FROM PUBLISHER]

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]