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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., (© 2023 The Authors. Journal of the American Geriatrics Society published by Wiley Periodicals LLC on behalf of The American Geriatrics Society.)

Background and Objectives: Temporal lobe epilepsy (TLE) is the most common adult form of epilepsy and is associated with a high risk of cognitive deficits and depressed mood. However, little is known about the role of environmental factors on cognition and mood in TLE. This cross-sectional study examined the relationship between neighborhood deprivation and neuropsychological function in adults with TLE., Methods: Neuropsychological data were obtained from a clinical registry of patients with TLE and included measures of intelligence, attention, processing speed, language, executive function, visuospatial skills, verbal/visual memory, depression, and anxiety. Home addresses were used to calculate the Area Deprivation Index (ADI) for each individual, which were separated into quintiles (i.e., quintile 1 = least disadvantaged and quintile 5 = most disadvantaged). Kruskal-Wallis tests compared quintile groups on cognitive domain scores and mood and anxiety scores. Multivariable regression models, with and without ADI, were estimated for overall cognitive phenotype and for mood and anxiety scores., Results: A total of 800 patients (median age 38 years; 58% female) met all inclusion criteria. Effects of disadvantage (increasing ADI) were observed across nearly all measured cognitive domains and with significant increases in symptoms of depression and anxiety. Furthermore, patients in more disadvantaged ADI quintiles had increased odds of a worse cognitive phenotype ( p = 0.013). Patients who self-identified as members of minoritized groups were overrepresented in the most disadvantaged ADI quintiles and were 2.91 (95% CI 1.87-4.54) times more likely to be in a severe cognitive phenotype than non-Hispanic White individuals ( p < 0.001). However, accounting for ADI attenuated this relationship, suggesting neighborhood deprivation may account for some of the relationship between race/ethnicity and cognitive phenotype (ADI-adjusted proportional odds ratio 1.82, 95% CI 1.37-2.42)., Discussion: These findings highlight the importance of environmental factors and regional characteristics in neuropsychological studies of epilepsy. There are many potential mechanisms by which neighborhood disadvantage can adversely affect cognition (e.g., fewer educational opportunities, limited access to health care, food insecurity/poor nutrition, and greater medical comorbidities). Future research will seek to investigate these potential mechanisms and determine whether structural and functional alterations in the brain moderate the relationship between ADI and cognition., (© 2023 American Academy of Neurology.)

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.

Background We evaluated whether a comprehensive ST-segment-elevation myocardial infarction protocol (CSP) focusing on guideline-directed medical therapy, transradial percutaneous coronary intervention, and rapid door-to-balloon time improves process and outcome metrics in patients with moderate or high socioeconomic deprivation. Methods and Results A total of 1761 patients with ST-segment-elevation myocardial infarction treated with percutaneous coronary intervention at a single hospital before (January 1, 2011-July 14, 2014) and after (July 15, 2014- July 15, 2019) CSP implementation were included in an observational cohort study. Neighborhood deprivation was assessed by the Area Deprivation Index and was categorized as low (≤50th percentile; 29.0%), moderate (51st -90th percentile; 40.8%), and high (>90th percentile; 30.2%). The primary process outcome was door-to-balloon time. Achievement of guideline-recommend door-to-balloon time goals improved in all deprivation groups after CSP implementation (low, 67.8% before CSP versus 88.5% after CSP; moderate, 50.7% before CSP versus 77.6% after CSP; high, 65.5% before CSP versus 85.6% after CSP; all P <0.001). Median door-to-balloon time among emergency department/in-hospital patients was significantly noninferior in higher versus lower deprivation groups after CSP (noninferiority limit=5 minutes; P noninferiority high versus moderate = 0.002, high versus low <0.001, moderate versus low = 0.02). In-hospital mortality, the primary clinical outcome, was significantly lower after CSP in patients with moderate/high deprivation in unadjusted (before CSP 7.0% versus after CSP 3.1%; odds ratio [OR], 0.42 [95% CI, 0.25-0.72]; P =0.002) and risk-adjusted (OR, 0.42 [95% CI, 0.23-0.77]; P =0.005) models. Conclusions A CSP was associated with improved ST-segment-elevation myocardial infarction care across all deprivation groups and reduced mortality in those from moderate or high deprivation neighborhoods. Standardized initiatives to reduce care variability may mitigate social determinants of health in time-sensitive conditions such as ST-segment-elevation myocardial infarction.

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., Competing Interests: The authors have declared that no competing interests exist.

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.

Using a multi-institutional EMR registry, we extracted housing status and evaluated the presence of several important comorbidities in order to describe the demographics and comorbidity burden of persons experiencing homelessness in northeast Ohio and compare this to non-homeless individuals of varying socioeconomic position. Of 1,974,766 patients in the EMR registry, we identified 15,920 (0.8%) as homeless, 351,279 (17.8%) as non-homeless and in the top quintile of area deprivation index (ADI), and 1,607,567 (81.4%) as non-homeless and in the lower four quintiles of area deprivation. The comorbidity burden was highest in the homeless population with depression (48.1%), anxiety (45.8%), hypertension (44.2%), cardiovascular disease (18.4%), and hepatitis (18.1%) among the most prevalent conditions. We conclude that it is possible to identify homeless individuals and document their comorbidity burden using a multi-institutional EMR registry, in order to guide future interventions to address the health of the homeless at the health-system and community level.

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., (© 2020 The American Geriatrics Society.)

Limitations of statistics currently used to assess balance in observation samples include their insensitivity to shape discrepancies and their dependence upon sample size. The Jensen-Shannon divergence (JSD) is an alternative approach to quantifying the lack of balance among treatment groups that does not have these limitations. The JSD is an information-theoretic statistic derived from relative entropy, with three specific advantages relative to using standardized difference scores. First, it is applicable to cases in which the covariate is categorical or continuous. Second, it generalizes to studies in which there are more than two exposure or treatment groups. Third, it is decomposable, allowing for the identification of specific covariate values, treatment groups or combinations thereof that are responsible for any observed imbalance., Competing Interests: Conflicts of Interest: The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Importance: Immune dysregulation can increase the risk of infection, malignant neoplasms, and cardiovascular disease, but improved methods are needed to identify and quantify immunologic hazard in the general population., Objective: To determine whether lymphopenia is associated with reduced survival in outpatients., Design, Setting, and Participants: This retrospective cohort study of the National Health and Nutrition Examination Survey (NHANES) included participants enrolled from January 1, 1999, to December 31, 2010, a large outpatient sample representative of the US adult population. Associations were evaluated between lymphopenia and other immunohematologic (IH) markers, clinical features, and survival during 12 years of follow-up, completed on December 31, 2011. Spearman correlations, Cox proportional hazards regression models, and Kaplan-Meier curves were used in univariable and multivariable models, allowing for nonlinear associations with bivariate cubic polynomials. Data were analyzed from September 1, 2018, through July 24, 2019., Exposures: Absolute lymphocyte counts (ALC), red blood cell distribution width (RDW), and C-reactive protein (CRP) level., Main Outcomes and Measures: All-cause survival., Results: Among the 31 178 participants, the median (interquartile range) age at baseline was 45 (30-63) years, 16 093 (51.6%) were women, 16 260 (52.2%) were nonwhite, and overall 12-year rate of survival was 82.8%. Relative lymphopenia (≤1500/μL) and severe lymphopenia (≤1000/μL) were observed in 20.1% and 3.0%, respectively, of this general population and were associated with increased risk of mortality (age- and sex-adjusted hazard ratios [HRs], 1.3 [95% CI, 1.2-1.4] and 1.8 [95% CI, 1.6-2.1], respectively) due to cardiovascular and noncardiovascular causes. Lymphopenia was also associated with worse survival in multivariable models, including traditional clinical risk factors, and this risk intensified when accompanied by bone marrow dysregulation (elevated RDW) and/or inflammation (elevated CRP level). Ten-year mortality ranged from 3.8% to 62.1% based on lymphopenia status, tertile of CRP level, and tertile of RDW. A high-risk IH profile was nearly twice as common as type 2 diabetes (19.3% and 10.0% of participants, respectively) and associated with a 3-fold risk of mortality (HR, 3.2; 95% CI, 2.6-4.0). Individuals aged 70 to 79 years with low IH risk had a better 10-year survival (74.1%) than those who were a decade younger with a high-risk IH profile (68.9%)., Conclusions and Relevance: These findings suggest that lymphopenia is associated with reduced survival independently of and additive to traditional risk factors, especially when accompanied by altered erythropoiesis and/or heightened inflammation. Immune risk may be analyzed as a multidimensional entity derived from routine tests, facilitating precision medicine and population health interventions.

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., Competing Interests: The authors have declared that no competing interests exist.