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      Area Deprivation Index and Cardiac Readmissions: Evaluating Risk‐Prediction in an Electronic Health Record

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          Abstract

          Background

          Assessment of the social determinants of post‐hospital cardiac care is needed. We examined the association and predictive ability of neighborhood‐level determinants (area deprivation index, ADI), readmission risk, and mortality for heart failure, myocardial ischemia, and atrial fibrillation.

          Methods and Results

          Using a retrospective (January 1, 2011–December 31, 2018) analysis of a large healthcare system, we assess the predictive ability of ADI on 30‐day and 1‐year readmission and mortality following hospitalization. Cox proportional hazards models analyzed time‐to‐event. Log rank analyses determined survival. C‐statistic and net reclassification index determined the model’s discriminative power. Covariates included age, sex, race, comorbidity, number of medications, length of stay, and insurance. The cohort (n=27 694) had a median follow‐up of 46.5 months. There were 14 469 (52.2%) men and 25 219 White (91.1%) patients. Patients in the highest ADI quintile (versus lowest) were more likely to be admitted within 1 year of index heart failure admission (hazard ratio [HR], 1.25; 95% CI, 1.03‒1.51). Patients with myocardial ischemia in the highest ADI quintile were twice as likely to be readmitted at 1 year (HR, 2.04; 95% CI, 1.44‒2.91]). Patients with atrial fibrillation living in areas with highest ADI were less likely to be admitted within 1 year (HR, 0.79; 95% CI, 0.65‒0.95). As ADI increased, risk of readmission increased, and risk reclassification was improved with ADI in the models. Patients in the highest ADI quintile were 25% more likely to die within a year (HR, 1.25 1.08‒1.44).

          Conclusions

          Residence in socioeconomically disadvantaged communities predicts rehospitalization and mortality. Measuring neighborhood deprivation can identify individuals at risk following cardiac hospitalization.

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          Most cited references49

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          Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond.

          Identification of key factors associated with the risk of developing cardiovascular disease and quantification of this risk using multivariable prediction algorithms are among the major advances made in preventive cardiology and cardiovascular epidemiology in the 20th century. The ongoing discovery of new risk markers by scientists presents opportunities and challenges for statisticians and clinicians to evaluate these biomarkers and to develop new risk formulations that incorporate them. One of the key questions is how best to assess and quantify the improvement in risk prediction offered by these new models. Demonstration of a statistically significant association of a new biomarker with cardiovascular risk is not enough. Some researchers have advanced that the improvement in the area under the receiver-operating-characteristic curve (AUC) should be the main criterion, whereas others argue that better measures of performance of prediction models are needed. In this paper, we address this question by introducing two new measures, one based on integrated sensitivity and specificity and the other on reclassification tables. These new measures offer incremental information over the AUC. We discuss the properties of these new measures and contrast them with the AUC. We also develop simple asymptotic tests of significance. We illustrate the use of these measures with an example from the Framingham Heart Study. We propose that scientists consider these types of measures in addition to the AUC when assessing the performance of newer biomarkers.
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            Making Neighborhood-Disadvantage Metrics Accessible — The Neighborhood Atlas

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              Neighborhood of residence and incidence of coronary heart disease.

              Where a person lives is not usually thought of as an independent predictor of his or her health, although physical and social features of places of residence may affect health and health-related behavior. Using data from the Atherosclerosis Risk in Communities Study, we examined the relation between characteristics of neighborhoods and the incidence of coronary heart disease. Participants were 45 to 64 years of age at base line and were sampled from four study sites in the United States: Forsyth County, North Carolina; Jackson, Mississippi; the northwestern suburbs of Minneapolis; and Washington County, Maryland. As proxies for neighborhoods, we used block groups containing an average of 1000 people, as defined by the U.S. Census. We constructed a summary score for the socioeconomic environment of each neighborhood that included information about wealth and income, education, and occupation. During a median of 9.1 years of follow-up, 615 coronary events occurred in 13,009 participants. Residents of disadvantaged neighborhoods (those with lower summary scores) had a higher risk of disease than residents of advantaged neighborhoods, even after we controlled for personal income, education, and occupation. Hazard ratios for coronary events in the most disadvantaged group of neighborhoods as compared with the most advantaged group--adjusted for age, study site, and personal socioeconomic indicators--were 1.7 among whites (95 percent confidence interval, 1.3 to 2.3) and 1.4 among blacks (95 percent confidence interval, 0.9 to 2.0). Neighborhood and personal socioeconomic indicators contributed independently to the risk of disease. Hazard ratios for coronary heart disease among low-income persons living in the most disadvantaged neighborhoods, as compared with high-income persons in the most advantaged neighborhoods were 3.1 among whites (95 percent confidence interval, 2.1 to 4.8) and 2.5 among blacks (95 percent confidence interval, 1.4 to 4.5). These associations remained unchanged after adjustment for established risk factors for coronary heart disease. Even after controlling for personal income, education, and occupation, we found that living in a disadvantaged neighborhood is associated with an increased incidence of coronary heart disease.
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                Author and article information

                Contributors
                Johnsonae2@upmc.edu
                Journal
                J Am Heart Assoc
                J Am Heart Assoc
                10.1002/(ISSN)2047-9980
                JAH3
                ahaoa
                Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
                John Wiley and Sons Inc. (Hoboken )
                2047-9980
                02 July 2021
                06 July 2021
                : 10
                : 13 ( doiID: 10.1002/jah3.v10.13 )
                : e020466
                Affiliations
                [ 1 ] Division of Cardiology Department of Medicine UPMC Heart and Vascular Institute University of Pittsburgh PA
                [ 2 ] Clinical Analytics Department UPMC Pittsburgh PA
                [ 3 ] Department of Preventive Medicine Feinberg School of Medicine Northwestern University Chicago IL
                Author notes
                [*] [* ] Correspondence to: Amber E. Johnson, MD, MS, 200 Lothrop St, Presbyterian South Tower, Third Floor, WE353.9, Pittsburgh, PA 15213. E‐mail: Johnsonae2@ 123456upmc.edu

                Author information
                https://orcid.org/0000-0003-1252-0735
                https://orcid.org/0000-0002-0063-6397
                https://orcid.org/0000-0002-5075-3604
                Article
                JAH36438
                10.1161/JAHA.120.020466
                8403312
                34212757
                01682e9f-244c-424f-b6dd-a96098ccdf00
                © 2021 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 05 January 2021
                : 19 May 2021
                Page count
                Figures: 3, Tables: 4, Pages: 12, Words: 18533
                Categories
                Original Research
                Original Research
                Health Services and Outcomes Research
                Custom metadata
                2.0
                July 6, 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.5 mode:remove_FC converted:16.08.2021

                Cardiovascular Medicine
                electronic health record,readmissions,risk prediction,social determinants of health,mortality/survival,quality and outcomes,risk factors

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