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Strategies to Address Racial and Ethnic Disparities in Health and Health Care for Chronic Conditions : An Evidence Map of Research From 2017 to 2024.

Racial and ethnic disparities in health and health care persist in the United States, adversely affecting outcomes in prevention and treatment of chronic conditions among adults. To map interventions aimed at reducing racial and ethnic disparities and improving health outcomes in the prevention and treatment of chronic conditions in adults. Searches of MEDLINE, CINAHL, and Scopus from January 2017 to April 2024, supplemented with gray literature. U.S.-based studies of interventions targeting racial and ethnic disparities in adults with chronic conditions. Information on intervention types, targets, outcomes, study designs, study settings, chronic conditions, and delivery personnel was extracted and categorized. Among 174 unique studies, 12 intervention types were identified, with self-management support and patient navigation the most common. Most interventions targeted patient behaviors; few studies addressed disparities directly or focused on underrepresented racial and ethnic marginalized groups. The lack of standardized terminology and the underrepresentation of certain racial and ethnic groups limit the evidence base. Although the literature search accurately reflects the current state of the literature, it also limits the body of evidence by excluding health disparities research conducted before January 2017, so significant findings from earlier studies may have been overlooked. The literature highlights diverse interventions targeting health disparities, but few studies evaluated their effectiveness in reducing the health disparities gaps. There is an urgent need for research focused on underrepresented racial and ethnic groups, particularly in promising areas such as patient navigation for cancer and diabetes self-management. Future research should prioritize robust study designs to assess the long-term effect and broader applicability of interventions, thus helping organizations and stakeholders to tailor strategies to community-specific needs. Agency for Healthcare Research and Quality.

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Development and Evaluation of a Comprehensive Prediction Model for Incident Coronary Heart Disease Using Genetic, Social, and Lifestyle-Psychological Factors: A Prospective Analysis of the UK Biobank.

Clinical risk calculators for coronary heart disease (CHD) do not include genetic, social, and lifestyle-psychological risk factors. To improve CHD risk prediction by developing and evaluating a prediction model that incorporated a polygenic risk score (PRS) and a polysocial score (PSS), the latter including social determinants of health and lifestyle-psychological factors. Cohort study. United Kingdom. UK Biobank participants recruited between 2006 and 2010. Incident CHD (myocardial infarction and/or coronary revascularization); 10-year clinical risk based on pooled cohort equations (PCE), Predicting Risk of cardiovascular disease EVENTs (PREVENT), and QRISK3; PRS (Polygenic Score Catalog identification: PGS000018) for CHD (PRSCHD); and PSSCHD from 100 related covariates. Machine-learning and time-to-event analyses and model performance indices. In 388 224 participants (age, 55.5 [SD, 8.1] years; 42.5% men; 94.9% White), the hazard ratio for 1 SD increase in PSSCHD for incident CHD was 1.43 (95% CI, 1.38 to 1.49; P < 0.001) and for 1 SD increase in PRSCHD was 1.59 (CI, 1.53 to 1.66, P < 0.001). Non-White persons had higher PSSCHD than White persons. The effects of PSSCHD and PRSCHD on CHD were independent and additive. At a 10-year CHD risk threshold of 7.5%, adding PSSCHD and PRSCHD to PCE reclassified 12% of participants, with 1.86 times higher CHD risk in the up- versus down-reclassified persons and showed superior performance compared with PCE as reflected by improved net benefit while maintaining good calibration relative to the clinical risk calculators. Similar results were seen when incorporating PSSCHD and PRSCHD into PREVENT and QRISK3. A predominantly White cohort; possible healthy participant effect and ecological fallacy. A PSSCHD was associated with incident CHD and its joint modeling with PRSCHD improved the performance of clinical risk calculators. National Human Genome Research Institute.

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