The COVID-19 is disproportionally affecting the poor, minorities and a broad range of vulnerable populations, due to its inequitable spread in areas of dense population and limited mitigation capacity due to high prevalence of chronic conditions or poor access to high quality public health and medical care. Moreover, the collateral effects of the pandemic due to the global economic downturn, and social isolation and movement restriction measures, are unequally affecting those in the lowest power strata of societies. To address the challenges to health equity and describe some of the approaches taken by governments and local organizations, we have compiled 13 country case studies from various regions around the world: China, Brazil, Thailand, Sub Saharan Africa, Nicaragua, Armenia, India, Guatemala, United States of America (USA), Israel, Australia, Colombia, and Belgium. This compilation is by no-means representative or all inclusive, and we encourage researchers to continue advancing global knowledge on COVID-19 health equity related issues, through rigorous research and generation of a strong evidence base of new empirical studies in this field.

Purpose: The purpose of this study was to test the feasibility of a new model of health care designed to improve the quality of life and the efficiency of resource use for older adults with multimorbidity. Design and Methods: Guided Care enhances primary care by infusing the operative principles of seven chronic care innovations: disease management, self-management, case management, lifestyle modification, transitional care, caregiver education and support, and geriatric evaluation and management. To practice Guided Care, a registered nurse completes an educational program and uses a customized electronic health record in working with two to five primary care physicians to meet the health care needs of 50 to 60 older patients with multimorbidity. For each patient, the nurse performs a standardized comprehensive home assessment and then collaborates with the physician, the patient, and the caregiver to create two comprehensive, evidence-based management plans: a Care Guide for health care professionals, and an Action Plan for the patient and caregiver. Based in the primary care office, the nurse then regularly monitors the patient's chronic conditions, coaches the patient in self-management, coordinates the efforts of all involved health care professionals, smoothes the patient's transitions between sites of care, provides education and support for family caregivers, and facilitates access to community resources. Results: A 1-year pilot test in a community-based primary care practice suggested that Guided Care is feasible and acceptable to physicians, patients, and caregivers. Implications: If successful in a controlled trial, Guided Care could improve the quality of life and efficiency of health care for older adults with multimorbidity.

Background: Readmission prevention should begin as early as possible during the index admission. Early identification may help target patients for within-hospital readmission prevention interventions. Objectives: To develop and validate a 30-day readmission prediction model using data from electronic health records available before the index admission. Research Design: Retrospective cohort study of admissions between January 1 and March 31, 2010. Subjects: Adult enrollees of Clalit Health Services, an integrated delivery system, admitted to an internal medicine ward in any hospital in Israel. Measures: All-cause 30-day emergency readmissions. A prediction score based on before admission electronic health record and administrative data (the Preadmission Readmission Detection Model—PREADM) was developed using a preprocessing variable selection step with decision trees and neural network algorithms. Admissions with a recent prior hospitalization were excluded and automatically flagged as “high-risk.” Selected variables were entered into multivariable logistic regression, with a derivation (two-thirds) and a validation cohort (one-third). Results: The derivation dataset comprised 17,334 admissions, of which 2913 (16.8%) resulted in a 30-day readmission. The PREADM includes 11 variables: chronic conditions, prior health services use, body mass index, and geographical location. The c-statistic was 0.70 in the derivation set and of 0.69 in the validation set. Adding length of stay did not change the discriminatory power of the model. Conclusions: The PREADM is designed for use by health plans for early high-risk case identification, presenting discriminatory power better than or similar to that of previously reported models, most of which include data available only upon discharge.

This study describes referral completion from the perspectives of patients and primary care physicians and identifies predictors of adherence to the referral recommendation. We observed a cohort of 776 referred patients from the offices of 133 physicians in 81 practices and 30 states. Referring physicians and patients completed self-administered questionnaires at the time of the referral decision and 3 months later. Physicians reported that 79.2% of patients referred had a specialist visit, and 83.0% of patients indicated they completed the referral. The most common reasons for not completing the referral were "lack of time" and patient belief that the "health problem had resolved." The kappa statistic for patient-physician agreement on referral completion was 0.34, indicating only fair concordance. Patients in Medicaid plans were less likely than others to complete the referral, and more likely to experience a health plan denial. A longer duration of the patient relationship with the primary care physician and physician/staff scheduling of the specialty appointment were both positive predictors of referral completion. About 8 in 10 patients referred from primary care complete a specialty referral within 3 months. Findings from this study suggest that referral completion rates may be increased by assisting patients with scheduling their specialty appointments and promoting continuity of care.