Exploring the impact of missingness on racial disparities in predictive performance of a machine learning model for emergency department triage.

Objective: To investigate how missing data in the patient problem list may impact racial disparities in the predictive performance of a machine learning (ML) model for emergency department (ED) triage.

Materials And Methods: Racial disparities may exist in the missingness of EHR data (eg, systematic differences in access, testing, and/or treatment) that can impact model predictions across racialized patient groups. We use an ML model that predicts patients' risk for adverse events to produce triage-level recommendations, patterned after a clinical decision support tool deployed at multiple EDs.

Evaluating equity in performance of an electronic health record-based 6-month mortality risk model to trigger palliative care consultation: a retrospective model validation analysis.

Objective: Evaluate predictive performance of an electronic health record (EHR)-based, inpatient 6-month mortality risk model developed to trigger palliative care consultation among patient groups stratified by age, race, ethnicity, insurance and socioeconomic status (SES), which may vary due to social forces (eg, racism) that shape health, healthcare and health data.

Design: Retrospective evaluation of prediction model.

Setting: Three urban hospitals within a single health system.

Effects of Neighborhood-level Data on Performance and Algorithmic Equity of a Model That Predicts 30-day Heart Failure Readmissions at an Urban Academic Medical Center.

Background: Socioeconomic data may improve predictions of clinical events. However, owing to structural racism, algorithms may not perform equitably across racial subgroups. Therefore, we sought to compare the predictive performance overall, and by racial subgroup, of commonly used predictor variables for heart failure readmission with and without the area deprivation index (ADI), a neighborhood-level socioeconomic measure.

Factors Associated with Loss to Follow-up among Cervical Cancer Patients in Rwanda.

Background: Cervical cancer is among the most common cancers affecting women globally. Where treatment is available in low- and middle-income countries, many women become lost to follow-up (LTFU) at various points of care.

Objective: This study assessed predictors of LTFU among cervical cancer patients in rural Rwanda.

Recognizing Racism in Medicine: A Student-Organized and Community-Engaged Health Professional Conference.

This piece details the evaluation and implementation of a student-led educational intervention designed to train health professionals on the impact of racism in health care and provide tools to mitigate it. In addition, this conference, cosponsored by medical, nursing, and social work training programs, facilitates development of networks of providers with the knowledge and skills to recognize and address racism in health care. The conference included 2 keynote speakers, an interprofessional panel, and 15 workshops.

Small area estimation of under-5 mortality in Bangladesh, Cameroon, Chad, Mozambique, Uganda, and Zambia using spatially misaligned data.

Background: The under-5 mortality rate (U5MR) is an important metric of child health and survival. Country-level estimates of U5MR are readily available, but efforts to estimate U5MR subnationally have been limited, in part, due to spatial misalignment of available data sources (e.g.

Child and Adolescent Health From 1990 to 2015: Findings From the Global Burden of Diseases, Injuries, and Risk Factors 2015 Study.

Importance: Comprehensive and timely monitoring of disease burden in all age groups, including children and adolescents, is essential for improving population health.

Objective: To quantify and describe levels and trends of mortality and nonfatal health outcomes among children and adolescents from 1990 to 2015 to provide a framework for policy discussion.

Evidence Review: Cause-specific mortality and nonfatal health outcomes were analyzed for 195 countries and territories by age group, sex, and year from 1990 to 2015 using standardized approaches for data processing and statistical modeling, with subsequent analysis of the findings to describe levels and trends across geography and time among children and adolescents 19 years or younger.