Patient assignment and quality performance: a misaligned system.

Objectives: To assess the congruence between patient assignment and established patients as well as their association with Healthcare Effectiveness Data and Information Set (HEDIS) quality performance.

Study Design: A retrospective cross-sectional analysis from January 2020 to February 2022.

Methods: The study setting is a fully integrated health care delivery system in Phoenix, Arizona.

Evolution of COVID-19 Health Disparities in Arizona.

COVID-19 burdens are disproportionally high in underserved and vulnerable communities in Arizona. As the pandemic progressed, it is unclear if the initial associated health disparities have changed. This study aims to elicit the dynamic landscape of COVID-19 disparities at the community level and identify newly emerging vulnerable subpopulations.

Framework for Multisector Alignment Research.

Context: Public health collaboratives are effective platforms to develop interventions for improving population health. Most collaboratives are limited to the public health and health care delivery sectors; however, multisector collaboratives are becoming more recognized as a strategy for combining efforts from medical, public health, social services, and other sectors.

Program: Based on a 4-year multisector collaborative project, we identify concepts for widening the lens to conduct multisector alignment research.

Dynamic incorporation of prior knowledge from multiple domains in biomarker discovery.

Background: In biomarker discovery, applying domain knowledge is an effective approach to eliminating false positive features, prioritizing functionally impactful markers and facilitating the interpretation of predictive signatures. Several computational methods have been developed that formulate the knowledge-based biomarker discovery as a feature selection problem guided by prior information. These methods often require that prior information is encoded as a single score and the algorithms are optimized for biological knowledge of a specific type.

Matched Forest: supervised learning for high-dimensional matched case-control studies.

Motivation: Matched case-control analysis is widely used in biomedical studies to identify exposure variables associated with health conditions. The matching is used to improve the efficiency. Existing variable selection methods for matched case-control studies are challenged in high-dimensional settings where interactions among variables are also important.