Integrating Social Drivers of Health into Hospital Ratings with Application to the 100 Top Hospitals Study.

The objective was to investigate the relationship between social drivers of health (SDOH) and hospital performance within the 100 Top Hospitals study, exploring methods to recognize hospitals serving marginalized communities. Publicly available data sourced from the Centers for Medicare and Medicaid Services and the 2023 100 Top Hospitals study was used. The study employed multivariable hierarchical generalized linear regression models to assess the association between an SDOH composite variable derived using principal component analysis and overall hospital performance measures within the 100 Top Hospitals study.

Evaluating individual intervention components: making decisions based on the results of a factorial screening experiment.

The multiphase optimization strategy (MOST) is a framework for not only evaluating but also optimizing behavioral interventions. A tool critical for MOST is the screening experiment, which enables efficient gathering of information for deciding which components to include in an optimized intervention. This article outlines a procedure for making decisions based on data from a factorial screening experiment.

Factorial experiments: efficient tools for evaluation of intervention components.

Background: An understanding of the individual and combined effects of a set of intervention components is important for moving the science of preventive medicine interventions forward. This understanding can often be achieved in an efficient and economical way via a factorial experiment, in which two or more independent variables are manipulated. The factorial experiment is a complement to the RCT; the two designs address different research questions.

Functional data analysis for dynamical system identification of behavioral processes.

Efficient new technology has made it straightforward for behavioral scientists to collect anywhere from several dozen to several thousand dense, repeated measurements on one or more time-varying variables. These intensive longitudinal data (ILD) are ideal for examining complex change over time but present new challenges that illustrate the need for more advanced analytic methods. For example, in ILD the temporal spacing of observations may be irregular, and individuals may be sampled at different times.

Understanding real-world implementation quality and "active ingredients" of PBIS.

Programs delivered in the "real world" often look substantially different from what was originally intended by program developers. Depending on which components of a program are being trimmed or altered, such modifications may seriously undermine the effectiveness of a program. In the present study, these issues are explored within a widely used school-based, non-curricular intervention, Positive Behavioral Intervention and Supports.