Effect of the Emory Healthy Kitchen Collaborative on Employee Health Habits and Body Weight: A 12-Month Workplace Wellness Trial.

Introduction: Teaching kitchens are being used to facilitate lifestyle changes with a focus on culinary and nutrition programs to improve health behaviors. Less is known regarding their use as a worksite wellness program and their influence on employees' quality of life, body weight, and adoption of healthy behaviors. We evaluated changes in self-reported healthy behaviors, overall health, and weight during a one-year multidisciplinary teaching kitchen program.

A Teaching Kitchen Program Improves Employee Micronutrient and Healthy Dietary Consumption.

Objective: To measure changes in micronutrient adequacy and diet quality in healthcare and university employees who underwent a 10-week teaching kitchen program.

Methods: Thirty-eight healthcare and university employees participated in a 10-week teaching kitchen program. Twenty-seven completed self-administered, 24-hour dietary recalls to measure dietary intake at baseline and 3-months.

Hospitalizations reduce health care utilization of household members.

Objective: To examine whether the financial burden of hospitalizations affects the health care utilization of household members of the admitted patient.

Data Sources: We utilized health care claims data from the Massachusetts All-Payer Claims Database, 2010-2015, to identify emergency hospitalizations of patients on family insurance plans and the health care utilization of the family members on those plans.

Study Design: We used an event-study analysis to compare health care spending and utilization of family members of a hospitalized individual and family members of an individual who was hospitalized 1 year later.

Uncertainty in lung cancer stage for survival estimation via set-valued classification.

The difficulty in identifying cancer stage in health care claims data has limited oncology quality of care and health outcomes research. We fit prediction algorithms for classifying lung cancer stage into three classes (stages I/II, stage III, and stage IV) using claims data, and then demonstrate a method for incorporating the classification uncertainty in survival estimation. Leveraging set-valued classification and split conformal inference, we show how a fixed algorithm developed in one cohort of data may be deployed in another, while rigorously accounting for uncertainty from the initial classification step.

Comparing risk adjustment estimation methods under data availability constraints.

The Italian National Healthcare Service relies on per capita allocation for healthcare funds, despite having a highly detailed and wide range of data to potentially build a complex risk-adjustment formula. However, heterogeneity in data availability limits the development of a national model. This paper implements and ealuates machine learning (ML) and standard risk-adjustment models on different data scenarios that a Region or Country may face, to optimize information with the most predictive model.

Cost Analysis of Developing, Implementing, and Evaluating a Multi-Disciplinary Teaching Kitchen.

Introduction: Teaching kitchens have emerged as strategies to deliver lifestyle medicine principles and practices. However, a better understanding of their implementation, delivery cost, and potential benefits are needed. This article provides a comprehensive analysis of the costs associated with the development, deployment, and evaluation of the Emory Healthy Kitchen Collaborative (EHKC) teaching kitchen clinical trial.

Flourishing in Healthcare Trainees: Psychological Well-Being and the Conserved Transcriptional Response to Adversity.

While much attention has been paid to healthcare provider and trainee burnout, less is known about provider well-being (i.e., flourishing) or about the effects of well-being on immune function.

Development of Culinary and Self-Care Programs in Diverse Settings: Theoretical Considerations and Available Evidence.

Culinary-based self-care programs are innovative and increasingly utilized models for catalyzing behavior change and improving health and well-being. The content, duration, and delivery of existing programs vary considerably. Between January and August 2019, we developed a teaching kitchen and self-care curriculum, which was administered as part of a year-long worksite well-being program to employees at an academic healthcare system.

Prolonged Symptoms After COVID-19 Infection in Outpatients.

We review 127 encounters for polymerase chain reaction-confirmed coronavirus disease 2019 (COVID-19) infection at a multidisciplinary outpatient clinic. We describe the symptomatology, time course, exam, and radiographic findings in this population. Patients with COVID-19 can experience persistent symptoms, primarily respiratory in nature, which can be severe enough to warrant hospitalization.

Early Child Development Assessments and Their Associations with Long-Term Academic and Economic Outcomes: A Systematic Review.

Developmental screening instruments were designed as diagnostic tools, but there is growing interest in understanding whether select tools can also be used systematically in research to examine intervention impacts on long-term outcomes. As such, this systematic review aims to examine associations between child development assessment tools and educational attainment, academic achievement, or wealth. We included studies identified in PubMed, PsycINFO, and Educational Resources Information Center if they reported an association between at least one tool from a pre-established list and one outcome of interest after age 10.

COVID-19 pandemic in the United States.

Objectives: The paper highlights US health policy and technology responses to the COVID-19 pandemic from January 1, 2020 - August 9, 2020.

Methods: A review of primary data sources in the US was conducted. The data were summarized to describe national and state-level trends in the spread of COVID-19 and in policy and technology solutions.

Non-hospitalized Adults with COVID-19 Differ Noticeably from Hospitalized Adults in Their Demographic, Clinical, and Social Characteristics.

The characteristics of patients with coronavirus disease 2019 (COVID-19) have primarily been described in hospitalized adults. Characterization of COVID-19 in ambulatory care is needed for a better understanding of its evolving epidemiology. Our aim is to provide a description of the demographics, comorbidities, clinical presentation, and social factors in confirmed SARS-CoV-2-positive non-hospitalized adults.

Purpose in life is a robust protective factor of reported cognitive decline among late middle-aged adults: The Emory Healthy Aging Study.

Background: Cognitive abilities tend to decline in advanced age. A novel protective factor of cognitive decline in advanced age is purpose-in-life (PiL), a trait-like tendency to derive life meanings and purpose. However, whether PiL protects against cognitive decline in late-middle-age is unclear.

Rationale and Design of the Emory Healthy Aging and Emory Healthy Brain Studies.

Understanding the relationships between health and aging is essential for delaying morbidity and maximizing independence in aging populations as life expectancies increase. Loss of cognitive function is a feared age-associated condition and growing public health concern. Alzheimer's disease (AD), the most common cause of dementia, has no curative therapies.

Data transformations to improve the performance of health plan payment methods.

The conventional method for developing health care plan payment systems uses observed data to study alternative algorithms and set incentives for the health care system. In this paper, we take a different approach and transform the input data rather than the algorithm, so that the data used reflect the desired spending levels rather than the observed spending levels. We present a general economic model that incorporates the previously overlooked two-way relationship between health plan payment and insurer actions.

Classifying Stage IV Lung Cancer From Health Care Claims: A Comparison of Multiple Analytic Approaches.

Purpose: Cancer stage is a key determinant of outcomes; however, stage is not available in claims-based data sources used for real-world evaluations. We compare multiple methods for classifying lung cancer stage from claims data.

Methods: Our study used the linked SEER-Medicare data.

Sample Selection for Medicare Risk Adjustment Due to Systematically Missing Data.

Objective: To assess the issue of nonrepresentative sampling in Medicare Advantage (MA) risk adjustment.

Data Sources: Medicare enrollment and claims data from 2008 to 2011.

Data Extraction: Risk adjustment predictor variables were created from 2008 to 2010 Part A and B claims and the Medicare Beneficiary Summary File.

Mental Health Risk Adjustment with Clinical Categories and Machine Learning.

Objective: To propose nonparametric ensemble machine learning for mental health and substance use disorders (MHSUD) spending risk adjustment formulas, including considering Clinical Classification Software (CCS) categories as diagnostic covariates over the commonly used Hierarchical Condition Category (HCC) system.

Data Sources: 2012-2013 Truven MarketScan database.

Study Design: We implement 21 algorithms to predict MHSUD spending, as well as a weighted combination of these algorithms called super learning.

Classifying Lung Cancer Severity with Ensemble Machine Learning in Health Care Claims Data.

Research in oncology quality of care and health outcomes has been limited by the difficulty of identifying cancer stage in health care claims data. Using linked cancer registry and Medicare claims data, we develop a tool for classifying lung cancer patients receiving chemotherapy into early vs. late stage cancer by () deploying ensemble machine learning for prediction, () establishing a set of classification rules for the predicted probabilities, and () considering an augmented set of administrative claims data.