Racial/Ethnic Disparities in Food Pantry Use and Barriers in Massachusetts during the First Year of the COVID-19 Pandemic.

This study sought to describe racial disparities in food insecurity, food pantry use, and barriers to and experiences with food pantries during the first year of the COVID-19 pandemic. We surveyed 2928 adults in Massachusetts regarding food access in the year before and during the first year of the pandemic. Weighted multivariable logistic regression models assessed racial differences in barriers to and experiences with pantry use during the pandemic. Black and Latino adults experienced the highest prevalence of food insecurity and pantry use. Additionally, Black and Latino adults reported more barriers to, but less stigma around, pantry use compared to White adults. Latino adults were less likely to know about pantry hours/locations and encounter staff who spoke their language. Black and Latino adults were also more likely to find pantry hours/locations inconvenient and have difficulty with transportation. The COVID-19 pandemic resulted in increased food insecurity, and food access inequities persisted. Programmatic policies to improve pantry access in communities of color could include increasing the hours/days that pantries are open, increasing bilingual staff, providing transportation or delivery, and creating multilingual public awareness campaigns on how to locate pantries.

Exploration of Machine Learning and Statistical Techniques in Development of a Low-Cost Screening Method Featuring the Global Diet Quality Score for Detecting Prediabetes in Rural India.

BACKGROUND: The prevalence of type 2 diabetes has increased substantially in India over the past 3 decades. Undiagnosed diabetes presents a public health challenge, especially in rural areas, where access to laboratory testing for diagnosis may not be readily available. OBJECTIVES: The present work explores the use of several machine learning and statistical methods in the development of a predictive tool to screen for prediabetes using survey data from an FFQ to compute the Global Diet Quality Score (GDQS). METHODS: The outcome variable prediabetes status (yes/no) used throughout this study was determined based upon a fasting blood glucose measurement ≥100 mg/dL. The algorithms utilized included the generalized linear model (GLM), random forest, least absolute shrinkage and selection operator (LASSO), elastic net (EN), and generalized linear mixed model (GLMM) with family unit as a (cluster) random (intercept) effect to account for intrafamily correlation. Model performance was assessed on held-out test data, and comparisons made with respect to area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS: The GLMM, GLM, LASSO, and random forest modeling techniques each performed quite well (AUCs >0.70) and included the GDQS food groups and age, among other predictors. The fully adjusted GLMM, which included a random intercept for family unit, achieved slightly superior results (AUC of 0.72) in classifying the prediabetes outcome in these cluster-correlated data. CONCLUSIONS: The models presented in the current work show promise in identifying individuals at risk of developing diabetes, although further studies are necessary to assess other potentially impactful predictors, as well as the consistency and generalizability of model performance. In addition, future studies to examine the utility of the GDQS in screening for other noncommunicable diseases are recommended.