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ARS Home » Northeast Area » Burlington, Vermont » Food Systems Research Unit » Research » Research Project #445147

Research Project: Rural Diet Diversity in New England

Location: Food Systems Research Unit

Project Number: 8090-10700-001-002-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Sep 1, 2023
End Date: Aug 31, 2025

Diet quality for rural U.S. residents falls far short of national recommendations. Most analyses use data from the National Health and Nutrition Examination Survey (NHANES), which are geographically aggregated. Community, state, and region-specific findings are scarce. Accordingly, there is a need for complementary analysis on the diversity and variance of rural dietary health at the regional scale. The goal of this project is to better understand the variability of diets in Northern New England and the factors associated with healthier outcomes. In pursuit of this goal the project has three objectives: 1. Generate a list of community characteristics that potentially are causally related to dietary quality among “positive deviant” rural communities and residents in northern New England, i.e. those that tend toward higher Healthy Eating Index scores. 2. Evaluate and compare the healthfulness of rural and nonrural diets in northern New England using household-level grocery store scanner data collected in Maine, New Hampshire, and Vermont. 3. Provide evidence that can be used to inform a larger study on heterogeneity in rural diet quality and ultimately used to guide evidence-informed interventions targeting rural disparities in cardiometabolic diseases.

Explanatory qualitative data will be collected to help inform data needs for the quantitative analyses and to support interpretations of the data, specifically to understand why some communities, sub-populations, or households score higher on the Healthy Eating Index (HEI) in their purchases. Cooperator scientists will conduct a series (6-8 total) of focus groups with key nutrition and health stakeholders (e.g., district staff from state health departments, nutrition professionals from Extension Services, and key personnel for hunger relief organizations) in rural communities across Maine, New Hampshire, and Vermont to explore contextual factors and strategies that support healthy eating in rural northern New England. Quantitative analyses will use household-level IRI scanner data to identify the alignment of food purchases with the Dietary Guidelines for Americans using the HEI and explore dietary diversity and variability among households in northern New England. Cooperator Scientists will use Consumer Network data from a subset of households identified as the “static panel” who consistently report purchases. The USDA’s Purchase to Plate Crosswalk tool will be used to link food purchase data to the USDA’s Food and Nutrient Database for Dietary Studies. Data analysis will begin with the creation of a consumption measure for every component of the HEI for each represented household in each quarter analyzed. The primary measure of dietary quality will be each household’s overall and component HEI scores. Once HEI scores have been estimated for each household, Cooperator scientists will test the measure of alignment between these purchases and the USDA’s Thrifty, Low-Cost, Moderate-Cost, and Liberal Food Plans, which represent nutritious dietary patterns at different cost levels. Drawing on household demographics and USDA recommendations for consumption and expenditure by age and sex, Cooperator scientists will calculate household-specific recommended expenditure shares for each food category. These analyses will be used to create empirical distributions of HEI, total food expenditure, and Expenditure Share Alignment for rural areas. From the USDA’s rural codes (urban, large rural, small rural, and isolated), Cooperator scientists will classify how diet quality and grocery spending differ within and between rural areas. The HEI dataset will be combined with external data sources that provide demographic, environmental, and economic information, with geography functioning as a match variable, to elaborate findings with diverse information on county and sub-county characteristics. A series of regression models will be used to distinguish which rural characteristics are related to significant differences in HEI scores and food purchases and expenditure.