Comparing demographic and health characteristics of new and existing SNAP recipients: application of a machine learning algorithm

Abstract

The Supplemental Nutrition Assistance Program (SNAP) expanded significantly after the Great Recession of 2008-2009, but no studies have characterized this new group of recipients. Few data sets provide details on whether an individual is a new or established recipient of SNAP. We sought to identify new and existing SNAP recipients, and to examine differences in sociodemographic characteristics, health, nutritional status, and food purchasing behavior between new and existing recipients of SNAP after the recession. We created a probabilistic algorithm to identify new and existing SNAP recipients using the 1999-2013 waves of the Panel Study of Income Dynamics. We applied this algorithm to the National Household Food Acquisition and Purchase Survey (FoodAPS), fielded during 2012-2013, to predict which individuals were likely to be new SNAP recipients. We then compared health and nutrition characteristics between new, existing, and never recipients of SNAP in FoodAPS. New adult SNAP recipients had higher socioeconomic status, better self-reported health, and greater food security relative to existing recipients, and were more likely to smoke relative to never recipients. New child SNAP recipients were less likely to eat all meals and had lower BMI relative to existing recipients. New SNAP households exhibited differences in food access and expenditures, although dietary quality was similar to that of existing SNAP households. We developed a novel algorithm for predicting new and existing SNAP recipiency that can be applied to other data sets,and subsequently demonstrated differences in health characteristics between new and existing recipients. The expansion of SNAP since the Great Recession enrolled a population that differed from the existing SNAP population and that may benefit from different types of nutritional and health services than those traditionally offered.