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

Abstract

Background 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. Objective 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. Methods 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. Results 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. Conclusion 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.

A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects

“… Prediction Survey United States 4524 Body mass index LASSO regression Stimulant medications and demographic factors were most strongly associated with body mass index. ( Hamad et al, 2019 ) 2019 To examine differences in sociodemographic characteristics, health, nutritional status, and food purchasing behavior between new and existing recipients of SNAP after the recession. Prediction Survey United States 21806 Household-level nutritional characteristics LASSO regression Given that new recipients are generally better off than existing recipients, it may be more impactful from a public health perspective to instead intervene among those existing recipients who may have more long-standing challenging socioeconomic circumstances.…”

Moving Food Assistance into the Digital Age: A Scoping Review

“…Studies implementing or evaluating inventory management tools involved tracking products going in and out of food pantries or banks, both from the donor-and clientperspectives [25,36,40,43,48,50,54,55,64,65,69,74]. Two of these tools were developed by academics, and two were developed by food pantries.…”

“…Three studies aimed to create digital tools to improve the use and acceptability of public health interventions [50,53,73]. This included combining online grocery shopping with nutrition education to improve healthy food options and consumption at food pantries.…”

Harnessing Artificial Intelligence to Improve Food Assistance: A Scoping Review of Machine Learning Tools

“…The pool of SNAP users has changed dramatically since the Great Recession. 71 Hamad et al are considering how new SNAP users may have different needs and backgrounds than those prior to 2008. 71 This information is critical to informing policymakers and SNAP program officers on how best to meet these changing needs.…”

“…71 Hamad et al are considering how new SNAP users may have different needs and backgrounds than those prior to 2008. 71 This information is critical to informing policymakers and SNAP program officers on how best to meet these changing needs. This topic was not considered less important within the corpus according to topic modeling than the detection of fraud.…”