(P24-018-24) Predictors of Frequency of Ultra-Processed Food Purchase Among Adult Consumers in Addis Ababa, Ethiopia

Objectives: To identify predictors of ultra-processed food purchase patterns among consumers in Addis Ababa, Ethiopia.

Methods: In 2022–23, adult (≥18 years) consumers (n=1200) were recruited outside supermarkets or souqs in Addis Ababa, Ethiopia as part of a randomized controlled trial evaluating front-of-package labels. Consumers were asked how frequently they purchased ultra-processed foods for themselves and others. Logistic regression was used to examine the relationship between purchase frequency (once per week or more versus 2-3 times per month) and potential predictors, including age, gender, education, number of children in the household, and market type. 

Results: Participants reported a mean age of 32 years, 53% were female, and 60% had a college diploma/degree or higher education. In multivariable-adjusted models, female gender (odds ratio, OR=1.35, 95%CI [1.06, 1.72]), younger age (≤32 years, OR=2.99, 95%CI [2.29, 3.89]), and having at least a college diploma (OR=1.33, 95%CI [1.03, 1.71]) were associated with more frequent purchase of ultra-processed foods for oneself. Higher education level was also associated with more frequent purchases for others (OR=1.51, 95%CI [1.18, 1.93]), as was having children in the family (OR=2.51, 95%CI [1.94, 3.24]).

Conclusions: Women, younger adults, consumers with higher education, and those living in households with children report more frequent purchases of ultra-processed foods in Addis Ababa, Ethiopia. Further research is needed to understand the dynamics that drive behavior towards ultra-processed food purchases in Ethiopia and to create healthier food environments that include affordable access to unprocessed and minimally processed food choices.

Funding Sources: This study was supported by Resolve to Save Lives. Resolve to Save Lives is funded by Bloomberg Philanthropies, the Bill and Melinda Gates Foundation, and Gates Philanthropy Partners, which is funded with support from the Chan Zuckerberg Initiative.