Food insecurity, participating in the Supplemental Nutrition Assistance Program, and the degree to which patients anticipate help from clinics to find food in Los Angeles County.

Healthcare clinics are uniquely positioned to screen for food insecurity and refer patients to food resources. This study examines this approach to address this social condition. A 2018 intercept survey of 1,103 adult patients recruited from across 11 clinic waiting rooms in Los Angeles County was conducted to describe the prevalence of food insecurity and whether Supplemental Nutrition Assistance Program (SNAP) participation and the degree to which patients anticipated their clinics to help them locate food varied by socio-demographic factors. The prevalence of food insecurity was high for this low-income survey sample (63.4%); 72% of Spanish-speaking Latinx reported experiencing it. For those who experienced food insecurity, older age was associated with lower odds of SNAP participation. Spanish-speaking Latinx had higher odds of anticipating help from a clinic to find food relative to English-speaking Latinx (Adjusted Odds Ratio 1.88, 95% Confidence Interval: 1.18, 2.98). An exploratory analysis showed that common reasons for not enrolling in SNAP included older adults not knowing how to apply to the program and Spanish-speaking Latinx worrying about citizenship status as it relates to the eligibility process. Findings revealed disparities in the prevalence of food insecurity and SNAP participation among patients of Los Angeles' low income clinics. Information from this study can help inform low-income clinics' efforts to intervene on food insecurity in their patient population. Copyright © 2020 Elsevier Inc. All rights reserved.

Citation

Julia I Caldwell, Dipa Shah-Patel, Deborah A Cohen, Alina I Palimaru, Tony Kuo. Food insecurity, participating in the Supplemental Nutrition Assistance Program, and the degree to which patients anticipate help from clinics to find food in Los Angeles County. Preventive medicine. 2020 Dec;141:106297

Mesh Tags

PMID: 33164847

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