Primary Submission Category: Place/Communities
Alignment of quantitative variables with caregiver-identified neighborhood features salient for food purchasing decisions
Authors: Félice Lê-Scherban, Victoria Ryan, Jayla Norman, Kelly A. Courts, Zachary Fusfeld, Maggie Beverly,
Presenting Author: Félice Lê-Scherban*
Introduction: Neighborhood conditions may be a promising target for interventions to mitigate the effects of food insecurity on child health, but doing so requires identifying salient neighborhood features. Our objective was to identify quantitative variables corresponding to neighborhood factors caregivers of young children find important for their families’ food choices. Methods: In 9 focus groups in English and Spanish among caregivers (n=51) of young children aged <5 years in Philadelphia, PA, participants identified features salient for their families’ food choices. Focus group transcripts were coded using a start-list method. Next, an iterative process was used with input from a scientific and community advisory board to translate caregiver-identified features into quantitative, area-level variables for linkage with pediatric patient addresses from medical records. Results: Caregivers identified including 8 related to the food retail environment (e.g., stores with high-quality products). Other features included transportation, safety concerns, and structural determinants such as economic disinvestment. Quantitative variables with high alignment to the focus group features were identified for 6 features, 7 features had medium/low-alignment variables, and no aligning variables were identified for 4 features. Advisory board input included defining relevant walking distances and abandoning inadequately aligned variables. Variable sources included the National Establishments Time Series database, American Community Survey, and prior population-based surveys in the study area. Variables are being linked with addresses corresponding to 63,670 children living in the Philadelphia metro area 2007-2023. Conclusions: Incorporating caregiver-identified neighborhood features into the identification of quantitative variables may increase the robustness and utility of statistical analyses of neighborhood impacts on child health.