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Translating caregiver-identified neighborhood features into quantitative variables for statistical analyses of effects on child health
Journal article   Open access   Peer reviewed

Translating caregiver-identified neighborhood features into quantitative variables for statistical analyses of effects on child health

Félice Lê-Scherban, Jayla Norman, Victoria Ryan, Maggie Beverly, Natalie M Rodrigues, Matthew Jannetti, Kelly A Courts, Zachary Fusfeld, Amy Carroll-Scott, Stephanie L Mayne, …
Annals of epidemiology, v 121, 110149
Sep 2026
DOI: https://doi.org/10.1016/j.annepidem.2026.110149
PMID: 42288206
Featured in Collection :   Drexel's Newest Publications
url
https://doi.org/10.1016/j.annepidem.2026.110149View
Published, Version of Record (VoR) Open Access via Drexel Libraries Read and Publish Program 2026 Open CC BY V4.0

Abstract

mixed methods research Neighborhood characteristics access to healthy foods Food Security
Neighborhoods are hypothesized to affect weight-related outcomes in part through their influence on household food choices, but quantitative study findings have been mixed. This may reflect mismatch between variables analyzed and residents' experiences. Our objective was to identify quantitative variables corresponding to neighborhood features caregivers of young children find important for families' food choices. In 9 focus groups in English and Spanish among low-income caregivers (n=51) of young children aged <5 years in Philadelphia, PA, participants identified features relevant for their families' food choices. An iterative, qualitative approach incorporating community member input was used to translate caregiver-identified features into quantitative, area-level variables for linkage with pediatric patient addresses. Caregivers identified 17 features related to food retail (8 features; e.g., healthy food availability, cost), logistical concerns (4 features; e.g., transportation), social environment (3 features; e.g., safety), and structural factors (2 features; gentrification, economic disinvestment). Highly aligned quantitative variables were identified for 5 features; medium/low for 7; and no aligning variables for 5. Variable sources included commercial and public databases, and prior population-based surveys. Incorporating qualitative approaches, including explicitly assessing alignment of quantitative analyses with residents' experiences, may help improve conceptual clarity and usefulness of epidemiological neighborhoods research.

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