BMC public health · 2026
Socioeconomic inequalities in online fresh food delivery use and associations with the neighborhood food environment.
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Liu Bochu, Liu Chuhao, Burgoine Thomas, Chen Guangping, Wang Yue, Wang Lan
Аннотация
BACKGROUND: Online delivery services may reduce physical barriers to acquiring fresh foods, but evidence on their socioeconomic patterning and associations with the neighborhood food environment remains limited. We examined socioeconomic inequalities in online fresh food ordering and its associations with the physical food environment.
METHODS: We conducted a cross-sectional study linking citywide transaction records for online green grocery, supermarket, and fruit delivery orders to neighborhood sociodemographic characteristics across fine-grained grids (n = 11,581) in Shanghai, China. We assessed socioeconomic inequalities in online fresh food delivery use by population composition (shares of female, older, young, low-income, and car-free residents) using concentration curves. We then estimated associations of online fresh food order counts with population composition and the physical food environment (counts of supermarkets, grocery stores, fruit stores, and fast-food outlets within a 1 km street-network buffer) using Bayesian spatial zero-inflated Poisson regression models with a residential population offset, which account for spatial autocorrelation, excessive zeros, and overdispersion in grid-level order counts.
RESULTS: Concentration curves ranked by the proportion of low-income residents fell below the line of equality, indicating that online fresh food orders were disproportionately concentrated in more economically advantaged neighborhoods. In Bayesian spatial regression models, grid-level order rates were lower in neighborhoods with higher shares of older, young, and low-income residents, but higher in areas with a greater proportion of car-free residents. Greater densities of nearby supermarkets and grocery stores were associated with lower online order rates across all order types. In the zero-inflation component of the models, a higher number of grocery stores was associated with a lower probability of zero green grocery orders, and greater supermarket access was associated with a lower probability of zero supermarket orders.
CONCLUSIONS: Online fresh food delivery is used disproportionately in socioeconomically advantaged neighborhoods, highlighting persistent inequalities in access to healthy foods. Yet, higher use in areas with a higher share of car-free residents and in places with fewer nearby retailers suggests that delivery services can partially offset mobility constraints and limited physical food options, offering a potential avenue for improving fresh food access in underserved communities.
METHODS: We conducted a cross-sectional study linking citywide transaction records for online green grocery, supermarket, and fruit delivery orders to neighborhood sociodemographic characteristics across fine-grained grids (n = 11,581) in Shanghai, China. We assessed socioeconomic inequalities in online fresh food delivery use by population composition (shares of female, older, young, low-income, and car-free residents) using concentration curves. We then estimated associations of online fresh food order counts with population composition and the physical food environment (counts of supermarkets, grocery stores, fruit stores, and fast-food outlets within a 1 km street-network buffer) using Bayesian spatial zero-inflated Poisson regression models with a residential population offset, which account for spatial autocorrelation, excessive zeros, and overdispersion in grid-level order counts.
RESULTS: Concentration curves ranked by the proportion of low-income residents fell below the line of equality, indicating that online fresh food orders were disproportionately concentrated in more economically advantaged neighborhoods. In Bayesian spatial regression models, grid-level order rates were lower in neighborhoods with higher shares of older, young, and low-income residents, but higher in areas with a greater proportion of car-free residents. Greater densities of nearby supermarkets and grocery stores were associated with lower online order rates across all order types. In the zero-inflation component of the models, a higher number of grocery stores was associated with a lower probability of zero green grocery orders, and greater supermarket access was associated with a lower probability of zero supermarket orders.
CONCLUSIONS: Online fresh food delivery is used disproportionately in socioeconomically advantaged neighborhoods, highlighting persistent inequalities in access to healthy foods. Yet, higher use in areas with a higher share of car-free residents and in places with fewer nearby retailers suggests that delivery services can partially offset mobility constraints and limited physical food options, offering a potential avenue for improving fresh food access in underserved communities.