Evidence map›Paper›PMID 42286524›Full record

ArticleBMC public health2026

Socioeconomic inequalities in online fresh food delivery use and associations with the neighborhood food environment.

Bochu Liu, Chuhao Liu, Thomas Burgoine, Guangping Chen, Yue Wang, Lan Wang

Abstract read
In one paragraph

Article in BMC public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Bochu LiuDepartment of Urban Planning, College of Architecture and Urban Planning, Tongji University, Shanghai, 200092, China.
Chuhao LiuGraduate School of Interdisciplinary Information Studies, The University of Tokyo, Tokyo, 113-0033, Japan.
Thomas BurgoineIMS Epidemiology, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.
Guangping ChenCollege of Urban and Environmental Sciences, Central China Normal University, Wuhan, 430079, China. george.chen@ccnu.edu.cn.
Yue WangRajax Network Technology (ele.me) Eleme Research Center, Hangzhou, 310000, China.
Lan WangDepartment of Urban Planning, College of Architecture and Urban Planning, Tongji University, Shanghai, 200092, China. wanglan@tongji.edu.cn.

Funding

National Natural Science Foundation of China 42401279Shanghai Rising-Star Program 24YF2747900
6 · The paper itself

Abstract

backgroundOnline 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.

methodsWe 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.

resultsConcentration 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.

conclusionsOnline 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.

Indexed as

CommerceFood SupplyInternetNeighborhood CharacteristicsResidence CharacteristicsChinaCross-Sectional StudiesDigital MediaFemaleFruitHumansLow Socioeconomic StatusMaleSocioeconomic Disparities in HealthSocioeconomic FactorsFood accessFood consumptionFruit and vegetable consumptionNeighborhood food environmentOnline food deliverySocioeconomic inequalityUrban China

Identifiers

PMID42286524
PMCPMC13488120

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.