Evidence map›Paper›PMID 41530762›Full record

ArticleBMC public health2026

Inequities in food access during the COVID-19 pandemic: A multilevel, mixed methods pilot study.

Megha R Aepala, Alice Guan, Tessa Cruz, Jamaica Sowell, Brenda Mathias, Katherine Lin, Analena Hope Hassberg, Salma Shariff-Marco, Mindy C DeRouen, Antwi Akom

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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
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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

5 · Who and what money

Authors and funding

10 authors.

Megha R AepalaDepartment of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA.
Alice GuanDepartment of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA.
Tessa CruzThe Social Innovation and Urban Opportunity Lab, Streetwyze, UCSF & San Francisco State University, Oakland, CA, USA.
Jamaica SowellRoots Community Health Center, Oakland, CA, USA.
Brenda MathiasThe Social Innovation and Urban Opportunity Lab, Streetwyze, UCSF & San Francisco State University, Oakland, CA, USA.
Katherine LinDepartment of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA.
Analena Hope HassbergDepartment of Sociology, California State University, Los Angeles, CA, USA.
Salma Shariff-MarcoDepartment of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA.
Mindy C DeRouenDepartment of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA. Mindy.Hebert-DeRouen@ucsf.edu.
Antwi AkomThe Social Innovation and Urban Opportunity Lab, Streetwyze, UCSF & San Francisco State University, Oakland, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInnovative data integration may serve to inform rapid, local responses to community needs. We conducted a mixed methods pilot study among communities of color or low-income in the San Francisco Bay Area amid the COVID-19 pandemic to assess a hypothesized data model to inform rapid response efforts.

methodsBetween 2020-2021, we collected (1) qualitative data through neighborhood reports submitted via Streetwyze, a mobile neighborhood mapping platform; (2) survey data on social and economic circumstances; and (3) geospatial data among residents of three counties. Qualitative data were coded and then integrated with survey and geospatial data. We used descriptive analyses to examine participants' experiences with food in their neighborhoods.

resultsAmong 51 participants, seventy percent of participants reported food insecurity before and after the pandemic began in March 2020. Within neighborhood reports, food was the most frequently occurring sub-theme within the Goods and Resources parent themes (68% and 49% of reports, respectively). Security (88%), resource programs (88%), outdoor space (84%), and equity (83%) were more likely to be mentioned by participants who were food insecure compared to those who were not (12%, 12%, 16%, 17%, respectively). Mentions of food in neighborhood reports more often occurred in census tracts with lower socioeconomic status and more area-level food insecurity.

conclusionIndividuals who were food insecure reported a constellation of needs beyond food, including needs related to safety and greater social equity. Our data model illustrates the potential for rapid assessment of community residents' experiences to provide enhanced understanding of community-level needs and effective support in the face of changing circumstances.

Indexed as

COVID-19Food InsecurityFood SupplyAdultFemaleHumansMaleMiddle AgedNeighborhood CharacteristicsPandemicsPilot ProjectsPovertyQualitative ResearchResidence CharacteristicsSan FranciscoSocioeconomic Disparities in HealthCommunity-based participatory researchFood insecurityMixed methodsMobile health technology

Identifiers

PMID41530762
PMCPMC12888245

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

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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.