Evidence map›Paper›PMID 36694616›Full record

ArticleCities (London, England)2023

Lessons from COVID-19 pandemic: Analysis of unequal access to food stores using the Gini coefficient.

Chong-En Li, Zih-Hong Lin, Yi-Ya Hsu, Nae-Wen Kuo

Abstract read
In one paragraph

Article in Cities (London, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

4 authors.

Chong-En LiDepartment of Geography, National Taiwan Normal University, Taipei, Taiwan.
Zih-Hong LinSchool of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW 2052, Australia.
Yi-Ya HsuSchool of Built Environment, University of New South Wales, Sydney, NSW 2052, Australia.
Nae-Wen KuoDepartment of Geography, National Taiwan Normal University, Taipei, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 has dramatically altered daily life worldwide, with some urban residents resorting to panic buying at the beginning of the pandemic. Large-scale lockdowns and restaurant closures have increased the need for grocery shopping. Such shifts in consumer patterns have altered supply-demand systems. Insufficient food store availability increases the likelihood of crowding and thus increases the probability of viral infection. People who live without easy access to food stores also face high infection risks when forced to travel long distances for grocery shopping. The COVID-19 pandemic has demonstrated the importance of the number and distribution of food stores to virus transmission. Food access is also a core factor of urban resilience during the pandemic. This study used the Gini coefficient to investigate the fairness of accessibility to food stores at the city and village levels, with Taipei City chosen as the research area. Different spatial scales were considered, and we calculated the equality of food access for older (≥65 years old) and non-older populations separately to determine whether one group faces greater inequality. At the city level, both older and non-older populations in Taipei have reasonable access (Gini coefficient between 0.3 and 0.4), with mean Gini coefficients of 0.3616 and 0.3655, respectively. This city-level analysis represents the overall degree of unequal access to food stores. At the village level, eight villages (1.8 %; total

Indexed as

COVID-19EqualityFood store accessibilityGini coefficientTaipei City

Identifiers

PMID36694616
PMCPMC9852324

What OpenQuestion 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 OpenQuestion graph.