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Assessing risk in the retail environment during the COVID-19 pandemic.

ABSTRACT: The COVID-19 pandemic has caused unprecedented disruption, particularly in retail. Where essential demand cannot be fulfilled online, or where more stringent measures have been relaxed, customers must visit shop premises in person. This naturally gives rise to some risk of susceptible individuals (customers or staff) becoming infected. It is essential to minimize this risk as far as possible while retaining economic viability of the shop. We therefore explore and compare the spread of COVID-19 in different shopping situations involving person-to-person interactions: (i) free-flowing, unstructured shopping; (ii) structured shopping (e.g. a queue). We examine which of (i) or (ii) may be preferable for minimizing the spread of COVID-19 in a given shop, subject to constraints such as the geometry of the shop; compliance of the population to local guidelines; and additional safety measures which may be available to the organizers of the shop. We derive a series of conclusions, such as unidirectional free movement being preferable to bidirectional shopping, and that the number of servers should be maximized as long as they can be well protected from infection.

SUBMITTER: Budd C

PROVIDER: S-EPMC8113911 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCOVID-19 mitigation strategies have had an untold effect on food retail stores and restaurants. Early evidence from New York City (NYC) indicated that these strategies, among decreased travel from China and increased fears of viral transmission and xenophobia, were leading to mass closures of businesses in Manhattan's Chinatown. The constantly evolving COVID -19 crisis has caused research design and methodology to fundamentally shift, requiring adaptable strategies to address emerging and existing public health problems such as food security that may result from closures of food outlets.ObjectiveWe describe innovative approaches used to evaluate changes to the food retail environment amidst the constraints of the pandemic in an urban center heavily burdened by COVID-19. Included are challenges faced, lessons learned and future opportunities.MethodsFirst, we identified six diverse neighborhoods in NYC: two lower-resourced, two higher-resourced, and two Chinese ethnic enclaves. We then developed a census of food outlets in these six neighborhoods using state and local licensing databases. To ascertain the status (open vs. closed) of outlets pre-pandemic, we employed a manual web-scraping technique. We used a similar method to determine the status of outlets during the pandemic. Two independent online sources were required to confirm the status of outlets. If two sources could not confirm the status, we conducted phone call checks and/or in-person visits.ResultsThe final baseline database included 2585 food outlets across six neighborhoods. Ascertaining the status of food outlets was more difficult in lower-resourced neighborhoods and Chinese ethnic enclaves compared to higher-resourced areas. Higher-resourced neighborhoods required fewer phone call and in-person checks for both restaurants and food retailers than other neighborhoods.ConclusionsOur multi-step data collection approach maximized safety and efficiency while minimizing cost and resources. Challenges in remote data collection varied by neighborhood and may reflect the different resources or social capital of the communities; understanding neighborhood-specific constraints prior to data collection may streamline the process.

Project description:(1) Background: To date, there are limited data in low- and middle-income countries (LMICs) that collect, monitor, and evaluate food environments in standardized ways. The development of a pilot survey tool, tailored to LMICs and focused on retail food environments, is necessary for improving public health nutrition. (2) Methods: A novel survey tool was developed and piloted in a sample of village retail food environments (n = 224) in Matiari, Pakistan between October 2020 to April 2021. Villages were randomly selected, and food outlets were surveyed within a 500-m radius from each village center. Descriptive statistics (counts and percentages) were used to describe the characteristics of food outlets and the availability of food. To test whether there was a difference in characteristics or in the mean of number of healthy, unhealthy, and total food items available by village size, a χ2 test or one-way ANOVA was conducted, respectively. (3) Results: In total, 1484 food outlets were surveyed for food accessibility, availability, and promotion across small (n = 54), medium (n = 112), and large villages (n = 58). In small and medium-sized villages, mobile food vendors were the predominant food outlet type (47.8% and 45.1%, respectively), whereas in large villages, corner stores (36%) were more prominent. The mean number of total food items (p < 0.006) and unhealthy food items (p < 0.001) available in food outlets differed by village size. The proportion of food outlets with available fruits, meat and poultry, water, and sugar-sweetened beverages also differed by village size (p < 0.001). (4) Conclusions: This study informs the global evidence gap in the current understanding of food environments in various ethnically diverse and dynamic LMICs, and the developed methodology will be useful to other LMICs for measuring and monitoring the food environment, especially among vulnerable population groups. This work complements current national and provincial survey efforts in Pakistan.

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Assessing risk in the retail environment during the COVID-19 pandemic.

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