Background COVID-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. Objective We 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. Methods First, 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. Results The 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. Conclusions Our 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.

Background The COVID-19 pandemic has significantly disrupted the food retail environment. However, its impact on fresh fruit and vegetable vendors remains unclear; these are often smaller, more community centered, and may lack the financial infrastructure to withstand supply and demand changes induced by such crises. Objective This study documents the methodology used to assess fresh fruit and vegetable vendor closures in New York City (NYC) following the start of the COVID-19 pandemic by using Google Street View, the new Apple Look Around database, and in-person checks. Methods In total, 6 NYC neighborhoods (in Manhattan and Brooklyn) were selected for analysis; these included two socioeconomically advantaged neighborhoods (Upper East Side, Park Slope), two socioeconomically disadvantaged neighborhoods (East Harlem, Brownsville), and two Chinese ethnic neighborhoods (Chinatown, Sunset Park). For each neighborhood, Google Street View was used to virtually walk down each street and identify vendors (stores, storefronts, street vendors, or wholesalers) that were open and active in 2019 (ie, both produce and vendor personnel were present at a location). Past vendor surveillance (when available) was used to guide these virtual walks. Each identified vendor was geotagged as a Google Maps pinpoint that research assistants then physically visited. Using the “notes” feature of Google Maps as a data collection tool, notes were made on which of three categories best described each vendor: (1) open, (2) open with a more limited setup (eg, certain sections of the vendor unit that were open and active in 2019 were missing or closed during in-person checks), or (3) closed/absent. Results Of the 135 open vendors identified in 2019 imagery data, 35% (n=47) were absent/closed and 10% (n=13) were open with more limited setups following the beginning of the COVID-19 pandemic. When comparing boroughs, 35% (28/80) of vendors in Manhattan were absent/closed, as were 35% (19/55) of vendors in Brooklyn. Although Google Street View was able to provide 2019 street view imagery data for most neighborhoods, Apple Look Around was required for 2019 imagery data for some areas of Park Slope. Past surveillance data helped to identify 3 additional established vendors in Chinatown that had been missed in street view imagery. The Google Maps “notes” feature was used by multiple research assistants simultaneously to rapidly collect observational data on mobile devices. Conclusions The methodology employed enabled the identification of closures in the fresh fruit and vegetable retail environment and can be used to assess closures in other contexts. The use of past baseline surveillance data to aid vendor identification was valuable for identifying vendors that may have been absent or visually obstructed in the street view imagery data. Data collection using Google Maps likewise has the potential to enhance the efficiency of fieldwork in future studies.

A systematic assessment of the effect of COVID-19 on the food retail environment-an important determinant of health-has not been conducted. Our objective was to assess the impact of COVID-19 on closures of restaurants, food retail stores, and fresh produce vendors in New York City (NYC). We conducted a cross-sectional study following the peak of COVID-19 in six neighborhoods in NYC. Two Chinese ethnic neighborhoods and four higher/lower resourced comparison neighborhoods were selected a priori based on 14 sociodemographic indicators. The primary outcome was indefinite/temporary closures or absence of food businesses. Of 2720 food businesses identified, produce vendors and restaurants were more likely to close than food retail stores. A higher proportion of food businesses closed in Chinese ethnic neighborhoods vs. comparison neighborhoods. COVID-19 impacted food businesses in six NYC neighborhoods examined in this period, with the greatest effect observed for Chinese ethnic neighborhoods.

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In 2016, New York City (NYC) began enforcing a sodium warning regulation at chain restaurants, requiring placement of an icon next to any menu item containing >=2,300 mg sodium. As menu labeling may improve menu nutritional composition, we investigated whether sodium content of menu items changed following enforcement of the sodium warning icon. All menu offerings at 10 quick-service (QSR) and 3 full-service (FSR) chain restaurants were photographed in 2015 (baseline) and 2017 (follow-up) and matched to nutritional information from restaurant websites; items were categorized as being available at both baseline and follow-up, or at only one timepoint. Linear and logistic regression models, respectively, assessed changes in calculated mean sodium-per-serving and the odds of an item containing >=2,300 mg sodium. At baseline, mean per-serving sodium content was 2,160 mg at FSR and 1,070 mg at QSR, and 40.6% of FSR items and 7.2% of QSR items contained >=2,300 mg sodium per serving. Sodium content did not differ when comparing all items offered at follow-up to all offered at baseline (21 mg, 95% CI: -60,101), or when comparing new versus discontinued items (17 mg, 95% CI: -154, 187). At follow-up, there was a non-significant increase in the overall likelihood of items requiring a warning icon (OR=1.32, 95% CI:  0.97,1.79). When comparing new versus discontinued items, there was a twofold increase in the odds of requiring a warning icon (OR=2.08, 95% CI: 1.02,4.24). Our findings both highlight high sodium content of menu items at popular chain restaurants and underscore difficulties in motivating restaurants to reduce sodium levels.