OBJECTIVES Mobile food vendors (also known as street food vendors) may be important sources of food, particularly in minority and low-income communities. Unfortunately, there are no good data sources on where, when, or what vendors sell. The lack of a published assessment method may contribute to the relative exclusion of mobile food vendors from existing food-environment research. A goal of this study was to develop, pilot, and troubleshoot a method to assess mobile food vendors. STUDY DESIGN Cross-sectional assessment of mobile food vendors through direct observations and brief interviews. METHODS Using printed maps, investigators canvassed all streets in Bronx County, NY (excluding highways but including entrance and exit ramps) in 2010, looking for mobile food vendors. For each vendor identified, researchers recorded a unique identifier, the vendor’s location, and direct observations. Investigators also recorded vendors answers to where, when, and what they sold. RESULTS Of 372 identified vendors, 38% did not answer brief-interview questions (19% were “in transit”, 15% refused; others were absent from their carts/trucks/stands or with customers). About 7% of vendors who ultimately answered questions were reluctant to engage with researchers. Some vendors expressed concerns about regulatory authority; only 34% of vendors had visible permits or licenses and many vendors had improvised illegitimate-appearing set-ups. The majority of vendors (75% of those responding) felt most comfortable speaking Spanish; 5% preferred other non-English languages. Nearly a third of vendors changed selling locations (streets, neighborhoods, boroughs) day-to-day or even within a given day. There was considerable variability in times (hours, days, months) in which vendors reported doing business; for 86% of vendors, weather was a deciding factor. CONCLUSIONS Mobile food vendors have a variable and fluid presence in an urban environment. Variability in hours and locations, having most comfort with languages other than English, and reluctance to interact with individuals gathering data are principal challenges to assessment. Strategies to address assessment challenges that emerged form this project may help make mobile-vendor assessments more routine in food-environment research.
In food-environment research, an alternative to resource-intensive direct observation on the ground has been the use of commercial business lists. We sought to determine how well a frequently-used commercial business list measures a dense urban food environment like the Bronx. On 155 Bronx street segments, investigators compared two different levels for “matches” between the business list and direct ground observation: lenient (by business type) and strict (by business name). For each level of matching, researchers calculated sensitivities and positive predictive values (PPVs) for the business list overall and by broad business categories: General grocers (e.g., supermarkets), Specialty-food stores (e.g., produce markets), Restaurants, and Businesses not primarily selling food (e.g., newsstands). Even after cleaning the business list (e.g., for cases of multiple listings at a single location), and allowing for inexactness in listed street addresses and spellings of business names, the overall performance of the business list was poor. For strict “matches”, the business list had an overall sensitivity of 39.3% and PPV of 45.5%. Sensitivities and PPVs by broad business categories were not meaningfully different from overall values, although sensitivity for General grocers and PPV for Specialty-food stores were particularly low: 26.2% and 32.0% respectively. For lenient “matches”, sensitivities and PPVs were somewhat higher but still poor: 52.4–60.0% and 60.0–75.0% respectively. The business list is inadequate to measures the actual food environment in the Bronx. If results represent performance in other settings, findings from prior studies linking food environments to diet and diet-related health outcomes using such business lists are in question, and future studies of this type should avoid relying solely on such business lists.
This study describes mobile food vendors (street vendors) in Bronx, NY, considering neighborhood-level correlations with demographic, diet, and diet-related health measures from City data. Vendors offering exclusively “less-healthy” foods (e.g., chips, processed meats, sweets) outnumbered vendors offering exclusively “healthier” foods (e.g., produce, whole grains, nuts). Wet days and winter months reduced all vending on streets, but exclusively “less-healthy” vending most. In summer, exclusively “less-healthy” vending per capita inversely correlated with neighborhood-mean fruit-and-vegetable consumption and directly correlated with neighborhood-mean BMI and prevalences of hypertension and hypercholesterolemia (Spearman correlations 0.90-1.00, p values 0.037 to <0.001). In winter, “less-healthy” vending per capita directly correlated with proportions of Hispanic residents and those living in poverty (Spearman correlations 0.90, p values 0.037). Mobile food vending may contribute negatively to urban food-environment healthfulness overall, but exacerbation of demographic, diet, and diet-related health disparities may vary by weather, season, and neighborhood characteristics.
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