Table 2.
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Broad Business Categories | |||||
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Businesses selling any food or beverage | OVERALL (N = 234) | General grocers (N = 42) | Specialty-food stores (N = 26) | Restaurants (N = 110) | Businesses not primarily selling food (N = 56) |
By strict “matches” | |||||
Sensitivity | 39.3 (33.0, 45.9) | 26.2 (13.9, 42.0) | 30.8 (14.3, 51.8) | 45.5 (35.9, 55.2) | 41.1 (28.1, 55.0) |
Positive predictive value | 45.5 (38.5, 52.7) | 34.4 (18.6, 53.2) | 32.0 (14.9, 53.5) | 56.8 (45.8, 67.3) | 40.4 (27.6, 54.2) |
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By lenient “matches” | |||||
Sensitivity | 58.1 (51.5, 64.5) | 52.4 (36.4, 68.0) | 57.7 (36.9, 76.6) | 60.0 (50.2, 69.2) | 58.9 (45.0, 71.9) |
Positive predictive value | 67.3 (60.4, 73.7) | 68.8 (50.0, 83.9) | 60.0 (38.7, 78.9) | 75.0 (64.6, 83.6) | 57.9 (44.1, 70.9) |
All values in table are percentages. Values in parentheses are 95% confidence intervals. N values in header are the numbers of businesses directly observed on the ground. Strict match = two businesses with the same or consistent name: could have difference in notation and/or spelling, but seemingly the same business in both datasets (examples: “Parrilla Latina Restaurant” vs. “Parilla Dominicano”, “Franko Deli” vs. “Franco’s Heroes and Sandwiches”, “Jumbo Hamburger” vs. “Jimbo’s Hamburgers”); lenient match = two businesses that may have different names in each dataset but thought to be of a consistent business type based on names (examples: “Nacho Pizza” vs. “Original Tony’s Pizza”, “C-Town Supermarket” vs. “Bravo Supermarket”, “Tseng’s Ice Cream Shop” vs. “Baskin Robbins”).