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PURPOSE. Assessing food environments often requires using commercially available data. Disparate methods used for classifying food outlets in these databases call for creating a classification approach using common definitions. A systematic strategy for reclassifying food stores and restaurants, as they appear in commercial databases, into categories that differentiate the availability of healthy options is described here. DESIGN AND SETTING. Commercially available data on food outlets including names, addresses, North American Industry Classification System codes, and associated characteristics was obtained for five New Jersey communities. ANALYSIS. A reclassification methodology was developed using criteria and definitions from the literature to categorize food outlets based on availability of healthy options. Information in the database was supplemented by systematic Internet and key word searches, and from phone calls to food outlets. RESULTS. The methodology resulted in 622 supermarket/grocery stores, 183 convenience stores, and 148 specialty stores in the original data to be reclassified into 58 supermarkets, 30 grocery stores, 692 convenience stores, and 115 specialty stores. Outlets from the original list of 1485 full-service restaurants and 506 limited-service restaurants were reclassified as 563 full-service restaurants and 1247 limited-service restaurants. Reclassification resulted in less than one-seventh the number of supermarkets and grocery stores, more than three times the number of convenience stores, and twice as many limited-service restaurants-a much less healthy profile than the one generated by using exclusively the commercial databases. CONCLUSION. An explicit and replicable strategy is proposed for reclassifying food outlets in commercial databases into categories that differentiate on the basis of healthy food availability. The intent is to contribute towards building a consensus among researchers on definitions used in public health research for characterizing different types of food outlets.

Purpose: Examine variation in obesity among older adults relative to the joint influences of density of neighborhood fast food outlets and residents' behavioral, psychosocial, and sociodemographic characteristics., Design: Cross-sectional and multilevel design., Setting: Census block groups, used as a proxy for neighborhoods, within the metropolitan region's Urban Growth Boundary in Portland, Oregon., Subjects: A total of 1221 residents (mean age, 65 years) recruited randomly from 120 neighborhoods (48% response rate)., Measures: A geographic information system-based measure of fast food restaurant density across 120 neighborhoods was created. Residents within the sampled neighborhoods were assessed with respect to their body mass indices (BMI), frequency of visits to local fast food restaurants, fried food consumption, levels of physical activity, self-efficacy of eating fruits and vegetables, household income, and race/ethnicity., Analyses: Multilevel logistic regression analyses., Results: Significant associations were found between resident-level individual characteristics and the likelihood of being obese (BMI > or = 30) for neighborhoods with a high-density of fast food restaurants in comparison with those with a low density: odds ratios for obesity, 95% confidence intervals (CI), were 1.878 (CI, 1.006-3.496) for weekly visits to local fast food restaurants; 1.792 (CI, 1.006-3.190) for not meeting physical activity recommendations; 1.212 (CI, 1.057-1.391) for low confidence in eating healthy food; and 8.057 (CI, 1.705-38.086) for non-Hispanic black residents., Conclusion: Increased density of neighborhood fast food outlets was associated with unhealthy lifestyles, poorer psychosocial profiles, and increased risk of obesity among older adults.

Purpose: To explore the relationship between the placement of fast-food outlets and neighborhood-level socioeconomic variables by determining if indicators of lower socioeconomic status were predictive of exposure to fast food., Design: A descriptive analysis of the fast-food environment in a Canadian urban center, using secondary analysis of census data and Geographic Information Systems technology., Setting: Edmonton, Alberta, Canada., Measures: Neighborhoods were classified as High, Medium, or Low Access based on the number of fast-food opportunities available to them. Neighborhood-level socioeconomic data (income, education, employment, immigration status, and housing tenure) from the 2001 Statistics Canada federal census were obtained., Analysis: A discriminant function analysis was used to determine if any association existed between neighborhood demographic characteristics and accessibility of fast-food outlets., Results: Significant differences were found between the three levels of fast-food accessibility across the socioeconomic variables, with successively greater percentages of unemployment, low income, and renters in neighborhoods with increasingly greater access to fast-food restaurants. A high score on several of these variables was predictive of greater access to fast-food restaurants., Conclusion: Although a causal inference is not possible, these results suggest that the distribution of fast-food outlets relative to neighborhood-level socioeconomic status requires further attention in the process of explaining the increased rates of obesity observed in relatively deprived populations.

Purpose: Obesity accounts for approximately 300,000 deaths a year in the United States, and prevalence rates have been increasing over the past decade. The nutrition environment may be contributing to this epidemic. This study examined the relationship between fast food restaurants and obesity on a state-wide basis., Design: A one-time cross-sectional analysis of secondary data was used for this study., Setting: The setting for this study was the United States., Subjects: State-level data were used as the unit of analysis. Alaska was excluded as an outlier, and the District of Columbia was added (N = 50)., Measures: Measures included aggregate state-level means for square miles per fast food restaurant, population per fast food restaurant, population density, ethnicity, age, gender, physical inactivity, fruit and vegetable intake, and obesity rates. Data were obtained from the 2002 Behavioral Risk Factor and Surveillance Survey, the 2000 U.S. Census, and the 2002 U.S. Yellow Pages., Results: Multiple hierarchal regressions revealed that square miles per fast food restaurants and residents per restaurant accounted for 6% of the variance in state obesity rates after controlling for population density, ethnicity, age, gender, physical inactivity, and fruit and vegetable intake. The entire model explained 70% of the total variance in state obesity rates., Conclusions: These results indicate a correlational relationship between both the number of residents per fast food restaurant and the square miles per fast food restaurants with state-level obesity prevalence. Limitations include the use of correlational aggregate data.