Obesity And Urban Food

Individual characteristics and IBM data were adjusted to two – kilometer Addis food retail stores, with each supermarket reducing obesity odds, and convenience stores predicting higher obesity Odds for each respondent. Obesity rates in New Orleans are among the highest in the country and virtually no research has been conducted to study the associations between obesity and neighboring food retailers in the urban environment. This article appealed to me because a well balanced diet via healthier food retail access is pivotal to promote health in vulnerable groups within communities limited to unhealthy food options.Previous studies on Northeastern communities, mound mostly positive correlations between the presence of convenient stores and increased rates of obesity. Apropos, a correlation was found between supermarket access and reduced obesity rates. Other research has found neither correlation between these variables. Limited, frequent non- consistent research findings, have examined the relationship between food access and body weight in disproportionate fashion amongst the United States. In comparison, fewer studies were conducted in the urban South, which tend to have some the highest obesity rates in the country.New Orleans has high obesity prevalence, which is why some of its Leslie Mar, SCAN 195. 7731, 01. 1415 communities were a choice of study. Other conditions, such as the lack of a household car, make the city an appropriate setting for this study. This study hypothesizes, â€Å"†¦ That greater supermarket access would be associated with a lower odds for obesity while greater convenience store and fast food access would be associated with higher obesity odds Since supermarkets offer a wider selection of affordable healthy food, and fast food establishments offer inexpensive energy-dense foods, these associations were predicted accordingly.METHODS Participants A 2004-2005 survey from the New Orleans Behavior Risk Factor Surveillance Syst em (BARFS), was the local version of the national telephone survey coordinated by the IIS Centers Of Disease Control and Prevention. Participants were enrolled via a random digital dial method: a single random individual aged 18 or older. The final analytic sample, after various exclusions, consisted of 3,925 subjects with reported IBM information, physical activity levels, household income information, collected demographic data, and 167 census tracts in New Orleans.The majority of participants were female (66%), 35. 5% White (n=l ,394), 58% African American 3. % Latino (n=11 6), and 3. 6% Other (n=141). 40% (n=l ,585) of respondents lived at or below the poverty line. 16% (n=628) were ages 18-30 years, and 47. 4% were above 50 years. A majority of 38% attained college graduate or higher education. Measures Food stores and fast food restaurant sites open between 2004-2005 were provided by the Louisiana Office of Public Health food retailer database.They were categorized as either f ull time or part time based on percentage on food item sales (60% cutoff) and total annual sales. They fall into five categories: small food Stores, medium food Stores, supermarkets, convenience stores, ND general merchandise stores. Fast food restaurants were categorized as regional, national, or local chain. A 2 – km buffer around a center point of each respondent census tract, measured using Arctic 9. 2 (SERIES, Redheads, CA), was used to define their neighborhood food environment, because multiple forms of transport are commonplace in New Orleans.Food store/ fast food geographic access was formed by summing the number of each food retailers in each category within the 2-km radius. For IBM measurements, individuals reported their heights and weights with 30 keg/mm as the cutoff between obese and non obese. There were independent variables to account for the results: race/ethnicity, a Poverty index ratio was calculated based on a comparison between household income with the Census Bureau's poverty threshold for a household size. They were divided into three categories of less than 1. 00 (below poverty line), 1. 0-185, and greater than 1. 85. 15. 4% of respondents refused to provide this information, so their poverty index was adjusted using a â€Å"hot deck' imputation technique based on race/ethnicity and education level (less than high school, high school graduate, some college, college graduate). Moderate and vigorous activities were used as the physical activity indicators, and Leslie Mar, SCAN 195. 7731, 01. 14. 15 television viewing (2 hours or more classified as high) was calculated as well. For multivariate analyses, Hierarchical Linear Modeling (HELM), with the extolling procedure in Stats/SE 9. (Staccatos, College Station, TX) was used to simultaneously assess the influence of group-level and individual-level predictors on dependent variables. A series of models to produce a regression equation for each food retailer type access measure was created. RESULTS Within the 2-km of respondents' census tract centers, there was an average of . 49 supermarkets. There were no supermarkets among 26% of the respondents within this buffer, and households had more food retail and convenience stores than supermarkets; with fast food restaurants at a higher average than supermarkets.Of the sample, the overall prevalence of obesity was 26. 5% with highest ranking at 35. 1% (African Americans), females at 29. 2%, older respondents at 29. 9%, and individuals with poorer household incomes and less education. With a high average of small food stores (25. 18%), convenience stores (1 1 . 28%), and fast food restaurants (9. 7%), there as a relationship between obesity odds and food retailer count. There was no significant association between small, medium, and general merchandise store access.