Table of Evidence – Environment and Obesity



Table. Review of studies that explored the relationship between BMI and variables in the community or consumer food environment.

|Author |Overall Purpose |Sample: Number |Obesity |Food Environment Variable: Unit of |Other Variables |Findings for association of obesity |Limitations |

|(Year) | |& Location |Outcome |Measurement & Data Source(s) |(Source) |and the food environment | |

| | | |Variable | | | | |

|Burdette & |Examined the |7,020 |BMI percentile|Unit of Measurement: |Demographics* |No association between child |Cross-sectional design |

|Whitaker |relationship |low-income |for age and |Distance from residence to nearest fast|Playground proximity |overweight or at risk for overweight |Restaurant density data not fully explored. |

|(2004)38 |between overweight |preschool |sex |food restaurant |Number of serious |status and proximity to fast food |Political jurisdiction may not be the most |

| |status in kids and |children, 3 to |(Measured |Number of fast food restaurants in a |crimes and 911 call |restaurants. |accurate classification of food outlet |

| |the proximity of |5 years of age |height and |neighborhood (political jurisdiction) |rate (Cincinnati |No difference in percentage of |exposure. |

| |residences to fast | |weight) |Data Source: |police department) |overweight and non-overweight children|Neighborhoods lacked variation |

| |food restaurants. |Cincinnati, | |Location of fast food restaurants- U.S.| |living in neighborhoods without fast |Use of secondary aggregate data |

| | |Ohio | |Yellow Pages (phonebook and internet) | |food restaurants. |Did not control for individual dietary or |

| | | | | | | |physical activity practices. |

|Maddock |Examined the |Adults in 50 |BMI |Unit of Measurement: |Individual |Both the number of residents per fast |Ecologic/Cross-sectional design |

|(2004)41 |relationship |states (exact |(Self-report |Square miles per fast food restaurant |demographics* |food restaurant and the square miles |Self-report height and weight |

| |between fast food |number in |height and |Residents per fast food restaurant |Physical inactivity |per fast food restaurants were |Use of secondary aggregate data |

| |restaurants and |sample not |weight) |Data Sources: |Fruit & vegetable |significantly correlated with obesity |Sample size used was the |

| |obesity prevalence |identified) | |Location of fast food restaurants- 2002|intake |prevalence. |minimum needed for analysis |

| |rates on the state | | |U.S. Yellow Pages |Population density | |Categorized exposure at a state level (urban |

| |level. | | |Total residents and area of land per |Males per 100 female | |and rural environments were indistinguishable) |

| | | | |state – 2000 U.S. Census |Age of adults in | |Only included two fast food chains |

| | | | | |states | | |

|Simmons, et |Examined the |1,454 adults |BMI |Unit of Measurement: |Demographics* |No relationship between availability |Cross-sectional design |

|al. (2005)43|relationship | |(Measured |Number of eating places per 1000 |Weekly Activity |of eating places and prevalence of |Limited detail provided about data collection |

| |between selection |Victoria, |height and |residents |TV or video viewing |obesity was found. |and statistical analysis of food environment |

| |and availability of|Australia |weight) |Data Sources: |Fruit, vegetable, | |variables |

| |takeaway and | | |Location of takeaway and restaurant |dairy, & takeaway | |Lacked statistical adjustment for income |

| |restaurant food and| |Waist |food outlets – direct observation and |consumption | |Categorized exposure at a “shire” level which |

| |obesity among | |circum-ference|phone directory | | |was broad and undefined |

| |adults. | |(Measured) |Total residents per town – 2001 | | | |

| | | | |Australian census | | | |

|Sturm & |Examined the |6,918 children |BMI change |Unit of Measurement: |Demographics* |Food outlet density had no significant|Incongruent categorization of exposures (BMI |

|Datar |association between| |over 1 and 3 |Per capita number and types of food |Birth weight |effect on BMI gain. |was measured as change over time, but food |

|(2005)44 |food prices and |National |yrs |outlets in each child’s residential and|Physical Activity |Lower fruit and vegetable prices |environment variable was measured at one |

| |food outlet density|Sample, U.S. |(Measured |school zip codes |Television viewing |predicted a significantly lower gain |point.) |

| |and changes in the |(59 MSA, 37 |height and |Price of food groups by MSA |Parent activities with|in BMI. |Use of secondary aggregate data |

| |BMI among |states) |weight) |Data Sources: |children |Dairy prices or fast food prices did |Types of stores not differentiated (Small |

| |elementary school | | |Number of food outlets by zip code-1999| |not have a significant affect on BMI |grocery store indistinguishable from |

| |children. | | |U.S. Census Zip Code Business Patterns | |gain. |supermarkets) |

| | | | |files | |Lower meat prices predicted a higher |Limited sample size in subpopulations |

| | | | |Average food prices by MSA- 1999 4th | |gain in BMI, but the results were |Did not measure or control for dietary intake. |

| | | | |quarter ACCRA data | |insignificant. | |

|Jeffery, et |Examined the |1,033 adults |BMI |Unit of Measurement: |Demographics* |The fast food, non fast food, and |Cross-sectional design |

|al. |relationship | |(Self-report |Total number of restaurants and the |Physical Activity |total restaurants within different |Self-report height and weight |

|(2006)40 |between BMI and |Minnesota |height and |number of fast food restaurants within |Television viewing |mile radii of home and work addresses |GIS mapping by food outlets by Standard |

| |living or working | |weight) |circles with radii of 0.5, 1.0, and 2.0|Eating habits |were not positively associated with |Industrial Codes from database may be |

| |near fast food | | |miles with home and work addresses as |(emphasis on frequency|overall BMI. |inaccurate. |

| |restaurants. | | |center of the circles |of eating away from |A significant inverse relationship | |

| | | | |Data Source: |home) |between BMI and number of restaurants | |

| | | | |Location of fast food restaurants- | |near work addresses was found for men | |

| | | | |public domain database | |only. | |

|Inagami, |Examined the |2,144 |BMI |Unit of Measurement: |Demographics* – |Individuals’ BMI was greater when they|Cross-sectional design |

|Cohen, |relationship |households |(Self-report |Centroid-to-centroid distances between |aggregated for each |selected grocery stores in |Self-report height and weight |

|Finch, & |between individual | |height and |residential and grocery store census |residential |more-disadvantaged neighborhoods. |Did not distinguish between types of food |

|Asch |BMI, distance to |Los Angles, |weight) |tracts |neighborhood |Average grocery store neighborhood |stores |

|(2006)39 |and deprivation of |California | |Difference between residential and |Location of work, |scores for each census tract explained|Use of secondary aggregate data |

| |the census tract in| | |grocery store census tracts |entertainment, medical|BMI more than individual scores. |Did not control for individual dietary intake |

| |which individuals | | |Neighborhood “Disadvantage Score” |care, & worship |A distance of greater than or equal to|or physical activity |

| |shop for groceries.| | |(DSG-DSR) | |1.76 miles from home to grocery store |Grocery store neighborhood “Disadvantage Score”|

| | | | |Data Sources: | |was an independent predictor of a BMI |was proxy measurement for grocery store quality|

| | | | |Residential and grocery store census | |increase. |Centroid-to-centroid distances between census |

| | | | |tracts – Participant survey and 1990 | | |tracts are crude estimates. |

| | | | |U.S. Census | | | |

| | | | |Neighborhood “Disadvantage Score” – | | | |

| | | | |2000 U.S. Census | | | |

|Morland, |Examined the |10,763 adults |BMI |Unit of Measurement: |Demographics* |The presence of convenience stores vs.|Cross-sectional design |

|Diez Roux, &|relationship | |(Measured |Presence or absence of convenience |Diabetes |no convenience stores was associated |Use of secondary aggregate data |

|Wing |between the |Mississippi, |height and |stores, grocery stores, and/or |Hypertension |with a higher prevalence of overweight|Lack of individual shopping data may have lead |

|(2006)42 |availability of |North Carolina,|weight) |supermarkets in residential census |Hyper-cholestemia |and obesity in the census tract. |to misclassification of shopping census tract |

| |supermarket, |Maryland, | |tract |Physical Activity |The presence of supermarkets in census|Lack of food environment data between 1993 and |

| |grocery stores and |Minnesota | |Data Source: | |tracts was inversely related to the |1999. |

| |convenience stores | | |Location of food stores- local | |prevalence of overweight compared to |Only controlled for physical activity and no |

| |and cardiovascular | | |departments of environmental health and| |census tracts without supermarkets. |other neighborhood or dietary variables |

| |disease risk | | |state departments of agriculture in | | |Excluded data from some minorities due to |

| |factors. | | |1999 | | |inadequate sample size |

*Demographics include various population characteristics such as age, sex, race, ethnicity, education level, employment status, income level, marital status, or other social attribute. Varied by study.

BMI – Body Mass Index

GIS – Geographic Informational Systems

DSG-DSR – difference between “Disadvantage Scores” of residential and selected grocery store census tract

MSA – Metropolitan Statistical Area

ACCRA – American Chamber of Commerce Research Association

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