T H E S I E N O F L I F E S T Y L E C H A N G

Health Promoting Community Design; Weight Control

Local Concentration of Fast-Food Outlets IsAssociated With Poor Nutrition and ObesityDaniel J. Kruger, PhD; Emily Greenberg, BA; Julian B. Murphy, MPH; Lindsay A. DiFazio, MPA;Kathryn R. Youra, MS

AbstractPurpose. We investigated the relationship of the local availability of fast-food restaurant

locations with diet and obesity.Design. We geocoded addresses of survey respondents and fast-food restaurant locations to

assess the association between the local concentration of fast-food outlets, BMI, and fruit andvegetable consumption.

Setting. The survey was conducted in Genesee County, Michigan.Subjects. There ivere 1345 individuals included in this analysis, and the response rate was

25%.Measures. The Speak to Your Health! Community Survey included fruit and vegetable

consumption items from the Behavioral Risk Factor Surveillance System, height, weight, anddemographics. We used ArcGIS to map fast-food outlets and survey respondents.

Analysis. Stepwise linear regressions identified unique predictors of body mass index (BMI)and fruit and vegetable consumption.

Results. Survey respondents had 8 7 fast-food outlets within 2 miles of their home.Individuals living in close proximity to fast-food restaurants had higher BMIs t(}342)= 3.21,p < .001, and loiuer fruit and vegetable consumption, t(1342)= 2.67, p= .008.

Conclusion. Individuals may be at greater risk for adverse consequences of poor nutritionbecause of the patterns in local food availability, which may constrain the success of nutritionpromotion efforts. Efforts to decrease the local availability of unhealthy foods as well asprograms to help consumers identify strategies for obtaining healthy meals at fast-food outletsmay improve health outcomes. (AmJ Health Promot 2014;28[5]:340-343.)

Key Words: Food Fnvironment, Nutrition, Body Mass Index, GeographicInformation System, Preventioti Research. Manuscript format: research; Researchptirpose: relationship testing; Study design: survey; Outcome meastire; behavioral,biomtrie; Setting; local community; Health focus; nutrition, weight control;Strategy; culture change, built environment; Target population age; adults; Targetpopulation circumstances; education, geographic location, and race/ethnicity

Daniel J. Kruger, PhD; Emily Greenberg, BA; fitlian B. Murphy, MPH; Lindsay A. DiFazio,MPA; and Kathryn R. Youra, MS, are with the Prevention Research Center of Michigan,University of Michigan School of Public Health, Ann Arbor, Michigan.Send reprint requests to Daniel J. Kruger, PhD, School of Pubhc Health, University of Michigan,1420 Washington Heights, Ann Arbor, MI 48109-2029; kruger@umich.edu.This manuscript was submitted December 1, 2011; revisions were requested February 1, 2012 and Aprit 2, 2013; the manuscriptwas accepted for publication April 5, 2013.Copyright 2014 by American Journal of Health Promotion, Inc.0890-1171/14/$5.0 + 0DOI: 10.4278/ajhp.U1201-QUAN-437

PURPOSE

American rates of overweight andobesit)' are high and have increaseddramatically over the past few decades.About 66% of Americans are currentlyoverweight or obese, and these pro-portions grow each year.' There aremtiltiple contributing factors, includ-ing frequent fast-food consumption,which is associated with high-fat dietsand high body mass index (BMI),^ aswell as reduced vegetable,"* fruit,'' andfiber consumption,"* and lower physicalactivity.^ Lower cotisumption of fruitsand vegetables is associated with high-er risk for cardiovascular disease,'"cancer,^ ' and ischmie stroke, '"*which are the three leading causes ofdeath in the United States. ' Recentresearch has attempted to elucidatethe interaction between these factorsand identify causation for these trends.

The geographical concentration offast-food outlets may be associated withadverse health outcomes such as in-creases in all-cause mortality and co-morbidities related to overweight andobesity.'^'''' Across New Zealand,neighborhood median travel distancesto fast-food locations are at least twiceas far in the most economically affluentneighborhoods compared to the mosteconomically deprived neighbor-hoods.'* Several other studies foundsimilar associations between the geo-graphic concentration of fast-foodoutlets with neighborhood depriva-tion'"^"'' and minorities as a ptopor-

1 7 1 X

tion of the population. 'Concentration of fast-food outlets

may be associated with lower frtiit andvegetable consumption. A study ofdriving distance to fast-food outletsshowed a negative association between

340 American Journal of Health Promotion May/June2014, Vol. 28, No. 5

proximity and vegetable consumption.However, those living farthest fromfast-food outlets were also more likelyto be overweight.^^ Another studyfound that car ownership was animportant indicator for increased BMIin addition to fast-food outlet proxim-ity," ' which may explain the discrep-ancy between vegetable consvimptionand overweight. Other studies havealso found a negative association be-tween distance to fast-food outlets andfruit and vegetable constimptionamong school-age children. ''^"

Previous studies operationally de-fined access to fast food in terms of thenumber of locations or locations percapita in relatively large geographicalareas represented by postal codes." '^^ ^Other studies have shown that self-reported concentration of fast-foodoutlets increased the odds of fast-foodconsumption and unhealthy diets."Ceographical information system(GIS) methodologies enable a moreprecise and direct analysis using geo-graphically identified data. The GIS-derived density of supermarkets wasassociated with perceived availability ofproduce and low-fat foods, althoughthe density of smaller food outlets hadmixed relationships with the perceivedavailability of healthy food across pop-tilations.^ Other GIS analyses of foodstores found that lower local availabilityof healthy foods was associated withlower dietary quality and that eachaddition of a supermarket and sub-traction of a fast-food oudet or conve-nience store resulted in a decrease in

We anticipated that the availability ofexact addresses for both fast-foodlocations and respondents from acountywide health survey would en-hance the acctiracy of the analyses offast-food outlet concentration. Wepredicted that the local concentrationof fast-food outlets would be inverselyrelated to frtiit and vegetable con-sumption and directly related to BMI.

METHODS

DesignThe Speak to Your Health! Com-

munity Survey in Genesee County,Michigan, was developed through acommunity-based participatory re-search process by a survey committee

composed of community and universi-ty partners. The survey was designed tomonitor and understand local healthand concerns, monitor the impact ofhealth initiatives on health outcomes,and promote change that improves thehealth of Genesee County communi-ties. The project was reviewed andapproved by the University of MichiganHealth Sciences Institutional ReviewBoard.

SampleProfessional survey staff at the

Michigan Public Health Institute con-ducted a 25-minute computer-aidedtelephone interview (CATI) with ran-domly selected respondents. Randomsamples of households were drawnacross Genesee County census tracts.At least 20 residents aged 18 and overwere intemewed for each of the 39residential census tracts in Flint, Mich-igan, and at least 10 from each of the90 census tracts outside of Flint. Theresponse rate was 25%. As with mosttelephone surveys, women and olderindividuals were overrepresented com-pared to the population, and thosewith less than a high school educationwere underrepresented. Only house-holds with landline telephones wereincluded. There were 1688 respon-dents; of these, 80% had complete datafor the variables of interest and wereincluded in the current analyses(1345).

MeasuresFruit and vegetable intake was mea-

sured using the eight items from theBehavioral Risk Factor SurveillanceSystem (BRFSS) assessing frequency(servings per day) of intake of fruit

juice, other fruits, green salad, non-fried potatoes, dark green leafy vege-tables, dark yellow or orangevegetables, beans, and other vegeta-bles.^ ** These items were combined tocompute an index of fruit and vegeta-ble servings per day. We calculated BMIfrom self-reported height and weight.

Demographic items included gen-der, age, race, Flint vs. non-Flintresidence, and highest level of educa-tion completed (see Table 1). Weidentified fast-food restaurants (NorthAmerican Industry Classification Sys-tem no. 722211, Limited-Service Res-taurants) with a current localtelephone directory and supplementa-

Table 1Demographic and Diet-Related

Characteristics of Participants (N =1345)

Characteristic

Age in years (SD)Race

WhiteBiack '^'r -' iVIuitiraciaiOther

SexFemaieMale

EducationLess than high schooiHigh schooiTechnicai schooiSome coiiegeAssociate's degreeBacheior's degreeMaster's degree or higher

ResidenceCitySuburban or rurai

Fruits and vegetabies per dayAt ieast 5 per day

Adequate exerciseBody mass index (SD)*

UnderweightNormai weightOverweightObese

No. of StudyParticipants

54 17

67%26%3%4%

70%30%

11%31%

2%26%10%13%8%

46%54%

4 327%40%

29 72%

30%32%37%

* SD indicates standard deviation.

ry Internet searches. We mapped re-spondents and locations of fast-foodrestaurants with geographical informa-tion systems and calculated the num-ber of fast-food restaurants within a 2-mile buffer zone (Euclidean orstraight-line distance) of each surveyrespondent. Because exercise can in-fluence BMI, we included data fromBRFSS exercise items indicatingwhether or not respondents per-formed at least 30 minutes of moderateexercise per day for 5 days per week(according to the American College ofSports Medicine and American HeartAssociation 2007 physical activity rec-ommendations) .^ ^

American Journai of Heaith Promotion May/June 2014, Voi. 28, No. 5 341

Table 2Unique Predictors of Fruit and Vegetable Consumption (FVC), Body Mass Index

(BMI), and BMI Category

Predictors of FVCConstantGenderFast food

Predictors of BMIConstantNon-WhiteAdequate exerciseFast food

Predictors of BMI categoryConstantNon-WhiteAdequate exerciseGenderFast food

* SE indicates standard

B

4.631-0.540-0.034

28.3191.743

-1.7470.088

(underweight.2.9270.260

-0.1950.1510.009

error.

SE*

0.1500.1940.013

0.3500.4090.3860.028

30.86-0.076-0.073

80.970.116

-0.1210.087

normal, overweight, obese)0.0460.0540.0480.0510.003

64.300.137

-0.1120.0820.072

f

0.0012.782.67

0.0014.264.533.21

0.0014.854.032.952.55

P

0.0050.008

0.0010.0010.001

0.0010.0010.0030.011

The findings apply to householdswith landline telephones; those whocannot afford or chose not to ptirchaselandlines were not represented. Thehigh refusal rates likely result in theoverrepresentation of older and morehighly educated individuals, as well asfemales, as is typical in telephonesurveys. As geographic concentrationof fast-food outlets is directly associatedwith neighborhood deprivation'''"''and minorities representation,"''^ it islikely that otir results would hold andmight be even stronger if our samplemore closely resembled the local de-mographic. Future research incorpo-rating self-reported fast-foodconstimption may extend these find-ings, although self-report measures(including those used in this study)may be biased. We acknowledge limi-tations in the accuracy and precision ofour data. We did not confirm that therestaurants were operational at the

AnalysisWe used stepwise linear regressions

tising the SPSS 17.0 software programto identify unique predictors of thenumber of fruits and vegetables coti-stimed per day, BMI as a continuousmeasure, and BMI categories (under-weight, normal, ovenveight, obese). Weentered residence (Flint vs. non-Flint),race (White vs. non-White), age inyears, gender, education in years, andnumber of fast-food locations within 2miles as predictors of the number offrtiits and vegetables consumed perday. We included these variables andattainment of recommended exerciselevel as potential predictors of BMl.

RESULTS

There was an average of 8 7 fast-food restaurants within 2 miles ofrespondents, with a range from 0 to 29.The local concentration of fast-foodoutlets significantly predicted fruit andvegetable consumption and BMI in theexpected directions (see Table 2).Women had higher fruit and vegetableconsumpdon than did men. Non-Whites and those who did not meetrecommended exercise guidelines hadhigher BMIs. Race, exercise, gender,and fast-food concentration predicted

BMI categoiy (see Table 2). For everystandard deviation (SD) increase infast-food concentration, BMI increasedby 9% of a SD. This is a statisticallystnall effect, yet comparable to those insimilar studies." ' ' ' In post hoc analy-ses, we determined that the localconcentration of public parks or self-reported fear of crime did not explainany additional variance in BMl.

DISCUSSION

GIS enable powerful tests of hy-pothesized spatial relationships. Weused GIS to demonstrate that the localgeographical concentration of fast-food oudets is related to higher BMIand inversely related to the consimip-tion of frtiits and vegetables and thatthese relationships were not accountedfor by sociodemographic factors, stig-gesting that the presence of fast food isa risk factor for the advantaged as wellas the disadvantaged. These resultsbuild on previous associations fotnidwith fast-food prevalence in relativelylarge geographical areas' and percep-tions of local fast-food availability,^'' aswell as GIS-identified associations be-tween the local availability of healthyfoods and beneficial ntitritiotial otit-

SO WHAT? Implications for HealthPromotion Practitioners andResearchersWhat is already known on thistopic?

Previotis studies have found re-lationships between obesity ratesand the ntimber of fast-food loca-tions or locations per capita inrelatively large geographical areasrepresented by postal codes. Geo-graphical information systemmethodologies enable a more pre-cise and direct analysis with geo-graphically identified data.What does this article add?

High local concentration of fast-food outlets is a risk factor for highBMI and low fruit and vegetableconsttmption across the popula-tion.What are the implications for healthpromotion practice or research?

The local food environment mayconstrain the success of nutritionpromotion efforts. Efforts to de-crease the local availabilitv' of un-healthy foods, as well as programsto help consumers identify strate-gies for obtaining healthy meals atfast-food outlets, may improve nu-trition outcomes.

342 American Journal of Health Promotion May/June 2014, Vol. 28, No. 5

time of data collection. We usedstraight-line buffers to determine geo-graphic concentration of fast-foodoutlets. Analyses based on actual streetdistance or walking time may yieldstronger associations. Unmeasuredconfounders, such as local access toexercise opportunities, may potentiallyinfluence the results.

Individuals may be at greater risk foradverse consequences of poor nutri-tion because of the patterns in localfood availability, which may constrainthe success of nutrition promotionefforts. Efforts to increase the localavailability of healthy foods, as well asprograms to help consumers identifystrategies for obtaining healthy mealsat fast-food outlets, may improve nu-trition outcomes.

Acknowledgments

The Speak to Your Health.' Community Survey wassupported by Che Prevention Research Center of Michigan(Centers for Disease Control and Prevention grant no. U48/CCU515775) and the Genesee County Health Department.We thank the Survey Committee members for their assistanceand thank all those who participated in the project.

References1. National Center for Health Statistics,

Chartbook on Trends in the Health ofAmericans. Health, United States, 2006.Hyattsville, Md: Public Health Service:2006,

2. Bowman S, Vinyard B. Fast foodconsumption of U,S, adults: impact onenergy and nutrient intakes andoverweight status./Am CollNutr. 2004:23:163-168.

3. Jeffery R, Baxter J, McGuire M, Linde J.Are fast food restaurants anenvironmental risk factor for obesity? IntJBehav Nutr Phy 2006:3:2.

4. French S, Harnack L, Jeffrey R, Fast foodrestaurant use among women in thePound of Prevention study: dietary,behavioral, and demographic correlates,IntJ Obesity. 2000;24:1353-1359,

5. Ness AR, Powles JW. Fruit and vegetables,and cardiovascular disease: a review, IntJEpidemiol 1997:26:1-13,

6. Knekt P, Reunanen A, Jarvinen R, et al,Antioxidant vitamin intake and coronarymortality in a longitudinal populationstudy AmJEpidemiol. 1994:139:1180-1189.

7. Bazzano LA, He J, Ogden LG, et al. Fruitand vegetable intake and risk ofcardiovascular disease in US adults: thefirst National Health and NutritionExamination Survey epidemiologic follow-up study AmJ Ctin Nutr. 2002;76:93-99.

8. Riboli E, Norat T, Epidemiologic evidenceof the protective effect of fruit andvegetables on cancer risk. AmJ Clin Nutr.2003:78:559S-569S.

9. Block G, Patterson B, Subar A, Fruit,vegetables, and cancer prevention: areview of the epidemiological evidence.Nutr Cancer. 1992:18:1.

10. Joshipura KJ, Ascherio A, Manson JE, et al.Fruit and vegetable intake in relation torisk of ischmie stroke, JAMA. 1999:282:1233-1239.

11. Mokdad AH, Marks JS, Stroup DF,Gerberding JL. Actual causes of death inthe United States, WOO. JAMA. 2004:291:1238-1245,

12. California Center for Public HealthAdvocacy, PolicyLink, and the UCLACenter for Health Policy Research,Designed for Disease: The Link Between LocalFood Environments and Obesity and Diabetes.Davis, Calif: CCPHA: 2008,

13. Alter D, Eny K. The relationship betweentbe supply of fast-food chains andcardiovascular outcomes, CJ Public Health.2005:96:173-177.

14. Pearce J, Blakely T, Witten K, Bartie P.Neighborhood deprivation and access tofast-food retailing, AmJPrev Med 2007:32:375-382.

15. Macdonald L, Cummins S, Macintyre S.Neighbourhood fast food environmentand area deprivationsubstitution orconcentration? Appetite. 2007:49:251-254.

16. Cummins S, Macintyre S, Foodenvironments and obesityneighbourhood or nation? Int J Epidemiol2005:35:100-104.

17. Lewis L, Sloane D, Nascimento L, et al.African Americans' access to healthy foodoptions in south Los Angeles restaurants.AmJPubtic Health. 2005;95:668-673.

18. Block J, Scribner R, DeSalvo K Fast food,race/ethnicity, and income: a geographicanalysis. AmJPrev Med. 2004:27:211-217,

19, Pearce J, Hiscock R, Blakely T, Witten K, Anational study of the association betweenneighbourhood access to fast-food outletsand the diet and weight of local residents.Health & Place. 2009:15:193-197,

20, Ingami S, Cohen D, Brown A, Asch S. Bodymass index, neighborhood fast-food andrestaurant concentration, and carownership.JUrban Health. 2009:86:683-695.

21, Timperio A, Ball K, Roberts R, et al.Children's fruit and vegetable intake:associations with the neighbourhood foodenvironment. Prev Med 2008:46:331-335.

22. Davis B, Carpenter C. Proximity of fast-food restaurants to schools and adolescentobesity. Am J Public Health. 2009:99:505-510.

23. Fraser L, Edwards K. The associationbetween the geography of fast food outletsand childhood obesity rates in Leeds, UK.Health & Place. 2010:16:1124-1128.

24. Moore L, Diez Roux A, Netdeton J,Jacobs D, Franco M. Fast-foodconsumption, diet quality, andneighborhood exposure to fast food. AmJ Epidemiol 2009:170:29-36.

25. Moore L, Diez Roux A, Brines S,Comparing perception-based andGeographic Information System (GIS)-based characterizations of the local foodenvironment, / Urban Health. 2009:85:206-216,

26, Bodor J, Rice J, Farley T, Swalm C, Rose D.The association between obesity andurban food environments. / Urban Health.2010:87:771-781,

27, Franco M, Diez Roux A, Nettleton J, et al.Availability of healthy foods and dietarypatterns: the Multi-Ethnic Study ofAtherosclerosis. AmJ Ctin Nutr. 2009:89:897-904,

28. Townsend M, Kaiser L. Development of anevaluation tool to assess psychosocialindicators of fruit and vegetable intake fortwo federal programs. J Nutrition Education& Behav. 2005:37:170-184,

29, Haskell W, Lee I, Pate R, et al. Physicalactivity and public health: updatedrecommendation for adults from theAmerican College of Sports Medicine andthe American Heart Association. Med SdSpart Exer. 2007:39:1423-1434.

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