Scoping Assessment of Climate Change Adaptation Priorities in the

EXAMININGTHE IMPACT OF

FOODDESERTSON PUBLIC HEALTH

IN CHICAGO

FINDINGS

EXAMINING THE IMPACT OF FOOD DESERTS ON PUBLIC HEALTH IN CHICAGO

LaSalle Bank Corporation

135 South LaSalle Street

Suite 2325

Chicago, Illinois 60603

Robert S. Grossinger

Senior Vice President

Community & Sustainable Development

Dear Colleague:

LaSalle Bank is committed to building sustainable communities by providing

residents, small businesses and non-profit organizations with the resources and

assistance necessary to be healthy and vibrant members of the community.

With deep roots in the Chicago community, LaSalle Bank understands that many

of the city’s neighborhoods are challenged by limited access to healthy food. It has

been established that numerous areas of Chicago are considered “food deserts,”

neighborhoods with no or distant grocery stores but an abundance of fast food

restaurants and other retail outlets offering little or no nutritious food. Similarly,

public health officials and community advocates have been alarmed by statistics that

reflect the growing prevalence of obesity, diabetes, cancer, cardiovascular disease and

hypertension, particularly in these disadvantaged communities.

As rates of these and other chronic health problems continue to rise, researcher Mari

Gallagher proposed, and LaSalle Bank commissioned, a report to explore the health

consequences of food deserts. And, indeed, it appears that residents of food deserts

experience higher rates of certain diet-related health conditions.

We hope that the findings herein and discussion at the Stranded in the Food Desert

forum will reveal both the challenges and opportunities involved in providing equal

access to healthy food to affected neighborhoods – and that it will inspire new

understanding of this important community health issue, and action that begins to

bring solutions. Thank you to Mari Gallagher, Local Initiative Support Corporation

and the many organizations and individuals who contributed to this report.

Sincerely,


3

TABLE OF CONTENTSForeword 4

ExecutiveSummary 6

Methodology 11andDataDevelopment

Findings

QuantifyingFood 16 AccessPatterns

TheFoodBalanceScore 22

FoodBalanceand 23 CommunityHealth

SummaryofFindings 32

Author’sComments, 33ConclusionsandAcknowledgements

Bibliography 39


4

“Location,location,location,”isanexpressionthatcomesupalotinconversationsaboutthevalueofapieceofproperty.Whetherwe’retalkingaboutbuyinganewhouseorlocatingabusiness,everyoneknowsthatlocationmatters.Butwhatabouthealth?Isthelocationwhereyoulivealsoimportantforyourhealth?Sure,weallknowthatlivinginanareawithfouledwateroraircanaffectourhealth,butarethereotheraspectsofplacesthatcanmakeussickorkeepuswell?Theanswerismostassuredly“yes,”anditisnotbasedonjustrealestatelorebutonasolidfoundationoffactsthathavebeenaccumulatedoverthelastquartercenturyorso.Researchersfromepidemiologyandotherdisciplineshaveconsistentlyshown,forexample,thatthosewholiveindisadvantagedneighborhoodshaveworsehealthoutcomes.Forawiderangeofhealthproblemsacrosstheagespectrum–frominfantmortalitytodisabilityintheelderly,andspecificconditionssuchascardiovasculardisease,diabetes,andasthma–whereyoulivedoesmakeadifference.What’smore,livingindisadvantagedareasalsoisassociatedwithmoreobesity,depression,andphysicalinactivity,allfactorsthatincreaseyourriskofgettingsick.

AgoodexampleofthisisastudythatcolleaguesandIconducted20yearsagoinwhichwecomparedtheriskofdyingbetweenresidentsofOakland,California,wholivedinfederallydesignatedpovertyareasandthosewhodidnot.Groupsofcensustractsweregatheredtogetheranddesignatedasapovertyareabasedonlevelsofincome,unemployment,loweducation,substandardhousing,unskilledmaleworkers,andchildreninsingleparenthomes.Thisresultedina10-mile-longstripdividedbyafreewayinwhichhousingwasinterspersedwithwarehouses,manufacturing,andrailways.WetrackedthehealthexperienceofagroupofindividualswholivedinthisareaandotherpartsofOaklandformanyyears.Afternineyears,wediscoveredthatthosewholivedinthispovertyareahada71%greaterchanceofdyingduringthatperiodthanthosewholivedinotherpartsofOakland.Ofcourse,thetwogroupsofpeopledifferedinmanyways,butwhenwetookintoaccountdifferencesinincomeandeducation,healthcareaccess,race/ethnicity,smokingandotherhighriskbehaviors,depression,andotherfactorsbywhichtheydiffered,thoseinthepovertyareawerestill50%morelikelytodieduringthosenineyears!Sotheremusthavebeensomethingaboutthesocialandphysicalenvironmenttowhichresidentsofpovertyareaswereexposedthatmadethemsicker.Health–likepolitics–mustbelocal,addingnewmeaningtotheimportanceoflocation.

Howcoulditbethatmerelylivinginaplacecouldmakeyousick?Theevolvingperspectiveisthattheareasinwhichpeoplelivecandifferwidelyintheiraccesstohealth-promotingfactorsandexposurestohealth-damagingfactors.Furthermore,thesedifferentopportunitiesandrisksmayexplainmanyofthehealthdifferencesthatwesee

FOREWORD FROM GEORGE KAPLAN


5

betweenresidentsofdifferentplaces,aswellashealthdisparitiesbetweenracial,ethnicandsocioeconomicgroups.

Thisiswherethepresentreport,Examining the Impact of Food Deserts on Public Health in Chicago,makesarealcontribution.Bydemonstratingthatwhereyoulivemakesarealdifferencetowhereyoucanbuyfoodand,indirectly,thequalityofthatfood,thereportbeginstoexplainexactlyhowopportunitiesandrisksvarybyneighborhood.Butitdoesn’tstopthere.Italsoshowsimportantdifferencesbetweenneighborhoodsinthe“balance”ofgrocerystorestofastfoodoutlets.Thattherearelargedifferencesinthenutritionalchoicesavailablebetweengrocerystoresandfastfoodoutletsiswellknown,butwhatwasnotknownbeforethisreportisthatforsomeneighborhoodsthebalanceswingsfartothefastfoodside.Atfirstglance,wemightonlyguessthatthesedifferencesinfluencethehealthofthosewholiveintheneighborhoods.Thereportprovidesevidencethatsuggestsourguessesarecorrect.CleverlycombininginformationfromtheCityhealthdepartment,researchstudies,anddriver’slicenserecords,thereportillustratesthatbothaccesstomarketsandthebalanceofmarketstofastfoodoutletsareassociatedwithimportanthealthoutcomes.Thisconclusionappliesgenerallytoabroadswathofhealthoutcomes,rangingfrom“prematuremortality,”tocardiovasculardisease,diabetes,andcancerdeathstoobesityandhypertension,lendingsupporttotheoverallimportanceoftheargument.

Finally,awordonlanguageinthereport.Ifindtheuseoftheterm“fooddesert”particularlyinteresting.Adesertis,ofcourse,aplacedistinguishedbytheabsenceofvegetation,rain,etc.,whichisthesenseinwhichthewordisusedinthisreport.Fooddesertsaredefinedas“areaswithnoordistantgrocerystores.”Buttheword“desert”isalsoaverb–“toleavesomeonewithouthelporinadifficultsituationandnotcomeback.”Thisseemstometocaptureanimportantdimensionoffooddesertsnotconveyedbythenoun.Theverb“desert”focusesonactionandagency,emphasizingthatthelackofaccesstogoodfoodinsomeareasisnotanatural,accidentalphenomenonbutisinsteadtheresultofdecisionsmadeatmultiplelevelsbymultipleactors.Byfocusingonthislattermeaning,wecanfindroomforchangestobeeffected,fordifferentdecisionstobemadeinthefuture,formovementtowardactionsthatcanimproveaccesstohealthyfoodforthosewhohavebeendeserted.Indoingso,wecanhelpinatleastonewaytoimproveunevenopportunities,andperhapsprovidebetterhealthaswell.

GeorgeA.Kaplan,Ph.D.ThomasFrancisCollegiateProfessorofPublicHealthDepartmentofEpidemiologyUniversityofMichiganSchoolofPublicHealthandResearchProfessor,InstituteforSocialResearchDirector,CenterforSocialEpidemiologyandPopulationHealth


6

EXECUTIVE SUMMARYIn1923,longbeforetheriseofMcDonald’sgoldenarches,anadvertisementforbeefmadethisproclamationintheBridgeport Telegraph:

“Ninety percent of the diseases known to man are caused by cheap foodstuffs. You are what you eat.”

Thephrase“youarewhatyoueat”actuallydatesbacktothe17thCentury.Overtime,sciencehasrepeatedlydemonstratedthatnutritionalintakedirectlyaffectshealthoutcomes.Thatwearewhatweeatisamedicalfact.Buttowhatdegreedoeswhatweeatand,thus,ourhealth,dependonwhereweliveandthetypesoffoodwehaveaccessto?

Examining the Impact of Food Deserts on Public Health in Chicagoprobesthisveryquestion.Givenourfoundationalpremisethatthehealthandvitalityofurbancommunitiesisablock-by-blockphenomenon,ourfirsttaskistomeasurethedistancefromeveryCityofChicagoblocktothenearestgrocerystoreandfastfoodrestaurant.Next,wedevelopanempiricalscoretoquantifythebalanceoffoodchoiceavailabletoresidents.Finally,wecomparefoodaccessandfoodbalancedirectlytohealthoutcomes,holdingconstanteducation,income,andrace.Hereiswhatwefound:

• Residentsoffooddeserts–largegeographicareaswithnoordistantgrocerystores–facenutritionalchallengesevidentindiet-relatedcommunityhealthoutcomes.Thoseoutcomesworsenwhenthefooddeserthashighconcentrationsofnearbyfastfoodalternatives.WecallthistheFoodBalanceEffect.

• MajorityAfrican-AmericanandmajorityWhitecommunitiesthathaveout-of-balancefoodenvironmentswillhavehigherratesofresidentsdyingprematurelyfromdiabetesthatarestatisticallysignificant.Thisstatisticalforecasttakesintoaccountincome,education,andrace.African-Americancommunitieswillbethemostlikelytoexperiencethegreatesttotalyearsoflifelostfromdiabetesasaresult.Tomeasurethiseffect,wedevelopedaFoodBalanceScore:thedistancetotheclosestgrocerdividedbythedistancetotheclosestfastfoodrestaurantforeachblock,tract,andCommunityAreaintheCityofChicago.ThechartinFig. 1showstheaverageyearsofpotentiallifelost(YPLL)fordiabetesin2003byhigh,middle,andlowFoodBalanceScoresbyChicagoCommunityArea.Themoreoutofbalancethecommunity,thehigherthe

African-American and White communities that have out-of-balance food environments will have higher rates of residents dying prematurely from diabetes.

Diabetes is also a serious health issue confronting Latino and diverse communities.

Average YPLL* by Food Balance by Chicago Community

Diabetes *Years of potential life lost

40

30

20

10

0

High Score (worst)

Middle Score (average)

Low Score (best)

Fig. 1


lifelosttodiabetes.DiabetesisalsoaserioushealthissueconfrontingLatinoanddiversecommunities.Ourstudysuggeststhatfoodaccessandgeographicfoodbalance,however,arenotthekeycontributorstothosepoorhealthoutcomes.

Whilethecalculationofyearsofpotentiallifelostmightbeanewconcepttosome,almosteveryonecanrelatetodifferentialsamongdeathrates. Fig. 2showsthenumericyearsofpotentiallifelostoutcomesfromthepreviouschartalongwith2003deathratesfordiabetes.Thediabetesdeathrateforthemostout-of-balanceChicagocommunitiesismorethantwicetherateforallothercommunities.

ThemapinFig. 3showsFoodBalanceScoresbyraceforthetractsthatscoredintheworstthird.Theseareareasthathavenoordistantgrocerystores,butnearbyfastfoodrestaurants,wherethegreatestprematuredeathfromdiabeteswilllikelycontinuetooccur.

PrematuredeathduetocancerandcardiovasculardiseaseisalsogreaterforAfrican-American,White,andLatinocommunitieswherethereisgreaterimbalanceoffoodchoices.Whiletheseeffectsarenotstatisticallysignificant,thepatternrepeatsitselfinnearlyeveryinstanceofanalysis:ascommunitiesbecomemoreout-of-balanceintermsoffoodchoices,diet-relateddeathsandprematuredeathincrease.

African-Americansarethemostdisadvantagedwhenitcomestobalancedfoodchoices,althoughotherracialgroupsdosufferaswell.African-Americans,onaverage,travelthefarthestdistancetoanytypeofgrocerystore,andtheirlowaccesscommunitiesclusterstrikingly.Chicago’sfooddeserts,forthemostpart,areexclusivelyAfrican-American.

Chicago’s Most Out-of-Balance Tracts When It Comes to Food Access

WhiteAfrican-AmericanLatinoAsianDiverse

NotinGrouping

The map shows the most out-of-balance tracts that have no or few grocers but nearby fast food restaurants shaded by that tract’s majority race.

Average YPLL by Food Balance by Chicago Community

Cancer CVD

300

200

100

0

High Score (worst)


Low Score (best)

Cancer and Cardiovascular Disease by Chicago Community Areas by Food Balance Scores

Food Cancer Cardiovascular Disease FoodBalance BalanceGroupings YPLL Death Rate YPLL Death Rate Score

Worst 314 9.73 345 11.07 2.04

Middle 247 7.42 242 7.41 1.25

Best 204 6.68 185 5.72 0.87

Diabetes by Chicago Community Areas by Food Balance Scores

Food Death Rate Balance YPLL Per 1,000 Groupings Population

Worst 45.48 1.27

Middle 33.48 1.11

Best 25.36 0.56

Fig. 2

Fig. 3

Fig. 4

Fig. 5

7


8

Distance to Grocers by Tract in Chicago

The map shows distance to all types of grocery stores at the tract level. Red colored tracts are the farthest distance from grocers; we see that they form three key food deserts on Chicago’s West and South sides.

Chicago’s Food Deserts Are Nearly Exclusively African-American

The map shows only the tracts that are the farthest distance to grocers and shades them by race. We see that the three clusters of food deserts are primarily African-American, with the African-American majority tracts.

1ROGERSPARK2WESTRIDGE3UPTOWN4LINCOLNSQUARE5NORTHCENTER6LAKEVIEW7LINCOLNPARK8NEARNORTHSIDE9EDISONPARK10NORWOODPARK11JEFFERSONPARK12FORESTGLEN13NORTHPARK14ALBANYPARK15PORTAGEPARK16IRVINGPARK17DUNNING18MONTCLARE19BELMONTCRAGIN20HERMOSA21AVONDALE22LOGANSQUARE23HUMBOLDTPARK24WESTTOWN25AUSTIN26WESTGARFIELDPARK27EASTGARFIELDPARK28NEARWESTSIDE29NORTHLAWNDALE30SOUTHLAWNDALE31LOWERWESTSIDE32LOOP33NEARSOUTHSIDE34ARMOURSQUARE35DOUGLAS36OAKLAND37FULLERPARK38GRANDBOULEVARD39KENWOOD

40WASHINGTONPARK41HYDEPARK42WOODLAWN43SOUTHSHORE44CHATHAM45AVALONPARK46SOUTHCHICAGO47BURNSIDE48CALUMETHEIGHTS49ROSELAND50PULLMAN51SOUTHDEERING52EASTSIDE53WESTPULLMAN54RIVERDALE55HEGEWISCH56GARFIELDRIDGE57ARCHERHEIGHTS58BRIGHTONPARK59MCKINLEYPARK60BRIDGEPORT61NEWCITY62WESTELSDON63GAGEPARK64CLEARING65WESTLAWN66CHICAGOLAWN67WESTENGLEWOOD68ENGLEWOOD69GREATERGRANDCROSSING70ASHBURN71AUBURNGRESHAM72BEVERLY73WASHINGTONHEIGHTS74MOUNTGREENWOOD75MORGANPARK76O’HARE77EDGEWATER

Chicago Communities Chicago’s Food Deserts by Tract with Community Boundaries

9

10 1112

13

21

477

314

1715 16

5 6

18 19 20

21

22 7

2523 24 8

26 27 28 32

3334

3031

56

64

57

62

65

63

58

5960 35

36

37 38 39

40 41

4266

67

61

68

6943

44 45 46

47 48

70 71

72 73

7475

49 50

51

53

52

54 55

WhiteAfrican-AmericanLatinoAsianDiverse

NotinGrouping

ClosestDistance

AverageDistance

FarthestDistanceExcluded

FoodDesert

Fig. 6 Fig. 7

Fig. 8

29


9

InatypicalAfrican-Americanblock,thenearestgrocerystoreisroughlytwiceasdistantasthenearestfastfoodrestaurant.Thismeansthat,forAfrican-Americans,itismucheasiertoaccessfastfoodthanothertypesoffood.Followingadoctor’sdietaryrecommendationislikelyverydifficultforthehalfmillionplusAfrican-Americanswholiveinthe287worstgrocery-store-accesstracts.

Foodbalanceisastatisticallysignificantcontributortoincreasedratesofbodymassindex(obesity),butwhenweconducttheanalysisseparatelybydistinctfoodvenues,weseethatdistancetoagrocerystorehasanevengreaterimpactonbodymassindex.Asgrocerystoreaccessdecreases,obesityincreases,holdingeducationandincomeconstant.Sinceobesitycanfueltheonsetofotherdiseasesandchronichealthconditions,thedevelopmentofgrocerystoresinunderservedareaslikelywouldcontributepositivelytocommunityhealthandwellnessofAfrican-Americansandothergroups.Conversely,livinginafooddesertcanmeangreaterratesofobesity,prematuredeath,andlowerqualityoflife,especiallyformothersandchildren.

InChicago,bodymassindexpatternsclusterdramaticallybyraceandbyplace:theNorthandNorthwestsideshavethelowestratesofobesity,whiletheWestandSouthsideshavethehighestratesofobesity.Thedataisgroupedintoequaltertiles,notbyempiricaldefinitionsofobesityascalculatedbyBMI.

Allofthesefindingspointtooneconclusion:communitiesthathavenoordistantgrocerystores,orhaveanimbalanceofhealthyfoodoptions,willlikelyhaveincreasedprematuredeathandchronichealthconditions,holdingotherinfluencesconstant.Althoughwemustsetourfindingsinthecontextofthechallengesandlimitationsoflinkingcauseandeffectandofpredicting,withcertainty,theexactstatisticalmagnitudeoftherelationshipbetweenfoodaccessandhealth,itisclearthatfooddeserts,especiallythosewithanabundanceoffastfoodoptions,poseserioushealthandwellnesschallengestotheresidentswholivewithinthemandtotheCityofChicagoasawhole.

Thediet-relatedhealthoutcomesthatwefocuson–cancer,cardiovasculardisease,diabetes,obesity,andhypertension–stealtime,resources,vitality,andproductivity,andtheyreinforceeachother.AccordingtoarecentreportreleasedbytheCentersforDiseaseControlandPrevention,“obesitycanincreasetheriskof(adultonset)type2diabetesbyasmuchas34fold,anddiabetesisamajorriskfactorforamputations,blindness,kidneyfailure,andheartdisease.”ObesityaloneisestimatedtocosttheUnitedStateshealthcaresystem$100billionperyear(KoplanandFleming,2000).

Communities that have no or distant grocery stores, or have an imbalance of healthy food options, will likely have increased premature death and chronic health conditions.


10

Sixoutofevery10adultAmericansareoverweight,nearlyoneinthreeisobese,andhalfofallmealsareeatenoutsidethehome,mostlyatfastfoodrestaurants(UMNNews).AndaccordingtoastudybyLisaYoungandMarionNestleofNewYorkUniversity,foodportions,calories,andfatcontenthaveincreaseddramaticallyoverthelastfewdecades,notjustatfastfoodlocations,buteverywhere.Forexample,amuffintodayistypically333%largerthanwhattheUSDArecommends.Inthesetimes,choosinghealthyfoodsonaregularbasisisadifficultchallengeforanyone,butforresidentsofout-of-balancefooddeserts,itisnearlyimpossible.

ThecostsassociatedwiththeFoodBalanceEffectwillbebornedirectlybycommunitiesandtheirresidentsasitrelatestothequalityandlengthoflife,andindirectlybythehealthcareindustry,byemployers,bygovernmentagenciesandbyotherswhotakeonthefinancialburdenofpre-deathtreatments.TosimplydemonizethefastfoodindustryforthenegativehealthoutcomesassociatedwiththeFoodBalanceEffectwouldbetomissseveralkeypointsthatwediscussinthesectiontitledAuthor’s Comments, Conclusions, and Acknowledgements.Lessimportantnowiswhatthefastfoodindustryhasbeen.Moreimportantarewhatitisbecoming,whatitcouldbecome,thebestwaytomonitortheFoodBalanceEffectmovingforward,andhowtostandbehindandsupportAfrican-Americanand,indeed,allcommunityleaderswhostruggletosecurequalityandsustainablefoodvenuesintheirneighborhoods.

Asweallneedtoeattolive,foodmightnotonlybeourmostcommondenominatoraspeople,butalsoourmostunitingcallforcollective,strategicactionthattranscendsrace,place,class,andoutdateddevelopmentmodelsthatjustdon’tworkintheseunderservedcommunities.Identifyingmarketaswellasneeds-basedsolutionsthatpromoteaccesstonutritiousfoodsandhealthyfoodchoiceswillrequireinputandsupportfromthefooddesertresidentsthemselvesaswellasfromgrocers,banks,brokers,developers,planners,healthadvocates,educators,government,andfoundations–ultimatelyeveryone–toachieveevenamodestlevelofsuccess.

Wehopethatthisstudyprovidesonesmallstepinthedirectionofunderstandingthecomplexrelationshipbetweenfoodbalanceandcommunityhealth.

Weinviteyourcommentsandparticipationmovingforward.

Average Body Mass Index by Tertiles by Chicago Zip Code

The map shows the starkness of obesity patterns by Chicago Zip Code. Red-colored Zip Codes have the highest obesity.

Average Body Mass Index by Tertiles by Chicago Zip Code

This is the BMI map by Zip Code with the addition of our Food Balance Scores. Brown dots indicate areas that are the most out-of-balance; they have no or distant grocers but nearby fast food.

HignBMI

AverageBMI

LowBMIExcluded

WorstAverageBestExcluded

Food Balance Score

HignBMI

AverageBMI

LowBMIExcluded

Fig. 9

Fig. 10


11

METHODOLOGY AND DATA DEVELOPMENTOurmethodologybeginswithaverysimplepremise:thatthevitalityandhealthofanyurbancommunityisablock-by-blockphenomenon.Whenwethinkofconceptssuchas“home”and“community,”wetypicallythinkoftheveryblockwherewelive,andwhetherornotitisawell-balanced,life-supportingenvironment.Webeginourstudyoffoodaccessandhealthoutcomesattheblocklevelforthisreason.Wealsoanalyzepatternsbycensustract,officialCityofChicagoCommunityAreas,andbyZipCodes.Ourresearchobjectiveistocomparefoodaccessanddiet-relatedhealthoutcomes,holdingotherinfluencerssuchasincome,education,andraceconstanttothedegreepossiblegiventimeandresourceconstraints.Wetestthetheorythatabalancedfoodenvironmentisanimportantkeytocommunityhealth.Inotherwords,dofooddeserts(areaswithnoordistantgrocerystores)facenutritionalchallengesevidentindiet-relatedhealthoutcomes,anddothoseoutcomesworsenwhenthefooddeserthashighconcentrationsofnearbyfastfoodalternatives?Thehealthoutcomesthatwestudyarecancer,diet-relatedcardiovasculardiseases(heartdisease,hypertension,andstroke),diabetes,andratesofobesityandhypertension.

TheCityofChicagohas18,888census-definedblockswithnon-zeropopulations.Approximately7,000aremajorityWhite,7,300aremajorityAfrican-American,3,400aremajorityLatino,and900aremajoritydiverse,meaningthatnooneracehas50%ormoreofthepopulation.

Tocalculatetheaveragedistancetoafoodvenue(suchasagrocerystoreorfastfoodrestaurant),wemeasuredthedistancebetweenthegeographiccentersofeachblockandthelocationsofeachfoodvenueintheChicagovicinity.Thedistancefromthecenterofeachblocktoeachfoodvenuewascalculatedusingthelatitudeandthelongitudeofeachfoodvenueandofeachblockcenter.Ofthesedistances,theminimumdistancewascalculatedforeachblock,representingthedistancefromthatblocktothenearestfoodvenuebycategory:chaingrocers,smallgrocers,allgrocers,andfastfood.Tocontrolforpopulationdensity,foreachblockaweightwascreatedtoreflecttheshareofthecity’spopulationlivinginthatblock.Theaveragedistanceistheweightedaverageofthedistancefromeachblocktothenearestfoodvenue,withgreaterweightsgiventoblockswithlargernumbersofresidents.Thedistancescore,calculatedinmiles,isthedistancetheaveragepersonfromthatblockwouldneedtotraveltoreachafoodvenue.

Thissamemethodologywasusedtocalculatedistancesforcensustracts,officialCityofChicagoCommunityAreas,andforZipCodes,buildingupeachtimefromtheblocklevelsoastonotlosethetruepatternbylookingsolelyatoneaverageacrossalargergeography.

Similartoblocks,weexcludedtractswithverysmallorzeropopulations.Thereare77CommunityAreasinthecity;weexcludedtheLoopandO’Hare,astheyaremajordestinationcenters,andwouldskewthefindings.Inourdata

City of Chicago Blocks By Majority RaceWhiteAfrican-AmericanLatinoDiverseTotal

Number of Blocks

7,0997,3973,473

91918,888

Percent

37.6239.1018.404.87

100.00

Fig. 11


12

collectionoffastfoodvenues,weexcludedthoselocatedatMidwayAirport,butnotthoseinitssurroundingenvirons.Assuch,theentireCommunityAreathatincludesMidwaywasnotexcluded.

Totestourcoretheorythatfoodvenuebalancemattersforhealthoutcomes,wedevelopedaratioscore:thedistancetoanygrocerdividedbythedistancetoanyfastfoodvenue.Theaverageratiofortheentirecity,aZipCode,aCommunityArea,oracensustract,istheweightedaverageoftheratiosforeachblock,withgreaterweightgiventoblockswithlargernumbersofresidents,controllingfordensity.WecallthisratiotheFoodBalanceScore,andtheFoodBalanceEffect.

FoodvenuedatawascollectedforChicago,suburbsimmediatelyoutsideChicago,andareasimmediatelyoutsidetheSoutheasternsectionofChicagoinnearbyIndiana.Thisallowedustocalculatetheactualdistancetothenearestfoodvenue,ratherthanbeconstrainedbyartificialboundariessuchasconcentricrings,CommunityAreas,andsoon,thatmightnotreflecttherealitiesoffoodpurchasing.

Thedataarefromthefollowingsourcesandyears,unlessotherwisenotedinthereport.Someofourtablesandchartscompareseveralvariablesandhavelengthytitles.Wethereforedonotrepeatcoreinformationeachtime,suchastheyearandthesource,unlessitisneededforclarity.We are grateful to many colleagues for their generous contributions of data.WeacknowledgethemandothercontributorsinmoredetailinthesectiontitledAuthor’s Comments, Conclusions, and Acknowledgements.Andwhiletheircontributionsofdataandinsightsweretremendouslyimportant,theaccuracyoftheanalysisandtheinterpretationofthefindingswerestrictlytheresponsibilityoftheauthor.

Foodvenuedatasourcesinclude:

• DanielBlock,ChicagoStateUniversityandTheNortheasternIllinoisFoodSecurityAssessment(datacollectedthroughapastprojectfundedbyTheChicagoCommunityTrust)

• CityofChicagoDepartmentofPlanningandDevelopment

• ReferenceUSA

• MariGallagherResearchandConsultingGroup

Food Balance Theory

Food Balance Scores

Far Above 1: high score “worstoutcomes”–closertofastfood,fartherfromgrocers

Around 1: average score “averageofbalancedoutcomes”–equalaccesstogrocersandfastfood

Far Below 1: low score“bestoutcomes”–closertogrocer,fartherfromfastfood

Examples

Grocerystoreis1mileaway,andafastfoodrestaurantis.5milesaway1/.5 = 2

Grocerystoreis1mileaway,andafastfoodrestaurantis1mileaway1/1 = 1

Grocerystoreis.5milesaway,andafastfoodrestaurantis1mileaway.5/1 = .5

Fig. 12


13

Healthdatasourcesinclude:

• CookCounty,Illinoisdeathrecords

• OfficeofEpidemiology,ChicagoDepartmentofPublicHealth

• SiimSööt,UniversityofIllinois

• UniversityofMichiganSchoolofPublicHealth(fromTheChicagoStudy)

Foodvenuedataconsistsofgrocerystoresandfastfoodrestaurants.Itwasdevelopedandreleasedbydifferentsourcesatdifferentpointsintimeoverthepastfiveyears,wasmergedintoonefile,cleaned,andmadecurrentbyadditionaldatadevelopmentandfieldchecks.Nodatasetcanprovideacompleteandfullyaccuratepictureofthelocationsofcommercialvenuesacrosslarge,diverse,anddynamicurbangeographies.Nonetheless,webelievethatouroverallpatternsareaccurateandthattheyreflectthecurrentrealitiesoffoodaccessinChicagotoday.Therearethreetypesofgrocercategoriesthatwestudy:

1)Chaingrocerystores.ExamplesincludeJewel,WholeFoods,Dominick’sFinerFoods,TraderJoe’s,etc.

2)Smallerand/orindependentgrocers,includingthosethatfocusonfruitsandvegetables,suchasDelrayFarmsandPete’sProduce.Thiscategoryalsoincludesvarious-sizedLatino-focusedsupermarketsaswellaslargerindependentsupermarketssuchastheHydeParkCo-OpandHappyFoods.Weshortenthelabelforthiscategoryas“smaller”stores,butitdoesnotmeantoimplythattheyareall“small”intermsofsquarefootageorshelfspace.“Convenience”and“corner”grocerystoreswereexcluded.

3)Largeandsmaller/independentstorescombined,labeled“allgrocers.”

Fastfoodvenueswerecollectedinonecategoryandincludechainsaswellassmaller,independentoutlets.Generally,thedefinitionoffastfoodistake-outorself-carrytotableswithintherestaurant.Primarilywefocusedonburger,chicken,taco,andhotdogplaces.Sit-downrestaurantswithwaitstaff,coffeehouses,gasstations,andconveniencestoresthatmightserveready-made“fastfood”itemswereexcluded.Deli-typetake-outrestaurantswereexcludedwiththeexceptionofSubwayandMr.Submarinebecauseoftheirubiquityinmostmarkets.Wearenotsuggestingthatdelifoodisinherentlybadorgood,orthattraditionalfastfoodaloneorinmoderationdirectlycausespoorhealth.Ourfocusisonwhatweconsiderabalanceoffoodoptionsintestingourhypothesisthatitimpactshealthoutcomes.Wepositthatcommunitieswithnoordistantgrocerystoresandnearbyfastfoodrestaurantsandperhapsasubmarinesandwichshopareoutofbalance.

Cardiovasculardiseaseratesanddeathsweresegregatedbythosethatarediet-related,namely:essentialhypertension,hypertensiveheartdisease,hypertensiverenaldisease,hypertensiveheartandrenaldisease,acutemyocardialinfarction,subsequentmyocardialinfarction,certaincurrentcomplicationsfollowingacutemyocardialinfarction,otheracuteischaemicheartdiseases,chronicischaemicheartdisease,allcerebrovasculardiseases(stroke)andartheroscelorosis.Excludedwerecardiovasculardiseaseratesanddeaths:acuterheumaticfever,chronicrheumaticheartdiseases,pulmonary


14

heartdiseaseanddiseasesofpulmonarycirculationandotherformsofheartdiseasenotlinkedtodiet-relateddeaths,suchasonescausedbyviruses.

Wecalculateyearsofpotentiallifelost(YPLL)for2003forcancer,cardiovasculardisease,diabetes,andchronicliverdiseaseandcirrhosis,andfortotaldiet-relateddiseasesexcludingchronicliverdiseaseandcirrhosisforYPLLaggregationsbyCommunityArea.Forchronicliverdiseaseandcirrhosisweanalyzedfoodaccessimpactusinggrocer-to-fast-fooddistanceratioscoresthrougharegressionanalysis.Accesstoalcoholwouldalsobeausefulmeasure.Webeganthatanalysisthroughthelensofliquorlicensedata,butmoreworkneedstobedonetoisolateandunderstandpotentialimpactsfromthetypesofalcoholaccess.Alcoholispurchasedandconsumedinmanyvenues,includingsmallliquorstores,chaingrocerystores,pharmacies,andsit-downrestaurants.Ourfocusonalcoholaccesswasnotdeepenoughtodrawanyconclusions.

YPLLisastatisticthatmeasuresthetotalnumberoflifeyearslostduetoprematuredeathinapopulationfromacertaincause.Prematuredeathisusuallydefinedasdeathattheageoflessthan65or75years,orlessthantheaveragelifeexpectancy(McDonnell,1998,andwww.musc.edu/bmt737/Spr_1999/pj/ypll.html).

Inourcalculations,theYPLLrateisthetotalyearsoflifelostduetoaspecificdiseaseinpersonsunder65dividedbythepersonsinthatCommunityAreawhoareunder65.Eachdeathisweightedbasedonitsdistancefromage65.

2005driver’slicensedata,whichreportsheightandweight,wasusedtoconstructmeasuresofbodymassindex,whichisanacceptedmeasureforobesity.BMIcalculationswereattheZipCodelevel.ZipCodesinhighdestinationareas,namelytheLoopandO’Hare,wereexcludedsoasnottoskewthefindings.

WeuseregressionanalysistocomparefoodratioscoresandtheirimpactonYPLLandBMIoutcomes,holdingeducation,income,andraceconstant.

Werecognizethe“timefactor”challengeinstudyingfoodaccesstohealthoutcomes.Howmuchtimeneedstopassforthelackofnutritiousfoodaccesstohaveaneffectoncommunityhealth?Forexample,lackofadequatenutritioninchildhoodmightnotbeevidentuntillaterinlife.Oritcouldbeevidentinchildhoodobesitypatterns.Thisisnotachallengewecancontrolforinthisstudy,nordowespeculateonthetheoreticalpossibilitiesoftimefactorsonhealthimpacts.Weprimarilycompare2006foodaccesspatternsto2003CityofChicagodeathdata,2005ChicagoStudybytheUniversityofMichiganobesityandhypertensiondata,and2005driver’slicensesdatatoconstructbodymassindexscores,astheyarethemostcurrentdataavailabletous.Webelievethesearevalidcomparisons.Healthconditionsdonotdevelopovernight,nordonewgrocerystoresorfastfoodrestaurants.

Abiggerchallengeincorrelatingneighborhoodcharacteristicsandhealthisthatneighborhoodeffectsare,bydefinition,endogenoustothecompositionalcharacteristicsofneighborhoods.Theprojectrecognizesthemanycomplexmethodologicalchallengesinisolatingcauseandeffectandholdingconstantpotentialstatisticalconfounders,suchasincome,race,genetics,culture,foodpreferences,andself-selectionintospecifictypesofcommunities.Weutilize


15

nationalandlocalbestmethodologicalpractices,takingdeliberatemeasurestomaximizetherobustnessandaccuracyofourdataandmodels,andexercisecautionandcareinstatingourfindings.Weareparticularlymindfulofwhathasbeencalledthe“EcologicalFallacy.”

Herewepresentascenario,notbasedonactualindividualleveldata,butonethatshouldbekeptinmindwhendescribingoutcomesandfindings.Withineachcommunityofourscenario,therelationshipbetweenaccesstogrocerystoresandhealthispositive(betteraccessbetterhealth).Therelationshipisalsopositiveacrosstheentirepopulation(ifwepoolobservationsfromallthreecommunities,theregressionlinewouldcoincidewiththeCommunity2regressionline).

Ifweonlyhaveaveragesbycommunity,wecouldstillconcludethattherelationshipispositive:theecologicalregressionlinethatbestfitstheaveragevaluesforthethreecommunities–(H1avg,G1avg),(H2avg,G2avg),(H3avg,G3avg)–slopesupward.Buttherelationshipisstrongerwhenweusecommunityaveragesthanwhenweusedataonindividuals(eitheranalyzingallindividualspooledoranalyzingindividualsseparatelybycommunity).Thisisbecausethecommunitieshaveverydifferentcompositions(Community1:highhealth/highaccess;Community2:moderatehealth/moderateaccess;Community3:lowhealth/lowaccess).

Basedontheecologicalregression,themostwecansayis“communitieswithbetteraverageaccesstogrocerystoreshavebetteraveragehealth”–wecannotsaythat“anindividualpersonwhohasbetteraccesstogrocerystoreswillhavebetterhealth”(i.e.weneedtolimitourgeneralizationstotheunitofobservation–thecommunityinthiscase–thatweareusing,andnotgeneralizetosmallerunitsofobservation–theindividual).

Therefore,whilewedemonstrateinthisstudythepositiveassociationbetweenaccesstobetterfoodsandbetterdiet-relatedhealthoutcomesandconditions,particularlyconcerningcertaindiseasesandraces,wemustsetourfindingsinthecontextofthechallengesandlimitationsoflinkingcauseandeffectandofpredicting,withcertainty,theexactstatisticalmagnitudeoftherelationship.Nonetheless,westandbyourconclusionthatfooddeserts–particularlythosewithhighconcentrationsoffastfoodrestaurants–poseserioushealthandwellnesschallengestotheresidentswholivewithinthemandtoChicagoasawhole.

COMMUNITY 1 REGRESSION



H3 avg

H2 avg

H1 avg

HEALTH

GROCERY STORE ACCESSG3 avg G2 avg G1 avg

COMMUNITY 1 DATA

COMMUNITY 2 DATA

COMMUNITY 3 DATA

“ECOLOGICAL”REGRESSION

Fig. 13


16

FINDINGSOverviewExamining the Impact of Food Deserts on Public Health in Chicago quantifiesdifferenttypesoffoodaccessatthelowestgeographiespossiblethenteststhetheorythatabalancedfoodenvironment–shorterdistancestopurveyorsofawidevarietyofhealthyfoodssuchasgrocers,andlongerdistancestolesshealthyoptionssuchasfastfood–isdirectlylinkedtobettercommunityhealthoutcomes.Thefocusontheconnectionsbetweenthebuiltenvironmentandcommunityhealthisonlybeginningtoemergeasaprofessionalfield,butitsfoundationaltheoryisnotcompletelynovel.YenandKaplan(1999)demonstratedtheimpactofthepresenceoflocalcommercialstoresonmortality:deathsweremorelikelyinplaceswithfewerstores,evenaftercontrollingforavarietyofindividual-levelcharacteristicsassociatedwithahighermortalityrisk.Inourstudy,wedevelopedwhatwecalltheSandGlassTheorytounderstandandplaceintocontextthedynamicandcomplexrelationshipsandconditionsthatimpacthealthoutcomesoveralifetime,foodaccessandfoodbalancebeingonlyonestrandofmanyinfluencingfactors.Ourbuiltenvironmentdataincludesthelocationsofchaingrocers,smallorindependentgrocers,andallgrocers,aswellasarangeoffastfoodoutlets.Thediet-relatedhealthoutcomesthatwestudyarecancer,specificcardiovasculardiseases(suchasheartdisease,hypertension,andstroke),diabetes,andobesity.Becausewepositthatthehealthandvitalityofanyurbancommunityisablock-by-blockphenomenon,webeginouranalysisattheblocklevel,movinguptoananalysisbycensustract,officialCityofChicagoCommunityAreas,andZipCodes.WealsoanalyzetheresultsofdirectmeasurementsofobesityandhypertensionandcomparethoseresultstoourFoodBalanceScores.Ourfindingsareorganizedinto4sections:QuantifyingFoodAccessPatterns,FoodBalanceScore,FoodBalanceandCommunityHealth,andaverybriefSummaryofFindings.

QuantifyingFoodAccessPatternsTheCityofChicagohas18,888census-definedblockswithnon-zeropopulations.Approximately7,000aremajorityWhite,7,300aremajorityAfrican-American,3,400aremajorityLatino,and900aremajoritydiverse,meaningthatnooneracehas50%ormoreofthepopulation.

Whenweanalyzefoodaccessbyraceattheblocklevel,wefindthatmajorityAfrican-Americancommunitieshavethelowestaccessto1)chaingrocerystores,

Fig. 14

Fig. 15

Northern vs. Southern Chicago


2)independentandsmallergrocerystores,and3)allgrocerystores,butaboutroughlyequalaccesstofastfoodrestaurantscomparedtootherracialgroupings.Forexample,fromanaverageAfrican-Americanblock,onewouldneedtotravelaboutone-thirdofamile(roughly3blocks)toreachafastfoodrestaurant,butanadditionalone-thirdofamilefarther(atotalof6blocks)toreachsometypeofgrocerystoreandslightlyfartheryettoreachachaingrocerystore,whichtypicallyoffersmorevarietyofhealth-sensitivefooditems,fromno-andlow-fatand-saltproductstolactose-reducedmilk.LookingattransitpatternsinmajorityAfrican-Americanareas,weseethatcarownershipiscomparativelylow.Relianceonpublictransportation,ridesfromfriends,andwalkingareoftencitedasmethodstoreachfoodvenues,sosmalldifferencesindistancecouldmakeforlargedifferencesindietarychoicesavailable,andpotentiallylargedifferencesinhealthoutcomes.

Thesesamefoodaccesspatternsarefoundatthetractlevel.Onaverage:

• PeoplewholiveinmajorityWhite,Latino,andDiversetractstraveltheshortestdistancetoanytypeofgrocerystore(about.39miles).

• PeoplewholiveinmajorityAfrican-Americantractstravelthefarthestdistancetoanytypeofgrocerystore(.59miles).

• InAfrican-Americantracts,thedistancetoasmallorindependentgroceristhefarthest(.81miles)andthedistancetoachaingrocerisslightlyless(.77miles).Thisdispelsthemyththatsmallerand/orindependentgrocersaremorelikelythanchaingrocerstolocateinAfrican-Americancommunities.

• ThedistancetofastfoodisslightlyfartherinAfrican-Americantractsthanothertracts,butagrocerystoreisnearlytwiceasfar.Thismeansthat,forAfrican-American,itismucheasiertoaccessfastfoodthanothertypesoffood.

White

African-American

Hispanic

Asian Grocer Fast Food

17

City of Chicago Blocks by Majority RaceWhiteAfrican-AmericanLatinoDiverseTotal

Number of Blocks

7,0997,3973,473

91918,888

Percent

37.6239.1018.404.87

100.00

MajorityRaceWhiteAfr.-Am.LatinoDiverseChicago

ChainGrocers.57.77.62.52.65

SmallGrocers.54.86.42.53.62

AllGrocers.39.59.36.36.45

FastFood.35.34.34.30.34

Food Access by Avg. Distance in Miles by City of Chicago Block

Fig. 16

Fig. 17

Fig. 18

MajorityRaceWhiteAfr.-Am.LatinoDiverse

ChainGrocers.57.77.57.60

SmallGrocers.52.81.52.53

AllGrocers.39.58.39.38

FastFood.28.32.28.26

Food Access by Avg. Distance in Miles by City of Chicago Tract

TotalPop.(rounded)

1,099K

985K1,099K

167K

Avg.Income$52,334$27,485$33,437

$33,340

Fig. 19

Relative Access to Grocers and Fast Food in Chicago by Race

Thereare8majorityAsiantractsintheCityofChicagowhicharenotincludedinthistable.


BecauseChicago’sblocks,tracts,andcommunitiestendtobesegregatedbyrace,weseetheimpactofthesepatternsgeographicallyinthefollowingseriesofmaps.TheNorthsideofthecitytendstohavegreateraccesstoalltypesofgrocers,whiletheWestandSouthsidestendtohavelessaccess.LowaccesstractsontheSouthsidetendtocluster,formingfooddeserts.Fooddesertsarelargegeographicareaswithno,few,ordistantgrocerystores(LangandRayner1998;Whitehead1998;Fureyetal.2001;LangandRayner2002;Wrightetal.2005,Gallagher2006).

Innon-African-AmericantractsandontheNorthsideofChicagogenerally,thereisagreaterconcentrationoffastfood.

However,muchoftheconcentrationoffastfoodontheNorthSideisin“destination”entertainmentandeatingareas,wherethereisheavyvisitortraffic,andawidevarietyofnon-fastfoodrestaurantsandgrocerystores,resultinginmorefoodchoice.Innon-African-Americanmajoritytracts,thedistancetoagrocerystoreorfastfoodrestaurantisroughlyequal;fastfoodrestaurantsareonlyslightlycloserindistance.Thismeansthat,inmajorityWhite,Latino,anddiversetracts,therearemorechoiceswhenitcomestofood:itisalmostaseasytoaccessagrocerystoreasitistoaccessafastfoodrestaurantoranothertypeofrestaurant.Generally,bothgrocerystoresandfastfoodrestaurantsarepositivelycorrelatedwithincomepatterns:thehighertheincomeoftheconsumersinthatarea,thehighertheconcentrationofalltypesoffoodvenues.YetwefindthatfastfoodisofteninverselycorrelatedwithincomeincertainAfrican-Americanblocks,tracts,andcommunities:asincomegoesdown,grocerystoreconcentrationgoesdown,butfastfoodconcentrationgoesup.Furthermore,usingdifferentgeographicunitsandmethodsofanalysis,weconsistentlyfindthatAfrican-Americancommunitiesaremuchmorelikelytoclusterintofooddesertswherefastfoodoutletsaremoreprevalentthangrocers.

InFig. 24, weseethestarknessofthefooddesertpatternwhenweviewthefarthestdistancetertileoffoodaccesstoourcategoryofallgrocers.Inotherwords,weareshowingthe“worst”scoringthirdofcensustractsintheentirecity,whereonehastotravelthefarthestonaveragetoreachagrocerystore,andwecodethetractsbymajorityrace.

Distance to Chain Grocer by Tract

Distance to Small Grocer by Tract

Distance to Any Grocer by Tract

18

Fig. 20

Fig. 21

Fig. 22

ClosestDistance

AverageDistance

FarthestDistance

ClosestDistance

AverageDistance

FarthestDistance

ClosestDistance

AverageDistance

FarthestDistance


Distance to All Grocers by Farthest Distance Tertile Only by Race by Tract

1ROGERSPARK2WESTRIDGE3UPTOWN4LINCOLNSQUARE5NORTHCENTER6LAKEVIEW7LINCOLNPARK8NEARNORTHSIDE9EDISONPARK10NORWOODPARK11JEFFERSONPARK12FORESTGLEN13NORTHPARK14ALBANYPARK15PORTAGEPARK16IRVINGPARK17DUNNING18MONTCLARE19BELMONTCRAGIN20HERMOSA21AVONDALE22LOGANSQUARE23HUMBOLDTPARK24WESTTOWN25AUSTIN26WESTGARFIELDPARK27EASTGARFIELDPARK28NEARWESTSIDE29NORTHLAWNDALE30SOUTHLAWNDALE31LOWERWESTSIDE32LOOP33NEARSOUTHSIDE34ARMOURSQUARE35DOUGLAS36OAKLAND37FULLERPARK38GRANDBOULEVARD39KENWOOD

40WASHINGTONPARK41HYDEPARK42WOODLAWN43SOUTHSHORE44CHATHAM45AVALONPARK46SOUTHCHICAGO47BURNSIDE48CALUMETHEIGHTS49ROSELAND50PULLMAN51SOUTHDEERING52EASTSIDE53WESTPULLMAN54RIVERDALE55HEGEWISCH56GARFIELDRIDGE57ARCHERHEIGHTS58BRIGHTONPARK59MCKINLEYPARK60BRIDGEPORT61NEWCITY62WESTELSDON63GAGEPARK64CLEARING65WESTLAWN66CHICAGOLAWN67WESTENGLEWOOD68ENGLEWOOD69GREATERGRANDCROSSING70ASHBURN71AUBURNGRESHAM72BEVERLY73WASHINGTONHEIGHTS74MOUNTGREENWOOD75MORGANPARK76O’HARE77EDGEWATER

Chicago Communities Chicago’s Food Deserts by Tract with Community Boundaries

9

10 1112

13

21

477

314

1715 16

5 6

18 19 20

21

22 7

2523 24 8

26 27 28 32

3334

3031

56

64

57

62

65

63

58

5960 35

36

37 38 39

40 41

4266

67

61

68

6943

44 45 46

47 48

70 71

72 73

7475

49 50

51

53

52

54 55

FoodDesert

Fig.

19

Distance to Fast Food by Tract

Fig. 23 Fig. 24

ClosestDistance

AverageDistance

FarthestDistance

WhiteAfr.-Am.LatinoAsianDiverse

NotinGrouping

29

Note: In this map, red indicates closest distance.


20

Chicago’s Highest Concentration of Single Mothers with Children Under 18

as a Percentage of Total Population by Tractwith the Food Desert Overlay

Highestthirdconcentrationofsinglemothers

FoodDesert

Chicago’s Highest Concentration of Grandparents with Primary

Responsibility for Children Under 18 as a Percentage of Total Population

by Tract with the Food Desert Overlay

Highestthirdconcentrationofgrandparents

FoodDesert

Chicago’s Highest Concentration of Children Under 18

as a Percentage of Total Population by Tractwith the Food Desert Overlay

Highestthirdconcentrationofchildren

FoodDesert

Chicago’s Highest Concentration of Disabled Adults

as a Percentage of Total Population by Tract with the Food Desert Overlay

Highestthirdconcentrationofdisabledadults

FoodDesert

Chicago’s Highest Concentration of Disabled Children

as a Percentage of Total Population by Tract with the Food Desert Overlay

Highestthirdconcentrationofdisabledchildren

FoodDesert

Fig. 26 Fig. 27

Fig. 28 Fig. 29 Fig. 30


WecaneasilyseethatAfrican-Americantractsdominatethefooddesertmapandthattheyclusterdramatically.Themostvulnerablepopulationsaresinglemothersandchildren.Wealsoseeconcentrationsofgrandparentswithprimaryresponsibilityforgrandchildrenunder18yearsofage,andfordisabledpopulations.WhiledisabledchildrenaremoredisbursedthroughoutChicago,asthosechildrenage,andasadultsbecomedisabled,weseeagreaterconcentrationofthatpopulationinthefooddesert.

ItisalsoimportanttoaccountforwhatwewouldexpectthepatternsofWhite,African-American,Latino,anddiversetractstobeiftheyweredistributedequallyacrossthecitybytheirrespectiveweightsrelativetofoodaccessandfoodbalance.Analyzingthedetailsofallthreegrocerdistancetertilesandillustratingthembypercentagepointdifferentialsallowsustodothat.Italsoallowsustomoveawayfromsimpleaveragesacrossraces,whichcanmaskextremepatternswithinracialgroupings.

Thefarthestdistancetertilechartisanalternativedisplayofthedataonthefarthestdistancetertilemapthatconfirms–notaccountingforotherinfluencerssuchasincomeandstorelocationstrategies–thatAfrican-Americanmajoritytractsareover-representedinleast-access-to-grocery-storeoutcomes,basedonthetotalnumberofAfrican-AmericantractsinChicago.Inotherwords,wecalculatedthepercentageoftractsineachtertilebymajorityrace.Thenwecalculatedwhatthedistributionbyracewouldbeineachtertileifeachracewererepresentedinproportiontothetotaltractsofthatraceacrossthecity.Diversetractsareunder-representedintheworstoutcomestertile.Insomecases,however,diversetractsdoindeedhavenegativeoutcomesconcerningaccesstogrocerystores,buttheytendtobedispersed,notclustered,withothersimilarscoringtracts.Whitetractsareunder-representedinthe“worstoutcomes”grouping.Latinotractsscoreroughlywhereweexpectthemto–theyareonlymarginallyunder-representedinthetertile.Butwhenweanalyzetheshortestdistancetertilewiththeclosestorbestaccesstogrocerystores,weseethatLatinotractsareover-representedby31percentagepointsandthatdiversetractsareover-representedby53percentagepoints.Whitetractsareonlymarginallyover-representedandAfrican-Americantractsareunder-representedby23percentagepoints.

21

White 58 19.40% 35.34% -15.94%Afr.-Am. 189 54.00% 41.37% 12.63%Latino 21 13.73% 18.09% -4.38%Diverse 8 21.05% 4.49% -16.58%Theaveragedistanceinthisgroupingtoanygrocerystorewas.78miles.Therangewas.62to1.33miles.

Average Distance to All Grocery Stores by Chicago Census Tracts by Tertiles

Farthest Distance (worst outcomes)

White 118 38.80% 35.34% 3.45%Afr.-Am. 99 28.29% 41.37% -13.09%Latino 57 37.25% 18.09% 19.17%Diverse 8 21.05% 4.49% 16.58%Theaveragedistanceinthisgroupingtoanygrocerystorewas.41miles.Therangewas.38to.54miles.

Average Distance

White 125 41.81% 35.1% 6.71%Afr.-Am. 62 17.71% 41.1% -23.37%Latino 75 49.02% 18.0% 31.06%Diverse 22 57.89% 4.5% 53.43%Theaveragedistanceinthisgroupingtoanygrocerystorewas.25miles.Therangewas.07to.22miles.

Note: Therewere8Asianminoritycommunitiesinthisanalysis:zerointheshortestgrouping,2inthemiddlegrouping,and6inthefarthestgrouping.

Shortest Distance (best outcomes)

Number Percentage Whatpercentage Percentage oftracts oftotalin wouldbe point (848total) grouping ifequally differential distributed

Percentage Point Differential of Average Distance to All Grocery Stores

in Miles by Chicago Census Tracts

White African-American Latino Diverse -15.94% 12.63% -4.38% -16.58º%

20%

10%

0%

-10%

-20%

Farthest Distance Tertile

Fig. 31

Fig. 32


Insummary:• MajorityWhitetractshaveaboveaverageaccessto

grocerystores.OfallWhitetractsthatfallinthefarthestdistance(worst)tertile,onlyahandfulcluster,andtheyonlyclustermarginally.Combined,thereare219,265totalpeoplewholiveinthesefarthestdistanceWhitetracts;25.2%or55,261arestudentsinschoolthreeyearsofageandolder.

• MajorityLatinotracts,insomecases,havefaraboveaverageaccesstogrocerystores,andfordiversetractsaccessvariesfromverylowtoveryhigh,althoughweseeoverallaccessishigh.Somediversetractsarestable,whileothersmightbeeitherdecliningorgentrifying.Thereare72,849totalpeoplewholiveinthemajorityLatinofarthestgrocerdistancetracts;22,021arestudentsinschoolthreeyearsofageandolder.Formajoritydiversetracts,thereare25,329totalpeoplewholiveinthefarthestgrocerdistancetractsand30.2%or9,618arestudentsinschoolthreeyearsofageandolder.

• MajorityAfrican-Americantractshavetheleastaccesstogrocerystoresandthosetractsclusterstrikingly.Thereare521,488totalpeoplewholiveinthefarthestdistancemajorityAfrican-Americantracts–almosttwicethenumberofthefarthestdistancepopulationinWhite,Latino,anddiversetractscombined.OfthepopulationinAfrican-Americanfarthestdistancetracts,oneoutofthreeor172,082arestudentsinschoolthreeyearsofageandolder.

WehavealreadydemonstratedthatAfrican-Americanblocksandtracts1)haveloweraccesstogrocerystoresand2)thattheyhaveampleaccesstofastfoodrestaurants.Butdoareasthathavetheverylowestaccesstogrocerystoreshavecomparativelyhighratesoffastfoodrestaurants,andwhat,ifany,effectmightthathaveoncommunityhealth?

TheFoodBalanceScoreOurresearchobjectiveistocomparefoodaccessanddiet-relatedhealthoutcomesacrossraces,holdingotherinfluencersconstantsuchasincomeandeducationtothedegreepossiblegiventimeandresourceconstraints.Wetestthetheorythatabalancedfoodenvironmentisanimportantkeytocommunityhealth.Inotherwords,dofooddeserts(areaswithnoordistantgrocerystores)facenutritionalchallengesevidentindiet-relatedhealthoutcomes,anddooutcomesworsenwhenthefooddeserthashighconcentrationsofnearbyfastfoodalternatives?

22

Percentage Point Differential of Average Distance to All Grocery Stores

in Miles by Chicago Census Tracts

White African-American Latino Diverse 6.71% -23.37% 31.06% 53.43%

60%

40%

20%

0%

-20%

-40%

Shortest Distance Grouping

Fig. 33


Totestourcoretheorythatfoodvenuebalancemattersinhealthoutcomes,wedevelopedaratioscore:thedistancetotheclosestgrocerdividedbythedistancetotheclosestfastfoodvenue.Theaverageratiofortheentirecity,aZipCode,aCommunityArea,oracensustract,istheweightedaverageoftheratiosforeachblock,withgreaterweightgiventoblockswithlargernumbersofresidents.WecallthisratiotheFoodBalanceScore.

Fig. 35illustratesthehighestscoringFoodBalanceScoretertileoftractsintheCityofChicago.Inotherwords,weshowthe“worst”scoringtractsthatarethemostout-of-balanceintermsoffoodaccess.Thesearetheareaswithnoordistantgrocerystores,butcomparativelynearbyaccesstofastfoodrestaurants.

WeseeagainthestrongrepresentationandclusteringofmajorityAfrican-Americantractscomparedtoothertractsbyracialgroupings,particularlyonChicago’sSouthside.

African-AmericantractshavethehighestFoodBalanceScoreintheworstscoringtertileamongallracesandthegrocerystoredistanceforthosetractsisparticularlyfar–aquarterandathirdasfardependingonthetypeofgrocerystore.African-Americantracts,thoughtheyhavethelowestaveragemedianhouseholdincome,haveroughlythesameaccesstofastfoodrestaurantsastheotherracialgroupingsinthistertile(.25milesforAfrican-Americantracts,but.25fordiversetracts,.19forLatinotracts,and.18forWhitetracts).Whatmighttheimpactofthisout-of-balancefooddesertbeonhealthoutcomesforAfrican-Americans,andisthereahealthimpactforothermajorityracetractsthathavehighFoodBalanceScoresbutdonotcluster,oronlyclustermarginally?

FoodBalanceandCommunityHealthWeknowthattheracialdisparityinhealthintheUnitedStatesissubstantial.TheoveralldeathrateforAfrican-AmericanstodayiscomparabletotherateofWhites30yearsago(WilliamsandJackson,2005).WecanseethosedisparitiesbylookingathealthdatabyraceforChicagotractswithatleast20diet-relateddeathspertractfor2003.Whethermeasuredbyincome,education,oroccupation,socioeconomicstatus(SES)isastrongpredictorofhealthoutcomesandhealthvariationsamongracialgroups(Marmot,2002,WilliamsandJackson,2005).ManyadditionalfactorsbesidesSEScontributetopoorhealthandprematuredeath,suchasfoodpreference,genetics,andculture.Towhatdegree,ifany,doesfoodaccesscontributetonegativehealthoutcomes?

23

Highest Scoring Food Balance Score by Race by Tract

Food Balance Scores

Food Balance Scores

Far Above 1: high score “worstoutcomes”–closertofastfood,fartherfromgrocers

Around 1: average score “averageofbalancedoutcomes”–equalaccesstogrocersandfastfood

Far Below 1: low score“bestoutcomes”–closertogrocers,fartherfromfastfood

Examples

Grocerystoreis1mileaway,andafastfoodrestaurantis.5milesaway1/.5 = 2

Grocerystoreis1mileaway,andafastfoodrestaurantis1mileaway1/1 = 1

Grocerystoreis.5milesaway,andafastfoodrestaurantis1mileaway.5/1 = .5

Food Access by Distance in Miles by Highest Scoring Food Balance Score

by Tertile (worst outcomes)

By Majority Race of Tract

White

Afr.-Am.

Latino

Diverse

Food Balance Score

3.00

3.37

2.62

2.29

Average Household Income

$55,293

$26,505

$33,451

$36,024

Fig. 34

Fig. 35

Fig. 36

WhiteAfr.-Am.LatinoAsianDiverse

NotinGrouping


Weanalyze226tractswithatleast20deathsfromdiet-relatedcausespertractforyear2003.Ofthose226tracts,100aremajorityWhiteand97aremajorityAfrican-American.ThoseaggregatenumberswerelargeenoughtoanalyzeWhiteandAfrican-Americantractsfurther,controllingforraceandotherinfluencers,bysortingthemfirstbyraceandsecondbyfoodbalance,thensplittingthematthemedian.Thisresultsinfourgroupings:twoWhitegroups,tocomparetoeachother,andtwoAfrican-Americangroups,tocomparetoeachother.

Theout-of-balanceWhitetractgroup(withaFoodBalanceScoreof1.91)hasaslightlyhigherdiet-relateddeathrate(6.10diet-relateddeathsperthousand)thanthein-balanceWhitetractgroup(whichhasaFoodBalanceScoreof.96and5.96diet-relateddeathsper1,000),eventhoughincomeandeducationareslightlymorefavorableintheout-of-balancegroup.Thisisnotahugedifference,butitmovesinthedirectionofourtheory.Theout-of-balanceAfrican-Americantractgroup(withaveryhighFoodBalanceScoreof2.71)hasaconsiderablyhigherdiet-relateddeathrate(7.55diet-relateddeathsperthousand)thanthemorein-balanceAfrican-Americangroup(whichhasaFoodBalanceScoreof1.17and6.65diet-relateddeathsper1,000),eventhoughincomeandeducationareaboutthesame.Thisisalmostafullpercentagepointdifferential;itisabignumber.Wealsoseethatfortheout-of-balanceAfrican-Americangroup,thereisahighproportionofsinglemotherswithchildrenunder18yearsofage.

Thedatasuggestthattherecouldbeapositiverelationshipbetweenfoodbalanceandhealthoutcomes,withabiggerimpactonAfrican-Americantracts,asAfrican-AmericanincomesoverallarelowerthanWhiteincomes,meaningthattheyhavelessabilitytocompensateforlowgeographicaccesstogrocerystores.Saidanotherway,African-AmericansareprobablymorereliantonpublictransportationandhavelesseconomicabilitythanWhitestodrivebycarorcaboutoftheircommunitiestoagrocerystorewherehealthyfoodpurchasescanbemade.Thoseclarifyingassumptionsaside,itappearsthatfoodbalancedoesaffectdiet-relatedhealthoutcomesinbothAfrican-AmericanandWhitetracts.

24

Fig. 37

African-American and White Tract Groupings with 20 or More Diet Related Deaths in 2003 by Food Balance Scores

African-American White Tracts Tracts

7

6

5

4

3

2

1

0

Worst Food Balance

Better Food Balance

7.536.69

6.10 5.96


Wealsoseehealthdisparitiesamongraceswhenweanalyze2003diet-relatedcardiovasculardisease(CVD)deathratesbyhigh,middle,andlowscoringtertilesbyCommunityArea.African-Americancommunitiesarefarover-representedintheworsthealthoutcomestertile,andfarunder-representedinthebesthealthoutcomestertile.WhiteandLatinocommunitiesareunder-representedintheworsthealthoutcomestertileandover-representedinthebesthealthoutcomestertile.Diversecommunitieshaveanequaldistributionintermsofhealthoutcomes.

Wecalculateaverageyearsofpotentiallifelostanddeathratesfor2003forcancer,diet-relatedcardiovasculardisease,anddiabetesbyCommunityArea.Yearsofpotentiallifelost(YPLL)isastatisticthatmeasuresthetotalnumberoflifeyearslostduetoprematuredeath–notjusttherateofdeath–inapopulationfromacertaincause.WeseethatmajorityAfrican-Americancommunitieshavethegreatestnumberofyearsoflifelostforcancer,cardiovasculardisease(CVD),anddiabetes.Forexample,lookingatCVD,theaverage

25

Data is from the 2000 census unless otherwise noted.

Indicator

Food Balance Score

Diet-related deaths per thousand people of tracts with 20 or more diet-related in 2003 Cook County death records

Median household income

Total population

Females 15-34 years old

Single females with children under 18

Population over 21 with a disability

Number of grandparents responsible for grandchildren under 18

Percentage of 25+ population with a high school graduation or higher

Percentage of 25+ population with a BA degree or higher

Median age

50 Majority White Tracts with Higher Food Balance Scores

1.91

6.10

$50,864

304,382

49,576

4,748

48,843

2,007

82.10

34.58

37.6

50 Majority White Tracts with Lower Food Balance Scores

0.96

5.96

$47,211

316,015

47,462

4,672

54,818

2,722

79.37

27.12

38.0

49 Majority African-American Tracts with Higher Food Balance Scores

2.71

7.55

$33,323

237,934

31,764

12,217

45,602

3,049

71.16

12.70

33.2

48 Majority African-American Tracts with Lower Food Balance Scores

1.17

6.65

$32,625

287,846

42,251

17,847

58,210

3,762

72.35

14.92

33.3

High CVD Grouping Percentage Point Differential by Racial Goupings

of Chicago Communities

White African-American Latino Diverse -24.8 36.1 -11.8 3.1

40%

20%

0%

-20%

-40%

Worst Health Outcomes

Fig. 38

Fig. 39


African-Americancommunityin2003lostatotalof337yearsthatindividualsfromthecommunitywouldhavecollectivelylivedhadtheynotdiedprematurelyfromCVD.ThedeathrateforCVDwas8.07per1,000people–farmorethantwicetherateofanyotherracialgrouping.Infact,ineverysingleinstance,African-Americancommunitieshavetheworsthealthoutcomes.Towhatdegree,ifany,istheFoodBalanceEffectacontributortonegativehealthoutcomesofAfrican-Americanandothercommunities?

26

Low CVD Grouping Percentage Point Differential by Racial Goupings

of Chicago Communities

White African-American Latino Diverse 13.8 -28 12.5 1.3

20

10

0

-10

-20

-30

Best Health Outcomes

Tofurtherprobethisimportantquestion,ourfirsttaskistocompareYPLLoutcomestolevelsoffoodbalance.Inotherwords,wetakeeachofthe75CommunityAreasinouranalysisandrankthembytheirFoodBalanceScore.Higherscoresaremoreout-of-balancecommunitieswithnoordistantgrocerystoresbutnearbyfastfoodrestaurants.Whileallwecanshowisapositiveassociativepattern–notnecessarilycauseandeffect–weseethat,astheFoodBalanceScoreincreases,YPLLanddeathratesincreaseforeachdiet-relateddisease.Furthermore,thebestfoodbalancegroupinghashealthoutcomesthatareabovetheaverageofallcommunitiescombined.

Chicago Community Areas Years of Potential Life Lost and Death Rates Per 1,000 Population by Diseases by Race

Cancer Cancer CVD CVD Diabetes Diabetes YPLL DR YPLL RD DR

White 212 7.56 173 5.49 15 0.42

Afr.-Amer. 299 10.28 337 12.18 45 1.44

Latino 206 4.70 177 4.02 31 0.75

Diverse 281 5.55 295 5.86 50 1.01

Average 255 7.94 258 8.07 35 0.98

Chicago Community Areas Average Years of Potential Life Lost and Death Rates Per 1,000 Population by Diseases by Race by Food Balance Groupings

Food Balance Cancer Cardiovascular Disease Diabetes Food BalanceGroupings YPLL Death Rate YPLL Death Rate YPLL Death Rate All YPLL ScoreWorst 314.44 9.73 345.28 11.07 45.48 1.27 705.20 2.04

Middle 246.88 7.42 241.76 7.41 33.48 1.11 522.12 1.25

Best 204.04 6.68 185.48 5.72 25.36 0.56 414.88 0.87

Fig. 40

Fig. 41

Fig. 42


27

Average YPLL by Food Balance by Chicago Community

Cancer CVD Diabetes

300

200

100

0

High Score (worst)


Low Score (best)

Cancer YPLL by Chicago Community Area

CVD YPLL by Chicago Community Area

Diabetes YPLL by Chicago Community Area

Cancer YPLL by Chicago Community Area

by Food Balance Scores

CVD YPLL by Chicago Community Area


Diabetes YPLL by Chicago Community Area


Fig. 43

Fig. 44

Fig. 45

Fig. 46

Fig. 47

Fig. 48

Fig. 49

LowCancer

MediumCancer

HighCancer

LowCVD

MediumCVD

HighCVD

LowDiabetes

MediumDiabetes

HighDiabetes

WorstAverageBest

Food Balance Score

WorstAverageBest

Food Balance Score

WorstAverageBest

Food Balance Score


28

Percent Average Food Balance YPLL YPLL YPLL Total Food Balance high school Percent household Score Groups Cancer CVD Diabetes YPLL Scores or higher BA or higher income Highest scoring tertile, but of those 354 397 52 804 2.04 69.5 14.4 $15,464 20 African-American communities only

Average scoring tertile, but of those 231 264 39 534 1.24 72.4 17.5 $16,422 9 African-American communities only

Average Years of Potential Life Lost by Community Area by Race

Race,education,andincomeareimportantpredictorsofhealthoutcomesthatwealsoneedtoaccountfor.WhenwesortallcommunitiesbyFoodBalanceScoresintotertiles(thebest,middle,andworst),ourabilitytosortwithintertilesbyracediminishesbecauseoftheskeweddistributionacrosstertilesbyrace.Forexample,intheworstFoodBalancetertile,thereare20majorityAfrican-Americancommunitiesbutonly2majorityWhitecommunities,1majorityLatinocommunity,1majorityAsiancommunity,and1majoritydiversecommunity.Tocontrolforrace,thebestwecandoiscomparetwomajorityAfrican-Americangroupings–oneAfrican-Americangroupingintheworstfoodbalancetertile(20communities)tooneAfrican-Americangroupingintheaveragebalancetertile(9communities).Again,theworstfoodbalancetertilehasnoordistantgrocerystoresbutnearbyaccesstofastfoodrestaurants.Theaverageorin-balancefoodaccesstertilehasmoreofanequaldistanceoftheclosestgrocertotheclosestfastfoodoutlet.IneachcaseofthosetwoAfrican-Americangroupingcomparisons,theaverageYPLLbycancer,diet-relatedcardiovasculardisease,anddiabetesareallsubstantiallyhigherinthegroupingwiththeworstFoodBalanceScore.WhenwelookattotalYPLL,weseethatthedifferencebetweenin-balanceandout-of-balancemeansanincreaseinprematurediet-relatedyearsoflifelostbymorethan50%.Income,alsoaninfluenceronhealthoutcomes,isroughlyequalinbothgroupings.Educationattainmentmeasures,however,areslightlylowerintheworstFoodBalanceScoregrouping.Whilewecannotbecertainofadirectlink,thedataagainsuggestthatthereisapositiverelationshipbetweentheFoodBalanceScoreanddiet-relatedyearsoflifelostminimizingotherpossibleinfluencers.Inotherwords,ascommunitiesbecomemoreout-of-balancewithfoodpurchasingvenues,negativediet-relatedhealthoutcomesconsistentlyincrease.

Fig. 50


WeconductedaregressionanalysisbyChicagoCommunityAreaforyearsofpotentiallifelost(YPLL)controllingforrace,education(percentagehighschoolgraduateorhigher)andpercapitaincometostudytheeffectoffoodbalanceondiet-relatedcommunityhealth.WeappliedourFoodBalanceScore:theclosestdistancetoagrocerdividedbytheclosestdistancetoafastfoodrestaurantattheblocklevel,aggregatedup,block-by-block,totheCommunityArealevel.FordiabetesYPLL,thecoefficient(Dgrocery/Dfastfood)isintheexpectedlineardirection(positive)forallbutdiversecommunitiesandisstatisticallysignificantformajorityAfrican-Americancommunities(atthe5%level,meaningwecanconfidentlyaccountfor95%oftheregressionformajorityAfrican-Americancommunities)andformajorityWhitecommunities(atthe10%level,meaningwecanconfidentlyaccountfor90%oftheregressionformajorityWhitecommunities).Thismeansthatwecanbereasonablyconfidentaboutthedirectionoftheassociationandthestrengthofthestatisticalrelationship,especiallyforAfrican-Americancommunities.TheimpactoffoodbalanceonpredicteddiabetesYPLLcanbeseeninthefiguresatright.ForbothAfrican-AmericanandWhitecommunities,theregressionsindicatethatastheFoodBalanceScoreincreases,YPLLfromdiabetessteadilyincreases.Saidanotherway,majorityAfrican-AmericanandWhitecommunitiesthathavenoordistantgrocerystores,butnearbyaccesstofastfoodrestaurants,willhavestatisticallyhigherratesofresidentsdyingprematurelyfromdiabetes,andthatAfrican-Americancommunitiesarethemostlikelytoexperiencethegreatestlifelostintotalyearsduetothenumbersofthemlivinginaffectedareasandduetothestrengthoftheregression.

ForcancerYPLL,thecoefficientfor(Dgrocery/Dfastfood)ispositiveforAfrican-American,White,andLatinocommunities(meaningthatascommunitiesbecomemoreout-of-balance,diet-relateddeathsincrease)buttheseeffectsarenotstatisticallysignificant.Thesameistrueforcardiovasculardisease.Forchronicliverdiseaseandcirrhosis,theeffectispositiveandstatisticallysignificantonlyforAfrican-Americancommunities(atthe10%level,meaningwecanconfidentlyaccountfor90%oftheregressionforAfrican-Americans).Wehadn’texpectedtofindarelationshipbetweenchronicliverdiseaseandcirrhosisandtheFoodBalanceScore.Instead,weexpectedthataccesstoalcoholwouldbethemostusefulmeasuretotestahealtheffect.Webeganthatanalysisthroughthelensofliquorlicensedata,butmoreworkneedstobedonetoisolateandunderstandpotentialimpactsfromthetypesofalcoholaccess.

29

3.34.80

82

27

Food Balance Score

FittedValuesforDiabetesYPLL

Predicted Diabetes for Majority African-American Communities

Predicted Diabetes for Majority White Communities

Liver Disease and Cirrhosis Years of Potential Life Lost by Chicago

Community Area

Fig. 51

Fig. 52

Fig. 53

LowLiverDisease

MediumLiverDisease

HighLiverDisease

2.26.72

39

4.88

Food Balance Score

FittedValuesforDiabetesYPLL


Alcoholispurchasedandconsumedinmanyvenues,includingsmallliquorstores,chaingrocerystores,pharmacies,andsit-downrestaurants.Generally,ourfocusonalcoholaccesswasnotdeepenoughtodrawanyconclusions.However,inthefuture,wemighttestthetheorythatalcoholaccessfromliquorstoresasopposedtoothervenuesmightbepositivelycorrelatedtotheFoodBalanceScore.Inotherwords,asaccesstogrocerystoresdecreases,andaccesstofastfoodincreases(anout-of-balanceconditionandthusahighFoodBalanceScore),accesstoliquorstoresmightalsoincrease.

Ourstudyalsohadaccesstodatafromanextensiveresearchprojectthatincludesdirectmeasurementsonobesityandhypertensionfrom2001to2003.Fieldtechniciansactuallymeasuredtheseoutcomes;theywerenotself-reportedasinasurvey.Moreworkneedstobedonetolinkindividual-levelhealthdatatoindividual-levelfoodaccessdata.However,fromtheuniverseofapproximately3,000observationsfromthisrepresentativeChicagosample,wewereabletoanalyzeresultsbyCommunityAreaforthe23communitieswith50ormoreobservationseach.Notcontrollingforrace,wedividethose23communitiesatthemedianbyFoodBalanceScores.Whilewecannotcompletelycontrolforeducationandincomeeither,weseethattherelationshipbetweenFoodBalanceScoresandnegativehealthoutcomesrepeatsitself.Onaverage,communitieswithmoreout-of-balancefoodchoiceshave24%higherratesofobesityand27%higherratesofhypertension.

30

Wecontinueourstudyofobesitywithamorerobust,albeitself-reported,sampleofheightandweightfromall2005driver’slicenserecordsbyZipCode.Heightandweight,includedinthoserecords,allowsthecalculationofbodymassindex(BMI),anacceptedmeasureforobesity.Themapatleft(Fig. 54)showsastrikingclusteringpattern:Chicago’sNorthandNorthwestsideshavethelowestratesofBMIandtheWestandSouthsideshavethehighestratesofBMI.Thedataisgroupedintoequaltertiles,notbyempiricaldefinitionsofobesityascalculatedbyBMI.

Body Mass Index (Obesity) by Food Access Scores

by Zip Code

Dividedatthe Grocerto Household Percent Percent Percent PercentmedianbyFood fastfood income highschool BA obese hypertensiveBalanceScores score orhigher orhigher

Moreout-of-balancegrouping 1.54 $21,529 74% 23% 31% 33%(worstoutcomes)

In-balancegrouping .95 $25,928 73% 34% 25% 26%

Fig. 54

Fig. 55

Food Balance ScoreWorstAverageBestExcluded

HignBMI

AverageBMI

LowBMIExcluded


31

Itisdifficulttocompareoutcomesinthisfashionacrossorwithinracesbecauseofdifferencesinincomeandeducation,andbecauseoftheaveragingacrosssuchlargegeographies.BecausewearerestrictedtotheaverageBMIattheZipCodelevel,muchofthenuanceislost.Butthepatternisconsistentwithouroverallfindings.Weseethat,comparingtertiles,ascommunitiesbecomemoreout-of-balanceinfoodchoices,BMIincreases.

WhenweruntheregressionforBMIcontrollingforincomeandwhethertheZipCodeTabulationArea(ZCTA)hadamajorityofAfrican-Americans,amoreout-of-balancescorewasassociatedwithahigheraverageBMIthatwasstatisticallysignificant.Theregressiondoesnotcontrolforeducationbecause,asFig.57shows,thereisaverystrong,positivecorrelationbetweenincomeandeducationattheZCTAlevel.Becauseofthis,incomeandeducationcannotbeenteredintotheregressionsimultaneously.

Wealsofoundthatifeithertheaveragedistancetoagrocerortheaveragedistancetoafastfoodoutletisusedinsteadoftheratioofthesedistances(theFoodBalanceScore),bothindependentlyhaveapositiveandstatisticallysignificantassociationwithahigherBMI.Buttheregressionshowedthatifbothoftheseaveragedistancesareenteredatthesametime,onlytheaveragedistancetoagrocershowsupashavingastatisticallysignificantrelationshiptoBMI:aZCTAwheretheaveragedistancetoagrocerisonemilegreaterwillhaveanaverageBMIthatis1.643greaterthantheBMIinanotherwiseidenticalZCTA.

Median Percent Grocer to household 4 years Tertile grouping fast food income of college by Zip Codes score 1999 or more BMI

Highest scoring 2.22 $43,957 15 25.75

Average scoring 1.26 $41,234 16 25.30

Lowest scoring 0.94 $42,681 21 24.93

Median Percent # of Food household 4 years Zip Majority Balance income of college Codes race Score 1999 or more BMI

26 White 1.39 $52,467 24 24.58 14 African- 1.74 $29,899 9 26.50 American

5 Latino 1.03 $28,181 6 21.59

6 Diverse 1.60 $37,003 14 25.34

Fig. 56

Fig. 57

BMI Weight Status

Below18.5 Underweight

18.5–24.9 Normal

25.0–29.9 Overweight

30.0andAbove Obese

We remind our readers of the important point that equal tertiles, not the above weight status breaks, were used previously in the BMI by Zip Code maps.

Fig. 58


32

MeasuredbytheR-Squaredstatistic,theregressionsthatbestfitthedata(i.e.thatexplainthegreatestamountofthevariationinBMIacrossZCTAs)arethoseinthefollowingchart.

.60Education

.2 .40

50,000

100,000

Med

ian

Ho

use

ho

ldIn

com

e19

99

Income = 25,833 + 95,612 educationR2 = 0.512 RMSE = 11101.60 N = 50

SummaryofFindingsAllofthesefindingspointtooneconclusion:communitiesthathavenoordistantgrocerystoresbutnearbyfastfoodrestaurantsinstead–i.e.communitiesthatareoutofbalanceregardinghealthyfoodoptions–willlikelyhaveincreasedprematuredeathandchronichealthconditions,holdingotherinfluencersconstant.Althoughwemustsetourfindingsinthecontextofthechallengesandlimitationsoflinkingcauseandeffectandofpredicting,withcertainty,theexactstatisticalmagnitudeoftherelationshipbetweenfoodaccessandhealth,itisclearthatfooddeserts,especiallythosewithanabundanceoffastfoodoptions,poseserioushealthandwellnesschallengestotheresidentswholivewithinthemandtotheCityofChicagoasawhole.Mothers,children,thedisabled,andtheelderlyarethemostvulnerableresidentsofthefooddesert.Thecostsassociatedwiththiseffectwillbebornedirectlybythemasitrelatestothequalityandlengthoflife,andindirectlybytheheathcareindustry,byemployers,bygovernmentagenciesandbyotherswhotakeonthefinancialburdenofpre-deathtreatments.

Fig. 59


33

Author’s Comments, Conclusions and AcknowledgementsForsomeonewithastrongbackgroundinmarketdynamics,thestudyoffoodaccessasapathwaytocommunityhealthmightseematfirstglancelikeaprofessionalshift.Whatdrivesrealestatedevelopmentdecisions?Whatmakesagrocerystoreprojectcreditworthyintheeyesofitsfinanciers?Wherearetheuntappedbusinessopportunitiesinunderservedmarkets?Andhowdoesfoodavailabilitystealorextendproductivity,time,resources,andvitality?Inmanyminds,itisthislastquestionthatisoutofplaceintheset.YetasGeorgeKaplanpointsoutinhiseloquentForewordtothisreport,thelifeanddeathofanyrealestatedeal–andthelifeanddeathofmankind–arehighlyimpactedbythreethings:location,location,location.

Throughoutmyentirefirstcareerasacommunitydevelopmentpractitioner,Ilearnedhardlessonsaboutlocation.Infact,thereissomethingaboutlocationitselfthatbeckonscommunitydeveloperstobeginwith.LongagoImanagedacommercialstriponthefarSoutheastSideofChicagothathadanunsightly,sloping,vacantlotintheheartofthedistrict,strewnwithlitterandtires.Amanwaschaseddownandbeatentodeaththere.Apoliceman,thinkingbackontheincident,shookhisheadandsaidmatter-of-factly:“Thisisaprettybadlocation.”Itwas.Gangs.Drugs.Violence.Commercialdecay.Somethinghadtobedone.Workingwiththecommunity,weturnedthevacantlotintoagarden.Itsproutedflowers,vegetables,anartshow,andevenawedding.Forlotsofreasons,iteventuallyimprovedonthelocationradar,totheextentthatthelandbecamemorevaluableanddemandforitincreased.Theprivatesectorbuiltastorefrontonthesiteseveralyearslater,andthegardenwasnomore.Thelandreturnedtoitsintendedfunction.Incommunitydevelopment,andinneighborhoodmarkets,too,thecycleofboom,bustandrevivalisalwaysalocalcondition.

Itisthesamewithpublichealth.Locallandusedecisionsare,inmanyrespects,publichealthdecisions.Andwhileoneplotoflanddoesnotdirectlycauseeitherlifeordeath,orcommunityrevitalizationordecline,itcertainlycaninfluencethoseoutcomes.Asfarbackas1926,theSupremeCourtrenderedanopinionthatgovernmenthasaresponsibilitytopromoteandprotectpublichealth,andthatgovernmentcan,therefore,controllandusetothatend,whichtypicallyhappensthroughzoninglaws.Sotobeacommunityplannerandnotcareabouthealth,ortobeahealthofficialandnotcareaboutthebuiltenvironment,meansopportunitiesarelost.Butthereisanotherdimensionoflostopportunities:themarket.

Canthemarketdowellbydoingsomegood?Whynot?Ourstudyidentifiesahalfmillionpluspeoplewholiveinafooddesertwithnoordistantgrocerystoresbutnearbyaccesstofastfood.Asubstantialnumberofthemaresinglemothersandchildren.Myguessisthatwomen,morethananyoneelse,knowtheimportanceoffoodtostitchingtogetherthedelicatecontinuumoflife.Itisironicthatthesewomenarethemostdisenfranchisedfromthefoodmarketgiventhattheyprobablyvalueandunderstanditmorethananyotherconsumergroup.Foodisindeedourmostbasiccommondenominator,arguablymorethanhousingoranyothergood.Weallneedfoodregularlytolive,butourresponsetofoodasacommoditydiffers.Thecommunityhealthadvocatemightstresslocalneedsanddeficiencies,andnutritiousfoodasahumanright,whilemarketproponentsfocusonunitprices,salesvolume,profitmargins,andthecreditworthinessofthegrocerleaseholder.


Wearelivinginaworldofbiological,socialandeconomiccomplexity.RobertWright,inhisfascinatingbook,Nonzero: The Logic Of Human Destiny,talksabouthowthebestofthebest,evencompetitors,havecollaboratedovertimeinpursuitofmutualself-interestandbiological,socialandeconomicevolution–progress,assomewouldcallit.Inthisdayofinformation,technology,andmassproduction,afooddesertistheantithesisofprogress,andthecostsassociatedwithlivingwithinonewillbebornedirectlybythoseresidentsthroughtheirqualityandlengthoflife,andindirectlybythehealthcareindustry,byemployers,bygovernmentagencies,andbyotherswhotakeonthefinancialburdenofpre-deathtreatments.Therefore,wemightlookatfoodmarketsinunderservedareasthroughWright’sgametheorylenstoseehowamyriadgroupofactorsmightbandtogetherwithdistinctagendastomakethewheelturninanewway.Thiswouldbeprogressive,aswellassmart.

Grocerystorelocationsprovedinourstudytobeasignificantfactorinobesityoutcomes;thefartherthegrocerystore,thehighertheobesityrate.Thatpoorhealthoutcomescanbemoderatedbyaccesstofoodandtofoodbalanceisanimportantfinding.Translation:somethingcanbedoneaboutit.Thebuiltenvironmentconstitutesasetofdeliberatechoices:todevelop,subsidizeorfinanceagrocerystore–ornotto–happensbychoice,notbychance.

Onehundredbilliondollarsisasubstantialsumofmoneytospendeachyearonobesity.If5percentofthatwereinvestedingrocerystoredevelopmentpairedwithlocalcampaignsforhealthyeatingasananti-obesitysavingsmeasure,thatwouldgenerate$100millionperstatetoenhancethesustainabilityofnewbusinessopportunities.Small,mid-sizeandindependentgrocers–notjustchains–couldbethepointoffocus.

Cost-benefitscenariossuchastheseareworthyofexamination.Soisthefastfoodindustryintermsofitspotentialcontributionstoimprovedfoodaccess,foodchoiceandcommunityhealth.TosimplydemonizethefastfoodindustryforthenegativehealthoutcomesassociatedwiththeFoodBalanceEffectwouldbetomissseveralkeypoints.

First,thefastfoodindustryhasinvestedinareasotherwisedevoidoffoodoptions,whereasmanygrocersoverthelastfewdecadeshave,bycontrast,desertedtheseareas.Thefastfoodindustrydeliversahighlyconvenientandfillingjust-in-timefoodproductatalowpriceandisasourceofcommunityjobs.Itscompetitiveadvantageisimportantconsideringthatthefooddesertprimarilyconsistsofworkingfamiliesand,specificallyworkingmothers,whostrugglewiththepressureofmultiplechildren,multiplejobs,multiplebillsandmultiplelifestresses.Onlywithgreatnaivetécouldweassumethattheywouldspendalltheirtimecookingelaboratehealthymealsifonlygivenaccesstoagrocerystore.Furthermore,fastfoodoutletshaveflourishedinmarketsthatotherfoodvenuesandretailershavedesertedoravoided.Theyhaveachievedsustainabilityinthepuremarketsenseoftheword.Finally,thefastfoodindustryisevolving,albeitunderpressurefrombookslikeFast Food Nation,butevolvingnonethelessintoanewdimensionoffoodlinesthatincludehealthieroptionsandreducedsaturatedfatcontent.Thissuggeststhepotentialforforwardmovement,somethingbetter,progress.Lessimportantnowiswhatthefastfoodindustryhasbeen.Moreimportantarewhatitisbecoming,whatitcouldbecome,andhowtoimprovedatasystemstobettergaugemarketopportunitiesandcommunityhealthmovingforward.Asaformerpractitionerandnowaresearcherwhostillspendsconsiderabletimeconductingmarketanalysesandcommercialsiteassessments,Irepeatedlyseetheneedforimproveddatasystemsandbettermarketsizingmethods.Inurbanmicro-marketsespecially,“baddata”isakeyobstacletogrocerystoredevelopment.Withtherightskills,mindsetandresources,thiscanberemediedonamarket-by-marketbasis,butwhatcanbedonetomakeimproveddatasystemsmorewidelyavailableacrossmarketstoactorswhomakedevelopmentdecisionsandtocommunityleaderswhomonitorchange?

34


Thereareotherstrategicquestionsthatcanbeexploredaswell:

1) Whatcanthegroceryindustrylearnfromthefastfoodindustryinordertobettercompeteinunderservedmarkets?

2) Wouldfoundationsorothersprovidecreditenhancementtomakemid-sizeandindependentgrocersmoreviableinareasthatneedthem?

3)Canafranchiseofmicro-entrepreneursmimicsuccessfulcompaniessuchasPeaPodintheto-the-doordistributionofhealthyfooditems?Whatistheviabilityofmobilegrocerystores,suchasfruitandvegetabletrucksandevenbicyclecartsthatsellfreshsnacksandproduce?

4)Whatdoestheubiquitousvendingmachineindustryhavetoofferareasthatsufferfromfoodimbalance?Canpubliclyavailablevendingmachinesdispenseapples,low-fatyogurt,bagsofcarrotsandmultigrainbread,insteadofsodaandcandybars,andturnaprofit?Thisisafocusnowincertainpublicschoolsystemsacrossthecountry,butitcouldbeanopportunityeverywhere.

5) Individualswhoworkoftenspendmorethanhalfoftheirtimeawakeeachdayintheiremployers’environs.Byencouraginghealthyeatingandexerciseintheworkplace,canemployersincreaseemployeeproductivityandreducethecostsofemployer-supportedhealthcarecoverage?

6) Isa“foodliteracy”educationcampaignneeded?Doweallknowhowtoreadrecipesandfoodlabels,measureingredients,sizefoodportionsappropriately,cook,andmaintainhealthyfoodchoices?Foodliteracymightbeasimportantinsomecommunitiesasfinancialliteracy.Thetwocouldalsocomplementeachother.

7) Whatwouldshiftourcultureawayfromthetelevisionintothekitchen?Wouldbusyparents,particularlytime-pressuredsinglemothers,formcookingclubswherehealthyfoodispreparedinbulkandeaten,exchanged,orfrozenforlaterconsumption?Whatcancommunityorganizationsdotosupportandencouragehealthycookingandeatinghabits?

8)Last,whileeducationcampaignsarelaudable,isitdishonesttotellpeopletoeathealthyfoodsifthereisnorealisticplacenearbytoacquirethem?Thisbringsusfullcircletothecorechallengeathand:tobeabletochoosehealthyfoodsyoumustfirsthaveaccesstothem.Itallboilsdowntolocation.

Earlierinthisnarrative,Itoldthestoryoftheunsightlyvacantlotthatharboreddrugsandgangmembersanddiscouragedcommercialinvestment.Itwasaprettybadlocation.Thecommunitywasgalvanizedtoaction,andimprovementsweremade,butnotuntilamanwasbeatentodeaththere.

Knowingthatfoodimbalancestealslifeandvitalityfromcommunitiesandtheirresidentscanprovidethissameurgentcallforaction.Afirstbreath,thefinalone,andthedailyfoodthatsustainslifein-between,isacontinuumthateverymortalshares.Asweallneedtoeattolive,foodmightbetheunifyingthreadthattranscendsrace,place,class,andoutdateddevelopmentmodelsthatjustdon’tworkintheseunderservedcommunities.Identifyingmarketaswellasneeds-basedsolutionsthatpromoteaccesstonutritiousfoodsandhealthyfoodchoiceswillrequireinputandsupportfromthefooddesertresidentsthemselvesaswellasfromgrocers,banks,brokers,developers,planners,healthadvocates,educators,government,foundations–ultimatelyeveryone–toachieveevenamodestlevelofsuccess.

35


36

Examining the Impact of Food Deserts on Public Health in Chicagoassessesthelinkbetweenfoodimbalanceandthequalityandlengthoflife,andthequalityandcauseofdeath.Thisbodyofwork,anditscallforcollectiveactionandcommunitypartnership,wouldnothavebeenpossiblewithoutthegeneroussupportofLaSalleBank.I’mdeeplygratefultoLaSalleforunderwritingtheresearch,thereport,andtheforum,fortheirgracefulmannerofopeningnewdoorsinsupportoftheresearchwhilenotinterferingwithit,forconveningtheRoundtable,andfortheircouragetoexploreandhelpresolveasensitivecommunityhealthissuethathasrelevanceacrossurbanAmerica.LaSalle’sleadershiponthisprojectinChicagowillsetanewexamplenationwideforhowanontraditionalsetofactors–banks,theprivatesector,communitydevelopmentleaders,andothers–canaddressneighborhoodsustainabilitythroughthelensofcommunityhealthandwellness.Thiswillstrengthenbridgesacrossdiversedisciplinesand,moreimportantly,improveandextendlives.IamespeciallythankfulforthecontributionsandteamspiritprovidedbythefollowingLaSalleBankcolleagues:

RobertGrossinger,forchampioningtheprojectandprovidingitsvision.

LydiaMorken,forherday-to-dayprojectmanagementskills,hergentlenessandherstrengthtomakeitallhappen.

MaryLaraia,forhersupport,graceandinsights.

IlsaFlanagan,forhelpingusunderstandtheconnectionbetweencommunityhealthandsustainability.

DirkDonnel,forprovidingongoingsupportandenthusiasmthroughouttheproject.

MariaGuzman,forpleasantlydealingwithallmyphonecallsandemailsearlyintheprojectandforsoefficientlyhandlingmypaperwork.

AndreaLewis,forhermediasavvy,freshideasandsageadvice.

JaneBroski,MeganMeierandLaurenVidovich,fortheircapableworkonpublicrelations.

SharonMaddexforhermarketingandcommunicationsexpertiseandforhelpingdevelopattractiveandon-pointoutreachmaterials,includingthebeautifulforuminvitations,andPaulRohrforhissupportinmakingthathappen

CourtneyPakisandTracyCarlson,fororchestratinganoutstandingforumeventthatwillcatalyzenewfoodaccessopportunitiesinunderservedcommunities.

JamesMalone,forspearheadingtheFood DesertpagesonLaSalleBank’sWebsite,theWebcast,andothergreatideas.

JackieGieri,forherimportantworktodisseminatetheresearchfocusandfindingsinternallytoLaSalle’semployeecommunity.

OscarAndersonandNancyNord,fortheirwonderfuldesigncontributionsandforalwayslendingsupportwheneverneeded.

Andlast,butnotleast,NormanBobins,forputtingtogetherateamthatiscommittedtocommunity,communitysustainability,andprojectssuchasthisone.

IamalsogratefultotheentireteamattheLocalInitiativeSupportCorporation,forhostingtheForum,andfortheirsupportatthebeginningofmycareer,whentheyandtheSoutheastChicagoDevelopmentCommissionsponsoredmeinacommunity-focusedrealestatedevelopmenttrainingprogram,andyearslaterwhenLISCmadearecoverablepredevelopmentgrantwhichresultedinafull-servicegrocerystorein


anunderservedcommunityduringmytenureasexecutivedirectorforDevcorpNorth.TheexperienceandskillsthatIgainedthroughthesethreeorganizationsgreatlyimpactedmyorientationtocommunitydevelopment.

Regardingresearchcollaborators,notasinglepersonwasmoresupportivethanJosephFerrie,aneconomistatNorthwesternUniversity.Hisideas,expertisewithlargedatasets,statisticalmodels,andmappingsystems,andhands-oncontributionswereinstrumentaltotherigorofthemethodologyandthedeliverables,andtoourabilitytofinishtheprojectaheadofschedule.

IalsooweaworldofgratitudetoGeorgeKaplanoftheDepartmentofEpidemiologyattheUniversityofMichigan,forlendinghisworldclassreputationtotheproject,writingtheForewordtothereport,providingmentorship,andsufferingthroughreportdraftsandprovidingconstructivecomments.

JeffreyMorenoff,asociologistfromtheUniversityofMichigan,wastremendouslysupportiveinhiscontributionofdataofdirectobservationsofhealthstatisticsofarepresentativesampleofChicagoresidents.

KirstiBocskay,oftheChicagoDepartmentofPublicHealth,laboriouslyhelpedcalculatetheyearsofpotentiallifeloststatisticsaswellastabulateotherdatacriticaltotheproject’ssuccess.

DanielBlock,ofChicagoStateUniversityandtheNortheasternIllinoisFoodSecurityAssessment(fundedbytheChicagoCommunityTrust),wasakeycontributorofgrocerystoredata,andgenerouslyhelpedinformtheprojectthroughhisownresearch.MythanksalsotoLaDonnaRedmond,oftheInstituteforCommunityResourceDevelopment,who,alongwithmanyothersbeforeme,cutapathintounderstandingtheissuesoffoodinsecurityandnewopportunitiesforindependentgrocers.

SiimSööt,oftheUniversityofIllinoisatChicago,wasmostgraciousinsharingdriver’slicensedatafromwhichbodymassindex,ameasureofobesity,wascalculated.

TheCityofChicagosupportedthiseffortinnumerousways.LoriHealey,GinaCaruso,andFranSpencerfromtheChicagoDepartmentofPlanning&Developmentwereallextremelyhelpful,aswereTerryMason,JoeHarrington,andEricaSalemfromtheChicagoDepartmentofPublicHealth.IamespeciallyappreciativeofEricaformakingstacksofcommunityhealthfilesavailabletomeandforkindlyansweringmyendlessstreamofquestionsandrequestswhenshewasknee-deepinherownwork.AldermenMargaretLaurinoandMannyFloresalsoconvenedtheGrocerAccessTaskForcethatsetmanywheelsinmotion.

IamgratefultoMariaHibbs,ofthePartnershipforNewCommunities,andPeterSkosey,oftheMetropolitanPlanningCouncil,fortheirfriendshipandsupportofmyresearchonfoodaccess,andfortheirmanygooddeedstohelpChicagocommunities.

WilliamKling,ofAncelGlinkDiamondBush,hasbeenahelpfulandregularsupporterofmyresearchandhasconnectedmetonumerousresourcesandnewideas.

IamalsogratefultoSustainforitsworkaroundorganicandlocallygrownfood,andtoLynnPeemoellerforhercreativeideasaroundmobilegrocerystores.

WandaWhite-Gills,ofTeamworkEnglewood,generouslymadeherselfavailableasasoundingboard,bringinganimportantcommunityvoicetothediscussion.

PeterTatian,oftheUrbanInstitute,NationalNeighborhoodIndicatorsPartnership,generouslyhelpedconnecttheprojecttoanationalaudiencethatispassionateaboutthe“democratizationofdata”andwaystouseimproveddatasystemstohelpsupportunderservedneighborhoodsandtheirresidents.

37


38

JohnBeskeofJohnBeskeCommunicationswasresponsibleforabeyondimpressivereportillustration,whichhecompletedinrecordtime,andwhichbringsotherwisecomplicatedinformationtolife.I’malsothankfultoMarilynMarkleforhercreativecontributionstothereport’scover.

ThisreportwasalsoimprovedbycontributionsfromG.LamontBlackstone,ofG.L.Blackstone&Associates,LindaGoodman,ofGoodmanWilliams,LeonardHarris,ofChathamFoods,MattLongjohn,LaraJones,andChristyKeirig,oftheConsortiumtoLowerObesityinChicagoChildren,MaryLudgin,headofHeitman’sU.S.PrivateRealEstateGroup,LarryLund,ofRealEstatePlanningGroup,RuthRothstein,ofRosalindFranklinUniversityofMedicineandScience,MargieSchaps,ofHealth&MedicinePolicyResearchGroup,CynthiaStewart,oftheInternationalCouncilofShoppingCenters,andJohnSweeney,ofJPSInterests.

Finally,anacknowledgementofourForumFriends,whoprovidedinvitationlists,advice,andotherassistanceinthedevelopmentoftheforum.

•ChicagoDepartmentofPlanning&Development

•ChicagoDepartmentofPublicHealth

•ChicagoFoodSystemsCollaborative

•ConsortiumtoLowerObesityinChicagoChildren

•TheFoodTrust

•Health&MedicinePolicyResearchGroup

•InternationalCouncilonShoppingCenters

•InstituteforCommunityResourceDevelopment

•MetropolitanPlanningCouncil

•NortheasternIllinoisCommunityFoodSecurityAssessment

•UrbanInstitute,NationalNeighborhoodIndicatorsPartnership

MygratitudeagaintoLaSalleBankformakingthisteameffortpossible.WehopethisstudycontributestothehealthofallChicagoans.

Weinviteyourfeedbackandparticipationmovingforward.

MariGallagher,PrincipalMariGallagherResearch&ConsultingGroup1929WestPattersonChicago,IL60613(312)[email protected]


39

1. Department of Health and Human Services (U.S.) Healthy People 2010. Volume I. Washington: DHHS. November 2000.

2. Village of Euclid v. Ambler Realty Co., 272 U.S. 365 (1926) (USSC+).

3. Creating a Healthy Environment: The Impact of the Built Environment on Public Health. Richard Jackson, MD, MPH, Chris Kochtitzky.

4. American Dietetic Association Final Report of Findings. Prepared by Wirthlin Worldwide. 2002.

5. Child Trends DataBank: Overweight Children and Youth. March 10, 2006. Online.

6. The Influence of Community Factors on Health. A Policy Link Report. Fall, 2004.

7. “Supersizing America: Obesity Becomes an Epidemic.” Martha Coventry. UMNews. Winter, 2004.

8. “Solving the Problems of the Iowa Food Deserts: Food Insecurity and Civic Structure.” Lois Wright Morton, Ella Annette Bitto, Mary Jane Oakland, Mary Sand. Rural Sociology. 2005.

9. “Survey tracks trends in children’s dietary habits.” USDA/ARS Children’s Nutrition Research Center at Baylor College of Medicine. March, 2006.

10. “Obesity in the US.” American Obesity Association. AOA Fact Sheets. March, 2006.

11. U.S. Department of Labor Grocery Store Fact Sheet. April, 2006.

12. “Food Companies Criticized Over Health Commitments.” Kate Holton. Reuters. April, 2006.

13. “The Availability and Cost of Healthier Food Alternatives.” Karen Jetter and Diana Cassady. American Journal of Preventative Medicine. 2006; 30 (1)

14. Community Food Systems. MSU Urban and Regional Planning. Winter, 2004.

15. “ Neighborhood Health and Self-Reported Health Status: A Multilevel Analysis.” American Journal of Public Health. August, 1999.

16. Food Redlining: A Hidden Cause of Hunger. Tolerance in the News. March, 2006.

17. Neighborhoods and Health. American Medical Association. 2003.

18. “Is Economic Policy Health Policy?” American Journal of Epidemiology. 91, 351-3. Kaplan, G.A., Lynch, J.W. (2001).

19. “Land Use Planning and the Control of Alcohol, Tobacco, Firearms, and Fast Food Restaurants.” American Journal of Public Health. September, 2003.

20. The Need for More Supermarkets in Philadelphia: Food for Every Child.

21. “Local Services and Amenities, Neighborhood Social Capital, and Health.” Andrea Altschuler, Carole Somkin, and Nancy Adler. Social Science and Medicine. 2004.

22. “How Neighborhoods and Physical Functioning Are Related: The Roles of Neighborhood Socioeconomic Status, Perceived Neighborhood Strain, and Individual Health Risk Factors.” The Society of Behavioral Medicine. 2004.

23. “Social Sources of Racial Disparities in Health.” David Williams and Pamela Braboy Jackson. Health Affairs. 2005.

24. “Neighborhood Physical Conditions and Health.” American Journal of Public Health. March, 2003.

25. “Investigating Neighborhood and Area Effects on Health.” American Journal of Public Health. November, 2001.

26. “Maximum or Minimum Differentiation? Location Patterns of Retail Outlets.” The Review of Economics and Statistics. February, 2002.

27. “Fat land: Supersizing America.” The New York Times. January 12, 2003.

28. “Fast Food Linked to Child Obesity.” CBS News. January 5, 2003.

29. “Just Enough for You: About Food Portions.” U.S. Department of Health and Human Services and National Institutes of Health. No date.

30. “Prevalence of Overweight Among Children and Adolescents: United States, 1999-2002.” National Center for Health Statistics. March, 2006.

31. “Childhood Obesity in the United States: Facts and Figures.” Institute of Medicine. September, 2004.

32. “ Neighborhood Effects on Health: Exploring the Links and Assessing the Evidence.” Journal of Urban Affairs. Vol. 23. 2001.

33. “The Impact of the Built Environment On Health: An Emerging Field.” American Journal of Public Health. September, 2003.

34. “Racial Disparities in Self-Reported Health at Older Ages: What Difference Does The Neighborhood Make?” Journal of Gerontology. 2005.

35. “Built Environment: Healthy Communities, Healthy Homes, Healthy People.” Research Triangle Park. 2002.

36. “Developing Successful Retail in Underserved Urban Markets.” International Council of Shopping Centers. 2004.

37. “Food, Markets, and Healthy Communities: How Food Stores Accelerate Local Development and Enrich Residents’ Lives.” Local Initiatives Support Corporation (national), Tony Proscio, and MetLife Foundation, 2006.

38. “Healthy Food, Healthy Communities: Improving Access and Opportunities Through Food Retailing.” Policy Link and California Endowment. 2005.

39. “Obesity Epidemic Continues to Worsen in the United States.” Healthcare News. HarrisInteractive. March, 2006.

40. “Childhood Obesity: Costs, Treatment Patterns, Disparities in Care, and Prevalent Medical Conditions.” Thomson Medstat Research Brief.

41. “Prevalence of Overweight and Risk of Overweight Children Among 3-to-5-Year-Old Chicago Children, 2002-2003.” Journal of School Health. March, 2006.

42. “Childhood Obesity: Examining the Evidence.” ACOFP. March, 2006.

43. “Childhood Obesity on the Rise.” World on Health. June, 2002.

44. “Obesity in Youth.” American Obesity Association. March, 2006.

45. “Life After Webvan.” RED Herring. The Business of Technology. May, 2006.

46. “The Demand for Food Away From Home: Full Service or Fast Food.” ERS Research Brief. January, 2004.

47. “Patterns and Trends in Food Portion Sizes, 1977-1998.” JAMA. March, 2006.

48. “Neighborhood Racial Composition, Neighborhood Poverty, and the Spatial Accessibility of Supermarkets in Metropolitan Detroit.” Research and Practice. April, 2005.

49. “Life in the Fast Food Lane.” Chicago Tribune Magazine. March 5, 2006.

Bibliography

ReportauthoredbyMariGallagherResearch&ConsultingGroup1929WestPattersonChicago,IL60613(312)339-0640www.marigallagher.com©copyright2006MariGallagher

SponsoredbyLaSalleBanklasallebank.com(312)904-2866

Printed on 100% PC Recycled Paper

Paper Manufactured Using Wind Power

Documents

Scoping Assessment of Climate Change Adaptation Priorities in the