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New Measures of the Food Environment in Seattle-King

County

NUTR 500, 2009-02-19

Phil Hurvitz, PhCUrban Form Lab

College of Built Environments University of Washington

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Acknowledgements

• UWCOR/NIH-NIDDK• Urban Form Lab

– Eric Scharnhorst (food source classification)

• Group Health Cooperative– David Arterburn (health outcomes data)

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Overview

• What does “food environment” mean?– Food environment at different scales

• Time• Space

• Built Environment/Food Environment• Food Environment in a Spatial

Framework• So what?• Conclusions

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What does “food environment” mean?

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Food environments across temporal scales

• What is a “healthful” or “harmful” food?

• What is a healthful or harmful diet?

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Environmental effects across spatial scales

McGarigal and Marks 1995

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Food environments across spatial scales

• Cell• Organ• System• Organism/Individual• Community/Neighborhood• City• Region• Country/Continent/Globe

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Food environments across spatial scales

• Cell• Organ• System• Organism/Individual• Community/Neighborhood• City• Region• Country/Continent/Globe

basic biological “bench” science

“built environment”/urbanform EPI

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Built Environment/Food Environment

• Food environment = places to procure & consume food– Stores – Restaurants – Emergency food system

• What does access mean?

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Built Environment/Food Environment

• For food access, what conditions are necessary and what conditions sufficient'?

• Spatial proximity? – Necessary if transportation is limited

• Ease/convenience of getting to/from? – Necessary if mobility is limited

• Affordability – Necessary if income is limited

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Food Environment in a Spatial Framework

• How is food environment measured?• How can food environments be

summarized over area or population of interest?

• How are food environments related to other spatially explicit factors?

• Tools:– Microsoft Access: data storage, database

processing– ArcGIS 9.3: spatial analysis & mapping– R (with RODBC library): statistics & graphics

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Measuring the Food Environment

• Food processing & selling establishments are regulated by public health agencies

• Main responsibility = protect against foodborne illnesses

• Requires up-to-date address & identification information

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PHSKC Food License Data

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PHSKC Food License Data

???

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Food source classification

• No standards exist• We developed an ad hoc

classification system

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Food source classification

• An “L4” class was assigned to each of the >10,000 food sources

• Hierarchical nesting of classes to top level

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Address Geocoding

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Address Geocoding Algorithm

~20%

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Address Geocoding

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Extending the Map

• Having a map of the location of different food sources, while intrinsically interesting, is not an end goal in itself

• Food sources need to be conceptualized as part of an overall environment

• How are food locations related to other spatial factors?

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Different ways to summarize

• Count within a spatial tolerance• Proximity to closest• Mean distance within a spatial

tolerance (combines the 2 above approaches)

• Area-based summaries• Kernel density estimators

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Count within a spatial tolerance

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Proximity to closest

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Mean distance within a spatial tolerance

mean Euclidean distanceto all:717 m

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Mean distance within a spatial tolerance

mean network distanceto all:911m

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Area-based summaries: FFRD by tract

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Area-based summaries: FFRD by ZIP code area

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Area-based summaries: MAUP

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Area-based summaries: MAUPdoes this

location have lower

exposure

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Area-based summaries: MAUPdoes this

location have lower

exposure

than this?

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Area-based summaries: MAUP

why this

pattern?

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Kernel density estimators

cross-sectional viewsummation of XY Gaussians

3D & planimetric view

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Kernel density estimators

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So what?

• Relating measures to other things that matter– Mortality-based deprivation index– Median household income– Percent of residents living below poverty– Race/ethnicity– Health outcomes

• Obesity• Diabetes

Multivariate census-based deprivation index

Singh, G.K., Area deprivation and widening inequalities inUS mortality, 1969-1998. Am J Public Health, 2003. 93(7): p. 1137-43.

US CensusVariables

Higher SES

Lower SES

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Mortality rates higher among most deprived

Singh, 2003

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Relationship(s) between FFRD, SES, health?

• Based on existing data, do any relationships that appear?

• Caveats:– Area-based summaries (census tracts)– GHC data may not be generalizable to

tracts

ffdnskm2

95 98 101

0 . 0 5 7 4

0.379***

0 40 80

0.0862

.0 . 0 7 2 2

0.0 0.3 0.6

0.0919

.0 . 0 0 6 4

0.0 0.2 0.4

0.161**

010

20

0.177***

9598

101

deprivation 0.708*** 0.461*** 0.272***

0.386***

0.206***

0.232***

0.296***

medhhinc 0.510*** 0 . 0 1 1 9

0.137**

0.248***

0 . 0 3 7 1

2000

012

0000

0 . 0 3 7 3

0

4080

pctnonwhite 0.111*

0 . 0 4 6 4

0.108

*0.0934

.0 . 0 1 8 1

overwt 0.691*** 0.285***

0.423***

0.4

0.8

0.619***

0.0

0.3

0.6

obese 0.309***

0.398*** 0.585***

hyp 0.394***

0.2

0.5

0.424***

0.0

0.3

diabetes 0.629***

0 5 15 20000 100000 0.4 0.8 0.2 0.5 0.0 0.3

0.0

0.3

metsyn

FFR density, deprivation, health outcomes (male)

MALEthere seems to be a relationship between deprivation & some health outcomes

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ffdnskm2

95 98 101

0 . 0 5 7 4

0.379***

0 40 80

0.0862

.0 . 0 7 2 2

0.0 0.3 0.6

0.0919

.0 . 0 0 6 4

0.0 0.2 0.4

0.161**

010

20

0.177***

9598

101

deprivation 0.708*** 0.461*** 0.272***

0.386***

0.206***

0.232***

0.296***

medhhinc 0.510*** 0 . 0 1 1 9

0.137**

0.248***

0 . 0 3 7 1

2000

012

0000

0 . 0 3 7 3

0

4080

pctnonwhite 0.111*

0 . 0 4 6 4

0.108

*0.0934

.0 . 0 1 8 1

overwt 0.691*** 0.285***

0.423***

0.4

0.8

0.619***

0.0

0.3

0.6

obese 0.309***

0.398*** 0.585***

hyp 0.394***

0.2

0.5

0.424***

0.0

0.3

diabetes 0.629***

0 5 15 20000 100000 0.4 0.8 0.2 0.5 0.0 0.3

0.0

0.3

metsyn

FFR density, deprivation, health outcomes (male)

MALEdo you see a relationship between FFRD & other area-based variables?

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ffdnskm2

97 99 102

0 . 0 3 5 7

0.423

***

20 60

0 . 0 2 6 6

0.341***

0.0 0.3 0.6

0.302***

0.117

0.0 0.2 0.4

0.198**

010

20

0.275***

9799

102

deprivation 0.578*** 0.443*** 0.532*** 0.551*** 0.327***

0.414***

0.410***

medhhinc 0.495*** 0.143*

0.185**

0.256***

0.271***

2e+0

41e

+05

0.162*

2060

pctnonwhite 0.174*

0.240***

0.104

0.286***

0.155*

overwt 0.839*** 0.472*** 0.514***

0.3

0.6

0.716***

0.0

0.3

0.6

obese 0.382*** 0.490*** 0.680***

hyp 0.555***

0.2

0.5

0.601***

0.0

0.2

0.4

diabetes 0.727***

0 5 15 2e+04 1e+05 0.3 0.6 0.2 0.4 0.6 0.0 0.2 0.4

0.0

0.2

0.4

metsyn

FFR density, deprivation, health outcomes (female)

FEMALEthere seems to be a relationship between deprivation & some health outcomes

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Median household incomeffdnskm2

97 99 102

0 . 0 3 5 7

0.423

***

20 60

0 . 0 2 6 6

0.341***

0.0 0.3 0.6

0.302***

0.117

0.0 0.2 0.4

0.198**

010

20

0.275***

9799

102

deprivation 0.578*** 0.443*** 0.532*** 0.551*** 0.327***

0.414***

0.410***

medhhinc 0.495*** 0.143*

0.185**

0.256***

0.271***

2e+0

41e

+05

0.162*

2060

pctnonwhite 0.174*

0.240***

0.104

0.286***

0.155*

overwt 0.839*** 0.472*** 0.514***

0.3

0.6

0.716***

0.0

0.3

0.6

obese 0.382*** 0.490*** 0.680***

hyp 0.555***

0.2

0.5

0.601***

0.0

0.2

0.4

diabetes 0.727***

0 5 15 2e+04 1e+05 0.3 0.6 0.2 0.4 0.6 0.0 0.2 0.4

0.0

0.2

0.4

metsyn

FFR density, deprivation, health outcomes (female)

FEMALEthere seems to be a relationship between FFRD & some health outcomes – but in the direction we would expect?

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Conclusion

• Health outcomes follow SES gradients• Area-based fast food restaurant densities do

not appear to be related to either SES gradients or health outcomes

• Area-based spatial measurement & summary methods are fundamentally problematic

• Other built environment factors frequently ignored (e.g., road density, land use mix, employment density, transit hubs)

• Individual eating behavior, SES, and built environment data will be necessary to investigate causal relationships

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Phil Hurvitzgis.washington.edu/phurvitz

Higher SES

Lower SES

does this location have lower

exposure

than

this?