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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?
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