Spatial relationships between nutrition/activity indicators and built/natural environment measures in Iowa

John DeGrootejohn.degroote@uni.eduUNI GeoTREE CenterApril 3, 2013

UNI GeoTREE Center

• GeoInformatics Training, Research, Education, and Extension Center• Support the use of geospatial (GIS, RS, GPS, web

mapping) technologies at UNI and throughout Iowa• www.geotree.uni.edu• john.degroote@uni.edu

This Project

• Funded by a small seed grant from UNI • Purpose - pilot project to investigate

relationships between self-reported nutrition/activity and health related variables and environmental metrics derived using GIS

Project goals• Develop a spatially referenced database on energy

balance-related behavior, health outcomes, and environment • Examine patterns of Behavioral Risk Factor

Surveillance System (BRFSS) survey results in Iowa• Development of spatial processing algorithms and

models for deriving useful environmental metrics• Statistically investigate associations between

derived environmental measures and behavior and health outcome data

Background

• Proportion of population classified as overweight/obese has risen greatly nationally and in Iowa• Prevalence of adult obesity doubled between

1980 and 2002 while in children tripled (Ogden et al. 2006)• Iowa ranked 15th highest in nation on

overweight/obesity prevalence among all 54 states/territories (IDPH, 2013)

Background

• In Iowa 37% of adults considered overweight while 28% were considered obese based on 2007 Behavioral Risk Factor Surveillance System (BRFSS) (Centers for Disease Control and Prevention)• In a survey of 6200 7th and 8th graders in 2010-

2011 19% and 18% were considered obese or overweight respectively

http://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html

http://www.idph.state.ia.us/iowansfitforlife/common/pdf/bmi.pdf

Background

• There has been considerable research investigating relationship between environment and energy-related behaviors and outcomes•Often derived some environmental

metric for comparison to some measure such as BMI

Background

• Data often aggregated at some administrative unit such as zip-code or census tract• However, many have been somewhat

piecemeal • Investigate one to several individual

environmental variables in relation to some measure of obesity/overweight (e.g. BMI)

• Recently more holistic environment studies

Methodology

• Compile data on BRFSS and examine patterns in the state• Compile a variety of measures of

environment related to both food availability and access to recreation• Examine associations between BRFSS

averaged responses and environment measurements at various scales

Methodology

•We hoped to estimate a variety of environmental measures for each estimated household in the state using proximity, neighborhood, and network analyses• A novel method which could potentially be

used elsewhere• Estimate household locations using road

network and population density by census block

Disaggregated estimated housing units in Iowa (population density by Census block – road network used to estimate housing units based on urban, urban cluster, and rural classifications

By modeling households we could hopefully avoid situations like this in which total number of fast food restaurants and grocery stores are aggregated to the census tract. In this example, there would be no difference between the two houses at the center of the buffers.

Thus we would be able to end up with more spatially precise estimates of the environment (top figure) that could be potentially investigated in relation to health indicators (e.g. BMI). Bottom figure would be an example of aggregated to census values.

Also would be useful for targeting interventions.

BRFSS

• Behavioral Risk Factor Surveillance System (BRFSS)• Phone survey of adults throughout Iowa• We obtained data from 2005-2010 (~30k

responses) with zip-code and county of residence recorded• Questions about height & weight (BMI), fruit/veg

consumption, general health, exercise, etc.• Only examine zip-codes with at least 20

responses

Environmental Measures

• Derived a wide range of environmental metrics at county, zipcode, and household levels• Business data (restaurants, fast food, convenience

stores, bars, fitness facilities) from InfoUSA• Calculate neighborhood statistics so for each

location know how many businesses within a certain neighborhood

• Recreational trail density • Land use mix – estimate of diversity of land uses

Environmental Measures

• We have derived a wide range of environmental metrics at county, zipcode, and household levels• Area of recreational land use• Population and housing density (census block)• Network density• Average NDVI scores• Sprawl index• Slope

Grocery store density in Cedar Rapids (1 km neighborhood)

Fast food density in Cedar Rapids (1 km neighborhood)

ZipCode ResultsVariable GenHealth Exercise BMI

GenHealth 0.49 0.39

Exercise 0.49 0.26

MedHHIncome -0.42 -0.29 -0.19

AveBlkSize -0.30 -0.26 -0.12

TrailLength -0.11 -0.12 -0.15

TrailDensity -0.14 -0.15 -0.16

GroceryDensity 0.24

FfoodDensity -0.12 -0.14

ZipCode Results

• The general health, exercise and BMI all significantly associated• Higher income associated with better

health, more exercise, and lower BMI• Larger average block size (sprawl index)

associated with better health, more exercise, and lower BMI

ZipCode Results

•More trails and higher trail density associated with better health, more exercise, and lower BMI• Higher grocery density actually associated

with worse general health• Higher fast food density associated with

more exercise and lower BMIs

Business database concerns

Future Directions

• Derive environmental metric scores averaged by households for comparison to BRFSS data• Need to proportion by urban, urban cluster,

and rural household• Develop composite models of many

environmental variables

Example composite models

Future Directions

• Publish some of the research•Would be useful to have more

geographically precise health measurements• Seek further funding for this

Conclusions

• Demonstrated spatial variability in BMI and other variables in Iowa based on BRFSS• Developed a wide range of environmental

metrics at different levels of aggregation• Results are preliminary • Still a lot of work to do to organize data

properly for a systematicanalysis

Acknowledgements

• Student Brian Swedberg did a lot of data processing• Iowa Department of Public Health (Don

Shepherd) for providing BRFSS data• Disa Cornish UNI Center for Social

Behavioral Research for consulting on BRFSS data

THANK YOUQUESTIONS?

john.degroote@uni.edu