augmenting ehr with environmental data
TRANSCRIPT
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Caryn Roth, Yosef Khan, Randi Foraker, Peter EmbiSan Francisco, CA
March 21, 2013
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Outline
Background Study Design & Methodology
Results
Conclusions
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Outline
Background Study Design & Methodology
Results
Conclusions
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Percent Obese (BMI 30) by State
Centers for Disease Control and Prevention. Obesity and Overweight. BRFSS 2011
Body Mass Index (BMI) =
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Obesity-related illnesses cost $190.2billon/year
Institute of Medicine Report. Accelerating Progress in Obesity Prevention: Solving
the Weight of the Nation. 2012.
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Genetics, Lifestyle and Environment
Das UN. Obesity: genes, brain, gut, and environment. Nutrition. 2010;26(5):459-73.
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J ilcott Pitts SB, Edwards M, Moore J B, Shores KA, Dubose KD, McGranahan D.Obesity is Inversely Associated with Natural Amenities and Recreation Facilities
Per Capita. J ournal of physical activity & health. 2012.
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Wang MC, Kim S, Gonzalez AA, MacLeod KE, Winkleby MA. Socioeconomic and food-
related physical characteristics of the neighbourhood environment are associated withbody mass index. J ournal of epidemiology and community health. 2007.
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Secondary Use of EHR Data
Non-direct care use of PHIincluding but not limited toanalysis, research,quality/safety measurement,
public health, payment,provider certification oraccreditation, and marketingand other business including
strictly commercial activities
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Safran C, Bloomrosen M, Hammond W, Labkoff S, Markel-Fox S, et al. Toward anational framework for the secondary use of health data: an american medicalinformatics association white paper. J ournal of the American Medical Informatics
Association. 2007.
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Too much data or not enough?
Is it really too much data?
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Outline
Background Study Design & Methodology
Results
Conclusions
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Research Question
Can we apply secondary use of EHR-derived clinical data to study associationsbetween obesity and environmental factors?
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Study objectives
Investigate possible spatial associations
between access to fresh food andcommunity physical recreation facilities andthe prevalence of obesity and overweight inFranklin County, Ohio
Further investigate associations with respectto income level, education level, age, and
population characteristics
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Diverse Data Sources
OSUWMC Information Warehouse Neilson Marketing Data
North American Industry Classification
System (NAICS)/Zip Code BusinessPatterns
Other Public Data Sources
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OSUWMC Information Warehouse
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Information Warehouse Dataset
One year of inpatient and outpatient visits Patients aged 18-65
Address of record within Franklin County,
Ohio Most recent visit where height and weight
was recorded
Gender, race, year of birth and zip code
62,701 unique patient encounters
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Nielsen PrimeLocation
Consumer Behavior TrendsTelephone, mail, online surveys Barcode scanners, smartphone apps, etc. Extensive data:
Population Size Education Income & Poverty
Available through Ohio Department ofHealth
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713940: Fitness and Recreational Sports Centers
North American Industry Classification System(NAICS) and ZIP Code Business Patterns Website
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NAICS445110: Supermarkets and Other
Grocery Stores (Not Convenience)
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Farmers MarketsManual compilation
Farmers Markets
Manual Compilation
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All Potential Variables
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Source Variables
Information Warehouse Zip Code
Year of Birth
Gender
Race
Height & Weight BMI (outcome variable)PrimeLocation Median Household Income
Average Household Income
Median Household Effective Buying IncomeAverage Household Ef fective Buying Income
Famil ies below Poverty
Percent of Civilian Labor Force Unemployed
Population
Pop 25+, No High School Degree
Pop 25+, High School DegreePop 25+, College Degree or Higher
NAICS Fitness and recreational Sports Centers
Supermarkets and Other Grocery Stores
Manual Search Farmers Markets
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Methodology Overview
Collected and merged data by zip code
Calculated BMI for each patient
Developed multinomial logistic regressionmodel, clustered by zip code Examined co-linearity Used fractional polynomial model
comparisons
Assessed interactions Allowed all eliminated variables to re-
enter model
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Outline
Background Study Design & Methodology
Results
Conclusions
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BMI Categories
2077919567
22355
0
5000
10000
15000
20000
25000
Normal(18.5 BMI