Augmenting EHR With Environmental Data

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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