Carrie Tomasallo, PhD, MPHCarrie Tomasallo, PhD, MPH

Wisconsin Department of Health ServicesWisconsin Department of Health ServicesDivision of Public HealthDivision of Public HealthBureau of Environmental and Occupational Bureau of Environmental and Occupational HealthHealthcarrie.tomasallo@wisconsin.govcarrie.tomasallo@wisconsin.gov

Analysis of Asthma Prevalence Using Clinical Electronic Health Records and Air Pollution Data

Why study chronic disease risk Why study chronic disease risk factors present in the factors present in the environment & community?environment & community?

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RationaleRationale

A multilevel approach that includes A multilevel approach that includes an ecological viewpoint may help to an ecological viewpoint may help to explain heterogeneities in chronic explain heterogeneities in chronic disease expression across disease expression across socioeconomic, behavioral, and socioeconomic, behavioral, and geographic boundaries that remain geographic boundaries that remain largely unexplainedlargely unexplained

Improved knowledge for Improved knowledge for interventionsinterventions

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BackgroundBackground

Asthma affects 500,000 children and adults Asthma affects 500,000 children and adults in Wisconsinin Wisconsin

Wisconsin Behavioral Risk Factor Wisconsin Behavioral Risk Factor Surveillance System (BRFSS) data provide Surveillance System (BRFSS) data provide annual statewide asthma prevalence annual statewide asthma prevalence estimates estimates

Alternative Surveillance Data: Health Alternative Surveillance Data: Health information exchange between UW Health information exchange between UW Health and WI Division of Public Health and WI Division of Public Health (details on next slide)(details on next slide) 44

Electronic Health Record (EHR) data Electronic Health Record (EHR) data from UW Department of Family from UW Department of Family Medicine, Pediatrics, and Internal Medicine, Pediatrics, and Internal Medicine Clinics to identify a patient Medicine Clinics to identify a patient population with asthma at a census population with asthma at a census block levelblock level

HIPAA limited data setHIPAA limited data set

462,000 patients (45,000 462,000 patients (45,000 asthmatics) asthmatics)

UW eHealth – PHINEXUW eHealth – PHINEXUUniversity of niversity of WWisconsin isconsin EElectronic lectronic HealthHealth Record – Record – PPublic ublic HHealth ealth InInformation formation ExExchangechange

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UW ehealth – PHINEX UW ehealth – PHINEX (Clinic EHR Data, 2007-2011)(Clinic EHR Data, 2007-2011)

Item TotalPatients 462,497Encounters 8.9 MillionLab Results 11.5 MillionEncounter

Diagnoses15.1 Million

Problem List Diagnoses

2.1 Million

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Geographic Distribution of Patient Population, 2007-2011

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Specific AimsSpecific Aims

Examine asthma prevalence and risk Examine asthma prevalence and risk factors in clinical EHR and air pollution factors in clinical EHR and air pollution datadata

Frequency tables of asthma Frequency tables of asthma prevalenceprevalence

Multivariate regression modelingMultivariate regression modeling

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Determine areas and populations of Determine areas and populations of asthma disparityasthma disparity

GIS and spatial analyses of GIS and spatial analyses of population trendspopulation trends

Specific Aims Specific Aims cont.cont.

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Current Asthma Current Asthma DefinitionDefinitionAsthma diagnosis (ICD-9 code 493) Asthma diagnosis (ICD-9 code 493) in either encounter diagnosis or in either encounter diagnosis or problem diagnosis field of clinical problem diagnosis field of clinical EHREHR

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Air Pollution DataAir Pollution Data

EPA Criteria air pollutant data (CO, NOEPA Criteria air pollutant data (CO, NO22 PMPM2.52.5, PM, PM1010, SO, SO22) modeled from stack ) modeled from stack emissions and aggregated to the emissions and aggregated to the census block group (CBG)census block group (CBG)

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Air Pollution Data Air Pollution Data cont.cont.

EPA Airmod program used to construct a EPA Airmod program used to construct a distance decay model from each point distance decay model from each point sourcesource

Pollutant values aggregated to the CBGPollutant values aggregated to the CBG

A five-mile radius applied for each stack A five-mile radius applied for each stack

Values of zero apply in areas where no Values of zero apply in areas where no data points were locateddata points were located

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ResultsAsthma Prevalence

  # with Asthma

Percent (95% CI)

Overall 44,616 9.7 (9.6 – 9.7)

Sex    

Male 18,371 8.4 (8.3 – 8.5)

Female 26,245 10.8 (10.7 – 10.9)

Age Group    

0-4 3,859 8.5 (8.3 – 8.8)

5-11 5,084 13.5 (13.2 – 13.9)

12-17 4,112 12.3 (11.9 – 12.7)

18-34 10,042 9.9 (9.7 – 10.1)

35-64 17,828 9.5 (9.4 – 9.7)

65+ 3,691 6.4 (6.2 – 6.6)

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 # with Asthma

Percent (95% CI)

Race/Ethnicity    

White (non-Hispanic)

36,807 9.6 (9.5 – 9.7)

Black (non-Hispanic)

3,532 17.9 (17.3 – 18.5)

Other (non-Hispanic)

1,181 6.8 (6.4 – 7.2)

Hispanic 1,534 8.9 (8.5 – 9.4)

ResultsAsthma Prevalence cont.

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 # with Asthma

Percent (95% CI)

BMI (children)    

Normal/Underweight

7,851 12.8 (12.5 – 13.1) 

Overweight 1,891 15.4 (14.7 – 16.1)

Obese 2,021   18.8 (18.0 – 19.6)

BMI (adults)    

Normal/Underweight

7,363 8.7 (8.5 – 10.6) 

Overweight 7,869 9.3 (9.9 – 10.3)

Obese 8,709 12.1 (13.6 – 14.1)

Morbidly Obese 3,127 18.3 (13.6 – 14.1)

ResultsAsthma Prevalence cont.

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Prevalent Asthma = Prevalent Asthma =

Sex + Age + Race + BMI + Sex + Age + Race + BMI + Smoking Status + Household Smoking Status + Household Income + Income + Insurance Status + Insurance Status + Air Pollutants Air Pollutants (CO, NO(CO, NO22, PM, PM2.52.5, PM, PM1010, , SOSO22) )

ResultsPredictive Model

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ResultsMultivariate Regression for AsthmaCovariate ORadj (95% CI)

SexMale referenceFemale 1.31 (1.28 – 1.35) Age Group (years)0-4 reference5-11 1.26 (1.20 – 1.33)12-17 1.14 (1.08 – 1.21)18-34 0.88 (0.83 – 0.92)35-64 0.74 (0.71 – 0.78)65+ 0.46 (0.42 – 0.50)

*Asthma Yes = 32,133; Asthma No = 222,964

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Covariate ORadj (95% CI)

Race/EthnicityWhite (non-Hispanic) referenceBlack (non-Hispanic) 1.67 (1.59 – 1.76)Other (non-Hispanic) 0.74 (0.69 – 0.79)Hispanic 0.86 (0.81 – 0.92)BMI StatusNormal or Underweight

reference

Overweight 1.16 (1.13 – 1.20)Obese 1.64 (1.59 – 1.69)

ResultsMultivariate Regression for Asthma cont.

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Covariate ORadj (95% CI)

Smoking Status

Never reference

Former 1.12 (1.08 – 1.15)Current 1.03 (0.99 – 1.07)Passive 1.13 (1.07 – 1.20)

Household Income

≥$75,000 reference

$50,000-<$75,000 0.95 (0.93 – 0.98)

<$50,000 0.94 (0.90 – 0.97)

ResultsMultivariate Regression for Asthma cont.

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Covariate ORadj (95% CI)

Insurance StatusCommercial referenceMedicare 1.23 (1.16 – 1.31)Medicaid 1.28 (1.23 – 1.33)Workers’ Compensation

0.87 (0.73 – 1.03)

No Insurance 0.41 (0.35 – 0.47)Criteria Air Pollutant*Average PM2.5 1.05 (1.01 – 1.09)

*Other criteria air pollutants (CO, NO2, SO2 and PM10) were not significant at the p=0.05 level

ResultsMultivariate Regression for Asthma cont.

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Clinic Patients with Asthma by Census Block Group, Dane County, WI

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Average Concentration of PM2.5 by Census Block Group, Dane County, WI

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ConclusionsConclusions

Between 2007-2011, EHR clinic data Between 2007-2011, EHR clinic data identified 45,000 asthmaticsidentified 45,000 asthmatics

Prevalence ranged from 6.4% (adults Prevalence ranged from 6.4% (adults 65+ years) to 13.5% (children 5-11 65+ years) to 13.5% (children 5-11 years)years)

Asthma prevalence highest among Asthma prevalence highest among Black non-Hispanics (17.9%)Black non-Hispanics (17.9%)

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Conclusions Conclusions cont.cont.

Factors associated with asthma Factors associated with asthma prevalence:prevalence:– Younger age, female genderYounger age, female gender– Black non-Hispanic race/ethnicityBlack non-Hispanic race/ethnicity– Former smoking statusFormer smoking status– Elevated BMIElevated BMI– Reporting government insuranceReporting government insurance

– Average PMAverage PM2.52.5 value value

Identified asthma patients at the Identified asthma patients at the neighborhood level in Madison, WIneighborhood level in Madison, WI

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Future DirectionsFuture Directions

Refining air pollution model; alternate Refining air pollution model; alternate sources of data?sources of data?

Change definitions of asthma and Change definitions of asthma and asthma control asthma control

Follow clinical outcomes for an Follow clinical outcomes for an intervention groupintervention group

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PHINEX Asthma Group Collaborative PHINEX Asthma Group Collaborative Effort - Thank You!Effort - Thank You!

Co-Principal Investigators:•Theresa Guilbert - UW Pediatrics•Larry Hanrahan- UW Department of Family Medicine

•Bill Buckingham- UW Applied Population Laboratory•Tim Chang- UW Biostatistics and Medical Informatics•Kelly Cowan- UW Pediatrics•Aman Tandias- UW Department of Family Medicine

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UW eHealth PHINEX Theory and UW eHealth PHINEX Theory and MethodsMethodsWisconsin Medical Journal, June 2012Wisconsin Medical Journal, June 2012

http://www.wisconsinmedicalsociety.org/_WMS/publications/wmj/pdf/111/3/124.pdf

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