Mapping Heart Disease, Stroke and Other Chronic Diseases: A Program to Enhance GIS Capacity within Health Departments

Map Highlights from California; Kansas; New Mexico; South Dakota;Vermont; Cuyahoga County, Ohio; Cleveland, Ohio; and Denver, Colorado

Submitted to the US Centers for Disease Control and Prevention, Division for Heart Disease and Stroke Prevention, and the National Association of Chronic Disease Directors

Prepared by the Children’s Environmental Health Initiative at the School of Natural Resources and Environment, University of Michigan

May 2015

Acknowledgements

The following staff from each of the participating agencies provided valuable contributions to the success of this project’s ability to enhance the use of GIS within health departments for the prevention and treatment of heart disease, stroke, and other chronic diseases. In addition, we extend our deep appreciation to the Environmental Systems Research Institute (Esri) for their provision of software grants to the state and local health departments participating in this project. California Department of Public Health Brendan Darsie Janet Bates Madhurima Gadgil Shannon Conroy Xueying Zhang

Kansas Department of Health and Environment Trevor Christensen Cynthia Snyder Erika Welsh Virginia Barnes

New Mexico Department of Health Katharine VonRueden Bryan Patterson Christopher Lucero Bambi Bevill

South Dakota Department of Health Carrie Cushing Lexi Haux Ashley Miller Pamela Schochenmaier

Vermont Department of Health Marieke Jackson Jessie Curran Caitlyn Dayman Patrick Henry Barbara Carroll

Cleveland Department of Public Health, OH David Bruckman Jana Rush Vino Sundaram

Cuyahoga County Board of Health, OH Christopher Kippes Becky Gawelek Domenica McClintock Carl Preusser

Denver Public Health Department, CO Tracey Richers-Maruyama Teddy Montoya Jennifer Wieczorek Kaylynn Aiona Christie Mettenbrink

Erie County Department of Health, PA Jeff Quirk Laura Luther Mathew Elwell Valerie Bukowski

Lake County Division of Community Health Services, OH Katelyn Coan Kathy Durchik Kathy Milo Ron Graham

Tri-County Health Department, CO Alix Hopkins Christine Dermont-Heinrich Dani Searle Maura Proser

National Association of Chronic Disease Directors Margaret Casey

Children’s Environmental Health Initiative, University of Michigan Mara Geller Kafi Laramore-Josey Ruiyang Li Kaleah Mabin Marie Lynn Miranda Nicole Sandberg Ben Strauss Joshua Tootoo

US Centers for Disease Control and Prevention Michele Casper Sophia Greer Linda Schieb

chronic diseAse gis exchAnge To see additional maps that address heart disease, stroke and other chronic diseases, visit the Chronic Disease GIS Exchange at www.cdc.gov/dhdsp/maps/gisx. The site includes a map gallery, GIS training modules, and a wide range of GIS resources. Visitors to the site are also invited to submit their own map to the map gallery.

i

introduction

Geographic Information Systems (GIS) are powerful tools for enhancing the ability of health departments to address the public health burden of heart disease, stroke, and other chronic diseases. In order to build the capacity of health departments to utilize GIS for the surveillance and prevention of chronic diseases, the Division for Heart Disease and Stroke Prevention at the national Centers for Disease Control and Prevention (CDC) funds a collaborative training project with the National Association of Chronic Disease Directors and the University of Michigan. The central objective of this GIS Surveillance Training Project is to enhance the ability of health departments to integrate the use of GIS into daily operations that support existing priorities for surveillance and prevention of heart disease, stroke, and other chronic diseases. Staff members from health departments receive training regarding the use of GIS surveillance and mapping to address four major purposes:

• documenting geographic disparities • informing policy and program decisions • enhancing partnerships with external agencies • facilitating collaboration within agencies

In 2014, the following state health departments were competitively selected to participate in this GIS Surveillance Training Project: California, Kansas, New Mexico, South Dakota, and Vermont. The following local health departments were also selected to participate: Cuyahoga County, Ohio; Cleveland, Ohio; Lake County, Ohio; Erie County, Ohio; Denver, Colorado; and Tri-County, Colorado. The project is intentionally designed to develop a GIS infrastructure that can serve a vast array of chronic disease areas, yet with a focus on heart disease and stroke.

The maps displayed in this document highlight examples of how each participating health department produced maps to support their chronic disease priorities by documenting the burden, informing program and policy development, and enhancing partnerships. The extent of collaboration among chronic disease units within each health department is evident in the diversity of the teams that participated in the training and have continued to work to strengthen GIS infrastructure within their respective health departments.

ii

cAliforniA

Local Tobacco Retailer License (TRL) Ordinances and Tobacco Retailer Density in California, 2014

Del Norte

Siskiyou Modoc

Trinity Shasta

Lassen Humboldt

Tehama Plumas

Mendocino Glenn Butte Sierra

Colusa Yuba Nevada

Lake Sutter

Placer

Sonoma Yolo El Dorado

Sacramento Alpine

Napa Solano Amador

Marin Calaveras

Contra Costa San Joaquin Tuolumne Mono

Alameda San Mateo Mariposa

Stanislaus Santa Clara

Santa Cruz Number of tobacco retailers per 10,000 population Merced Madera

8 - 10

Fresno San Benito 11 - 15

Inyo

>15 Tulare Monterey

Kings

Local Tobacco Retailer License Ordinances

Kern San Luis Obispo

San Bernardino

Santa Barbara

Los Angeles Ventura

Riverside

Orange

San Diego Imperial

0 20 40 80 Miles ±

Key Points

• Increased exposure to tobacco marketing in retail stores leads to increased youth tobacco use and also decreases likelihood of quitting.

• Research has documented that TRL ordinances can be used to limit the location and density of tobacco retailers and thereby reduce the tobacco industry’s influence.(1)

• This map suggests that the density of tobacco retailers is smaller in most areas with a TRL ordi-nance than in areas without a TRL ordinance.

Data source: California Policy Evaluation Tracking System, California Board of Equalization Tobacco Licensing Lis, Census Buearu Produced by: California Tobacco Control ProgramSources: Esri, USGS, NOAA

1. Wooten H, McLaughlin I, Chen L, Fry C, Mongeon C, Graff S. Zoning and licensing to regulate the retail environment and achieve public health goals. Duke Forum for Law & Social Change. 2013;5(65:65-96).

1

Adult Type 2 Diabetes Prevalence andLocations of Lifestyle Change and Self-Management Programs, 2015

cAliforniA

Adult Type 2 Diabetes Prevalence and Locations of Lifestyle Change and Self-Management Programs, 2015

Copyright:© 2013 National Geographic Society, i-cubed, Sources: Esri, USGS, NOAA

0 40 80 Miles

Adult Type 2 Diabetes Prevalence (%)

2.7 - 5.7

5.8 - 6.5

6.6 - 7.3

7.4 - 8.8

8.9 - 13.2

Unstable

30 minute drive-time boundary

Lifestyle Change/Self-Management Programs

³National Diabetes Prevention Program (NDPP)

Diabetes Self-Management Education (DSME) Program

Chronic Disease Self-Management Program (CDSMP)

Key Points

• An estimated 1.9 million Californian adults (6.9%) have been diagnosed with type 2 diabetes.

• This map helps identify gaps in access to lifestyle change and self-management programs for diabetes and opportunities for collaborations among the different programs.

• Only 40 counties (69%) have DSME programs and approximately 10% of Californians live outside a 30-minute drive-time boundary of NDPPs, DSME programs, and CDSMPs.

Data Sources: Estimated percent of California adults (aged 18 and above) who were ever told by a doctor that they have Type 2 diabetes based on over 40,000 surveyed via the California Health Information Survey (CHIS) 2011-2012. Unstable estimates have relative standard errors ≥ 30%. NDPPs updated February 2015 from national registry (http://nccd.cdc.gov/DDT_DPRP/Registry.aspx). DSME programs updated February 2015 and include diabetes education programs accredited by the American Association of Diabetes Educators (http://www.diabeteseducator.org/ProfessionalResources/accred/Programs.htm) and/or recognized by the American Diabetes Association (http://professional.diabetes.org/erp_list.aspx). CDSMP workshops held January 2014 thru February 2015 via Partners in Care Foundation. S Conroy, Chronic Disease Control Branch, CDPH, March 2015.

2

Licensed tobacco retailer locations and poverty,Sed wick Count , Kansas

kAnsAs

Licensed Tobacco Retailer Locations and Poverty, Sedgwick County, Kansas

g y

3

Licensed Tobacco Retailer

Percent of population below poverty level by tract

0.3% - 4.9%

5% - 8.7%

8.8% - 15.8%

15.9% - 28%

28.1% - 53%

¯ 0 1.5 3 6 9 12 Miles

Key Points

• Prevalence of poverty among census tracts within Sedgwick County ranges from 0.3% to 53.0%.

• Tobacco retailers are more densely concentrated in poorer areas in Sedgwick County.

• The purpose of this map is to visually demonstrate how tobacco access is geo-graphically correlated with poverty.

Data Source: 2012 American Community Survey 5-year; 2014 Kansas Department of Revenue

Kansas Chronic Disease Self-Management Program Workshop SitesJuly 1, 2012 through June 30, 2014

kAnsAs

Kansas Chronic Disease Self-Management Program Workshop Sites, 2012-2014

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Derby

Norton

Newton

Topeka

Ottawa

Hugoton Liberal

Wichita

Kingman

Peabody

Merriam

Garnett

Grinnell Stockton

Sterling

Mt. Hope

Concordia

Manhattan

Hoisington

Great Bend

Greensburg

Burlington

Garden City

Clay Center

Coffeyville

Leavenworth

±0 30 6015 Miles

Kansas CityLenexa Olathe

Overland Park

Arthritis Prevalence Insufficient data

0.1% - 18.8%

18.9% - 24.7%

24.8% - 28.6%

28.7% - 33.8%

Workshops ! Yr1 workshops n=40

V Yr2 workshops n=23

Data Source: Arthritis prevalence: 2011 Behavioral Risk Factor Surveillance System, Bureau of Health Promotion, Kansas Department of Health and Environment; CDSMP workshops: 2012-2014 NCOA database

• The Kansas Arthritis Program tries to increase healthcare access for people with arthritis and other chronic conditions so that they can improve self-management.

• One purpose of this map is to deter-mine which areas with high prevalence of arthritis have not yet been targeted.

• The map will help the Bureau of Health Promotion strategically plan where new workshop sites will be implemented.

Key Points

4

N E W ME X I C O

Status of School District Wellness Policies in New Mexico, School Year 2013-2014

Gallup

Grants-Cibola Santa Rosa

Vaughn

Mesa Vista Chama Valley

Lake Arthur

Roswell

Central

Deming

Socorro

Silver City

Belen

Cobre

Zuni

Las Cruces

Hobbs

Dexter

Clovis

Aztec

Los Lunas

Farmington

Texico

Portales

Hagerman

Rio Rancho

District Wellness Policy Progress

In Proce

Updated & Approved Policies

Implemented New Policies

No Progress

Status of School District Wellness Policies

±0 40 80 120 16020 Miles

Key Points

• Healthy Kids New Mexico works with publicschool districts to update and strengthen their wellness policies to include language supporting healthy eating, physical activity, and staff wellness.

• This map shows the progress school districtsare making in updating and strengthening their wellness policies.

• This map is helpful for statewide programming, strategic planning, identifying gaps and opportuni-ties, and building collaborative partnerships across state agencies and organizations.

Data Source: Healthy Kids New Mexico program data 2013-2014

5

new mexico

New Mexico’s Evidence-Based Manage Your Chronic Disease (MyCD) and Tomando Control de su Salud Programs, 2010 -2015

Farmington Rio Arriba Colfax Union

San Juan

Taos

Española Mora Harding Los

Alamos

McKinley Sandoval San Miguel Santa Fe Las Vegas Rio Rancho

ABQ Quay Grants Bernalillo

Cibola Valencia Guadalupe

Torrance Curry

De Baca

Socorro

Roosevelt ´ Catron Lincoln Chaves _̂ MyCD

! MyCD and Tomando U Roswell

MyCD - Most Recent Sites (as of 9/2012) Sierra ![ Grant Lea

FY15 Counties Targeted for Expansion

30 Minute Drive Doña Ana Otero

Eddy !( Center of Population

Las Cruces Major Interstate Luna

Hidalgo 0 25 50 100 150 200

Miles

Esri, HERE, DeLorme, MapmyIndia, © OpenStreetMap contributors, and the GIS user community

New Mexico's Evidence-BasedManage Your Chronic Disease (MyCD) and Tomando Control de su Salud (Tomando) Programs: 2010-2015

30 Minute Drive Time to Six-Week Community Workshop Locations

• Most of New Mexico is considered rural orfrontier and there are large distances between central cities and outlying towns, making health-care access diffi cult for residents.

• The New Mexico Diabetes Prevention andControl Program is working with statewide part-ners to increase access to evidence-based chronic disease self-management programs.

• This map can be used by the state to implementnew programs in areas that still lack access to healthcare resources.

Key Points

6

Colorectal Cancer in South Dakota: Age-Adjusted Rates of Incidence,Distant (Stage IV) Incidence, and Mortality (2002-2011)

south dAkotA

Colorectal Cancer in South Dakota: Age-Adjusted Rates of Incidence, Distant (Stage IV) Incidence, and Mortality, 2002-2011

4.2- 14

.

14.3

- 17.

17.8

- 21.

21.8

- 35.

Distant (Stage IV) Incidence

Meade

Butte

Perkins

Corson

Dewey

Harding

Tripp

Day

Todd

Spink

Hand

Brown

Lyman

Ziebach

Pennington

Jackson

Haakon

Custer

Shannon

Sully

Stanley

Fall River

Faulk

Clark

Beadle

Jones

Hyde

Brule Mellette

Bennett

Roberts

Potter

Gregory

Lake

Edmunds

Grant

Deuel

Hughes

Aurora

Marshall McPherson

Clay

Miner

Turner

Kingsbury Brookings

Campbell

Lincoln

Hamlin

Walworth

Jerauld Moody

Hutchinson

Minnehaha McCook

Codington

Buffalo Sanborn

Yankton

Douglas

Charles Mix

Lawrence

Union

Hanson Davison

Bon Homme

Meade

Butte

Perkins

Corson

Dewey

Harding

Tripp

Day

Todd

Spink

Hand

Brown

Lyman

Ziebach

Pennington

Jackson

Haakon

Custer

Shannon

Sully

Stanley

Fall River

Faulk

Clark

Beadle

Jones

Hyde

Brule Mellette

Bennett

Roberts

Potter

Gregory

Lake

Edmunds

Grant

Deuel

Hughes

Aurora

Marshall McPherson

Clay

Miner

Turner

Kingsbury Brookings

Campbell

Lincoln

Hamlin

Walworth

Jerauld Moody

Hutchinson

Minnehaha McCook

Codington

Buffalo Sanborn

Yankton

Douglas

Charles Mix

Lawrence

Union

Hanson Davison

Bon Homme

Meade

Butte

Perkins

Corson

Dewey

Harding

Tripp

Day

Todd

Spink

Hand

Brown

Lyman

Ziebach

Pennington

Jackson

Haakon

Custer

Shannon

Sully

Stanley

Fall River

Faulk

Clark

Beadle

Jones

Hyde

Brule Mellette

Bennett

Roberts

Potter

Gregory

Lake

Edmunds

Grant

Deuel

Hughes

Aurora

Marshall McPherson

Clay

Miner

Turner

Kingsbury Brookings

Campbell

Lincoln

Hamlin

Walworth

Jerauld Moody

Hutchinson

Minnehaha McCook

Codington

Buffalo Sanborn

Yankton

Douglas

Charles Mix

Lawrence

Union

Hanson Davison

Bon Homme

Source: SD Cancer Registry; SD Department of Health

Incidence Rates

17.2

- 46.2

46.3

- 51.3

51.4

- 60.5

60.6

- 82.2

Mortality Rates

2 7 7 5

Distant (Stage IV) Incidence Rates

0.0-6

.1

6.2-8.1

8.2- 10

. 4

10.5- 21

. 3

Incidence

Mortality

*

*

*

0 45 90 180 Miles *Rates per 100,000 age-adjusted to the 2000 US standard population

Key Points • Colorectal cancer accounted for 9.7% of all cancer cases reported in South Dakota in 2011.

• According to the U.S. Preventive Services Task Force, regular screening, beginning at age 50, is the key to preventing colorectal cancer.

• These maps will assist the state with plan-ning interventions and determining where additional screening services are needed.

7

SO U T H D A KOT A

Diabetes Prevalence and Prevention Programs Among South Dakota Adults, 2011

#

#

#

#

#

#

##

#

#

#

#

#

#

#

#

##

#

#

#

#

#

#

#

#

#

Meade

Butte

Perkins

Corson

Dewey

Harding

Tripp

Day

Spink

Hand

Brown

Lyman

Ziebach

Jackson

Pennington

Custer

Haakon

Shannon

Sully

Stanley

Fall River

Faulk

Clark

Beadle

Jones

Hyde

Brule

Mellette

Bennett

Roberts

Potter

Gregory

L

Edmunds

Grant

D el

Hughes

Aurora

MarshallMcPherson

Clay

Charles Mix

Miner

Turner

KingsburyLawr e

Campbell

Lincol

Brookings

Union

Hamlin

Walworth

Jerauld Moody

Hutchinson

MinnehahaMcCook

Codington

Buffalo Sanborn

Yankton

HansonDavis

Douglas

Bon Homme0 6030 Miles±Self Management

Prevention Program

cent Diabetes Prevalence

Prevention Programs

#

#

#

##

##

#

#

#

#

#

#

#

#

#

#

##

#

#

#

#

#

# Diabetes Self Management

" Diabetes Prevention Program

Age-Adjusted Diabetes Prevalence 6.3 - 7.0

7.1 - 7.4

7.5 - 7.7

7.8 - 9.4

9.5 - 18.6

Prevention Programs

enc

Todd

on

ake

n

eu

Source: Data is age-adjusted BRFSS dataaccessed via CDC Diabetes Interactive Atlas

r

Diabetes Prevalence and Prevention Programs Among South Dakota Adults, 2011

8

Key Points

• Prevalence of diabetes within SouthDakota counties ranges from 6.3%-18.6%.

• Diabetes Self-Management and PreventionPrograms are proven ways to combat and control the disease.

• The purpose of this map is to determinethe need for and location of future diabetes self-management and prevention program sites.

*Drive Times to Certified Diabetes Educator (CDE) sites, 2014

Vermont

Drive Times to Certified Diabetes Educator (CDE) Sites*, 2014

!(

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!(

!( !(

!(

Drive time to CDE site*

!( !( 0-15 minutes 16-30 minutes 31-45 minutes More than 45 Minutes

!( CDE Sites (3 days/week)

¯ i0 25 5012.5 M les

• Only 18% of Vermonters live within 15 minutes of a site where a CDE is available at least 3 days a week.

• This map shows that there are several areas in Vermont where the drive time is more than 30 minutes to reach a CDE.

• The purpose of this map is to show areaswhere more Certified Diabetes Educators are needed.

Key Points

*Sites with CDE available at least 3 times per week.

For more information contact VDH-GIS@state.vt . us

Data sourc es: CDE s fr om the Vermon t Association of Dia bete s Educa tors pr v

o id ed th e pract ice locations and

their FTE information for each site as of September 2014.

9

Hospitalization Rates for Diseases of the Heartby Vermont County of Residence, 2007 - 2009

Vermont

Hospitalization Rates* for Diseases of the Heart by Vermont County of Residence, 2007-2009

County rates compared to State Rate** significantly lower not different significantly higher

2007 2008 State Rate = 91.0 State Rate = 91.6

Essex Essex Franklin Grand Orleans Franklin Orleans 92.7 122.8 106.1 Isle 91.6 84.7 97.8 66.6 Grand Isle Lamoille 100.2 Lamoille 82.1 68.6

Chittenden Caledonia Chittenden Caledonia 76.5 91.3 80.7 92.1 Washington Washington

82.5 80.1

Addison Addison Orange Orange 78.5 74.4 103.3 98.2

Windsor Windsor 95.3

Rutland 93.8 Rutland 105.8 106.6

Bennington Bennington 134.4 134.5

Windham Windham 81.5 79.8

§ 0 5 10 20 30 40 Miles

2009 State Rate = 82.8

Grand Isle Essex Franklin Orleans 93.2 115.3 82.8 89.3

Lamoille 74.6

Chittenden Caledonia 72.8 84.6

Washington 66.4

Addison Orange 77.6 103.5

Windsor 84.9 Rutland

104.1

Bennington 101.7

Windham 67.0

Key Points • These maps illustrate variation of hospi-talization rates among individual counties and show a slight decrease of the state rate over time.

• While the state rate has declined from 2007 to 2009, the total number of coun-ties with significantly higher rates has increased.

• The maps can be used to illustrate areas of Vermont with relatively higher or lower rates of hospitalization for heart disease.

*Rates are age-adjusted to the US 2000 standard population, per 10,000. Includes Vermont residents hospitalized in Vermont or neighboring states with a primary ICD-9-CM diagnosis code of 390-398, 402, 404, or 410-429. Data Source: Vermont Uniform Hospital Discharge Data Set, 2007-2009.

**Comparisons of county rates to state rates are considered statistically significant when confidence limits are non-overlapping.

10

cleVelAnd, ohio

Age-Adjusted Heart Disease Mortality Rates, by Neighborhood with Area Hospitals, City of Cleveland, 2008-2012

®

®

³

Overall Heart Disease Death Rates

300 250 200 150 100

50 0

211.5

268.3

196.4 184.9

Cuyahoga Cleveland Ohio Nation County

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Broadway-Slavic Village

Cudell

Stockyards

Detroit Shoreway

Kamm's Old Brooklyn

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University

Ohio City

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Goodrich-Kirtland Pk

v Hospitals

Rates significantly higher than City rate (268.3)

Insufficient Data

0.0

0.1 - 237.4

237.5 - 253.3 (City rate, blacks)

253.4 - 316.9

317.0 - 433.8

433.9 - 693.2

Black Age-Adjusted Heart DiseaseMortality Rates

White Age-Adjusted Heart Disease

v Hospitals

Rates significantly lower than City rate (268.3)

Rates significantly higher than City rate (268.3)

Insufficient Data

0.0

0.1 - 203.8

203.9 - 303.7 (City rate, white)

303.8 - 338.6

338.7 - 365.0

365.1 - 629.4

Mortality Rates

Key Points

• The average rate of heart disease mortality in Cleveland was 268.3 per 100,000, which is higher than the state average and national average.

• Five neighborhoods in each of the black and white demographics had heart disease mortality rates that were significantly higher than the city’s average.

• The purpose of this map is to highlight disparities in heart disease mortality within the city of Cleve-land so that public health officials can specify where chronic disease interventions are needed most.

Map is based on average annual age-adjusted heart disease mortality rate over the five year period in the City of Cleveland’s Statis-tical Planning Areas (SPA) as neighborhoods. Rate is determined by the number of deaths per 100,000. Age-adjusted to 2000 U.S. standard population. Data Source: Ohio Department of Health. Stroke deaths defined as ICD-10 codes: 100-109, I11, I13, I20-I51.

11

cuyAhogA county, ohio

Health Improvement Partnership - Cuyahoga Racial and Ethnic Approaches to Community Health Target Communities

Stroke Death Rate Age-Adjusted (per 100,000)

Percentage of 18 to 24 year olds with less than High School Education

0.0 - 6.4 0.0 - 20.0

18 to 24 year olds with less than High School Education

GF

GF

GFGF GF

GF

GF

GF GF

GF

GF

GF GF

GF

Stroke Deaths

0 2.5 Miles

20.1 - 29.0 6.5 - 13.8 29.1 - 40.0 13.9 - 24.3

Ü 40.1 - 55.0 24.4 - 36.9 55.1 - 150.0 37.0- 100.0 Data Insufficient

GF Federally Qualified City of Cleveland Health Center

(FQHC)

Target Communities Cuyahoga

County

Key Points

• Racial and Ethnic Approaches to Community Health (REACH) intends to increase access for better nutri-tion, more physical activity and improved chronic dis-ease prevention in 22 census tracts across Cleveland.

• These maps show REACH target communities and the locations of federally qualified health centers where hypertension programs are being implemented to help improve chronic disease management.

• These maps will be used to provide a baseline of the chronic disease burdens and social determinants of health within these target communities.

Source: Ohio Department of Health (ODH) Vital Statistics ICD-10 codes: I60-69. Age-Adjusted to the 2000 U.S. Standard Population and presented using 2010 census tracts. Map created and analysis performed by Epidemiology, Surveillance, and Informatics at the Cuyahoga County Board of Health, March 2015. ODH specifically disclaims responsibility for any analyses, interpretations or conclusions. Federally Qualified Health Centers data downloaded from the Health Resources and Services Administration (HRSA) Data Warehouse, April 28, 2014 http://datawarehouse.hrsa.gov/data/datadownload/hccdownload.aspx State and national rate source: Division for Heart Disease and Stroke Prevention: Interactive Atlas. Centers for Disease Control and Prevention. Available at http://nccd.cdc.gov/DHDSPAtlas/#. Accessed on 7/22/14.

12

0 0.05 0.1 Miles0.025

denVer , colorAdo

Smoking Violations on Denver Health Campus, Jan.-Aug. 2014

“No Smoking” Signs Ð

Smoking Violations 1

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8th Avenue

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Miles

6th Avenue 5

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Source: Esri, DigitalGlobe, GeoEye, i-cubed, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community

Key Points

• The Denver Public Health Chronic Disease Tobacco Team conducted weekly surveys of the Denver Health Campus to see if smoking violations were a serious issue on the hospital’s campus.

• The areas with the largest amount of smoking violations seem to be near major roadways and parking lots.

• Smoking violations represent smokers who were hospital system employees, patients, or visitors.

Data collected by the DPH Chronic Disease Tobacco Team during weekly campus audits.

13

Facilitating Collaboration The GIS Surveillance Training Program was intentionally designed to develop a GIS infrastructure that would facilitate collaboration among an array of chronic disease units within each health department, yet with a focus on heart disease and stroke. To that end, the staff members from each health department that participated in the training represented different chronic disease units. Each health department was led by a member of the heart disease and stroke unit (bold).The following lists the chronic disease units that were represented in each of the participating health departments:

California Department of Public Health

Name Chronic Disease Unit Brendan Darsie California Colon Cancer Control Program Janet Bates Chronic Disease Control Branch Madhurima Gadgil Chronic Disease Control Branch Shannon Conroy Chronic Disease Control Branch Xueying Zhang California Tobacco Control Program

Kansas Department of Health and Environment

Name Chronic Disease Unit Trevor Christensen Bureau of Health Promotion/ Epidemiology Cynthia Snyder Bureau of Health Promotion Erika Welsh Bureau of Health Promotion/Epidemiology Virginia Barnes Bureau of Health Promotion/Heart Disease and Stroke Program

New Mexico Department of Health

Name Chronic Disease Unit Katharine VonRueden Chronic Disease Prevention and Control Bureau/Epidemiology Bryan Patterson Emergency Medical Systems Bureau, Division of Epidemiology and Response Christopher Lucero Diabetes Prevention and Control Program Bambi Bevill Chronic Disease Prevention and Control Bureau/Public Health Division

14

Facilitating Collaboration South Dakota Department of Health

Name Chronic Disease Unit Carrie Cushing South Dakota Department of Health Lexi Haux Comprehensive Cancer Control Program Ashley Miller Office of Chronic Disease Prevention and Health Promotion/Epidemiology Pamela Schochenmaier Heart Disease and Stroke Prevention Program

Vermont Department of Health

Name Chronic Disease Unit Marieke Jackson Vermont Department of Health Jessie Curran Vermont Department of Health Caitlyn Dayman Epidemiology Patrick Henry Women, Infants, and Children Program Barbara Carroll Vermont Uniform Hospital Discharge Data

Local Health Departments

Cleveland Department of Public Health, OH David Bruckman, MS; Chief Systems Analyst / Biostatistician Jana Rush; Chief Epidemiologist Vino Sundaram; Epidemiologist

Erie County Department of Health, PA Jeff Quirk; Public Health Preparedness/Epidemiology Laura Luther; Program Coordinator Mathew Elwell; Public Health Preparedness Coordinator Valerie Bukowski; Erie County Department of Health

Cuyahoga County Board of Health, OH Becky Gawelek; Researcher Carl Preusser; Registered Sanitarian Christopher Kippes; Director Domenica McClintock; Supervisor

Lake County Division of Community Health Services, OH Katelyn Coan; Lake County Health District Kathy Durchik; Clinical Services Kathy Milo; Health Promotion and Planning Ron Graham; Health Director

Denver Public Health Department, CO Christie Mettenbrink; Epidemiologist Jennifer Wieczorek; Chronic Disease Manager Kaylynn Aiona; Statistical Research Specialist Teddy Montoya; Health Program Specialist Tracey Richers-Maruyama; Program Manager

Tri-County Health Department, CO Alix Hopkins; Nurse Manager Christine Dermont-Heinrich; Public Health Planner Dani Searle; Nutrition Maura Proser; Prevention Manager

15

16

PARTICI PANTS TO DATE

CaliforniaKansas

South Dakota

New Mexico

Utah

Idaho

Montana

Colorado

Nebraska

Oklahoma

Texas

Arkansas

Louisiana

Mississippi

Florida

South Carolina

Iowa

Minnesota

Wisconsin

OhioIndiana

Pennsylvania

New York

New Hampshire HampshireMassachusettsetts

Oregon

Washington

Nevada

Arizona

Wyoming

North Dakota

Missouri

Illinois

Kentucky

Tennessee

Alabama Georgia

Virginia

WestVirginia

Delaware

Trained state

Trained local clusters

NorthCarolina

Vermont

Maine

Kentucky

Pennsylvania

OhioIndiana

North

VirginiaVirginia

West

Michigan

Illinois

N

W

S E-mail: cehi.snre@umich.edu

http://cehi.snre.umich.edu