The Potential of Geographic Information Systems (GIS) to Facilitate Data-Driven Prevention

96
Barbara Seitz de Martinez, PhD, MLS, CPP Desiree Goetze, MPH, CHES, CPP Kaigang Li www.drugs.indiana.edu Partnerships to Facilitate Prevention and Recovery: Transformation Where It Counts! The Potential of Geographic Information Systems (GIS) to Facilitate Data-Driven Prevention

description

The Potential of Geographic Information Systems (GIS) to Facilitate Data-Driven Prevention. Barbara Seitz de Martinez, PhD, MLS, CPP Desiree Goetze, MPH, CHES, CPP Kaigang Li www.drugs.indiana.edu. Partnerships to Facilitate Prevention and Recovery: Transformation Where It Counts!. - PowerPoint PPT Presentation

Transcript of The Potential of Geographic Information Systems (GIS) to Facilitate Data-Driven Prevention

Page 1: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Barbara Seitz de Martinez, PhD, MLS, CPPDesiree Goetze, MPH, CHES, CPP

Kaigang Li www.drugs.indiana.edu

Partnerships to Facilitate Prevention and Recovery: Transformation Where It Counts!

The Potential of Geographic Information Systems (GIS)

to Facilitate Data-Driven Prevention

Page 2: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 2

What You Will Learn:Components of GIS system

How GIS Helps with Strategic Planning

How GIS Can Help You with Program Evaluation Surveillance System Track Change over Time and Space

How GIS Plays a Role in Research: Example: Impact of Smoking and Coalitions Morbidity and Mortality Making Sense of the Data

Page 3: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 3

Data… What is it?It’s up to you!

http://skywalker.cochise.edu/wellerr/rocks/sdrx/limestoneL.htm

www.breitbart.com/article.php?id=070312165704.qtgd3817&show_article=1&cat=us&image=large

Page 4: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 4

Use it to enhance quality of life It’s what you make of it!

http://www.bluffton.edu/~sullivanm/england/london/parliament/frmriver.jpg

Limestone detail, IU

House of Parliament, London, England

http://www.indexstock.com/content/artists/Rich%20Remsberg.asp

Page 5: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 5

Data-driven prevention…but you driveIt’s up to you!

http://newsinfo.iu.edu/asset/page/normal/1450.html

Little 500 Bike Race, IU

Page 6: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Components of a GIS System

Minimal Equipment and Personnel Skill Requirements

Page 7: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 7

GIS System: Levels of Complexity

Level 3: Complex data import & analyze

Level 2: Simple data imports & analysis

Level 1: Extract data

Page 8: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 8

Level 1 – Extract Data

Equipment: Hardware and Software

Data: Purchased Databases

www.tetrad.com

Kinds of Skills (Capacity Building)

MapInfo, PCensus, Maploader

(1)

Purchased GIS data

(1)

Basic ComputerAnd Printer

(1)

Basic ComputerSkills(1)

Page 9: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 9

Level 1 – Extract Data

MapInfo, PCensus, Maploader

(1)

Purchased GIS data

(1)

Basic ComputerSkills(1)

Single Parent Families 2006, Percent (AGS, 2006 est., 2007)

County Lake Co. IN U.S.Lone Parent Male (percent) 10.9 6.7 7.4Lone Parent Female (Percent) 28.9 23.2 25.6Single Parent Families (M+F) % 39.8 29.9 33

Page 10: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 10

Alcohol Spending per HouseholdPer Household Spending on Alcohol, 2006 est. (AGS, 2007)

  Adams Indiana U.S.

Spending on Alcohol 529.8 664.9 621.7

Beer and ale not at home 73.9 93 87

Wine away from home 36.1 45.4 42.5

Whiskey away from home 60.1 75.6 70.7

Alcohol On Out-Of-Town Trips 65.4 81.7 76.4

Spending on Alcohol for Consumption in the Home 292.9 367.2 343.4

Beer and ale at home 157.2 197.1 184.3

Wine at home 84.7 106.2 99.3

Whiskey at home 20.7 25.9 24.2

Whiskey and other Liquor at Home 51.1 63.9 59.7

Median Household Income

45,791.50 54,272.10

48,276.60

Total Spending Per HH as % of Median HH income 1.155 1.225 1.288

Rank for Spending as % of Median HH Income 67 40th of 51  

Page 11: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 11

Adults without a High School Diploma

Educational Attainment, 2006 est. (AGS, 2007)  Lake Co. Indiana U.S.Less than 9th grade 3 5 5.89th to 12th grade, no diploma 9.5 9.7 8.9Total, Less Than 9th or less than HS Diploma 12.5 14.6 14.7High school graduate 40.4 36.5 29.8Some college, no degree 20.8 19.7 19.9Associate degree 8.5 6.7 7.4Bachelor's degree 11.4 14.2 17.9Graduate or profession degree 6.4 8.2 10.3

Page 12: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 12

Data can seem overwhelmingToo much data can seem overwhelming.

http://entertainment.webshots.com/photo/1041702010015901341YseGEP

Page 13: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Lake County – Less Than High School Diploma

Source: AGS, 2007 estimates

Page 14: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Source: AGS, 2007 estimates

Lake County – Less Than High School Diploma

Page 15: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Census Tracts: 30300; 30100; 30200; 31000; 20400; 20600; 10202; 10700; 10500; 11300; 41200

Lake County – Less Than High School Diploma

Source: AGS, 2007 estimates

Page 16: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Lake County – Less Than High School Diploma

Page 17: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Family Conflict: Divorce

Divorce Rate, 2006 est. (AGS,2007)

County Lake Indiana U.S.

Divorced (Percent) 10.2 10.7 9.7

Page 18: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Lake County – Divorce Rate

Source: AGS, 2007 estimates

Page 19: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Census Tracts: 20600; 10203; 10202; 10201; 10900; 11300; 11700; 11600;11900; 41600; 12600

Source: AGS, 2007 estimates

Lake County – Divorce Rate

Page 20: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention
Page 21: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 21

Level 2 – Your Data / Maps

Equipment: Geocoding Software Color Printer

Data: Local data Addresses

Skills (Capacity Building) Patience, precision Microsoft Excel, Microsoft Access

Page 22: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Software Required

MapInfo, PCensus, Maploader

(1)

Mapmarker Geocoding software, Excel & Access

(2)

Page 23: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Data Complexity

Purchased GIS data

(1)

Imported data (free or purchased)

(2)

Page 24: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Examples of Data

AGS, Claritas™,Map files

(1)

Program Data, Address Data, Health Data (public

or purchased)(2)

Page 25: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Skills Required

Basic Computer(1)

Geocoding, Excel and Access

(2)

Page 26: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 26

Afternoons R.O.C.K. in Indiana

Indiana Prevention Resource Center

Afternoons R.O.C.K. Programs, 2005-2006 School Year

Afternoons R.O.C.K. in Indiana, 2006

Page 27: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 27

Places of Worship

Indiana Prevention Resource Center

Places of Worship, 2005

Info USA, American Church List, 2005

Page 28: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 28

Youth Serving Agencies

Info USA, American Church List, 2005

Youth Serving Agencies, 2005

Indiana Prevention Resource Center

Page 29: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 29

Public Libraries and Branches

Indiana State Library, 2006

Main Library, 2006

Branch Library, 2006

Indiana Prevention Resource Center

Page 30: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 30

Schools

Indiana Department of Education, 2005

School

Indiana Prevention Resource Center

Page 31: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 31Marion County Schools, Alcohol Retail Outlets by School District

Public SchoolOther SchoolAlcohol Retail Outlet

Marion County Alcohol Retail Outlets by Census Tract

Page 32: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 32Marion County Schools, Alcohol Retail Outlets by School District

Public SchoolOther SchoolAlcohol Retail Outlet

Marion County Alcohol Retail Outlets by School District

Page 33: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 33Marion County Schools, Alcohol Retail Outlets by School District

Public SchoolOther SchoolAlcohol Retail Outlet

Marion County Alcohol Retail Outlets by School District

Page 34: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Franklin Township School DistrictPublic SchoolOther SchoolAlcohol Retail Outlet

Page 35: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

M S D Perry Township School DistrictPublic SchoolOther SchoolAlcohol Retail Outlet

Page 36: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

MSD Washington Township SchoolsPublic SchoolOther SchoolAlcohol Retail Outlet

Page 37: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Indianapolis Public SchoolsPublic SchoolOther SchoolAlcohol Retail Outlet

Page 38: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 38Marion County Universities and Alcohol Retail Outlets

UniversityAlcohol Retail Outlet Butler

Indianapolis Downtown Campus of Ivy Tech

IUPUI

Marion College

U of Indy

Page 39: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 39

1 Mile Radius around Butler University

Page 40: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 40

1 Mile around University of Indianapolis

Page 41: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 41

1 Mile around Downtown Campus of IVY TECH

Page 42: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 42

1 Mile around Marion College

Page 43: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 43

1 Mile around IUPUI

Page 44: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 44

1 and 2 Miles around IUPUI

Page 45: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 45

Jefferson County Profile, 6.9

TRIP Inspection Data (using data for 2005 from IN State Excise Police), ATC 2006 

  Jefferson IndianaIntensity of Inspection 2 1No. Insp per 1,000 Youth, 10-17 13 12Population 10-17 3,686 726,218Total Population 32,343 6,270,352Total No. of Tobacco Retail Outlets 2005 33 6,401Total inspections Completed 49 8,503Total Failed Inspections 10 1,076Percent, Failed Inspections 20 13Percent, Passed Inspections 80 87Ranking (1-81) for % Failed Inspections 22  Ranking (1-81) for % Passed Inspections 62  

Table 6.9: Intensity of TRIP Inspections and Related Statistics, Calculations for 2005 Based on Data from the TRIP Program (ATC, Indiana State Excise Police, 2006)

Page 46: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Schools in Proximity to Failed Trip Inspections – Close-up

Jefferson County

Tobacco Outlets That Failed TRIP Inspections in 2006Children at School

Page 47: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Schools in Proximity to Failed Trip Inspections – Close-up

Madison, Indiana

Pope John XXIII School

Grace Baptist School

Shawe Memorial High School

Tobacco Outlets That Failed TRIP Inspections in 2006Children at School

Source: ISP, Excise Police, 2007

Page 48: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 48

Vigo County Profile, 6.5

Table 6.5: Clandestine Methamphetamine Lab Seizures, (ISP, 2007)(ATC, Indiana State Excise Police, 2006)

Methamphetamine Laboratory Seizures, 1998-2005 (ISP, 2006)

Vigo Indiana1998 (Any Agency) 1 431999 (Any Agency) 18 1292000 (Any Agency) 52 3142001 (Any Agency) 66 5422002 (Any Agency) 105 9882003 (Any Agency) 108 1,2782004 (Any Agency) 166 1,5492005 (IPS) 19 9892005 (Any Agency) 83 1,303

Page 49: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Map: Meth Lab Busts

Indiana Prevention Resource Center

Source: IN State Police, 2007

Total lab busts in 2006, 993

Page 50: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Map: Meth Lab Busts (prism)

Source: IN State Police, 2007 Total lab busts in 2006, 993

Indiana Prevention Resource Center

Page 51: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 51

Level 3 –Data import & analyze

Equipment: Geocoding Software SPSS

Data: Local data Public Purchased

Skills (Capacity Building) Higher level computer skills Higher level statistical skills

Page 52: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Software Required

MapInfo, PCensus, Maploader

(1)

Mapmarker Geocoding software, Excel & Access

(2)

SPSS (3)

Page 53: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Levels of Data Complexity

Purchased GIS data

(1)

Imported data (free or purchased)

(2)

More complex imported data(free or purchased)

(3)

Page 54: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Examples of Data

AGS, Claritas™(1)

Program Data, Address Data, Health Data (public

or purchased)(2)

Mortality Report Data Morbidity Data

(public or purchased) (3)

Page 55: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Skill Complexity

Basic Computer(1)

Geocoding, Excel and Access

(2)

Excel, Access, SPSS(3)

Page 56: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 56

Alcohol Retail Outlets – Density/Map

Indiana Alcohol Tobacco Commission, 2006

Indiana Prevention Resource Center

Alcohol Sales Outlets Per Capita, (IN ATC, 2006)

  Monroe Co. Indiana

Total Population (2006 est.) 121,477 6,310,320

Number of Outlets (March 2006) 251 11,011

Outlets Per Capita 0.0021 0.0017

Outlets Per 1,000 Persons 2.07 1.74

Page 57: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 57

Property Crime Indices

Above US (9), 101.55-194

Above IN (12), 95.55-194

Top Quarter (23), 64-194

Mid Range (46), 19-64

Lowest Quarter (23), 4-19AGS, Crime Indices2004 (2005)Indiana Prevention Resource Center

Bottom Quarter, Mid Range, Top Quarter (includes over IN & over US)

Page 58: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Using local data and GIS to analyzecounty level comprehensive tobacco

prevention in Indiana

Page 59: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 59

Environmental Strategies +Theoretical bases for environmental approaches

to prevention

Describe the elements of a comprehensive tobacco prevention program

Discuss how local data and GIS software can be used to evaluate the effectiveness of public health efforts

Analyze morbidity and mortality to determine public health impact within the community

Page 60: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 60

Indiana Map

Source: http://www.in.gov/itpc/Research2.asp?CatID=8

Page 61: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 61

Smoking Rates by Region

20

22

24

26

28

30

32

34

36

2002 2003 2004 2005

NWNCNECCESWSECW

Source: http://www.in.gov/itpc/Research2.asp?CatID=8

Page 62: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 62

NC & CW Regional Smoking Rates

0

5

10

15

20

25

30

35

2002 2003 2004 2005

NCCW

http://www.in.gov/itpc/Research2.asp?CatID=8

Page 63: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 63

Smoking Rates by Region

20

22

24

26

28

30

32

34

36

2002 2003 2004 2005

NWNCNECCESWSECW

Source: http://www.in.gov/itpc/Research2.asp?CatID=8

Page 64: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 64

Demographics: Education (Source: AGS, 2006 est., 2007)

Educational Attainment, Less Than a High School Diploma

  Less Than a High School Diploma RANK

North West 15% 7

North Central 18% 4

North East 17% 5

Central 14% 8

Central East 19% 2

South West 19% 2

South East 20% 1

Central West 16% 6

Page 65: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 65

Data frustrates …there will be obstacles

http://entertainment.webshots.com/photo/1040516534015901341MFYhbt

Page 66: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 66

Map: Education: Any College Degree (prism map)

AGS, Core Demographics,2005 estimates (2006)

NCCW

Page 67: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 67

Demographics: Race/Ethnicity (AGS, 2005 est.,2006)

Race/Ethnicity as Percent

  White Black Hispanic Other

North West 88% 7% 6% 2%

North Central 93% 2% 5% 1%

North East 95% 1% 3% 1%

Central 93% 3% 3% 2%

Central East 95% 2% 1% 1%

South West 96% 2% 1% 1%

South East 96% 1% 1% 0%

Central West 95% 2% 1% 1%

State of Indiana 87% 8% 4% 3%

Page 68: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 68

Map: Race/Ethnicity by County (prism)

AGS, Core Demographics,2005 estimates (2006)

Page 69: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 69

Demographics: Income (Source: AGS, 2006 est., 2007)

Text

Text

Income: Median Household, Median Family and Rankings

 

Median Household Income

RANK, Median Household Income

Less Than a High School

Diploma

RANK, High School Diploma or higher

North West $58,919 2 15% 2

North Central $53,495 4 18% 5North East $53,026 5 17% 4

Central $66,189 1 14% 1

Central East $44,933 8 19% 6

South West $50,541 6 19% 6

South East $54,290 3 20% 8

Central West $49,427 7 16% 3State of Indiana $58,575  

Page 70: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 70

Demographics: Income (Source: AGS, 2006 est., 2007)

Text

Text

Income: Median Household, Median Family and Rankings

 

RANK, Diversity

RANK, Median Household Income

RANK, High School

Diploma or higher

RANK, Tobacco Use

North West 1 2 2 5

North Central 2 4 5 8

North East 4 5 4 7

Central 3 1 1 4

Central East 5 8 6 2

South West 7 6 6 6

South East 8 3 8 3

Central West 5 7 3 1

Page 71: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 71

Map: Median Family Income (contour map)

AGS, Core Demographics,2005 estimates (2006)

NC

CW

Page 72: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 72

AGS, Core Demographics,2003 estimates (2004)

Adult Cigarette Smokers

Page 73: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 73

Indiana Adult Smoking Rates

22

23

24

25

26

27

28

29

30

2000 2001 2002 2003 2004 2005 2006

Indiana Smoking Rate

http://www.in.gov/itpc/Research2.asp?CatID=8

Page 74: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 74

Smoking Rates by Region

20

22

24

26

28

30

32

34

36

2002 2003 2004 2005

NWNCNECCESWSECW

Source: http://www.in.gov/itpc/Research2.asp?CatID=8

Page 75: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 75

NC & CW Regional Smoking Rates

0

5

10

15

20

25

30

35

2002 2003 2004 2005

NCCW

http://www.in.gov/itpc/Research2.asp?CatID=8

Page 76: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 76

Bronchitis

Source: Indiana State Department of Health, October 19, 2007

CW and CE regions Top 2 counties (CW) 3 of the top 5 counties

Only 20 counties reported deaths from bronchitis. All reported less than 5 deaths, making the rates “unstable.” But by using 2 regions, CW+CE compared to the rest of the state, the results are more reliable.

Region Deaths Mean Age-Adj Rate/100,000

CW+CE 8 3.05

Other 6 regions

15 2.0

Page 77: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 77

Chronic Airway Obstruction

Source: Indiana State Department of Health, October 19, 2007

CW and CE regions 3 of the top 4 counties 4 of the top 6 counties 5 of the top 9 (=10% of IN counties) 2 more of these 9 border these regions 7 of the top 9 counties in or border

Age-adjusted chronic airway obstruction mortality rate, 2005

Page 78: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 78

Emphysema

Source: Indiana State Department of Health, October 19, 2007

CW region Top county by more than 8 points

Age-adjusted emphysema mortality rate, 2005

Page 79: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 79

Malignant Neoplasm of the Trachea, Lung, Bronchus

CW and CE regions 4 of the top 10% of counties (9) 4 of the top 6 counties

Age-adjusted lung cancer mortality rate, 2005

33.8% 31.8%

Source: Indiana State Department of Health, October 19, 2007

Page 80: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 80

Ischemic Heart Disease

CW and CE regions Top county in CW 3 of the top 4 counties in CW 5 of the top 10 in CW or CE 8 of the top 16 in CW or CE 12 of the top 16 either in or borderingthe CW or CE regions.

Age-adjusted Ischemic Heart Disease mortality rate, 2005

33.8% 31.8%

Source: Indiana State Department of Health, October 19, 2007

Page 81: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 81

Malignant Neoplasm of the Esophagus

Age-adjusted malignant neoplasm of the esophagus mortality rate, 2005

Malignant Neoplasm of the EsophagusDoes not SEEM REMARKABLE.

Source: Indiana State Department of Health, October 19, 2007

Page 82: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 82

CVD

CVD SEEMS NOT REMARKABLE.

33.8%

Age-adjusted CVD mortality rate, 2005

Source: Indiana State Department of Health, October 19, 2007

Page 83: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 83

Morbidity Data

Cancer of Trachea, Lung, BronchusChronic Airway ObstructionBronchitis/EmphysemaCancer of the LipCancer of the EsophagusCancer of the LarynxAortic AneurysmSIDS

Source: Indiana State Department of Health, October 19, 2007

Page 84: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 84

Lung Cancer Incidence

CW region – rate of lung cancer incidence top county 2 of the top 5 counties 3 of the top 7counties

NE and NC region 2 lowest counties (NE) 3 of the 4 lowest counties 4 of the 6 lowest counties

Rank for rate of lung cancer incidence, 2003

33.8%31.8%

25.1%

25.1%

Source: Health Service Demand Estimates 2005.   Franking, TN:  Planning 2.0, 2006. 

Page 85: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 85

Lung Cancer Incidence

Region Smoking Mean Rate Tob-PrCos

CW 34% 79.7 1/12CE 31% 70.2 3/11

SE 31% 78.1 13/14

C 29% 73.3 1/12

NW 28% 70.1 0/5

SW 27% 70.2 7/16

NC 25% 67.6 0/5NE 26% 60 0/9

Perhaps tobacco production is associated with heavier smoking?IN State Department of Health, published in: Indiana Cancer Facts & Figures, 2006.  American Cancer Society, Great Lakes Division, Inc., 2006.

Page 86: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 86

Cancer of the Trachea, Lung, Bronchus

CW and CE regions 2 of top 3 (CW) 4 of top 9 counties (CW or CE) 6 of top 11 counties (CW or CE) 8 of top 11 counties (in or bordering CW or CE)

Rate/100,000 of DRG082 (C33), Cancer, 2005

33.8% 31.8%

Region Counties % IN Cos % T-R

CW/CE 24 26% of 92 66% of 3

CW/CE 24 26% of 92 44% of 9

CW/CE 24 26% of 92 54% of 11Source: Health Service Demand Estimates 2005.   Franking, TN:  Planning 2.0, 2006. 

Page 87: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 87

DRG088 Bronchitis, Emphysema and Chronic Airway Obstruction

CW and CE regions 3 of top 9 (top 10% of IN counties) (3 additional counties border CW or CE) 6 of the top 9 are in or border CW or CE 11 of top 18 counties (top 20%) in CW or CE

Rate/100,000 of DRG088, 2005

33.8%31.8%

Region Counties % IN Cos % T-R

CW/CE 24 26% of 92 33% of 9

CW/CE 24 26% of 92 61% of 18

Source: Health Service Demand Estimates 2005.   Franking, TN:  Planning 2.0, 2006. 

Page 88: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 88

DRG172 Malignant Neoplasm of the Esophagus

Rate/100,000 of DRG172, 2005

33.8% 31.8%

CW and CE regions 3 of top 9 (top 10% of IN counties) (3 additional counties border CW or CE) 6 of the top 9 are in or border CW or CE 10 of top 18 counties (top 20%) in CW or CE 55.5% of the top 18 counties are in CW/CE. These 2 regions constitute 26% of IN counties.

Source: Health Service Demand Estimates 2005.   Franking, TN:  Planning 2.0, 2006. 

Page 89: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 89

Elements Used for this Study

Community CoalitionPublic Education / Marketing Cessation ProgramsSchool – Based Programs Indoor Air PoliciesYouth Use Law EnforcementHigh Risk PopulationFunding Level

Page 90: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 90

Guiding PrinciplesIs it comprehensive?

Is it well funded?

Is it sustainable over time?

Does it operate free of the tobacco industry?

Does it address high risk populations?

Page 91: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 91

Regional FindingsCOUNTY SMOKING

RATESMOKING RATE RANK (1 = low)

COALITION STRENGTH RANK (1 = high)

NORTHWEST 28% 4 8

NORTH CENTRAL 25% 1 3

NORTH EAST 26% 2 6

CENTRAL 29% 5 1

CENTRAL EAST 32% 7 7

SOUTH WEST 27% 3 2

SOUTH EAST 31% 6 4

CENTRAL WEST 34% 8 5http://www.in.gov/itpc/Research2.asp?CatID=8

Page 92: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 92

Tobacco Coalition Strength

2825 26

2932

2731

34

0

5

10

15

20

25

30

35

40

NW NC

NE C

CE

SW SE

CW

TOBACCO COALITION SMOKING RATE INDIANA

http://www.in.gov/itpc/Research2.asp?CatID=8

Page 93: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 93

What You Have Learned:Components of GIS systemHow GIS Helps with Strategic Planning

Demographics Assessment – Needs and Resources Relationship Identification (where/who) Decision-Making Cultural Competency Obtain Funding

How GIS Can Help You with Program Evaluation Surveillance System Track Change over Time and Space

Page 94: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 94

Don’t let the task get you down.

http://entertainment.webshots.com/photo/1041702014015901341TIYRAl

Page 95: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

23年 4月 22日 www.drugs.indiana.edu 95

Without data you won’t get far.

http://entertainment.webshots.com/photo/1040516516015901341VzRwAP

Page 96: The Potential of  Geographic Information Systems (GIS)  to Facilitate Data-Driven Prevention

Indiana Prevention Resource Center501 N. Morton, Suite110Bloomington, IN 47404

Phone: (800) 346-3077Fax: (812) 855-4940

E-mail: [email protected]: http://www.drugs.indiana.edu

A special thanks to: Jennifer Kelley, MPH, Mikyung Jun, PhD, Aaron Jones, MPH, and Erika Pitcher for their assistance.