Geographic Information Systems for Resource Allocation

Michael Bryan, Chronic Disease Epidemiologist

Geographic Information Systems for Resource

Allocation

Presentation to Georgia Public Health Association

April 12, 2011

ACCESS

Access to affordable, quality health

care in our communities

RESPONSIBLE

Responsible health planning

and use of health care resources

HEALTHY

Healthy behaviors and

improved health

outcomes

DCH Mission

FY 2011

DCH InitiativesFY 2011

Continuity of Operations Preparedness

Customer Service

Emergency Preparedness

Financial & Program Integrity

Health Care Consumerism

Health Improvement

Health Care Transformation

Public Health

Workforce Development

Outline

• Objectives• Background• Cardiovascular Disease and Socioeconomic Status

Example• Conclusion

Objectives

• To demonstrate the use of GIS for program development and health message targeting– To visualize and describe the spatial relationship

between county-level socioeconomic indicators and cardiovascular disease morbidity in Georgia

– To visualize the distribution of CVD morbidity on the block group level

Number of Farms by County, Georgia 1997Without GIS Visualization Capabilities

County No. Farms County No. Farms County No. Farms County No. Farms County No. Farms County No. Farms

Rabun 122 Gwinnett 303 Hancock 103 Muscogee 39 Appling 494 Decatur 335Towns 121 Barrow 361 Butts 148 Effingham 203 Randolph 119 Grady 462Fannin 151 Polk 344 Heard 160 Bleckley 221 Chatham 42 Thomas 421Murray 238 Paulding 218 Spalding 193 Marion 147 Turner 230 Seminole 183Whitfield 325 Cobb 128 Glascock 76 Candler 264 Ben Hill 159 Charlton 75Catoosa 215 Oglethorpe 319 Jefferson 356 Chattahoochee 13 Worth 406 Lowndes 373Union 256 Clarke 80 Burke 346 Macon 282 Wayne 276 Echols 67Walker 478 Wilkes 298 Washington 327 Treutlen 157 Coffee 656 Camden 46Dade 175 Lincoln 163 Meriwether 257 Dodge 491 Clay 56 Brooks 430

Gilmer 267 DeKalb 46 Troup 221 Schley 91 Irwin 288

Habersham 407 Oconee 305 Pike 252 Pulaski 161 Bacon 324

White 284 Walton 493 Lamar 188 Taylor 196 Lee 157

Lumpkin 198 Haralson 260 Monroe 179 Toombs 401 Dougherty 139

Stephens 188 Morgan 390 Baldwin 137 Montgomery 252 Calhoun 122

Gordon 535 Carroll 702 Jones 157 Tattnall 589 Tift 359

Dawson 160 Douglas 107 Screven 325 Wheeler 176 Pierce 379

Chattooga 278 Rockdale 102 Wilkinson 88 Dooly 259 Early 279

Floyd 437 Greene 198 Upson 185 Evans 183 Berrien 399

Pickens 194 Newton 260 Jenkins 248 Bryan 61 Ware 274

Franklin 699 Taliaferro 55 Bibb 149 Webster 76 Baker 131

Hall 666 Columbia 169 Twiggs 98 Stewart 77 Mitchell 464

Hart 460 McDuffie 217 Talbot 111 Sumter 314 Atkinson 196

Banks 446 Clayton 54 Harris 207 Telfair 271 Brantley 207

Bartow 400 Henry 327 Crawford 123 Wilcox 273 McIntosh 24

Cherokee 493 Warren 134 Emanuel 441 Liberty 43 Cook 226

Forsyth 434 Fayette 184 Johnson 288 Crisp 213 Colquitt 634

Jackson 719 Richmond 106 Laurens 688 Long 64 Miller 251

Elbert 320 Jasper 185 Peach 157 Quitman 17 Clinch 93

Madison 622 Coweta 316 Houston 249 Jeff Davis 220 Lanier 92

Fulton 257 Putnam 152 Bulloch 524 Terrell 174 Glynn 36

Number of Farms by County, Georgia 1997With GIS Visualization Capabilities

Research Question

• Do Geographic Information Systems help guide program development and health message targeting?

Background

What is GIS?

• A “database system in which most of the data are spatially indexed and upon which a set of procedures are operated in order to answer questions about spatial entities in the database.” (Antenucci 1991)

GIS Defined

• Database System– Database– Database management system (DBMS)– Relational Database Model

• Spatially Indexed– Data related to items in space, like objects, lines, or polygons

• Procedures– Ways to manipulate spatially indexed data in database system

Questions for GIS

• Where along I-85 is the highest fatal crash rate using 500 meter segments of the interstate as observational unit?

• What was the distribution of the Chlorine gas plume that occurred in Conyers, GA in 2004 beginning at 5am and ending at 5pm in 10 minute increments?

• Is the incidence of Lyme Disease in South Georgia associated with urbanization?

• Is county obesity prevalence associated with green space acreage? Sidewalk length?

What does GIS do?

• Capture Data– Identify objects and enter

data on these objects

Cardiovascular Disease (CVD) Discharges, Georgia 2008

What does GIS do?

• Integrate Data– Combine data from different sources and/or different

scalesCounty Population

Data from US Census Bureau

County CVD Hospital Discharges from GA Hospital Association

County Database

What does GIS do?

• Manipulate Data– Process data in database

County Population

CVD Cases County Population

CVD Discharges

CVD Morbidity

County Database

What does GIS do?

• Produce Maps• Produce Graphs and

Tables• Produce Reports• Geographically-based

analysis

Applications of GIS

• Targeting resources towards particular groups• Planning locations of health facilities and programs• Determining catchment areas and target

populations• Creating health profiles• Epidemiological research and analysis• Assessing health needs to provide health services

“We haven’t got GIS. It isn’t a problem for us. Why is it a problem for you?”

• Removes technology and tools available – To investigate impacts of exposures to human health– To monitor diseases and their risk factors– To determine health inequalities– To communicate with others

Geographic Information Systems and Health Interventions

• GIS helps identify areas or populations at risk of disease• GIS helps relate disease risk to potential areal risk factors

– Socioeconomic position– Amount of tobacco advertising– Availability and cost of healthy food– Availability and quality of public spaces – Sense of safety or crime– Exposure to chronic stress– Sources of social support


• Prioritize Target Populations • Adjust Intervention

Disease Risk

Risk Factors

Other Exposures

Target Population

Intervention Type


Number of Criterion

Complexity Utility of GIS

Socioeconomic Disparity and CVD Burden

• Burden of CVD greater in areas of lower socioeconomic position

• Socioeconomic inequality in burden of CVD is increasing with time

Source: Singh (2002)

Socioeconomic Disparity and CVD Risk Factor Burden

• Persons living in more deprived areas have– Increased risk of obesity– Increased smoking– Increased physical inactivity

Diez Roux (1997)

Socioeconomic Disparity and Health Interventions

• More deprived areas may be less susceptible to prevention efforts– Lower health knowledge– Lower probability of healthy behavior change– Less exposure to prevention messages

(Benjamin-Garner 2002; Bartley 2000)

Cardiovascular Disease Morbidity and Socioeconomic Indicators Example

CVD Program

• Objective: Increase hypertension and cholesterol screening rate– Target populations of highest CVD burden– Utilize socioeconomic status in program design

Data Sources

• GA Hospital Association – CVD Morbidity

• US Census Bureau– Education– Occupation– Income

• Bureau of Labor Statistics– Unemployment

• US Department of Agriculture– Poverty

Variables

• Age-Adjusted Cardiovascular Disease Morbidity• Local Economic Resource Index

– Unemployment Rate– Percent with at least Associate’s Degree– Family Median Income– Percent of working population in white collar occupation

• Poverty Prevalence

Cardiovascular Disease Morbidity• Age-adjusted to 2000 US

Standard Population• Deduplicated 2008 Hospital

Discharges • Principle Diagnosis

– ICD-9 codes 390-434 and 436-448– Ischemic Heart Disease– Hypertensive Heart Disease and

Hypertension– Stroke– Rheumatic Fever and Chronic

Rheumatic Disease

Burden of Cardiovascular Disease Morbidity, Georgia 2008

• 145,000 total hospitalizations due to any CVD• Average length of stay was 5 days• Average charge per hospitalization was $35,800• Total hospital charges were $4.9 billion• Direct healthcare cost and indirect cost estimated at

$11.7 billion

Local Economic Resource Index

• Summary Index of 4 measures:– Percent of Working Population in White Collar Occupation– Unemployment Rate– Median Family Income– Percent with Associate’s Degree or greater

• Variables categorized into quintiles• Quintiles assigned scores from 0 to 4• Scores of 0 represent the most economically

disadvantaged group while scores of 16 represent the most economically advantaged group

Poverty

• Percent of persons living below the US poverty line– Based on income thresholds that vary by family size and

composition• Captures economic deprivation• Meaningful across regions and time• Easily understood and interpretable

Age-Adjusted Cardiovascular Disease Morbidity*, Georgia 2008

• 112,694 deduplicated Hospital Discharges

• Range*: 210.3 – 2,212.1• Mean*: 1,466.3 (σ=384.4)

*Per 100,000 Deduplicated Hospital Discharges

Percent of Population with at least an Associate’s Degree, Georgia 2000

• Range: 7.6% - 46.1%• Median: 15.6%• Mean: 18.2% (σ=8.1%)

Percent of Working Population in White Collar Occupation, Georgia 2000


Median Family Income, Georgia 2000

• Range: $27,232 - $78,853• Median: $38,463• Mean: $40,411 (σ=$9,485)

Percent of Population Unemployed, Georgia 2007


Local Economic Resource Index, Georgia

• Range: 0 – 16• Median: 7• Mean: 8.0 (σ=4.7)

Percent of Population in Poverty, Georgia 2007


Socioeconomic Disparity and CVD Morbidity*, Georgia

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Significant

Summary of Visualization of CVD and Socioeconomic Indicators

• Those in the most economically disadvantaged Local Economic Resource quintile have a 20% higher morbidity than those in the most economically advantaged quintile

Socioeconomic Status

CVD Morbidity

What does GIS give to programs?

• Communication tool for themselves and their stakeholders

• Method of incorporating several variables of import – Facilitates data-based decision-making

3-D graphics to visualize CVD Morbidity and Socioeconomic Indicators Simultaneously

Cardiovascular Disease Morbidity by Block Group, Houston and Irwin County 2008Houston County Irwin County

Conclusions

GIS as Tool for CVD Program

• Visualize the burden of CVD morbidity and the economic position for each county

• Visualize the distribution of both disease burden and economic resources for all counties throughout Georgia

GIS as Programmatic Tool

• GIS provides:– Simple way to understand the relationship between

disease burden and disease risk factors– Means of incorporating any spatially referenced

variables of interest• Technological advances allow for a fine scale

picture of the health climate– Can target health programs accordingly

ReferencesArmstrong D et al. “Community occupational structure, medical and economic resources and coronary mortality among US blacks and whites,

1980-1988.” Annals of Epidemiology 1988; 8:184-191.Bartley M et al. “Social distribution of cardiovascular disease risk factors: change among men in England 1984-1993. J Epidemiol Community

Health 2000; 54; 806-814.Benjamin-Gamer R et al. “Sociodemographic differences in exposure to health information. Ethn Dis 2002; 12; 124-34.Curtis AJ and Lee WA. “Spatial Patterns of diabetes related health problems for vulnerable populations in Los Angeles.” International Journal of

Health Geographics 2010; 9(43); 1-10.Diez-Roux AV et al. “Neighborhood Environments and Coronary Heart Disease: A multilevel analysis.” American Journal of Epidemiology 1997;

146(1); 48-63.Diez-Roux AV et al. “Neighborhood of residence and incidence of Coronary Heart Disease.” New England Journal of Medicine 2001; 345 (2); 99-

106.Ezzati M et al. “The Reversal of Fortunes: Trends in County Mortality and Cross-County Mortality Disparities in the United States.” PLoS

Medicine 2008; 5(4); 0557-0568.Gesler WM et al. “Using mapping technology in health intervention research.” Nursing Outlook 2004; 52; 142-146.Lawlor DA et al. “Life-Course Socioeconomic Position, Area Deprivation and Coronary Heart Disease: Findings From the British Women’s Heart

and Health Study.” American Journal of Public Health 2005; 95(1); 91-97.Lyratzopoulos G et al. “Deprivation and trends in blood pressure, cholesterol, body mass index and smoking among participants of a UK primary

care-based cardiovascular risk factor screening programme: both narrowing and widening in cardiovascular risk factor inequalities.” Heart 2006; 92; 1198-1206.

Singh GK. “Area Deprivation and Widening Inequalities in US Mortality, 1969-1998.” American Journal of Public Health 2003; 93(7); 1137-1143.Singh GK and Siahpush M. “Increasing inequalities in all-cause and cardiovascular mortality among US adults aged 25-64 years by area

socioeconomic status, 1969-1998.” International Journal of Epidemiology; 31; 600-613.Smith DP et al. “Re(surveying the uses of Geographical Information Systems in Health Authorities 1991-2001.” Area 2003; 35(1); 74-83.Sundquist J et al. “Cardiovascular risk factors and the neighborhood environment: a multilevel analysis.” International Journal of Epidemiology

1999. 28; 841-845.

Acknowledgements

• Rana Bayakly, MPH, Chronic Disease, Healthy Behavior, Injury, Environmental Epidemiology Director

• Lydia Clarkson, MPH, Cardiovascular Disease Unit Lead

• Jim Steiner, Data Manager• All others who helped in any way

Thank You

Michael Bryan, [email protected]

(404) 463-3748

Socioeconomic Disparity

• CVD burden decreasing more slowly in areas of lower socioeconomic position– Life expectancy rising more slowly – Mortality/morbidity decreasing less rapidly in men

Top 2.5%Bottom 2.5%

White Collar Occupations

• Management Occupations, except farm managers• Business and financial operations occupations• Professional and related occupations• Sales and office occupations

Socioeconomic Indicators

Indicator Range Median Mean(σ)

High Education 7.6% - 46.1% 15.6% 18.2% (8.1%)

White Collar 29.9% - 72.3% 45.2% 47.2% (8.5%)

Median Family Income

$27,232 - $78,853 $38,463 $40,411 ($9,485)

Unemployment Rate 3.0% - 9.5% 4.9% 5.1% (1.2%)

Poverty 5.2% - 36.2% 18.3% 18.6% (6.4%)

GIS as Programmatic Tool

• Impact on programmatic decision-making – Given limited resources, should the program target

counties that have higher levels of economic resources or counties that have lower levels economic resources?

– Should different programs and messages be implemented in areas based on a better comprehension of an area’s economic resources and disease burden?

The Health Message Conundrum

CVD Morbidity

Economic Resources

Low

Low

High

High

The Health Message Conundrum

CVD Morbidity

Economic Resources

Low

Low

High

High

Examples of CVD Morbidity by Local Economic Resource (LER) Index

County LER Quintile CVD Morbidity*

Clay 0 417

Quitman 0 253

Catoosa 4 316

Dade 3 210

Jones 4 1800

Houston 4 1758

Marion 0 1799

Twiggs 1 1749*Age-Adjusted Deduplicated Hospital Discharges per 100,000 population

Examples of CVD Morbidity by Local Economic Resource (LER) Index

County LER Quintile CVD Morbidity*

Clay 0 417

Quitman 0 253

Catoosa 4 316

Dade 3 210

Jones 4 1800

Houston 4 1758

Marion 0 1799

Twiggs 1 1749*Age-Adjusted Deduplicated Hospital Discharges per 100,000 population

Health & Medicine

Geographic Information Systems for Resource Allocation