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Geographic Information Systems for Resource Allocation

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Geographic Information Systems for Resource Allocation

  1. 1. Geographic Information Systems for Resource Allocation Presentation to Georgia Public Health Association April 12, 2011 Michael Bryan, Chronic Disease Epidemiologist
  2. 2. DCH Mission 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
  3. 3. DCH Initiatives FY 2011 FY 2011 Continuity of Operations Preparedness Customer Service Emergency Preparedness Financial & Program Integrity Health Care Consumerism Health Improvement Health Care Transformation Public Health Workforce Development
  4. 4. Outline <ul><li>Objectives </li></ul><ul><li>Background </li></ul><ul><li>Cardiovascular Disease and Socioeconomic Status Example </li></ul><ul><li>Conclusion </li></ul>
  5. 5. Objectives <ul><li>To demonstrate the use of GIS for program development and health message targeting </li></ul><ul><ul><li>To visualize and describe the spatial relationship between county-level socioeconomic indicators and cardiovascular disease morbidity in Georgia </li></ul></ul><ul><ul><li>To visualize the distribution of CVD morbidity on the block group level </li></ul></ul>
  6. 6. Number of Farms by County, Georgia 1997 Without 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 335 Towns 121 Barrow 361 Butts 148 Effingham 203 Randolph 119 Grady 462 Fannin 151 Polk 344 Heard 160 Bleckley 221 Chatham 42 Thomas 421 Murray 238 Paulding 218 Spalding 193 Marion 147 Turner 230 Seminole 183 Whitfield 325 Cobb 128 Glascock 76 Candler 264 Ben Hill 159 Charlton 75 Catoosa 215 Oglethorpe 319 Jefferson 356 Chattahoochee 13 Worth 406 Lowndes 373 Union 256 Clarke 80 Burke 346 Macon 282 Wayne 276 Echols 67 Walker 478 Wilkes 298 Washington 327 Treutlen 157 Coffee 656 Camden 46 Dade 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
  7. 7. Number of Farms by County, Georgia 1997 With GIS Visualization Capabilities
  8. 8. Research Question <ul><li>Do Geographic Information Systems help guide program development and health message targeting? </li></ul>
  9. 9. Background
  10. 10. What is GIS? <ul><li>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) </li></ul>
  11. 11. GIS Defined <ul><li>Database System </li></ul><ul><ul><li>Database </li></ul></ul><ul><ul><li>Database management system (DBMS) </li></ul></ul><ul><ul><li>Relational Database Model </li></ul></ul><ul><li>Spatially Indexed </li></ul><ul><ul><li>Data related to items in space, like objects, lines, or polygons </li></ul></ul><ul><li>Procedures </li></ul><ul><ul><li>Ways to manipulate spatially indexed data in database system </li></ul></ul>
  12. 12. Questions for GIS <ul><li>Where along I-85 is the highest fatal crash rate using 500 meter segments of the interstate as observational unit? </li></ul><ul><li>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? </li></ul><ul><li>Is the incidence of Lyme Disease in South Georgia associated with urbanization? </li></ul><ul><li>Is county obesity prevalence associated with green space acreage? Sidewalk length? </li></ul>
  13. 13. What does GIS do? <ul><li>Capture Data </li></ul><ul><ul><li>Identify objects and enter data on these objects </li></ul></ul>Cardiovascular Disease (CVD) Discharges, Georgia 2008
  14. 14. What does GIS do? <ul><li>Integrate Data </li></ul><ul><ul><li>Combine data from different sources and/or different scales </li></ul></ul>County Population Data from US Census Bureau County CVD Hospital Discharges from GA Hospital Association County Database
  15. 15. What does GIS do? <ul><li>Manipulate Data </li></ul><ul><ul><li>Process data in database </li></ul></ul>County Population CVD Cases County Population CVD Discharges CVD Morbidity County Database
  16. 16. What does GIS do? <ul><li>Produce Maps </li></ul><ul><li>Produce Graphs and Tables </li></ul><ul><li>Produce Reports </li></ul><ul><li>Geographically-based analysis </li></ul>
  17. 17. Applications of GIS <ul><li>Targeting resources towards particular groups </li></ul><ul><li>Planning locations of health facilities and programs </li></ul><ul><li>Determining catchment areas and target populations </li></ul><ul><li>Creating health profiles </li></ul><ul><li>Epidemiological research and analysis </li></ul><ul><li>Assessing health needs to provide health services </li></ul>
  18. 18. “ We haven’t got GIS. It isn’t a problem for us. Why is it a problem for you?” <ul><li>Removes technology and tools available </li></ul><ul><ul><li>To investigate impacts of exposures to human health </li></ul></ul><ul><ul><li>To monitor diseases and their risk factors </li></ul></ul><ul><ul><li>To determine health inequalities </li></ul></ul><ul><ul><li>To communicate with others </li></ul></ul>
  19. 19. Geographic Information Systems and Health Interventions <ul><li>GIS helps identify areas or populations at risk of disease </li></ul><ul><li>GIS helps relate disease risk to potential areal risk factors </li></ul><ul><ul><li>Socioeconomic position </li></ul></ul><ul><ul><li>Amount of tobacco advertising </li></ul></ul><ul><ul><li>Availability and cost of healthy food </li></ul></ul><ul><ul><li>Availability and quality of public spaces </li></ul></ul><ul><ul><li>Sense of safety or crime </li></ul></ul><ul><ul><li>Exposure to chronic stress </li></ul></ul><ul><ul><li>Sources of social support </li></ul></ul>
  20. 20. Geographic Information Systems and Health Interventions <ul><li>Prioritize Target Populations </li></ul><ul><li>Adjust Intervention </li></ul>Disease Risk Risk Factors Other Exposures Target Population Intervention Type
  21. 21. Geographic Information Systems and Health Interventions Number of Criterion Complexity Utility of GIS
  22. 22. Socioeconomic Disparity and CVD Burden <ul><li>Burden of CVD greater in areas of lower socioeconomic position </li></ul><ul><li>Socioeconomic inequality in burden of CVD is increasing with time </li></ul>Source: Singh (2002)
  23. 23. Socioeconomic Disparity and CVD Risk Factor Burden <ul><li>Persons living in more deprived areas have </li></ul><ul><ul><li>Increased risk of obesity </li></ul></ul><ul><ul><li>Increased smoking </li></ul></ul><ul><ul><li>Increased physical inactivity </li></ul></ul>Diez Roux (1997)
  24. 24. Socioeconomic Disparity and Health Interventions <ul><li>More deprived areas may be less susceptible to prevention efforts </li></ul><ul><ul><li>Lower health knowledge </li></ul></ul><ul><ul><li>Lower probability of healthy behavior change </li></ul></ul><ul><ul><li>Less exposure to prevention messages </li></ul></ul><ul><ul><li>(Benjamin-Garner 2002; Bartley 2000) </li></ul></ul>
  25. 25. Cardiovascular Disease Morbidity and Socioeconomic Indicators Example
  26. 26. CVD Program <ul><li>Objective: Increase hypertension and cholesterol screening rate </li></ul><ul><ul><li>Target populations of highest CVD burden </li></ul></ul><ul><ul><li>Utilize socioeconomic status in program design </li></ul></ul>
  27. 27. Data Sources <ul><li>GA Hospital Association </li></ul><ul><ul><li>CVD Morbidity </li></ul></ul><ul><li>US Census Bureau </li></ul><ul><ul><li>Education </li></ul></ul><ul><ul><li>Occupation </li></ul></ul><ul><ul><li>Income </li></ul></ul><ul><li>Bureau of Labor Statistics </li></ul><ul><ul><li>Unemployment </li></ul></ul><ul><li>US Department of Agriculture </li></ul><ul><ul><li>Poverty </li></ul></ul>
  28. 28. Variables <ul><li>Age-Adjusted Cardiovascular Disease Morbidity </li></ul><ul><li>Local Economic Resource Index </li></ul><ul><ul><li>Unemployment Rate </li></ul></ul><ul><ul><li>Percent with at least Associate’s Degree </li></ul></ul><ul><ul><li>Family Median Income </li></ul></ul><ul><ul><li>Percent of working population in white collar occupation </li></ul></ul><ul><li>Poverty Prevalence </li></ul>
  29. 29. Cardiovascular Disease Morbidity <ul><li>Age-adjusted to 2000 US Standard Population </li></ul><ul><li>Deduplicated 2008 Hospital Discharges </li></ul><ul><li>Principle Diagnosis </li></ul><ul><ul><li>ICD-9 codes 390-434 and 436-448 </li></ul></ul><ul><ul><li>Ischemic Heart Disease </li></ul></ul><ul><ul><li>Hypertensive Heart Disease and Hypertension </li></ul></ul><ul><ul><li>Stroke </li></ul></ul><ul><ul><li>Rheumatic Fever and Chronic Rheumatic Disease </li></ul></ul>
  30. 30. Burden of Cardiovascular Disease Morbidity, Georgia 2008 <ul><li>145,000 total hospitalizations due to any CVD </li></ul><ul><li>Average length of stay was 5 days </li></ul><ul><li>Average charge per hospitalization was $35,800 </li></ul><ul><li>Total hospital charges were $4.9 billion </li></ul><ul><li>Direct healthcare cost and indirect cost estimated at $11.7 billion </li></ul>
  31. 31. Local Economic Resource Index <ul><li>Summary Index of 4 measures: </li></ul><ul><ul><li>Percent of Working Population in White Collar Occupation </li></ul></ul><ul><ul><li>Unemployment Rate </li></ul></ul><ul><ul><li>Median Family Income </li></ul></ul><ul><ul><li>Percent with Associate’s Degree or greater </li></ul></ul><ul><li>Variables categorized into quintiles </li></ul><ul><li>Quintiles assigned scores from 0 to 4 </li></ul><ul><li>Scores of 0 represent the most economically disadvantaged group while scores of 16 represent the most economically advantaged group </li></ul>
  32. 32. Poverty <ul><li>Percent of persons living below the US poverty line </li></ul><ul><ul><li>Based on income thresholds that vary by family size and composition </li></ul></ul><ul><li>Captures economic deprivation </li></ul><ul><li>Meaningful across regions and time </li></ul><ul><li>Easily understood and interpretable </li></ul>
  33. 33. Age-Adjusted Cardiovascular Disease Morbidity*, Georgia 2008 <ul><li>112,694 deduplicated Hospital Discharges </li></ul><ul><li>Range*: 210.3 – 2,212.1 </li></ul><ul><li>Mean*: 1,466.3 ( σ =384.4) </li></ul>*Per 100,000 Deduplicated Hospital Discharges
  34. 34. Percent of Population with at least an Associate’s Degree, Georgia 2000 <ul><li>Range: 7.6% - 46.1% </li></ul><ul><li>Median: 15.6% </li></ul><ul><li>Mean: 18.2% ( σ =8.1%) </li></ul>
  35. 35. Percent of Working Population in White Collar Occupation, Georgia 2000 <ul><li>Range: 29.9% - 72.3% </li></ul><ul><li>Median: 45.2% </li></ul><ul><li>Mean: 47.2% ( σ =8.5%) </li></ul>
  36. 36. Median Family Income, Georgia 2000 <ul><li>Range: $27,232 - $78,853 </li></ul><ul><li>Median: $38,463 </li></ul><ul><li>Mean: $40,411 ( σ =$9,485) </li></ul>
  37. 37. Percent of Population Unemployed, Georgia 2007 <ul><li>Range: 3.0% - 9.5% </li></ul><ul><li>Median: 4.9% </li></ul><ul><li>Mean: 5.1% ( σ =1.2%) </li></ul>
  38. 38. Local Economic Resource Index, Georgia <ul><li>Range: 0 – 16 </li></ul><ul><li>Median: 7 </li></ul><ul><li>Mean: 8.0 ( σ =4.7) </li></ul>
  39. 39. Percent of Population in Poverty, Georgia 2007 <ul><li>Range: 5.2% - 36.2% </li></ul><ul><li>Median: 18.3% </li></ul><ul><li>Mean: 18.6% ( σ =6.4%) </li></ul>
  40. 40. Socioeconomic Disparity and CVD Morbidity*, Georgia *Age-Adjusted Hospital Discharges per 100,000 population Significant
  41. 41. Summary of Visualization of CVD and Socioeconomic Indicators <ul><li>Those in the most economically disadvantaged Local Economic Resource quintile have a 20% higher morbidity than those in the most economically advantaged quintile </li></ul>Socioeconomic Status CVD Morbidity
  42. 42. What does GIS give to programs? <ul><li>Communication tool for themselves and their stakeholders </li></ul><ul><li>Method of incorporating several variables of import </li></ul><ul><ul><li>Facilitates data-based decision-making </li></ul></ul>
  43. 43. 3-D graphics to visualize CVD Morbidity and Socioeconomic Indicators Simultaneously
  44. 44. Cardiovascular Disease Morbidity by Block Group, Houston and Irwin County 2008 Houston County Irwin County
  45. 45. Conclusions
  46. 46. GIS as Tool for CVD Program <ul><li>Visualize the burden of CVD morbidity and the economic position for each county </li></ul><ul><li>Visualize the distribution of both disease burden and economic resources for all counties throughout Georgia </li></ul>
  47. 47. GIS as Programmatic Tool <ul><li>GIS provides: </li></ul><ul><ul><li>Simple way to understand the relationship between disease burden and disease risk factors </li></ul></ul><ul><ul><li>Means of incorporating any spatially referenced variables of interest </li></ul></ul><ul><li>Technological advances allow for a fine scale picture of the health climate </li></ul><ul><ul><li>Can target health programs accordingly </li></ul></ul>
  48. 48. References <ul><li>Armstrong 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. </li></ul><ul><li>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. </li></ul><ul><li>Benjamin-Gamer R et al. “Sociodemographic differences in exposure to health information. Ethn Dis 2002; 12; 124-34. </li></ul><ul><li>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. </li></ul><ul><li>Diez-Roux AV et al. “Neighborhood Environments and Coronary Heart Disease: A multilevel analysis.” American Journal of Epidemiology 1997; 146(1); 48-63. </li></ul><ul><li>Diez-Roux AV et al. “Neighborhood of residence and incidence of Coronary Heart Disease.” New England Journal of Medicine 2001; 345 (2); 99-106. </li></ul><ul><li>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. </li></ul><ul><li>Gesler WM et al. “Using mapping technology in health intervention research.” Nursing Outlook 2004; 52; 142-146. </li></ul><ul><li>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. </li></ul><ul><li>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. </li></ul><ul><li>Singh GK. “Area Deprivation and Widening Inequalities in US Mortality, 1969-1998.” American Journal of Public Health 2003; 93(7); 1137-1143. </li></ul><ul><li>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. </li></ul><ul><li>Smith DP et al. “Re(surveying the uses of Geographical Information Systems in Health Authorities 1991-2001.” Area 2003; 35(1); 74-83. </li></ul><ul><li>Sundquist J et al. “Cardiovascular risk factors and the neighborhood environment: a multilevel analysis.” International Journal of Epidemiology 1999. 28; 841-845. </li></ul>
  49. 49. Acknowledgements <ul><li>Rana Bayakly, MPH, Chronic Disease, Healthy Behavior, Injury, Environmental Epidemiology Director </li></ul><ul><li>Lydia Clarkson, MPH, Cardiovascular Disease Unit Lead </li></ul><ul><li>Jim Steiner, Data Manager </li></ul><ul><li>All others who helped in any way </li></ul>
  50. 50. Thank You Michael Bryan, MPH [email_address] (404) 463-3748
  51. 51. Socioeconomic Disparity <ul><li>CVD burden decreasing more slowly in areas of lower socioeconomic position </li></ul><ul><ul><li>Life expectancy rising more slowly </li></ul></ul><ul><ul><li>Mortality/morbidity decreasing less rapidly in men </li></ul></ul>Top 2.5% Bottom 2.5%
  52. 52. White Collar Occupations <ul><li>Management Occupations, except farm managers </li></ul><ul><li>Business and financial operations occupations </li></ul><ul><li>Professional and related occupations </li></ul><ul><li>Sales and office occupations </li></ul>
  53. 53. 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%)
  54. 54. GIS as Programmatic Tool <ul><li>Impact on programmatic decision-making </li></ul><ul><ul><li>Given limited resources, should the program target counties that have higher levels of economic resources or counties that have lower levels economic resources? </li></ul></ul><ul><ul><li>Should different programs and messages be implemented in areas based on a better comprehension of an area’s economic resources and disease burden? </li></ul></ul>
  55. 55. The Health Message Conundrum CVD Morbidity Economic Resources Low Low High High
  56. 56. The Health Message Conundrum CVD Morbidity Economic Resources Low Low High High
  57. 57. Examples of CVD Morbidity by Local Economic Resource (LER) Index *Age-Adjusted Deduplicated Hospital Discharges per 100,000 population 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
  58. 58. Examples of CVD Morbidity by Local Economic Resource (LER) Index *Age-Adjusted Deduplicated Hospital Discharges per 100,000 population 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

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