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1、Medical Geography: Health Care Accessibility and ImplicationsFahui WangFred B. Kniffen Professor of Geography Louisiana State UniversityJune 17, 2010 CNUContentso The issue of accesso GIS-based accessibility measureso Optimization towards equal accesso Application in healthcare access n PHSA designa

2、tion and late-stage cancero Concluding commentsAccess Matters!o Convenience of access to activitiesn job, school, healthcare, park, public service, shopping o Access defines “l(fā)ocation”o Spatialn Where you areo Nonspatialn Who you areo Social justice and public policyMeasuremento How scarce are the s

3、ervices?n Supply vs. Demand (ratio)o How far are demands from supplies?n Distancen Road networkn Transportation meansPrimitive measureso Distance (time) from supplyo Simply potential modelo Supply-demand ratio within an areao FCA with a radius/boxo FCA with a travel time rangejijjidSAFrom S/D Ratio

4、to FCAo Simply S/D Ratioo FCA with a RadiusnImprovements o Within-unit variationo Cross-bordernLimitationso Straight-lineo S-D beyond radius2-step floating catchment area: 2SFCAo Scale availability at each supply location o Sum up accessible supplies around each demand location2SFCA Recap1a. For eac

5、h supply location j, select all demand locations k within a travel time range from j (catchment Cj).1b. Compute the supply to demand ratio within Cj: 2a. For each demand location i, search all supply locations j within the travel time range from i (catchment Zi).2b. Sum up the supply to demand ratio

6、s at these supply locations:jCkkjjPSR/ijiZjCkkjZjjiPSRA)/(Generalizing “distance decay”o as a continuous function n a power or exponential function n a Gaussian functiono as a discrete variablen dichotomous as in 2SFCA n multiple as in E2SFCAo a hybrid approachn a kernel density function n a three-z

7、one approach Generalizing “distance decay”Validation o Modeling actual supply-demand interactionsn Individual travel behaviorn Aggregated travel patternOptimization o Towards equal accessibilityo Formulated as a planning problem o minimizing accessibility gaps among demand locations by adjusting the

8、 amounts of supplies at given supply locations. A Quadratic Programming ApproachoSubject to minjjijjaVdS112)(minS1 S2. Sn SmkkjkjdDV1whereValue of optimizationo Emphasizing the equality issue o Identifying over- and under-capacity of supplyo Guiding planning and public policy in closing the gapAppli

9、cation in Healthcare Access: HPSA designationo U.S. ranks behind in health performanceo deep disparities in access to care and health outcomeso Federal programs: MUP & HPSAs o Calls for better measure of accessibility Consolidating nonspatial variables025507512.5MilesSpatial Accessibility by 2-s

10、tep FCA MethodLegendAccessibility Score 0.003876 - 0.0048860.003218 - 0.0038750.002464 - 0.0032170.001727 - 0.0024630.000986 - 0.0017260.000000 - 0.000985County Boundary05102.5MilesSpatial Accessibility by 2-step FCA MethodLegendAccessibility Score 0.003876 - 0.0048860.003218 - 0.0038750.002464 - 0.

11、0032170.001727 - 0.0024630.000986 - 0.0017260.000000 - 0.000985County Boundary025507512.5MilesSocioeconomic Disadvantage Scores (Factor 1)LegendFACTOR1-2.011300 - -0.462800-0.462799 - 0.1000000.100001 - 0.9333000.933301 - 1.8886001.888601 - 3.1617003.161701 - 5.872100County Boundary05102.5MilesSocio

12、economic Disadvantage Scores (Factor 1)LegendFACTOR1-2.011300 - -0.462800-0.462799 - 0.1000000.100001 - 0.9333000.933301 - 1.8886001.888601 - 3.1617003.161701 - 5.872100County Boundary025507512.5MilesSocio-cultural Barrier Scores (Factor 2)LegendFACTOR2-1.597400 - -0.454300-0.454299 - -0.014900-0.01

13、4899 - 0.7280000.728001 - 1.7687001.768701 - 3.2223003.222301 - 6.145200County Boundary05102.5MilesSocio-cultural Barrier Scores (Factor 2)LegendFACTOR2-1.597400 - -0.454300-0.454299 - -0.014900-0.014899 - 0.7280000.728001 - 1.7687001.768701 - 3.2223003.222301 - 6.145200County Boundary025507512.5Mil

14、esHigh Health Care Need Scores (Factor 3)LegendFACTOR3-7.376800 - -1.760400-1.760399 - -0.702300-0.702299 - 0.0231000.023101 - 0.7152000.715201 - 1.6974001.697401 - 5.786100County Boundary05102.5MilesHigh Health Care Need Scores (Factor 3)LegendFACTOR3-7.376800 - -1.760400-1.760399 - -0.702300-0.702

15、299 - 0.0231000.023101 - 0.7152000.715201 - 1.6974001.697401 - 5.786100County BoundaryIntegrating Spatial and Nonspatial Factorso Geographic AreanPrimary Indicator (spatial accessibility score) 1/3500nPrimary Indicator (spatial accessibility score) 1/3000 AND secondary indicator (factor 3) 1 standar

16、d deviation above mean o Population GroupnPrimary Indicator (factor 1) 1 standard deviation above meannPrimary Indicator (factor 1) standard deviation above mean and secondary indicator (factor 2) 1 standard deviation above mean025507512.5MilesIntegrated DesignationLegendPoor Spatial AccessMargianl

17、Spatial Access and High NeedsPoor Socioeconomic StatusMarginal Socioeconomic Statusand Socio-Cultural BarrierCounty Boundary(Spatial access criteria: score 1/3500 or 1/3000 if ther is high needs)025507512.5MilesExisting Health Professional Shortage Area (HPSA)LegendCounty BoundaryService Area (whole

18、 county)Population Group (partial county)Population Group (by Township)Service Area (by Township)Population Group (by Census Tract)Service Area (by Census Tract)Application in healthcare access (vs. late-stage cancer)Cancer mortalityLate-stage diagnosisHealthcare accessRisk breakdownsUrban vs. rural

19、Travel time to cancer screening facilityAccess to primary careSpatial: where we areIndividual attributesCommunity characteristics (contextual)Nonspatial: who we areCultureKnowledge(Dis)advantageHealth insuranceAccess to primary careAvailability and accessibilityof screening servicesQuality of health

20、 careMultilevel approachSocio-economic disadvantageSocial networks and institutionsLocal resourcesPERSONPLACERural-urban continuumChicagoChicago suburbsOther metro areasLarge town (10-50k) RuralMultilevel Model CoefficientsBreastColorectalLungProstateChic_sub-.181* -.065* -.001-.092* -.087* -.029-.0

21、07-.012-.017-.189* -.105* -.062Other_metro-.278* -.146* -.110* -.131* -.122* -.056-.172* -.183* -.252* -.128* -.039-.012Large town-.336* -.162* -.167* -.272* -.250* -.198* -.331* -.338* -.431* -.191* -.065-.063Rural-.207* -.032-.063-.169* -.156* -.085-.204* -.212* -.323* -.164* -.035-.038Age50.547*.541*.216*.216*.195*.188*.129.128Age70-.279* -.279*-.082* -.083*-.234* -.233*-.174* -.177*Black.373*.370*.083*.077*-.068* -.085*.289*.279*Income-.134*-.047-.110*-.087Non_Engli.012*.002-.001.003Access-37.6*20.0-27.2*-9.9Time.0020.000Findin