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Spatial Econometric Model of Healthcare Spending

Spatial Econometric Model of Healthcare Spending

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Spatial Econometric Model of Healthcare Spending

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  1. Spatial Econometric Model of Healthcare Spending Garen Evans MISSISSIPPI STATE UNIVERSITY LOCAL!

  2. Background Health Care spending as Percentage of GSP

  3. Health Care Spending • Hospitals • Professional Services • Long Term Care • home health care, nursing homes • Personal Medical Supplies • durables, drugs, supplies • Other

  4. U.S. Personal Healthcare Spending* * Millions of 2004 dollars

  5. US PHC Spending, 1995-2004

  6. Change in PHC Spending, 1995-2004

  7. PHC Spending in Mississippi

  8. Personal Healthcare Spending as a Percentage of Gross State Product, 2004

  9. Local Health Care Spending? • National • Personal health care spending • Sector detail • Hospitals, home health care, etc. • State • Place-based • Residence-based • County ?

  10. County-Level Spending • Usage: • Quantify importance of health care in small economies • Often combined with input-output analysis. • Leverage interest in local health care • eg., Critical Care Access Hospital designation • Gauge effectiveness of healthcare policy as an economic engine • Test global hypotheses

  11. County-level Spending • Non-structural approach • Product of LPC-adjusted state per-capita spending and local population • Patient-origin analysis • National benchmarks • Trade area capture • Structural approach • Identify factors related to health care spending

  12. Health Care Spending • Factors that affect spending: • Demographic • Population distributions • Socioeconomic • Income • Market-related • Physician concentration • Policy • Managed care

  13. Demographic • Age 65+ tend to use six times the healthcare compared to younger persons • Martin, 2005 • At least one chronic condition by age 70 • Neese, 2002 • Out-of-pocket spending for chronic conditions varies with age • Hwang, 2001

  14. Socioeconomic • Higher growth in per-capita income leads to growth in per-capita private spending. • Smith, 1998 • Almost 18% of per-capita spending due to income growth. • Peden, 1995 • Spending for children in poverty was 14% higher than average. • Holahan, 2001

  15. Market Factors • Uninsured spend less than those with Medicaid • Holahan, 2001 • High physician concentration generates higher levels of spending • Martin, 2002 • Large provider networks exert leverage over insurers when negotiating prices. • Brudevold, 2004

  16. Policy factors • High levels of enrollment in HMOs reduces spending growth • Staines, 1993; Cutler, 1997. • Medicaid managed care enrollment not a significant predictor of Medicaid expenditures. (Only state per capita income and regional differences were significant predictors of Medicaid costs. ) • Weech-Maldonado, 1995

  17. Objectives • Develop local spending model. • Counties in Mississippi • Cross-sectional • Examine relationship of factors associated with healthcare spending. • Explore space.

  18. Data • Health Spending Impact Model (HSIM) • County-level health care spending estimates • Based on state-level per-capita spending • Local Purchase Coefficients • Hospitals • Physicians, Dentists, et al. • Long Term Care • Medical Supplies • Other

  19. Statewide Spending Population 2.9 million Hospital Care $7.3 billion Per-Capita $2,517

  20. Local Hospital Spending 52.2% of Oktibbeha County residents received hospital care in other counties. LPC is 47.8% or… $1,202 per-capita Pop 42,454 Total: $51 million

  21. Percentage of residents discharged from local hospital Mean: 41.2% Std Dev.: 27.6%

  22. County-level per-capita spending for health care Mean: $3,576 Max: $5,189 Min: $956 11 < 1 SD (13%) 16 > 1 SD (19.5%)

  23. Data • Socioeconomic/Demographic • Per-capita income – Woods and Poole • Poverty rate - Small Area Income & Poverty Estimates; US Census. • Market • Hospital – MSDH Report on Hospitals • Diabetes (mortality) – MSDH Vital Statistics • Insurance • Small Area Health Insurance Estimates (SAHIE; US Census) 2001

  24. Spatial clustering can occur in behavioral risk factors and outcomes Mobley, 2006. Spatial lag can lead to biased and inconsistent estimators Anselin, 2006 Spatial Weights

  25. Summary Statistics PCI: $000 COVER: % not covered by health insurance HOSP: dummy (1=hospital) POVRTY: Percentage of population at below 100% poverty rate. DIABET: mortality per 100,000 population LSPC: local spending per capita, $000 RHO1: rook-based spatial weights RHO2: queen-based spatial weights

  26. #1 BASELINE MODEL LSPC = f(PCI, COVER, POVRTY, DIABET, HOSP) + - - + + #2 SPATIAL LAG MODEL (ROOK-BASED WEIGHTS) LSPC = f(PCI, COVER, POVRTY, DIABET, HOSP, RHO1) + #3 SPATIAL LAG MODEL (QUEEN-BASED WEIGHTS) LSPC = f(PCI, COVER, POVRTY, DIABET, HOSP, RHO2) + Models

  27. Results

  28. Rook-based Queen-based LSPC Moran Scatterplots

  29. LSPC, rook LSPC, queen Local Indicators of Spatial Association (LISA)

  30. Summary • Per-capita income, presence of hospital, poverty rate, and insurance coverage help explain local per-capita spending for healthcare services. • Space matters in the analysis of healthcare spending

  31. Summary • Space is significant, but does not appear to be substantial… • 1.94% of variation in the rook model. • 2.63% of variation in the queen model. • Negative Rho implies dissimilarity in neighboring areas.

  32. Working paper and presentation is online: http://giwiganz.com/garen/NARSC07 Garen Evans gevans@ext.msstate.edu 662-325-2750