Spatial Econometric Model of Healthcare Spending LOCAL Garen

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

Background Health Care spending as Percentage of GSP

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

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

US PHC Spending, 1995 -2004

Change in PHC Spending, 1995 -2004

PHC Spending in Mississippi

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

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

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

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

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

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

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

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

Policy factors High levels of enrollment in HMOs reduces spending growth n 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. ) n Weech-Maldonado, 1995

Objectives 1. Develop local spending model. 1. 2. Counties in Mississippi Cross-sectional 2. Examine relationship of factors associated with healthcare spending. 3. Explore space.

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

Statewide Spending Population 2. 9 million Hospital Care $7. 3 billion Per-Capita $2, 517

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

Percentage of residents discharged from local hospital Mean: 41. 2% Std Dev. : 27. 6%

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%)

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

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

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 RHO 1: rook-based spatial weights RHO 2: queen-based spatial weights

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

Results

LSPC Moran Scatterplots Rook-based Queen-based

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

Summary 1. Per-capita income, presence of hospital, poverty rate, and insurance coverage help explain local per-capita spending for healthcare services. 2. Space matters in the analysis of healthcare spending

Summary 3. Space is significant, but does not appear to be substantial… n n 1. 94% of variation in the rook model. 2. 63% of variation in the queen model. 4. Negative Rho implies dissimilarity in neighboring areas.

Working paper and presentation is online: http: //giwiganz. com/garen/NARSC 07 Garen Evans gevans@ext. msstate. edu 662 -325 -2750