Spatial Econometric Model of Healthcare Spending

Garen Evans

MISSISSIPPI STATE UNIVERSITY

LOCAL!

Background

Health Care spending as Percentage of GSP

Health Care Spending

HospitalsProfessional ServicesLong Term Care home health care, nursing homes

Personal Medical Supplies durables, drugs, supplies

Other

U.S. Personal Healthcare Spending*

37% 34%

10%16%

3%0

100000

200000

300000

400000

500000

600000

Hospitals Professionals Long Term Supplies Other

* Millions of 2004 dollars

US PHC Spending, 1995-2004

10.5%

11.0%

11.5%

12.0%

12.5%

13.0%

13.5%

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

Spending Pecent of GDP

Change in PHC Spending, 1995-2004

198% 212% 219% 218%

166%

370%

152%180% 184%

276%

213%

0%

50%

100%

150%

200%

250%

300%

350%

400%

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PHC Spending in Mississippi

0% 20% 40% 60% 80% 100%

1995199619971998199920002001200220032004

Hospital Care Physician and Clinical Services Other Professional Services Dental Services Home Health Care Prescription Drugs Other Non-Durable Medical Products Durable Medical Products Nursing Home Care Other Personal Health Care

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

Local Health Care Spending?

National Personal health care spending Sector detail

Hospitals, home health care, etc.

State Place-based Residence-based

County ?

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

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

Health Care Spending

Factors that affect spending: Demographic

Population distributions Socioeconomic

Income Market-related

Physician concentration Policy

Managed care

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

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

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

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

Objectives

1. Develop local spending model.Counties in MississippiCross-sectional

2. Examine relationship of factors associated with healthcare spending.

3. Explore space.

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

Statewide Spending

Population2.9 million

Hospital Care$7.3 billion

Per-Capita$2,517

Local Hospital Spending52.2% of Oktibbeha

County residents received hospital

care in other counties.

LPC is 47.8% or…$1,202 per-capita

Pop 42,454Total: $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: $5,189Min: $956

11 < 1 SD (13%)16 > 1 SD (19.5%)

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

Spatial Weights

Spatial clustering can occur in behavioral risk factors and outcomes

Mobley, 2006.

Spatial lag can lead to biased and inconsistent estimators

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

RHO1: rook-based spatial weights

RHO2: queen-based spatial weights

Variable N Mean Standard Deviation

Minimum Maximum

PCI 82 17.814 3.126 10.416 26.876 COVER 82 18.64 4.138 11.4 29.2 HOSP 82 0.89 0.315 0 1 POVRTY 82 20.039 5.0437 8.6 32.4 DIABET 82 26.14 21.296 0 95 LSPC 82 3.577 0.956 0.557 5.189 RHO1 82 3.5813 .40722 2.6215 4.5747 RHO2 82 3.5855 .39281 2.7195 4.5421

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, RHO1)

+

#3 SPATIAL LAG MODEL (QUEEN-BASED WEIGHTS)

LSPC = f(PCI, COVER, POVRTY, DIABET, HOSP, RHO2)

+

ResultsModel OLS Baseline Spatial Lag, Rook Spatial Lag, Queen Variable Coef. T Coef. T Coef. T PCI 0.1447 5.4** .1317 4.9** 0.1307 4.9** COVER -0.1167 -2.8** -.1154 -2.8** -0.1149 -2.8** HOSP 1.447 6.4** 1.3351 5.8** 1.3116 5.8** POVRTY 0.112 3.2** 0.10474 3.0** 0.10417 3.05** DIABET 0.00698 2.2* 0.00519 1.6 0.00500 1.6 RHO - - -0.368 -2.0* -0.442 -2.4* K -0.5387 -0.7 1.278 1.1 1.588 1.4 N 82 82 82 R2 0.6285 0.6479 0.6548

*, ** - These coefficients are statistically significant at the 0.05, 0.01 level.

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…

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/NARSC07

Garen Evansgevans@ext.msstate.edu662-325-2750