Determinants for
Healthcare Expenditure
Growth
Presented by LaToyia Floyd
Wayne State University
Fall 2013
Overview
 Introduction
 Health Care Trends
 Literature Review
 Data
 Regression Model
 Empirical results
 Conclusion
Introduction
 Health care expenditure
 Current trends vs growth rates: past vs future state
 Health care reform in the US
 Equity Issues – who gets access to healthcare
 Providing insurance for the uninsured
 Quality issues – health outcomes
 Quality of life; preventive care
 Efficiency Issues – best utilization of resources
 Cost: front end investment vs back end exploitation
 Re-organization of primary health care
 Expansion of Mid-level provider utilization
Introduction
 Re-organization of primary health care
 Improving quality
 checks and balances
 Coordination post hospitalization
 Helping the equity conundrum – who receives healthcare
 Distribute burden of service across resources
 Low overhead clinics which serve local communities
 Multi-payment structures
 Re-introducing fee-for-service
Health Care Trends
 % Change in spending downward slope
Average Annual Percent Change in National Health
Expenditures, 1960-2010
Source: Kaiser Family Foundation calculations using NHE data from Centers for Medicare and Medicaid Services, Office of the Actuary, National
Health Statistics Group, at http://www.cms.hhs.gov/NationalHealthExpendData/ (see Historical; National Health Expenditures by type of service
and source of funds, CY 1960-2010; file nhe2010.zip).
Health Care Trends
 Baby Boomers – increase dependence on health care
% of Population 65 or Older Across Countries
20.0%
15.0%
Australia
Canada
10.0%
Norway
United States
5.0%
0.0%
Year
1964
1969
1974
1979
1984
1989
1994
1999
2004
Source: OECD health statistics database
2009
Health Care Trends
 Distribution of National Health Expenditures, by Type
of Service (in Billions), 2010
Note: Other Personal Health
Care includes, for example,
dental and other professional
health services, durable medical
equipment, etc. Other Health
Spending includes, for example,
administration and net cost of
private health insurance, public
health activity, research, and
structures and equipment, etc.
Source: Kaiser Family Foundation calculations using NHE data from Centers for Medicare and Medicaid Services, Office of the Actuary, National
Health Statistics Group, at http://www.cms.hhs.gov/NationalHealthExpendData/ (see Historical; National Health Expenditures by type of service
and source of funds, CY 1960-2010; file nhe2010.zip).
Health Care Trends
 % Distribution for source of spending
1970
2010
Hospital Care
1970
2010
Physician & Clinical
Services
1970
2010
Retail Prescription
Drugs
1970
2010
Nursing Care Facilities &
Continuing Care Retirement
Communities
Literature Review
 Barros (1998) The Black Box of Health care Expenditure
Growth
 What contributes to the growth rate of health expenditure –
future expansion
 Contributions to level of health care expenditure – current and
past factors
 Fuchs (1974) Who Shall Live
 Substitution of inputs – can this apply to healthcare resources
such as providers
 Macinko, Starfield and Shi (2003) Contribution of Primary
Care Systems to Health Outcomes for OECD Countries
 Strong relationship between strength of primary care system
and health outcomes
Data
 OECD Database
 4 countries: USA, Canada, Norway and Australia
 Sample sizes (~40 data points)
 Difficulty finding variables that fit into model
meaningfully
Data
% Change in Health Care Expenditures
0.16
0.14
0.12
0.10
United States
Norway
Canada
Australia
0.08
0.06
0.04
0.02
0.00
1990
1995
2000
Year
2005
2010
Data
Descriptive Statistics: NORWAY compared to US
VAR
Mean
Sample size
Variance
Norway
39
2,036.92206
2,673,473.90896
US
39
3,420.43427
5,853,666.01922
Summary
Degrees Of Freedom
Test Statistics
2.95879
Hypothesized Mean
Difference
Pooled Variance
4,263,569.96409
0.00427
t Critical Value (5%)
1.99601
0.00213
t Critical Value (5%)
1.66792
0.2124
p-level
0.00294
p-level
0.99572
67
0.E+0
Two-tailed distribution
p-level
One-tailed distribution
p-level
G-criterion
Test Statistics
Critical Value (5%)
0.18367
Pagurova criterion
Test Statistics
2.95879
Ratio of variances parameter
0.31353
Critical Value (5%)
0.02516
Data
Descriptive Statistics: CANADA compared to US
VAR
Canada
US
Sample size
Mean
39
39
1,918.69179
3,420.43427
Variance
1,391,987.49144
5,853,666.01922
Summary
Degrees Of Freedom
55
Hypothesized Mean Difference
0.E+0
Test Statistics
3.48409
Pooled Variance
3,622,826.75533
Two-tailed distribution
p-level
0.00098
t Critical Value (5%)
2.00404
One-tailed distribution
p-level
0.00049
t Critical Value (5%)
1.67303
G-criterion
Test Statistics
Critical Value (5%)
0.25225
0.18367
p-level
0.00194
Pagurova criterion
Test Statistics
3.48409
p-level
0.99901
Ratio of variances parameter
0.19211
Critical Value (5%)
0.02518
Data
Descriptive Statistics: AUSTRALIA compared to US
VAR
Sample size
Australia
39
US
39
Mean
1,580.52576
3,420.43427
Variance
1,112,673.77351
5,853,666.01922
Summary
Degrees Of Freedom
Test Statistics
52
4.35338
Hypothesized Mean Difference
Pooled Variance
0.E+0
3,483,169.89637
Two-tailed distribution
p-level
0.00006
t Critical Value (5%)
2.00665
One-tailed distribution
p-level
0.00003
t Critical Value (5%)
1.67469
G-criterion
Test Statistics
Critical Value (5%)
0.32385
0.18367
p-level
0.00015
Pagurova criterion
Test Statistics
Ratio of variances parameter
4.35338
0.15972
p-level
Critical Value (5%)
0.99994
0.02519
Regression Model
 Dependent variable: Total health care expenditure, per
capita PPP
 Independent variables:
 Administration and Health Insurance, per capita PPP
 Pharmaceuticals and non-durable medical goods, per
capita PPP
 Total number of curative (acute) beds, per 1,000
 Preventative measures, per capita PPP
 Home care expenditures, per capita PPP
Regression Model
 Time series regression, detrended
 Country specific comparison
 Model
 Total Expenditure on Healthcaret = 0 + 1 GDPt1 +
2(Administration)t1 + 3(Pharma)t3 + 4(tot. curative)t4 +
5(preventive)t5 + 6t + ut
 Detrending accomplished by adding time trend variable,
6t
Empirical Results
 Significant Variables
Empirical Results
 Elasticities: logs of variables for USA
 Increasing returns to scale for number of Curative beds
Regression Model
 Dependent variable: Total health care expenditure, per
capita PPP
 Independent variables: Total of 10, discussing 5 today
 Pharmaceuticals and non-durable medical goods, per
capita PPP
 Total number of curative (acute) beds, per 1,000
 Total hospital beds per million population
 Limited data points
 Practicing physicians per 1,000 population
Regression Model
Number of Hospitals per Million Population
80
70
60
50
Australia
Canada
40
Norway
United States
30
20
10
0
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Regression Model
Practising Physicians per 1,000 Population
4.5
Number of Physicians
4.0
3.5
Australia
Norway
3.0
United States
2.5
2.0
2005
2006
2007
2008
Year
2009
2010
2011
Conclusion
 Number of Curative beds a factor in health care growth
 Clue into organizational restructuring
 Further studying on independent variables
 Number of hospitals
 Could the decrease in the number of hospitals contribute to
lower percentage growth of healthcare
 Practicing physicians
 New medical schools
 Leverage mid-level providers
 Co-integration between variables
 Endogenous effects vs exogenous effects on model
 Insurance structuring
 ER expansion