1. Determinants for Healthcare Expenditure Growth Presented by LaToyia Floyd Wayne State University Fall 2013

2. Overview Introduction Health Care Trends Literature Review Data Regression Model Empirical results Conclusion

3. 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

4. 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

5. % Change in spending downward slope Health Care Trends 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).http://www.cms.hhs.gov/NationalHealthExpendData/

6. Health Care Trends Baby Boomers – increase dependence on health care Source: OECD health statistics database

7. Health Care Trends Distribution of National Health Expenditures, by Type of Service (in Billions), 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).http://www.cms.hhs.gov/NationalHealthExpendData/ 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.

8. Health Care Trends % Distribution for source of spending 1970 2010 Hospital Care Physician & Clinical Services Retail Prescription Drugs Nursing Care Facilities & Continuing Care Retirement Communities

9. 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

10. Data OECD Database 4 countries: USA, Canada, Norway and Australia Sample sizes (~40 data points) Difficulty finding variables that fit into model meaningfully

11. Data

12. Descriptive Statistics: NORWAY compared to US VARSample size Mean Variance Norway39 2,036.92206 2,673,473.90896 US39 3,420.43427 5,853,666.01922 Summary Degrees Of Freedom67 Hypothesized Mean Difference 0.E+0 Test Statistics2.95879 Pooled Variance 4,263,569.96409 Two-tailed distribution p-level0.00427 t Critical Value (5%) 1.99601 One-tailed distribution p-level0.00213 t Critical Value (5%) 1.66792 G-criterion Test Statistics0.2124 p-level 0.00294 Critical Value (5%)0.18367 Pagurova criterion Test Statistics2.95879 p-level 0.99572 Ratio of variances parameter0.31353 Critical Value (5%) 0.02516

13. Data Descriptive Statistics: CANADA compared to US VARSample sizeMeanVariance Canada391,918.691791,391,987.49144 US393,420.434275,853,666.01922 Summary Degrees Of Freedom55Hypothesized Mean Difference0.E+0 Test Statistics3.48409Pooled Variance3,622,826.75533 Two-tailed distribution p-level0.00098t Critical Value (5%)2.00404 One-tailed distribution p-level0.00049t Critical Value (5%)1.67303 G-criterion Test Statistics0.25225p-level0.00194 Critical Value (5%)0.18367 Pagurova criterion Test Statistics3.48409p-level0.99901 Ratio of variances parameter0.19211Critical Value (5%)0.02518

14. Data Descriptive Statistics: AUSTRALIA compared to US VARSample sizeMeanVariance Australia391,580.525761,112,673.77351 US393,420.434275,853,666.01922 Summary Degrees Of Freedom52Hypothesized Mean Difference0.E+0 Test Statistics4.35338Pooled Variance3,483,169.89637 Two-tailed distribution p-level0.00006t Critical Value (5%)2.00665 One-tailed distribution p-level0.00003t Critical Value (5%)1.67469 G-criterion Test Statistics0.32385p-level0.00015 Critical Value (5%)0.18367 Pagurova criterion Test Statistics4.35338p-level0.99994 Ratio of variances parameter0.15972Critical Value (5%)0.02519

15. 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

16. Regression Model Time series regression, detrended Country specific comparison Model Total Expenditure on Healthcare t =  0 +  1 GDP t1 +  2 (Administration) t1 +  3 (Pharma) t3 +  4(tot. curative) t4 +  5 (preventive) t5 +  6 t + u t Detrending accomplished by adding time trend variable,  6 t

17. Empirical Results Significant Variables

18. Empirical Results Elasticities: logs of variables for USA Increasing returns to scale for number of Curative beds

19. 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

20. Regression Model

22. 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