1. Estimating Needed Capacity of Nursing Home and Hospital Beds Presented by Megan Stratman and Matt Spellman

2. Motivation  To build a statistical model to estimate needed capacity of nursing home beds and hospital beds within Metropolitan Statistical Areas (MSA)  To use this model to determine whether the Eugene- Springfield MSA has too many or too few nursing home and hospital beds

3. Introduction  Current hospital situation in Eugene-Springfield  Implications of Certificate of Need (CON)

4. Outline of Presentation  Review of Literature – to help understand what past studies’ have determined  Building our regression models  Estimating and interpreting coefficients  Applying regression results to Eugene-Springfield MSA to determine whether below or above needed capacity of nursing home and hospital beds

5. Review of Literature Nursing Homes  Age  Gender  Marital Status  Functional Impairments  Educational Attainment  Income  Market Competitiveness

6. Review of Literature Hospitals  Birth Rate  Death Rate  Constraint Function:  “Break-even”  Demand (Population, Insurance Coverage, etc)  Government Regulation

7. Review of Literature Certificate of Need (CON)  Ambiguous effect on supply of beds  Proponents: contains costs and maintain quality  Opponents: restricts competition

8. Methodology and Hypotheses Building Two Models  Dependent variable:  Model 1: Nursing Home Beds  Model 2: Hospital Beds  Independent variables in each model:  Demographic  Presence of CON regulations  By building these models and running regression analyses, we can determine which variables impact needed bed capacity

9. Methodology and Hypotheses Nursing Homes—Selected Variables Variables Hypothesized Signs Rationale Total Population 65+ (in 1,000s) + Larger population of elderly, larger demand Males 65+ (in 1,000s) - Relatively shorter lifespan, wife able to provide in-home care Presence of CON (1=yes) - Market distortion restricts supply 65+ Below Poverty Level (in 1,000s) - Unable to afford nursing home care; ambiguous effect: Medicaid, past studies Disability 65+ (in 1,000s) + Increased functional impairments increase need for living assistance Some College 65+ (in 1,000s) + Undetermined interaction; higher education may imply longer lifespan

10. Methodology and Hypotheses Hospitals—Selected Variables Variables Hypothesized Signs Rationale Total Population (in 1,000s) + Larger total population, larger demand Total Population 65+ (in 1,000s) + Larger population of elderly, larger demand Females 15-44 (in 1,000s) + Child-bearing age increases demand of beds Presence of CON (1=yes) - Market distortion restricts supply Population Insured + Insured are more likely to use hospital facilities

11. Methodology and Hypotheses Data Collection  National data according to Metropolitan Statistical Areas (MSA)  Sources:  U.S. Census Bureau (demographics)  www.medicare.gov (nursing home beds)  American Hospital Association (hospital beds)  Kaiser Family Foundation (insurance coverage)

12. Regression Results Nursing Homes Explanatory Variables Base ModelAdd CONAdd PovertyAdd DisabilityAdd College Total Pop 65+ (in 1,000s) 228.82*** (29.24) 228.42*** (29.42) 282.73*** (44.29) 275.88*** (40.70) 284.52*** (41.42) Males 65+ (in 1,000s) -456.28*** (69.29) -455.145*** (69.73) -565.28*** (96.31) -563.79*** (91.79) -591.86*** (101.19) CON 1=yes -214.84* (127.03) -265.40** (133.77) -283.00** (126.27) -285.21** (121.22) Below Poverty Level (in 1,000s) -98.90** (45.63) -132.89 (85.57) -124.82 (89.42) Disability 65+ (in 1,000s) 31.59 (50.69) 26.83 (53.96) College 65+ (in 1,000s) 9.34 (26.43) R-squared0.95130.95160.95600.95630.9564

13. Methodology and Hypotheses Interpreting the Coefficient on Total Males 65+ Coefficient = -455 Hold Total Population Constant Increase Males by 1,000 Decrease Females by 1,000

14. Methodology and Hypotheses Interpreting the Coefficient on CON  Dummy variable used to control for qualitative data  Presence of CON = 1  No CON = 0  If CON is present, insert 1 into equation  nhbeds = 228(totalpop65)– 455(male65) – 214(CON)  nhbeds = 228(totalpop65)– 455(male65) – 214(1)  nhbeds = 228(totalpop65)– 455(male65) – 214  If CON is not present, insert 0 into equation  nhbeds = 228(totalpop65)– 455(male65) – 214(CON)  nhbeds = 228(totalpop65)– 455(male65) – 214(0)  nhbeds = 228(totalpop65)– 455(male65)  Coefficient on CON has no impact on regression estimate

15. Regression Results Hospitals Explanatory Variables Base Model Add Total Pop 65+ Add Females 15-44 Add Insurance Add CON Total Pop (in 1000s) 2.713*** (0.255) 1.372*** (0.337) 0.401 (0.295) 1.903 (3.340) 1.371*** (0.335) Total Pop 65+ (in 1000s) 13.169*** (3.152) 12.391*** (2.937) 13.015*** (2.721) 13.207*** (3.139) Females 15- 44 (in 1000s) 5.420*** (1.685) Population Insured -0.602 (2.967) CON 1=yes -58.440 (99.629) R-squared0.83200.87050.87810.87060.8707

16. Application of Models Final Regression Equations  Nursing Home Beds  Hospital Beds

17. Application of Model MSA Total Pop 65+ Male 65+ Presence of CON 1=yes 65+ Below Poverty Eugene-Springfield42,95418,14013,149 Medford-Ashland28,99112,63511,944 Spokane, WA51,94921,19814,021 Albuquerque, NM80,42133,99507,213 Nursing Homes Estimated # of Beds Actual # of Beds 1,6831,229 966596 2,4283,686 3,1772,302

18. Application of Models Nursing Homes (actual – estimated) - 454- 370 + 1258 - 875

19. Application of Model MSATotal Pop Total Pop 65+ Eugene-Springfield322,95942,954 Medford-Ashland181,26928,991 Spokane, WA417,93951,494 Albuquerque, NM712,73880,421 Hospitals Estimated # of Beds Actual # of Beds 1,318586 940384 1,5671,625 2,3461,970

20. Application of Models Hospitals (actual – estimated) - 732 - 556 + 58 - 376

21. Review of Regression Building  Estimated two models  Nursing home beds  Hospital beds  Using the models, we estimated needed capacity of beds in the Eugene-Springfield MSA

22. Conclusions  Nursing Home Beds:  CON regulations have an impact by restricting supply of beds, relative to supply in a competitive market  Hospital Beds:  CON mimics how a competitive market would function  State fixed effects may help explain the differences in bed supply; note Washington vs. Oregon  Influencing factors: preferences of residents; substitutes and alternatives; others unable to be captured empirically

23. Thank You Any Questions?