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

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

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

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

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

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

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

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

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

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 (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

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

Regression Results Nursing Homes Explanatory Variables Base ModelAdd CONAdd PovertyAdd DisabilityAdd College Total Pop 65+ (in 1,000s) *** (29.24) *** (29.42) *** (44.29) *** (40.70) *** (41.42) Males 65+ (in 1,000s) *** (69.29) *** (69.73) *** (96.31) *** (91.79) *** (101.19) CON 1=yes * (127.03) ** (133.77) ** (126.27) ** (121.22) Below Poverty Level (in 1,000s) ** (45.63) (85.57) (89.42) Disability 65+ (in 1,000s) (50.69) (53.96) College 65+ (in 1,000s) 9.34 (26.43) R-squared

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

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

Regression Results Hospitals Explanatory Variables Base Model Add Total Pop 65+ Add Females Add Insurance Add CON Total Pop (in 1000s) 2.713*** (0.255) 1.372*** (0.337) (0.295) (3.340) 1.371*** (0.335) Total Pop 65+ (in 1000s) *** (3.152) *** (2.937) *** (2.721) *** (3.139) Females (in 1000s) 5.420*** (1.685) Population Insured (2.967) CON 1=yes (99.629) R-squared

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

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, ,4283,686 3,1772,302

Application of Models Nursing Homes (actual – estimated)

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, ,5671,625 2,3461,970

Application of Models Hospitals (actual – estimated)

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

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

Thank You Any Questions?