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Nursing Home Quality as a Public Good David C. Grabowski Harvard Medical School Joseph J. Angelelli Pennsylvania State University Jonathan Gruber Massachusetts.

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Presentation on theme: "Nursing Home Quality as a Public Good David C. Grabowski Harvard Medical School Joseph J. Angelelli Pennsylvania State University Jonathan Gruber Massachusetts."— Presentation transcript:

1 Nursing Home Quality as a Public Good David C. Grabowski Harvard Medical School Joseph J. Angelelli Pennsylvania State University Jonathan Gruber Massachusetts Institute of Technology

2 Medicaid and Private-pay prices, 1998

3 Uniform Quality Assumption Most economists have assumed that quality is uniform within facilities across payer types –Federal law prohibiting discrimination by payer type –Economies of joint production –Professional norms –Lack of individual data If this is the case, Medicaid can free-ride on private-payers –Studies find association between payer mix and quality.

4 Criticisms of uniform quality assumption Oversight of federal law very difficult Economies of joint production not relevant for most direct patient care activities –e.g., assisting residents with bathing, dressing, eating, toileting, and walking

5 Our Contribution To test whether quality is uniform across Medicaid and private-pay patients within nursing homes. –We use a range of process and outcome based measures of quality –We exploit both within-home and within- person variation in payer type and quality –We exploit Medicaid-private pay rate differentials across states

6 Data Minimum Data Set (MDS) surveys from KS, ME, MS, ND, OH, SD & WA –MDS collected at least quarterly for all patients, 1998 (4 th qtr) thru 2002 –Data combines existing patients with new admissions –Eliminate short-stay Medicare patients –Total sample: 1,626,628 assessments for 359,768 patients from 1,537 facilities. Facility information from OSCAR system Rates collected from state Medicaid cost reports

7 Quality Measures Pain Pressure ulcers Physical restraints Incontinence Catheters Bedfast Anti-psychotics Feeding Tubes Urinary tract infection Wound infection Falls Depression

8 Methods (cont.) NH fixed effects model Y int = α + β 1 MEDICAID int + β 2 OTHER int + δX int + γZ nt + α t + λ n + ε int Patient fixed effects models Y int = α + β 1 MEDICAID int + β 2 OTHER int + δX int + γZ nt + α t + μ i + ε int

9 Timing of the Medicaid Effect Y int = α + Σ -k<j<m θ j MEDICAID j int + β 1 OTHER int + δX int + γZ nt + α t + μ i + ε int Replace Medicaid dummy with three lead (or greater) and three (or greater) lag transition terms in the patient-level fixed effects model. Restrict model to only those individuals observed 7+ periods and excludes Medicaid transitions with fewer than 3 assessments pre- and post-transition

10 OutcomeFacility FEsPatient FEsDep Var Mean Pain 0.008 (11.76)-0.009 (7.64)0.13 Pressure ulcers-0.007 (12.38)-0.011 (11.10)0.08 Restraints-0.002 (4.75) 0.006 (7.21)0.09 Incontinence 0.018 (24.25)0.013 (11.86)0.54 Catheters-0.010 (20.24)-0.014 (23.11)0.08 Bedfast-0.002 (4.24)-0.007 (11.60)0.06 Anti-psychotics0.009 (8.67)0.005 (3.68)0.19 Feeding tubes-0.002 (3.99)-0.008 (19.83)0.07 Urinary infection-0.004 (6.62)-0.013 (11.49)0.09 Wound infection-0.003 (13.58)-0.004 (9.92)0.02 Falls-0.022 (31.82)-0.019 (14.07)0.16 Depression0.034 (36.79)0.023 (23.25)0.43

11 Transition Results: Total Sample PeriodIncontinenceDepression T-3+-0.006 (0.99)-0.008 (1.48) T-2-0.00004 (0.01)0.0003 (0.05) T-10.003 (0.51)0.004 (0.74) Transition0.004 (0.54)0.009 (1.56) T+10.001 (1.65)0.015 (2.50) T+20.014 (2.15)0.017 (2.84) T+3+0.014 (2.23)0.014 (2.52)

12 Transition Results: New Admits Only PeriodIncontinenceDepression T-3+0.006 (0.63)-0.006 (0.75) T-20.010 (1.05)0.011 (1.25) T-10.008 (0.84)0.013 (1.49) Transition0.007 (0.76)0.019 (2.27) T+10.015 (1.58)0.025 (2.94) T+20.017 (1.75)0.025 (2.98) T+3+0.017 (1.84)0.021 (2.52)

13 Alternate Specification Another potentially exogenous source of variation is the difference between the private-pay price and Medicaid Larger rate differential should entail worse Medicaid quality Thus, we examine a model that interacts the ratio of rates (Medicaid/private-pay) with payer source Results do not support differential quality

14 Conclusions The results support the uniform quality assumption used in most economic studies of the NH sector –Little evidence of a Medicaid causal effect There is the potential for “free ridership” on the part of state Medicaid programs Segregation by payer type –“Driven to tiers”


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