Use of Long Term Care Hospitals and Outcomes of Care for Acute Care Ventilator Patients 1 Presented by Kathleen Dalton, PhD Co-investigator Barbara Gage, PhD Presented at Academy Health, June 2008 Funding Source: CMS Contract # TO Cornwallis Road ■ P.O. Box ■ Research Triangle Park, NC Phone

Background Long Term Care Hospitals (LTCHs) care for medically complex patients with expected length of stay >=25 days. 80% of admissions are direct hospital transfers. Transfer creates large additional DRG payment Account for <2% total Medicare discharges Number of LTCH facilities is small but growing 281 in FY 2001 to 392 by FY 2006 (+ 40%). Recent moratorium on new construction imposed by CMS (May 2008) 2

Background Wide geographic variation in supply of LTCH beds and in local LTCH transfer rates Most common LTCH user : Medicare Vent DRGs Transfer rates range from 10%  40% in areas that have LTCHs Less than 1% in areas that don’t Previous work (RTI, Rand, MedPAC) suggests that use of LTCHs is associated with higher cost, but possibly lower mortality Has been difficult to control for selection 3

Design Issues Can’t randomize patients to PAC settings In the absence of experimental data, there are options: Use regression to control for observable differences in patients Simulate experimental conditions using propensity grouping Exploit geographic differences as a potential “natural experiment” or as instrumental variable 4

Objectives for this Study Revisit earlier models looking effects of LTCH use on episode costs and clinical outcomes, but with improved control for selection Changes in approach: Restrict study sample to areas where referral decision is not supply-constrained Restrict episode cases to DRGs with historically high LTCH use Use stratified propensity-score model to mimic random assignment, within groups of episodes ranked by likelihood of LTCH transfer 5

Study Question In areas with adequate LTCH supply, what are the differences in cost or clinical outcomes between ventilator patients who are transferred to LTCHs and those who remain in acute care settings? Note: Companion study (presented elsewhere) asks: What happens in places with no LTCHs? What are the area-level differences? 6

Sample & Data Sources CY 2004 cases with Vent DRGs (475 and 483), from selected high-LTCH supply areas From FY 2004 and 2005 hospital and SNF claims, follow beneficiary until episode is closed by: Discharge home followed by 60+ days without further admission Discharge into non-Medicare long-term care without further readmission (censored observations) Death Cases with death <=7 days from index admission excluded (presumed no referral decision) 7

Location of LTCHs and LTCH Outcomes Study Areas 8

Mechanics of Stratified Propensity Model  Model LTCH transfer = f (patient attributes at index (acute) admission)  Rank cases by predicted Prob(LTCH)  Establish groups within which patient predictors are similarly distributed between LTCH users and non-users. Called “balancing propensity groups”, to simulating random assignment.  Interact propensity group dummy on actual LTCH use in second-stage estimates (allows separate estimates of effect by group)  Outcome Equations Outcome = f ( (LTCH transfer X propensity group dummy), patient attributes at index admission, acute-care facility characteristics, alternate post-acute care (PAC) transfer dummy ) 9

Prediction Variables for LTCH Transfer Patient level only Age, gender, race, disability status, Medicaid Diagnoses (reason for admission; selected Elixhauser co- morbidities) Procedures (tracheotomy; vent>96 hrs; selected major surgical procedures) Utilization (ICU use; Outlier $ as % PPS $) Strongest clinical predictor is tracheotomy and acute care vent support > 96 hours. Pseudo-R 2 for prediction equation = 0.28 Clustered for hospital effects 10

Sample Descriptive Statistics by Propensity Group Group Number of cases Percent referred to LTCH Mean episode length (days) Mean Medicare utilization (days) Mean Medicare inpatient payments Percent died ( episode + 60 days) Percent home <= 60 days index admit Percent readmit within 60 days any home discharge 18618% $ 30,083 $ 30,08333%56%16% % $ 32,865 $ 32,86539%49%16% % $ 47,374 $ 47,37455%31%12% % $ 121,198 $ 121,19851%23%9% % $ 135,819 $ 135,81954%20%9% % $ 129,096 $ 129,09659%17%7% Total433232% $ 69,633 $ 69,63347%36%13% 11

Sample Descriptive Statistics: Post -Acute Care (PAC) Use by Propensity Group 12

Second Stage Outcome Estimations Log-linear model for utilization and cost; logits for mortality, home discharge and readmissions Retained patient predictors from LTCH transfer model Added characteristics of index hospital Size Type of control Presence of hospital-based Rehab or SNF Teaching status Alternative SNF or IRF referral destinations Reference case is “propensity group 1” with “no post- acute transfer” 13

Findings Summary Strong differences in impact of LTCH transfer across propensity groups. Lowest probability (least complex): associated with higher cost; same or worse clinical outcomes Highest probability (most complex): associated with same or lower costs, but generally better clinical outcomes Implication: research designs should incorporate a priori expectation of heterogeneity  avoid estimating sample average “treatment effects” 14

Inpatient Utilization Differentials by Propensity Group and LTCH Transfer Status (from elasticity coefficients) Plots are percent difference in outcome measures, by propensity group. *** Blue: LTCH transfers Green: No PAC *** Reference category: Group 1, No PAC Transfer 15

Medicare Payment Differentials by Propensity Group and LTCH Transfer Status (from elasticity coefficients) Plots are percent difference in outcome measures, by propensity group. *** Blue: LTCH transfers Green: No PAC *** Reference category: Group 1, No PAC Transfer 16

Mortality Differentials by Propensity Group and LTCH Transfer Status (odds ratios) 17 Plots are adjusted odds ratios, by propensity group. *** Blue: LTCH transfers Green: No PAC *** Reference category: Group 1, No PAC Transfer

Home Discharge Differentials by Propensity Group and LTCH Transfer Status (odds ratios) 18 Plots are adjusted odds ratios, by propensity group. *** Blue: LTCH transfers Green: No PAC *** Reference category: Group 1, No PAC Transfer

Readmission Differentials by Propensity Group and LTCH Transfer Status (odds ratios) 19 Plots are adjusted odds ratios, by propensity group. *** Blue: LTCH transfers Green: No PAC *** Reference category: Group 1, No PAC Transfer

Interpretation Important take-away message is probably not the point estimates, but the patterns formed by the LTCH versus non-PAC lines. Point estimates are different but line patterns are almost identical for estimations on cases from other high-LTCH supply areas Results could tell two plausible stories, depending on our faith in propensity scores… 20

Story 1 Assumes Adequate Control for Selection: For the most complex cases, LTCHs may be a good policy investment: Same or lower utilization, same or lower cost Lower mortality For less complex patients, LTCH transfers bring no benefit Longer stays, higher costs No clinical benefits. Policy implications: Small number of LTCH beds appropriate throughout the country Need to restrict supply and referral criteria 21

Story 2: Uncontrolled Confounding? “Too sick to transfer”: unobserved instability in the severely ill 22 N on-tracheotomy cases with unobserved prognosis for long recovery

Techniques to test… Can use distance measures as instrumental variables to test for exogenous referral decision within the propensity groups (2-stage substitution or residual inclusion models) Stratified estimations pose some problems, but thus far tests have failed to reject H 0 that LTCH transfer is exogenous within lowest and highest propensity groups  No statistical evidence of uncontrolled confounding 23

Other Limitations & Design Considerations Claims data don’t tell us if patient is on vent at time of index discharge May benefit from time-to-event design for dichotomous outcomes LTCH transfer is treated as a binary choice rather than one among multiple PAC referral options. It may need to be modeled as multinomial choice? series of nested decisions? 24