1. 1 An Autoregressive latent Trajectory Model of Resident Outcome Improvement in Nursing Homes Thomas T.H. Wan, Ph.D., M.H.S. Professor and Director Public Affairs Doctoral Program College of Health and Public Affairs University of Central Florida May 18, 2005

2. 2 Introduction  The quality of nursing home care has been a serious concern.  Nursing homes are under increased scrutiny and regulation due to reports of inadequate or deficient care  Little is known about the trajectories of resident outcomes that are directly related to nurse staffing, nursing care deficiency rating, and rehabilitation.  It is a challenging to develop a theoretically informed framework to guide the longitudinal analysis of nursing homes’ quality performance.

3. 3 Theoretical Framework

4. 4 Research Questions  What are the factors associated with the improvement of resident outcomes at the facility level?  Can previous levels influence later levels of quality performance measured by resident outcomes?  Do time-specific measures of nursing related variables influence the improvement of resident outcomes, while the lagged effects of quality measure and influences of contextual factors are simultaneously considered?

5. 5 Purpose of the Study  Using 7 waves of data with autoregressive latent trajectory modeling, we assess the relationships of staffing, nursing care adequacy, and rehabilitative care to each wave of quality improvement, holding constant the contextual factors of nursing homes in the investigation of individual change trajectories.  The hypothesis is that, while controlling for facility and contextual factors, nursing homes with higher nurse staffing, more rehabilitative care, and fewer nursing care deficiencies will show improved resident outcomes.

6. 6 Data & Methods OSCAR (Online Survey, Certification, and Reporting System) CMS contracts state surveyors to review and rate each nursing facility annually. Contains hundreds of variables on every U.S. Medicare- or Medicaid- certified nursing facility. Data are considered to be accurate reflections of actual deficiencies or citations.

7. 7 Measurement Panel data (1997-2003): N=11,197 Major outcome variable: Quality index  Change in incidence rate of adverse outcomes (pressure ulcers, physical restraints, and catheters) Time-varying variables, measured annually  Nurse staffing  Nursing care deficiencies or citations  Rehabilitation (% receiving rehab services) Eight time-invariant covariates  Bed size  Private ownership (for profit =1; not for-profit = 0)  Chain affiliation (chain = 1; non-chain = 0)  Average acuity level  % Medicare residents  Region ( South = 1; non-South = 0)  Urban (urban=1; rural=0)  % elders (75+) in the county

8. 8 A Balanced Score Card Approach Nursing Home Quality: Resident Outcomes A weighted aggregate measure of quality: Rate change in the incidents such as developing pressure ulcers, having physical restraints, & on catheters per year Declining incidents = a positive rate =better resident outcomes Top 25 percentile of 11,197 facilities rated as high quality nursing homes (N=2,799)

9. 9 The Distribution of the Best Quality (top 25%) Nursing Homes in US (1997-2003)

10. 10 Results  Cross-sectional analysis  Trend analysis  Longitudinal modeling Cross-lagged model Parallel growth curve model Autoregressive latent trajectory model

11. 11 1. Cross-Sectional Regression Analysis of Resident Outcomes in 1997

12. 12 The Better Practice is associated with Homes with a smaller bed size Being for-profit Caring for more Medicare residents Having residents with lower acuity levels Located in the region other than the South Having a high level of nurse staffing Certified with lower frequencies of nursing care deficiencies

13. 13 2.Trends of Four Indicators (1997-2003)

14. 14 3. Autoregressive Model Goodness of Fit Statistics: X2 = 14,311 with 315 degrees of freedom GFI =.925; AGFI =.903; TLI =.899; RMSEA =.063

15. 15 4. Parallel Process Growth Model GOF statistics: Chi-square =5,369 (196 DF) GFU =.954; AGFI =.958; TLI =.958; RMSEA =.049

16. 16 Findings of the Parallel Process GC Model  The QI growth curve shows a steady improvement in resident outcome.  The Nurse care deficiency growth curve shows a decline in 7 years.  The rehabilitation services use increased after 2000.  The change trajectories in resident outcomes are positively associated the increase in rehabilitation service use and negatively associated with the slope of nursing care deficiencies.  The increase in rehabilitative services enhances improved resident outcomes in 2000-2003, but not earlier years.

17. 17 5. Autoregressive latent Trajectory Model

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19. 19 Findings of ALT Model  The lagged effect of QI is an important factor that should be statistically controlled in growth curve modeling.  The intercept factor, representing the baseline of quality, was well predicted by eight contextual and facility characteristics variables.  The slope or change trajectory of quality was only weakly predicted by them.  The improved quality in resident outcomes was associated with facilities having fewer nursing care deficiency citations than did their counterparts.

20. 20 Conclusions  Complimentary results were revealed from both cross-sectional & longitudinal analyses.  Parallel process growth curve modeling demonstrates its potential utility for policy research.  ALT is a power analytical approach to confirmatory analysis and data mining under a theoretically specified framework.  The best practice in nursing home quality is directly associated with reduced nursing care deficiencies.

21. 21 Thank you Email: twan@mail.ucf.edu