1. Can Care Managers Assist Older Adults to delay Nursing Home Placement? Development of a Risk Index to Predict Transfers from Home and Community-Based Waiver Programs to Nursing Homes Sandra L. Spoelstra, PhD, RN Midwest Nursing Research Society April 14 th, 2012 Dearborn, Michigan

2. Acknowledgements  Dr. Barbara Given, PhD, RN, FAAN University Distinguished Faculty  Charles Given, PhD & Raza Haque, MD  Mei You, MS Statistician  Mike Daeschlein, MDCH Long-Term Care Division  20 Waiver Agencies, 356 Care Managers, & 20,000 Clients

3. Objectives  Participants will be presented factors related to nursing home placement in frail vulnerable elderly in the community setting.  Participants will develop an understanding of how to assist care managers to identify nursing home placement risk.

4. Background  With adults aged 65 and older currently comprising 15% of the population and growing exponentially.  concern is mounting as to how to care for this growing demographic group.  It will be important to find ways to deliver high- quality care tailored to the needs of clients in order to allow these individuals to remain living in the community.

5. Purpose  This research examines the risk of nursing home placement (NHP) an inception cohort of vulnerable group of low-income elderly persons in the State of Michigan Home & Community- Based Waiver program between 2002—2007.  Focus: developing a risk index to identify waiver clients who transferred to NH 2 years.

6. The Present Study  From literature review factors were examined in the Minimum Data Set-Home Care (MDS-HC).  Examined how change between next to last assessment and last assessment increases risk of NHP.  Different gradations of change in each variable were examined.

7. Data Source  From the State Medicaid data warehouse:  MDS-HC assessments  Medicaid claim files  Michigan death certificate information

8. Sample & Setting  Federal 1915(c) HCBS waiver program in the State of Michigan  Clients must meet Medicaid-defined nursing facility level-of-care criteria (ADLs/IADLs, <300% of poverty level, & a caregiver).  Age >65  Between 2002—2007

9. 14,568 Eligible & had an MDS-HC 14,568 Eligible & had an MDS-HC 12,839 Had 2+ MDS-HC Assessments 1,729 Had One MDS-HC Assessment 2,426 NHP in 2 Years 4,099 Stayed in the Waiver Program >2 Years 1,567 Stayed in the Program <2 Years 4747 Other (3,983 died in the program; 764 lost follow up) 4747 Other (3,983 died in the program; 764 lost follow up) Sample Analyzed N=6525 Sample Analyzed N=6525 Development Group N=3263 Confirmation Group N=3262

10. Variables Examined  Age, sex, race  Physical function  dressing, eating, toileting, transferring, and bathing  Cognitive function  Falls  Caregiver informal support hours  Nursing home placement (from claim files)

11. Two Level Model  Deterioration was defined as increased number of ADL dependency, increased the cognitive scale, and increased falls comparing next to last assessment and last assessment.  Limitation: two clients might both be defined as having no deterioration, if one remained independent and the other was fully dependent at both assessments, as deterioration in condition is the issue in our risk model

12. Three Level Model  Deterioration further divided into whether clients had ADL dependencies or 2+ cognitive performance deficits or had falls at last MDS.  Cases that remained were independent at both MDS assessment; or improved at the final MDS when compared with their second to last MDS.  Few cases reported improvement in any dimension.

13. Validation of Risk Model  The sample was split in half using a simple random sampling.  N=3263 to develop the risk index (development sample).  N=3262 to validate the risk index (confirmation sample).

14. Development Sample  Risk factors were entered in model as predictors  NHP <2 years was the dependent variable in the logistic model.  Risk factors that were not significant were removed 1- by-1 until all were p <0.05.  Two risk indexes based on the summed beta weights multiplied by the risk factors for either deterioration alone (the 2-level model) or deterioration and dependency (the 3-level model) were developed.  5 points were added to each index score so that all scores were positive.

15. Confirmation Sample  Applied same estimated beta weights from the development sample to the confirmation sample.  Computed risk indices, and compared the Association of Predicted Probabilities and Observed Responses.  Mann-Whitney non parametric method was used to compare statistical differences between the 2- level model and the 3-level model.

16. Results  N=6525  2426 (37%) transferred to NH in <2 years. Clients at high risk of NHP are over 75 years of age, of Caucasian race, were in a NH before, wished to reside in another setting, were more likely to have been hospitalized in the past 90 days, and reported behavioral problems at the last assessment. Informal caregivers & living arrangement did not impact the model.  Each factor produced between 10-25% greater rates of NHP than clients without those factors.

17. Changes between the 2-MDS Assessments  In the 3-level model, deterioration in cognitive status and physical function is a more sensitive indicator of NHP than the level of dependence alone at the last observations.  No deterioration in falls, but reported falls at the last MDS produced rates of NHP’s (45.7%) to (46%) that were similar in the group who remained at home or those with NHP.

18. Association of Predicted Probabilities & Observed Responses  Area under Curve (AUC) of Receiver Operating Curves (ROCs), % of concordance plus a half percentage of ties:  Development sample 2-level model was 0.72 3-level model was 0.73  Confirmation sample 2-level model was 0.70 3-level model was 0.72  Compared ROCs 3-level & 2-level model to see which was better at predicting NHP.

20. Examining Change in Index on NHP  Categorized risk index into 7 levels, increasing each level by a magnitude of 0.5.  Summed each level according to the proportion of clients who entered a NH. Relationship between a 0.5 unit increase in the risk index and the probability that clients transfer to a NH. Beginning with scores of 4.0 to 4.49, each half unit increase in the risk index produced around a 10% increase in the rate of NHP.  For example, as the risk index score increases (from 2.5 to 6.0), the rate of NHP increased from 21% to 77%.

22. Correspondence Between NHP & Risk Index  Correspondence of probability, sensitivity, and specificity. Assuming a score of 5 on the risk index, then the probability of transferring to a NH is approximately 50%. Using this 50%, we examined sensitivity & specificity A sensitivity of approximately 0.4 & specificity close to 0.9.  This means that for a risk index score >5, we will be able to correctly identify 40% of those clients who will actually go to a NH.  For those clients with a score <5 correctly identifying 90% of those clients who are not going to transfer to a NH.

24. Discussion  The utility of this risk index for waiver program staff comes from the fact that this model can be easily produced from information that is already being collected in the MDS-HC assessments.  If collected on a laptop computer, the risk index could be calculated in real time.  Information from the prior observation, paired with current risk index scores, could be used to produce a risk score that would reveal the rates of deterioration over consecutive assessments.

25. Discussion  Waiver staff could target education and/or services for clients and their caregivers towards those areas with greater vulnerability.  Example: if cognitive status was declining, then caregivers could be informed about how to manage persons with these declines. If hospitalizations existed in the past 90 days, then waiver agents could examine the reasons for the hospital admissions and determine what might be done by waiver staff, and to engage the client, and/or their caregiver, and the primary care physician in actions to prevent hospitalization.

26. Discussion  The index might guide resource allocation needs to be tested.  The data does indicate how it might be used to address this decision. Decisions should not rely solely on the overall score, but on the individual changes in each of the risk index components.  The 3-level model was superior to the 2-level model that focused solely on deterioration.

27. Limitations  To assure that floor effects were addressed, examined the prevalence all factors. Age was divided as 65-75, and 76+ and then clients were classified according to no change, or change in one, two, or all three measures. This change score was then compared with the number and percent of cases with a maximum score on each measure (a score of 5 on the ADL index, 6 on cognitive performance, and 9+ on falls) at the next to last contact.  Only 9% of all patients with no change had a maximum score on ADLs, and 1% had maximum scores on cognitive performance and falls at the next to last contact.

28. Research Implications  Future research could focus on development of the laptop application so that this risk index could be used in the home setting.  Future research could also focus on how the risk index functions in a real world setting, and what actions care managers are able to take to delay or prevent NHP, and what cost saving are experienced.

29. Conclusions  This index defining risk of transfer to a nursing home could be a valuable adjuvant to clinical observations.  If waiver agents are aware of those clients at greater risk, they could target services or intervene to delay or prevent NHP.

30. Conclusion  With the increasing pressure to lower costs of health care, especially for the dually eligible, efforts such as this capitalize on existing information, and deliver it to agencies so that they can make more informed decisions with respect to how to service clients in wavier programs.