Sally C. Stearns 1 Laura P. D’Arcy 1 Daria Pelech 2

1. Using the NNHS versus the LEHD & NHC to Assess Whether Nursing Home Staff Turnover Affects Resident Outcomes Sally C. Stearns1 Laura P. D’Arcy1 Daria Pelech2 1The University of North Carolina at Chapel Hill 2Duke University UNC Institute on Aging September 22, 2009 Supported by the National Institute on Aging and the Demography and Economics of Aging Research (DEAR) Program at the Carolina Population Center (Grant 5-P30-AG024376) Facilitated by the National Center for Health Statistics and the Triangle Census Research Data Center

2. Disclaimer • This research was carried out at the Triangle Census Bureau Research Data Center facility. The results and conclusions of the paper are those of the authors and do not indicate concurrence by the Census Bureau. These results have been screened to avoid revealing confidential data.

3. Overview Turnover among nursing home staff problematic High annual rates for nursing assistants (68% to 170%) High costs to facilities May compromise quality of care Evidence on effect of turnover on outcomes Mixed or inconclusive results Most studies: Don’t address endogeneity of turnover and outcome Use small/non-representative samples Use aggregated facility data

4. Research Question (Pilot) • What is the effect of facility-level turnover among certified nursing assistant (CNA) staff on resident-level outcomes? • Real dearth of information nursing home staff turnover data • Pilot study conducted at RDC used 2004 National Nursing Home Survey • Merged facility and area data with resident surveys • Good methods • Facility fixed effects • Proposed instrumental variables for endogeneity of turnover • But turnover data are single point in time (not annual) per facility

5. Area Economic Indicators - Employment - Housing Value Turnover or Churning Other Facility Characteristics Resident Characteristics - Sociodemographic - Medical/clinical - Functional Resident Outcomes (Bad) - Hospital Use - ER Use - Ulcers - Pain - Falls - Any of the Above Conceptual Model (1)

6. Conceptual Model (2) Area Economic Indicators - Employment - Housing Value Turnover or Churning Other Facility Characteristics Resident Characteristics - Sociodemographic - Medical/clinical - Functional Resident Outcomes (Bad) - Hospital Use - ER Use - Ulcers - Pain - Falls - Any of the Above

7. Empirical Model: Pilot • Turnover=f(Facility characteristics, area IV) • Estimated using single year facility-level observations • Bad Outcomes=f(Turnover, resident characteristics, other facility characteristics) • Single year multiple resident-level observations per facility for cross sectional pilot study

8. Area Instruments: Pilot & Proposed Study County unemployment Median home value Median income Percent housing units vacant NA hourly mean wage Food/beverage server hourly mean wage HHI total certified beds

9. Data: Pilot Study • 2004 National Nursing Home Survey • Started with1,140 facilities and 13,425 residents • Needed to work at Triangle Census Research to access file created by NCHS • Can not merge public use versions of facility & resident surveys • Exclusions (age<65 or missing data) resulted in a analysis file of 9,279 residents at 981 facilities • Range of 1 to 12 residents per facility

10. Turnover Measures: Pilot • Two measures: • Turnover among certified nursing assistants (CNAs) in the past three months (annualized) • Average over all residents: 52% • Proportion of CNAs on staff for less than one year • Average over all residents: 37%

11. Outcome Measures: Pilot • Resident-level observations of: • Hospital Admission in past 90 days (7%) • ED visits in past 90 days (8%) • Any pressure ulcer (10%) • Fell in past 30 days (16%) • Fell in past 31-180 days (28%) • Any pain in past 7 days (25%) • Any negative health outcome above (55%)

12. Methods: Pilot • Linear probability models • Facilitates FE and IV estimation • OK if reasonable variance in dependent variables • Adjusted for survey weights and clustering • Three types of models estimated: • Naïve LPM • Facility Fixed Effects • Facility Fixed Effects – Instrumental Variables

13. Results: Pilot Study Any Bad Outcome (mean of 0.55) • FE are arguably the best estimates: • Increase in CNA turnover of 0.1 associated with 0.0025 increase in likelihood of bad outcome • Increase in proportion of CNAs at facility less than one year of 0.1 associated with 0.0094 increase in likelihood of bad outcome

14. Summary: Pilot • FE estimates show modest effect of turnover or low retention on bad outcomes • Other observed facility characteristics had comparable effects • High occupancy or lack of care plan increased bad outcomes • For-profit status or offering fully paid health insurance for the CNA’s family decreased bad outcomes • Effects were strongest for “any pain” outcome • IV estimates larger, but: • Weak instruments • Cross-sectional area instruments can not explain within-facility variation in resident outcomes

15. Policy Implications: Pilot • Interventions to reduce CNA turnover are likely beneficial and may reduce cost, but other observed and unobserved facility characteristics may have as great of an effect on resident outcomes • Comprehensive programs to ensure quality administration and oversight at facilities may be required to jointly reduce CNA turnover and improve resident outcomes

16. Limitations: Pilot Study Have not: Allowed for non-linear effects of turnover or low retention Controlled for staffing levels (though is picked up in fixed effects, so estimation is quasi-reduced form) Can not distinguish between turnover once in many positions versus lots of turnover in a few positions Cross-sectional data IV correction may not work due to: Weak instruments Intrinsic problem that cross-sectional IVs can not explain within-facility variation in outcomes

17. Research Question (Revised) What is the effect of facility (establishment) churning on facility-level resident outcomes? Proposed Study: Merge Quality Workforce Indicator (turnover) data with Nursing Home Compare Longitudinal facility-level panel will: Facilitate IV approach Provide within-facility variation in turnover over time But lots of limitations, so is it worth it?

18. Proposed Study • Nursing Home Compare (NHC) • www.medicare.gov/nhcompare/ • Annual facility-level records since 2003 of facility characteristics, inspection results, residents, staff and ratings • Would enable annual panel from 2003-2008 for up to 17,000 nursing homes (~15,000 free-standing??) • Quarterly Workforce Indicators (QWI) • Generated from Longitudinal Employment Household Data (LEHD) • Provides measure of turnover for all employees at a firm • But only available for approximately 30 states • Currently available through 200? (at least 2004)

19. Empirical Model: Proposed Study Turnover=f(Facility characteristics, area IV) Estimated using panel of annual facility-level observations Bad Outcomes=f(Turnover, resident characteristics, other facility characteristics) Facility-level observations for proposed longitudinal study

20. Proposed Study Challenges • 1. Limitations to turnover measure from QWI • Cannot distinguish employees or turnover by position (e.g., nurses vs CNAs vs gardeners) • Establishment (facility) level measures available only through a multiple imputation process • 2. Merging NHC and imputed turnover • Can not get employer identification number (EIN) for NHC facilities • Need to merge by name & address

21. 1a. QWI Turnover Measure • QWI uniquely identifies: • Firm (SEIN) • Establishment (SEINUNIT) • Provides firm-level turnover measure = turnover at time t for firm k • FA is # of full quarter accessions • FS is # of full quarter separations • F is average full quarter employment

22. 1b. QWI Turnover Measure • Need to use multiple imputation to get establishment (facility) turnover • Process developed by John Abowd at Cornell • Generates most likely establishment for each employee based on distance, employee distribution within firm, employee work history, and period of establishment existence • Imputation validated in Minnesota (which associates establishments & employees) and appears to work for 99.5% of employers.

23. 2. Linking NHC Data to QWI • Nursing home is equivalent to establishment (SEINUNIT), but EIN not available • Name, address, zipcode available; in theory can get Medicare provider number or ***possibly*** even the EIN from Centers for Medicare & Medicaid services • Two possible paths for linkage (but both have problems) • Via the Business Register Bridge (BRB) • *MAYBE* via the Geocoded Address List (GAL)

24. Proposed Study Worth It? • Even if match does not work, arguably valuable to Census & other researchers to know that linkage is not currently feasible • If linkage works sufficiently well, then: • Valuable to Census/researchers to know matching for other studies feasible • Longitudinal panel of annual observations on facility turnover and aggregated resident outcomes would enable strong FE and IV estimation of relationship