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 Hill2Duke University

UNC Institute on AgingSeptember 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

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.

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

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

Conceptual Model (1)

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 (2)

Area Economic Indicators - Employment - Housing Value

Turnover orChurning

Other Facility Characteristics

Resident Characteristics - Sociodemographic - Medical/clinical - Functional

Resident Outcomes (Bad) - Hospital Use - ER Use - Ulcers - Pain - Falls - Any of the Above

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

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

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

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%

/1004* vacanciesFTElast weekin workedFTEs

Months 3Last in Left FTEsTurnover CNA

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%)

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

Results: Pilot Study Any Bad Outcome (mean of 0.55)

Models with: Turnover Only Low Retention Only Turnover and Low Ret1 2 3 4 5 6 7 8 9

Estimation Method: OLS FE FE-IV OLS FE FE-IV OLS FE FE-IV

Turnover (0.52) 0.013* 0.030** 0.268** 0.012 0.025** 0.043

Low Retention (0.37) 0.031 0.112** 0.835** 0.034 0.094** 0.726**

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

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

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

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

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?

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)

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

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

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

FTkt (FA kt FSkt ) /2

Fkt

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.

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)

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