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Using the NNHS versus the LEHD & NHC to Assess Whether Nursing Home Staff Turnover Affects Resident Outcomes. Sally C. Stearns 1 Laura P. D’Arcy 1 Daria Pelech 2 1 The University of North Carolina at Chapel Hill 2 Duke University UNC Institute on Aging September 22, 2009 - PowerPoint PPT Presentation
Citation preview
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