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e effects of RN staffing hours on nursing home quality:two-stage model
ang Yuol Lee a,*, Mary A. Blegen b, Charlene Harrington c,1
lege of Nursing, Eulji University, 771-77 Gyeryong-ro, Jung-gu, Daejeon, South Korea
partment of Community Health Systems, University of California, San Francisco, 2 Koret Way, San Francisco, CA 94143-0608,
ed States
partment of Social and Behavioral Sciences, University of California, San Francisco, 3333 California Street, Suite 455, San Francisco,
4118-0612, United States
What is already known about the topic?
ursing home staffing levels are associated with qualityf care. These studies have been conducted using on largeational samples in the U.S.hile previous studies have examined these relation-ips, most have not taken into account the biasesat can occur when there are potential endogenous
relationship between RN staffing and the qualityoutcomes.
What this paper adds
� There are potential endogenous relationships betweenRN staffing and the quality outcomes (pressure ulcers,urinary tract infections, and weight loss).� We used a two-stage regression model to account
for these endogenous relationships where nursinghomes with more RNs may attract residents withhigher care needs are expected to hire more RNs.Ordinary least squares regression models were used toexamine two process quality indicators (antipsychotic
T I C L E I N F O
le history:
ived 23 April 2013
ived in revised form 1 October 2013
pted 4 October 2013
ords:
sing homes
sing home quality
sing home staffing
se staffing
lity indicators
lity of care
A B S T R A C T
Objectives/background: Based on structure-process-outcome approach, this study exam-
ined the association of registered nurse (RN) staffing hours and five quality indicators,
including two process measures (catheter use and antipsychotic drug use) and three
outcome measures (pressure ulcers, urinary tract infections, and weight loss).
Setting/participants: We used data on resident assessments, RN staffing, organizational
characteristics, and market factors to examine the quality of 195 nursing homes operating
in a rural state of United States — Colorado.
Design/methods: Two-stage least squares regression models were performed to address
the endogenous relationships between RN staffing and the outcome-related quality
indicators, and ordinary least squares regression was used for the process-related ones.
This analysis focused on the relationship of RN staffing to nursing home quality indicators,
controlling for organizational characteristics, resources, resident casemix, and market
factors with clustering to control for geographical differences.
Results: Higher RN hours were associated with fewer pressure ulcers, but RN hours were
not related to the other quality indicators.
Conclusions: The study finding shows the importance of understanding the role of ‘nurse
staffing’ under nursing home care, as well as the significance of associated/contextual
factors with nursing home quality even in a small rural state.
� 2013 Elsevier Ltd. All rights reserved.
Corresponding author. Tel.: +82 42 259 1719.
E-mail addresses: [email protected] (H.Y. Lee),
[email protected] (M.A. Blegen),
[email protected] (C. Harrington).
Tel.: +1 415 476 4030.
Contents lists available at ScienceDirect
International Journal of Nursing Studies
journal homepage: www.elsevier.com/ijns
0-7489/$ – see front matter � 2013 Elsevier Ltd. All rights reserved.
://dx.doi.org/10.1016/j.ijnurstu.2013.10.007
H.Y. Lee et al. / International Journal of Nursing Studies 51 (2014) 409–417410
drug use and catheter use), which were not found to beendogenous.� This study result shows that less competitive environ-
ment and homes with higher percentage of Medicaid andMedicare patients were significantly associated withhigher percentage of catheter use.
Poor quality of care in nursing homes has been apersistent problem in the United States for many years(GAO, 1987, 2003, 2007, 2009; Institute of Medicine [IOM],2003). More than 1.3 million people live in nursing homesand almost one-third of nursing homes have quality andsafety problems in care (Harrington et al., 2006). Somecommon quality problems in nursing homes have beenidentified including resident weight loss, pressure ulcers,infections, and other treatable or preventable problems(Schnelle et al., 2004; U.S. Centers for Medicare andMedicaid Services [CMS], 2001).
Nursing home quality indicators were developed in1995 to identify poor quality of resident care and tosupport quality assurance and improvement activities innursing homes (Karon et al., 1999; Zimmerman et al.,1995). The quality indicators included indicators of poornursing care (process measures) and outcome measuresfor nursing home residents, identified from the MinimumData Set (MDS). The MDS is a national mandatory uniformresident assessment tool that includes measures offunctional, behavioral, social, and clinical characteristicsof nursing home residents (Harris and Clauser, 2002;Morris et al., 1990; Zimmerman et al., 1995). Nursinghomes are required to submit MDS data electronically tothe CMS on a quarterly basis and CMS computes andreports the quality measures for each nursing home (Arlinget al., 2007).
This paper examined the factors associated with fiveselected quality indicators in Colorado nursing homes. Thefocus was on the relationship of RN staffing and qualitymeasures, controlling for organizational characteristics,resident casemix, facility resources, and market factors.
This study also examines the relationships betweenstaffing and resident outcomes in a small rural whereasprevious studies generally use large national samples.Colorado was selected for this study because data wereavailable describing RN staffing, resident outcomes, andnursing home characteristics collected for another projectin 2000. Colorado is sparsely populated in some areas witha variation in nursing home staffing across geographicalareas and the state had lower staffing levels than theaverage state in 2000 (Harrington et al., 2006) and lowminimum staffing requirements (Mueller et al., 2006).Although these data are from 2000, Colorado has notchanged its minimum direct care nursing staffing standardof 2.0 hours per resident per day since 1988 (Harrington,2010). This study addressed the question of whetherColorado’s RN staffing levels would be associated withquality indicators in its nursing homes, as has been foundin some other studies. Although Colorado nursing homesmay have improved in quality over time, we expect thatthe basic relationships between quality indicators, staffinglevels, and other facility factors would be similar to thepresent time.
1. Conceptual framework
Many studies have documented the importance ofnursing staff in both the process and the outcomes ofnursing home care. Higher RN staffing has been found to berelated to improved outcomes such as: improved func-tional status for residents, lower mortality rates, increaseddischarge from the nursing home, fewer pressure ulcers,less restraint usage, fewer catheterizations and urinarytract infections, and less antibiotic use (Bostick et al., 2006;Castle, 2008; Spilsbury et al., 2011).
For this study, we selected 5 measures based on aliterature review that showed these were importantmeasures to examine because they were expected to berelated to staffing levels. While other measures could havebeen selected, it was necessary to limit the number ofquality indicators examined in this study.
1.1. Pressure ulcers
Pressure ulcers, as adverse patient outcomes, have beenfrequently used to measure the quality of nursing care innursing homes. A recent study, using a longitudinal fixed-effect design to treat the potential endogeneity of thestaffing and pressure ulcers, found that a 50 percent increasein RN hours per resident day resulted in a 66 percent declinein the rate of pressure ulcers (Konetzka et al., 2008). HigherRN staffing levels was associated with lower rates ofpressure ulcers in another study (Weech-Maldonado et al.,2004). Bostick (2004) also found that more RN hours wererelated to fewer pressure ulcers. Horn et al. (2005) showedthat RN and total staffing hours were associated with fewerpressure ulcers, but RN staffing was a stronger predictorthan total hours. Castle and Anderson (2011) also showedthat lower RN staffing was related to higher pressure sores ina dynamic panel model of US nursing homes.
1.2. Urinary tract infections
Recently, a five-state study examined the relationshipof RN staffing with urinary tract infections (UTIs) as anadverse patient outcome (Konetzka et al., 2008). Konetzkaet al. (2008) found that increased RN staffing reduced therates of UTIs with a significant magnitude, controlling forother factors. Higher RN staffing presumably improves thesupervision and nursing care to prevent poor residentoutcomes.
1.3. Weight loss
Weight loss is a problem in nursing homes that is oftenrelated to poor quality of care. One study indicated thatnursing homes with a lower prevalence of weight lossprovided better care and more assistance during meals(Simmons et al., 2003). Other studies found that more RNcare hours were significantly associated with less weightloss (Horn et al., 2005) and more nursing assistant carehours were significantly related to lower rates of weightloss (Dyck, 2007). In contrast, Bostick (2004) did not find arelationship between nurse staffing hours and theprevalence of weight loss.
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H.Y. Lee et al. / International Journal of Nursing Studies 51 (2014) 409–417 411
Antipsychotic drug use
The use of antipsychotic drugs without an indication of need for such medications is considered a sign of poorlity care (Zimmerman et al., 1995). In a six-state study
ng generalized estimating equations, increased treat-nt of depression with antidepressants was associatedh facilities with more professional nursing staff (Lapane
Hughes, 2004). RN staffing levels were not signifi-tly associated with the use of antipsychotic drugs inther study (Weech-Maldonado et al., 2004).
Catheter use
The use of urinary catheters is discouraged for nursinge residents because it can lead to urinary tract
ctions and discomfort. One study found that more staffing hours were significantly related to a lower usecatheters (Horn et al., 2005). Another study thatsified nursing homes into high quality and poor qualityups found that more RNs and licensed nurses wereted to more catheter use in the poor quality group but
relationship was not found in the high quality groupabowski and Castle, 2004). Castle and Anderson (2011)
showed that lower RN staffing was related to higherheter use in US nursing homes.Based on the literature review, the main hypothesised in this study is that higher RN staffing will be
ociated with improved quality indicators.
ther factors associated with quality
There are many other factors in addition to staffingels that can affect nursing home quality, includinglity characteristics, resident casemix (or acuity) andrket factors.
Facility characteristics
Previous studies have demonstrated that non-profites had more desirable resident outcomes such as fewer
ssure ulcers, decreased antipsychotic drug use, and fewerheters (Aaronson et al., 1994; Grabowski and Hirth, 2003;ghes et al., 2000). Hughes et al. (2000) found that non-fit nursing homes and the number of certified nursinges per 100 beds had lower antipsychotic drug use, whileprofit nursing homes, large facilities, and chains were
ociated with increased antipsychotic drug use (Hughesal., 2000). Smaller facilities are likely to have bettercomes; facilities with the best resident outcomes had adian size of 80 beds, and facilities with poor residentcomes were larger (median of 120 beds) (Rantz et al.,4). Based on the studies, we expect that for-profitlities, larger facilities, and chains will be associated withrer quality indicators.
Higher state Medicaid payment rates have been asso-ed with a lower likelihood of having poor quality (baseddeficiencies) (Grabowski and Castle, 2004). Coloradocials set Medicaid reimbursement rates based on eachsing home’s specific facility costs, with adjustments toe the resident casemix into account, cost of living
adjustments for different geographical regions, and ceilingsor limits on costs. The average Colorado Medicaid nursinghome rate was set at $111 per resident per day in 2000,which was a level that was 20th out of 50 states (Grabowskiet al., 2004). Colorado Medicaid rates have increased overtime but its basic facility-specific reimbursement metho-dology with casemix adjustments has remained the samesince 2000. Facilities that receive higher Medicaid reim-bursement in Colorado were expected to have higher qualityof care as measured by the quality indicators.
Several studies have indicated that the percentage ofMedicare and Medicaid residents affects the outcome ratesfor each nursing home (Aaronson et al., 1994; Carter andPorell, 2003; Castle, 2002; Grabowski and Castle, 2004).Medicare payments per day are higher than Medicaidpayments, but are lower than private payers, which affectnursing home resources and quality of care. Nursing homeswith higher percentages of Medicare short-term skillednursing residents and residents with dementia were foundto be associated with increased medication use (Hugheset al., 2000). Facilities with high percentages of Medicaidresidents have been found to have low quality (Mor et al.,2004). Facilities with higher percentages of Medicareresidents and lower percentages of Medicaid residentsshould have improved quality indicators.
2.2. Resident casemix
Facilities that have higher resident casemix (acuity)may have a higher risk for poor outcomes of care. Casemixrefers to nursing home residents’ needs for assistance withactivities of daily living (ADLs). While it would have beenideal to have Minimum Data Set (MDS) data to computeResource Utilization Group (RUG) scores for the analysis,these data were not publicly available for the year of thestudy. Resident casemix has often been measured by theaverage ADL dependency score of residents in three ADLs:eating, toileting, and transferring to and from the bed,chair, or a standing position (Harrington and Swan, 2003;Harrington et al., 1998, 2007). The casemix measures inthis study are used as control measures.
2.3. Market characteristics
Areas with a greater non-profit market share had alower likelihood of poor quality measures including theprevalence of pressure ulcers and indwelling catheter use(Grabowski and Castle, 2004; Grabowski and Hirth, 2003).One study found that less competition within a geogra-phical market was associated with low quality whenmeasured by an increase in the use of catheters, but thedifferences in pressure ulcers rates were not statisticallysignificant (Grabowski and Castle, 2004). Facilities withgreater market concentration (less competition) andfacilities in areas with fewer excess beds should havebetter quality measures.
3. Methods
This study used a cross-sectional design to describerelationships between the staffing and the facility and
H.Y. Lee et al. / International Journal of Nursing Studies 51 (2014) 409–417412
market characteristics with three outcome quality indi-cators and two process indicators. The population in thisstudy was all nursing homes in Colorado in operationduring the year 2000 (199 nursing homes, excludinghospital-based nursing homes, veteran’s homes, andspecialized care facilities) such as hospice and rehabilita-tion care units. Of the 199 nursing homes, three nursinghomes were excluded from the data analyses because ofmissing values for market characteristics, and anothernursing home was excluded because data did not appearaccurate (100% of residents were restrained). After datacleaning, the study included 195 free-standing nursinghomes.
3.1. Data sources
This study used secondary data from five adminis-trative databases including the Online Survey Certificationand Reporting (OSCAR) data, Minimum Data Set (MDS)version 2.0, quarterly staffing data from the Colorado stateinspections, state Medicaid reimbursement data, and theArea Resources File (ARF). The basic dataset was created bya research group at the University of Colorado HealthScience Center. All the quality indicators were from theMinimum Data Set version 2.0. Since all statistical datawere for facilities (and not individual residents), the studywas exempt from human subjects requirements.
The staffing data were reported to the state nursinghome inspectors on a quarterly basis and included dailyestimates of all worked hours of direct care and admin-istrative staff, number of residents, and sources of paymentfor residents. The staffing data, reviewed by statesurveyors, were considered to be more reliable andaccurate than the OSCAR staffing data that are reportedfor a two-week period at the time of the annual survey by
each facility to the Centers for Medicaid and MedicaidServices (CMS) (Weech-Maldonado et al., 2004).
The primary source for the facility characteristics wasfrom the OSCAR administrative data collected at the timeof the annual nursing home survey (e.g. bed size, owner-ship, chain affiliation, the percent of Medicare andMedicaid residents, and the ADL dependency score forthe resident casemix data). These data were generallyconsidered to be accurate. The percent of residents withnew cognitive impairment was aggregate data from theMinimum Data Set for residents. The state Medicaid ratefor each facility was obtained from the Medicaid agency forColorado.
The ARF data were used for the market characteristics(e.g. the Herfindahl index and excess beds) and the censusdata on the population aged 65 and over and females in thelabor force. These data are compiled at the county level.Because there was a significant disparity in the supply ofnursing home beds among counties, we averaged allcounty-level market characteristics within each of the 15Health Services Areas in Colorado.
3.2. Quality indicators
Table 1 presents the operational definitions of qualityindicators from the MDS data that were developed andtested by researchers at the Center for Health SystemsResearch and Analysis (CHSRA), University of Wisconsin-Madison from the MDS. Systematic empirical analyses andfield-testing has established high levels of content, face,and convergent validity of the MDS items and the qualityindicators (Hawes et al., 1995; Karon et al., 1999; Morriset al., 1990; Zimmerman et al., 1995). The three outcomequality indicators used for this study: pressure ulcers,urinary tract infections, weight loss, have been found
Table 1
Quality Indicators from MDS 2.0.
Variable names Definitions
Process measures
% of antipsychotic drug use (Quality Indicator 19) Prevalence of residents using antipsychotic drugs without psychotic or related conditions on
the most recent assessment. Denominator for this quality indicator excludes residents with psychotic disorders,
Tourette’s syndrome, Huntington’s disease or those with hallucinations on the most recent assessment or full
assessment. This quality indicator is risk adjusted. Residents who exhibit both cognitive impairment and behavior
problems the most recent assessment are considered at HIGH RISK to receive antipsychotic medication(s). All
others (except those excluded) are considered at LOW RISK.
% of indwelling catheter use (Quality Indicator 10) Prevalence of residents with indwelling catheters on the most recent assessment. The
denominator is all residents.
Outcome measures
% of low risk pressure ulcers (Quality Indicator 24) Prevalence of low-risk residents who have been assessed with a pressure ulcer(s) stage 1–4
on the most recent assessment or on the ICD-9 code. The denominator is all residents on the most recent
assessment. This quality indicator is risk adjusted. Residents are considered high risk for the development of
pressure ulcers if they have any one or more of the following conditions: they are impaired for bed mobility or
transfer; or are comatose; or have malnutrition; or have an end stage disease on the most recent assessment. All
other residents are considered to be low risk. Residents at low risk that flag should be reviewed since this would be
considered a sentinel health event.
% of urinary tract infections (Quality Indicator 12) Prevalence of residents who were identified as having had a urinary tract infection on the
most recent assessment. This quality indicator is not risk adjusted and the denominator is all residents.
% of weight loss (Quality Indicator 13) Prevalence of residents noted with weight loss (5% or more in the last 30 days or 10% or more
in the last 6 months) on the most recent assessment. This quality indicator is not risk adjusted and the denominator
is all residents.
Source: Center for Health Systems Research and Analysis, 1999. Facility Guide for the Nursing Home Quality Indicators. Prepared for the Health Care
Financing Administration. Madison, Wisconsin. University of Wisconsin-Madison. September. Accessed May 30, 2011. https://www.qtso.com/download/
mds/facman.pdf.
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H.Y. Lee et al. / International Journal of Nursing Studies 51 (2014) 409–417 413
pirically to differentiate facilities by quality (Rantzl., 2004).
Staffing
For measuring nurse staffing levels, this study used RNfing hours per resident day. This staffing measure
ludes all RN direct care and administrative hours thatre actual productive hours worked. The state dataection form separates paid productive hours from non-ductive hours (for vacation time and sick leave). Total
hours per day were divided by the average residents per.Of five quality indicators, three outcome qualityicators were assumed theoretically to be endogenoush RN staffing hours per resident day because nursing
es that have higher percent of residents with pressureers, urinary tract infections, and weight loss would hirere RNs. On the other hand, if the homes have more RNs,n patients at risk for those problems would be morely to be attracted to high-staffed nursing homes. Theogenous relationship was treated with a two-staget squares regression model instituting two instru-
ntal variables. The process measures were not con-red endogenous because facilities with poor quality
icators may need more RN staffing but homes withre RNs are not expected to have poor processes of care.
Facility characteristics
Facility size was measured by the number of beds in thelity. For-profit homes were coded as 1 compared with-profit and government-owned homes that were coded
0. Chain-affiliated homes were also coded as 1 if thesing homes were owned by a chain, while non-chainsre coded as 0.
Resident casemix
For the casemix measure, the ADL dependency scores calculated from OSCAR data using a 1–3 scale based on
amount of help needed in eating, toileting, andsferring. Each facility reported for each resident the
p needed as: minimal assistance (scored as 1), moderateistance (scored as 2), and extensive assistance (scored), then these were averaged for all residents. A higherber indicates a higher need for assistance (from 1 to 3).
measure of the percent of new residents with cognitiveairment was retrieved from MDS data.
Market characteristics
Health Services Areas were used to define the marketas rather than counties because there were a number ofnties that did not have nursing homes. Herfindahlex was calculated with the number of beds for eachlity, divided by the total nursing home beds in eachlth Services Area (HSA) and then the proportion for
h home was squared and summed to create an index forh Health Services Area. Higher values of the indexicate more concentration and less competition with
scores that ranged from 0 to 1. The percent of excess bedsin the Health Services Area was calculated by dividing thetotal number of residents by the total number of nursinghome beds in each Health Services Area and thenmultiplied by 100.
3.7. Instrumental variables
The percent of population over 65 and the percentfemale in the workforce were used for the instrumentalvariables of RN staffing hours. These data were from theARF files and county rates were averaged for each HealthServices Area.
4. Analytical model
Descriptive statistics were prepared showing themeans and standard deviations of the variables in themodel. To check for the possibility of multicollinearity,Pearson correlation coefficients for all independent vari-ables were calculated and found not to be highly correlatedeach other (most were less than .4 and the highestcorrelation was .6).
Separate regression analyses were conducted for eachquality indicator to examine the effects of estimated RNstaffing along with other facility, resident and marketfactors. We used 2-stage least squares (2SLS) regressionmodels in Stata 10.0 with instrumental variables for thethree outcome quality indicators. Two instruments (per-centage of population aged over 65 and percentage offemales in the labor workforce) were selected to estimateRN staffing hours per resident day at the first stage of the2SLS model. These instrumental variables were ones thatpredicted RN staffing levels but that did not predict thequality indicators.
The Durbin–Wu–Hausman endogeneity test showedthat there was endogeneity between RN staffing hours andpressure ulcers at the significance level of .05 (DurbinChi2 = 2.59, p = .1075; Wu–Hausman F[1,182] = 2.45,p = .1192; robust regression F[1,14] = 4.95, p = .0430). Thetwo instruments were strong enough to predict staffing(partial R-squared = .355). The p-values of the F test andcomparison of R-square between excluded and includedinstrument models were used to determine whether theinstruments were weak at the p value 0.05 level (robustF[2,14] = 6.75, p < .01; minimum eigenvalue = 3.45). Theminimum eigenvalue provided was evaluated based on theWald test reported in the Stata 10.0 output and was foundto be significant (p < .05), and therefore, the model showedthat the instruments were valid. A set of over-identifica-tion tests used in Stata 10.0 to check for redundancy didnot find the model to be over-identified.
Although only pressure ulcers showed endogeneitystatistically for RN staffing hours per resident day, thisstudy applied a conservative approach by using 2SLSregression models for all three outcome quality indicators.Estimated RN staffing hours were added to the second-stage regression model for pressure ulcers.
For the two process quality indicators, a simpleordinary least square (OLS) regression was used becauseendogeneity was not found between staffing and the
H.Y. Lee et al. / International Journal of Nursing Studies 51 (2014) 409–417414
process measures. To control for the potential bias of themarket data aggregated at the Health Services Area level,the Stata’s cluster procedures for the 15 Health ServicesAreas were employed in all regression models.
5. Results
Table 2 presents descriptive findings in this study.Pressure ulcers averaged 7.8% with a range of 0–27.8.Urinary tract infections averaged 8.1 percent with a rangeof 0.6–33. Weight loss ranged between 2.4 percent and28.1 percent with a mean of 12.4. Antipsychotic drug useranged between 0.3 percent and 77.1 percent with a meanof 15.4. Indwelling catheter use averaged 7.0 percent witha range between 0 percent and 30 percent. The RN staffingranged from 0.2 to 1.6 hours per resident day.
5.1. Pressure ulcers
In the second stage equation in Table 3, higher RN hourswere significantly associated with 11.3 percent lower ratesof pressure ulcers. In addition, Medicaid reimbursementrates and the percent of Medicare residents were positivelyassociated with higher rates of pressure ulcers. An increasein Medicare residents was also related to higher rates ofpressure ulcers. The total explained variance of pressureulcers was 14.17 percent (F = 96.97, p < .01).
5.2. Other quality indicators
In Table 3, estimated RN staffing hours were not relatedto urinary tract infections, while facility size was inverselyassociated with urinary tract infections (b = �.014;p < .05). The total explained variance of urinary tractinfections was 13.89 percent (F = 19.30, p < .01). EstimatedRN staffing hours were not related to weight loss. A higher
percentage of Medicare residents was associated withhigher weight loss. The total explained variance of weightloss was 17.74 percent (F = 29.37, p < .01).
In Table 4, antipsychotic drug use was not associatedwith RN staffing but higher use was associated with higherpercentages of Medicare residents (b = �.351, p < .01). Thetotal explained variance of the antipsychotic drug use was20.25 percent (F = 38.45, p < .01). RN staffing hours werenot related to the rates of catheter use. Medicaidreimbursement rates, the percent Medicare residents,and the percent Medicaid residents were all positivelyassociated with the percent of catheter use.
6. Discussion
Theoretically, higher RN staffing hours should besignificantly associated with better process and out-come-related quality indicators. Higher scores of depen-dency must be controlled for in the statistical modelbecause dependency of nursing homes should increase theRN staffing hours in each home. Controlling for theendogenous relationship between dependency and staff-ing and for market and contextual factors, we found thathigher RN staffing hours were associated with an 11%lower prevalence of pressure ulcers. This study result alsoconfirmed the endogeneity between staffing and pressureulcers that future research studies should take into account(Konetzka et al., 2008). The positive results were similar tothose found in other studies that did not take endogeneityinto account (Bostick, 2004; Horn et al., 2005; Weech-Maldonado et al., 2004). This result shows a robustrelationship between RN staffing hours and pressure ulcercare even for facilities in a small rural state.
These findings provide important implications forstaffing practices. Nursing home providers in the US andinternationally make efforts to reduce RN and total nurse
Table 2
Descriptive Statistics (N = 195).
Outcome variables Mean S.D. Range
2 Quality indicators: process of care
Antipsychotic drug use (%) 15.4 10.1 .3–77.1
Indwelling catheter use (%) 7.0 4.5 0–30
3 Quality indicators: outcome of care
Low risk pressure ulcers (%) 7.8 3.8 0–27.8
Urinary tract infections (%) 8.1 4.3 .6–33.0
Weight loss (%) 12.4 4.5 2.4–28.1
Nurse staffing variable
Registered nurse (RN) staffing hours per resident day 0.6 .2 0.2–1.6
Facility characteristics
Number of total beds (facility size) 96.3 46.3 28–264
For-profit 71.2% (139/195)
Chain-affiliated 68.2% (133/195)
Medicaid reimbursement rates $115.1 14.8 $71–149
Medicaid patients (%) 63.7 22.5 0–98.7
Medicare patients (%) 6.5 6.3 0–34.8
Resident casemix
Average ADL dependency score 1.99 0.4 1–2.9
New cognitive impairment patients (%) 14.1 11.0 0–80.6
Market characteristics (clustered by 15 Health Services Area)
Herfindahl index 0.22 0.25 .02–1.00
Excess nursing home beds (year 2000) 17.4 2.0 14.5–21.7
Proportion of populations aged 65+ (year 2000) 12.4 3.60 8–22
Percent females in employed workforce (year 2000) 45.6 1.2 42.9–47.9
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H.Y. Lee et al. / International Journal of Nursing Studies 51 (2014) 409–417 415
fing in nursing homes as a way to control their costs.s study, however, shows the importance of RN staffingpreventing pressure ulcers and the potential negativects of reducing RN staffing on quality.
RN staffing levels were not associated with urinary tractctions, weight loss, antipsychotic drug use, and
heter use, controlling for other factors. This was intrast to the negative relationship between RN staffing
urinary tract infections (Konetzka et al., 2008), weight (Horn et al., 2005), antipsychotic drug use (Lapane and
ghes, 2004), and catheter use (Horn et al., 2005) found iner studies. But the findings were consistent with the
of associations between RN staffing and weight lossstick, 2004) and antipsychotic drug use (Weech-ldonado et al., 2004) previously reported.There are several possible explanations for the lack oftionship between staffing and the other four qualityicators. First, the small number of nursing homes in thedy may have reduced the power to detect statisticallyificant relationships in a cross sectional analysis.
Second, only the effects of RN staffing hours were countedin this study. It may be that direct care or total nursinghours are more important in preventing urinary tractinfections, weight loss, antipsychotic drug use, andcatheter use than RN staffing (Schnelle et al., 2004).Where there are sufficient numbers of nursing assistants ortotal nurse staffing staff, residents may receive more fluidsto drink, more assistance with eating to prevent weightloss, more time with the residents to reduce drug use andmore frequent toileting to prevent the use of catheters. Inaddition, it would have been ideal to have had MinimumData Set information, which were unavailable at the timeof the study, to calculate casemix using Resource Utiliza-tion Groups (RUGs) rather than the physical functioningscores that were used in the study. Future studies shoulduse data from the new Minimum Data Set 3.0 to examinethe relationship between casemix, staffing and qualityindicators.
Third, there were unmeasured aspects of nurse staffingthat were not included, such as education, experience,
le 3
-stage least squares regression models for outcome quality measures (N = 195).
gression coefficients (robust standard errors) 1st stage 2nd stage 2nd stage 2nd stage
RN staffing hours per resident day Pressure ulcers UTIs Weight loss
dogenous variable
stimated RN staffing hours per resident day �11.272**
(5.026)
3.090
(4.017)
�2.488
(9.105)
cility characteristics
Number of total beds (facility size) �.001**
(.001)
�.029***
(.009)
�.014**
(.006)
�.005
(.015)
For-profit �.081***
(.020)
�.718
(.740)
�.948
(.819)
.922
(1.043)
Chain-affiliated �.032
(.033)
�.744
(.759)
�.456
(.674)
�.953
(.821)
Medicaid reimbursement rates ($) .003***
(.001)
.081***
(.023)
.034
(.028)
.014
(.037)
Medicaid residents (%) �.002***
(.001)
�.016
(.020)
.004
(.013)
�.033
(.020)
Medicare residents (%) .005
(.003)
.250***
(.066)
.117
(.059)
.200**
(.090)
Resident casemix
ADL dependency (1–3) �.018
(.029)
.924
(.570)
1.873
(1.229)
1.693
(.897)
New cognitive impairment (%) �.003***
(.001)
�.019
(.023)
.005
(.012)
.005
(.029)
Market factors
Herfindahl index (0–1) �.032
(.146)
�2.460
(2.676)
1.025
(1.907)
.113
(4.137)
Excess beds (%) �.022***
(.006)
�.247
(.171)
�.100
(.182)
�.238
(.278)
strument variables
pulation aged 65+ (%) �.017**
(.007)
male in labor workforce (%) �.019
(.016)
Intercept 2.000**
(.016)
11.429
(6.417)
1.489
(6.305)
14.357
(7.873)
R-squared .3550 .1417 .1389 .1774
tatistics 82.01*** 96.97*** 19.30*** 29.37***
: N = 195, 2-stage least squares regression models were used with robust clustering (by 15 Health Services Areas) procedure in Stata 10.0; robust
dard errors adjusted for 15 clusters were reported in parenthesis.
egression coefficients less than .05 are shown in bold.
It is the estimated values of RN staffing hours per resident day, which came from the first stage regression model for RN staffing hours per resident day
then inserted to the second stage regression models for each 3 outcome quality measures as an independent variable.
p-value < .05
* p-value < .01
H.Y. Lee et al. / International Journal of Nursing Studies 51 (2014) 409–417416
expertise, staff turnover rates, agency staff and otherfactors that have been found to influence residentoutcomes (Castle and Engberg, 2007, 2008a,b). Fourth,the controls for casemix used in this study (e.g. ADLdependency and new cognitive impairment) may not havebeen adequate to reflect the complex nature of casemix.Moreover, the secondary data sets used in the study couldhave had some measurement errors.
Finally, there may be a threshold level that is necessaryfor RN staffing levels to have an effect on some qualityindicators. A CMS study (2001) found that qualityimproves in a linear relationship with RN staffing andthey found that 0.75 RN hours per resident day wereneeded for long stay residents to prevent harm andjeopardy to residents. This study found that Colorado hadRN staffing levels of 0.6 hours per resident day in 2000,which was lower than the recommended RN level (CMS,2001), so RN levels may not have been high enough toimprove other process and outcome measures.
The study identified a few organizational character-istics that were related to quality indicators. Largerfacilities had lower rates of pressure ulcers and urinary
tract infections, which was inconsistent with the findingsfrom Rantz et al. (2004). Large facilities may have a morestandardized way of managing or maintaining quality andbetter resident outcomes in their facilities. For-profit andchain facilities were not found to have poorer qualityindicators than other facilities unlike the findings in someprevious studies. It appears that there were few differencesin the quality indicators by ownership type in Colorado.
Higher Medicaid reimbursement rates were associatedwith higher rates of pressure ulcers and catheter use,perhaps because there were higher resource costs asso-ciated with managing these types of residents. Higherpercentages of Medicare residents in a facility wereassociated with higher rates of pressure ulcers, weightloss, and catheter use, but lower use of antipsychoticsprobably because Medicare residents have higher overallresident acuity and their care is focused on short-termmanagement (compared to Medicaid residents). Marketfactors were not significant in this study except that higherpercentages of excess beds (greater competition for futureresidents) were related to lower catheter use. If facilitiesare competing for residents in an area, they are expected tomake efforts to compete on quality, which could result inlower catheter use.
This study had a number of limitations. It was a cross-sectional study that focused on only one state so that thefindings should be applied with caution for other areas orcountries. Because the data were for 2000, the relation-ships between staffing and the quality indicators may havechanged although the staffing levels in Colorado haveremained the same since 2000. The study was limitedbecause data on other types of staff, staffing training, staffturnover, and other factors that may be related to qualityindicators were not available for the model. This study ofColorado should be repeated using more current data toconfirm the findings from this study. Moreover usingColorado data over time may identify relationships withthe quality indicators that were not found on the one-yearcross-sectional analysis.
7. Conclusion
This study shows that nursing home characteristics andRN staffing levels have different impacts depending onresident outcomes. Pressure ulcers were improved bymore RN hours, while controlling for other factors, even ina rural state. The other quality indicators were notassociated RN staffing levels, but they were associatedwith some facility characteristics such as size and thepercent of Medicare and Medicaid residents. This studysuggests the need for further studies of quality indicatorswith other types of nurse staffing and with a greater focuson improving the controls for resident casemix.
Conflict of interest
None declared.
Funding
UCSF Graduate Student Research Award $900 andCentury Club Dissertation Award for UCSF Nursing student$1000.
Table 4
Ordinary least square regression models for process quality measures
(N = 195).
Regression coefficients
(robust standard errors)
Antipsychotic
drug use
Catheter
use
Staffing variable
RN staffing hours per resident day .176
(4.496)
�.684
(1.883)
Facility characteristics
Number of total beds (facility size) �.000
(.019)
�.006
(.008)
For-profit 4.421
(2.46)
�.072
(.946)
Chain-affiliated 1.264
(2.045)
�1.006
(.569)
Medicaid reimbursement rates ($) �.030
(.045)
.067**
(.026)
Medicaid residents (%) .082
(.041)
.036***
(.011)
Medicare residents (%) �.351**
(.144)
.150***
(.042)
Resident casemix
ADL dependency (1–3) �4.921
(3.181)
1.383
(.659)
New cognitive impairment (%) �.007
(.033)
.003
(.061)
Market factors
Herfindahl index (0–1) �8.845
(4.518)
�2.128
(2.618)
Excess beds (%) �.135
(.508)
�.319**
(.146)
Intercept 24.890
(16.238)
.591
(3.606)
R-squared .2025 .1131
F statistics 38.45*** 15.89***
Note: N = 195, simple ordinary least squares regression models were used
with robust clustering (by 15 Health Services Areas) procedure in Stata
SE10.0; Robust standard errors were reported in parenthesis.
All regression coefficients less than .05 are shown in bold.
** p-value � .05
*** p-value � .01
Eth
Subnum
Ref
Aaro
Arlin
Bost
Bost
Cart
Cast
Cast
Cast
Cast
Cast
Cast
Cen
Dyc
Grab
Grab
Grab
Harr
Harr
Harr
Harr
Harr
Harr
H.Y. Lee et al. / International Journal of Nursing Studies 51 (2014) 409–417 417
ical approval
The study’s ethical approval was given by UCSF Humanject Protection Program on April 11th, 2008 (Approvalber #06030098, project number #06030096).
erences
nson, W.E., Zinn, J.S., Rosko, M.D., 1994. Do for-profit and not-for-profit nursing homes behave differently? The Gerontologist 34, 775–786.g, G., Lewis, T., Kane, R., Mueller, C., Flood, S., 2007. Improving quality
assessment through multilevel modeling: the case of Nursing HomeCompare. Health Services Research 42, 1177–1199.ick, J.E., 2004. Relationship of nursing personnel and nursing homecare quality. Journal of Nursing Care Quality 19, 130–136.ick, J.E., Rantz, M.J., Flesner, M.K., Riggs, C.J., 2006. Systematic reviewof studies of staffing and quality in nursing homes. Journal of Amer-ican Medical Director Association 7, 366–376.er, M.W., Porell, F.W., 2003. Variations in hospitalization rates amongnursing home residents: the role of facility and market attributes. TheGerontologist 43, 175–191.le, N.G., 2002. Nursing homes with persistent deficiency citations forphysical restraints use. Medical Care 40, 868–878.le, N., 2008. Nursing home caregiver staffing levels and quality ofcare: a literature review. Journal of Applied Gerontology 27, 375–405.le, N.G., Anderson, R.A., 2011. Caregiver staffing in nursing homes andtheir influence on quality of care using dynamic panel estimationmethods. Medical Care 49 (6) 545–552.le, N., Engberg, J., 2007. The influence of staffing characteristics onquality of care in nursing homes. Health Services Research 42, 1822–1847.le, N., Engberg, J., 2008a. Further examination of the influence ofcaregiver staffing levels on nursing home quality. The Gerontologist48, 464–476.le, N., Engberg, J., 2008b. The influence of agency staffing on quality ofcare in nursing homes. Journal of Aging & Social Policy 20, 437–453.ter for Medicare and Medicaid Services (CMS), 2001. Appropriatenessof Minimum Nurse Staffing Ratios in Nursing Homes, Phase II Report.Department of Health and Human Services, Washington, DC.k, M.J., 2007. Nursing staffing and resident outcomes in nursinghomes: weight loss and dehydration. Journal of Nursing Care Quality22, 59–65.owski, D.C., Castle, N.G., 2004. Nursing homes with persistent high
and low quality. Medical Care Research and Review 61, 89–115.owski, D.C., Hirth, R.A., 2003. Competitive spillovers across non-
profit and for-profit nursing homes. Journal of Health Economics22, 1–22.owski, D.C., Feng, Z., Intrator, O., Mor, V., 2004. Recent trends in state
nursing home payment policies. Health Affairs, W4-363–W4-373.ington, C., 2010. Nursing Home Staffing Standards in State Statutes
and Regulations. University of California, San Francisco, CA. http://www.pascenter.org/documents/Staffing_regulations_12_10.pdf(accessed July 2011).ington, C., Carrillo, H., LaCava, C., 2006. Nursing Facilities, Staffing,
Residents and Facility Deficiencies, 1999 Through 2005. Departmentof Social and Behavioral Sciences University of California, San Fran-cisco, CA.ington, C., Carrillo, H., Mullan, J., Swan, J.H., 1998. Nursing facility
staffing in the states: the 1991 to 1995 period. Medical Care Researchand Review 55, 334–363.ington, C., Swan, J.H., 2003. Nursing home staffing, turnover, and case
mix. Medical Care Research and Review 60, 366–392.ington, C., Swan, J.H., Carrillo, H., 2007. Nurse staffing levels and
Medicaid reimbursement rates in nursing facilities. Health ServicesResearch 42, 1105–1129.is, Y., Clauser, S.B., 2002. Achieving improvement through nursing
home quality measurement. Health Care Financing Review 23, 5–18.
Hawes, C., Morris, J.N., Phillips, C.D., Mor, V., Fries, B.E., Nonemaker, S.,1995. Reliability estimates for the Minimum Data Set for nursinghome resident assessment and care screening (MDS). The Gerontol-ogist 35, 172–178.
Horn, S.D., Buerhaus, P., Bergstrom, N., Smout, R.J., 2005. RN staffingtime and outcomes of long-stay nursing home residents: pressureulcers and other adverse outcomes are less likely as RNs spendmore time on direct patient care. American Journal of Nursing 105,58–70.
Hughes, C.M., Lapane, K.L., Mor, V., 2000. Influence of facility character-istics on use of antipsychotic medication in nursing homes. MedicalCare 12, 1164–1173.
Institute of Medicine, 2003. Keeping Patients Safe: Transforming theWork Environment of Nurses. National Academy of Medicine,Washington, DC.
Karon, S.L., Sainfort, F., Zimmerman, D.R., 1999. Stability of nursing homequality indicators over time. Medical Care 37, 570–579.
Konetzka, T.R., Stearns, S.C., Park, J., 2008. The staffing-outcomes relation-ship in nursing homes. Health Services Research 43, 1025–1042.
Lapane, K.L., Hughes, C.M., 2004. Which organizational characteristics areassociated with increased management of depression using antide-pressants in US nursing homes? Medical Care 42, 992–1000.
Mor, V., Zinn, J., Angelelli, J., Teno, J.M., Miller, S.C., 2004. Driven to tiers:socioeconomic and racial disparities in the quality of nursing homecare. Milbank Quarterly 82, 227–256.
Morris, J.N., Hawes, C., Fries, B.E., Phillips, C.D., Mor, V., Katz, S., Murphy,K., et al., 1990. Designing the national resident assessment instru-ment for nursing homes. The Gerontologist 30, 293–307.
Mueller, C., Arling, G., Kane, R., Bershadsky, J., Holland, D., Joy, A., 2006.Nursing home staffing standards: their relationship to nurse staffinglevels. The Gerontologist 46, 74–80.
Rantz, M.J., Hicks, L., Grando, V., Petroski, G.F., Madson, R.W., Mehr, D.R.,et al., 2004. Nursing home quality, cost, staffing, and staff mix. TheGerontologist 44, 24–38.
Schnelle, J.F., Simmons, S.F., Harrington, C., Cadogan, M., Carcia, E., Bates-Jensen, B.M., 2004. Relationship of nursing home staffing to quality ofcare. Health Services Research 39, 225–250.
Simmons, S.F., Gracia, E.T., Cadogan, M.P., Al-Samarrai, N.R., Levy-Storms, L.F., Osterweil, D., Schnelle, J.F., 2003. The Minimum DataSet weight-loss quality indicator: does it reflect difference in careprocess related to weight loss? Journal of American GeriatricsSociety 51, 1410–1418.
Spilsbury, K., Hewitt, C., Stirk, L., Bowman, C., 2011. The relationshipbetween nurse staffing and quality of care in nursing homes: asystematic review. International Journal Nursing Studies (Epub aheadof print).
U.S. General Accounting Office (GAO), 1987. Report to the Chairman,Subcommittee on Health and Long-Term Care, Select Committee onAging, House of Representatives. Medicare and Medicaid: StrongerEnforcement of Nursing Home Requirements Needed. U.S. GAO,Washington, DC.
U.S. General Accounting Office, 2003. Nursing Homes Quality: Prevalenceof Serious Problems, While Declining, Reinforces Importance ofEnhanced Oversight – Report to Congressional Requesters GAO-03-561. General Accounting Office, Washington, DC.
U.S. Government Accountability Office, 2007. Nursing Homes: Efforts toStrengthen Federal Enforcement Have Not Deterred Some Homesfrom Repeatedly Harming Residents. GAO-07-241. GAO, Washington,DC.
U.S. Government Accountability Office, 2009. Nursing Homes: Addressingthe factors underlying Understatement of Serious Care ProblemsRequires Sustained CMS and State Commitment. GAO-10-70. GAO,Washington, DC. http://www.gao.gov/new.items/d1070.pdf.
Weech-Maldonado, R., Meret-Hanke, L., Neff, M.C., Mor, V., 2004. Nursestaffing patterns and quality of care in nursing homes. Health CareManagement Review 29, 107–116.
Zimmerman, D.R., Karon, S.L., Arling, G., Clark, B.R., Collins, T., Ross, R.,et al., 1995. The development and testing of nursing home qualityindicators. Health Care Financing Review 16, 107–127.
