Research in Gerontological Nursing

Empirical Research 

Influence of Nurse Staffing Levels on Resident Weight Loss Within German Nursing Homes

Jaroslava Zimmermann, MA; Holger Pfaff, PhD

Abstract

In Germany, there is no empirical evidence regarding the relationship between nurse staffing levels and care outcomes in nursing homes. The current study explored differences in nurse staffing levels between facilities with and without resident weight loss. The analyses were conducted at the facility level (N = 166) and involved weight loss assessment of 8,665 residents. Separate models for residents with and without cognitive impairment were computed. The regression analyses showed that nursing facilities where no weight loss occurred among residents without cognitive impairment had a lower number of residents per RN and additional care staff. However, no differences were found between facilities with and without weight loss among residents with cognitive disabilities. Further research is needed to identify factors leading to weight loss among residents with cognitive disabilities, including teamwork and work climate in nursing homes.

[Res Gerontol Nurs. 2018; 11(1):48–56.]

Abstract

In Germany, there is no empirical evidence regarding the relationship between nurse staffing levels and care outcomes in nursing homes. The current study explored differences in nurse staffing levels between facilities with and without resident weight loss. The analyses were conducted at the facility level (N = 166) and involved weight loss assessment of 8,665 residents. Separate models for residents with and without cognitive impairment were computed. The regression analyses showed that nursing facilities where no weight loss occurred among residents without cognitive impairment had a lower number of residents per RN and additional care staff. However, no differences were found between facilities with and without weight loss among residents with cognitive disabilities. Further research is needed to identify factors leading to weight loss among residents with cognitive disabilities, including teamwork and work climate in nursing homes.

[Res Gerontol Nurs. 2018; 11(1):48–56.]

Nursing home research has found that certain nursing home conditions, such as low nurse staffing levels, negatively influence health status of residents (Castle & Engberg, 2008; Dyck, 2007; Woo, Chi, Hui, Chan, & Sham, 2005), or, as defined by Donabedian (1988), nursing care outcomes. Donabedian (1988) assumed that nursing care outcomes relate to structural and process quality. To the researchers' best knowledge, no previous studies have examined the relationship between nursing staff levels and nursing care outcomes in Germany. The recent German quality reporting standards for nursing homes address only structural and care process quality. Therefore, little is known about patient outcomes of nursing care. Previous studies have investigated the prevalence of pressure ulcers (Kottner, Dassen, & Heinze, 2011; Wingenfeld, 2015), falls (Kottner et al., 2011), health care–associated infections (Engelhart et al., 2009), and malnutrition among nursing home residents (Reuther, van Nie, Meijers, Halfens, & Bartholomeyczik, 2013; Stange, Poeschl, Stehle, Sieber, & Volkert, 2013; Volkert, Pauly, Stehle, & Sieber, 2011); however, these outcomes were not linked to staffing levels.

One of the first projects in Germany to systematically examine nursing home quality outcomes nationwide was Project EQisA. EQisA stands for “Ergebnisqualität in der stationären Altenhilfe,” which roughly translates to “quality outcomes in inpatient elderly care.” EQisA, which was a cooperative project of the Diocesan Caritas Association in Cologne and the Institute of Nursing Science at Bielefeld University, developed and evaluated nursing home outcomes in 229 facilities with more than 21,200 residents (Kelleter, 2017). It was conceived to improve quality indicators in terms of resident outcomes, which were defined in the work of Wingenfeld et al. (2011), conducted on behalf of the Federal Ministry of Health and Federal Ministry for Family Affairs, Senior Citizens, Women, and Youth in Germany. Within EQisA, a unique database was produced for Germany. This database was used to conduct the secondary analyses in the current study.

There is evidence of higher prevalence of malnutrition and risk of malnutrition in German nursing homes (Reuther et al., 2013; Stange et al., 2013; Volkert et al., 2011) than in other countries (Bell, Tamura, Masaki, & Amella, 2013). Furthermore, the increasing shortage of qualified nursing personnel in Germany has been known and discussed for many years. The work of Aiken, Clarke, Sloane, Sochalski, and Silber (2002) reported that in understaffed hospitals, nurses experienced higher levels of emotional exhaustion and patients had increased risks of mortality and failure-to-rescue within 30 days of admission. Because unintentional weight loss increases mortality risk (Alibhai, Greenwood, & Payette, 2005), the link between nurse staffing levels and weight loss among residents was examined in the current study. Specifically, the main focus was on differences in staffing levels between facilities with and without weight loss among residents.

Determinants of Resident Weight Loss

The most common reasons for nursing home admissions are functional or cognitive disabilities (Gaugler, Duval, Anderson, & Kane, 2007), which are often associated with unintentional weight loss (Verbrugghe et al., 2013). In a retrospective study on geriatric hospital patients, Wirth, Smoliner, Sieber, and Volkert (2011) concluded that weight loss among patients with dementia and cognitive dysfunction is caused by loss of fat mass, which may be a consequence of cognitive disabilities. Comparable results supporting a relationship between malnutrition and cognitive impairment were presented in a Korean study (Lee et al., 2009) performed among older individuals living within the community. Saka, Kaya, Ozturk, Erten, and Karan (2010), who studied patients admitted to an outpatient clinic, found that 44% of participants had poor nutritional status. Malnutrition was in turn associated with depression, dementia, functional impairment, and multimorbidity.

In addition to malnutrition being a possible symptom of chronic disease, many studies have suggested that poor nutritional status of residents might be an indicator of organizational deficiencies in nursing facilities. For instance, international nursing home research revealed that certain organizational conditions, such as inadequate management and staffing structures, insufficient staff training and education, inappropriate physical environments and equipment within eating areas, and poor food quality and quantity may contribute to resident weight loss (Bostick, Rantz, Flesner, & Riggs, 2006; Crogan, Shultz, Adams, & Massey, 2001).

Although the relationship between staffing levels and adverse nursing home outcomes has been relatively well investigated, there are no clear results regarding the link between nurse staffing and weight loss in nursing home residents. A retrospective analysis of Horn, Buerhaus, Bergstrom, and Smout (2005), using data from 82 U.S. facilities, revealed that each 10-minute increase in direct care time by RNs per resident per day decreased the percentage of residents with weight loss. However, an exact percentage was not reported. A study conducted in Hong Kong (Woo et al., 2005) that included 1,820 long-term institutions concluded that malnutrition was associated with lower staffing levels. Across 2,951 facilities, Dyck (2007) found that residents with at least 3 hours per day of nursing assisted care had a 17% lower probability of weight loss incidence. In addition, in a large study that examined data from 17,552 nursing homes, Bowblis (2011) found that implementation of minimum direct care staffing requirements improved certain aspects of care quality, including the incidence of significant weight change in residents. However, two additional studies reported no significant relationship between nurse staffing variables and the prevalence of weight loss (Bostick, 2004; Rantz et al., 2004).

Taking this disparate evidence into account, the connection between nurse staffing levels and resident weight loss remains unclear. Approximately one half of the examined studies provided evidence for the staffing and weight loss hypothesis, whereas the remainder obtained no evidence. Therefore, the current study aimed to contribute to the staffing–weight loss discussion by adding German data in hope that this addition would further elucidate the issue. This study intended to test the hypothesis that there is a relationship between staffing levels and weight loss in German nursing homes.

Method

Source of Data

Data regarding resident weight loss were collected within the EQisA project. As part of the project, 15 quality indicators were regularly collected and evaluated. Five data collection periods took place from 2012 to 2016, with increasing numbers of participating nursing homes in each. The quality indicators measured improvement or maintenance of functional abilities and autonomy, improvement or maintenance of independence to organize daily life and social contacts, prevalence of pressure ulcers, prevalence of falls with serious consequences, prevalence of weight loss, and use of physical restraints among cognitively impaired residents. All residents who had signed a declaration of consent to participate in the survey were included in the data collection. The ethics commission approved the study design of the EQisA. Each facility had specially trained RNs to assess resident outcomes (Kelleter, 2017; Wingenfeld et al., 2011).

Facility characteristics were also gathered as part of the project. Characteristics included number of occupied and free beds, special care unit status, dementia and other special care concepts, mortality rate, and number of full-time equivalent employees, according to their qualifications and working area. Participation in the project was voluntary for nursing homes. Data from the past round of collection, conducted in Spring 2016, were used in the current analyses. All participating facilities agreed that data could be used for this research study. The sample comprised 166 nursing homes with complete data about resident weight loss, in which 8,665 residents were assessed.

Model Specification

Weight Loss. The weight loss indicator was measured as percentage of residents per nursing home who had unintentionally lost more than 10% of their weight within the past 6 months. This definition of unintentional weight loss among older adults is frequently used in clinical studies (Stange et al., 2013; Wallace & Schwartz, 2002). The weight loss rates were calculated from the weight history found within care documentation. As mentioned above, residents with cognitive dysfunctions are at higher risk of weight loss than other residents. To facilitate risk-adjustment for this high-risk group, the analyses were conducted separately for residents with no, or slight, cognitive impairment, and at least considerable cognitive impairment. Cognitive status was measured with an assessment tool based on a new definition of nursing care dependency in Germany (Wingenfeld, Büscher, & Gansweid, 2008), which was recognized within the Second Bill to Strengthen Long-Term Care on January 1, 2017 (Bundesministerium der Justiz, 2015). Residents who were terminally ill, had been diagnosed with cancer, had experienced limb amputation, or were undergoing diuretic therapy were excluded from the analyses. This exclusion was made because the excluded illnesses are typically accompanied by eminent weight loss with a medical cause (Alibhai et al., 2005; Bush et al., 2014; Littman et al., 2015); thus, weight loss cannot be avoided using nursing interventions. To achieve improved comparability across facilities, residents weighed outside the survey period were also excluded from the analysis (Wingenfeld et al., 2011). These exclusions were made within project EQisA. Overall, 5,829 residents met the exclusion criteria.

Because data on weight loss were gathered in the form of rates per nursing home, only aggregate data of resident outcomes at the facility level could be analyzed. For many facilities, these rates were equal to zero. Because the goal of this study was to identify differences between facilities with and without weight loss among residents, all participating facilities were divided into two categories. The first category included facilities with no resident weight loss (coded as 0), and the second contained facilities with at least one incidence of weight loss (coded as 1). As mentioned above, the risk-adjustment of weight loss was based on resident cognitive status. Two models were computed for all facilities to consider these two risk groups separately (Castle & Engberg, 2008): (a) one where the dependent variable was “weight loss among residents with no, or slight, cognitive impairment (WL NCI)”; and (b) one where the dependent variable was “weight loss among residents with at least considerable cognitive impairment (WL CI).” In each facility, there was a mix of residents who were and were not cognitively impaired. More details are provided in Table 1.

Dependent, Independent, and Control Variables Used in Analyses

Table 1:

Dependent, Independent, and Control Variables Used in Analyses

Staffing. Staffing was defined as staff-to-resident ratio for each care staff category. The number of staff was measured in full-time equivalents. As such, part- and full-time workers were included. In Germany, there is a distinction between RNs, nursing assistants (NAs), and additional care staff (ACS). RNs must complete 3 years of training in theory and practical nursing. For leadership positions, they must receive 460 hours of advanced training. NAs and ACS do not need any formal nursing education. With regard to the field of activity, RNs assume responsibility for the entire nursing process and supervision of other nursing and care staff. NAs can perform straightforward tasks, such as changing bandages, assisting with medication intake, or measuring blood pressure. ACS accompany, support, and facilitate activity for residents in an everyday capacity (e.g., having conversations, helping with the preparation of meals). RNs take responsibility for nutritional care, including identification of malnutrition risk. They may delegate some tasks such as feeding to NAs, but only when NAs are under supervision of qualified nursing staff. It is regulated by law that a minimum of 50% of all care staff in nursing facilities must be RNs. In this sample, the mean RN ratio was 56.68% (range = 31.65% to 90.57%), which was comparable to the national average in 2015 (Statistisches Bundesamt, 2017).

Organizational Factors. Many nursing home studies have reported other organizational factors that influence the quality of nursing homes (Bostick et al., 2006). In the current study, the most common factors were considered, such as facility size, occupancy rate, region, rural or urban location, resident case mix, and number of residents with slight and high risk of weight loss.

In Germany, there are differences in nursing standards for long-term care between federal states; therefore, the region variable was included. In the study sample, most facilities were in the federal states of North Rhine-Westphalia and Bavaria. There were few facilities in other federal states, such as Baden-Wuerttemberg, Rhineland-Palatinate, Schleswig-Holstein, Hesse, and Saarland. The region variable was included as a dummy variable comparing facilities in North Rhine-Westphalia to other federal states. Choosing other federal states as the reference category provided equivalent results in the tests.

Until December 31, 2016, the staffing level standards varied by region and were contingent on resident dependency levels (DLs) defined by the German nursing insurance system (Wagner, 2017). There were four DLs: 0 (low need for assistance), 1 (need for assistance for ≥90 minutes per day; basic care needs of ≥45 minutes per day), 2 (need for assistance for ≥180 minutes per day; basic care needs of ≥120 minutes per day), and 3 (need for assistance for ≥300 minutes per day; basic care needs of ≥240 minutes per day). The mean DL for each facility was calculated as a weighted index. Because DL did not consider cognitive status (this has changed with an introduction of the Second Bill to Strengthen Long-Term Care on January 1, 2017), the number of residents with no or slight cognitive impairment per facility (NCI) and the number of residents with at least considerable cognitive impairment (CI) were included as controls. Nevertheless, these variables included only residents who were assessed for weight loss. As mentioned above, there was no information about cognitive status of residents who were excluded in project EQisA.

Unlike many studies conducted in the United States, ownership and/or payer mix were not considered as control variables. In the study sample, most facilities were owned by non-profit organizations, especially the Caritas Association, and there were few private or governmental facilities. Furthermore, although a majority of nursing facilities in Germany (53%) have non-profit owners (Statistisches Bundesamt, 2017), the current results do not accurately represent the variety of nursing facilities in Germany. This deficit in sampling accuracy is one of the consequences of voluntary participation in the project EQisA. In addition, regarding payer mix, there are no differences between contributions of private pay residents and social welfare contributions to facilities in Germany.

Data Analyses

Logistic regression was used to examine the relationship between staffing levels and resident weight loss. To test multicollinearity among independent variables, variance inflation factor (VIF) was computed. For both models, values of VIF ranged between 1.082 and 2.709, which is generally considered evidence of no multicollinearity among factors. The model was specified in two steps. First, control variables were included. Second, staffing variables, which were the primary focus of the current study, were added. A total of 166 nursing facilities and 8,665 residents were included in the analyses. SPSS 23 was used for all analyses.

Results

The logistic regression analyses revealed significant associations between the RN- and ACS-to-resident ratios and the incidence of WL NCI (Table 2). Literally translated, holding all other facility characteristics constant, an increase of one resident per RN enhanced the odds of facilities having WL NCI by 129.7% (odds ratio [OR] = 2.297; 95% confidence interval [CI] [1.343, 3.928]; p = 0.002) and an increase of one resident per ACS was associated with an 18.4% increase in the odds of facilities having WL NCI (OR = 1.184; 95% CI [1.024, 1.370]; p = 0.023). NA-to-resident ratios were not associated with WL NCI. As for other facility characteristics, occupancy rate and number of NCI residents were significantly related to incidence of WL NCI in the partial and fully specified models. Specifically, with all other factors being constant, a 1% decrease in occupancy was associated with 5.8% increased odds of facilities having WL NCI (OR = 0.942; 95% CI [0.892, 0.996]; p = 0.034), whereas increasing the number of NCI residents led to 9.6% increased odds of the facilities having WL NCI (OR = 1.096; 95% CI [1.035, 1.159]; p = 0.002).

Odds Ratios With 95% Confidence Intervals for Weight Loss Among Residents With No, or Slight, Cognitive Impairment

Table 2:

Odds Ratios With 95% Confidence Intervals for Weight Loss Among Residents With No, or Slight, Cognitive Impairment

Although the model of WL CI was statistically significant, there were no significant relationships between the independent variables and WL CI (Table 3).

Odds Ratios With 95% Confidence Intervals for Weight Loss Among Residents With At Least Considerable Cognitive Impairment

Table 3:

Odds Ratios With 95% Confidence Intervals for Weight Loss Among Residents With At Least Considerable Cognitive Impairment

Discussion

The results support findings of previous research on weight loss prevalence (Stange et al., 2013; Volkert et al., 2011), showing that unintentional weight loss is still an ongoing problem in German long-term care facilities. The goal of the current study was to determine differences in staffing levels between facilities with and without unintentional weight loss among residents. Surprisingly, the analyses revealed differences among facilities with and without WL NCI. There were no significant differences between facilities with and without WL CI.

Facilities without WL NCI had higher staffing levels of RNs and ACS compared to facilities with WL NCI. In Germany, assistance with eating and feeding falls within RNs' duties. As results of Crogan et al. (2001) and Kayser-Jones and Schell (1997) have shown, inadequate RN staffing in nursing facilities can lead to high workloads and stress among nursing staff. Furthermore, due to lack of time when RNs are inadequately staffed, RNs are often unable to deliver sufficient nutritional care to residents. In addition, Zúñiga et al. (2015) found that good teamwork and a safe work climate were related to better quality of care in Swiss nursing homes. Thus, a lack of communication and unclear role distribution among staff might negatively influence nutritional care.

The association between ACS staffing levels and WL NCI suggests that ACS are also involved in nutritional care, although ACS responsibilities are limited to assisting with activities and support of residents. A survey report of the National Association of Statutory Health Insurance Funds showed that 67% of ACS help residents with eating and feeding every day, whereas 81% help with drinking (Geerdes & Schwinger, 2012). Given the current results, it could be conjectured that the ACS in the study sample were partly or wholly responsible for nutritional care, because of the time pressure and high workload of the RNs. For this reason, having facilities that are well staffed with ACS might lower the risk of WL NCI.

Second, facilities without WL NCI had higher occupancy rates than facilities with WL NCI. A study by Kim, Harrington, and Greene (2009) reported that lower occupancy was associated with a higher mortality rate. Mor, Zinn, Angelelli, Teno, and Miller (2004) regarded lower occupancy as one of the characteristics of nursing homes situated in poor and disadvantaged communities with lower staffing levels and higher quality deficiencies. On the other hand, two studies (Castle & Fogel, 1998; Castle & Longest, 2006) identified an association between higher occupancy and greater use of physical restraints, as well as higher pressure ulcer incidence and contracture rates.

Third, facilities with WL NCI tend to have more NCI residents. This trend might reflect the variance in resident structure within facilities not captured by the resident case-mix variable because resident case-mix was computed based on the stages of resident DLs according to the German Care Insurance Act. This distinction included only individuals who were physically impaired, without regard for cognitive diseases. Information about cognitive status of all residents in assessed facilities was not available. Residents with dementia require special care that is more challenging for nursing staff. Accordingly, nursing facilities with higher numbers of residents with dementia might have worse care quality than facilities with fewer or no residents who are cognitively impaired (Castle & Longest, 2006; Wagner, McDonald, & Castle, 2013). Future studies should use variables that better adjust for resident structure, including the different stages of cognitive disability.

There were no statistically significant differences between facilities with and without WL CI. With regard to staffing levels, findings are consistent with results of Rantz et al. (2004) and Bostick (2004). One possible explanation might be the relative homogeneity of the nursing facilities in the project EQisA. As mentioned above, most of the nursing facilities belonged to the Caritas Association, and Caritas facilities must follow common (Christian) values and standards, and provide services of a certain quality. Another explanation might be that other factors, which are not included in the analyses, might better explain the disparities among the facilities. Copeman (2000) and Crogan et al. (2001) found, for example, that insufficient staff training and education increased risk of resident weight loss. Particularly among individuals with dementia, feeding difficulties are common, which make patients especially sensitive to incompetent handling by caregivers (Watson & Green, 2006). To address this, further research is needed that includes facilities owned by other non-profit, government, or private associations, as well as further training of staff in managing residents with dementia.

Limitations

There are some limitations of the current study. First, the study sample was not selected randomly, but rather conveniently, as the participation of nursing homes in the EQisA project was voluntary. Second, the majority of nursing homes belonged to the Caritas Association, which limits the generalizability of the results. Third, due to data availability, these analyses were conducted only at the facility level. Future analyses should involve resident-specific data. Finally, this study differentiated between facilities within which weight loss had and had not occurred in the past 6 months. In future studies, the full range of nursing home outcomes should be considered.

Conclusion

The study suggests that determinants of nursing home outcomes may differ between resident groups within facilities. For residents who are not cognitively impaired, the ratio of residents to nursing staff appeared to be a relevant organizational factor of weight loss. However, for those with cognitive disease, resident-to-staff ratios seemed to have no effect. Factors of weight loss among residents with dementia could not be identified. In coming years, the number of individuals with dementia is expected to rise, especially in developed countries with high life expectancy; however, in international research on nursing home quality, this resident group remains mostly unnoticed.

Further research is needed in this field to provide general recommendations for German nursing settings, as this study is one of the first of its kind. In the assessed facilities, sufficient nurse staffing levels should be ensured, as RN ratio differed from the regulatory required minimum. The current study showed that ACS assistance with eating may improve nutritional care of residents without cognitive impairment. In consideration of nursing staff shortages in Germany, deployment of ACS should be reconsidered by legislators. Moreover, according to the Care Statistics (Statistisches Bundesamt, 2017), in 2015, 40.5% of staff working in nursing facilities were 50 and older. Over the past few years, in Germany and elsewhere, requirements of nursing staff have markedly changed due to altered resident structure, including increased numbers of residents with dementia or of very old age. Thus, facilities should ensure that qualifications of nursing staff comply with demands; further training should be made available as necessary.

Finally, relevance of nursing care processes in prevention of unintentional weight loss should be mentioned, which might be generalized to all developed countries. Interventions, such as application of an appropriate nutritional risk assessment, should be planned and performed in a timely fashion. Especially for residents with dementia, provided care should consider the preferences of residents; for example, preparing and eating favorite meals together with care staff to stimulate their appetite. On the other hand, to prevent disruptive behavior, a calm atmosphere should be created during mealtimes, which requires competent care staff and a cooperative team.

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Dependent, Independent, and Control Variables Used in Analyses

VariableOperational DefinitionMean (SD) or %
Dependent variables
  WL NCIFacilities without weight loss among residents who were not cognitively impaired (reference category)67.5
Facilities with weight loss among residents who were not cognitively impaired32.5
  WL CIFacilities without weight loss among residents who were cognitively impaired (referen ce category)31.3
Facilities with weight loss among residents who were cognitively impaired68.7
Staffing variables
  RNRatio of residents to RNs4.69 (0.95)
  NARatio of residents to NAs6.40 (2.73)
  ACSRatio of residents to ACS20.94 (3.14)
Control variables
  LocationMetropolitan (reference category)35.5
Urban27.7
Rural6.7
  RegionNorth Rhine-Westphalia (reference category)46.4
Other federal states53.6
  Facility sizeNumber of beds94.28 (34.48)
  OccupancyOccupancy rate93.19 (8.05)
  Resident case mixAverage resident dependency level (DL) per facility (1–5: 1 = no DL; 2 = DL 0; 3 = DL 1; 4 = DL 2; 5 = DL 3)3.71 (0.25)
  NCI residentsNumber of residents with no cognitive impairment assessed for weight loss23.58 (9.45)
  CI residentsNumber of residents with cognitive impairment assessed for weight loss36.02 (11.01)

Odds Ratios With 95% Confidence Intervals for Weight Loss Among Residents With No, or Slight, Cognitive Impairment

VariableFacility ControlsStaffing Variables Added
Location
  Metropolitanreference categoryreference category
  Urban1.127 [0.457, 2.780]0.773 [0.285, 2.098]
  Rural0.576 [0.217, 1.528]0.486 [0.170, 1.392]
Region
  North Rhine-Westphaliareference categoryreference category
  Other federal states0.708 [0.288, 1.742]0.673 [0.236, 1.918]
Facility size0.997 [0.981, 1.013]0.993 [0.975, 1.011]
Occupancy0.951* [0.905, 0.999]0.942* [0.892, 0.996]
Resident case mix0.178 [0.029, 1.075]0.473 [0.051, 4.362]
NCI residents1.077** [1.023, 1.132]1.096** [1.035, 1.159]
RN staffing2.297** [1.343, 3.928]
NA staffing0.944 [0.717, 1.243]
ACS staffing1.184* [1.024, 1.370]
Pseudo R2 (Nagelkerke's)0.24***0.39***

Odds Ratios With 95% Confidence Intervals for Weight Loss Among Residents With At Least Considerable Cognitive Impairment

VariableFacility ControlsStaffing Variables Added
Location
  Metropolitanreference categoryreference category
  Urban0.664 [0.263, 1.680]0.658 [0.254, 1.705]
  Rural1.175 [0.461, 2.993]1.234 [0.482, 3.159]
Region
  North Rhine-Westphaliareference categoryreference category
  Other federal states1.605 [0.675, 3.814]1.295 [0.521, 3.222]
Facility size1.006 [0.988, 1.024]1.005 [0.987, 1.024]
Occupancy1.049 [0.997, 1.104]1.048 [0.995, 1.104]
Resident case mix2.085 [0.379,11.471]1.531 [0.241, 9.740]
CI residents1.046 [0.999, 1.095]1.047 [0.999, 1.098]
RN staffing1.103 [0.717, 1.698]
NA staffing0.885 [0.731, 1.072]
ACS staffing0.965 [0.854, 1.089]
Pseudo R2 (Nagelkerke's)0.18**0.20**
Authors

Ms. Zimmermann is Research Associate and PhD Student, Graduate School GROW - Gerontological Research on Well-Being, and Dr. Pfaff is Professor, Quality Development and Evaluation in Rehabilitation, Faculty of Human Sciences and Faculty of Medicine, University of Cologne, Cologne, Germany.

The authors have disclosed no potential conflicts of interest, financial or otherwise. This study was a part of a dissertation project conducted within the scope of the Graduate School GROW - Gerontological Research on Well-Being, and was supported in part by the North Rhine-Westphalian funding scheme Fortschrittskollegs. The authors acknowledge Dr. Heidemarie Kelleter and the Diocesan Caritas Association in Cologne for providing results of the Project EQisA used for the analyses in this study.

Address correspondence to Jaroslava Zimmermann, MA, PhD Student, Graduate School GROW – Gerontological Research on Well-Being, University of Cologne, Albertus-Magnus-Platz, Cologne 50923, Germany; e-mail: jaroslava.zimmermann@uni-koeln.de.

Received: September 21, 2017
Accepted: November 22, 2017

10.3928/19404921-20180109-01

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