OBJECTIVE: A clear link has been demonstrated between lower nurse staffing levels in hospitals and adverse patient outcomes, but the results of studies of such relationships in long-term care facilities haven’t been as clear. This study explored the time nurses spent in direct care and how it affected outcomes in long-stay (two weeks or longer) nursing home residents.

METHODS: In a retrospective study of data collected as part of the National Pressure Ulcer Long-Term Study (NPULS), we analyzed data on 1,376 residents of 82 long-term care facilities whose lengths of stay were 14 days or longer, who were at risk of developing pressure ulcers but had none at study entry, and who had a Braden Scale score of 17 or less. Primary data came from residents’ medical records during 12-week periods in 1996 and 1997. Dependent variables included development of pressure ulcer or urinary tract infection (UTI), weight loss, deterioration in the ability to perform activities of daily living (ADLs), and hospitalization. Independent variables included resident demographics, severity of illness, nutritional and incontinence interventions, medications, and nurse staffing time.

RESULTS: More RN direct care time per resident per day (examined in 10-minute increments up to 30 to 40 minutes per resident per day) was associated with fewer pressure ulcers, hospitalizations, and UTIs; less weight loss, catheterization, and deterioration in the ability to perform ADLs; and greater use of oral standard medical nutritional supplements. More certified nursing assistant and licensed practical nurse time was associated with fewer pressure ulcers but did not improve other outcomes.

CONCLUSIONS: The researchers controlled for important variables in long-stay nursing home residents at risk for pressure ulcers and found that more RN direct care time per resident per day was strongly associated with better outcomes. There’s an urgent need for further research to confirm these findings and, if confirmed, for improving RN staffing in nursing homes to decrease avoidable adverse outcomes and suffering.

This study is a secondary analysis of a subset of data from the National Pressure Ulcer Long-Term Study (NPULS). Nurse staffing time was defined as the number of minutes (for RNs and LPNs) or hours (for CNAs) of direct care provided by RNs, LPNs, and CNAs per resident per day. The primary outcome variable was pressure ulcer development, but other adverse events in the database, including urinary tract infection (UTIs), weight loss, deterioration in ADLs, and hospitalization, were also assessed, as were two care processes (catheterization and administration of nutritional supplements) associated with pressure ulcer development. Details of the NPULS inclusion criteria, data collection methodology, and measurements of resident characteristics, treatment factors, and clinical outcomes are described in More on Methods and Statistics: National Pressure Ulcer Long-Term Care Study Database, page 66.

The existing NPULS database contained 1,524 residents from 95 facilities who didn’t have a pressure ulcer at study entry and for whom nurse staffing information was available. For a total of 148 residents at 13 facilities, RN direct care was 40 or more minutes (median, 81.3 minutes). We excluded these facilities and residents from our analyses because their characteristics differed significantly from long-stay nursing home residents who had less than 40 minutes per resident per day of RN direct care time. For example, the greater-than-or-equal-to-40-minute group remained in the NPULS study for significantly fewer days (65.5 versus 78.6 days, P < 0.001) and had significantly higher average scores on the Comprehensive Severity Index (CSI) in month 1 (81.8 versus 61.0, P < 0.001), significantly more enteral feeding (27.7% versus 15.1%, P < 0.001), significantly less hospitalization (2% versus 11.7%, P < 0.001), and a significantly higher death rate (10.1% versus 5%, P = 0.01). (For more on the CSI, see More on Methods and Statistics&#58; National Pressure Ulcer Long-Term Care Study Database, page 66.) Therefore, to avoid skewing, the current study focuses on residents from 82 facilities in 19 states who received 40 minutes or less per resident per day of RN direct care and were thus more typical of long-stay nursing home residents. Data for the present analyses come from a subset (N = 1,376) of NPULS residents, all of whom had no pressure ulcer at study start. For details of statistical analyses see More on Methods and Statistics, page 66.

RESULTS

Across 82 facilities, the mean RN time per resident per day was 16 minutes (median = 15.1, range = 0.7 to 36.9), the mean LPN time per resident per day was 30.6 minutes (median = 31.5, range = 0 to 122.0), and the mean CNA time per resident per day was 1.7 hours (median = 1.9, range = 0.04 to 6.1). Facility ownership status was not significantly associated with nurse staffing levels. Nursing time per resident per day by for-profit and nonprofit status, respectively, was 15.1 versus 19 minutes for RNs (P = 0.08), 31.9 versus 26.6 minutes for LPNs (P = 0.26), and 1.7 versus 2 hours for CNAs (P = 0.14).

We examined numerous possible ranges for CNA time per resident per day. Only one outcome, the development of pressure ulcers, was significantly associated with CNA time ([chi]² = 17.8, P < 0.001): 16% of the residents who had a CNA time of 2.25 hours or more per day (median = 3.1 hours) developed at least one pressure ulcer, as did 23% of those who had a CNA time of 2 to 2.24 hours per day (median = 2.1 hours) and 32% of those who had less than 2 hours per day (median = 1.8 hours).

Two time ranges for LPN time per resident per day produced the only difference in outcomes: less than 45 minutes (median = 29.3 minutes) and 45 minutes or more (median = 51.8 minutes). More LPN time was associated with fewer pressure ulcers ([chi]² = 7.22, P = 0.007). However, more LPN time was also associated with more deterioration in the ability to perform ADLs ([chi]² = 22.6, P < 0.001) and greater catheter use ([chi]² = 7.61, P = 0.006) in bivariate analyses, but did not remain in multivariate analyses when other variables were controlled for.

RN, LPN, and CNA times were not highly correlated with study duration, CSI score in month 1, or resident age (-0.18 < r < 0.17), even though a few correlations were statistically significant. The correlation of RN time with CSI score was r = -0.04 (P = 0.10); the correlation of RN time with study duration was r = 0.10 (P < 0.001). The correlation of RN time with CNA time was r = 0.238 (P = 0.033), the correlation of RN time with LPN time was r = -0.037 (P = 0.745), and the correlation of LPN time with CNA time was r = 0.563 (P < 0.001). Interaction terms between staff time variables and also between staff time and other resident, treatment, and facility variables were allowed to enter models but none was significant.

The strongest predictor of hospitalization was higher severity of illness in month 1 (Wald [chi]² = 57.32). Catheter use and longer study duration also were significantly associated with increased likelihood of hospitalization. Again, RN direct care time of 30 to 40 minutes per resident per day was associated with the least likelihood of hospitalization, and ownership status was not significant.

All variables found to be significant in logistic regressions remained significant when either hierarchical or Cox models were applied. RN direct care time of 30 to 40 minutes per resident per day was always among the most significant predictors of better outcomes.

DISCUSSION

This study provides a comprehensive assessment of associations of average nursing time with five adverse clinical outcomes (development of pressure ulcers and UTIs, deterioration of ability to perform ADLs, weight loss, and hospitalization) and two care processes (catheterization and administration of nutritional supplements) after controlling for physical limitations; medical diagnosis; severity of illness; and other resident, treatment, and facility factors for long-stay, at-risk nursing home residents. In each analysis, RN staffing of 30 to 40 minutes per resident per day was strongly associated with better outcomes, and that association was much stronger than those for LPN and CNA times.

We first put the time per resident per day into the models as a continuous variable. For the outcome of pressure ulcer development, RN, LPN, and CNA time was significant. For the other outcomes, only RN time was significant. However, knowing only that more time is better wouldn’t be that helpful to nursing homes and policy makers. Therefore, we looked at 5-minute increments for each staff type and rates of outcomes. The outcomes were flat, having little variation, except where we ultimately chose to break the data as described: 10-minute increments for RNs, one break at 45 minutes for LPNs, and two breaks for CNAs.

It’s important to note several limitations to this study. First, this was an observational study, not an interventional study. Noninterventional studies can only show associations that reflect assumptions about causality, unlike randomized, controlled trials, which can demonstrate causality with more certainty. Second, nurse staffing data were not resident specific; we didn’t conduct time and motion studies to determine the exact number of minutes each type of nurse spent performing specific interventions for each resident. Third, the time spent on specific nursing activities was not documented. Also, we didn’t include facilities with an average RN direct care time of 40 minutes or longer per resident per day, for the reasons specified above. Future studies should look at facilities with the highest levels of staffing to determine whether the relationships found in our study continue to hold. Although this study identifies associations between nurse staffing and patient outcomes, these associations provide directions for further research and practice changes.

Although the original data from NPULS were collected in the mid 1990s, the results are relevant to today’s nursing home environment. We are implementing these findings in nursing homes today, and the same factors correlate comparably with outcomes.

Studies that determine the effectiveness of processes of care performed in response to specific risk factors are needed to inform practice. We also need to continue to develop models of care; better documentation systems that support practice decisions; and health information systems that guide, document, and evaluate practice. However, these analyses suggest that, in addition, we need adequate RN staffing if we are going to improve the nursing home experience and resident outcomes.

MORE ON METHODS AND STATISTICS

No resident identifiers were included in the database, except those permitted by the privacy rule established under the Health Insurance Portability and Accountability Act of 1996. This research complied with the rules for human experimentation as outlined in the Declaration of Helsinki and was approved as qualifying for expedited review status by the institutional review boards.

We trained 19 data abstractors (employed by the study facilities) to use the data collection instrument and the Comprehensive Severity Index (CSI) software system (for more on CSI, see below) and measured their reliability by comparison with experienced trainers; this ensured the accuracy of data collection. The criterion for accuracy of the abstractor’s data collection (interrater reliability) was agreement with the trainer on at least 90% of the measurements for each resident; if this was not achieved, the abstractor received additional training until 90% agreement was achieved.

For each resident, we retrospectively reviewed 12 consecutive weeks of medical records, data from the long-term care Minimum Data Set (MDS; an assessment tool used by all Medicare-certified long-term care facilities), and other written records such as physician orders and medication logs. If the resident was discharged during the 12 weeks under review, records for the actual length of stay were included. For newly admitted residents, the review of records began on the resident’s admission date (the study start date) and continued for 12 weeks. For existing residents with a pressure ulcer, the review included the four weeks before its identification and extended for eight weeks afterward. For existing residents without a pressure ulcer, the review included records beginning when residents were identified as being at risk (having a Braden Scale score of 17 or less) and extended for 12 weeks afterward. All study periods fell between February 1996 and October 1997.

Resident treatment characteristics included medications, nutritional interventions, pressure ulcer management strategies, and incontinence interventions. Treatment with “nutritional supplement” means the use of oral medical nutritional products at any time during the study period. Chart data were used to determine whether catheterization had occurred.

Facility variables included nurse staffing time, ownership status, existence of skin-care teams, use of outside skin-care consultants, use of in-house nurses who floated to understaffed units, and hours of dietitian time per month. Nurse staffing time was reported by each facility as the number of hours of direct care provided to residents by RNs, LPNs, and CNAs per 24-hour period for each month of the study, as was resident census per month for the same time periods. Time spent on managerial and administrative tasks was not included in RN direct care time. For each facility we computed average monthly RN, LPN, and CNA minutes or hours per resident per day for each month during the whole study period. These numbers had almost no variation so we didn’t use the monthly averages but instead used the aver-age over the whole study period.

Resident outcomes included development of pressure ulcer (stages I through IV), urinary tract infection (UTI), or weight loss; deterioration in the ability to perform ADLs; and hospitalization. Pressure ulcer documentation was abstracted from medical records; ulcers smaller than 0.25 cm² were eliminated. A pressure ulcer was considered new if the first pressure ulcer assessment or treatment occurred 15 days or longer after the study began.

UTI was defined according to the criteria of the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM; code 599.0). Weight loss was defined according to MDS section K (oral–nutritional status), item 3a (“Weight loss 5% or more in last 30 days; or 10% or more in last 180 days”), or from recorded chart weights showing a 5% or greater weight loss in study month 1 ([weight obtained closest to the end of the first month minus the first weight] divided by the first weight) or a weight loss of 10 lbs. or more in three months (the last weight obtained minus the first weight).

Deterioration of the ability to perform ADLs was defined according to section G.9 of the MDS, in which such deterioration, as compared to the patient’s status 90 days previously or since the most recent assessment, if sooner, is documented. Hospitalization dates and deaths were abstracted from resident records.

In our study of RN staffing time and outcomes of long-stay nursing home residents, we conducted bivariate analyses using [chi]² tests to examine associations between increments of nursing time and resident outcomes and between increments of nursing time and the use of treatments associated with pressure ulcer prevention. Logistic regression analyses were used to determine associations among resident, treatment, and facility characteristics and outcomes. Multicollinearity and goodness of fit were assessed. The importance of each predictor was determined by coefficient magnitude, odds ratios, and 95% confidence intervals.

Discrimination c statistic was assessed using the area under the receiver-operating-characteristic curve. The Hosmer–Lemeshow goodness-of-fit test was used to evaluate calibration. A nonsignificant P value indicates that the estimated probabilities weren’t significantly different from actual rates of occurrence and is further indication that the model was a good fit.

Facility-level measures, including staff time, were the same for all residents in a facility. Hierarchical models to account for clustering by facility were constructed and compared to the nonhierarchical models. Based on hierarchical and nonhierarchical analyses having the same significant variables, we elected to present findings from nonhierarchical models with resident as the unit of analysis (for more, see Hierarchical Models, page 68). Variables such as staff time and other facility-level measures were allowed to enter models. We also computed Cox proportional hazards regressions to evaluate the robustness of the findings. All analyses were performed with SAS statistical software (version 8.2, SAS Institute).

Hierarchical Models

The article by Horn and colleagues notes that hierarchical models were used to account for “clustering by facility” (See More on Methods&#58; Statistical Analyses, page 67). What does this mean and why is it important in such studies?

In nursing and health care research, it can be difficult to isolate a few key variables in order to understand their effects on patient outcomes. For example, a researcher may ask “Why do people in nursing homes get pressure ulcers?” As Horn points out, many factors contribute to pressure ulcer development. Multivariate analysis is a statistical method for assessing which factors (independent variables) influence specific resident outcomes (dependent variables). In this case, the authors examined several independent variables (staffing time, facility characteristics, and history of pressure ulcers, among others) and several dependent variables (development of pressure ulcers, weight loss, and ability to perform activities of daily living, among others).

Hierarchical analysis is a type of multivariate analysis that addresses two additional issues. One is the clustering mentioned by Horn and colleagues. Some observations are clustered—another term is nested—within others, creating a possible correlation among participants. Consider an example from educational research, a field in which hierarchical analysis has a longer history than it does in health care research (this example is taken from the Raudenbush text, below). In any school system, many individual students are “nested” within a single class, and many classes are “nested” within a single school. In addition to their individual differences, students in the same class may share other factors in common—for example, the influence of a particular teacher—that affects their test scores. Therefore, when comparing test scores across classes, we want to know the degree of this within-class correlation. This correlation within a group of study subjects is tested using the intraclass correlation coefficient (ICC), a measure of the degree of resemblance of smaller units nested within larger ones. The magnitude of the ICC influences estimates of variance and tests of significance. (Other statistical effects of this nesting aren’t explored here, but readers who are interested in a more thorough statistical discussion can refer to the texts listed below.)

The second issue is, what do the additional layers—or levels, in hierarchical terminology—contribute to our understanding of the research question? We have to make at least two assumptions about these “higher” levels (in our example, the class and school levels, as opposed to the individual student). First, results across classes or schools aren’t necessarily a simple aggregate of individual students’ results. Second, class- and school-level factors may contribute to an explanation of the outcome of interest—for example, variations in grade-point averages in a particular class or school, above what individual student characteristics may contribute (as would be found, for example, if teachers assigned grades using different standards).

As Raudenbush suggests, once you become aware of the issue, you notice that hierarchical (multilevel) problems are everywhere. In health care research, we often encounter multilevel problems: patients nested within clinicians nested within primary care practices or, as another example, residents nested within units nested within nursing homes. Hierarchical analyses and models are used because nonhierachical techniques, such as logistic regression, don’t take nesting or ICC into account.

Consequently, Horn and colleagues employed a hierarchical analysis to “take into account” the possible ICC among nursing home patients. Facility variables, such as nurse staffing time and ownership status, were considered as second-level variables to see whether they contributed to resident outcomes over and above the effect of the first-level variables. They compared these results to those of (nonhierarchical) logistic regression analysis. By indicating that the hierarchical analysis yielded no statistically significant differences from the nonhierarchical analysis, the authors suggest that the ICC is negligible, that there’s not necessarily a second-level contribution, and that the study findings could be accurately presented using logistic regression.

Goldstein H. Multilevel statistical models. 3rd ed. London: E. Arnold; 2003.

Hox JJ. Multilevel analysis&#58; techniques and applications. Mahwah, NJ: Lawrence Erlbaum Associates; 2002.

Kreft IGG, de Leeuw J. Introducing multilevel modeling. Thousand Oaks, CA: Sage Publications; 1998.

Raudenbush SW, Bryk AS. Hierarchical linear models&#58; applications and data analysis methods. 2nd ed. Thousand Oaks, CA: Sage Publications; 2002.

Snijders TAB, Bosker RJ. Multilevel analysis&#58; an introduction to basic and advanced multilevel modeling. Thousand Oaks, CA: Sage Publications; 1999.

REFERENCES