| HOME | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|---|
| ||||||||||||||||||||||||||||||||
RESEARCH ARTICLE |
a Department of Social and Behavioral Sciences, University of California, San Francisco
b Center for Health Systems Research and Analysis, University of Wisconsin, Madison
Charlene Harrington, Department of Social and Behavioral Sciences, University of California, 3333 California Street, Suite 455, San Francisco, CA 94118 E-mail: chas{at}itsa.ucsf.edu.
 |
Abstract |
|---|
 TOP Abstract DeficienciesIndependent VariablesMethodsResultsDiscussionReferences |
|---|
Methods. Data were from the On-Line Survey, Certification, and Reporting System for all certified nursing homes in the United States. Regression models examined total deficiencies, quality of care, quality of life, and other deficiencies.
Results. Fewer registered nurse hours and nursing assistant hours were associated with total deficiencies and quality of care deficiencies, when other variables were controlled. Fewer nursing assistant staff and other care staff hours were associated with quality of life deficiencies. Fewer administrative staff hours were associated with other deficiencies. Facilities that had more depressed and demented residents, that were smaller, and that were nonprofit or government-owned had fewer deficiencies. Facilities with more residents with urinary incontinence and pressure sores and with higher percentages of Medicaid residents had more deficiencies, when staffing and resident characteristics were controlled.
Discussion. Facility characteristics and states were stronger predictors of deficiencies than were staffing hours and resident characteristics. Because only a small portion of the total variance in deficiencies could be explained, much work remains to explore factors that influence deficiencies.
USING registered nurses (RNs) and other nursing personnel as well as many other types of health professionals and care providers, nursing homes in the United States provide care to about 1.6 million residents (Harrington, Carrillo, Thollaug, and Summers 2000
). Studies have documented the importance of nursing in both the process of care and the outcomes of nursing home care (Aaronson, Zinn, and Rosko 1994; Cherry 1991; Linn, Gurel, and Linn 1977; Munroe 1990; Nyman 1988; Spector and Takada 1991). Nyman 1988 found that nurse hours per patient day were positively related to facility quality measures. Munroe 1990 found a positive significant relationship between the quality (measured by deficiencies) and higher ratios of RN and licensed vocational or licensed practical nurse (LVN/LPN) hours per resident day and concluded that higher ratios of licensed nurses may be more important than total nursing hours. Cherry 1991 found that increased RN hours were positively associated with a composite of good outcome measures (fewer decubitus ulcers, catheterized residents, and urinary tract infections and less antibiotic use).
Spector and Takada 1991 reported that higher staff levels and lower RN turnover rates were related to improvements in resident functioning. Lower staffing was associated with high rates of urinary catheter use, low rates of skin care, and low resident participation in activities. Cohen and Spector 1996 found that higher ratios of RNs to residents, adjusted for resident case mix, reduced the likelihood of death and that higher ratios of LVN/LPNs significantly improved resident functional outcomes. A recent study of Minnesota nursing homes found that in the 1st year after a patient's admission to a nursing home, the licensed (but not nonlicensed) nursing hours were significantly related to improved functional ability, increased probability of discharge home, and decreased probability of death (Bliesmer, Kane, and Shannon 1998). The role of professional nursing staff disappeared, however, for chronic care residents.
Few studies have examined other types of care staff in nursing homes such as physician and therapy services. Some of these have found a positive relationship between such services and outcomes (Chiodo, Gerety, Mulrow, Rhodes, and Tuley 1992; Karuza and Katz 1994; Kochersberger, Hielema, and Westlund 1994; Mulrow et al. 1994; Przybylski et al. 1996).
In this study we had two aims. First, we described and categorized the types of federal deficiencies issued to facilities by state licensing and certification surveyors for different types of care problems. Deficiencies are evaluations of poor quality made by state surveyors under the federal nursing home certification regulations. Second, we used multiple regression analyses to test the association of facility deficiencies with facility staffing hours per resident day, resident characteristics, facility characteristics, and states. The general hypothesis we tested was that staff hours per resident day in nursing homes would be negatively associated with higher numbers of deficiencies, when controlling statistically for resident characteristics, facility characteristics, and states.
|
Deficiencies |
|---|
|
TOPAbstractDeficienciesIndependent VariablesMethodsResultsDiscussionReferences |
|---|
The OSCAR deficiency data are generally considered to be accurate reflections of the actual deficiencies issued by surveyors. The data also accurately reflect problems that exist in the facilities because deficiencies are subject to extensive review by state officials and facilities and are subject to appeal by providers through an administrative or judicial process. Because providers do not want deficiencies on their record, they challenge deficiencies they consider unwarranted.
The federal regulations have 179 specific standards for nursing facility care in 17 major categories. In this study, we analyzed the total number of deficiencies for each facility. In addition, we grouped the deficiencies into three categories: (a) quality of care, (b) quality of life, and (c) other deficiencies. Quality of care included 72 specific items in the following federal survey categories: resident assessment, quality of care, nursing services, dietary services, physician services, rehabilitative services, dental services, pharmacy services, and infection control. For example, prevention of pressure sores, falls, and prevention of physical decline are separate requirements under the quality of care category. The quality of life category included 77 specific items on resident's rights; admission, transfer, and discharge rights (including resident rights); resident behavior and facility practices (includes resident rights); quality of life; and physical environment (which could affect quality of life). Other deficiencies included 30 specific items on administration and medical records. Because surveyors have some discretion in the specific individual deficiencies that they apply to quality problems that they detect, grouping deficiencies into a few broad categories has the advantage of reducing some variability inherent in the survey process. The three broad categories we used appeared to be conceptually separate and easier to analyze that the 17 categories used by HCFA.
|
Independent Variables |
|---|
|
TOPAbstractDeficienciesIndependent VariablesMethodsResultsDiscussionReferences |
|---|
Staffing hours.
Federal standards have been established for different types of staff in nursing facilities as a means of ensuring adequate nursing home care. Nursing home reform legislation in the Omnibus Budget Reconciliation Act of 1987 (Omnibus Budget Reconciliation Act of 1987) increased the minimum standards for nursing home staffing. All nursing homes certified for Medicare and Medicaid residents must have an RN director of nursing, an RN on duty for 8 hours a day 7 days a week, and a licensed nurse (either an RN or LVN/LPN) on duty around the clock (Omnibus Budget Reconciliation Act of 1987 [OBRA]). In addition, the law requires sufficient nursing staff to provide nursing and related services to residents to enable residents to reach or maintain the highest practicable level of physical, mental, and psychosocial well-being. Regulations also require social activities; medically related social services; dietary services; physician and emergency care services; and pharmacy, dental, and rehabilitation services including physical, speech, and occupational therapies (OBRA 1987; U.S. Department of Health and Human Services, Health Care Financing Administration 1995a 1995b 1995c). Facilities have discretion in going beyond the minimal federal requirements in the numbers and types of staff they use to provide care. Staffing types and hours vary considerably across facilities (Harrington and Carrillo 1999; Harrington, Carrillo, Mullan, and Swan 1998).
The major focus of this study was staffing hours per resident day in nursing facilities. The staffing was examined for six separate categories. First, nursing staff was categorized as all RN hours including nurse administrators, LVN/LPN, and nursing assistant hours separately. Other care staff included the professional staff and their assistants (e.g., physicians, pharmacists, dietary staff, therapists, activities staff, and social workers). All facility administrative staff (excluding nursing) was combined into a separate category. Housekeeping and other staff formed a fourth category. Finally, all these groups of staff were combined for the total staff in nursing facilities.
The general hypothesis was that staffing hours would be inversely related to deficiencies, when resident characteristics, facility characteristics, and states were controlled. Specifically, we hypothesized that fewer nursing staff hours per resident day would be associated with higher numbers of total deficiencies and with quality of care and quality of life deficiencies. We expected that fewer other care staff hours (e.g., therapists and activities staff) per resident day would be related to more quality of care and quality of life deficiencies. We expected that fewer administrative and housekeeping staff hours per resident day would be related to more deficiencies in the "other" deficiency category.
Resident characteristics.
Nursing homes vary in the types of residents they serve, typically referred to as case mix variation. A strong relationship among resident characteristics, nurse staffing time requirements, and nursing costs in nursing facilities has been shown in several studies (Arling, Nordquist, and Capitman 1987; Fries and Cooney 1985; Fries et al. 1994; Schneider, Fries, Foley, Desmond, and Gormley 1988). These studies have shown the importance of activities of daily living (ADLs) and other resident characteristics on facility resource requirements.
Resident characteristics or case mix may increase the number of deficiencies that a facility receives, when staff hours and other factors are controlled. This may occur if surveyors identify the resident characteristics or case mix when they begin the survey and focus their survey attention on specific residents with problems. We expected that resident characteristics such as functional impairment, cognitive and emotional status, incontinence, and pressure sores would be positively related to deficiencies, because residents with these problems are probably at greater risk of poor outcomes and generally require more effort and expertise in caregiving. The hypothesis we tested was that higher levels of case mix would be positively associated with the number of deficiencies in total and with quality of life and quality of care deficiencies in particular, when staffing hours, facility characteristics, and states were controlled. Because of the potential complex relationship between resident characteristics and deficiencies, we examined deficiencies with and without resident characteristics in the models.
Facility characteristics.
We examined the following facility characteristics: size, whether or not a facility was hospital based, certification status, percentage of Medicaid residents, and type of ownership.
; Nyman 1988; Ullmann 1987). Other studies have found a negative relationship between size and staffing (Fottler, Smith, and James 1981). Controlling for both staffing hours and resident characteristics, we expected that larger sized nursing facilities would be related to deficiencies because larger facilities may be more difficult to manage and/or may result in a less positive environment for residents. ; Kanda and Mezey 1991), because of residents' eligibility for Medicare skilled nursing reimbursements. Medicare payment levels have traditionally been higher than those in Medicaid facilities (Buchanan, Madel, and Persons 1991). Because SNFs have higher payment rates than other certification categories, they should be negatively related to deficiencies, whereas Medicaid-only facilities, which have the lowest reimbursement rates, may be positively associated with deficiencies. Controlling for staffing hours and resident characteristics, we expected that SNFs would have fewer deficiencies because of a number of factors not captured in the model, such as the expertise and experience of direct care staff, organizational and administrative expertise, facility support services, and other unmeasured factors. ; Buchanan et al. 1991; Cohen and Dubay 1990; Dor 1989; Holahan and Cohen 1987; Holahan, Rowland, Feder, and Heslam 1993; Swan, Harrington, and Grant 1993). Studies have shown that high percentages of Medicaid residents result in lower staffing levels and conversely that high percentages of Medicare residents result in higher staffing levels (Fottler et al. 1981; Harrington et al. 1998; Nyman 1988; Zinn 1993). Nyman 1988 found that facilities with greater proportions of Medicaid residents appeared to have lower quality of care. Facilities with greater proportions of Medicaid residents may not have to compete on quality, and such facilities may have fewer resources because Medicaid reimbursement rates are lower than those of Medicare. Therefore, we hypothesized that higher percentages of Medicaid residents would be positively associated with deficiencies even after controlling for staffing hours, resident characteristics, and other factors. ; Davis 1993; Spector, Selden, and Cohen 1998), so they should have fewer deficiencies. We expected that nonprofit and government facilities would have fewer deficiencies compared with for-profit facilities, controlling for staffing hours, resident characteristics, and other factors. We expected that unmeasured variables for nonprofit facilities, such as organizational philosophy, training and skill of staff, and turnover of staff, may all result in fewer deficiencies.
State or regional factors.
Although the deficiency data are generally considered accurate, there are some variations in the surveyor procedures and practices for determining deficiencies across the 50 states and the District of Columbia. The U.S. General Accounting Office 1998a, General Accounting Office 1998b has been critical of the state survey agencies in their poor identification of quality of care problems and their poor records in enforcing the federal standards. The U.S. Department of Health and Human Services, Health Care Financing Administration (1998a, 1998b) also identified problems with its survey process and has been attempting to improve the process. Thus, state and regional variables were included as control variables in separate models. No hypotheses were made about particular states.
|
Methods |
|---|
|
TOPAbstractDeficienciesIndependent VariablesMethodsResultsDiscussionReferences |
|---|
Data Cleaning
All federally certified nursing facilities for Medicare and Medicaid in the United States were included in this study, except for the trust territories and Puerto Rico. Intermediate care facilities for the mentally retarded were excluded. We eliminated duplicate provider records by matching facility records by state, city, facility name, address, and telephone number (372 facilities were eliminated). Another 1,496 facilities with survey dates prior to or after the study period were removed. Facilities with incomplete information were also removed (26 facilities). At that point, the resulting sample was 15,536 facilities. We corrected errors in reporting of total beds and residents by setting the maximum number to the number of certified skilled nursing beds in the facility (for 2,315 facilities).
Staffing data.
We cleaned staffing data using the total nursing data. Because facilities with 15 or fewer residents had significantly higher staffing hours than did larger facilities, to an extent judged to be unlikely to be accurate, 1,054 such facilities were eliminated. We then used means and standard deviations to examine staffing for the four certification categories. Because the staffing data showed a number of outliers that appeared to be erroneous and resulted in skewed distribution, all facilities that reported staffing of more than two standard deviations above the mean were removed (159 facilities). Thus, the total number of facilities in the study was 13,770. Excluded facilities were smaller, more likely to be hospital based, more likely to be SNFs, and had a lower proportion of Medicaid residents.
Each of the categories of staff included all the full-time, part-time, and contract staff for that category. To compute the staffing hours per resident day, we multiplied the total staffing payroll full-time employees reported for a 2-week period by 70 h and then divided by the total number of residents and by the 14 days in the reporting period (the procedure used by HCFA). The staffing data were collected at the time of each regular survey and were not audited by state surveyors.
Resident characteristics.
Several variables were selected for resident characteristics from the OSCAR file. We summed the scores for each of three ADLs (eating, toileting, and transferring) to construct an ADL index (1 = lowest need for assistance, 2 = moderate need, 3 = greatest need). Thus, the summary ADL scale ranged from 3 to 9 with a score of 9 being the most dependent. Three mobility variables were used for a mobility index: 1 = independent in ambulation, 2 = in a chair all or most of the time, and 3 = bedfast all or most of the time, based on the number of residents at each level. The percentages of residents reported with depression, dementia, behavioral symptoms, incontinence, and pressure sores were calculated for the study.
Other independent variables.
For the size of a nursing facility, the total beds were reported from OSCAR in a set of four dichotomous variables: those with less than 60 beds, with 60–119 beds, with 120–160 beds, and with more than 160 beds. A dichotomous variable indicated whether or not the facility was hospital based. For certification status, nursing facilities for Medicaid with a distinct part for SNF care (Category 3) and nursing facilities dually certified for Medicaid and Medicare (Category 2) were combined and used as the comparison category. We used three dichotomous variables to indicate each facility's certification for SNFs, facilities with a combination of Medicare and Medicaid residents, and facilities with Medicaid-only residents. Nonprofit and government-owned facilities were combined and indicated by a dichotomous variable and compared with profit-making facilities. We included state dummy variables in the model to take into account state variation in deficiencies. Census regions were indicated by a set of four dichotomous variables (midwest, northeast, south, and west) and were examined separately from states.
Analytic Approach
We used bivariate analyses to examine relationships between key variables and deficiencies. Pearson product correlations were conducted between the independent variables. Although some of the independent variables showed relationships with each other, the correlations were modest, suggesting that multicollinearity was not likely to be problematic. Tolerance tests in the regression analysis also did not show multicollinearity to be a problem.
There is no general agreement on the best way to measure and analyze deficiencies, so we tested several approaches. We used ordinary least squares (OLS) regression modeling with SPSS 1998 to examine factors associated with the number of deficiencies issued to each facility to test the hypotheses for the study. We inspected the residual normality of the total deficiencies distribution and found that there was no strong violation of the normality assumptions for OLS modeling. In this case, the distribution of residuals was found to be more symmetric than the dependent variable. The skewness in the dependent variable was due largely to skewness in the distribution of the independent variables and not to non-normality of the residuals.
We found the same distribution pattern when we used a logistic regression to analyze whether any deficiency was cited (deficiency = 1, no deficiency = 0). Then, using the log of deficiencies, we computed an OLS regression on the deficiencies excluding those facilities with zero deficiencies. The findings, which were similar, are reported in the Results section.
Finally, we also conducted alternative logistic regressions including (a) whether the facility was in the quartile with the greatest number of deficiencies (i.e., more than eight deficiencies were cited) and (b) whether any deficiencies were evaluated by surveyors as indicating substandard care. The pseudo R2 statistic for binary response variables is the proportion of variation in the dependent variable "explained" by the independent variables in the model. The logistic regressions were significant and informative but not as effective and useful in targeting facilities with outlying deficiencies as the OLS regressions.
|
Results |
|---|
|
TOPAbstractDeficienciesIndependent VariablesMethodsResultsDiscussionReferences |
|---|
|
Table 1 shows the distribution of resident characteristics. On a 9-point scale, the mean ADL index was 5.78. The mean resident mobility was 1.42 on a 3-point scale. Half of the residents had urinary incontinence, 42% had dementia, 26% had behavioral symptoms, 18.6% had depression, and 6.5% had pressure sores. Most nursing homes (62%) had fewer than 119 beds. Only 8.7% of facilities were hospital based, and 3.76% were SNFs. Eighteen percent were certified as nursing facilities with Medicaid-only residents. The majority of residents were on Medicaid (65.6%), with 9.7% on Medicare and the remainder private pay. Of the facilities, 68% were for-profit.
Regression Models for Deficiencies
Table 2 shows the results of OLS regression models of deficiencies. As expected, lower levels of RN staff were significantly associated with total care deficiencies and quality of care but not with quality of life deficiencies and other deficiencies. LVN/LPN hours were not related to deficiencies. Lower levels of nursing assistant staff were associated with more total care deficiencies, quality of care deficiencies, and quality of life deficiencies but not with other deficiencies. Lower levels of other care staff were associated with increased quality of life deficiencies but not with total deficiencies or quality of care deficiencies. As expected, lower levels of administrative staff were associated with higher deficiencies in the "other" category that included administrative deficiencies. Contrary to the hypotheses, lower levels of housekeeping and other staff had no relationship to deficiencies.
|
Smaller facilities (16–60 beds and 60–119 beds) were less likely to have quality of care and quality of life deficiencies and had fewer total deficiencies than larger facilities. Neither hospital affiliation nor certification status was associated with deficiencies. Facilities with greater percentages of Medicaid residents were more likely to have deficiencies of all types. Nonprofit and government facilities had fewer deficiencies of all types than for-profit facilities.
States were also significant predictors of deficiencies (see Table 2 for summary F value). The regression model was also tested with the four census regions as substitutes for the state dummy variables (data not shown). The midwest, south, and west each had higher numbers of all types of deficiencies than did the northeast comparison group. The highest coefficients were found in the western region.
Table 3 shows the alternative regression models for deficiencies. A logistic regression shows the findings for facilities that had any deficiency; these findings were similar to those in Table 2 except that LVN/LPN hours were significant and nurse assistant hours were not. When the regression for the log of deficiencies (excluding those facilities with no deficiencies) shown in Table 3 was compared with the OLS regression for the total deficiencies (Table 2 ), the findings were similar. With respect to staffing levels, RN and nurse assistant hours were negative predictors of deficiencies. Overall, the alternative models yielded the same general conclusions and showed the robustness of the OLS model.
|
|
Discussion |
|---|
|
TOPAbstractDeficienciesIndependent VariablesMethodsResultsDiscussionReferences |
|---|
), fewer RN staff hours were associated with more quality of care deficiencies. Fewer nursing assistant hours, as expected, had a consistent, significant negative relationship with total, quality of care, and quality of life deficiencies. Fewer other care staff hours were associated with more quality of life deficiencies. On the other hand, staffing hours and resident characteristics did not explain much of the variation in deficiencies when case mix and facility characteristics and other factors were controlled for in the model. Because we were able to explain only a small portion of the variance, much work remains to explore these important relationships with deficiencies. The relationship between resident characteristics and deficiencies was complex. Contrary to our hypotheses, lower scores on ADL impairment were also related to more quality of life deficiencies, perhaps because surveyors considered that more independent residents need more activities and stimulation than more impaired residents. Surprisingly, depression and dementia were also associated with reductions in the number of deficiencies. This finding raises questions about whether quality problems associated with the psychosocial characteristics are difficult for surveyors to identify; or perhaps surveyors are less concerned about quality of care and life for cognitively impaired residents, and this is reflected in fewer deficiencies.
As expected, pressure sores were related to increased numbers of deficiencies of all types because they are visible and quantifiable measures of poor quality. As expected, urinary incontinence was also associated with more deficiencies. Urinary incontinence is a treatable problem in nursing homes and has also been the focus of the survey process, which may explain the association with deficiencies. On the other hand, surveyors may tend to cite facilities when they observe problems that may be more of an indicator of case mix than of quality problems. In other words, it may be difficult for surveyors to determine when poor outcomes are related to the disease process or to poor and inadequate care.
As expected, higher percentages of behavioral problems, urinary incontinence, and pressure sores were positively associated with quality of life deficiencies. These easily observable problems may have a negative impact on individual residents and could also have a negative impact on the overall environment of the facility. Future studies should examine these relationships in greater detail.
Consistent with our hypothesis, larger facilities were associated with more deficiencies (when staffing and residents were controlled), perhaps because larger facilities are less able to monitor and ensure both quality of care and quality of life requirements for residents. Alternatively, surveyors' judgments may not be very sensitive to facility size. As expected, facilities with more Medicaid residents were associated with more deficiencies, even when we controlled for staffing and resident characteristics, suggesting poorer care in such facilities. This finding is consistent with previous studies by Nyman 1988 that suggest poorer quality of care occurs in facilities with higher proportions of Medicaid residents, especially in areas where there is excess demand for services. The findings may be related to the reduced need to compete on the basis of quality or to lower Medicaid reimbursement rates compared with Medicare and private pay rates. Confirming our hypothesis and the findings from other studies, nonprofit and government facilities were associated with fewer deficiencies (Aaronson et al. 1994; Cohen and Spector 1996). These facilities may be providing higher quality of care than the for-profits because they may reinvest their net revenues into the facilities. These facilities may have more staff expertise and lower turnover rates and other characteristics that were unobserved variables. On the other hand, surveyors may be more sympathetic to nonprofit and government nursing homes than the for-profit facilities.
State variations in deficiencies confirm that HCFA should pay greater attention to achieving uniformity in the state survey procedures. Lower levels of deficiencies in the northeastern region compared with other regions may indicate that facilities in the northeast have higher quality of care or that the region has weaker survey procedures. Regional differences could also be a proxy for other market factors not included in the model. It would be troubling if the differences were related to different survey procedures and/or less enforcement, because the federal program strives to ensure standard procedures and enforcement across regions. The U.S. Department of Health and Human Services, Health Care Financing Administration (1998a, 1998b) has admitted it has problems with the effective implementation of its survey process. HCFA should use findings from this study to examine the differences in deficiencies across states and regions and to eliminate variation in the survey and enforcement process.
There are important issues to be addressed concerning the quality of OSCAR data. The staffing data reported in OSCAR are collected for only a 2-week period at the time of the annual survey and generally are not audited by surveyors. Some facilities may increase their staff during the period around the survey. Thus, the reports may overstate the actual staffing in facilities. Unfortunately, no other data source on staffing across states is available. We have compared the average staffing hours reported on OSCAR for all facilities in California for the 1996–97 period with the annual staffing data submitted to California on the facility cost reports for the same period. This comparison did show that the annual nursing staffing was on average only 0.1 h lower than the OSCAR data, suggesting that the data are similar. Another limitation is that the available staffing data do not capture differences in education levels, capability, motivation, and experience of staff. It may be that these unmeasured factors—such as educational level, experience, number of nurses in advanced practice, turnover rates, and other information—are more important than the hours and specific types of staff used. Staffing reports made on a quarterly basis would be more informative than those made for a 2-week period. Thus, if staffing has a limited effect, it may reflect some limitations in the data. Given the importance of the issues surrounding staffing and its relationship to quality, HCFA should undertake a more detailed, rigorous study of the appropriateness of current staffing data and the accuracy and representativeness of OSCAR staffing data.
Another issue is whether the deficiencies are an accurate reflection of quality in the facilities. The identification of quality problems in nursing homes is a difficult task for surveyors. As discussed previously, there are good reasons to believe that the deficiencies reported do identify problems within the facilities. However, there may be additional problems that are not reflected in any deficiencies. The U.S. General Accounting Office 1998b had independent surveys conducted by a reputable research team. The research team conducted surveys concurrently with California state surveyors and found additional deficiencies that were not identified by the California state survey agency, but they did not identify false positive deficiencies. Thus, there may be false negatives in surveyors' identification of deficiencies (see also U.S. General Accounting Office 1999). There may, of course, also be false positives with deficiencies that are undetected.
Deficiencies for quality violations are relatively infrequent, as we have shown. Moreover, the average number of deficiencies per facility has been declining steadily from 8.8 deficiencies in 1991 to 4.9 in 1997 (Harrington and Carrillo 1999; U.S. Office of the Inspector General Department of Health and Human Services 1999). Thus, the accuracy and measurement issues of deficiencies are even more important. If HCFA is able to improve the reliability and consistency of the survey process and the citing of deficiencies within and across states, then the relationships between independent and dependent variables may be improved.
Finally, the technology of appropriately identifying quality problems in nursing facilities can be improved. It is hoped that the use of new quality indicators (QIs) will improve the identification of problems (Zimmerman et al. 1995). These indicators, developed by Zimmerman and colleagues 1995, identify 30 resident problems with data from the Minimum Data Set (MDS) assessment forms that must be completed for each resident upon admission and annually, with changes in conditions reported to HCFA on a quarterly basis. Such MDS data on individual residents, although it is also not regularly audited by state surveyors, should be more accurate than the resident data reported on the OSCAR report. Once the MDS data become available for researchers, the data should be a valuable tool in studying the relationships between staffing, resident characteristics, and facility characteristics.
The reporting of QIs will allow surveyors and researchers to identify the condition of residents upon admission and then over time. Future studies should examine changes in resident conditions over time, controlling for the initial condition of residents. This may improve the accuracy of detecting true quality problems that are the result of poor care, taking into account the resident's initial condition and/or resident co-morbidities. Surveyors can use the QIs to target individual residents in facilities where potential quality of care may be occurring so that surveyors can examine these residents during the survey process. Surveyors can also use the QIs to identify specific facilities with higher or lower proportions of residents with potential quality problems, and this technology may improve the accuracy of identifying problems and issuing deficiencies. Researchers should continue to explore the relationships among staffing, resident characteristics, facility characteristics, and quality. As databases are improved, perhaps the relationships may become more clearly understood.
|
Acknowledgments |
|---|
Received for publication April 9, 1999. Accepted for publication April 12, 2000.
|
References |
|---|
|
TOPAbstractDeficienciesIndependent VariablesMethodsResultsDiscussionReferences |
|---|
This article has been cited by other articles:
 |
|
 |
|
  N. G. Castle State Differences and Facility Differences in Nursing Home Staff Turnover Journal of Applied Gerontology, November 1, 2008; 27(5): 609 - 630. [Abstract] [PDF]
|
|
|
|
|
|
N. G. Castle Nursing Home Caregiver Staffing Levels and Quality of Care: A Literature Review Journal of Applied Gerontology, August 1, 2008; 27(4): 375 - 405. [Abstract] [PDF]
|
|
|
|
 |
|
 J. Engberg, N. G. Castle, and D. McCaffrey Physical Restraint Initiation in Nursing Homes and Subsequent Resident Health Gerontologist, August 1, 2008; 48(4): 442 - 452. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 N. G. Castle Nursing Home Evacuation Plans Am J Public Health, July 1, 2008; 98(7): 1235 - 1240. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Goodson, W. Jang, and M. Rantz Nursing Home Care Quality: Insights From a Bayesian Network Approach Gerontologist, June 1, 2008; 48(3): 338 - 348. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
N. G. Castle, J. Engberg, and A. Men Nursing Home Staff Turnover: Impact on Nursing Home Compare Quality Measures Gerontologist, October 1, 2007; 47(5): 650 - 661. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
G. Arling, R. L. Kane, C. Mueller, J. Bershadsky, and H. B. Degenholtz Nursing Effort and Quality of Care for Nursing Home Residents Gerontologist, October 1, 2007; 47(5): 672 - 682. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. H. Swan and C. Harrington California Nursing Facility Quality and Union Environments J Aging Health, April 1, 2007; 19(2): 183 - 199. [Abstract] [PDF]
|
|
|
|
|
|
B. A. Kash, N. G. Castle, G. S. Naufal, and C. Hawes Effect of staff turnover on staffing: a closer look at registered nurses, licensed vocational nurses, and certified nursing assistants. Gerontologist, October 1, 2006; 46(5): 609 - 619. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C. Donoghue and N. G. Castle Voluntary and Involuntary Nursing Home Staff Turnover Research on Aging, July 1, 2006; 28(4): 454 - 472. [Abstract] [PDF]
|
|
|
|
|
|
P. M. Keith Nursing Home Administrators' Attribution of Antifacility Bias to Ombudsman Programs Journal of Applied Gerontology, April 1, 2006; 25(2): 120 - 136. [Abstract] [PDF]
|
|
|
|
|
|
N. G. Castle and J. Engberg Organizational Characteristics Associated With Staff Turnover in Nursing Homes Gerontologist, February 1, 2006; 46(1): 62 - 73. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Zinn, W. Spector, L. Hsieh, and D. B. Mukamel Do Trends in the Reporting of Quality Measures on the Nursing Home Compare Web Site Differ by Nursing Home Characteristics? Gerontologist, December 1, 2005; 45(6): 720 - 730. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. L. Gruber-Baldini, S. Zimmerman, M. Boustani, L. C. Watson, C. S. Williams, and P. S. Reed Characteristics Associated With Depression in Long-Term Care Residents With Dementia Gerontologist, October 1, 2005; 45(suppl_1): 50 - 55. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. D. Phillips, S. Holan, M. Sherman, M. L. Williams, and C. Hawes Rurality and Nursing Home Quality: Results from a National Sample of Nursing Home Admissions Am J Public Health, October 1, 2004; 94(10): 1717 - 1722. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. L. Troyer and H. G. Thompson Jr. The Impact of Litigation on Nursing Home Quality Journal of Health Politics Policy and Law, February 1, 2004; 29(1): 11 - 42. [Abstract] [PDF]
|
|
|
|
|
|
C. Harrington, J. T. Mullan, and H. Carrillo State Nursing Home Enforcement Systems Journal of Health Politics Policy and Law, February 1, 2004; 29(1): 43 - 74. [Abstract] [PDF]
|
|
|
|
|
|
L. Polivka, J. R. Salmon, K. Hyer, C. Johnson, and D. Hedgecock The Nursing Home Problem in Florida Gerontologist, April 1, 2003; 43(90002): 7 - 18. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. M. Wiener An Assessment of Strategies for Improving Quality of Care in Nursing Homes Gerontologist, April 1, 2003; 43(90002): 19 - 27. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. Harrington, J. O'Meara, M. Kitchener, L. P. Simon, and J. F. Schnelle Designing a Report Card for Nursing Facilities: What Information Is Needed and Why Gerontologist, April 1, 2003; 43(90002): 47 - 57. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Angelelli, V. Mor, O. Intrator, Z. Feng, and J. Zinn Oversight of Nursing Homes: Pruning the Tree or Just Spotting Bad Apples? Gerontologist, April 1, 2003; 43(90002): 67 - 75. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Mezey, N. N. Dubler, E. Mitty, and A. A. Brody What Impact Do Setting and Transitions Have on the Quality of Life at the End of Life and the Quality of the Dying Process? Gerontologist, October 1, 2002; 42(90003): 54 - 67. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. Walshe and C. Harrington Regulation of Nursing Facilities in the United States: An Analysis of Resources and Performance of State Survey Agencies Gerontologist, August 1, 2002; 42(4): 475 - 487. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. Mezey and T. Fulmer The Future History of Gerontological Nursing J. Gerontol. A Biol. Sci. Med. Sci., July 1, 2002; 57(7): M438 - 441. [Abstract] [Full Text]
|
|
|
|
|
|
N. G. Castle Administrator Turnover and Quality of Care in Nursing Homes Gerontologist, December 1, 2001; 41(6): 757 - 767. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. T. Mullan and C. Harrington Nursing Home Deficiencies in the United States: A Confirmatory Factor Analysis Research on Aging, September 1, 2001; 23(5): 503 - 531. [Abstract] [PDF]
|
|
|
|
 |
|
 C. Harrington Regulating nursing homes: Residential nursing facilities in the United States BMJ, September 1, 2001; 323(7311): 507 - 510. [Full Text] [PDF]
|
|
|
|
 |
|
 C. Harrington Nursing Facility Staffing Policy: A Case Study for Political Change Policy Politics Nursing Practice, May 1, 2001; 2(2): 117 - 127. [Abstract] [PDF]
|
|
| ||||||||||||||||||||||||||||||||
| HOME | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|---|
