Quality of care and long-term care administrators' education: does it make a difference?

JanuaryYMarch & 2015

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Quality of care and long-term care administrators’ education: Does it make a difference? Nicholas G. Castle Jessica Furnier Jamie C. Ferguson-Rome Douglas Olson Jennifer Johs-Artisensi Background: Long-term care administrators (administrators) can influence the care nursing home residents receive. However, little research has examined what factors of administrators are associated with how they influence care. Purposes: In this research, the association between administrators’ education and quality of nursing home care is examined. In addition, the association between state educational and training requirements and quality of nursing home care is examined. Methodology: Information collected from 3,941 administrators was matched with secondary data, including Nursing Home Compare; the Online Survey, Certification and Reporting data; and the Area Resource File. The quality indicators restraint use, catheter use, inadequate pain management, low-risk residents with pressure ulcers, and high-risk residents with pressure ulcers were examined. Findings: Positive, statistically significant associations were found between the education level of administrators and all five quality indicators. Likewise, positive statistically significant associations were found between state educational requirements and state training requirements and the five quality indicators. Practice Implications: If these associations hold true, then promoting further educational attainment of individual administrators may influence quality of care. The state educational requirements and training requirements for administrators’ licensure may represent an additional means of influencing the quality of care in nursing homes.

Key words: administration, nursing homes, quality Nicholas G. Castle, PhD, is Professor, Graduate School of Public Health, University of Pittsburgh, Pennsylvania. E-mail: [email protected]. Jessica Furnier, MHA, is Project Manager, University of Pittsburgh Medical Center, Pennsylvania. Jamie C. Ferguson-Rome, MHA, is Faculty Instructor and Project Director, Graduate School of Public Health, University of Pittsburgh, Pennsylvania. Douglas Olson, PhD, MBA, FACHCA, NHA, is Visiting Faculty, George Washington University; Professor, Health Care Administration Program; and Director, Center for Health Administration and Aging Services Excellence, University of Wisconsin-Eau Claire. Jennifer Johs-Artisensi, MPH, PhD, is Associate Professor & Program Director, Health Care Administration, Department of Management and Marketing, College of Business, University of Wisconsin-Eau Claire. The authors have disclosed that they have no significant relationship with, or financial interest in, any commercial companies pertaining to this article. DOI: 10.1097/HMR.0000000000000007 Health Care Manage Rev, 2015, 40(1), 35Y45 Copyright B 2015 Wolters Kluwer Health | Lippincott Williams & Wilkins

Copyright © 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.

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Health Care Management Review

T

he effectiveness, quality of care, and quality of life in nursing homes are influenced by long-term care administrators (administrators; Wing & Salsber, 2001). However, information on what administrative factors are associated with the ability to influence nursing home care is scant. In this research, the association between longterm care administrators’ education and state licensure requirements (with respect to education and training) with the quality of nursing home care is examined. Nursing home daily operations are traditionally managed by the top management team, which includes the administrator, the director of nursing (DON), key department heads, and depending on the organization, a medical director. Dana and Olson (2007) discussed and reviewed the importance and uniqueness of the significant role of leadership in long-term care. The administrator typically directs his or her leadership team and performs a variety of daily tasks including budgeting, guiding staff education and training, assuring compliance with regulations, and paying attention to market and community needs. A comprehensive list of these duties is provided by Castle, Ferguson, and Hughes (2009). A growing body of research is showing the important influence administrators have on nursing home operations, including quality of care. For example, turnover of administrators is associated with the turnover of caregivers (Castle, 2005). Researchers have also found that inadequate licensure of long-term care administrators (reflected in administrative deficiency citations in certification surveys) was related to poorer resident outcomes (Castle & Longest, 2006). Thus, administrators may potentially influence the care nursing home residents receive (Castle et al., 2009). Little research has examined the association between administrators’ education and training with respect to quality of nursing home care. Key educational and training standards come from each state’s mandated licensing requirements. A second educational component influencing care is the actual educational level achieved by each administrator.

Licensing Requirements Each state has different minimum training and educational requirements in place since federal regulations, passed in 1967, mandated that administrators be licensed by their state (Center for Health Workforce Studies, 2004; National Council of State Boards of Nursing, 2008). A National Advisory Council created recommendations for states on governance, education, examination requirements, and training and experience requirements (McCoy, 1971). These recommendations were not mandatory but rather were intended to provide guidance to individual state legislators (McCoy, 1971). The educational requirements of the National Advisory Council included administrator applicants having a bacca-


laureate degree (to be effective after January 1, 1980) increasing to a master’s degree (to be effective after January 1, 1985; McCoy, 1971). With respect to training, the National Advisory Council recommended all applicants serve for 1 year as an administrator-in-training (AIT; McCoy, 1971). Most states did not (and still do not) implement these licensure regulations within National Advisory Council’s recommendations (Center for Health Workforce Studies, 2004; National Council of State Boards of Nursing, 2008). For example, Alabama uses an associate’s degree as an educational requirement, and Iowa uses 720 hours as a requirement for the AIT. Overall, 26 states use a baccalaureate degree as an educational requirement (none use a master’s degree), and 15 use 1 year as a requirement for the AIT (most of the remainder use less than 1 year). The Omnibus Budget Reconciliation Act (1987) gave the Department of Health and Human Services the task of determining and setting minimum administrator licensing standards. There are, however, still no federal standards in place, leaving individual states the responsibility and flexibility of determining their own licensing criteria (Center for Health Workforce Studies, 2004; National Council of State Boards of Nursing, 2008). Of note, these are one of the few Omnibus Budget Reconciliation Act 1987 tasks yet to be implemented. Potential administrators in a majority of the states are required to complete an approved specialized education program, complete a certain amount of field experience (i.e., AIT program), and pass an examination. Many states require a candidate to pass not only a federal licensing examination (conducted through the National Association of Long-Term Care Administrator Boards [NAB]) but also an individual state licensing exam. Administrators have a multitude of daily responsibilities, including compliance and regulatory oversight (i.e., certification), budgeting concerns (i.e., reimbursement rates), staffing issues (i.e., turnover), and resident care and satisfaction (Castle et al., 2009). When working in these diverse situations involved with nursing home management, training and educational requirements are likely important. We believe that more rigorous training and increased educational requirements provide administrators with a better platform to provide higher quality care for residents and thus hypothesize the following: (H1) Administrators working in states with high educational requirements will be associated with better quality of care. (H2) Administrators working in states with more advanced training requirements will be associated with better quality of care.

Educational Background A study conducted by Decker and Castle (2011) examined the 2004 National Nursing Home Survey and found that 32% of administrators had received a graduate degree, 50%


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Administrators’ Education

had a bachelor’s degree, and 17% had a high school or associate degree. However, the association between education and quality of care was not examined in this study. Forbes-Thompson, Gajewski, Scott-Cawiezell, and Dunton (2006) questioned the preparation of long-term care administrators to meet the challenges of leadership roles in nursing homes after finding that three fourths of administrators had only a baccalaureate or less. Aroian, Patsdaughter, and Wyszynski (2000) have also noted the need for further educational preparation among administrators because of the growing complexity of care provided in nursing homes. Castle and Fogel (2002) found better resident outcomes in nursing homes where the administrators had a professional affiliation (i.e., with the American College of Health Care Administrators) compared with nursing homes where administrators were not affiliated with a professional association. Higher levels of education, in general, have also been associated with increased performance (Chevalier, Harmon, Walker, & Zhu, 2004). Results of some hospital-based studies have suggested that baccalaureate-prepared nurses are more likely to show professional behaviors important to patient safety (Aiken, Clarke, Cheung, Sloane, & Silber, 2003). This includes additional problem solving, performance of complex functions, and effective communication, although outcomes were not examined in this study (Aiken et al., 2003). We believe that a more advanced educational background provides administrators with a better opportunity to provide quality care for residents and thus hypothesize the following: (H3) Administrators with more advanced educational backgrounds will be associated with better quality of care. Conceptual framework. The conceptual framework that guided this study is based on work by Donabedian (2003). Donabedian describes health care organizations in terms of structure, process, and outcomes. Structure is defined as the conditions under which care is provided (materials, human resources, organizational characteristics). Process includes the activities that are done to provide health care. Outcomes are results or changes that can be attributed to health care. This comprehensive framework has been widely applied in health systems research, including several nursing home studies (e.g., Decker, 2008). The structural element we focus on in this research is the human resources of administrators, specifically education and training. The benefits of education are also emphasized in human capital theories. Higher education is believed to promote better use of information, skills to manage, empowerment, and improved self-efficacy (Sweetland, 1996). In our case, the conceptual framework of structure, process, and outcomes was used as we were interested in the relationship between education (including both state requirements and actual educational levels) and outcomes.

Methods Sample and Data Several sources were used as data in this research. Educational and training requirements for administrator licensure were obtained through an online search including state regulatory and licensing agencies and the NAB (2007; www.nabweb.org). A rank score was developed for each state (described below) based on the outcomes of the searches. The findings were linked with nursing home quality information representing five measures (physical restraint use, pain management, catheter use, low-risk residents with pressure ulcers, and high-risk residents with pressure ulcers) coming from a federal report card (Nursing Home Compare) and with demographic information from primary data collected from 3,941 administrators. In addition, characteristics of the nursing homes came from the Online Survey, Certification and Reporting (OSCAR) data, whereas local market characteristics came from the Area Resource File (ARF).

Web Search of Licensure Requirements An online search for each state was conducted and consisted of searching for the rules and statutes regarding administrator qualifications and licensure. Individual state-specific information was found through the NAB. In addition, some states have individual advisory boards that oversee licensure. When available, information from state advisory boards was utilized as well.

Administrator Questionnaire Quality-of-care outcomes can be influenced by demographics and job skills; therefore, primary data collection from long-term care administrators was examined. The primary data were collected through a mail questionnaire. The survey was sent to 6,000 administrators, with the mailing sample created using information from the OSCAR data (described further below). For each survey, the OSCAR facility identification number was also retained, so that the primary data could be matched with the OSCAR and facility characteristics of the sample. The sample was randomly chosen from all nursing homes listed in the OSCAR, which includes facilities from the 48 contiguous states. Small nursing homes (G30 beds) and hospital-based facilities were excluded from the survey sample. These facilities were excluded because small nursing homes are less likely to have full-time administrators and hospital-based facilities are considered to be uncharacteristic of the nursing home industry (Castle, Engberg, & Men, 2007).


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Demographic and job skill information items included in the questionnaire were gender, age, race, education, tenure in the nursing home, tenure as an administrator, number of long-term care administrators working in the facility in the past 3 years, and number of DONs working in the facility in the past 3 years. These items were included because prior research has shown that they provide descriptive information on top management working in the nursing homes (see review by Castle et al., 2009). Moreover, tenure in the nursing home, tenure as an administrator, and number of long-term care administrators working in the facility in the past 3 years are associated with quality of care (Castle et al., 2009). In addition, Medicaid reimbursement rates are included in the analyses (as external factors). This information for each state came from primary data collected by the authors. This additional data collection included using a Web-based survey sent to state Medicaid departments (and by using follow-up telephone calls). This generally followed a process used and published by others (e.g., Grabowski, Feng, Intrator, & Mor, 2004) and is not described here.


in the United States. The inspections survey records of many characteristics of the nursing home and aggregate characteristics of residents, which are commonly used as a secondary source of nursing home characteristics (e.g., Decker, 2008). The primary data collected were merged with the OSCAR to obtain nursing home information (i.e., profit status and occupancy rates). The specific variables used in this analysis are considered to be the most reliable. The reliability of some of the data, such as staffing levels, is subject to debate (e.g., Kash, Hawes, & Phillips, 2007), but these variables were not used from this data source (as they were included in the primary data collection). ARF. Local economic data on employment and nursing home market concentration were used from the ARF. The ARF contains information on more than 6,000 health, social, and economic indicators for all U.S. counties (extensive details regarding this data can be found at www.hrsa.gov).

Analytic Models

Nursing home compare. As quality indicators for the analyses, some of the quality measures reported on the Nursing Home Compare Web site were used (www.Medicare .gov/NHCompare). Nursing Home Compare is a Web-based report card providing information for all Medicare- and/or Medicaid-certified nursing homes. The quality measures reported are advantageous in several respects. They were subject to extensive testing, are derived from the Minimum Data Set, are readily available, and represent measures relevant to both consumers and providers (Abt Associates, 2004). Moreover, these quality measures are becoming commonly used in empirical research (e.g., Alexander, 2008). Nineteen quality measures were available at the time of this study (i.e., 2010). Five of these quality measures were examined in this investigation (restraint use, catheter use, inadequate pain management, low-risk residents with pressure ulcers, and high-risk residents with pressure ulcers). Prior research identified these five quality measures to be time sensitive (Castle & Engberg, 2008). That is, these quality measures can change quickly, whereas the other quality measures may develop over a longer period (e.g., need for help with daily activities). This was important because the tenure of some administrators may be short; thus, these five measures may better capture the potential influence of administrators on outcomes.

Table 1 lists the descriptive statistics of the quality, staffing, facility, and market variables used in this analysis. The variables included in the analyses were derived from the prior research in this area that has examined nursing home quality. For the most part, the definitions of the variables used are self-evident. However, more detail is provided for the state licensure (education and training) rank scores. To compare the state education and training licensure requirements, a score was assigned to each state for each measure. Scores for education and training were assigned to each state based on the minimum level of education required and the duration of training, respectively. For educational requirements, a score of 1 was assigned to states requiring a minimum of a high school diploma. A score of 2 was assigned to states requiring an associate’s degree or up to 60 college credits or semester hours. States requiring a bachelor’s degree received a score of 3. Scores for training were determined by ordering the states with the lowest number of required hours (e.g., North Carolina, 120 hours) to the states with the highest number of required hours (e.g., Maryland, 2,080 hours). The states were then grouped into low, medium, and high training requirements. The high group included all states with a 1-year (or more) training requirement, as this was recommended by the National Advisory Council. The low group included all states with a 6-month (or less) as training requirements. A state-by-state list of educational and training requirements is available from the authors.

OSCAR. The OSCAR data are collected as part of state and federal nursing home inspections. Facilities that accept residents with Medicare and/or Medicaid payments are surveyed annually. This includes most (i.e., 97%) nursing homes

Analyses. Descriptive statistics for the background variables collected are presented (e.g., demographics and job characteristics). The OSCAR and ARF were also used to examine the characteristics of the facility sample (i.e.,

Secondary Data


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Table 1

Descriptive statistics of nursing homes Variables (operational definition of variable)

Mean or percent

Dependent variables Quality measures Physical restraint use (percent of residents with physical restraint use) 11.3% With moderate to severe pain (percent of residents with moderate-to-severe pain) 4.9% Low-risk residents with pressure sores (percent of low-risk residents with pressure sores) 5.4% High-risk residents with pressure sores (percent of high-risk residents with pressure sores) 5.1% Had a catheter inserted and left in bladder (percent of residents that had a catheter 6.5% inserted and left in bladder) Independent variables 2.2 Education rank (A rank of 1 was assigned to states requiring a minimum of a high school diploma or below. a rank of 2 was assigned to states requiring an associate’s degree or up to 60 credits or semester hours. States requiring a bachelor’s degree or more received a rank of 3.) 2.1 Training rank (The states ordered from the lowest number of required hours to the states with the highest number of required hours. The states were then ranked into low, medium, and high training requirements.) Nurse aide staffing (FTE nurse aides per resident) 0.32 LPN staffing (FTE LPNs per resident) 0.12 RN staffing (FTE RNs per resident) 0.07 0.12 Resident case mix (The score for three ADLs [eating, toileting, and transferring] constructed by giving a score of 1 for low assistance, 2 for moderate assistance, and 3 for high need for assistance summed for each ADL.) Psychiatric condition (proportion of residents diagnosed with psychiatric conditions) 0.16 Dementia (proportion of residents diagnosed with dementia [defined as an ICD-9 code 0.43 indicating a specific dementing illness]) 1.59 Quality-of-care deficiency citations (deficiency citations representing the sum of 19 different deficiency citations [F-tags are 309, 310, 311, 312, 314, 316, 317, 318, 319, 321, 322, 323, 324, 325, 328, 329, 330, 333, 353]; within-state rankings (percentiles) used for analyses). J, K, or L deficiency citations (any deficiency citation at J, K, or L level [see Table 2]; 0.04 within-state rankings [percentiles] used for the analyses)a Medicaid resident occupancy (percent of residents with Medicaid as payor) 63% Size (number of beds in the nursing home) 109 For-profit ownership (for-profit or not-for-profit ownership) 65% Chain member (whether member of a nursing home chain or not) 56% Occupancy rate (percent of beds occupied by residents) 83% Medicaid reimbursement rate (the state average daily [i.e., per diem] payment rate 132.4 for Medicaid residents ($); the rates are adjusted to constant 2,000 dollars using the Consumer Price Index)b 2,076 Competition (Herfindahl Index; each facility’s percentage share of beds in the county/squared market shares of all facilities in the county)c 146 Elderly per square mile (average number of elderly per square mile [aged 65 years and above] in the county)c 27,324 Per capita income (average income [$] for all county residents)c

Standard deviation

8.1 5.2 3.3 4.9 7.1

V V 0.14 0.09 0.11 0.26

0.17 0.20 1.84

0.38 26 73 V V 17 31.62

2,384 419 7,966

Note. N = 3,913 nursing homes. Variables primarily came from the Online Survey, Certification, And Reporting (OSCAR); with elderly in the county and per capita income coming from the Area Resource File (ARF); and, Medicaid reimbursement rates coming from primary data. The figures presented are from the 2010 data. FTE = full-time equivalent; LTC = long-term care; ADLs = activities of daily living; RNs = registered nurses; LPNs = licensed practical nurses. a

Variation in the use of nursing home deficiency citations is known to occur from state to state. Using the percentile distribution of deficiency citations within each state gives to a more consistent comparison of deficiency citations across states.

b

The unit of analysis for this variable is the state (unless otherwise noted, for all other variables the facility is the unit of analysis).

c

The unit of analysis for this variable is the county.


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sample representativeness and nonresponse). Characteristics of the facility sample are also presented. We examined the level of collinearity among the independent variables and multicollinearity, by using the variance inflation factor test (the correlation between the variables was generally low). The quality measures are counts of specific negative events per nursing home, each divided by the number of residents at risk for that negative event expressed as a percent. For many nursing homes in the sample, these counts were low. Negative binomial regression is a standard method commonly used to examine data dispersed in this way (Hilbe, 2011). Thus, negative binomial regression (Hilbe, 2011) was used in the multivariate analyses. The negative binomial coefficients are reported in incidence rate ratio (IRR) form. An IRR is similar to an odds ratio, that is, estimates greater than one represent a positive association between the explanatory variable and the outcome. In our case, high values of the quality measures are indicative of poor quality, and IRRs less than one are representative of better quality (although we are careful to note that these analyses should not be interpreted as causal). The education variable (i.e., a variable of interest) was a categorical variable with a bachelor’s degree (i.e., what appears to be the norm) as the reference category. Thus, in the analyses, other levels of education are compared with the bachelor’s degree category. The state educational requirement (i.e., a second variable of interest) was a categorical variable with a bachelor’s degree as the reference category. Therefore, in the analyses, other levels of state educational requirements are compared with the state bachelor’s degree category. The state training requirements (i.e., the third variable of interest) was a categorical variable with high (i.e., what was recommended by the National Advisory Council) as the reference category. Thus, in the analyses, other categories of state training requirements (i.e., low and medium) are compared with this category. Possible correlations of the quality measures within markets exist. This can bias the standard errors of the estimates. To account for this, the HuberYWhite sandwich estimator (i.e., robust standard errors) clustered by county was used for all of the multivariate analyses (Hastie, Tibshirani, & Friedman, 2001).

Results Three thousand nine hundred thirteen long-term care administrators returned the questionnaire, giving a response rate of 65%. No significant differences on facility characteristics (i.e., bed size, ownership, chain membership, and private-pay census) or market characteristics (e.g., competition) existed for respondent compared with nonrespondent facilities (results not shown). In addition, no significant differences between the nursing homes participating in this study and the national statistics were identified (results not


Table 2

Descriptive statistics of long-term care administrators Mean or Standard percent deviation Demographic characteristics Male Age (years) Race (% Caucasian) Education High school diploma Associate degree Baccalaureate degree Master’s or higher degree Employment characteristics Years of experience as an administrator Tenure (months) Turnover of administrators (past 3 years) Turnover of directors of nursing (past 3 years) Member of a professional society

78% 51.2 67%

V 10.1 V

1.9% 9.7% 55.3% 33.2%

V V V

12.2

7.4

31 3.3

15.0 2.5

3.7

2.6

83%

V

Note. N = 3,913 administrators.

shown). Table 1 presents the descriptive characteristics of the participating nursing homes. The background variables describing the participating administrators are shown in Table 2. Administrators were most likely to be men (i.e., 78%) and on average of 51 years old, with 67% Caucasian. Table 2 also shows a breakdown of the education level of administrators: 1.9% have a high school diploma, 9.7% have an associate’s degree, 55.3% have a bachelor’s degree, and 33.2% reported having a master’s degree or higher. Table 3 displays the regression results examining the effects of state educational requirements of administrators on the five quality indicators. Positive, statistically significant associations were found between educational requirements and all five quality indicators. Using column one as an example, facilities in states with low educational requirements for licensure are likely to have levels of physical restraint use of 23% higher in comparison with those with high educational requirements (i.e., a bachelor’s degree). In general, the findings support the hypothesis that administrators working in states with high educational requirements will be associated with better quality of care (i.e., H1). Table 3 displays the regression results examining the effects of state training requirements of administrators on the five quality indicators. Positive, statistically significant associations were found between training requirements and all five quality indicators. For example, facilities in states


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with low training requirements (G6 months) for licensure are likely to have levels of physical restraint use of 24% higher in comparison with those with high training requirements (i.e., 1 year or more). In general, the findings support the hypothesis that administrators working in states with high training requirements will be associated with better quality of care (i.e., H2). Table 3 also displays the regression results examining the effects of actual levels of education of administrators on the five quality indicators. Positive, statistically significant associations were found between the education of administrators and all five quality indicators. This shows that, for example, facilities in which the administrator has a high school diploma have physical restraint use scores of 38% higher than facilities in which the administrator has a bachelor’s degree. In general, the findings support the hypothesis that administrators with more advanced educational backgrounds will be associated with better quality of care (i.e., H3).

Discussion As a 2001 report from the Institute of Medicine pointed out, nursing home quality is low. In 2008, approximately 25% of all facilities received deficiency citations for causing harm or immediate jeopardy to residents (Harrington, Carrillo, & Blank, 2009). This low quality has persisted, and achieving quality improvements has proven difficult. Long-term care administrators are instrumental in achieving quality improvements in nursing homes. The goals of the recently passed Patient Protection and Affordable Care Act are to expand coverage, improve quality, and lower the costs of providing health care (Ernst & Young, 2010). One of the means by which it aims to accomplish the goal of improving quality is through providing grants to enhance the education of the health care workforce. However, it focuses entirely upon direct care providers, individuals with direct patient contact (U.S. Senate, 2010). The findings of this study suggest that this approach may be insufficient in addressing the issue of nursing home quality. More educational and training opportunities may be needed, for as Siegel, Leo, Young, and Castle (in press) have shown currently, 30% of administrators report that they were not prepared for their first administrator position. Our results indicate a positive association between educational level of administrators and quality of care on all five quality indicators. Thus, it is expected that quality of care could improve if the educational background of longterm care administrators was at a higher level (if these crosssectional findings were replicated in causal analyses). Point estimates for this association are large. For example, in facilities in which the administrator has only a high school diploma, the likelihood is that physical restraint use could potentially decrease by about one third if the facility were

to employ an administrator with a bachelor’s degree. There is a likelihood that physical restraint use would decrease by an even greater amount (about three eights) if the facility were to employ an administrator with a master’s degree (not shown). It is important to note, however, that it is possible that facilities with better quality attract administrators with a higher level of education, so although the associations identified are strong, our findings do not show a causal relationship. Our results also indicate positive associations between state minimum educational requirements and quality of care on all five measures. State minimum educational requirements are associated with all of the quality indicators. However, the IRRs of these associations are generally smaller than those identified for the individual administrator educational levels discussed above. Previous research in quality improvement has shown that poor quality is often the result of broken processes. Although direct care providers have a significant role in the delivery of quality care, administrators oversee the processes through which care is delivered and thus impact quality as well. A more comprehensive approach to quality improvement in nursing homes may be through policy that also targets the qualifications and training of long-term care administrators. With the caveat that the analyses are cross-sectional, our data suggest that significant improvements in quality could be realized by (1) raising the minimum educational level for administrators from a high school diploma to at least an associate’s degree or, better still, a bachelor’s degree and (2) raising the minimum state training requirements for administrators to 1 year. It is important to note, however, that increasing educational standards and training requirements may hinder nursing homes in the short term. Nursing homes have traditionally had difficulties retaining staff. Increasing minimum educational and training prerequisites may decrease the applicant pool, further exacerbating staffing difficulties. Singh (1997) asserted that ‘‘licensure policies directly impact the supply of administrators, and have a strong influence on the level of training and skills administrators possess in the relevant domains of practice’’ (p. 6). Nevertheless, the longterm impact of improved quality that may potentially result from increased educational and training licensure requirements outweighs any possible short-term disadvantages.

Limitations and Suggestions for Future Research A limitation of this study is that, with the cross-sectional data used, it is not possible to disentangle causal direction. We cannot infer that high administrator education or training necessarily influences better quality of care. A plausible argument could be made that the causal direction may actually occur in the opposite direction. High- (better) quality nursing homes hire better educated/trained administrators; moreover, better educated/trained administrators may want


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Table 3

Regression results examining the association of long-term care administrators level of education and state licensure requirements on nursing home quality indicators

Variables STATE LICENSURE Education Rank Low (GBachelor’s Degree) (vs. Bachelor’s Degree) Medium (GBachelor’s Degree) (vs. Bachelor’s Degree) Training Rank Low (G6 months) (vs. One-year or more) Medium (6 to G12 months) (vs. one-year or more) ADMINISTRATOR EDUCATION High School Diploma (vs. Bachelor’s Degree) Associate’s Degree (vs. Bachelor’s Degree) Master’s Degree (or higher) (vs. Bachelor’s Degree) Pseudo-R2

(1)

(2)

Physical Restraint Use Incident Rate Ratios [95%CI]

Inadequate Pain Management Incident Rate Ratios [95%CI]

1.23** [1.02Y1.25] 1.14*** [1.01Y1.19]

1.22** [1.05Y1.26] 1.16** [1.04Y1.18]

1.24*** [1.04Y1.21] 1.16** [1.01Y1.20]

1.15** [1.02Y1.18] 1.18** [1.03Y1.21]

1.38*** [1.05Y1.40] 1.24* [1.02Y1.27] 0.87** [0.77Y0.98] 0.24

1.34** [1.03Y1.37] 1.16*** [1.09Y1.21] 0.90* [0.85Y0.98] 0.26

Notes: The Quality Measures are counts of specific negative events per nursing home, each divided by the number of residents at risk for that negative event and recorded as a percent. Thus, the Quality Measures are recorded as a percent (i.e., 0Y100%) so that higher percent scores represent better quality. Pseudo R2 = McFadden’s pseudo R-squared. It is calculated as 1 - ll[model] / ll[null]. Regression results for administrator education and state licensure are presented for parsimony; all variables in Table 2 are included in each model. The regression models also include as controls: -Top Management Factors (tenure, turnover (past 3 years), age, member of a professional association). -Staffing Factors (nurse aide staffing (FTEs per resident), LPN staffing (FTEs per resident), RN staffing (FTEs per resident). -Resident Factors (resident case-mix, psychiatric condition, dementia). -Organizational Factors (Medicaid resident occupancy, size, ownership, chain member, occupancy rate). -External Factors (Medicaid reimbursement rate, competition, elderly in county, per capita income). CI = confidence interval. *p G .05. **p G .01. ***p G .001.

to work in high-quality facilities. Thus, our results should be interpreted as associations, and longitudinal data should be used in the future to determine the causal direction of the effects. The analyses do not account for potential migratory effects. Survey respondents are not necessarily originally licensed in the state in which they are currently practicing. Although long-term care administrators must obtain licensure in the state in which they are practicing, many states have varying reciprocity agreements. These agreements enable administrators previously licensed in a different state to obtain licensure through an alternate process, which does not necessarily include meeting the same minimum educational requirements as required for new administrators. An additional limitation is that the minimum level of training required in many states is linked to the level of edu-

cation achieved by the applicant. For example, in Alabama, an applicant with a bachelor’s degree in health care administration must complete 500 hours of training, whereas an applicant with a master’s degree in the same field must only complete 200 hours. Also, some states allow previous work experience to substitute for training programs. Response bias may have occurred in the survey of longterm care administrators. Individuals with higher educational levels have exhibited behavior suggesting a greater willingness to go beyond the minimum. Thus, these individuals may be more likely to respond to an optional survey. In many nursing homes, top management also includes a DON and other key leaders. All aspects of this study could be repeated with a focus on DON characteristics. Future studies could involve incorporating additional qualification


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(3)

(4)

(5)

Low-Risk Residents With Pressure Sores Incident Rate Ratios [95%CI]

High-Risk Residents With Pressure Sores Incident Rate Ratios [95%CI]

Residents With Catheters Incident Rate Ratios [95%CI]

1.28*** [1.04Y1.31] 1.17* [1.03Y1.21]

1.19** [1.03Y1.24] 1.13** [1.02Y1.19]

1.21** [1.02Y1.25] 1.22* [1.02Y1.30]

1.19*** [1.02Y1.22] 1.09 [0.98Y1.15]

1.20** [1.03Y1.23] 1.14 [0.99Y1.21]

1.18** [1.02Y1.22] 1.15* [1.01Y1.20]

1.26** [1.02Y1.31] 1.19** [1.01Y1.23] 0.85* [0.81Y0.97] 0.27

1.29** [1.05Y1.35] 1.18** [1.03Y1.25] 0.83** [0.80Y0.95] 0.28

1.28** [1.03Y1.32] 1.14* [1.02Y1.17] 0.81** [0.72Y0.96] 0.22

requirements into the scoring rubric, such as the minimum age requirements for taking the licensure exam and requirements (such as passing scores) of the state examinations. In addition, states have varying requirements for maintaining licensure (such as continuing education). The stringency of continuing educational requirements and their impact on quality could be evaluated. This research shows the importance of education. However, we do not examine whether the diversity or quality of the educational curriculums have any impact. Degrees from different disciplines (e.g., business administration vs. biology) may influence quality of care. In addition, skills learned outside a traditional educational setting may also be important. There are many skills that can be learned or finessed through mentorship with successful leaders. Mentoring has been said to be ‘‘the most powerful educational

tool we can provide our early careerists’’ (Kriegel, 2012, p. 63). Mentoring and training programs outside the formal classroom setting teach administrators how to tackle the unknown and how to handle a multitude of events. This type of relationship between a senior leader and an early leader can be rewarding to both parties by reinforcing skills, improving confidence, and gaining responsibility (Kriegel, 2012). A national mentoring initiative based on this premise was recently launched by the American College of Health Care Administrators (www.achca.org/index.php/mentoring).

Conclusion Long-term care services have seen an increased demand and are expecting an even higher demand now that the baby


44


boomers are coming into retirement age. The United States has approximately 16,000 licensed nursing homes (Centers for Disease Control and Prevention, National Center for Health Statistics, 2004). About 3.5 million people are cared for in nursing homes annually (Centers for Disease Control and Prevention, National Center for Health Statistics, 2004). The number of people who seek care in nursing homes and the projected increased demand on nursing homes can put a strain on direct care workers; however, top management also experiences the added strain. Our findings add an important factor to the body of literature examining the relationship between long-term care administrator educational characteristics and licensure requirements and quality of care. We found support for our hypotheses. Educational level of the administrator is positively associated with better quality of care. The educational and training standards for administrator licensure requirements are also positively associated with better quality of care. Thus, providing tentative evidence that increasing the degree of rigor of the requirements for administrator licensure, particularly regarding education and training, may be used as low-cost policy options will help improve the quality of care in nursing homes. References Abt Associates. (2004). National nursing home quality measures: User’s manual (v1.2). Cambridge, MA: Author. Available at http://www.cms.hhs.gov/quality/nhqi/ Aiken, L. H., Clarke, S. P., Cheung, R. B., Sloane, D. M., & Silber, J. H. (2003). Educational levels of hospital nurses and surgical patient mortality. Journal of the American Medical Association, 290(12), 1617Y1623. Alexander, G. L. (2008). An analysis of nursing home quality measures and staffing. Quality Management in Health Care, 17(3), 242Y251. Aroian, J. F., Patsdaughter, C. A., & Wyszynski, M. E. (2000). DONs in long-term care facilities: Contemporary roles, current credentials, and educational needs. Nursing Economics, 18, 149Y156. Castle, N. G. (2005). Turnover begets turnover. The Gerontologist, 45, 186Y195. Castle, N. G., & Engberg, J. (2008). Further examination of the influence of caregiver staffing levels on nursing home quality. The Gerontologist, 48, 464Y476. Castle, N. G., Engberg, J., & Men, A. (2007). Nursing home staff turnover: Impact on nursing home compare quality measures. The Gerontologist, 47(5), 650Y661. Castle, N. G., Ferguson, J. C., & Hughes, K. (2009). Humanism in nursing homes: The impact of top management. Journal of Health and Human Services Administration, 31(4), 483Y516. Castle, N. G., & Fogel, B. (2002). Professional association membership by nursing facility administrators and quality of care? Health Care Management Review, 27(2), 7Y17. Castle, N. G., & Longest, B. B. (2006). Administrative deficiency citations and quality of care in nursing homes. Health Services Management Research, 19, 144Y152.


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