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Intended and Unintended Consequences of Minimum Staffing Standards for Nursing Homes



Staffing is the dominant input in the production of nursing home services. Because of concerns about understaffing in many US nursing homes, a number of states have adopted minimum staffing standards. Focusing on policy changes in California and Ohio, this paper examined the effects of minimum nursing hours per resident day regulations on nursing home staffing levels and care quality. Panel data analyses of facility-level nursing inputs and quality revealed that minimum staffing standards increased total nursing hours per resident day by 5% on average. However, because the minimum staffing standards treated all direct care staff uniformly and ignored indirect care staff, the regulation had the unintended consequences of both lowering the direct care nursing skill mix (i.e., fewer professional nurses relative to nurse aides) and reducing the absolute level of indirect care staff. Overall, the staffing regulations led to a reduction in severe deficiency citations and improvement in certain health conditions that required intensive nursing care. (This is the peer reviewed version of the following article: Health Econ. 2015 Jul; 24(7):822-39, which has been published in final form at This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.)
Nursing Home Staffing Standards
Intended and Unintended Consequences of Minimum Staffing Standards for Nursing
Min M. Chen
College of Business Administration
Florida International University
11200 SW 8th Street, Miami, FL 33199
David C. Grabowski
Harvard Medical School
Department of Health Care Policy
180 Longwood Avenue, Boston, MA 02115-5899
Staffing is the dominant input in the production of nursing home services. Because of
concerns about understaffing in many U.S. nursing homes, a number of states have adopted
minimum staffing standards. Focusing on policy changes in California and Ohio, this paper
examined the effects of minimum nursing hours per resident day regulations on nursing home
staffing levels and care quality. Panel data analyses of facility-level nursing inputs and quality
revealed that minimum staffing standards increased total nursing hours per resident day by 5%
on average. However, because the minimum staffing standards treated all direct care staff
uniformly and ignored indirect care staff, the regulation had the unintended consequences of
both lowering the direct care nursing skill mix (i.e., fewer professional nurses relative to nurse
aides) and reducing the absolute level of indirect care staff. Overall, the staffing regulations led
to a reduction in severe deficiency citations and improvement in certain health conditions that
required intensive nursing care.
JEL Classification: I11, I18, L51, L88
Keywords: Staffing, Health care quality, Nursing input, Nursing home, Regulation
Email: Phone: (305) 348-4201. Fax: 1-619-924-8356.
Nursing Home Staffing Standards
Minimum nurse staffing regulations in hospitals, nursing homes, and home health
agencies have become more common in recent years. With the objective to increase the quality
of health care services, these regulations set a minimum ratio of staff to patients or minimum
nursing hours per patient day (Harrington and Carrillo, 1999; Spetz, 2001; GAO, 2003).
However, no consensus exists among researchers, medical professionals, and policy makers on
the effectiveness of such regulations. The debate is of particular relevance to nursing homes,
given that staffing is the dominant input in the production of nursing home services.
Proponents emphasize the legislation’s potential to improve the quality of care
provided to residents. They argue that the current nurse staffing levels are so low as to jeopardize
the well-being of residents. Some evidence supports this argument for nursing homes, but very
little evidence suggests that regulations improve quality in hospitals (Donaldson and Shapiro,
2010). Indeed, two separate literature reviews have concluded that higher nursing home staffing
is associated with better quality of care (Bostick et al., 2006; Collier and Harrington, 2008).
Nursing homes with low staffing levels, especially low registered nurse (RN) levels, tend to have
higher rates of poor resident outcomes such as pressure ulcers, catheterization, lost ability to
perform daily living activities, and depression. Staffing standards may also improve working
conditions, which would increase job satisfaction and reduce nursing turnover and burnout.
Critics of minimum staffing standards, on the other hand, have raised several issues
with these policies. First, economists are generally against policies that do not allow providers to
Nursing Home Staffing Standards
choose the most efficient mix of inputs in the production of care (Buchan, 2005; Buerhaus, et al.,
2009). If a nursing home can more efficiently produce particular outcomes with fewer staff, they
should not be required to hire more staff to meet a minimum standard. Second, because prices in
health care are often set administratively, providers cannot necessarily raise output prices to
account for the increased labor costs under minimum staffing standards (Walshe, 2001; Gaynor
2006). As such, these regulations may cause providers to substitute away from other inputs to
care in order to pay the higher labor costs. Importantly, minimum staffing standards may take
resources away from other areas such as indirect care staff (e.g., activities) or facility
infrastructure (Bowblis and Hyer, 2013). A final criticism of these policies is that they are often
not adequately enforced - because of the cost burden the regulation places on providers and the
severe nursing shortage in many local markets (GAO, 1999a; Wiener, 2003). For example, using
cost report data from the California Office of Statewide Health Planning, Harrington and
O’Meara (2006) estimated that 27% of nursing homes failed to comply with the state’s minimum
staffing standards in 2003.
The purpose of this paper is to explore the causal relationship between the imposition
of minimum nurse staffing standards in nursing homes and outcomes including nursing home
staffing and quality of care. We focus on two states, California and Ohio, that changed their
staffing regulations for nursing homes in 2000 and 2002. We analyzed a panel data set of 45,738
nursing home-year observations in California, Ohio and control states
from 1996 through 2006,
The control states are Alabama, Kentucky, Nebraska, Nevada, New Hampshire, New York, North Dakota,
South Dakota, Virginia and Washington.
Nursing Home Staffing Standards
including detailed information on nursing home characteristics, resident census, payment source
and quality indicators measuring different dimensions of quality. We analyzed the effect of nurse
staffing laws on the nursing home staffing level and quality of care using a
differences-in-differences (DID) research design and several different specifications. The results
show that total nursing hours per resident day (HPRD) in the treated group of nursing homes
increased by about 5% relative to comparison facilities nursing homes in other states which
have no state-level staffing standards. However, because the staffing standards were broadly
applied based on total direct care hours, the increased nursing inputs were certified nursing
assistants (CNAs) and licensed practical nurses (LPNs) rather than RNs. As a result, nursing skill
mix decreased under the minimum staffing standards. Moreover, we also found another
consequence of these staffing standards, which is a substitution away from indirect care staff. In
terms of quality, our results suggested a positive and statistically significant impact of staffing
standards on health output quality as measured by a reduction in deficiency citations both total
counts and presence of severe deficiency citations. Other quality measures were generally not
impacted by the minimum staffing standard. The effects of the regulations, however, depended
on a nursing homes staffing level and market competition at baseline. Nursing homes that
ranked in the bottom quartile for staffing prior to regulation were more likely to increase LPNs
and CNAs, substitute away from indirect care staff and improve in quality. Similarly, nursing
homes facing greater competition had a stronger response to the regulations.
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2.1 Background
Poor nursing home quality has been a longstanding problem (Institute of Medicine,
1986). Given the importance of nurse staffing in resident safety and quality of care, minimum
staffing standards have been a major subject of debate among policymakers (Harrington 2005a,
b, Park and Stearns 2009). The Omnibus Budget Reconciliation Act of 1987 (OBRA-87)
created a national set of standards of care (including staffing standards) for people living in
Medicare and Medicaid certified nursing facilities. The staffing regulations established under
OBRA-87 required that certified nursing homes have LPNs on duty 24 hours a day; an RN on
duty at least 8 hours a day, 7 days a week and an RN director of nursing in place (Omnibus
Budget Reconciliation Act of 1987, 1987). Importantly, OBRA-87 did not mandate a specific
staff-to-resident ratio or minimum nurse HPRD.
Following OBRA-87, a number of states introduced legislation to establish or increase
minimum staffing standards using either minimum staffing levels or minimum staff-to-resident
ratios. Some bills were passed and signed into law, while others were stalled or failed.
In 2000,
California AB1731 (Shelley) was signed into law (Chapter 451, Statutes of 2000). In addition to
raising the minimum nursing staff requirement from 3.0 to 3.2 hours of direct resident care per
day, the law eliminated the policy of allowing RN or LPN hours to be counted double towards
meeting the prior staffing standard. Before the policy change, more than 97 percent of nursing
By 2007, 37 states had established their own staffing standards as a part of their state nursing facility licensing
requirements. Among them, 13 states established their current standards in the year 2000 or later. States without
their own minimum standards follow the federal guidelines. Details of the state standards are presented in
Appendix tables A.1 and A.2.
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homes met the then-current 3.0 staffing standard (Klutz 2001) set in 1990 (Chapter 502,
Statutes of 1990). Similarly, Ohio increased its minimum total direct care hours from 1.6 to
2.75 in 2002.
Given that the prior minimum staffing standards were longstanding in both
states, the large one-time regulatory change eliminated potential complicating factors from
frequent policy changes
and improved identification. Together, these two states accounted for
about 15%
of all U.S. nursing homes.
A remarkable feature of the laws passed by both California and Ohio are that the
minimum nursing staff requirements were specified as total direct care hours, giving the same
weight to RN, LPN, and CNA hours. RNs, LPNs and CNAs have different educational
requirements and scopes of practice. To become an RN, an individual must obtain an Associate
or a Bachelor degree of Science in Nursing (which normally takes 3-4 years to complete) or a
3-year diploma program in registered nursing. RNs have a significantly expanded scope of
practice compared with that of LPNs and they often delegate tasks to LPNs and CNAs and
assume a supervisory role. LPNs are required to complete an LPN program provided by
vocational schools or community colleges, which generally takes 1-2 years, and pass a licensing
exam. LPN scope of practice varies across states rather significantly (Seago et al., 2004). LPNs
can perform some complex procedures but not to the extent of RNs. CNAs constitute the bulk
Refer to Ohio H.B.No.78 for specifics of the regulation. The previous regulation was implemented in 1974 and
amended in 1992 (Harrington, 2010).
For instance, Minnesota had relevant regulation changes on 1996, 2000 and 2001. Similarly, Maine’s current
standards took effect in 2001, but it had changes to the previous standards in 1999 and 2000.
According to authors’ calculation based on the On-line Survey, Certification, and Reporting (OSCAR)
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of the direct care workforce, assisting residents with daily tasks such as bathing, dressing,
transferring, and feeding (Bureau of Labor Statistics, 2012). The training and qualifications to
become a CNA vary across states, but the minimum requirements generally include 75 hours of
training, among which 16 hours must be supervised clinical training as mandated by the
Nursing Home Reform Act (1987).
The cost to a nursing home of complying with the minimum standards depends on the
extent of compliance before the regulation, the composition of the nurse skill mix, and the
wages of nursing personnel. In our data, the percentage of nursing homes complying with the
minimum standards in California was quite stable at around 35% from 1996 to 1999. It then
increased abruptly to 45% in 2000, and kept increasing to almost double at 70% by 2004. This
is consistent with the findings from a California Department of Health Services (2001) report to
the legislature which used a sample of 111 nursing homes. In Ohio, more than 70% of nursing
homes met the requirement prior to the regulation due to a less stringent standard, and the
compliance rate increased by about 20 percentage points to 90% after the adoption of the
regulation. State health departments conduct on-site licensure inspections to ensure that nursing
homes meet the requirements for licensure and certification. A nursing facility that is not in
compliance with the minimum staffing level or any other specific requirements may receive a
deficiency citation in that dimension and must submit a plan of correction. If the harm is serious
or the problem persists, a severe deficiency citation can be issued and the nursing home may be
subject to penalties. The most common penalty imposed is a civil monetary penalty. Depending
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on the scope and the severity of the substantiated violations, the amount of the fine can range
from $50 to $10,000 per day or $1,000 to $10,000 per violation. Nursing homes with repeated
or especially severe violations may be subject to more severe sanctions, such as license
suspension and revocation of the Medicare and/or Medicaid reimbursement for services
provided (Chen and Serfes, 2012).
According to a 1999 survey by the Bureau of Labor Statistics (Wiatrowski, 2000), the
average hourly wage rates for RNs, LPNs, and CNAs were $19, $14, and $8, respectively. To
increase one hour of direct care nursing to meet the standard, a typical nursing home with 100
residents would need to pay $292,000 in wages if they hired only CNAs, while the total
additional wage costs would be $594,000 if they hired only RNs. Based on the On-line Survey,
Certification, and Reporting Database, the total additional annual wage cost to meet the higher
minimum nurse staffing standards would be $202m in California and $24.4m in Ohio if nursing
homes hired only RNs. By comparison, if nursing homes hired only CNAs to meet the
increased staffing standards, the additional cost would be roughly $85m in California and
$10.3m in Ohio.
Although both states increased the minimum total direct care hours under their
staffing laws, the California law also disallowed the double counting of RNs and LPNs towards
meeting its staffing threshold. Moreover, the California standard of 3.2 hours per resident day is
slightly higher than the Ohio standard of 2.75. As a result, the pressure to employ low cost
staffboth new CNAs and a reallocation from RNs to LPNsis expected to be magnified in
Nursing Home Staffing Standards
California relative to Ohio.
2.2 Conceptual Framework
Nursing home administrators choose the combination of inputs to maximize the
objective of the nursing home; however, minimum staffing requirement might require the facility
to change the amount of nursing staff regardless of the facility’s objective function. In this
context, the staffing requirements are based on total staff, making no distinction for RNs, LPNs,
and NAs. Because Medicaid and Medicare nursing home payments are set administratively,
providers cannot necessarily raise output prices for these public-pay residents to account for the
increased labor costs under minimum staffing standards.
How nursing homes respond to staffing regulations depend on both their initial staffing
level and the market environment. Nursing homes that fall short of the minimum requirements
will have a strong incentive to increase their staffing if the requirements are binding. On the
other hand, the increased cost of complying with the requirements may lead to reallocation of
their resources and substitute inputs away from other dimensions that are not directly regulated
such as indirect staff. Higher nurse staffing has generally been found to have a positive effect on
quality, while the decrease in unregulated dimensions may have detrimental effects.
It is theoretically ambiguous whether and how nursing homes with staffing exceeding
the standards will respond to the standards. They may not change staffing in a perfectly
competitive market in which consumers cannot observe product quality prior to purchase (Leland,
1979; Shapiro, 1983). As other nursing homes respond to the regulation by hiring more nursing
staff, which raises wages, the nursing homes above the minimum standards may respond to the
Nursing Home Staffing Standards
higher wages by lowering their staffing ratio. If this happens, quality in a nursing home that is
above the standards could get worse. On the other hand, Ronnen (1991) and Crampes and
Hollander (1995) note that in an imperfectly competitive market with complete quality
information, when facilities that fall short of the standards increase quality, other facilities also
increase their quality in order to soften the intensified price competition from their improved
competitors. These models all assume that providers compete on both price and quality. However,
a large share of nursing home payments are set administratively by Medicaid and Medicare, and
nursing homes can only set the price for private-pay residents. Although the Medicaid rate is
generally 20 to 30 percent lower than private-pay price, nursing home quality is assumed to be a
public good across long-stay residents regardless of the disparity in payment rates (Grabowski, et
al., 2008). The main incentive for nursing homes to compete on quality is to attract private-pay
residents who are willing to pay more for better quality of care (Cohen and Spector, 1996).
Gaynor (2006) concludes that when prices are regulated, greater competition will lead to higher
quality. Quality competition has potentially become stronger in recent years because of “Nursing
Home Compare,a web-based nursing home report card initiative introduced by the Centers for
Medicare and Medicaid Services (CMS) that started to report data on various dimensions of
quality since 2002 (Grabowski and Town, 2011).
The standard economic models suggest that the competitiveness of the market may
influence the strategies and behaviors nursing homes pursue to maximize their objectives.
However, the effects of staffing standards on quality might be complicated, depending on a
decision maker’s quality elasticity of demand and quality information. There could also be a
Nursing Home Staffing Standards
dynamic effect in which the staffing standards causes changes in factors that are not directly
observable such as improved organizational process, higher productivity and greater quality
Based on this framework, these regulations lead to a series of testable implications:
Hypothesis 1: Nursing homes below the staffing standard will respond to the minimum staffing
requirements by employing greater numbers of low cost CNAs.
Hypothesis 2: The increase in staffing in response to the minimum requirements will be strongest
in those facilities that are most deficient prior to the regulation.
Hypothesis 3: Nursing homes below the staffing standard will substitute away from other
unregulated inputs such as indirect care staff (e.g., activities) in order to pay the higher nurse
staff labor costs under the minimum staffing regulation.
Hypothesis 4: Nursing homes above the staffing standard will decrease staffing if the price of
staffing is increased by the minimum staffing regulation.
Hypothesis 5: The increase in staffing in response to the minimum requirements will improve the
quality of care.
Hypothesis 6: The response to the staffing regulation will be strongest in more competitive
2.3 Prior Literature
Several early studies (e.g. Janelli, et al. (1994); Moseley (1996)) investigated the impact of
federal staffing standards in selected states and found a decrease in restraint use and catheter use
among nursing home residents after the implementation of federal standards. Zhang and
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Grabowski (2004) used national data and stronger methods to examine the effects of the Nursing
Home Reform Act staffing requirements and found both a significant increase in nursing home
staffing levels and quality improvements from 1987 to 1993. Several studies used cross-sectional
data to evaluate state-level staffing standards and found that states with higher staffing standards
generally had higher staffing levels (e.g. Harrington (2005a, 2005b), Mueller et al.,2006).
The most recent generation of studies has used panel data methods to investigate the
impact of the state minimum staffing standards. Park and Stearns (2009) examined the effect of
state level staffing policy changes between 1998 and 2001. Using a national sample, they found
that the implementation of mandated standards led to small staffing increases for facilities with
staffing initially below the new standards and also an improvement in selected health outcomes.
Lin (2010) explored the differential impacts of minimum staffing requirements on licensed
nurses and direct-care nurses respectively and found that while the former reduced deficiency
citations, the latter had no significant effect on quality. Using national facility-level data for the
period 1999 through 2004, Bowblis (2011) found that higher minimum staffing requirements
increased nurse staffing levels, although the effect on skill mix depended on the Medicaid share
within the facility. He found that minimum standards had a mixed effect on care practices but
generally improved resident outcomes and lowered survey deficiencies. Hyer and colleagues
(2009) found a substitution away from RNs and towards LPNs and NAs following the adoption
of the Florida minimum staffing standards. In a study specific to California, Tong (2011) used
whether each facility was constrained by the minimum staffing change at baseline as an
instrument for a subsequent staffing increase. She found that the minimum staffing standard
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increased direct care staffing among lower skilled workers, which led to a decline in on-site
mortality. Matsudaira (forthcoming) found a similar increase in nurse aide hours proportional to
the gap between their initial staffing level and legislated minimum threshold in California but no
corresponding increase in the quality of care at these facilities. The difference in results across
these two California studies is likely related to the use of mortality as an outcome in the Tong
study, as compared to a broader mix of quality measures in the Matsudaira study.
In terms of the unintended consequences of minimum staffing standards, Bowblis and
Hyer (2013) found substitution away from support staff (e.g., housekeeping) in the context of
increased minimum staffing standards for direct care workers. Thomas and colleagues (2010)
found a similar decline in the indirect workforce following the adoption of a more stringent
staffing standard in Florida.
In summary, the existing literature generally suggests that overall nurse staffing has
increased in response to minimum staffing standards, but this response has largely resulted in a
lower nursing skill mix as nursing homes have responded to these broad staffing standards by
hiring more NAs. Evidence also suggests that nursing homes decrease indirect care staff in the
context of minimum direct care staffing standards. Finally, the majority of the studies show a
modest positive quality response to the staffing standards.
2.4 Our Contribution
Previous studies either use a national sample (which may include states with frequent
policy changes and lower nonbinding staffing standards) or use a single state and identify the
effects using instrumental variable approach. Our research design is different from both of these
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approaches in that we identify effects in two large states that experienced a major, one-time
policy change and we compare these states against nursing homes in other states that did not
employ a minimum staffing standard at any point over the study period. As such, our study is the
first in this literature to combine the strength of the national approach (non-adopting comparison
states) with the strength of the single-state approach (one-time, binding regulation).
This paper further contributes to this literature by using a long panel (from 1996 to
2006) to identify not only the immediate impact but also the intermediate and long-term causal
effects of minimum staffing standards on staffing levels and a large number of quality of care
measures. It may be the case that the effect of these regulations dissipates or magnifies over time.
Moreover, the detailed data on differentiated input and various quality measures before and after
the policy change can provide insight on heterogeneous responses to regulation. Specifically, we
examine whether the response is stronger among those nursing homes that are most deficient
prior to the regulation and also whether the response is stronger in more competitive markets.
To estimate the effects of minimum staffing standards on nursing home quality, we
employed the 1996-2006 On-line Survey, Certification, and Reporting Database (OSCAR), in
conjunction with the state regulation data.
OSCAR is an administrative database collected by
State regulation data are collected from the University of California at San Francisco 2000-2001 and 2007
Survey of Nursing Home Staffing Standards (Harrington, 2010), and a Department of Health & Human
Services (DHHS) 2003 report on state case studies of minimum nursing staff ratios (DHHS, 2003). When there
was ambiguity or missing information, we reviewed the state government’s website and checked the relevant
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CMS, which provides information on nursing home operations, resident census, and regulatory
compliance status. Every Medicare and Medicaid certified nursing home is required to be
surveyed at least once during a 15-month period, usually once a year,
to determine their
eligibility for maintaining certification status. Because approximately 96% of nursing homes
are certified by Medicare or Medicaid (Grabowski et al., 2008), OSCAR covers almost the
entire universe of nursing homes.
We used staffing measures from OSCAR to construct overall nursing HPRD and also
RN, LPN, and CNA HPRD. According to CMS (2004), HPRD means the average hours
worked by the licensed nurses or nursing assistants divided by total number of residents.
Staffing measures in OSCAR were reported in full time equivalents (FTEs)
over a two week
period. The FTEs were converted to HPRD using this formula:
kRe 14/)70*(
, k =
RN, LPN and CNA (Harrington et al., 1998). We converted FTEs to total staffing hours by
taking the total nursing staff FTEs and multiplying by 70 work hours for the period. We then
divided the total staffing hours by 14 days in the reporting period and then by the total number
of residents. For example, if there are four CNAs, two LPNs and one RN working full time and
state codes and senate or assembly bills. We also contacted the state licensing and certification offices and
ombudsman programs to ensure the accuracy of the timing and nature of the regulation.
The statewide average interval for these surveys must not exceed 12 months. Based on concerns that facilities
could mask certain deficiencies if they could predict the survey timing, the Health Care Financing
Administration (now the Centers for Medicare and Medicaid Services) directed states in 1999 to (i) avoid
scheduling a home’s survey for the same month of the year as the home’s previous standard survey and (ii)
conduct at least 10 percent of standard surveys outside the normal work day (either on weekends, early in the
morning, or late in the evening) (GAO, 2003).
The full time equivalents are reported by full-time, part-time and contract labor, respectively.
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another two CNAs each working 14 hours a week, this nursing home would report 4.8
=4+2*(14/35) FTEs for CNAs, 2 FTEs for LPNs and 1 FTE for RN. The total hours of nursing
care is (4.8+2+1)*5=39. Suppose the total number of residents (totres) equals 10, then dividing
39 total hours of nursing care by 10, we obtain 3.9 hours per resident day. This example would
comply with California law.
We also investigated the impact of the minimum staffing standards on indirect care
staff. Specifically, we constructed measures of housekeeping, food service and activity staff
HPRD in the same way as the direct care staffing HPRD described in the preceding texts. We
also summed these three indirect care staff types together to construct an overall indirect staff
We measured overall nursing home quality by the total number of assigned survey
deficiencies and an indicator that a severe health deficiency was assigned. We also constructed
five facility-level quality measures expressed as the share of total residents living in the nursing
home at the time of the OSCAR survey. The five quality measures, the share of residents with
contractures, physical restraints, psychoactive medications, pressure ulcers, and urethral
catheters, are standard measures of nursing home quality (Abt Associates 2004; Castle and
Engberg, 2005). Contractures and pressure ulcers have been frequently used in the medical care
literature to measure adverse resident health outcomes (Grabowski, et al., 2008; Cowles, 2002).
The rates of catheter use and physical restraint use were considered to be indicators of care
process quality (Zinn, 1993; Cawley, et al., 2006; Park and Stearns, 2009). Antipsychotic
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medications are commonly prescribed to nursing home residents despite their possible side
effects (Avorn, et al., 1989; Gellad et al., 2012). Also, these measures are likely to be closely
related to nurse staffing. Conditions such as contractures and pressure ulcers can be prevented
or significantly reduced with adequate nursing care. More medical supervision or a better
understanding by staff members of the purpose and side effects of commonly used psychoactive
drugs can reduce inappropriate use and adverse events. Finally, they pass the identification test,
which will be detailed in the next section, showing similar trends in the experiment states
versus the control states prior to the regulation.
To control for heterogeneity associated with quality changes over time, we
constructed covariates employing time-varying facility characteristics contained in OSCAR.
These variables include bed size, number of residents, occupancy rate, acuity index,
mix, ownership status (for-profit, nonprofit, and government), whether the facility is hospital
based, and whether the facility is part of a chain. We also constructed a county-level
Herfindahl-Hirschman Index (HHI) as a proxy for market competition and estimate the impact
of minimum staffing standards on various outcomes conditional on market competition at
baseline. This index was constructed by summing the squared market shares based on the
number of beds of all facilities in a county. The index ranges from 0 to 1, with lower values
signifying a lower concentration of facilities and thus more competitive market.
The acuity index is the sum of average activities of daily living (ADL) index and a special treatments index.
The special treatments index is defined as the sum of the proportion of residents receiving respiratory care,
suctioning, intravenous therapy, tracheotomy care, and intravenous feeding. ADL index is the sum of the
proportion of residents with certain characteristics times their associated weights. Cowles (2002) provides the
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We identified and excluded occasionally misreported staffing values using the
following rules in Bowblis (2011): (1) observations with greater than 24 hours of staffing per
resident day; (2) observations with zero staffing (except for activities staff); and (3) among
nursing homes that do not fall into the first two categories, those that are outside four standard
deviations of the mean staffing value. The original database contains 48,799 observations from
5,599 facilities from the twelve states studied during the period from 1996 to 2006. 113
facilities (2%) were excluded based on the exclusion criteria. The resulting sample consists of
5,486 unique nursing facilities with a total of 45,738 survey observations.
We used a DID design to identify effects of minimum staffing standards, by
contrasting outcomes in nursing homes that were subject to state-level minimum staffing
standards relative to outcomes in nursing homes located in ten states that did not have a
minimum staffing regulation in place at any point over our study period. Specifically, the
control states are Alabama, Kentucky, Nebraska, Nevada, New Hampshire, New York, North
Dakota, South Dakota, Virginia, and Washington.
The identifying assumption underlying our
model is that the experiment states (OH, CA) are affected by the same unobservable shocks as
these ten control states. We will discuss how we test this assumption later in this section.
Regressions conditional on staffing or competition at baseline contain 44,108 observations because 1,630
observations appear in the database after baseline date.
In a set of sensitivity analyses, all the results that we present in the next section are robust to limiting the
control states to Kentucky, which borders Ohio, and Nevada, which borders California.
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We began by using a straightforward two-way fixed-effects framework to estimate the
average effect of minimum nurse staffing standards on the outcomes of interest. Specifically,
we used the standard DID model of the following form:
jtstjstjst vtFacilityYearMSSXY
++++++= *
is a vector of observable nursing home characteristics, including certification status,
ownership type, size, occupancy rate, and resident acuity index (refer to the complete list in
Table I).
is a vector of year dummies that controls for unobserved impacts that are
common to all facilities but vary by year such as changes in federal laws and changes in
is a vector of nursing home dummies to control for the unobserved,
time-invariant differences across nursing homes that might be correlated with variation in
minimum staffing standards.
is an indicator variable that equals 1 if nursing facility j in
state s and year t is subject to minimum staffing standards and 0 otherwise. If nursing homes do
respond to the minimum staffing standard on average, we expect > 0. is identified by the
relationship between within-facility variations over time in the outcomes of interest and the
variation of minimum staffing regulations across states. To address concerns about unobserved
variations across states and over time that might be correlated with minimum staffing policy
changes, we added state specific trends (
) to our specification. This specification allows
each state to have its own time trend related to the specific measure of quality. It also controls
for any additional source of state-specific heterogeneity over time such as unobserved
demographic changes.
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We estimated equation (1) using a series of different outcome variables Y. First, we
estimated the model with total hours of direct staff per resident day. Second, the detailed
staffing information contained in OSCAR enables us to investigate the change in staff mix,
which is the HPRD delivered by RNs, LPNs, and CNAs, respectively. Third, we estimated the
effect of the minimum staffing standard on indirect care staff HPRD collectively and then
separately for housekeeping, food service and activity staff. Fourth, we used total count of
deficiency citations, an indicator of the presence of severe deficiency and five health outcome
measures (proportion of residents with contractures, pressure ulcers, physical restraints,
catheterization, psychoactive medications) to study the effect on nursing home quality of care.
Finally, we estimated each of the regressions conditional on staffing and competition at baseline,
respectively. We ranked facilities into different quartiles based on their total HPRD (TOTHPRD)
or the county-level Herfindahl-Hirschman index where the facility was located.
We then
included the interactions between MSS and indicators of which quartile the facility ranked in
terms of both staffing and market competitiveness at baseline. These interaction terms allow us
to assess the differential impact of staffing policy changes across nursing homes.
To examine the dynamics in the timing of the impact of the minimum staffing
standards, we enriched the basic specification to allow for separate short-term,
intermediate-term and long-term effects of the regulations as follows:
The total HPRD and HHI at baseline are defined by the last value of total HPRD and HHI prior to the
implementation of staffing requirements.
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++++++++= *3,32,21,1
We broke the post period into short-term (within 1 year after the regulation, measured
), intermediate-term (within 2 years after the regulation, measured by
) and
long-term (3 or more years after the regulation, measured by
). The omitted reference
category is the year prior to the regulation (period 0).
The underlying identifying assumption for estimating equation (1) is that, absent the
regulation, outcomes would have similar trends in the treatment and control states. If this
assumption does not hold, that is, if, for example, nursing homes anticipate the implementation
of the regulations or the regulations come into place when nursing intensity is increasing
anyway, then our estimates would be biased. We conducted a partial test of this identifying
assumption by examining if the experiment states have significant changes in TOTHPRD in the
periods prior to the regulation. We therefore estimated the following model:
is an indicator variable equal to 1 if it is 2 years prior to the regulation enforcement,
is an indicator variable equal to 1 if it is 3 years prior to the regulation enforcement
and MSSst,-3 is an indicator variable equal to 1 if it is four or more years prior to the
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The standard errors in all the regression analyses were clustered at the level of the
facility to allow for an arbitrary covariance matrix within the clusters (Bertrand, et al., 2004).
Table I presents summary statistics of the key dependent and independent variables
used in the specifications. The top panel shows an increase in the mean HPRD of LPN, CNA
and total HPRD in California and Ohio after the regulation. We observe a decrease in mean RN
HPRD. On average, about 7.9 deficiencies were found in nursing homes before the regulation
and the total count slightly increased to 8.6 after the regulation was implemented. However, the
percentage of nursing homes with a severe deficiency decreased by 60%. A high prevalence of
psychoactive drug usage was present in nursing homes, with about half of residents receiving
psychoactive medications. Nearly 1/3 of residents had contractures, and about 7% residents had
pressure ulcers. The bottom panel presents summary statistics for independent variables and
other nursing home characteristics including size (measured by the number of beds and total
residents), occupancy rate, acuity level, payer source, and ownership status.
The first analysis examines the relationship between the minimum staffing
requirements and staffing levels under different model specifications (Table II). Column 1
presents benchmark estimates of speciation (1) without adding state specific trends. Column 2
presents the difference-in-differences estimates of specification (1), in which the prechange and
postchange in total HPRD in the treatment states are compared to the prechange and postchange
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in total HPRD in the control states. The minimum staffing regulation was associated with a
0.175 increase in direct care hours per resident day; a 5% increase given that the mean
TOTHPRD in California and Ohio before regulation was around 3.3.
Column 3 presents estimated coefficients of specification (2) when the basic
specification is enriched to allow for separate short-term, intermediate-term and long-term
effects of the regulations. The estimates show a positive and statistically significant impact of
minimum staffing standards on total direct nursing care hours, and the impact persists after it
has been in place for 3 or more years. The policy changes were associated with a 0.14 increase
in total HPRD during the first year of regulation and a 0.19 increase after 3 or more years after
the regulation was issued. The first year phase-in may be due to a lag in enforcement.
Column 4 presents the results of estimating specification (3) when both preregulation
and postregulation dummies are included. The small and insignificant lead coefficients confirm
that there is no significant difference in changes of total direct care hours per resident day
between treatment and control states prior to the regulation relative to period 0 (the year right
before the regulation). This provides support for the identifying assumption that, absent the
minimum staffing regulation, the treatment and control states would follow the same trend in
total HPRD. In results not reported here, we found that the other coefficient estimates also make
intuitive sense. For example, total nurse staffing level increased the most in nursing homes with
Although Assembly Bill 1107 addressing nursing homes in California became effective on January 1, 2000,
facilities were notified that enforcement of the new standards would begin in April of 2000 (Matsudaira,
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a higher share of Medicare-financed residents, while it increased the least in nursing homes
with a higher share of Medicaid-financed residents. This result likely relates to the greater
demand responsiveness and higher payment levels associated with Medicare relative to
Column 5 presents the results when we added market and state variables such as a
county-level HHI and the state Medicaid per-diem rate to our basic specification as a robustness
check. The results of our specification are robust to those generated by including the market
variables. Given that the Medicaid rate data were not available for years after 2004 and the lack
of within-facility variation in the HHI over time (within-facility SD=0.025), we opted to use our
original specification (1) with state specific time trends to control for additional source of
We examined the components of the change in total direct care staffing. The results
reported in Table III reveal that 71% (0.125 out of 0.175) of the increase in total direct care
staffing hours per resident day came from the increase in CNA hours while the rest came from
the increase in LPN input intensity. Given the double counting of RNs and LPNs in California
prior to the regulations, we also examined the effect of the regulations in California and Ohio in
separate regressions (results available upon request). Interestingly, the results suggest that the
shift in Ohio was largely because of to an increase in CNA hours, while the shift in California
was because of both an increase in NA and LPN hours.
Nursing Home Staffing Standards
The results in Table III also indicate that nursing homes responded quite differently to the
minimum staffing standards based on their initial staffing level and the market competitiveness.
Nursing homes that ranked in the bottom quartile at baseline on total staffing significantly
increased all three types of nursing staff HPRDs. On the contrary, nursing homes that ranked in
the top quartile at baseline saw no change in RN and LPN and even reduced NA HPRDs the
type of staffing hours that could be adjusted most easily. The results conditional on competition
at baseline show a parallel pattern. Nursing homes that were located in the most competitive
markets (i.e., ranked in the bottom quartile at baseline on HHI) increased their LPN and NA
staffing significantly, while nursing homes located in the least competitive markets saw no
impact on all three types of nursing staff.
In order to analyze whether nurse staffing requirements caused nursing facilities to
divert resources from other dimensions, we estimated the effects of the minimum staffing
standard on indirect staff HPRD. We observed a statistically significant decrease in all three
types of indirect staff HPRD and the combined indirect staff HPRD, ranging from 2.8 to 3.3%
(when compared with the corresponding mean HPRD at baseline). Nursing homes that initially
ranked in the top quartile of total staffing had the largest decrease in support staff HPRD.
Moreover, nursing homes with greater market power on average reduced their support staff
HPRD more.
In order to deal with the nonlinearity of count variables, we estimated a negative
Except for activities staff.
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binomial model with facility fixed effects, year fixed effects, and state time trends. Table V
reports the average marginal effects. We found that the increased minimum staffing standards
significantly decreased the total number of deficiencies by 2.8% and severe deficiencies by
24%. In terms of the other five quality measures, we observed a significant decline in the rate of
contractures (5.7%) following the adoption of the minimum staffing standards (Table VI). The
other four measuresphysical restraint use, pressure ulcers, urethral catheter use and
psychoactive medication usewere not found to be statistically significant at conventional
levels. Nursing homes that were located in more competitive markets consistently had
significantly fewer deficiency citations and lower incidence of contractures and psychoactive
mediation use. Similarly, the facilities that were in the bottom staffing quartile at baseline
experienced the strongest improvement in reducing deficiency citations, pressure ulcers, and
contractures. The use of urethral catheterization had no significant change in both the top and
bottom staffing quartile. The use of physical restraints increased among facilities in the bottom
quartile, but not the top. There was a significant drop in the psychoactive medication use among
facilities in the top quartile of staffing, but not the bottom.
We further investigated the validity of the identifying assumption by estimating
model (3) with other outcomes in addition to total hours of direct staff per resident day.
Appendix table B reports results from these additional robustness checks. We found no
evidence of significant differences in nursing home quality measured by process of care and
Nursing Home Staffing Standards
certain health outcomes in periods prior to the regulations relative to period 0.
This partially
confirms the validity of the identifying assumption that absent the regulations, states would
have had similar trends in staffing and other quality measures. It also suggests that the
regulations do not appear to have been implemented in response to pre-existing trends in
nursing home quality (Finkelstein, 2004).
In this paper, we examined nursing homes response to minimum staffing standards,
an important regulatory tool often used to raise product quality in health care. The main
findings can be summarized as follows. First, we observed a significant increase in nursing
homes’ total direct care HPRD after the imposition of a minimum staffing standard. Second,
because the minimum staffing standard included all direct care workers, the regulation led to
the hiring of additional CNAs and LPNs rather than higher wage RNs. Third, we found that
nursing homes responded to the minimum staffing standard for direct care workers by
employing fewer indirect care workers such as housekeeping, food service and activities staff.
Fourth, the staffing regulations were found to improve quality of care as measured by survey
deficiencies and contractures but other quality measures (physical restraints, antipsychotic
medications, pressure ulcers, and catheters) remained unchanged. Finally, the results were
strongest in those nursing homes that were particularly deficient in staffing at baseline or were
In results not report here, we do not find significant difference in other nursing home quality measures (e.g.
direct care HPRD, support staff HPRD and lack of severe deficiency citations) in periods prior to the
regulations relative to period 0.
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located in more competitive markets.
These results offer several important contributions to this literature. We have selected
two treatment states with relatively clean policy changes. That is, these states made a single
minimum staffing standard change, with long preperiod and postperiod with no other changes
in the policy. We were also able to construct a comparison group of ten states that made no
policy change over this period of study. As a result, we were able to examine pre-trend and
post-trend in our outcomes of interest. Importantly, we did not observe any trends in staffing or
deficiencies in the years prior to the adoption of the staffing regulation. This suggests we have
identified a valid experiment and our empirical results are unbiased. Similarly, with our
relatively long follow-up period, we were able to examine how staffing and deficiencies evolve
in the years following adoption of the policy. Like many policies, we observed a slight lag in
the response to the staffing standard, perhaps because of some leeway that facilities were
allowed initially under the new regulations.
From a policy perspective, these results suggest staffing regulations have had a positive
impact on certain nursing home quality measures. However, the crude nature of the direct care
staffing requirements has important policy implications for the composition of the nursing
home workforce. Not surprisingly, nursing homes have largely increased LPN and CNA staff in
response to the minimum staffing laws that employ a total direct care HPRD threshold. The cost
of an LPN is 74% as much as an RN, while the cost of an NA is 42% of an RN (Wiatrowski,
2000). Simple economics suggests that nursing homes will largely buy cheaper labor when
Nursing Home Staffing Standards
faced with these crude staffing requirements. Nevertheless, the increased hiring of LPNs and
CNAs led to fewer deficiencies and contractures. This result is somewhat different from other
studies suggesting RN staffing is particularly important for nursing home quality (Bostick et al.,
2006). However, this result may reflect the problem of local average treatment effects,
whereby our estimates reflect the true answer for a particular population but not for others. That
is, additional CNAs or LPNs may be quite productive in nursing homes with low staff levels,
but in order to improve quality in nursing homes with average or higher staffing levels, more
RNs may be necessary. Moreover, we did not observe a statistically meaningful effect on other
quality measures such as pressure ulcers. It may be the case that greater RNs are necessary to
improve these outcomes. This is an issue to consider in future research, especially in a state that
specifically mandated a higher number of RN HPRD under a new staffing regulation.
Our study also adds value to the nursing home minimum staffing literature by
considering heterogeneous treatment effects by staffing at baseline and also by the
competitiveness of the market. In both cases, we found support for the idea that the effects are
strongest in those facilities that had the lowest staffing at baseline and also in those more
competitive markets. From a policy perspective, the finding that these regulations, as intended,
are targeting those lowest staffed facilities at baseline is reassuring. However, policymakers
may need to consider additional policies in those less competitive markets in which facilities
did not respond as strongly to the regulations. Moreover, the finding that facilities in the top
quartile reduced NA staffing and experienced no increase in RN and LPN staffing suggests that
Nursing Home Staffing Standards
the increased demand for nursing personnel from the nursing homes below the staffing
standards may have led to an increase in the price of labor for facilities already in compliance
with the minimum. In response to this higher wage price, these facilities may have decreased
NA staffing. This is consistent with the previous finding by Mark, et al. (2009) where RNs in
California metropolitan areas experienced real wage growth after California’s RN staffing law
was implemented.
The general nature of the staffing regulations may also cause what is known in the
economics literature as the multitasking problem. If the goal of the regulator is
multidimensional and not all dimensions are regulated, then the regulation will distort effort
away from unregulated objectives that may be important to patient well-being (Holmstrom and
Milgrom, 1991). Previous nursing home research has found evidence of the multitasking
problem in the context of quality report cards (e.g., Konetzka et al., 2013; Mukamel et al.,
2010). Our research offers further support for multitasking in that nursing homes decreased
indirect care staff in the context of higher mandated direct care staff standards. Although we
ultimately found fewer deficiencies and contractures under the minimum staffing standards,
lower indirect care staff HPRD may have implications for nursing home quality of life, an
important yet difficult to measure output.
Some states have attempted to avoid this multitasking problem through the use of a
wage pass-through policy, which earmarks additional Medicaid payments to nursing homes for
the explicit purpose of increasing compensation for direct-care workers. Feng and colleagues
Nursing Home Staffing Standards
(2010) found that wage-pass through policies increased CNA staffing by 3-4%. However, a
wage pass-through policy requires state Medicaid programs to target new dollars to nurse
staffing, whereas a staffing regulation leaves it open to the provider to determine how to
allocate resources. In a period of state budget shortfalls in which the available financing for
wage-pass through policies may be limited, policymakers may be willing to tolerate the
unintended consequences of a minimum direct care staff standard in order to increase direct
staffing and improve certain measures of quality.
This study is limited in several ways. First, although we undertook a series of checks
to ensure the validity of our identification strategy, we could not rule out unobservables that
were present in those states adopting minimum standards and those facilities that were below
the staffing threshold at baseline. Second, our quality measures (e.g., pressure ulcers) were
aggregated at the facility level and we cannot rule out the sorting of different residents across
nursing homes following the regulations. Finally, the standard limitations apply to our DID
model in that there may be different trends in those facilities subject to the regulations relative
to the comparison facilities.
In summary, this paper has found that minimum staffing standards had a positive
impact on both direct care staffing and certain measures of nursing home quality of care.
However, we have also found evidence that these regulations caused nursing homes to decrease
the number of indirect care staff. Further, these effects were found to differ based on the staffing
level and market competition at baseline. Given these tradeoffs, future research will need to
Nursing Home Staffing Standards
study the complex relationship between regulation, staffing, competition, input use, and quality.
It is also important to continue to monitor both the intended and unintended consequences of
these policies.
Nursing Home Staffing Standards
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Table I. Summary Statistics for Staffing, Nursing Home Characteristics and Quality Measures
by Regulation Status
Full Sample1
California and Ohio
Dependent Variables
Nurse staffing hours per resident day
Total HPRD
Support staff hours per resident
Food services
Overall Quality of care variables
Total deficiency count
Indicator of severe deficiency
Percentage of Resident with
Pressure ulcers
urethral catheterization
Physical restraint
Psychoactive medication
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Number of beds
Total Residents
Occupancy Rate
Hospital Based
Acuity Index
Percentage of Medicaid Residents
Percentage of Medicare Residents
Percentage of Private Pay/Other
Herfindahl-Hirschman Index
Medicaid Rate2
No. of Facility-Year Observations
Notes: 1. The full sample includes California, Ohio and ten control states: Alabama, Kentucky, Nebraska,
Nevada, New Hampshire, New York, North Dakota, South Dakota, Virginia and Washington. These control
states did not have any direct care regulation in effect, nor any changes in such regulation from 1996 to 2006. 2.
Medicaid daily rate is CPI-adjusted using 2004 dollars and covers 1996 to 2004.
Standard deviations are reported in brackets. * Significant at 10%; ** significant at 5%; *** significant at 1%.
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Table II. Effects of Minimum Staffing Standards on Total Staff Hours Per Resident Day
Staffing standards (All Post Years)
Staffing standards (4 or More Years Prior to
Staffing standards (3 Years Prior to Regulation)
Staffing standards (2 Years Prior to Regulation)
Staffing standards (Year of the Regulation)
Staffing standards (Post 2 Years)
Staffing standards (Post 3 or More Years)
Includes market variables
Includes state time trends
Notes: All models include year fixed effects and facility fixed effects. Standard errors are clustered at the level
of the facility.
* Significant at 10%; ** significant at 5%; *** significant at 1%.
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Table III. Effects on Nursing Skill Mix: Measured by Staffing Hours Per Resident Day
MSST (All Post Years)
Conditional on Staffing at Baseline
Bottom Quartile
Top Quartile
Conditional on Competitiveness at Baseline
Bottom Quartile
Top Quartile
Notes: All regressions include facility characteristics, year fixed effects, facility fixed effects and state-specific time
trends. Robust standard errors clustered by state are reported in parentheses.
* Significant at 10%; ** significant at 5%; *** significant at 1%.
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Table IV. The Impact of Nursing Home Staffing Standards on Indirect Care Staffing Hours Per Resident
Food Service
Activities Staff
MSST (All Post Years)
Conditional on Staffing at Baseline
Bottom Quartile
Top Quartile
Conditional on Competitiveness at Baseline
Bottom Quartile
Top Quartile
* Significant at 10%; ** significant at 5%; *** significant at 1%.
Nursing Home Staffing Standards
Table V. The Impact of Nursing Home Staffing Standards on Overall Quality: Measured
by Deficiency Citations
Total Count of Deficiency Citations
Indicator of Severe Citations
MSST (All Post Years)
Staffing standard (Year of
the Regulation)
Staffing standard (Post 2
Staffing standard (Post 3 or
More Years)
Conditional on Staffing at Baseline
Bottom Quartile
Top Quartile
Conditional on Competitiveness at Baseline
Bottom Quartile
Top Quartile
* Significant at 10%; ** significant at 5%; *** significant at 1%.
Nursing Home Staffing Standards
Table VI. The Impact of Nursing Home Staffing Standards on Process of Care and Health Outcomes
Pressure Ulcers
Physical Restraint
Psychoactive Medication
MSST (All Post Years)
Conditional on Staffing at Baseline
Bottom Quartile
Top Quartile
Conditional on Competitiveness at Baseline
Bottom Quartile
Top Quartile
* Significant at 10%; ** significant at 5%; *** significant at 1%.
Nursing Home Staffing Standards
Appendix A: State Minimum Nursing Home Staffing Standards
Table A.1. Overview of State Minimum Nursing Home Staffing Standards
First Year of Current
Regulation in Effect
AL, AZ*, HI, KY, MO*, NE, NH, NY, ND, NV, SD,
Notes: *. Arizona rescinded regulation in 1997. Missouri rescinded regulation in 1998. Prior to 2000,
Iowa had separate ratios for Skilled Nursing Facilities (SNF) and Intermediate Care Facilities (ICF). In
2000 the state rescinded ratio for SNF, leaving previous ICF ratio to apply to all facilities.
Table A.2. Changes in Selected State Minimum Total Staffing Standards, 1999-2002
Number of
Nursing Homes
First Year of
Current Regulation
in Effect
Year of Previous
HPRD Changes
3.10 (0.3 RN)
1996, 2000
2.41 (0.54 RN+LPN)
1999, 2000
Sources: OSCAR data 1996-2006; DHS 2003 report, UCSF 2000-2001 and 2007 Survey of Nursing Home
Staffing Standards, conversations with state officials and/or ombudsmen.
Nursing Home Staffing Standards
Appendix B: Robustness Checks
Appendix Table B. Pre and Post Estimates of the Effect of the Staffing Requirements on Process of Care and Health Outcomes
Pressure Ulcers
Staffing standards (4 or More Years Prior to Regulation)
Staffing standards (3 Years Prior to Regulation)
Staffing standards (2 Years Prior to Regulation)
Staffing standards (Year of the Regulation)
Staffing standards (Post 2 Years)
Staffing standards (Post 3 or More Years)
* Significant at 10%; ** significant at 5%; *** significant at 1%.
... To account for these changes in the measurement, I standardize the case-mix (expected) total nurse staffing HPRD measure using the mean and standard deviation for each respective measurement period. 8 Previous research has demonstrated that nursing home residents typically enter a nursing home located in their county of residence 9 and the consensus within the literature is to use the county as the relevant geographic market for the construction of competition measures (see, e.g., Bowblis (2012), Chen and Grabowski (2015), Konetzka et al. (2008), and Zhao (2016) who all construct county-level Herfindahl-Hirschman Index). To account for the potential influence of competition and economic forces that may influence staffing and quality of care, I include the quarterly total nursing homes operating in the county (calculated from the PBJ data), the annual county-level real income per capita from the Census Bureau, and the county-level unemployment rate from the U.S. Bureau of Labor Statistics. ...
... Previous research has documented that the imposition of minimum staffing requirements has the potential to reduce non-nurse staffing (Bowblis & Hyer, 2013;Chen & Grabowski, 2015). Given that the non-nurse composition of each nursing home's care team is included through binaries and HPRD for specific occupations and occupational groups, the inclusion of these factors may potentially be influenced by the downgrade policies threatening the validity of the findings. ...
... 6 As a robustness test, I also evaluate this intent-to-treat group relative to a control group consisting of nursing homes with 6 or fewer days each quarter without an RN present during the 2017Q1-2017Q4 period across an abbreviated time horizon (2017Q1-2019Q4) which excludes the second policy announcement period. Results for these models (reported in Tables A5-A7) Bowblis and North (2011) estimate the value at 83%. 10 As a robustness tests, I also evaluate the results including these factors as a series of extended binaries for the quarterly presence of non-nursing staff and with the exclusion of these controls (Tables A11-A13) given that previous researchers have found that the imposition of minimum staffing requirements has the potential to reduce non-nursing staff (Bowblis & Hyer, 2013;Chen & Grabowski, 2015). 11 In a supplemental analysis (not reported), I evaluated compliance status (using three categories (1) never compliant, post-policy compliant, and always compliant in 2017 and 2019) using multinomial logit models with an abbreviated set of nursing home-specific characteristics from 2017. ...
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Policymakers have historically attempted to influence quality in nursing homes through the imposition of minimum staffing standards and through the public dissemination of quality on websites like Care Compare. One current Federal standard necessitates a registered nurse (RN) on duty for at least eight consecutive hours each day. In 2018, the Centers for Medicare and Medicaid Services announced that they would incentivize compliance with this requirement by downgrading nursing homes with 7+ days without an RN present during the quarter by one star on their Care Compare staffing domain quality rating. This study evaluates the impact of this new enforcement mechanism. Using an intent‐to‐treat sample of nursing homes at risk for downgrade with difference‐in‐differences and event study models, it finds that the policy increased compliance and staffing levels. Using the policy to instrument for full compliance, it finds that the daily presence of an RN causally improves several quality dimensions.
... Even though the STM studies were not designed to determine if a NH was sufficiently staffed, one of the reasons the authors justified the use of the STM studies to construct the benchmark is the nursing staff times associated with the STM studies are associated with higher staffing levels than the STRIVE study. Increasing staffing levels has merit in potentially improving the quality of care as there is a consensus that higher nursing staff levels are generally associated with higher quality (Bowblis, 2011;Chen & Grabowski, 2015;Park & Stearns, 2009;Tong, 2011). However, the full consequences of using any staffing benchmark needs to be known before policymakers should consider implementing them. ...
... Numerous studies have examined the association with NH quality and nursing staff levels, with one study questioning the quality of the literature (Armijo-Olivo et al., 2020). While the relationship is complex and multiple studies find inconclusive evidence (Backhaus et al., 2014;Harrington et al., 2012), the consensus is that higher nursing staff levels are generally associated with higher quality (Bowblis, 2011;Chen & Grabowski, 2015;Park & Stearns, 2009;Tong, 2011). ...
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Background and Objectives Despite concerns about the adequacy of nursing home (NH) staffing, the federal agency responsible for NH certification and regulation has never adopted an explicit quantitative nursing staff standard. Harrington and colleagues (2020) have proposed a benchmark for this purpose based on the 1995/97 Staff Time Measurement (STM) studies. This paper aims to assess the extent to which NHs staff to this proposed STM benchmark, the extent to which regulators already implicitly apply the STM benchmark, and compute the additional operating expenses NHs would incur to adhere to the STM benchmark. Research Design and Methods Using NH Compare Archive data, the STM benchmark was compared to staffing levels reported by the facility and whether NHs received a nursing staff deficiency. Using financial information from Medicare Cost Reports, the additional annual operating expenses required to staff to the STM benchmark were calculated for each state and nationwide. Results The vast majority of NHs did not staff to the STM benchmark; 80.2% for registered nurses and 60.0% for total nursing staff. Deficiency patterns showed that NH regulators were not using the STM benchmark to determine sufficiency of nursing staff. Implementing the STM benchmark as a regulatory standard would increase operating expenses for 59.1% of NHs, at an average annual cost of half million dollars per facility. The nationwide increase in operating expense is estimated to be at least$4.9 billion per year. Discussion and Implications Without clear guidance on the staffing level needed to be sufficiently staffed, most NHs are subject to a community standard of care, which some have argued could be associated with suboptimal staffing levels. Implementing an acuity-based benchmark could result in improved staffing levels, but also comes with significant economic costs. The STM benchmark is not economically feasible at current Medicare and Medicaid reimbursement levels.
... More expensive workers, such as nurses, may be displaced by less expensive or unqualified workers, where substitution is possible. (Although this may not have negative consequences for the quality of care; see, for example, Chen and Grabowski, 2014.) Ikegami et al (2014) suggest that the reason nursing agencies in Japan have not expanded as fast as home help agencies is because nurses are three times as expensive as care workers. ...
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Introduction Public responsibility for long-term care (LTC) – in particular, care for frail older people – has expanded rapidly in most advanced nations in the past two or three decades. A key issue is resource allocation: how much money to spend and on what. But although the LTC field has drawn more and more attention from researchers – we know far more about how various approaches work than ever before – patterns of resource allocation have not been adequately studied. As a recent report indicates, ‘the current available statistics about public LTC programs are somewhat patchy’ (Carrera et al, 2013, p 31). Actually, information is available about LTC expenditure in most individual countries, and recently several admirable surveys of LTC policy across several countries have appeared (see, for example, Colombo et al, 2011; Riedel and Kraus, 2011; Mot et al, 2012; Rodrigues et al, 2012; Genet et al, 2013; OECD, 2013; Ranci and Pavaloni, 2013; Mor et al, 2014). However, systematic comparative analysis of expenditure and coverage of national LTC systems has been lacking. The objective is simple; the task is quite difficult. Two of us discovered this in trying to compare expenditures in just three countries, Germany, Japan and the US (Campbell et al, 2010). It took far longer than we expected and required many delicate decisions to match up the categories. The present study takes on seven countries, a number small enough to manage the necessary mutual adjusting with our limited time and resources, but large enough to represent significant models of LTC policy. To draw on quite conventional images in the welfare state literature, we have Sweden in social-democratic Northern Europe, Italy in familial Southern Europe, Germany in corporatist mid-continent, Australia, the US and England as quite different versions of the Anglo-Saxon ‘residual’ model, and Japan as the relatively new entry that shares aspects of all the other models. This chapter presents details of each country's approach to LTC and how their policies have changed over time. This chapter is essentially a ‘snapshot’ cross-sectional analysis of spending and coverage data. Since our contribution is largely methodological, we begin by explaining how we have tried to deal with the inherent problems of comparing LTC policy. There are four key approaches.
... More expensive workers, such as nurses, may be displaced by less expensive or unqualified workers, where substitution is possible. (Although this may not have negative consequences for the quality of care; see, for example, Chen and Grabowski, 2014.) Ikegami et al (2014) suggest that the reason nursing agencies in Japan have not expanded as fast as home help agencies is because nurses are three times as expensive as care workers. ...
Full-text available
The previous chapters have examined a range of strategies across OECD countries for organising, regulating and funding long-term care (LTC) public services for dependent older people in the face of the rising needs of ageing societies. These chapters have highlighted the main LTC models and reforms adopted internationally, provided a critical assessment of their successes and limitations, and drawn key recommendations for future policies. This concluding chapter reviews their findings in order to map and discuss the most important challenges and dilemmas that LTC policies will face in the years to come. Rising demand Future policies must take account of the expected rise in demand for LTC for older people. Chapter Two, by Wittenberg, shows that demand for LTC is expected to rise throughout the developed and developing world. The number of older people needing care is projected to rise partly because the large post- Second World War baby boom cohorts are starting to reach old age, and partly because increasing life expectancy means that a rising proportion of older people are surviving into late old age. The growth in demand for formal LTC is a complex issue which requires careful consideration. Need for care is not determined simply by age. Much will depend on whether there is a compression or expansion of disability, that is, whether, as total life expectancy increases, the number of years with severe disability remains constant, rises or falls. The issue of the compression or expansion of disability is the subject of much continuing debate. It would not seem prudent for policymakers to count on future reductions in the prevalence of severe disability among older people to offset the rising demand for LTC that will result from population ageing. An expansion of severe disability, including dementia-related disability, cannot be excluded. Demand for care services is not solely a function of the numbers needing care. It also depends on the availability of alternatives to services, the cost of care services, incomes, and expectations and preferences. The main alternative to formal care services is unpaid care by family and friends. Uncertainty about future supply of unpaid care, as discussed in the next section, could have a major upward impact on demand for formal services.
... More expensive workers, such as nurses, may be displaced by less expensive or unqualified workers, where substitution is possible. (Although this may not have negative consequences for the quality of care; see, for example, Chen and Grabowski, 2014.) Ikegami et al (2014) suggest that the reason nursing agencies in Japan have not expanded as fast as home help agencies is because nurses are three times as expensive as care workers. ...
Full-text available
Introduction A key policy debate in long-term care (LTC) policies across OECD countries today can be summarised by the following question: what measures and strategies can be adopted to optimise resources? New policies are required for balancing finances and access to care, with different options on the table and waiting for governments’ decisions. This chapter looks at changes over time in public resource allocation among LTC users in the same OECD countries considered in the previous chapter (except for Australia). As in Chapter Four, this chapter focuses exclusively on public care inputs, defined as those inputs that are (at least partially) publicly funded, and looks at users aged 65 and over. Chapter Four led the way to reconsidering how public resources are allocated in different LTC systems through an in-depth analysis of current spending. To complement that analysis, this chapter adopts a long-term perspective, investigating the changes that have occurred over the last 20–25 years in three crucial dimensions of resource allocation: the mix of LTC services for older people, their intensity, and their coverage. The countries considered are representative of the OECD environment with respect to both the overall welfare models and the models of LTC policies. Concerning the former, as Campbell et al have noted in Chapter Four, ‘we have Sweden in social-democratic Northern Europe, Italy in familial Southern Europe, Germany in corporatist mid-continent, Australia, the US and England as quite different versions of the Anglo-Saxon “residual” model, and Japan as the relatively new entry that shares aspects of all the other models.’ From the point of view of LTC policies, the sample of countries selected represents the different models in the OECD context: • • Universal coverage within a single programme: this model guarantees people access to formal services without taking into account users’ income or assets as eligibility criteria. It is also organised as a single system, separated or integrated with the overall health system (Germany, Japan and Sweden). • • Mixed systems: in this case, LTC is provided through a mix of different universal programmes and benefits operating alongside, or a mix of universal and meanstested LTC entitlements (England and Italy). • • Means-tested systems: under this type of scheme, LTC coverage is provided through safety-net programmes. In countries using this system, income and/ or asset tests are used to define thresholds for eligibility to publicly funded care.
... Open access more than half of OECD countries report a shortage of LTC caregivers. 2 Media and researchers have increasingly expressed concerns about LTC staffing levels being too low, affecting quality of resident care and safety. [6][7][8][9] In acute care, multiple studies have demonstrated that better nurse staffing (ie, more care hours per client and day and more qualified care teams) is associated with better client outcomes. 10 However, the results of these studies may not be directly applicable to LTC. ...
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Introduction Especially in acute care, evidence points to an association between care staffing and resident outcomes. However, this evidence is more limited in residential long-term care (LTC). Due to fundamental differences in the population of care recipients, organisational processes and staffing models, studies in acute care may not be applicable to LTC settings. We especially lack evidence on the complex interplay among nurse staffing and organisational context factors such as leadership, work culture or communication, and how these complex interactions influence resident outcomes. Our systematic review will identify and synthesise the available evidence on how nurse staffing and organisational context in residential LTC interact and how this impacts resident outcomes. Methods and analysis We will systematically search the databases MEDLINE, EMBASE, CINAHL, Scopus and PsycINFO from inception for quantitative research studies and systematically conducted reviews that statistically modelled interactions among nurse staffing and organisational context variables. We will include original studies that included nurse staffing and organisational context in LTC as independent variables, modelled interactions between these variables and described associations of these interactions with resident outcomes. Two reviewers will independently screen titles/abstracts and full texts for inclusion. They will also screen contents of key journals, publications of key authors and reference lists of all included studies. Discrepancies at any stage of the process will be resolved by consensus. Data extraction will be performed by one research team member and checked by a second team member. Two reviewers will independently assess the methodological quality of included studies using four validated checklists appropriate for different research designs. We will conduct a meta-analysis if pooling is possible. Otherwise, we will synthesise results using thematic analysis and vote counting. Ethics and dissemination Ethical approval is not required as this project does not involve primary data collection. The results of this study will be disseminated via peer-reviewed publications and conference presentation. PROSPERO Registration number CRD42021272671.
... Reports about insufficient care in nursing homes are common (e.g., Tscharnke (2009)). A central aspect of such reports and in scientific debates on care quality are nurse-to-resident staffing ratios (see e.g., Chen and Grabowski (2015) or Tong (2011), on US nursing homes). Although there are several attempts to measure outcome quality objectively, this remains difficult in the LTC context due to a lack of objective and comparable data (Castle & Ferguson, 2010). ...
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Many countries limit public and private reimbursement for nursing care costs for social or financial reasons. Still, quality varies across nursing homes. We explore the causal link between case‐mix adjusted nurse staffing ratios as an indicator of care quality and different price components in Swiss nursing homes. The Swiss reimbursement system limits and subsidizes the care price at the cantonal level, which implicitly limits staffing ratios, while the residents cover the nursing home‐specific lodging price privately. To estimate causal effects, we exploit (i) the exogeneity of the Swiss care price regulation, (ii) nursing‐home fixed effects estimations and (iii) instrumental variables for the lodging price. Our estimates show a positive impact of prices on certified staffing ratios. We find that a 10% increase in care prices increases certified staffing ratios by 3–4%. A comparable 10% increase in lodging prices raises certified staffing ratios by 1.5–10% (depending on the model). Our findings highlight that price limits for nursing care impose a limit on staffing ratios. Furthermore, our results indicate that providers circumvent price limits by increasing lodging prices that are privately covered. Thus, this cost shifting implicitly shifts the financial burden to the residents.
Age-Friendly Health Systems is a movement to ensure that all care and support for and with older adults across all settings is age-friendly care. Age-Friendly Health Systems provide staff, leadership, and care partner education based on the 4M Framework (What Matters, Medications, Mentation, Mobility). Nursing homes and other settings are often left out of local, state, or federal strategic plans on aging. In addition, limited quality and quantity of nursing home staff impact new program implementation. We consider how programs and services to support older adults can create and sustain an Age-Friendly Ecosystem, including a meaningful role for nursing homes.
This paper examines how workload affects the provision of care in a large but understudied segment of the healthcare sector – maternity wards. I use detailed patient-level administrative data on childbirth, and exploit quasi-random assignment of unscheduled patients to different staffing ratios. I find that the probability of C-section increases at a decreasing rate with workload. I show that this result is not attributable to patients’ differential sorting across staffing levels. Instead, I find evidence that C-sections are used to alleviate midwives’ workload -they are faster than vaginal births and performed by physicians. I also exploit patient’s civil status to determine whether the effect varies with patient’s bargaining power -single women are on average more likely to be alone in the delivery room. Consistent with induced demand, only single patients are more likely to receive a C-section when admitted at high workload levels.
Background: Federal minimum nurse staffing levels for skilled nursing facilities (SNFs) were proposed in 2019 U.S. Congressional bills. We estimated costs and personnel needed to meet the proposed staffing levels, and examined characteristics of SNFs not meeting these thresholds. Methods: This was a cross-sectional analysis of 2019Q4 payroll data, the Hospital Wage Index, and other administrative data for 14,964 Medicare and Medicaid-certified SNFs. We examined characteristics of SNFs not meeting proposed minimum thresholds: 4.1 total nursing hours per resident day (HPRD); 0.75 registered nurse (RN) HPRD; 0.54 licensed practical nurse (LPN) HPRD; and 2.81 certified nursing assistant (CNA) HPRD. For SNFs falling below the thresholds, we calculated the additional HPRD needed, along with the associated full-time equivalent (FTE) personnel and salary costs. Results: In 2019, 25.0% of SNFs met the minimum 4.1 total nursing HPRD, while 31.0%, 84.5%, and 10.7% met the RN, LPN, and CNA thresholds, respectively. Only 5.0% met all four categories. In adjusted analyses, factors most strongly associated with SNFs not meeting the proposed minimums were: higher Medicaid census, larger bed size, for-profit ownership, higher county SNF competition; and, for RNs specifically, higher community poverty and lower Medicare census. Rural SNFs were less likely to meet all categories and this was explained primarily by county SNF competition. We estimate that achieving the proposed federal minimums across SNFs nationwide would require an estimated additional 35,804 RN, 3509 LPN, and 116,929 CNA FTEs at $7.25 billion annually in salary costs based on current wage rates and prepandemic resident census levels. Conclusions: Achieving proposed minimum nurse staffing levels in SNFs will require substantial financial investment in the workforce and targeted support of low-resource facilities. Extensive recruitment and retention efforts are needed to overcome supply constraints, particularly in the aftermath of the COVID-19 pandemic.
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The goal of this paper is to identify key issues concerning the nature of competition in health care markets and its impacts on quality and social welfare and to identify pertinent findings from the theoretical and empirical literature on this topic. The theoretical literature in economics on competition and quality, the theoretical literature in health economics on this topic, and the empirical findings on competition and quality in health care markets are surveyed and their findings assessed. Theory is clear that competition increases quality and improves consumer welfare when prices are regulated (for prices above marginal cost), although the impacts on social welfare are ambiguous. When firms set both price and quality, both the positive and normative impacts of competition are ambiguous. The body of empirical work in this area is growing rapidly. At present it consists entirely of work on hospital markets. The bulk of the empirical evidence for Medicare patients shows that quality is higher in more competitive markets. The empirical results for privately insured patients are mixed across studies.
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Minimum quality standards (MQS) constitute an important regulatory tool that can be used to raise product qualities, to benefit consumers and to increase market participation. One of the main assumptions in the existing literature is that firms must comply with standards. Nevertheless, in many industries, and in particular the service industry, quality observability and enforceability are not perfect. Some low quality firms do not comply with standards. What are the welfare implications of an MQS regulation in such an environment? We develop a price competition model of vertical differentiation that accounts for these empirical observations. Contrary to well-established results in the literature, MQS can increase quality disparity between firms and raise hedonic prices. Some consumers get hurt and market participation decreases. Note: This is a pre-print of an article published in Journal of Regulatory Economics. The final authenticated version is available online at: Also available at SSRN:
Conference Paper
Purpose: Despite substantial regulatory oversight, quality of care in nursing homes remains problematic. This article assesses strategies for improving quality of care in these facilities. Design and Methods: This article reviews the research literature on eight strategies: strengthening the regulatory process, improving information systems for quality monitoring, strengthening the caregiving workforce, providing consumers with more information, strengthening consumer advocacy, increasing Medicare and Medicaid reimbursement, developing and implementing practice guidelines, and changing the culture of nursing facilities. Results: Although individual approaches vary, several themes emerge. First, several strategies require substantially more resources and will increase costs. Second, the research literature does not provide much guidance as to the effectiveness of these options. Third, several strategies assume a degree of data sophistication on the part of nursing homes that may not exist. Fourth, regulation is likely to continue to be the main strategy of quality assurance. Finally, the political saliency of nursing home quality issues is uneven. Implications: Quality of care in nursing homes is a major issue for which there is no simple solution.
Introduction In the standard economic treatment of the principal–agent problem, compensation systems serve the dual function of allocating risks and rewarding productive work. A tension between these two functions arises when the agent is risk averse, for providing the agent with effective work incentives often forces him to bear unwanted risk. Existing formal models that have analyzed this tension, however, have produced only limited results. It remains a puzzle for this theory that employment contracts so often specify fixed wages and more generally that incentives within firms appear to be so muted, especially compared to those of the market. Also, the models have remained too intractable to effectively address broader organizational issues such as asset ownership, job design, and allocation of authority. In this article, we will analyze a principal–agent model that (i) can account for paying fixed wages even when good, objective output measures are available and agents are highly responsive to incentive pay; (ii) can make recommendations and predictions about ownership patterns even when contracts can take full account of all observable variables and court enforcement is perfect; (iii) can explain why employment is sometimes superior to independent contracting even when there are no productive advantages to specific physical or human capital and no financial market imperfections to limit the agent's borrowings; (iv) can explain bureaucratic constraints; and (v) can shed light on how tasks get allocated to different jobs.
This article estimates the impact of minimum sta¢ ng requirements on the nursing home market using a unique national panel over the 1996-2005 period. This study reveals that, given a half-hour increase in the minimum nursing hours per resident day for licensed nurses, quality of patient care increases by 25 percent. This quality-increasing e¤ect is mainly driven by low-quality nursing homes increasing their quality of care to meet the new standards. By contrast, minimum sta¢ ng requirements for direct-care nurses do not have any signi…cant impact on quality. This lack of impact may be explained by nursing home providers circumventing this regulation by hiring less expensive and less skilled laborers as substitutes for direct-care nurses. the seminar participants at the BU microeconomics dissertation workshop, the BU empirical micro workshop, the 5th International Industrial Organization Conference, the 34th Conference European Association for Research in Industrial Economics, and the 2009 North American Summer Meeting of the Econometric Society, for their helpful suggestions and comments.
This paper investigate the causal eect of a regulation for California nursing homes that required a minimum number of nurse hours per patient day on the quality of health care measured both by patient outcomes and deciency citations from facility inspections. The research design employed is based on the insight that compliance with the law induces a kinked relationship between average nurse employment increases and initial stang levels in the period following adoption: firms should increase employment of nurses in proportion to the gap between their initial staffing level and the legislated minimum threshold. If higher nurse stang causes better quality, then the changes in quality outcomes should mirror these changes. Despite inducing increases in nurse aide hours worked of up to 30 percent for some firms, and about 10 percent across all firms initially out of compliance, I find no impact of the staffing regulation on patient outcomes or overall facility quality.
Objective: To study the effect of minimum nurse staffing requirements on the subsequent employment of nursing home support staff. Data sources: Nursing home data from the Online Survey Certification and Reporting (OSCAR) System merged with state nurse staffing requirements. Study design: Facility-level housekeeping, food service, and activities staff levels are regressed on nurse staffing requirements and other controls using fixed effect panel regression. Data extraction method: OSCAR surveys from 1999 to 2004. Principal findings: Increases in state direct care and licensed nurse staffing requirements are associated with decreases in the staffing levels of all types of support staff. Conclusions: Increased nursing home nurse staffing requirements lead to input substitution in the form of reduced support staffing levels.
Public reporting of health care quality has become a popular tool for incenting quality improvement. A fundamental question about public reporting is whether it causes providers to select healthier patients for treatment. In the nursing home post-acute setting, where patients must achieve a minimum length of stay to be included in quality measures, selection may take the form of discharge from the nursing home using rehospitalization, a particularly costly and undesirable outcome. We study the population of post-acute patients of skilled nursing facilities nationwide during 1999-2005 to assess whether selective rehospitalization occurred when public reporting was instituted in 2002, using multiple quasi-experimental designs to identify effects. We find that after public reporting was implemented, rehospitalizations before the length-of-stay cutoff increased. We conclude that nursing homes rehospitalize higher-risk post-acute patients to improve scores, providing evidence for selection behavior on the part of nursing home providers in the presence of public reporting.
Background: Antipsychotic medications are commonly prescribed to nursing home residents despite their well-established adverse event profiles. Because little is known about their use in Veterans Affairs (VA) nursing homes [ie, Community Living Centers (CLCs)], we assessed the prevalence and risk factors for antipsychotic use in older residents of VA CLCs. Methods: This cross-sectional study included 3692 Veterans age 65 or older who were admitted between January 2004 and June 2005 to one of 133 VA CLCs and had a stay of ≥90 days. We used VA Pharmacy Benefits Management data to examine antipsychotic use and VA Medical SAS datasets and the Minimum Data Set to identify evidence-based indications for antipsychotic use (eg, schizophrenia, dementia with psychosis). We used multivariable logistic regression and generalized estimating equations to identify factors independently associated with antipsychotic receipt. Results: Overall, 948/3692(25.7%) residents received an antipsychotic, of which 59.3% had an evidence-based indication for use. Residents with aggressive behavior [odds ratio (OR)=2.74, 95% confidence interval (CI), 2.04-3.67] and polypharmacy (9+ drugs; OR=1.84, 95% CI, 1.41-2.40) were more likely to receive antipsychotics, as were users of antidepressants (OR=1.37, 95% CI, 1.14-1.66), anxiolytic/hypnotics (OR=2.30, 95% CI, 1.64-3.23), or drugs for dementia (OR=1.52, 95% CI, 1.21-1.92). Those residing in Alzheimer/dementia special care units were also more likely to receive an antipsychotic (OR=1.66, 95% CI, 1.26-2.21). Veterans with dementia but no documented psychosis were as likely as those with an evidence-based indication to receive an antipsychotic (OR=1.10, 95% CI, 0.82-1.47). Conclusions: Antipsychotic use is common among VA nursing home residents aged 65 and older, including those without a documented evidence-based indication for use. Further quality improvement efforts are needed to reduce potentially inappropriate antipsychotic prescribing.