Objectives. We examined the relationship between nursing home staffing levels
and worker injury rates in 445 nursing homes in 3 states.
Methods.
from 3 states (Ohio, West Virginia, and Maryland) for the year 2000. We then
linked these data to Medicare’s Online Survey, Certification and Reporting system
to obtain nursing home staffing details and organizational descriptors. We used
ordinary least squares and log-transformed regression models to examine the association
between worker injury rate and nursing home staffing and organizational
characteristics.We obtained First Reports of Injury and workers’ compensation data
Objectives. We examined the relationship between nursing home staffing levels
Methods.
from 3 states (Ohio, West Virginia, and Maryland) for the year 2000. We then
linked these data to Medicare’s Online Survey, Certification and Reporting system
to obtain nursing home staffing details and organizational descriptors. We used
ordinary least squares and log-transformed regression models to examine the association
between worker injury rate and nursing home staffing and organizational
characteristics.We obtained First Reports of Injury and workers’ compensation data
Results.
worker injury rates in nursing homes after we adjusted for organizational characteristics
and state dummy variables (Total nursing hours per resident day were significantly associated withP=.0004).
Conclusions.
important impact on worker health. These findings were supported for multiple
facilities across different states; therefore, policies and resources that increase
staffing levels in nursing homes are warranted. (
1220–1225. doi:10.2105/AJPH.2004.045070)Our findings suggest that nursing home staffing levels have anAm J Public Health. 2005;95:
Staffing and Worker Injury in Nursing Homes
|Alison M. Trinkoff, ScD, Meg Johantgen, PhD, Carles Muntaner, MD, PhD, and Rong Le, MS
care by the Institute of Medicine noted that
there is empirical evidence that shows back
injuries among nurses are associated with
staffing levels.
worker injuries among resident care staff in
nursing homes has been documented,17 Although the extent of18–20
there have been few studies about the association
between injuries and staffing.
The occurrence of these injuries has important
implications for staff retention. Owen
and Garg
reported they had back pain said they had
made at least 1 job change in order to decrease
the number of nursing home residents
that had to be lifted and transferred.
Turnover among unlicensed personnel was
even higher,
among nursing assistants in one facility.21 found that 20% of nurses who22 with 23% annual turnover reported
23
30% reported they planned to quit
their jobs.In a statewide survey of nursing assistants,24
We used an ecological design that was
based on administrative data to examine the
association between staffing rates and worker
injuries. To do this, we analyzed the association
between staffing variables (total nursing
care hours per resident day) and adverse
worker outcomes (reported worker injuries)
at the institutional level. Analyses were also
adjusted for resident acuity, profit status,
nursing home size, and availability of nurse
aide training.
METHODS
Study Design
Our descriptive correlational design used
administrative data to examine the association
between worker injuries and the organizational
characteristics of nursing homes in
Medicare-approved facilities in 3 states: Ohio,
West Virginia, and Maryland. We used nursing
homes listed in Medicare’s Online Survey,
Certification and Reporting (OSCAR) Year
2000 database as the sampling frame for our
study.
Data Sources and Measures
We obtained staffing and organizational
descriptors from the OSCAR database. Our
variables included number of beds, special
services, RN and other personnel staffing,
type of nursing home ownership, and resident
acuity. These data are routinely collected by
the Centers for Medicare and Medicaid Services
(CMS) to support the survey and certification
function and to monitor deficiencies
and quality of care in US nursing homes that
The health care industry is one of the most
dangerous industries, ranking with construction,
trucking, and meatpacking in nonfatal
injury rates.
population, nursing homes have become
major care providers to the elderly within
the health care industry.
elderly and disabled Americans reside in
nursing homes, and nursing assistants provide
the majority of their care.
to the Bureau of Labor Statistics, the rate
of worker injuries within nursing and personal
care facilities is second among all
industries.
top 10 industries for musculoskeletal problems,
which is the major cause of worker
absenteeism, workers’ compensation claims,
and worker injury and illness.
rates of musculoskeletal injury have been
reported among nursing home workers
compared with rates among workers in
other occupations.1 Because of the growing elderly2 About 1.5 million3 According4 Nursing homes are among the1,4–7 Higher8,9
Nursing home employees working in directcare
facilities perform many physically taxing
activities, such as lifting heavy loads, working
in awkward postures, and transferring residents.
6,10–14
technology that supports patient care is physically
straining. The increased worker injury
rates likely result from increased exposure to
hazardous conditions and diminished recovery
time between exposures.Additionally, manipulating the15
Worker injuries in health care institutions
associated with staffing levels and skill mix
have been previously examined. Because
health care institutions have been required
to perform more efficiently, the resultant
changes are lower staffing levels and higher
patient loads, both of which have been shown
to increase worker injury. In a study of 12
hospitals in the Minneapolis–St Paul, Minnesota,
area that used data from 1990 to
1994, Shogren and Calkins
when registered nurse (RN) positions were
decreased by 9%, work-related illnesses and
injuries among nurses increased by 65%.
A review of the impact of staffing on health16 found that
July 2005, Vol 95, No. 7 | American Journal of Public HealthTrinkoff et al. | Peer Reviewed | Research and Practice | 1221
RESEARCH AND PRACTICE
receive Medicare or Medicaid funds. Because
the OSCAR data are continually updated by
overwriting the previous data, we purchased
historical data and documentation from the
Cowles Research Group.
the 3 states in our sample were extracted
from this large database.
We used First Report of Injury (FROI)
databases for 2 of the states, Ohio and West
Virginia, to measure worker injuries. This is
believed to be the best source of injury reports
because the process of filing workers’
compensation claims has many systematic
biases that can lead to suboptimal ascertainment
of injury.
workers’ compensation claims tend to be filed
for only the most severe injuries,
was also important to include worker injury
data from these claims to examine the study
question. Therefore, we also used workers’
compensation claims data from Maryland to
calculate worker injuries.
FROI and workers’ compensation data
were obtained from state agencies. The FROI
data are comparable to Occupational Safety
and Health Administration OSHA-200 log
data, but they are obtainable at the state
level for some states. Although injury data
were obtained for individual workers, we
aggregated injury data to the organizational
level for the analyses. All reported injuries
were included, regardless of type, although
the overwhelming majority of injuries were
musculoskeletal in origin (predominantly
back injuries).
Worker injury rates by skilled nursing facility
were calculated with formulas for injury
incidence from the Bureau of Labor Statistics’25 OSCAR data for26 On the other hand, because27 we felt it
Occupational Safety and Health Definitions.
28
the total number of nonfatal injuries
among RNs, licensed practical nurses (LPNs),
and aides for each facility and divided the
aggregate by the sum of the full-time equivalents
(FTE) for these 3 employee categories.
Multiplying the rates by 100 allows reporting
per 100 FTE.
Staffing variables were created with coding
rules designed by Harrington et al.
data were reported for a 14-day period, and
we used the coding rules to convert staffing
data to staffing hours per resident day by taking
the total nursing staff FTEs reported for a
2-week period and multiplying by 70 work
hours for the period. We divided the total
staffing hours by the total number of residents
and then by 14 days in the reporting
period. In accordance with Harrington et
al.,
contract positions for RNs; directors of nursing
were excluded. We included all LPNs and
licensed vocational nurses, and, for nursing
aide staffing, we included all certified nursing
assistants, nursing assistants in training, and
medication aides.To produce an overall rate, we aggregated29 FTE29 we included all full-time, part-time, and
File Construction
We applied exclusion criteria to remove
nursing homes from the database based on
the recommendations by Harrington et al
(1) too small—those with fewer than 15 beds,
(2) hospital based, (3) no RN hours—having
60 or more beds but no RN hours, (4) extra
RN hours—more than 12 RN hours per resident
day, (5) few nursing staff hours—less
than 0.5 total nursing hours per resident day,
and (6) excess nursing hours—more than 12
total nursing hours per resident day. We excluded
skilled nursing facilities that had excess
nursing hours to remove those facilities
that function as acute care step-down facilities
and therefore do not reflect the staffing
patterns for long-term care providers. For example,
in West Virginia, after we applied
each of the exclusion criteria the original
sample of 133 facilities decreased to 129
after deleting small facilities, to 103 after
deleting hospital-based facilities, and to 102
after deleting facilities with excess nursing
staff hours; only 77% of the original facilities
remained.
Worker injury data required considerable
cleanup. We culled the data received from
the states to extract injuries that occurred in
nursing homes during 2000. For the databases
that included a Standard Industrial
Code (http://www.osha.gov/pls/imis/sic_
manual.html), nursing homes were identified
with an 805 code (skilled nursing, intermediate
nursing, and nursing and personal care
facilities). Upon review of these codes, we determined
that some facilities were not nursing
homes (assisted living, temporary staffing
agency, system corporate office) and deleted
them. Through further analysis and recoding,
we retained only those records that represented
injuries to RNs, LPNs, and aides. To
facilitate analysis and linkage of the databases,
we assigned the CMS 6-digit provider
number for each facility to each worker injury
record. Because facility names in the injury
database were written as text with abbreviations,
common names, and corporation
names, direct linkage to the OSCAR name
was not always possible. In such cases, we
used the CMS Nursing Home Compare database
and other sources to match the nursing
home with its address.29:
Data Analysis
Statistical analyses were performed with
SAS, version 8.2 (SAS Institute Inc, Cary,
NC). We used descriptive statistics to examine
the association between organizational
characteristics and facility by state. We used
multivariate regression to identify the independent
effect of these organizational characteristics—
particularly staffing—on worker
injury rate. Because linked-facility sample
sizes for West Virginia and Maryland were
small, we combined nursing home data from
all 3 states into 1 file (n=445 linked facilities)
to eliminate concerns about adequacy
of power for these analyses. We included
state dummy variables in regression models
because of systematic differences across the
states. Before we analyzed the association
between worker injury rate and nursing
home characteristics (acuity index, total residents,
percentage of Medicaid, location,
profit status, aide training, and nursing hours
per resident day), we screened the data for
normality, missing values, outliers, and multicollinearity.
Acuity was measured with the
Acuindex, which was developed as part of
the work on the CMS Minimum Data Set.
The Acuindex takes into account the proportion
of residents with activities of daily living
dependencies and the proportion requiring
special treatments (e.g., suctioning, parenteral
feeding). Because this measure reflects
resident care burden, it also could influence
worker injury rates across facilities.
Therefore, we included acuity in our analysis
to control for variation in case mix. We
defined facilities with aide training as those
facilities with an approved Nurse Aide Training
and Competency Evaluation Program.
Among predictors, the percentage of Medic
| Research and Practice | Peer Reviewed |1222Trinkoff et al. American Journal of Public Health | July 2005, Vol 95, No. 7
RESEARCH AND PRACTICE
TABLE 1—Nursing Home Characteristics in Ohio, Maryland, and West Virginia: Online
Survey, Certification, and Reporting System Database, 2000
Ohio Maryland West Virginia
(n = 778) (n = 196) (n = 102)
Nurse Staffing, mean (SD)
RN hours per resident day 0.57 (0.27) 0.54 (0.40) 0.35 (0.17)
LPN hours per resident day 0.76 (0.30) 0.62 (0.29) 0.80 (0.52)
Aide hours per resident day 2.16 (0.59) 2.30 (0.66) 2.14 (0.62)
Total nursing hours per resident day 3.49 (0.80) 3.46 (0.94) 3.29 (0.88)
Proportion RN hours out of total nursing hours per 0.16 (0.07) 0.15 (0.08) 0.11 (0.05)
resident day
Acuity index, mean (SD) 10.33 (1.22) 11.05 (1.35) 10.74 (1.08)
Total residents, mean (SD) 87.76 (46.43) 113.29 (57.35) 89.48 (35.32)
Percentage residents enrolled in Medicaid, mean (SD) 68.12 (19.01) 59.68 (27.23) 75.84 (13.76)
Location, number (%)
Urban 584 (75.1) 174 (88.8) 35 (34.3)
Rural 194 (24.9) 22 (11.2) 67 (65.7)
Ownership, number (%)
Profit 606 (77.9) 127 (64.8) 82 (80.4)
Nonprofit/government 172 (22.1) 69 (35.2) 20 (19.6)
Aide training available, number (%) 289 (37.2) 73 (37.2) 65 (63.7)
Note.RN = registered nurse; LPN = licensed practical nurse.
aid residents was highly correlated with the
acuity index in all 3 states. The percentage
of Medicaid was then dropped from further
multivariate analysis. Additionally, because
injury rates were highly skewed, we modeled
the log of total injuries.
RESULTS
We examined the characteristics of nursing
homes in the 3 states for the entire sample
(Table 1). Ohio had the most nursing homes
(n=778) of the 3 states sampled, followed by
Maryland (n=196) and West Virginia (n=
102). Staffing levels were less distinct across
the states, with all 3 states having total nursing
hours per resident day (sum of RN, LPN,
and nursing aide staffing) that ranged from an
average of 3.3 to 3.5 hours. However, West
Virginia had a far lower average number of
RN hours per resident day (0.35) compared
with Maryland (0.54) and Ohio (0.57), which
indicated a lower skill mix. Our analysis of
the overall nursing home characteristics by
state showed that while acuity was similar
across states, total residents, percentage of
Medicaid residents, profit status, aide training,
and location differed. Maryland facilities had
more residents on average, a lower proportion
of for-profit facilities, and the lowest percentage
of Medicaid residents. In West Virginia,
two thirds of the facilities were in rural
areas, whereas in Ohio and Maryland, at least
three fourths were in urban areas. The proportion
of facilities with aide training was
twice as high in West Virginia compared with
Ohio and Maryland.
Many worker injury records were listed
under a corporation and could not be linked
to a specific facility; therefore, we used only
the subset of nursing homes that could be
linked in our analyses. To assess the impact
of this reduced sample on the variables of
interest, we compared the characteristics of
the skilled nursing facilities included in the
OSCAR sample with the sample that remained
after we linked the OSCAR and
worker injury data. As shown in Table 2,
there were no large differences in the average
staffing levels for the linked sample compared
with the OSCAR sample. The organizational
characteristics also were similar. We
then compared the staffing means by state
for the total OSCAR sample with the means
by state for the linked sample (Tables 1 and 3).
Once again, there was little or no change in
average staffing (nursing hours per resident
day) by state, before and after linkage. As a
result, we proceeded with the linked sample
for our analysis.
Table 3 shows injury rates for the 3 states.
We found that overall injury rates in Maryland
were the lowest, which was expected because
the injury rates were calculated from
workers’ compensation claims rather than
from FROI data. However, the almost total
lack of claims filed by nurse aides in Maryland
was unexpected. We also found other
differences in rates by state, which likely reflected
differences in injury reporting and
coding.
The results of the ordinary least squares regression
showed that total nurse hours per
resident day was significantly associated with
worker injuries after we adjusted for acuity,
profit status, aide training, total residents, and
state (
25% of the variance in worker injury was explained
by the model (Table 4). For each additional
hour increase in nursing care, injuries
were predicted to decrease by 2.4 per 100
FTEs. The number of total residents also had
a significant negative effect: as size increased,
worker injuries decreased. To examine this
further, we stratified nursing homes by number
of residents and found that injury rates
were lower in homes where there were more
residents, although staffing did not vary. Because
of the apparent underreporting of injury
rates among nurse aides in Maryland,
we reran the regression models and excluded
Maryland. The results were the same (data
not shown).
Because the injury rates were skewed, we
regressed log-transformed injury rates on untransformed
predictors. For the log-transformed
rates, all relationships and parameter estimates
that were significant in the untransformed
model remained significant, although
the elimination of positive skewness understandably
increased the predictability of the
model to 38% of total variance (Table 4).
Each additional hour of nursing care decreased
the injury rate by nearly 16%. Thus,
for every unit increase in staffing (total hours
of nursing care), worker injury rates decreased
by 2 per 100 FTEs.P=.0004). Our analysis showed that
July 2005, Vol 95, No. 7 | American Journal of Public HealthTrinkoff et al. | Peer Reviewed | Research and Practice | 1223
RESEARCH AND PRACTICE
TABLE 3—Mean Nursing Home Staffing and Injury Rates for Linked Sample, by State:
Online Survey, Certification and Reporting System Database, 2000
Ohio (n = 323) Maryland (n = 76) West Virginia (n = 45)
Mean (SD) Mean (SD) Mean (SD)
Nurse staffing
RN hours per resident day 0.57 (0.27) 0.52 (0.29) 0.35 (0.19)
LPN hours per resident day 0.79 (0.33) 0.60 (0.30) 0.77 (0.68)
Aide hours per resident day 2.24 (0.66) 2.29 (0.39) 2.09 (0.47)
Total nursing hours per resident day 3.60 (0.80) 3.41 (0.70) 3.20 (0.82)
Injuries (per 100 FTE)
RN 4.45 (8.63) 2.67 (8.06) 24.21 (22.78)
LPN 5.55 (11.54) 18.21 (20.42) 10.66 (17.92)
Aide 16.62 (21.35) 0.15 (0.59) 45.01 (96.31)
Total 11.60 (11.94) 3.09 (2.46) 26.83 (18.50)
Note.RN = registered nurse; LPN = licensed practical nurse; FTE = full-time equivalent.
TABLE 2—Characteristics of Skilled Nursing Facilities Included in Total Sample vs. Linked
Facilities: Online Survey, Certification, and Reporting (OSCAR) System Database, 2000
Total OSCAR Sample Sample After Linking OSCAR
for 3 States (n = 1076) and Injury Data (n = 445)
Nurse staffing, mean (SD)
RN hours per resident day 0.54 (0.30) 0.54 (0.27)
LPN hours per resident day 0.74 (0.33) 0.76 (0.38)
Aide hours per resident day 2.18 (0.60) 2.23 (0.61)
Total nursing hours per resident day 3.46 (0.83) 3.52 (0.85)
Proportion RN hours out of total nursing hours per 0.16 (0.07) 0.15 (0.06)
resident day
Acuity index, mean (SD) 10.50 (1.26) 10.59 (1.28)
Total residents, mean (SD) 92.57 (48.66) 96.47 (51.82)
Percentage residents enrolled in Medicaid, mean (SD) 67.32 (20.78) 70.21 (19.17)
Location, number (%)
Urban 793 (73.7) 333 (74.8)
Rural 283 (26.3) 112 (25.2)
Ownership, number (%)
Profit 815 (75.7) 345 (77.5)
Nonprofit/government 261 (24.3) 100 (22.5)
Aide training available, number (%) 427 (39.7) 176 (39.6)
Note.RN = registered nurse; LPN = licensed practical nurse.
DISCUSSION
We combined facility-level data from nursing
homes in 3 states and found that worker
injury rates were strongly associated with
staffing levels. Findings were consistent
across the 3 states despite differences in
data collection, injury classifications, and
reporting procedures. Additionally, a sizable
proportion of variance in worker injury was
explained by staffing. These data support
smaller studies of single nursing homes and
hospitals, which have also shown this association.
17,30
allowed us to circumvent some of the limitations
of individual-level research on working
conditions.
may be difficult if not impossible to ascertain
because of turnover, floating among health
care workers, and multiple workers caring
for a single resident. Examining injuries to
all resident care workers who worked in
long-term care facilities in 3 states (a total of
more than 400 facilities) enhanced the generalizability
of the findings.
The consistency of the association between
staffing and injury across states and facilities
is noteworthy and supports the credibility of
the findings, although there are limitations to
our study. The ecological design did not allow
us to make inferences about individual workers.Using an ecological framework31 Individual-level associations
32
hindered our ability to link across databases,
and the presence of missing data in certain
fields (e.g., occupation) also reduced the completeness
of the data analysis. Despite these
limitations, comparison of descriptors from
nursing homes in the original OSCAR sampling
frame with those in the linked frame
showed surprisingly few differences.
As expected, state variables were highly
significant, which underscores the importance
of adjusting for them in a combined model.
A minimal number of injuries were reported
by nurse aides in Maryland. Although the
exact reason for this is unknown, the injured
aides in Maryland most likely did not file
workers’ compensation claims, probably
owing to a lack of awareness; posting the law
in the workplace is not required. Also, the injury
definitions can reduce the likelihood of
filing claims, e.g., back injuries in Maryland
must have an acute onset to be claimable.
Furthermore, claims in Maryland must be
filed and signed by the injured employee—a
provider or other party cannot initiate the
claim—which may serve as a disincentive to
file among those who have insecure jobs.
Profit status and acuity were not significantly
associated with worker injury when
state, size, and staffing were controlled. On
the other hand, Banuszak-Holl and HinesMissing data from the injury databases22
found that nursing turnover, a factor correlated
with injuries, was higher among forprofit
nursing homes, which also tended to
have lower staffing ratios.
true for our sample. The lack of impact of
aide training was unexpected, because training
has been associated with lower injury
rates,
the impact of staffing in these studies. Adjust
| Research and Practice | Peer Reviewed |33 This was also17 although we did not take into account1224Trinkoff et al. American Journal of Public Health | July 2005, Vol 95, No. 7
RESEARCH AND PRACTICE
TABLE 4—Ordinary Least Squares Regression (OLS) and Log-Transformed Models of Worker
Injury per 100 FTE on Staffing (nursing hours PRD), For-Profit ownership, Aide Training,
Acuity, Total Residents and State: 2000 (N=445)
OLS Model Log-Transformed Model
Worker injury/100FTE Worker Injury/100 FTE
β
Nursing hours PRD –2.39 0.67 0.0004 –0.16 0.047 0.001
For-profit ownership 0.24 1.41 0.862 0.06 0.10 0.571
Aide training –0.16 1.18 0.892 –0.01 0.08 0.933
Acuity (Acuindex) –0.01 0.45 0.986 –0.03 0.03 0.385
Total residents –0.03 0.01 0.003 –0.01 0.00 < .0001
West Virginia (reference = Maryland) 21.82 2.27 < .0001 2.04 0.16 < .0001
Ohio (reference = Maryland) 7.77 1.59 < .0001 1.03 0.11 < .0001SE(β) P (β) β SE(β) P (β)
Note.
OLS modelFTE = full time equivalent; PRD = per resident day; SE = standard error.R2 = 0.25; log-transformed model R2 = 0.38.
ing for differences in resident acuity removed
case mix as a potential source of confounding,
which was important because nursing homes
with more dependent residents may have
higher rates of worker injury. It is also possible
that such homes have more assistive
equipment that reduces injury risk to workers.
22
home staffing are often made on the basis of
staff-to-resident ratio or hours per resident
day, with no accounting for differences in
acuity. This is reflected in our data, wherein
the acuity index from the OSCAR database
was not correlated with staffing (
Ongoing research is being conducted to examine
the association between acuity and
staffing in nursing homes.
The OSCAR data also have limitations.
The Centers for Medicare and Medicaid Services
performs edit checks on the OSCAR
data to identify errors. Straker
1995 OSCAR data with data from the Ohio
Department of Health to examine consistency
in several variables, including staffing. Staffing
correlations per patient day were 0.61, although
self-reports did not typically cover the
same period reported as the OSCAR assessment.
Another study examined actual payroll
and found correlations less than 0.5 between
the data reported in both the OSCAR and the
payroll,
exclusion criteria.
As for the worker injury data, some injuries
will be missed even with the use of
FROI data. For example, workers may seek
injury care from their regular health provider
and fail to mention that the injury is workrelated.The current approaches to nursingr =0.03).34 compared35 although these analyses had strict
26
generally a more complete source of potentially
claimable injuries to health care workers
than workers’ compensation data.
the hours worked would exclude paid nonwork
time, although we had no way to remove
this from our analysis. However, this
time is minimal among nurses, who often skip
breaks and lunches and perform uncompensated
overtime because of short staffing.Despite such limitations, FROIs are27 Ideally,36,37
Because injury data from the 3 states were
treated similarly in our analysis, these distinctions
should not affect the ability to associate
injuries with staffing.
Despite our successful attempt at using different
worker injury databases from multiple
states in this analysis, there should be standardization
of both reported data and definitions
of worker injury.
at the facility level should be available
even when facilities manage injuries at the
corporate level to allow for analysis of staffing
and related outcomes. The National Quality
Forum now recommends that staffing and
skill mix be examined as performance measures
for evaluating health care quality.38 Outcomes data reported39
Our study has shown that the impact of
staffing is also important for worker health.
By improving staffing levels in nursing homes,
both workers and residents will benefit. With
the impending shortage of long-term care
workers, it is imperative that we promote the
health of this essential group of care providers;
they will be increasingly needed to care
for an aging population.
About the Authors
Alison M. Trinkoff, Carles Muntaner, and Rong Le are
with the Department of Family and Community Health,
and Meg Johantgen is with the Department of Organizational
Systems and Adult Health, University of Maryland
School of Nursing, Baltimore, Md. Carlos Muntaner is also
with the Center for Addiction and Mental Health, University
of Toronto, Toronto, Ontario, and the Institute of Work
and Health, Toronto.
Requests for reprints should be sent to Alison M.
Trinkoff, ScD, University of Maryland School of Nursing,
655 W Lombard St, Rm 625, Baltimore, MD 21201
(email: trinkoff@son.umaryland.edu).
This article was accepted September 9, 2004.
Contributors
A.M. Trinkoff originated the study, supervised its implementation,
and led the writing. M. Johantgen created the
database and directed the data analysis. C. Muntaner
assisted with the study, analysis of findings, and article
preparation. R. Le assisted with the study and completed
the analyses. All authors originated ideas, interpreted
findings, and reviewed drafts of the article.
Acknowledgments
Support was provided by the Agency for Healthcare Research
and Quality (grant number R01 HS11990).
Human Participant Protection
The project was reviewed by the institutional review
board of the University of Maryland and was determined
to be exempt from the institutional review
board approval process according to DHHS 45 CFR
46.101.b (4).
References
1. Bureau of Labor Statistics.
Illness in 1998.
oshwc/osh/os/osnr0009.pdf. Accessed October 10,
2000.
2. Kinsella K, Velkoff VA.Workplace Injuries andAvailable at: http://www.bls.gov/iif/An Aging World: 2001.
Available at: http://www.census.gov/prod/2001pubs/
p95-01-1.pdf. Accessed July 9, 2002.
3. American Association of Retired Persons (AARP).
Across the States: Profiles of Long-Term Care Systems.
Washington, DC: Long-Term Care Policy Institute;
2000.
4. Bureau of Labor Statistics.
and Illnesses: Characteristics and Resulting Time Away
from Work, 1998.
news.release/History/osh2.04202000.news. Accessed
October 10, 2000.
5. Bureau of Labor Statistics.
and Illnesses: Characteristics and Resulting Time Away
from Work, 1999.
news.release/history/osh2.03282001.news. Accessed
May 17, 2001.
6. Collins JW, Owen BD. NIOSH research initiativesLost-Worktime InjuriesAvailable at: ftp://ftp.bls.gov/pub/Lost-Worktime InjuriesAvailable at: ftp://ftp.bls.gov/pub/
July 2005, Vol 95, No. 7 | American Journal of Public HealthTrinkoff et al. | Peer Reviewed | Research and Practice | 1225
RESEARCH AND PRACTICE
to prevent back injuries to nursing assistants, aides, and
orderlies in nursing home.
421–424.
7. Personick ME. Nursing home aides experience increase
in serious injuries.
30–37.
8. Meyer J, Muntaner C. Injuries in home health care
workers: An analysis of occupational morbidity from a
state compensation database.
295–301.
9. SHARP: Safety and Health Assessment and Research
for Prevention Program.
of the Back and Upper Extremity in Washington State,
1989–1996.
of Labor and Industries; 1998. Technical Report
No.: 40-1-1997.
10. Allen A. On-the-job injury: a costly problem.
Anesth Nurs.
11. Brulin C, Gerdle B, Granlund B, Hoog J, Knutson A,
Sundelin G. Physical and psychological work-related
risk factors associated with musculoskeletal symptoms
among home care personnel.
12:104–110.
12. Marras WS, Davis KG, Kirking BC, Bertsche PK.
A comprehensive analysis of low-back disorder risk
and spinal loading during the transferring and repositioning
of patients using different techniques.Am J Ind Med. 1996;29:Mon Labor Rev. 1990;113:Am J Ind Med. 1999;35:Work-Related DisordersOlympia, Wash: Washington State DepartmentJ Post1990;5:367–368.Scand J Caring Sci. 1998;Ergonomics.
1999;42:904–926.
13. Sosnowitz B, Hriceniak J. Neonatal intensive care
units can be hazardous to nurses’ health.J Perinatol.
1998;8:253–257.
14. Trinkoff AM, Storr CL, Lipscomb JA. Physically
demanding work and inadequate sleep, pain medication
use, and absenteeism in registered nurses.
Environ Med.
15. Bongers PM, De Winter CR, Kompier MAJ, Hildebrandt
VH. Psychosocial factors at work and musculoskeletal
disease.
19:297–312.
16. Shogren E, Calkins A.
nurses Association Research Project on Occupational Injury/
Illness in Minnesota Between 1990–1994.
Minn: Minnesota Nurses Association;1997.
17. Wunderlich GS, Sloan FA, Davis CK, eds.
Staff in Hospitals and Nursing Homes.
National Academy Press; 1996.
18. Evidence-Based Practice Center (Oregon Health
and Science University).
Conditions on Patient Safety
Assessment No. 74. Rockville, Md: Agency for Healthcare
Research and Quality; 2003. AHRQ Publication
No. 03-E024. Contract number 290-97-0018.
19. Muntaner C, Lynch J, Oates G. The social class
determinants of income inequality and social cohesion.J Occup2001;43:355–363.Scand J Work Environ Health. 1993;Findings of the MinnesotaSt. Paul,NursingWashington, DC:The Effect of Healthcare Working. Evidence Report/Technology
Int J Health Serv.
20. Myers D, Silverstein B, Nelson NA. Predictors of
shoulder and back injuries in nursing home workers: a
prospective study.
21. Owen BD, Garg A. Patient handling tasks perceived
to be most stressful by nursing assistants. In:
Mital A, editor
Safety 1.
775–781.
22. Banaszak-Holl J, Hines MA. Factors associated
with nursing staff turnover.
512–517.
23. Remsburg RE, Armacost KA, Bennett RG. Improving
nursing assistant turnover and stability rates
in a long-term care facility.
203–208.
24. Parsons SK, Simmons WP, Penn K, Furlough M.
Determinants of satisfaction and turnover among nursing
assistants. The results of a statewide survey
Nurs.
25.
longtermcareinfo.com/crg/. Accessed May 17, 2001.
26. Boden LI. Workers’ compensation in the United
States: high costs, low benefits.1999;29:699–732.Am J Ind Med. 2002;41:466–476.. Advances in Industrial Ergonomics andLondon, UK: Taylor & Francis; 1989:Gerontologist. 1996;36:Geriatr Nurs. 1999;20:. J Gerontol2003;29:51–58.Cowles Research Group. Available at: http://www.Annu Rev Public Health.
1995;16:189–218.
27. Wickizer TM, Franklin G, Plaeger-Brockway R,
Mootz RD. Improving the quality of workers’ compensation
health care delivery: the Washington state occupational
health services project.
5–33.
28. Bureau of Labor Statistics.
Health Definitions.
oshdef.htm. Accessed June 29, 2004.
29. Harrington C, Carrillo H, Thollaug S, Summers P,
Wellin V.
Deficiencies, 1993 Through 1999.
http://www.cms.hhs.gov/medicaid/services/nursfac99.
pdf. Accessed October 12, 2000.
30. Clarke SP, Sloane DM, Aiken LH. Effects of hospital
staffing and organizational climate on needlestick
injuries to nurses.
1115–1119.
31. Petrisek AC, Mor V. Hospice in nursing homes.Milbank Q. 2001;79:Occupational Safety andAvailable at: http://stats.bls.gov/iif/Nursing Facilities, Staffing, Residents, and FacilityAvailable at:Am J Public Health. 2002;92:
Gerontologist.
32. Robinson WS. Ecological correlations and the
behavior of individuals.
351–357.
33. Harrington C, Woolhandler S, Mullan J, Carrillo H,
Himmelstein DU. Does investor-ownership of nursing
homes compromise the quality of care.
Serv.
34. Straker, J.
and Staff Data: A Comparison with the Ohio Department
of Health Annual Survey of Long-Term Care Facilities.1999;39:279–290.Am Sociol Rev. 1950;15:Int J Health2002;32:315–325.Reliability of OSCAR Occupancy, Census
Miami, Fla: Scripps Gerontology Center, Miami University;
1999. Technical Report Series 3-01.
35. Centers for Medicare and Medicaid Services.
to Congress: Appropriateness of Minimum Nurse
Staffing Ratios in Nursing Homes Phase II Final Report;
2001 Dec. Contract No.: 500-95-0062-T.O.3.
at: http://www.cms.hhs.gov/medicaid/reports/
rp1201home.asp. Accessed July 9, 2002.
36. Shulman B.
Jobs Fail 30 Million Americans.
Norton & Co Inc; 2003.
37. Gass TE, Vladeck BC.
of a Nursing Home Aide.
University Press; 2004.
38. Johantgen M, Trinkoff A, Gray-Siracusa K,
Muntaner C, Nielsen K. Using state administrative data
to study nonfatal worker injuries: challenges and opportunities.ReportAvailableThe Betrayal of Work: How Low-WageNew York, NY: WWNobody’s Home: Candid ReflectionsNew York, NY: Cornell
J Safety Res.
39. National Quality Forum.
Standards for Nursing-Sensitive Performance Measurement.2004;35:309–315.National Voluntary Consensus
Available at: http://www.qualityforum.org/
nursing_sensitive_performance_measurement.html.
Accessed February 4, 2004.
T
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and worker injury rates in 445 nursing homes in 3 states.