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Sehgal AR. Impact of Quality Improvement Efforts on Race and Sex Disparities in Hemodialysis. JAMA. 2003;289(8):996–1000. doi:10.1001/jama.289.8.996
Author Affiliations: Division of Nephrology and Center for Health Care Research and Policy, MetroHealth Medical Center, and Departments of Medicine, Biomedical Ethics, and Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio.
Context By improving the process of care, quality improvement efforts have the
potential to reduce race and sex disparities. However, little is known about
whether reductions actually occur. National quality improvement activities
targeting hemodialysis patients provide an opportunity to examine this issue.
Objective To determine the effect of quality improvement efforts on race and sex
disparities among hemodialysis patients.
Design, Setting, and Subjects Longitudinal study of 58 700 randomly selected hemodialysis patients
from throughout the United States in 1993 through 2000.
Intervention Medicare-funded quality improvement project involving monitoring of
patient outcomes, feedback of performance data, and education of clinicians
at dialysis centers.
Main Outcome Measures Changes in hemodialysis dose (Kt/V), anemia management (hemoglobin level),
and nutritional status (albumin level).
Results The proportion of all patients with an adequate hemodialysis dose increased
2-fold. In 1993, 46% of white patients and 36% of black patients received
an adequate hemodialysis dose compared with 2000 when the proportions were
87% and 84%, respectively. Thus, the gap between white and black patients
decreased from 10% to 3% (P<.001). The gap between
female and male patients decreased from 23% to 9% over the same period (P = .008). The proportion of all patients with adequate
hemoglobin levels increased 3-fold. The proportion of all patients with adequate
albumin levels remained unchanged. Race and sex disparities in anemia management
and nutritional status did not change significantly.
Conclusions Quality improvement efforts have a variable impact on race and sex disparities
in health outcomes. Further work is needed to determine how quality improvement
methods can be targeted to reduce health disparities.
Race and sex disparities in health outcomes have been extensively documented.1 For example, blacks and women are less likely to receive
kidney transplantation than whites and men.2,3 By
improving the process of care, quality improvement efforts have the potential
to reduce race and sex disparities in health outcomes.4,5 Alternatively,
the patient, clinician, and societal factors that created disparities in the
first place may persist and result in a continued gap between whites and blacks
(or men and women) even as outcomes for both white and black patients improve.6,7 Examples of such factors include affordability
of health care, geographic access, transportation, education, knowledge, literacy,
health beliefs, racial concordance between patient and clinician, patient
attitudes and preferences, competing demands such as work or child care, and
Little is known about the actual impact of quality improvement activities
on health disparities.8 It would be particularly
interesting to know if successful quality improvement efforts that do not
specifically target race or sex have a beneficial impact on health disparities.
This study examines the impact of national quality improvement activities
on race and sex disparities among hemodialysis patients.9,10
The Centers for Medicare and Medicaid Services (formerly the Health
Care Financing Administration) randomly selected several thousand adult hemodialysis
patients each year as part of a quality improvement project.9,10 The
number of subjects sampled annually increased gradually from 6141 in 1993
to 8416 in 2000.11,12
The national intervention involved several steps.9,10 First,
a work group composed of individuals with expertise in hemodialysis treatment
identified indicators that represent key components of dialysis care. As such,
the indicators could be used to trigger quality improvement activities. This
work group selected (1) urea reduction ratio as an indicator of adequate hemodialysis
dose; (2) hematocrit as an indicator of anemia management; (3) albumin level
as an indicator of nutritional status; and (4) blood pressure. These indicators
are all linked with a hemodialysis patient's mortality, morbidity, and/or
quality of life.13-15 For
example, a 5-point increase in urea reduction ratio and a 0.1-g/dL increase
in albumin level are associated with 11% and 13% reductions in mortality risk,
respectively.14,16 In addition,
treatment of anemia is associated with improvements in many aspects of a hemodialysis
patient's quality of life, including energy and activity level, sleep, disease
symptoms, and psychological affect.13 Second,
regional quality oversight organizations (called End Stage Renal Disease Networks)
monitored these indicators every October, November, and December for the national
random patient sample. Work groups later modified the indicators by substituting
Kt/V (a related measure of hemodialysis dose in which K represents dialyzer
clearance [expressed in milliliters per minute] and is multiplied by time
and divided by the volume of water a patient's body contains) for urea reduction
ratio, substituting hemoglobin for hematocrit, and eliminating blood pressure.
Third, the Centers for Medicare and Medicaid Services distributed region-specific
performance data to all clinicians. Fourth, the End Stage Renal Disease Networks
sent educational material to clinicians, conducted workshops, and supervised
poorly performing facilities.
The Centers for Medicare and Medicaid Services asked dialysis facility
staff to abstract medical records to obtain quality indicator data for each
patient in the national random sample for the months of October, November,
and December. The Centers for Medicare and Medicaid Services then averaged
the first monthly value for hemodialysis dose, hematocrit/hemoglobin level,
and albumin level and compared patient-specific indicators against the following
guideline-based benchmarks: urea reduction ratio of 65 or higher (or Kt/V
≥1.2), hematocrit of 33% or higher (or hemoglobin ≥11 g/dL), and albumin
level of 3.5 g/dL or higher with the bromcresol green method (or ≥3.2 g/dL
with the bromcresol purple method).17-19 From
publicly available annual reports, these data were obtained in sufficient
detail to perform the analyses presented in this article.11,12,20-25 The
use of aggregational data from publicly available reports exempted this study
from institutional review board approval. The first year of the project involved
data collection only, while subsequent years involved data collection, feedback,
and educational activities.
The proportion of all patients who achieved an adequate hemodialysis
dose for each year from 1993 through 2000 was examined, as well as the proportion
of whites and blacks achieving these benchmarks. Logistic regression was used
to examine the relationship between achieving an adequate hemodialysis dose
and (1) race, (2) year, and (3) race × year interaction. A statistically
significant interaction between race and year indicated that the gap between
whites and blacks in achieving an adequate hemodialysis dose changed over
this period. The relationship between achieving an adequate hemodialysis dose
and (1) sex, (2) year, and (3) sex × year interaction was examined.
Similar analyses to examine other quality indicators were used. P<.05 was the level of significance used in this study and JMP software
(version 3.2, SAS Institute Inc, Cary, NC) was used to perform statistical
Of 58 700 subjects, 53% were white, 37% were black, 52% were men,
38% had renal failure due to diabetes mellitus, and 27% had renal failure
due to hypertension. The age distribution was 18 to 44 years, 18%; 45 to 64
years, 37%; and 65 years or older, 45%.
The proportion of all patients with an adequate hemodialysis dose increased
2-fold from 43% in 1993 to 86% in 2000. In 1993, 46% of white patients and
36% of black patients received an adequate dose (Figure 1). Corresponding figures for 2000 were 87% and 84%, respectively.
Thus, the gap between white and black patients decreased from 10% to 3% (parameter
estimate for race × year interaction = −0.015; 95% confidence
interval [CI], −0.024 to −0.006; P<.001).
In 1993, 54% of female patients and 31% of male patients received an adequate
hemodialysis dose (Figure 1). Corresponding
figures for 2000 were 91% and 82%, respectively. Thus, the gap between female
and male patients decreased from 23% to 9% (parameter estimate for sex ×
year interaction = −0.012; 95% CI, −0.021 to −0.003; P = .008). In addition, the magnitude of gaps between whites
and blacks and between women and men varied by region. Eleven regions had
race gaps of 4% or less (Figure 2).
However, no region had similarly small sex gaps (Figure 2).
The proportion of all patients with an adequate hemoglobin level increased
3-fold, from 26% in 1993 to 74% in 2000. As indicated in Figure 3, the gap between white and black patients varied from 2%
to 6% during this period. There was no significant change in the magnitude
of the gap during the interval (P = .90). The gap
between male and female patients varied from 2% to 7%, and did not change
significantly during the period (P = .14).
The proportion of all patients with an adequate albumin level did not
change significantly from 78% in 1993 to 80% in 2000. The gap between black
and white patients varied from 2% to 6%, and did not change significantly
during this period (P = .43). The gap between male
and female patients varied from 3% to 7% and did not change significantly
during this period (P = .09).
Dramatic improvements in adequate hemodialysis dose from 1993 through
2000 were accompanied by reductions of about two thirds in race and sex gaps
in a large, nationally representative sample. Similarly large improvements
in anemia management were not accompanied by reductions in race and sex gaps.
In addition, neither overall nutritional status nor associated race and sex
gaps changed during this interval. The reduction in race and sex gaps in hemodialysis
dose suggests that quality improvement efforts may reduce disparities. However,
sizeable gaps were still present for hemodialysis dose in 2000. This, along
with persistent gaps related to anemia and nutrition, indicates that current
quality improvement efforts may be insufficient to eliminate race and sex
These findings raise several points. First, the observed changes appear
to be due to the Medicare quality improvement project. While it is not possible
to establish a causal relationship without a concurrent control group, a previous
study found evidence for a dose-response relationship. Specifically, the End
Stage Renal Disease Networks that engaged in more intensive intervention had
larger quality improvements.10 It is also worth
noting that there was an approximately 5% decrease in hemodialysis patient
mortality rates during the study period.26 This
improvement is consistent with several previous studies that noted a link
between intermediate outcomes, such as hemodialysis dose, and global outcomes,
such as mortality and morbidity.14-16,27,28
Second, all of the quality indicators did not improve. The 3 indicators
examined require different levels of involvement by clinicians and patients.
Optimizing dialysis dose requires clinicians to adjust dialysis prescriptions
(eg, by increasing blood flow rate) and patients to stay for the full treatment
time.29 Clinicians also play a key role in
anemia management (eg, by administering intravenous erythropoeitin). However,
patient medical factors may limit the response to erythropoietin.30 By contrast, improvements in albumin levels largely
depend on patients' ability to follow dietary recommendations (eg, to increase
intake of protein-containing foods) and on nonnutritional factors such as
chronic inflammation.31,32 The
ability of clinicians to increase dialysis prescriptions or administer drugs
during treatment may explain why adequate dialysis doses and hemoglobin levels
improved while albumin levels did not.
Third, race and sex disparities were not eliminated. This is especially
concerning because the magnitude of quality improvement from 1993 through
2000 was often much larger than baseline race and sex gaps. For example, the
proportion of all patients receiving an adequate dialysis dose increased by
about 45% from 1993 to 2000 (from approximately 40% to 85%) while the baseline
race gap was 10% (Figure 1). Because
Medicare covers the cost of dialysis-related care, the persistent race and
sex gaps cannot be attributed to lack of health insurance.33 A
combination of patient and clinician factors is likely to be responsible for
health disparities. For example, black and male patients are larger on average
than white and female patients. As a result, they may need a longer treatment
time to achieve an adequate dialysis dose.34 However,
nephrologists often fail to increase prescribed treatment time appropriately
for larger patients.35 Blacks and men are also
more likely to shorten or skip treatments than whites or women.36 Similarly,
the proerythropoietic effect of androgens may contribute to the differences
in hemoglobin levels between men and women.30 However,
clinicians should be able to overcome this biological difference by administering
a larger dose of erythropoietin.18
Fourth, quality improvement methods should be better used to eliminate
disparities. The marked regional differences in the magnitude of disparities
(Figure 2) suggest that such disparities
are not an inherent feature of dialysis treatment. Studying patient and clinician
factors in regions with minimal disparities may help determine what interventions
are needed in regions with larger disparities. Increasing the overall intensity
of quality improvement efforts may also be helpful. Two earlier reports suggested
that intensive treatment of hypertension and depression in the context of
clinical trials largely eliminated disparities.37,38 By
contrast, the intensity of intervention and follow-up in quality improvement
activities is typically less than that in clinical trials.
Fifth, there are different ways to quanitate health disparities. Instead
of focusing on absolute differences, the results presented in the figures
could also be used to calculate relative measures of disparity. For example,
the absolute difference between whites and blacks in 1993 was 10% (Figure 1). This corresponds to a white-to-black
odds ratio of 1.51 (95% CI, 1.36-1.69). In 2000, the absolute difference was
3% while the odds ratio was 1.27 (95% CI, 1.12-1.45). Absolute and relative
measures generally provide complementary information, but sometimes give apparently
conflicting results when evaluating changes over time.1,39 It
is also worth noting that a reduction in disparities between whites and blacks
may occur in several ways: (1) whites improve but blacks improve even more,
(2) whites remain unchanged and blacks improve, and (3) whites worsen and
blacks remain unchanged or improve.
Limitations of the study include the fact that other local and regional
quality improvement activities were probably going on at the same time as
the Medicare-funded national initiative.19 Thus,
the observed changes may represent the cumulative effect of multiple quality
improvement activities. Nevertheless, these cumulative activities had a variable
impact on health disparities. In addition, demographic and medical characteristics
such as socioeconomic status and comorbid conditions were not available and
could not be adjusted for. However, previous work with hemodialysis patients
suggests that process-of-care factors are much more important than such demographic
and medical characteristics in determining quality outcomes.29,40 Data
to determine which facility characteristics are predictive of greatest improvement
were not available. Further work is needed to examine clinician characteristics,
to study disparities among nonrenal patients, and to explore other types of
health disparities, such as those related to patient socioeconomic status
and comorbid conditions.
In conclusion, quality improvement methods are promising, but insufficient
in their current form to eliminate health disparities among hemodialysis patients.
Race and sex disparities should be targeted as part of quality improvement
activities. Outcomes of whites, blacks, men, and women should be monitored
separately, and race- and sex-specific quality improvement methods should
be developed when appropriate.5,8
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