Assignment 1
jamex.a.bowher2s
Article3TheQualityofSurgicalandPneumonia.pdf
The Quality of Surgical and Pneumonia Care in Minority-Serving and Racially Integrated Hospitals Darrell J. Gaskin, Hossein Zare, Adil H. Haider, and Thomas A. LaVeist
Objective. To explore the association between quality of care for surgical and pneu- monia patients and the racial/ethnic composition of hospitals’ patients. Data Source. Our primary data were surgical and pneumonia processes of care indi- cators from the 2012 Medicare Hospital Compare Data. We merged this data with information from the 2011 American Hospital Association Annual Survey of Hospi- tals. We computed the racial and ethnic composition of hospital patients using 2008 data from the Healthcare Costs and Utilization Project. Study Design. The sample included 1,198 acute care general hospitals from 11 states: AZ, CA, FL, IA, MA, MD, NC, NJ, NY, WA, and WI. We compared quality across minority-serving, racially integrated, and majority-white hospitals using unconditional quantile regression models controlling for hospital and market characteristics. Principal Findings. We found quality differences between the lowest performing minority-serving, racially integrated, and majority-white hospitals. As we moved from 10th to 90th quantile, the quality differences between hospitals by patients’ racial com- position disappeared. In other words, the best minority-serving and racially integrated hospitals performed as well as the best majority hospitals. Conclusions. Efforts to improve quality of care for patients in minority-serving and racially integrated hospitals should focus on the lowest performers. Key Words. Hospitals, disparities, quality
Prior studies have found that racial and ethnic disparities in hospital quality are due to differences across hospitals rather than differences within hospitals (Lucas et al. 2006; Goldman and Dudley 2008; Culler et al. 2010; Hasnain- Wynia et al. 2010; Mayr et al. 2010; Popescu et al. 2010). This calls into ques- tion the quality of care provided by hospitals serving minority patients. These studies imply that minority-serving hospitals underperform on quality indica- tors when compared to other hospitals. Possible explanations for the poor
© Health Research and Educational Trust DOI: 10.1111/1475-6773.12394 RESEARCH ARTICLE
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Health Services Research
performance of minority-serving hospitals are a lack of resources (Jha, Orav, and Epstein 2010a; Haider et al. 2013), inadequate budgets, and a culture of mediocre or poor quality. Also, minority-serving hospitals may face chal- lenges attracting and retaining clinical and administrative expertise because of their payer mix. Another explanation is that minority-serving hospitals are unfairly labeled as low quality because inadequate risk adjustment fails to account for unobservable risk factors associated with their vulnerable patient populations (Lucas et al. 2006; Jha, Orav, and Epstein 2011).
Numerous studies have sought to identify characteristics of hospitals that are associated with low-quality care. Researchers have found that minority patients are more likely to receive care in safety net and Medicaid-dependent hospitals (Kind et al. 2010; Ly et al. 2010), hospitals that are located in poor communities (Lucas et al. 2006; Sarrazin, Campbell, and Rosenthal 2009; Culler et al. 2010; Ly et al. 2010), teaching hospitals (Gaskin et al. 2008; Jha et al. 2010b; Kind et al. 2010; Haider et al. 2013), public hospitals (Gaskin et al. 2008, 2011; Ly et al. 2010), hospitals with fewer nurses per patient-day (Ly et al. 2010), and hospitals with greater lengths of stay (Joynt, Orav, and Jha 2011). These characteristics associated with minority patient use are also associated with quality.
These systematic differences in hospital quality associated with the racial and ethnic composition of the patient population create a challenge for policy makers interested in improving hospital quality. The Affordable Care Act cre- ated the Centers for Medicare and Medicaid Services (CMS) Innovation Cen- ter to develop, test, and promulgate new payment reforms (PPACA 2010b) that would incentivize providers to improve quality (PPACA 2010a). One of the criteria for evaluating new payment reforms is whether it reduces dispari- ties. The association between racial and ethnic composition of patients and hospital quality raises concerns that policies that financially penalize poor per- formance will have the unintended consequence of increasing disparities in quality (Karve et al. 2008; Ly et al. 2010; Joynt, Orav, and Jha 2011). Some
Address correspondence to Darrell J. Gaskin, Ph.D., Department of Health Policy and Manage- ment, Hopkins Center of Health Disparities Solutions, Johns Hopkins Bloomberg School of Pub- lic Health, 624 North Broadway, Suite #441, Baltimore, MD 21205; e-mail: [email protected]. Hossein Zare, Ph.D., is with the Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Faculty Appointments & Services, University of Maryland University College (UMUC), Adelphi, MD. Adil H. Haider, M.D., M.P.H., F.A.C.S., is with the Center for Surgery and Public Health, Brigham and Women’s Hospitals, Boston, MA. Thomas A. LaVeist, Ph.D., is with the Department of Health Policy and Management, Hopkins Center of Health Disparities Solutions, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
2 HSR: Health Services Research
researchers warn that pay-for-performance policies would have the perverse effect of reducing resources in hospitals that serve higher proportions of minority patients because these hospitals may be unable to meet quality stan- dards due to systemic factors beyond their control (Jha, Orav, and Epstein 2010a; Ly et al. 2010). Hence, reimbursement policies designed to improve quality may compromise minority-serving hospitals’ ability to provide high- quality care to their patients.
While studies have found hospitals’ percentage of minority patients is inversely related, this finding negatively impacts the image of minority-serv- ing hospitals (Epstein, Gray, and Schlesinger 2010; Kind et al. 2010; Mayr et al. 2010) and ignores high-performing minority-serving hospitals. In this study, we compare quality between minority-serving and nonminority-serv- ing hospitals to determine whether the best minority-serving hospitals are comparable to the best nonminority-serving hospitals. We used quality indica- tors from the Medicare Hospital Compare database. This database contains quality indicators for a broad range of services such as surgical, pneumonia, heart attack, heart failure, emergency department, preventive care, children’s asthma, stroke care, blood clot prevention and treatment, and pregnancy and delivery care (CMS 2012b). In this article, we focus on surgical and pneumo- nia process of care measures. We selected these two processes of care because they are objective measures that are not subject to patient perception or inter- pretation. Also they are less subject to inadequate risk adjustment due to patients’ underlying unobserved health conditions.
DATA AND METHODS
Data
In this study, we focused on acute care general hospitals from 11 states: AZ, CA, FL, IA, MA, MD, NC, NJ, NY, WA, and WI. We selected these states because we could use their state inpatient discharge data to measure the racial and ethnic composition of their hospitals’ patients. We used 2012 data from Medicare Hospital Compare to measure hospital quality (CMS 2012b). Specifically, we used 18 indicators of timely and effective care measures: 12 surgical care indicators and 6 quality process indicators for pneumonia (Table 1). To measure the racial and ethnic composition of hospitals’ patients, we obtained 2008 state inpatient discharge (SID) data from Healthcare Cost and Utilization Project (HCUP) for the 11 states (SID 2008).
Disparities in Hospital Quality 3
T ab
le 1:
C o m p ar in g S u rg ic al
an d P n eu
m o n ia
P ro ce ss
o f C ar e M ea su re s in
M aj o ri ty -W
h it e H o sp it al s w it h M in o ri ty -
Se rv in g an
d R ac ia ll y In te gr at ed
H o sp it al s, w it h at le as tT
h ir ty
O b se rv at io n s fo r E ac h In d ic at o r
Q ua li ty In di ca to rs
Ty pe
of H os pi ta lb y R ac e/ E th ni cC
om po si ti on
of P at ie nt s
p va lu es fr om
t- te st s
M aj or it y- W hi te
R ac ia ll y In te gr at ed
M in or it y- Se rv in g
W hi te ve rs us
R ac ia ll y
In te gr at ed
M aj or it y W hi te
ve rs us
M in or it y- Se rv in g
O bs .
M ea n (S D )
O bs .
M ea n (S D )
O bs .
M ea n (S D )
R o w
S u rg ic al p ro ce ss o fc ar e m ea su re s
1 P er ce n to
fs u rg er y p at ie n ts gi ve n an
an ti b io ti c at th e ri gh
tt im
e (w
it h in
1 h o u r
b ef o re
su rg er y)
to h el p p re ve n ti n fe ct io n
6 5 5
97 .3 5 (2 .7 7)
2 07
97 .4 0 (2 .9 4)
12 6
9 6. 5 3 (4 .0 3)
0. 81 79
0. 00
5 0
2 P er ce n to
fs u rg er y p at ie n ts w h o se
p re ve n ti ve
an ti b io ti cs
w er e st o p p ed
at th e ri gh
tt im
e (w
it h in
2 4 h o u rs af te r su rg er y)
6 5 3
9 6. 2 4 (2 .9 5)
2 07
9 5. 07
(4 .4 8)
12 4
9 3. 0 6 (5 .1 7)
0. 00
00 0. 00
00
3 P er ce n to
fs u rg er y p at ie n ts w h o w er e gi ve n
th e ri gh
tk in d o fa n ti b io ti c to
h el p p re ve n t
in fe ct io n
6 5 5
97 .8 3 (1 .9 5)
2 07
97 .4 7 (2 .6 2)
12 6
9 6. 07
(3 .6 4)
0. 0 37 4
0. 00
00
4 P er ce n to
fs u rg er y p at ie n ts w h o go
t tr ea tm
en ta tr ig h tt im
e (w
it h in
2 4 h o u rs b ef o re
o r af te r su rg er y)
to h el p p re ve n tb
lo o d cl ot
6 4 8
9 4. 81
(4 .2 3)
21 0
9 4. 27
(5 .3 0)
13 1
9 2. 8 3 (6 .8 0)
0. 13 2 8
0. 00
00
5 P er ce n to
fs u rg er y p at ie n ts w h o se
d o ct o rs o rd er ed
tr ea tm
en ts to
p re ve n tb
lo o d cl o ts af te r ce rt ai n
ty p es
o fs u rg er ie s
6 4 9
9 5. 91
(3 .9 7)
21 0
9 5. 4 8 (5 .1 9)
13 1
9 3. 75
(7 .1 5)
0. 2 0 2 6
0. 00
00
6 P er ce n to
fa ll h ea rt su rg er y p at ie n ts
w h o se
b lo o d su ga r is ke p tu
n d er
go o d co n tr o li n th e d ay s ri gh
t af te r su rg er y
2 0 4
9 5. 0 6 (3 .1 7)
10 8
9 4. 4 5 (3 .2 8)
3 9
9 3. 9 6 (4 .0 3)
0. 11 27
0. 0 5 9 5
C on ti nu ed
4 HSR: Health Services Research
T ab
le 1.
C on ti nu ed
Q ua li ty In di ca to rs
Ty pe
of H os pi ta lb y R ac e/ E th ni cC
om po si ti on
of P at ie nt s
p va lu es fr om
t- te st s
M aj or it y- W hi te
R ac ia ll y In te gr at ed
M in or it y- Se rv in g
W hi te ve rs us
R ac ia ll y
In te gr at ed
M aj or it y W hi te
ve rs us
M in or it y- Se rv in g
O bs .
M ea n (S D )
O bs .
M ea n (S D )
O bs .
M ea n (S D )
7 P er ce n to
fs u rg er y p at ie n ts n ee d in g
h ai r re m o ve d fr o m
th e su rg ic al ar ea
b ef o re
su rg er y w h o h ad
h ai r re m o ve d
u si n g a sa fe r m et h o d
6 6 4
9 9. 6 4 (0 .9 5)
21 2
9 9. 2 8 (2 .3 9)
13 7
99 .2 2 (1 .7 6)
0. 00 14
0. 00
01
8 P er ce n to
fs u rg er y p at ie n tw
h o se
u ri n ar y
ca th et er s w er e re m o ve d o n th e fi rs to
r se co n d d ay
af te r su rg er y
6 0 2
9 2. 3 8 (6 .4 8)
2 00
91 .3 2 (6 .7 8)
11 2
89 .5 4 (9 .7 7)
0. 0 4 67
0. 00
01
9 P er ce n to
fs u rg er y p at ie n ts w h o w er e
ta ki n g h ea rt d ru gs
co ll ec te d b et a
b lo ck er s b ef o re
co m in g to
th e h o sp it al
58 1
9 5. 16
(3 .9 4)
19 4
9 4. 5 8 (4 .6 4)
10 1
9 2. 72
(6 .1 3)
0. 0 9 01
0. 00
00
10 P er ce n to
fo u tp at ie n ts h av in g su rg er y
w h o go
ta n an
ti b io ti c at th e ri gh
tt im
e, w it h in
1 h o u r b ef o re
su rg er y
5 37
9 4. 3 8 (5 .4 5)
16 6
9 4. 10
(4 .7 2)
9 9
91 .0 4 (7 .3 5)
0. 5 4 3 3
0. 00
00
11 P er ce n to
fo u tp at ie n ts h av in g su rg er y
w h o go
tr ig h tk
in d o fa n ti b io ti c( s)
5 3 8
9 5. 10
(4 .3 3)
16 6
9 4. 3 6 (4 .9 1)
97 93
.2 2 (5 .6 4)
0. 0 6 2 0
0. 00
02
12 P er ce n to
fp at ie n ts h av in g su rg er y
w h o w er e ac ti ve ly w ar m ed
in th e
o p er at in g ro om
o r w h o se
b o d y
te m p er at u re
w as
n ea r n o rm
al b y th e en
d o fs u rg er y
6 6 9
9 9. 6 8 (1 .0 3)
21 2
9 9. 4 8 (2 .1 4)
13 5
99 .2 5 (2 .0 6)
0. 0 6 37
0. 00
03
C on ti nu ed
Disparities in Hospital Quality 5
T ab
le 1.
C on ti nu ed
Q ua li ty In di ca to rs
Ty pe
of H os pi ta lb y R ac e/ E th ni cC
om po si ti on
of P at ie nt s
p va lu es fr om
t- te st s
M aj or it y- W hi te
R ac ia ll y In te gr at ed
M in or it y- Se rv in g
W hi te ve rs us
R ac ia ll y
In te gr at ed
M aj or it y W hi te
ve rs us
M in or it y- Se rv in g
O bs .
M ea n (S D )
O bs .
M ea n (S D )
O bs .
M ea n (S D )
P n eu
m o n ia p ro ce ss o fc ar e m ea su re s
1 P er ce n to
fp n eu
m o n ia p at ie n ts as se ss ed
an d gi ve n p n eu
m o co cc al va cc in at io n
70 0
9 3. 6 3 (7 .5 8)
21 3
9 2. 2 9 (9 .1 9)
13 8
8 9. 16
(1 4. 9 8)
0. 0 3 2 5
0. 00
00
2 P er ce n to
fp n eu
m o n ia p at ie n ts gi ve n
in it ia la n ti b io ti c( s) w it h in
6 h o u rs
af te r ar ri va l
6 9 2
9 5. 9 9 (3 .2 7)
21 2
9 4. 79
(4 .8 9)
13 8
9 3. 00
(6 .3 2)
0. 00
00 0. 00
00
3 P er ce n to
fp n eu
m o n ia p at ie n ts w h o se
in it ia lE
R b lo o d cu lt u re
w as
p er fo rm
ed p ri o r to
ad m in is tr at io n
o ffi
rs td
o se
o fa n ti b io ti cs
6 6 2
9 6. 76
(2 .8 7)
21 4
9 5. 8 5 (3 .6 7)
13 8
9 4. 37
(5 .4 1)
0. 00
02 0. 00
00
4 P er ce n to
fp n eu
m o n ia p at ie n ts gi ve n
sm o ki n g ce ss at io n ad
vi ce /c o u n se li n g
47 8
9 8. 14
(3 .3 3)
16 2
9 8. 0 9 (3 .5 7)
8 8
97 .5 2 (6 .0 4)
0. 87 2 3
0. 16
8 9
5 P er ce n to
fp n eu
m o n ia p at ie n ts gi ve n th e
m o st ap
p ro p ri at e in it ia la n ti b io ti c( s)
6 5 2
9 4. 4 5 (4 .6 4)
21 0
9 4. 4 6 (4 .1 6)
13 4
9 4. 01
(3 .8 6)
0. 97 40
0. 3 05
8
6 P er ce n to
fp n eu
m o n ia p at ie n ts as se ss ed
an d gi ve n in fl u en
za va cc in at io n
6 0 3
9 3. 07
(6 .7 5)
2 0 6
9 0. 5 6 (9 .6 5)
12 7
87 .9 4 (1 2. 47
) 0. 00
00 0. 00
00
*M aj o ri ty
w h it e (i f m in o ri ty
p at ie n t d is ch ar ge s: 0–
3 4%
); in te gr at ed
(i f m in o ri ty
p at ie n ts d is ch ar ge s: 3 5–
6 4%
); m in o ri ty
(i f m in o ri ty
p at ie n t d is ch ar ge s
≥ 6 5%
). † P ro b ab
il it y ch ec ke d m aj o ri ty -w
h it e h o sp it al s w it h in te gr at ed
an d m in o ri ty -s er vi n g h o sp it al s se p ar at el y.
‡ D if fe re n to
b se rv at io n re la te d to
so m e m is si n g O b s.
§ B o n fe rr o n i co rr ec ti o n (a
= 0. 0 5;
0. 00 14 ) em
p lo ye d to
d et er m in e th e st at is ti ca l d if fe re n ce s in
m ea n s b et w ee n m aj o ri ty -w
h it e,
ra ci al ly
in te gr at ed
, an
d m in o ri ty -s er vi n g h o sp it al s.
¶ O b se rv at io n fo r ea ch
in d ic at o r is d if fe re n tb
ec au
se o n ly h o sp it al w it h at le as t3
0 o b se rv at io n s fo r ea ch
in d ic at o r in cl u d ed
. So ur ce :A
u th o rs ’ an
al ys is fr o m
th e M ed
ic ar e C o m p ar e (2 01 0–
2 01 3)
an d S ta te In p at ie n tD
is ch ar ge
(H C U P )( 2 00
8) .
6 HSR: Health Services Research
We used categorical and continuous measures of minority-serving status. The race and ethnicity data from the SID data were used to compute the per- centage of minority patients in each hospital. In addition, we created a categor- ical variable, designating whether a hospital is minority-serving, racially integrated, or majority-white. We defined minority-serving hospitals as those whose patient census was 65 percent or more minority, racially integrated hospi- tals as those whose patient census was between 35 and 65 percent minority, and majority-white hospitals as those whose patient census was less than 35 per- cent minority. These cutoffs, while somewhat arbitrary, have been used in a prior study to describe race-ethnic composition of zip codes (Gaskin et al. 2009, 2012). These cutoffs are a recognition of possible differences between racially segregated and integrated environments. America has a troubled his- tory of racial–ethnic segregation in housing and public accommodations where predominantly white spaces are rich in resources, predominantly minority spaces have limited resources, and racially integrated spaces are frag- ile and subject to reduction in resources due to possible white flight. Sociolo- gists have documented associations between racial–ethnic composition and resource allocation (Massey, Condran, and Denton 1987; Williams and Col- lins 2001; Wilson 2012). The use of categorical variables is useful because it allows us to interpret the magnitudes of the differences between hospitals in terms that commonly describe the racial and ethnic composition of spaces. However, to demonstrate that our findings are robust to these definitions, we estimated the models using continuous variables to see if the associations we observe in continuous models are similar to the categorical models.
We used data from the 2011 American Hospital Association (AHA) Sur- vey of Hospitals to measure hospital characteristics (AHA 2011). Our analysis controlled for bed size, ownership status, teaching status, percentage of dis- charges for Medicare and Medicaid patients, urban–rural location, tertiary and high-tech services index, community service index, and full-time equiva- lent (FTE) total personnel per adjusted admission. We used the interns and res- idents to bed (IRB) ratio to determine teaching status. Hospitals with an IRB ratio greater than zero but less than 0.25 were designated as minor-teaching, those with an IRB ratio greater than 0.25 were designated as major-teaching, and nonteaching hospitals had an IRB ratio of zero. To control for patient cost severity, we use the case mix index reported by CMS (2012a).
The tertiary high-tech services index was the count of specialty services the hospital provided from a list of 29: computed tomography (CT) scanner, electron beam computed tomography, full-field digital mammography, mag- netic resonance imaging, intraoperative magnetic resonance imaging,
Disparities in Hospital Quality 7
multislice spiral computed tomography <64 slice, positron emission tomogra- phy (PET), positron emission tomography/CT (PET/CT), single photon emission computerized tomography, ultrasound, image-guided radiation ther- apy, intensity-modulated radiation therapy, proton beam therapy, shaped beam radiation, system and stereotactic radio-surgery, robotic surgery, trans- plant services (bone marrow, heart, kidney, liver, lung, tissue and other trans- plant), burn care, trauma center, extracorporeal shock-wave lithotripter, therapeutic radiology, adult cardiac surgery, and neonatal intensive care. We also controlled the number of community services provided by hospitals by creating a community index from a list of 11 services: community outreach, health fairs, community health education, health screenings, immunization program, indigent care clinic, linguistic/translation services, meals on wheels, mobile health services, teen outreach services, and rural health clinic.
In addition to hospital-level factors, we controlled for market/area-level factors. We measured hospital competition using a county-level Herfindahl Index and included county median household income (Census 2010) and an urban–rural county designation. State fixed effects were used to control differ- ences between government policies, practice variations, reimbursement methodologies, and data collection methods that vary across states. There are some missing data for some of our control variables. To maintain the sample size, we imputed values for these covariates using the mean and included in our regression models a dummy variable to indicate which observation had imputed data (Firebaugh 2008).
Statistical Analysis
We conducted bivariate and multivariate analyses. We used the t-test for com- paring each measure between minority-serving with majority-white hospitals and racially integrated and majority-white hospitals (Table 1). We employed a Bonferroni correction (a = 0.05; 0.0014) to determine the statistical differ- ences in means between majority-white, racially integrated, and minority- serving hospitals (Weisstein 2014).
We estimated unconditional quantile regression models to determine whether the quality of surgical and pneumonia care were lower in minority- serving and racially integrated hospitals compared to majority-white hospitals. This model developed by Firpo, Fortin, and Lemieux uses recentered influ- ence function to estimate impact of changes in explanatory variables on quan- tiles of unconditional distribution for the outcome variables (Firpo, Fortin, and Lemieux 2009b). We included state fixed effects; therefore, to adjust for
8 HSR: Health Services Research
any clustering by states, we reported bootstrapped standard errors using the rifreg command developed by Firpo, Fortin, and Lemieux (2009a). To control heteroscedasticity, we created adjusted admission by dividing total admission to total FTE and weighted regression mode by the square root of adjusted admissions. As a sensitivity analysis, we estimated the models using the percentage of minority patients as the key independent variables instead of the categorical variables to test whether our findings were sensitive to our definitions of racially integrated and minority-serving hospitals. All of the models were estimated using STATA 11. We reported results from standard weighted least squares and unconditional quantile regression models.
RESULTS
Descriptive Analysis
Our sample included 1,198 acute care general hospitals in the 11 states that had valid surgery and pneumonia process of care quality indicators. The sam- ple varied across quality indicators because some hospitals did not report all the indicators. The percentage of hospitals reporting for surgery and pneumo- nia process of care quality indicators ranged from 80 to 95 percent. There are 825 majority-white hospitals, 224 racially integrated hospitals, and 149 minor- ity-serving hospitals in the sample. We restricted our analysis to hospitals that had at least 30 observations for each indicator. Depending on the indicator, the number of observations varied from 204 to 700 majority-white hospitals, 108 to 214 racially integrated hospitals, and 39 to 138 minority-serving hospi- tals. We report the average score for each of the quality measures by racial–ethnic composition of the hospital in Table 1. We found differences in performance between majority-white, racially integrated, and minority-serv- ing hospitals. Compared to majority-white hospitals, racially integrated hospi- tals had statistically lower scores for 2 of the 12 surgical indicators and three of six pneumonia indicators used in regression. Compared to majority-white hospitals, minority-serving hospitals had statistically lower scores for 11 of the 12 indicators and four of six pneumonia indicators. We used p < .0014 as the threshold for significance because we are making 36 comparisons.
The observed differences in quality for minority-serving hospitals could be due to other hospital characteristics (Table 2). Compared to majority-white hospitals, minority-serving hospitals were larger, more likely to have public or for-profit ownership, and more likely to be a major or minor-teaching hospi- tal. Compared to majority-white hospitals, minority-serving hospitals had a
Disparities in Hospital Quality 9
T ab
le 2:
D es cr ip ti ve
S ta ti st ic s fo r In d ep
en d en
ta n d C o n tr o lV
ar ia b le s in
M aj o ri ty -W
h it e, R ac ia ll y In te gr at ed
,a n d
M in o ri ty -S er vi n g H o sp it al s
C on tr ol V ar ia bl es
M aj or it y W hi te
R ac ia ll y In te gr at ed
M in or it y- Se rv in g
p va lu es fr om
t- te st s
O bs .
M ea n/ P er c.
St d. D ev .
O bs .
M ea n/
P er c.
St d. D ev .
O bs .
M ea n/
P er c.
St d. D ev .
M aj or it y
W hi te ve rs us
R ac ia ll y
In te gr at ed
M aj or it y
W hi te
ve rs us
M in or it y-
Se rv in g
H o sp it al ch ar ac te ri st ic s
H o sp it al si ze
To ta lh
o sp it al b ed
s 8 2 5
18 0. 8 5
�1 73
.0 4
2 2 4
3 4 5. 3
�2 9 6. 0 9
14 9
2 8 0. 77
�2 3 9. 0 2
0. 00
0 0. 00
0 A d ju st ed
ad m is si o n
8 2 5
16 ,5 31
�1 5, 17 9
2 2 4
2 8, 27 1
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�1 7, 4 81
0. 00
0 0. 00
0
F T E p er
ad ju st ed
ad m is si o n
8 2 5
17 .7 5
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2 2 4
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14 9
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O w n er sh ip
ty p e (R ef :N
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0. 67 1
0. 0 2 4
Te ac h in g ca te go
ri es
(R ef :n
o n te ac h in g)
N o n te ac h in g (R ef )
8 2 5
0. 67
�0 .4 7
2 2 4
0. 3 8
�0 .4 8
14 9
0. 31
�0 .4 7
0. 00
0 0. 00
0 M in or -t ea ch in g
(≤ 0. 2 5)
8 2 5
0. 2 9
�0 .4 5
2 2 4
0. 47
�0 .5
14 9
0. 5 2
�0 .5
0. 00
0 0. 00
0
M aj o r- te ac h in g
(> 0. 2 5)
8 2 5
0. 0 4
�0 .2
2 2 4
0. 15
�0 .3 6
14 9
0. 17
�0 .3 7
0. 00
0 0. 00
0
P at ie n tc h ar ac te ri st ic s
M ed
ic ai d
8 2 5
0. 12
�0 .0 9
2 2 4
0. 15
�0 .1
14 8
0. 2 4
�0 .1 5
0. 00
0 0. 00
0 M ed
ic ar e
8 2 5
0. 4 2
�0 .1 3
2 2 4
0. 2 8
�0 .1 3
14 9
0. 2 4
�0 .1 3
0. 00
0 0. 00
0
C on ti nu ed
10 HSR: Health Services Research
T ab
le 2.
C on ti nu ed
C on tr ol V ar ia bl es
M aj or it y W hi te
R ac ia ll y In te gr at ed
M in or it y- Se rv in g
p va lu es fr om
t- te st s
O bs .
M ea n/ P er c.
St d. D ev .
O bs .
M ea n/
P er c.
St d. D ev .
O bs .
M ea n/
P er c.
St d. D ev .
M aj or it y
W hi te ve rs us
R ac ia ll y
In te gr at ed
M aj or it y
W hi te
ve rs us
M in or it y-
Se rv in g
C as e m ix
in d ex
8 2 5
1. 4 8
�0 .2 1
2 2 4
1. 5 9
�0 .2 6
14 9
1. 47
�0 .2 2
0. 00
0 0. 5 8 3
Se rv ic e m ix
H i- te ch
se rv ic es
In d ex
8 2 5
8. 27
�4 .4
2 2 4
10 .6 4
�5 .4 4
14 9
7. 9 9
�4 .0 5
0. 00
0 0. 4 6 3
H o sp it al
C o m m u n it y in d ex
8 2 5
5. 2 9
�1 .8 7
2 2 4
5. 76
�2 .0 8
14 9
5. 5 8
�1 .9 6
0. 00 1
0. 0 91
M ar ke tc h ar ac te ri st ic s
U rb an
(= 1, Y es )
8 2 5
0. 5 9
�0 .4 9
2 2 4
0. 8 6
�0 .3 5
14 9
0. 81
�0 .3 9
0. 00
0 0. 00
0 H er fi n d ah
li n d ex
8 2 5
0. 51
�0 .3 4
2 2 4
0. 2 8
�0 .3
14 9
0. 2
�0 .2 5
0. 00
0 0. 00
0 H ig h co m p et it iv e;
H I ≤ 0. 18
5 (= 1, Y es )
8 2 5
0. 2
�0 .4
2 2 4
0. 5 9
�0 .4 9
14 9
0. 6 9
�0 .4 6
0. 00
0 0. 00
0
M od
er at e co m p et it iv e;
0. 18
5 < H I ≤ 0. 51 6
(= 1, Y es )
8 2 5
0. 3 8
�0 .4 9
2 2 4
0. 2 3
�0 .4 2
14 9
0. 21
�0 .4 1
0. 00
0 0. 00
0
L o w co m p et it iv e;
H I > 0. 51 6 (= 1, Y es )
8 2 5
0. 4 2
�0 .4 9
2 2 4
0. 18
�0 .3 8
14 9
0. 0 9
�0 .2 9
0. 00
0 0. 00
0
M ed
ia n h o u se h o ld
in co m e ($ 10 ,0 00
) 8 2 5
5. 6 4
�1 .5 1
2 2 4
6. 2 6
�1 .5 9
14 9
6. 11
�1 .5 3
0. 00
4 0. 00 1
*W e o n ly in cl u d ed
h o sp it al s w it h va li d d at a fo r su rg ic al /p n eu
m o n ia p ro ce ss o fc ar e m ea su re s.
† H er fi n d ah
li n d ex
ca lc u la te d in
co un
ty le ve lf o r ea ch
h o sp it al .
‡ T h er e ar e 16
8 (1 4%
) m is si n g va lu es
fo r H i-T
ec h se rv ic es
an d H o sp it al
C o m m u n it y in d ex
es an
d 21 3 (1 7. 7%
) m is si n g va lu es
fo r ca se
m ix
in d ex
; w e
re p la ce d th em
b y m ea n an
d in cl u d ed
d u m m y in
th e m o d el s.
So ur ce :A
u th o rs ’ an
al ys is fr o m
th e M ed
ic ar e C o m p ar e (2 01 0–
2 01 3) ,H
C U P (2 00
8) ,A
m er ic an
H os p it al A ss oc ia ti o n [A
H A ]( 2 01 1) ,a n d C en
su s (2 01 0) .
Disparities in Hospital Quality 11
higher percentage of Medicaid patients and a lower percentage of Medicare patients. Minority-serving hospitals were more likely to be located in urban areas, with more hospital competition and higher median household incomes. Also, there were systematic differences between majority-white and racially integrated hospitals. Specifically, racially integrated hospitals were larger and more likely to be teaching hospitals. They served more Medicaid patients, fewer Medicare patients, and had a higher case mix index. Compared to majority-white hospitals, they provided more high-tech services and commu- nity services. Similar to minority-serving hospitals, racially integrated hospi- tals were more likely to be located in urban areas, with more hospital competition and higher median household incomes.
Multivariate Analysis
Table 3 reported the results of the unconditional quantile coefficient (UQF) for the all surgical and pneumonia process of care indicators. We found dif- ferences in performance between minority-serving, racially integrated, and majority-white hospitals after controlling for other hospital characteristics and market-level factors. These differences varied across quantiles. Minor- ity-serving hospitals in the bottom 10th quantile performed lower than com- parable majority-white hospitals on 5 of 12 surgical care process indicators and three of six pneumonia process indicators while minority-serving hospi- tals in the 90th quantile performed as well as majority-white hospitals on all of the indicators, that is, the differences are close to zero and statistically insignificant. The pattern of the UQF suggests that there are significant differences between the worst minority-serving hospitals and the worst majority-white hospitals, but the best minority-serving hospitals performed as well as the best majority-white hospitals. The differences in quality between minority-serving and majority-white hospitals developed and increased as one moves from the best performing hospitals to the worst performing hospitals.
Similarly, the UQF offer a nuanced story for racially integrated hospitals compared to majority-white hospitals. We do see a pattern where racially integrated hospitals in the bottom 10th quantile performed lower than comparable majority-white hospitals for 2 of the 12 surgical process indicators and 2 of 6 pneumonia process indicators. However for all indicators, racially integrated hospitals at the 90th quantile performed as well as comparable majority-white hospitals.
12 HSR: Health Services Research
T ab
le 3:
U n co n d it io n al
Q u an
ti le
R es u lt s fo r S u rg ic al
an d P n eu
m o n ia
P ro ce ss
o f C ar e M ea su re , C o m p ar in g R ac ia ll y
In te gr at ed
an d M in o ri ty -S er vi n g H o sp it al s w it h M aj o ri ty
W h it e H o sp it al s
R ow
R ac ia ll y In te gr at ed
H os pi ta ls
M in or it y- Se rv in g H os pi ta ls
O bs .
Q ua li ty In di ca to rs (D
ep en de nt V ar ia bl e)
O L S
Q 10
Q 5 0
Q 9 0
O L S
Q 10
Q 5 0
Q 9 0
S u rg ic al p ro ce ss o fc ar e m ea su re s
1 P er ce n to
fs u rg er y p at ie n ts gi ve n an
an ti b io ti c at th e ri gh
tt im
e (w
it h in
1 h o u r b ef o re
su rg er y)
to h el p
p re ve n ti n fe ct io n
�0 .2 70
(0 .3 2 3)
�3 .2 16 *
(1 .2 84
) 0. 00
7 (0 .2 5 3)
0. 0 6 8
(0 .1 62
) �0
.3 9 6
(0 .4 11 )
�1 .2 9 2
(1 .6 8 6)
�0 .5 17
(0 .3 3 6)
0. 41 3
(0 .2 13 )
9 8 8
2 P er ce n to
fs u rg er y p at ie n ts w h o se
p re ve n ti ve
an ti b io ti cs
w er e st o p p ed
at th e ri gh
tt im
e (w
it h in
2 4 h o u rs
af te r su rg er y)
�1 .5 3 2* **
(0 .3 71 )
�3 .6 9 0* *
(1 .4 28
) �0
.6 5 3*
(0 .3 01 )
�0 .4 46
(0 .2 9 0)
�2 .4 00
** *
(0 .4 75
) �7
.6 3 9* *
(2 .3 2 3)
�1 .2 3 2* *
(0 .4 3 4)
�0 .5 4 9
(0 .4 13 )
9 8 4
3 P er ce n to
fs u rg er y p at ie n ts w h o w er e
gi ve n th e ri gh
tk in d o fa n ti b io ti c
to h el p p re ve n ti n fe ct io n
�0 .5 67
* (0 .2 4 4)
�1 .3 16
(0 .8 8 0)
�0 .0 75
(0 .1 8 5)
�0 .1 6 5
(0 .1 4 5)
�1 .2 6 4* **
(0 .3 10 )
�2 .9 5 8*
(1 .2 4 9)
�0 .2 5 4
(0 .2 5 2)
�0 .1 0 5
(0 .1 5 4)
9 8 8
4 P er ce n to
fs u rg er y p at ie n ts w h o go
t tr ea tm
en ta tr ig h tt im
e (w
it h in
2 4 h o u rs b ef o re
or af te r su rg er y)
to h el p
p re ve n tb
lo o d cl o t
�0 .7 4 6
(0 .4 8 2)
�2 .6 4 8
(1 .8 96
) �0
.0 3 3
(0 .5 3 2)
0. 4 4 5
(0 .3 67
) �1
.3 18 *
(0 .6 17 )
�3 .9 81
(2 .5 6 8)
�0 .1 6 2
(0 .7 00
) 0. 21 7
(0 .4 6 0)
9 8 9
5 P er ce n to
fs u rg er y p at ie n ts w h o se
d o ct o rs o rd er ed
tr ea tm
en ts to
p re ve n tb
lo o d cl o ts af te r ce rt ai n
ty p es
o fs u rg er ie s
�0 .7 9 5
(0 .4 73
) �3
.7 6 3
(2 .3 97
) �0
.1 5 0
(0 .4 0 4)
0. 17 6
(0 .2 91 )
�1 .4 6 6*
(0 .6 0 5)
�5 .5 3 6
(3 .3 2 4)
�0 .2 2 0
(0 .5 4 4)
�0 .0 2 8
(0 .3 77 )
9 9 0
6 P er ce n to
fa ll h ea rt su rg er y p at ie n ts
w h o se
b lo o d su ga r is ke p tu
n d er
go o d co n tr o li n th e d ay s ri gh
t af te r su rg er y
�0 .6 5 3
(0 .5 15 )
�1 .3 9 9
(1 .2 10 )
�0 .6 67
(0 .7 8 2)
�0 .6 42
(0 .6 2 4)
�0 .5 12
(0 .7 4 5)
�0 .8 61
(1 .9 3 8)
�0 .0 5 8
(1 .0 5 9)
�0 .8 2 5
(0 .8 0 6)
3 51
C on ti nu ed
Disparities in Hospital Quality 13
T ab
le 3.
C on ti nu ed
R ow
R ac ia ll y In te gr at ed
H os pi ta ls
M in or it y- Se rv in g H os pi ta ls
O bs .
Q ua li ty In di ca to rs (D
ep en de nt V ar ia bl e)
O L S
Q 10
Q 5 0
Q 9 0
O L S
Q 10
Q 5 0
Q 9 0
7 P er ce n to
fs u rg er y p at ie n ts n ee d in g
h ai r re m o ve d fr o m
th e su rg ic al
ar ea
b ef o re
su rg er y w h o h ad
h ai r
re m o ve d us in g a sa fe r m et h o d
�0 .8 3 2* **
(0 .1 8 5)
�0 .5 18
(0 .2 9 3)
�0 .0 2 2
(0 .0 14 )
�0 .0 2 2
(0 .0 14 )
�0 .2 51
(0 .2 3 3)
�0 .4 5 0
(0 .2 4 5)
�0 .0 27
(0 .0 19 )
�0 .0 27
(0 .0 18 )
1, 01 3
8 P er ce n to
fs u rg er y p at ie n ts w h o se
u ri n ar y ca th et er s w er e re m o ve d
o n th e fi rs to
r se co n d d ay
af te r su rg er y
�1 .4 9 2*
(0 .7 2 2)
�3 .8 4 8
(2 .2 5 8)
�1 .0 5 0
(0 .7 8 2)
0. 27 3
(0 .5 2 3)
�3 .3 6 8* **
(0 .9 5 2)
�7 .8 4 9*
(3 .9 2 0)
�1 .1 5 9
(1 .0 17 )
0. 4 5 6
(0 .5 97
) 91 4
9 P er ce n to
fs u rg er y p at ie n ts w h o
w er e ta ki n g h ea rt d ru gs
co ll ec te d
b et a b lo ck er s b ef o re
co m in g to
th e h o sp it al
�0 .2 4 2
(0 .4 71 )
�0 .9 14
(1 .2 3 6)
�0 .1 4 6
(0 .5 40
) 0. 0 6 8
(0 .3 76
) �1
.2 83
* (0 .6 4 0)
�6 .1 4 2*
(2 .7 3 6)
�0 .8 9 3
(0 .7 79
) 0. 17 2
(0 .4 4 5)
87 6
10 P er ce n to
fo u tp at ie n ts h av in g
su rg er y w h o go
ta n an
ti b io ti c
at th e ri gh
tt im
e, w it h in
1 h o u r
b ef o re
su rg er y
�0 .3 21
(0 .6 76
) 0. 0 2 2
(1 .6 3 9)
�0 .3 3 2
(0 .5 62
) 0. 2 2 2
(0 .4 18 )
�2 .5 9 8* *
(0 .8 9 4)
�6 .9 2 0* *
(2 .4 3 0)
�2 .4 21 ** *
(0 .6 4 9)
0. 12
4 (0 .5 5 9)
80 2
11 P er ce n to
fo u tp at ie n ts h av in g
su rg er y w h o go
tr ig h tk
in d
o fa n ti b io ti c( s)
�0 .7 3 8
(0 .5 0 2)
�1 .5 00
(1 .4 4 3)
�0 .9 3 5
(0 .5 14 )
�0 .2 9 5
(0 .2 91 )
�1 .1 6 4
(0 .6 72
) �1
.1 4 9
(2 .5 6 5)
�1 .4 87
* (0 .6 87
) 0. 01 6
(0 .3 9 0)
8 01
12 P er ce n to
fp at ie n ts h av in g su rg er y
w h o w er e ac ti ve ly w ar m ed
in th e
o p er at in g ro om
o r w h o se
b o d y
te m p er at u re
w as
n ea r n o rm
al b y
th e en
d of
su rg er y
�0 .3 9 5*
(0 .1 5 6)
�0 .0 09
(0 .0 8 4)
�0 .0 2 5
(0 .0 15 )
�0 .0 2 5
(0 .0 2 3)
�0 .3 0 3
(0 .1 9 8)
�0 .1 3 5
(0 .1 17 )
�0 .0 3 6*
(0 .0 18 )
�0 .0 3 6
(0 .0 31 )
1, 01 6
C on ti nu
ed
14 HSR: Health Services Research
T ab
le 3.
C on ti nu ed
R ow
R ac ia ll y In te gr at ed
H os pi ta ls
M in or it y- Se rv in g H os pi ta ls
O bs .
Q ua li ty In di ca to rs (D
ep en de nt V ar ia bl e)
O L S
Q 10
Q 5 0
Q 9 0
O L S
Q 10
Q 5 0
Q 9 0
P n eu
m o n ia p ro ce ss o fc ar e m ea su re s
1 P er ce n to
fp n eu
m o n ia p at ie n ts
as se ss ed
an d gi ve n p n eu
m o co cc al
va cc in at io n
�1 .9 0 6*
(0 .9 63
) �1
.8 11
(3 .5 33
) �0
.9 88
(0 .5 9 3)
0. 2 2 6
(0 .4 0 5)
�3 .0 77 *
(1 .2 01 )
�3 .0 71
(4 .8 2 9)
�0 .8 79
(0 .9 07
) 0. 16
2 (0 .5 84
) 1, 0 51
2 P er ce n to
fp n eu
m o n ia p at ie n ts
gi ve n in it ia la n ti b io ti c( s)
w it h in
6 h o u rs af te r ar ri va l
�0 .4 07
(0 .3 84
) �0
.1 00
(1 .0 3 0)
�0 .5 51
(0 .3 6 8)
0. 21 4
(0 .3 2 8)
�0 .9 8 4*
(0 .4 79
) �3
.3 67
* (1 .6 37
) �0
.8 71
(0 .4 8 3)
�0 .2 77
(0 .3 25
) 1, 0 4 2
3 P er ce n to
fp n eu
m o n ia p at ie n ts
w h o se
in it ia lE
R b lo o d cu lt u re
w as
p er fo rm
ed p ri o r to
ad m in is tr at io n o ffi
rs td
o se
o f
an ti b io ti cs
�1 .0 15 **
(0 .3 32
) �3
.1 9 0* *
(1 .1 72
) �0
.4 31
(0 .2 8 8)
0. 0 47
(0 .1 8 8)
�1 .5 6 5* **
(0 .4 18 )
�4 .4 8 2* *
(1 .5 3 6)
�1 .1 37
** (0 .3 5 5)
�0 .2 4 4
(0 .2 43
) 1, 01 4
4 P er ce n to
fp n eu
m o n ia p at ie n ts
gi ve n sm
o ki n g ce ss at io n
ad vi ce /c o u n se li n g
�0 .0 4 3
(0 .4 25
) �0
.4 8 0
(1 .3 8 9)
�0 .0 65
(0 .1 22
) �0
.0 6 5
(0 .1 18 )
�0 .2 2 6
(0 .5 5 0)
0. 3 0 4
(1 .5 78
) �0
.1 21
(0 .1 2 9)
�0 .1 21
(0 .1 31 )
72 8
5 P er ce n to
fp n eu
m o n ia p at ie n ts
gi ve n th e m o st ap
p ro p ri at e
in it ia la n ti b io ti c( s)
�0 .6 8 8
(0 .5 08
) �1
.9 17
(1 .3 97
) �0
.4 22
(0 .4 6 2)
0. 3 0 2
(0 .3 2 6)
�0 .2 8 8
(0 .6 3 3)
�0 .2 8 2
(1 .6 75
) �1
.1 3 9*
(0 .5 4 8)
�0 .4 5 0
(0 .4 44
) 9 9 6
6 P er ce n to
fp n eu
m o n ia
p at ie n ts as se ss ed
an d gi ve n
in fl u en
za va cc in at io n
�2 .4 8 6* *
(0 .8 39
) �7
.2 13 *
(2 .8 66
) �0
.6 12
(0 .7 5 0)
0. 4 41
(0 .4 81
) �3
.0 5 5* *
(1 .0 73
) �8
.6 27
* (4 .0 2 2)
�1 .7 8 4
(0 .9 6 8)
0. 2 3 4
(0 .6 43
) 9 3 6
*F o r ea ch
in d ic at o r, o b se rv at io n s fo r al lq
u an
ti le s ar e eq
u al .
† In
th e re gr es si o n m o d el ,n
o t- fo r- p ro fi th
o sp it al s co n si d er ed
as a re fe re n ce
gr o u p .
‡ In
th e re gr es si o n m o d el ,t h e h ig h -c o m p et it iv e h o sp it al s co n si d er ed
as a re fe re n ce
gr o u p .
§ P ar en
th es es
fo r q u an
ti le s re p or te d b o o ts tr ap
S E fo r re ce nt er ed
in fl ue nc e fu nc ti on
re gr es si o n an
d S E fo r O L S ;a ll m o d el s w ei gh
te d b y 1/ sq rt (b d -t o t) .
¶ O b se rv at io n fo r ea ch
in d ic at o r is d if fe re n tb
ec au
se o n ly h o sp it al w it h at le as t3
0 o b se rv at io n s fo r ea ch
in d ic at o r w as
in cl u d ed
. * * * p < .0 01 ;* * p < .0 1;
* p < .0 5.
So ur ce :A
u th o rs ’ an
al ys is fr o m
th e M ed
ic ar e C o m p ar e (2 01 0–
2 01 3) ,H
C U P (2 00
8) ,A
m er ic an
H o sp it al A ss o ci at io n [A
H A ]( 2 01 1) ,a n d C en
su s (2 01 0) .
Disparities in Hospital Quality 15
Sensitivity Analysis
In Table 4, we reported the coefficients from the Recentered Influence Func- tion using percentage of minority discharge as the key independent variables. The patterns of the coefficients are consistent with our findings from Table 3. In the 10th quantile, the estimated associations of the percentage of minority discharges were negative and significant for 7 of the 12 surgical indicators and 2 of the 6 pneumonia indicators. In contrast in the 90th quantile, they have 2 of the 12 surgical indicators and none of the pneumonia indicators had statisti- cally significant negative associations with the percentage of minority dis- charges. As one moves from the 10th to the 90th quantile, the coefficients increase from negative values to values close to zero. This yields a different interpretation than the traditional ordinary least squares (OLS) estimates, which would suggest that there is a simple negative association for 8 of the 12 surgical and 4 of 6 pneumonia indicators. Considering differences in quantile, it seems the disparities in quality among hospitals by minority-serving status are larger for lower quantiles and almost nonexistent for the upper quantiles. This is a much different conclusion than one drawn from the OLS estimates. Also, because minority-serving and racially integrated hospitals are more likely to be large, urban, or teaching hospitals compared to majority white hospitals, we conducted stratified analyses by these hospital characteristics to determine if our findings are robust. We found that within size, location, and teaching categories that significant differences in quality existed primarily among the worst performing hospitals. These results are available by request from the authors.
DISCUSSION
Prior studies have argued that hospitals serving higher percentages of minority patients performed lower than other hospitals on quality indicators (Epstein, Gray, and Schlesinger 2010; Hasnain-Wynia et al. 2010; Kind et al. 2010; Joynt and Jha 2011; Joynt, Orav, and Jha 2011). A recent study reported that black patients tend to live closer to high-quality hospitals, but they are more likely to use lower quality hospitals for surgery (Dimick et al. 2013). These findings could create a misperception that minority-serving hospitals are uni- formly lower quality providers. Or even worse, it promotes a more dangerous axiom that hospital quality is inevitably lowered as the proportion of minority patients increases. Also, these findings offer little insight as to why some hospi-
16 HSR: Health Services Research
T ab
le 4:
U n co n d it io n al Q u an
ti le R es u lt s fo r S u rg ic al an
d P n eu
m o n ia P ro ce ss o fC
ar e M ea su re
in H o sp it al s
R ow
Q ua li ty In di ca to rs (D
ep en de nt
V ar ia bl es )
O L S
R ac ia l/ E th ni cG
ro up sC
om po si ti on
as a C on ti nu ou sV
ar ia bl e
O bs .
Q 10
Q 25
Q 5 0
Q 75
Q 9 0
S u rg ic al p ro ce ss o fc ar e m ea su re s
1 P er ce n to
fs u rg er y p at ie n ts
gi ve n an
an ti b io ti c at th e
ri gh
tt im
e (w
it h in
1 h o u r
b ef o re
su rg er y)
to h el p
p re ve n ti n fe ct io n
�0 .0 07 (0 .0 0 6)
�0 .0 30
(0 .0 2 5)
�0 .0 18 (0 .0 0 9)
�0 .0 07 (0 .0 05
) 0. 00 1 (0 .0 0 3)
0. 00
3 (0 .0 0 3)
98 8
2 P er ce n to
fs u rg er y p at ie n ts
w h o se
p re ve n ti ve
an ti b io ti cs
w er e st o p p ed
at th e ri gh
tt im
e (w
it h in
2 4 h o u rs af te r su rg er y)
�0 .0 3 9* **
(0 .0 07
) �0
.1 10 ** *
(0 .0 31 )
�0 .0 4 2* **
(0 .0 12
) �0
.0 2 0* *
(0 .0 06
) �0
.0 10 (0 .0 0 5)
�0 .0 13 *
(0 .0 0 6)
98 4
3 P er ce n to
fs u rg er y p at ie n ts
w h o w er e gi ve n th e ri gh
t ki n d o fa n ti b io ti c to
h el p
p re ve n ti n fe ct io n
�0 .0 2 0* **
(0 .0 0 4)
�0 .0 48
* (0 .0 19 )
�0 .0 2 4* *
(0 .0 0 8)
�0 .0 04 (0 .0 04
) �0
.0 05
* (0 .0 0 2)
�0 .0 04 (0 .0 0 2)
98 8
4 P er ce n to
fs u rg er y p at ie n ts
w h o go
tt re at m en
ta t
ri gh
tt im
e (w
it h in
2 4 h o u rs b ef o re
o r af te r
su rg er y)
to h el p p re ve n t
b lo o d cl o t
�0 .0 2 2* *
(0 .0 0 9)
�0 .0 60
(0 .0 3 6)
�0 .0 3 9*
(0 .0 17 )
�0 .0 02 (0 .0 10 )
0. 00 1 (0 .0 0 6)
0. 00
2 (0 .0 0 6)
98 9
5 P er ce n to
fs u rg er y p at ie n ts
w h o se
d o ct o rs o rd er ed
tr ea tm
en ts to
p re ve n t
b lo o d cl o ts af te r ce rt ai n
ty p es
o fs u rg er ie s
�0 .0 2 6* *
(0 .0 0 8)
�0 .1 0 3*
(0 .0 4 9)
�0 .0 3 8*
(0 .0 17 )
�0 .0 07 (0 .0 08
) 0. 00
3 (0 .0 0 6)
0. 00 1 (0 .0 0 5)
99 0
C on ti nu
ed
Disparities in Hospital Quality 17
T ab
le 4.
C on ti nu ed
R ow
Q ua li ty In di ca to rs (D
ep en de nt
V ar ia bl es )
O L S
R ac ia l/ E th ni cG
ro up sC
om po si ti on
as a C on ti nu ou sV
ar ia bl e
O bs .
Q 10
Q 25
Q 5 0
Q 75
Q 9 0
6 P er ce n to
fa ll h ea rt
su rg er y p at ie n ts w h o se
b lo o d su ga r is ke p tu
n d er
go o d co n tr o li n th e d ay s
ri gh
ta ft er
su rg er y
�0 .0 12 (0 .0 11 )
�0 .0 06 (0 .0 2 6)
�0 .0 14 (0 .0 2 0)
�0 .0 13 (0 .0 16
) �0
.0 3 0*
(0 .0 12 )
�0 .0 2 3
(0 .0 13 )
3 51
7 P er ce n to
fs u rg er y p at ie n ts
n ee d in g h ai r re m o ve d
fr o m
th e su rg ic al ar ea
b ef o re
su rg er y w h o h ad
h ai r re m o ve d u si n g a
sa fe r m et h o d
�0 .0 08
* (0 .0 0 3)
�0 .0 09
* (0 .0 05
) �0
.0 03 (0 .0 01 )
�0 .0 01 (0 .0 00
) �0
.0 01
* (0 .0 00
) �0
.0 01 *
(0 .0 00
) 1, 01 3
8 P er ce n to
fs u rg er y p at ie n t
w h o se
u ri n ar y ca th et er s
w er e re m o ve d o n th e
fi rs to
r se co n d d ay
af te r
su rg er y
�0 .0 5 4* **
(0 .0 13 )
�0 .1 2 5*
(0 .0 57
) �0
.0 66
* (0 .0 2 8)
�0 .0 2 5
(0 .0 15 )
�0 .0 07 (0 .0 10 )
�0 .0 02 (0 .0 0 9)
91 4
9 P er ce n to
fs u rg er y p at ie n ts
w h o w er e ta ki n g h ea rt
d ru gs
co ll ec te d b et a
b lo ck er s b ef o re
co m in g
to th e h o sp it al
�0 .0 2 6* *
(0 .0 0 9)
�0 .0 9 6* *
(0 .0 3 6)
�0 .0 39
** (0 .0 13 )
�0 .0 19 (0 .0 10
) �0
.0 00 (0 .0 07
) �0
.0 01 (0 .0 07
) 87 6
C on ti nu ed
18 HSR: Health Services Research
T ab
le 4.
C on ti nu ed
R ow
Q ua li ty In di ca to rs (D
ep en de nt
V ar ia bl es )
O L S
R ac ia l/ E th ni cG
ro up sC
om po si ti on
as a C on ti nu ou sV
ar ia bl e
O bs .
Q 10
Q 25
Q 5 0
Q 75
Q 9 0
10 P er ce n to
fo u tp at ie n ts
h av in g su rg er y w h o go
t an
an ti b io ti c at th e ri gh
t ti m e, w it h in
1 h o u r
b ef or e su rg er y
�0 .0 3 9* *
(0 .0 12 )
�0 .1 00
* (0 .0 41 )
�0 .0 51 *
(0 .0 2 0)
�0 .0 3 2* **
(0 .0 0 9)
�0 .0 08 (0 .0 0 9)
�0 .0 02 (0 .0 0 8)
8 0 2
11 P er ce n to
fo u tp at ie n ts
h av in g su rg er y w h o go
t ri gh
tk in d o fa n ti b io ti c( s)
�0 .0 2 2*
(0 .0 09
) �0
.0 2 5
(0 .0 37
) �0
.0 41 **
(0 .0 15 )
�0 .0 2 6* *
(0 .0 0 9)
�0 .0 13 *
(0 .0 0 6)
�0 .0 01 (0 .0 0 6)
8 01
12 P er ce n to
fp at ie n ts h av in g
su rg er y w h o w er e
ac ti ve ly w ar m ed
in th e
op er at in g ro o m
o r
w h o se
b o d y te m p er at u re
w as
n ea r n o rm
al b y th e
en d o fs u rg er y
�0 .0 05 (0 .0 03
) �0
.0 02 (0 .0 0 2)
�0 .0 03
* (0 .0 01
) �0
.0 01 (0 .0 00
) �0
.0 01 (0 .0 00
) �0
.0 01 (0 .0 00
) 1, 01 6
P n eu
m o n ia
1 P er ce n to
fp n eu
m o n ia
p at ie n ts as se ss ed
an d
gi ve n p n eu
m o co cc al
va cc in at io n
�0 .0 5 3* *
(0 .0 17 )
�0 .0 3 6
(0 .0 6 8)
�0 .0 73
* (0 .0 3 3)
�0 .0 2 6*
(0 .0 13 )
�0 .0 12 (0 .0 10 )
�0 .0 02 (0 .0 0 8)
1, 0 51
2 P er ce n to
fp n eu
m o n ia
p at ie n ts gi ve n in it ia l
an ti b io ti c( s) w it h in
6 h o u rs af te r ar ri va l
�0 .0 17 *
(0 .0 07
) �0
.0 47
* (0 .0 2 2)
�0 .0 2 9*
(0 .0 12 )
�0 .0 13 (0 .0 07
) �0
.0 06 (0 .0 0 5)
�0 .0 00 (0 .0 0 5)
1, 0 4 2
C on ti nu ed
Disparities in Hospital Quality 19
T ab
le 4.
C on ti nu ed
R ow
Q ua li ty In di ca to rs (D
ep en de nt
V ar ia bl es )
O L S
R ac ia l/ E th ni cG
ro up sC
om po si ti on
as a C on ti nu ou sV
ar ia bl e
O bs .
Q 10
Q 25
Q 5 0
Q 75
Q 9 0
3 P er ce n to
fp n eu
m o n ia
p at ie n ts w h o se
in it ia lE
R b lo o d cu lt u re
w as
p er fo rm
ed p ri o r to
ad m in is tr at io n o ffi
rs t
d o se
o fa n ti b io ti cs .
�0 .0 2 5* **
(0 .0 0 6)
�0 .0 6 6* *
(0 .0 23
) �0
.0 3 0* *
(0 .0 10 )
�0 .0 2 2* **
(0 .0 0 6)
�0 .0 11 *
(0 .0 0 5)
�0 .0 03 (0 .0 0 3)
1, 01 4
4 P er ce n to
fp n eu
m o n ia
p at ie n ts gi ve n sm
o ki n g
ce ss at io n ad
vi ce /
co u n se li n g
�0 .0 00 (0 .0 0 8)
0. 00
8 (0 .0 23
) 0. 00
2 (0 .0 14 )
�0 .0 03 (0 .0 0 2)
�0 .0 03 (0 .0 0 2)
�0 .0 03 (0 .0 0 2)
72 8
5 P er ce n to
fp n eu
m o n ia
p at ie n ts gi ve n th e m o st
ap p ro p ri at e in it ia l
an ti b io ti c( s)
�0 .0 07 (0 .0 0 9)
�0 .0 14 (0 .0 26
) 0. 00 1 (0 .0 13 )
�0 .0 13 (0 .0 0 8)
�0 .0 06 (0 .0 0 6)
�0 .0 05 (0 .0 0 6)
9 9 6
6 P er ce n to
fp n eu
m o n ia
p at ie n ts as se ss ed
an d
gi ve n in fl u en
za va cc in at io n
�0 .0 4 5* *
(0 .0 15 )
�0 .1 0 6
(0 .0 57
) �0
.0 8 4* *
(0 .0 2 9)
�0 .0 27
* (0 .0 13 )
�0 .0 27
* (0 .0 13 )
�0 .0 02 (0 .0 0 9)
9 3 6
*F o r ea ch
in d ic at o r, o b se rv at io n s fo r al lq
u an
ti le s ar e eq
u al .
† In
th e re gr es si o n m o d el ,n
o t- fo r- p ro fi th
o sp it al s co n si d er ed
as a re fe re n ce
gr o u p .
‡ In
th e re gr es si o n m o d el ,t h e h ig h -c o m p et it iv e h o sp it al s co n si d er ed
as a re fe re n ce
gr o u p .
§ P ar en
th es es
fo r q u an
ti le s re p o rt ed
b o o ts tr ap
S E fo r re ce nt er ed
in fl ue nc e fu nc ti on
re gr es si o n an
d S E fo r O L S ;a ll m o d el s w ei gh
te d b y 1/ sq rt (b d -t o t) .
¶ O b se rv at io n fo r ea ch
in d ic at o r is d if fe re n tb
ec au
se o n ly h o sp it al w it h at le as t3
0 o b se rv at io n s fo r ea ch
in d ic at o r w as
in cl u d ed
. * * * p < .0 01 ;* * p < .0 1;
* p < .0 5.
So ur ce :A
u th o rs ’ an
al ys is fr o m
th e M ed
ic ar e C o m p ar e (2 01 0–
2 01 3) ,H
C U P (2 00
8) ,A
m er ic an
H o sp it al A ss oc ia ti o n [A
H A ]( 2 01 1) ,a n d C en
su s (2 01 0) .
20 HSR: Health Services Research
tals are performing poorly for their minority patients. Our findings indicated that the best minority-serving and racially integrated hospitals provide care on par with the best majority-white hospitals for surgery and pneumonia care. Differences in quality were evident among the worst minority-serving and racially integrated hospitals when compared to the worst majority-white hos- pitals. In effect, the lowest quality minority-serving hospitals bring down the mean quality rating for the entire group of minority-serving hospitals and cre- ate an inaccurate perception of all minority-serving hospitals.
The Patient Protection and Affordable Care Act (ACA) requires pay- ment reform in the Medicare, Medicaid, and Children’s Health Insurance Pro- gram programs to improve quality (PPACA 2010a). Specifically, the ACA created the CMS Innovation Center to develop, test, and promulgate new payment reforms that would incentivize providers to improve quality. One of the Innovation Center’s criteria for evaluating a payment reform program is its impact on disparities. Payment reforms that disproportionately penalize minority-serving hospitals may be viewed as “unfair” and could weaken already fragile health care delivery systems serving minority patients. A few studies have looked at the impact of pay-for-performance on minority-serving hospitals (Ryan 2010; Blustein et al. 2011); these studies do not show that these reimbursement methodologies increase disparities. However, recent implementation of financial penalties for high rates of hospital readmissions disproportionately fell on hospitals serving minority patients (Lagoe, Nanno, and Luziani 2012; CMS 2013).
The National Quality Forum (NQF) expressed concern that the public reporting quality indicators and their use for evaluating hospital performance and reimbursement could have unintended negative consequences for hospi- tals serving disadvantaged populations (NQF 2014). They acknowledge that quality indicators are correlated with sociodemographic composition of the patient population, that is, race, ethnicity, language, and socioeconomic status. NQF recommended including sociodemographic factors in risk adjustment methodologies unless there are conceptual reasons or empirical evidence indi- cating that this adjustment is unnecessary or inappropriate. Also, they recom- mended stratification and the creation of peer groups of hospitals serving similar patients to address the possibility of unfairly penalizing hospital for unobserved patient related risk factors correlated with sociodemographic factors.
The overall policy goal is to improve quality of care in all hospitals and eliminate disparities. CMS and other policy makers should design reimburse- ment schemes that provide appropriate incentive structures for improving
Disparities in Hospital Quality 21
quality in all hospitals. Simply rewarding top-performing hospitals with finan- cial bonuses may do little to accomplish this overall goal, while penalizing low-performing hospitals could directly negatively impact care for low- income and minority patients. Alternative incentive schemes could be focused on improvement in individual hospitals with annual goals (say a 10 percent relative improvement in performance as a stretch goal). This would incen- tivize low performers to make substantial improvements, encourage them to develop of culture of quality improvement if stretch goals are updated annu- ally, and in the long term lead to high performance.
Researchers have suggested that the lack of resources is the primary rea- son for low-performing hospitals that serve predominantly minority popula- tions (Karve et al. 2008; Ryan 2010; Weissman et al. 2012). Public policy should go beyond providing financial incentives. If the poor performance of these hospitals is due to a lack of resources, then public policy should address possible deficits in clinical, managerial, and physical plant resources. Policy makers could allow low-performing hospitals to use grants, tax subsidies, and loan repayment programs to attract managerial and clinical expertise targeted to address specific quality problems. The Maryland Health Enterprise Zone Initiative is an example of such a policy. A few Maryland hospitals have used this program to provided needed medical services to poor underserved neigh- borhoods (Reece, Brown, and Sharfstein 2013). Similarly, tax-exempt capital financing could be made available to low-performing hospitals to improve facilities and address infrastructure needs related to specific quality problems.
Our analysis is somewhat limited in that our data do not include states from the Deep South that have large black populations, that is, Alabama, Arkansas, Georgia, Louisiana, and Mississippi. Moreover, our models are cross-sectional and cannot infer any causal inferences. This is why we strictly interpreted our estimates as associations rather than making causal inferences. More important, we do not believe that racial and ethnic composition of hos- pital patients intrinsically influences quality of care. Rather the racial and eth- nic composition of hospital patients is correlated with resources, practices, and policies that influence quality in the Donabedian sense (Donabedian 2002). Also, there are no minority-serving and racially integrated hospitals in Iowa. However, we estimated the models without hospitals from Iowa and compared the results. This sensitivity analysis found no significant changes in our results. Our racial composition measure is from 2008 HCUP data. These data typically have a 2-year lag, which makes it difficult to include contempo- raneous data in our analysis. However, we do not believe this leads to a mis- classification of hospitals because the racial composition of a hospital’s patient
22 HSR: Health Services Research
census does not change dramatically over short periods of time. A change in the demographics of the patient census would probably require hospitals to change their geographic market substantially. Also because we converted these data into a categorical measure, this problem affects only those hospitals with racial–ethnic compositions near the 35 and 65 percent cutoff points. Finally, quality differences among high-performing hospitals are difficult to determine because of ceiling effects associated with these quality measures. Top-performing minority-serving, racially integrated, and majority-white hos- pitals may differ along other important aspects that are not captured by these measures.
Policy Implications
We found that for surgical and pneumonia process of care measures, the best minority-serving and racially integrated hospitals performed as well as the best majority-white hospitals. However, low-performing, minority-serving, and racially integrated hospitals performed worst than low-performing major- ity-white hospitals for surgical and pneumonia quality indicators. Other stud- ies are needed to evaluate disparities in other quality indicators. Efforts to improve surgical and pneumonia care for patients in minority-serving and racially integrated hospitals should target the lowest performers. Policy mak- ers should consider alternative incentive programs to encourage low-perform- ing hospitals to meet quality standards. Future studies should focus on the quality differences within minority-serving and racially integrated hospitals. There may be some valuable lessons to be learned from high-performing, minority-serving, and racially integrated hospitals that could be replicated in their low-performing counterparts.
ACKNOWLEDGMENTS
Joint Acknowledgment/Disclosure Statement: This research was supported in part by grant number P60MD00214: Hopkins Center for Health Disparities Solu- tions, awarded by the National Institute on Minority Health and Health Dis- parities. Dr. Darrell J. Gaskin served as the principal investigator for this project. We appreciate Dr. Nicole M. Fortin for her points and suggestion on rifreg command.
Disclosures: None. Disclaimers: None.
Disparities in Hospital Quality 23
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26 HSR: Health Services Research
SUPPORTING INFORMATION
Additional supporting information may be found in the online version of this article:
Appendix SA1: Author Matrix.
Disparities in Hospital Quality 27
