Do Patients Hospitalized in High-Minority Hospitals Experience … · INTRODUCTION Racial and ethnic differences in the burden of cardiovascular disease (CVD) contribute significantly

For peer review only

Do Patients Hospitalized in High-Minority Hospitals

Experience More Diversion and Poorer Outcomes?

Journal: BMJ Open

Manuscript ID bmjopen-2015-010263

Article Type: Research

Date Submitted by the Author: 14-Oct-2015

Complete List of Authors: Shen, Yu-Chu; Naval Postgraduate School, Graduate School of Business and Public Policy; National Bureau of Economic Research Hsia, Renee; UCSF, Emergency Medicine

<b>Primary Subject Heading</b>:

Cardiovascular medicine

Secondary Subject Heading: Emergency medicine

Keywords: ambulance diversion, treatment, health outcomes, racial disparities

For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml

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Do Patients Hospitalized in High-Minority Hospitals Experience More Diversion and

Poorer Outcomes?

Yu-Chu Shen, PhD1, Renee Y. Hsia, MD, MSc

2

1 Graduate School of Business and Public Policy, Naval Postgraduate School, Monterey, CA,

USA; National Bureau of Economic Research, Cambridge MA, USA

2 Department of Emergency Medicine and Institute of Health Policy Studies, University of

California at San Francisco, San Francisco, CA, USA

Corresponding author:

Yu-Chu Shen, PhD

Email: [email protected]

Phone: (831) 656-2951

Address: Naval Postgraduate School

555 Dyer Road, Code GB

Monterey, CA 93943

Key Words: ambulance diversion, treatment, health outcomes, racial disparities

Word count: 3,045

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ABSTRACT

Objective: We investigated the association between ambulance diversion and differences in

access, treatment, and outcomes between black and white patients.

Design: Retrospective analysis.

Setting: We linked daily ambulance diversion logs from 26 California counties between 2001

and 2011 to Medicare patient records with acute myocardial infarction and categorized patients

according to hours of diversion for their nearest emergency departments on their day of

admission: 0, <6, 6 to <12, and >12 hours. We compared the amount of diversion time between

hospitals serving high volume of black patients and other hospitals. We then use multivariate

models to analyze changes in outcomes when patients faced different levels of diversion, and

compared that change between black and white patients.

Participants: 28,683 Medicare patients from 26 California counties between 2001 and 2011.

Main Outcome Measures: (1) access to hospitals with cardiac technology; (2) treatment

received, and (3) health outcomes (30-day, 90-day, and 1-year death and 30-day readmission).

Results: Hospitals serving high volume of black patients spent more hours in diversion status

compared to other hospitals. Patients faced with the highest level of diversion had the lowest

probability of being admitted to hospitals with cardiac technology compared with those facing no

diversion, by 4.6% for cardiac care intensive unit, and 3.5% for catheterization lab and CABG

facilities. Patients experiencing increased diversion also had a 4.9% decreased likelihood of

receiving catheterization and 9.7% higher 1-year mortality.

Conclusions: Hospitals serving high volume of black patients are more likely to be on

diversion, and diversion is associated with poorer access to cardiac technology, lower probability

of receiving revascularization, and worse long-term mortality outcomes.

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ARTICLE SUMMARY

Strengths and limitations of this study:

• Links unique daily diversion data from hospitals in 17 local emergency medical services

agencies (LEMSAs) in California with patient-level data from Medicare between 2001-

2011.

• Utilizes actual driving distance between a patient’s ZIP code and the nearest hospital’s

longitude and latitude coordinates to identify the closest ED to a patient.

• Analyzes three dimensions of patient care – access, treatment, and outcomes – to explore

potential disparities between black and white patients experiencing ambulance diversion.

• Limitations include: potential reporting bias due to self-reported data by LEMSAs, lack

of generalizability outside of California, and a small sample of black patients.

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INTRODUCTION

Racial and ethnic differences in the burden of cardiovascular disease (CVD) contribute

significantly to health disparities observed in the United States, and unfortunately have increased

over time.(1, 2) These disparities are particularly noticeable for critical and time-sensitive

diseases such as acute myocardial infarction (AMI),(3) with studies showing that black patients

are less likely than white patients to receive cardiac treatments such as angiography or

thrombolytic therapy after AMI.(4) Many potential explanations for these disparities have been

suggested, including individual patient factors such as a lack of awareness of AMI symptoms,(5)

physician bias,(6) and a distrust of the medical system that results in a hesitance to seek care.(7)

However, it is also important to consider the possibility that system-level mechanisms may be

partially responsible for these disparities,(8, 9) and particularly whether black Americans are less

likely than their white counterparts to receive needed treatment.(10)

One important system-level mechanism that is especially critical for time-sensitive

conditions such as AMI is ambulance diversion. Ambulance diversion occurs when emergency

departments (ED) are temporarily closed to ambulance traffic due to a variety of reasons, such as

overcrowding or lack of available resources, (11-17) and effectively creates a temporary

decrease in ED access. Past studies have found that ambulance diversion is associated with poor

health outcomes for patients suffering from AMI.(18, 19) A recent study further explored the

mechanisms through which ambulance diversions affect patients, and showed that patients whose

nearest hospital experiences significant diversion have poorer access to hospitals with cardiac

technologies, leading to a lower likelihood of receiving treatment with revascularization, and

increased mortality.(20) Few studies, however, have examined if ambulance diversion is

associated with health disparities.

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In this paper, we explore whether ambulance diversion affect black and white patients

differently. Using 100% of Medicare claims and daily ambulance diversion logs from 26

California counties between 2001 and 2011, we first compared the amount of ambulance

diversion time between hospitals serving large share of black patients and other hospitals. We

then analyzed changes in access, treatment, and outcomes between black and white patients

when both were exposed to ambulance diversion.

METHODS

Conceptual Model

Conceptually, ambulance diversion is a signal of a hospital operating beyond capacity,

and can affect patients who had to be diverted elsewhere as well as non-diverted patients within

the overcrowded ED. In order to better target potential areas for intervention, it is essential to

know exactly where the disparities occur. In our conceptual framework (Figure 1), we break

down the patient’s experience of ambulance diversion in stages and discuss the sources of

potential disparities at each stage. Figure 1 shows that in the first stage, a hospital experiences

resource constraints, mostly due to overcrowding in the ED, such that it cannot accept incoming

ambulance traffic. At this stage, a potential racial disparity exists if black patients are more

likely to be diverted than white patients because the ED closest to them is more likely to be on

diversion.

Some patients might then be routed to hospitals less technologically equipped to handle

complicated cardiac cases. At this stage, disparities could occur if black patients are more likely

to be diverted to less desirable settings than white patients, resulting in worse outcomes.

The decreased access to cardiac technology in turn could decrease the likelihood of

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patients receiving needed treatment. In addition, it is also possible that patients who need

advanced cardiac intervention during ambulance diversion periods have a lower likelihood of

receiving treatment even in a hospital equipped with cardiac technology, if crowding and limited

resources outstrip the capability of the staff to deploy their technology appropriately. Such

situation can affect patients who were already admitted to hospitals under diversion or patients

who were admitted to hospitals that have to treat the additional diverted patients. At this stage,

potential disparity exists if blacks receive inferior treatments compared to white patients, leading

to worse patient outcomes, when both are exposed to the same level of ambulance diversion.(17)

Our study therefore explores potential differences between black and white patients at

different stages of ambulance diversion. We first compare amount of ambulance diversion

between hospitals serving large share of black patients (henceforth “minority-serving hospitals”)

and others. We then examine whether racial disparities in ambulance diversion, if any, resulted

in differential health outcomes between black and white patients.

Data

We obtained patient data from 100% Medicare Provider Analysis and Review

(MedPAR), linked with vital files, between 2001 and 2011. We linked them with the Healthcare

Provider Cost Reporting Information System and American Hospital Association annual surveys

to obtain additional hospital-level information.

To identify each hospital’s daily ambulance diversion hours, we acquired daily

ambulance diversion logs provided by the California local emergency medical services agencies

(LEMSAs), which contain data for 17 out of the 23 LEMSAs that did not ban diversion for the

years of 2001-2011 (actual coverage dates vary by LEMSA). The 17 LEMSAs together represent

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88% of the California population. To identify the closest ED for a patient, we supplemented our

hospital data with longitude and latitude coordinates of the hospital’s physical address or heliport

(if one existed).(21) We obtained actual driving distance from the patient’s ZIP code centroid to

nearest hospital’s latitude and longitude coordinates based on Google Maps, using automation

codes developed in Stata.(22)

Patient Population

We identified the AMI population by extracting from 100% MedPAR records that had

410.x0 or 410.x1 as the principal diagnoses as done in previous studies,(18) were hospitalized

between 2001 and 2011, and reside in counties for which we had diversion data. We excluded

all patients who were not admitted through the ED, and patients whose admitting hospital was >

100 miles away from their mailing ZIP codes. For analysis of 30-day readmission, we excluded

patients who could not be readmitted to the hospital (for example, if the patient died during the

index admission) per CMS guidelines.(23)

Defining Minority Serving Hospitals

In the first part of our analysis, we performed a trend analysis of ambulance diversion

hours between hospitals serving large share of black patients and other hospitals. We

characterized hospitals’ share of black patients in 2 ways, using metrics from prior work. First,

we ranked each hospital by the proportion of total Medicare patient volume that is black at

baseline (2001),(24) and defined minority-serving hospitals as those who ranked in the top 10%.

Second, we also designated hospitals as a minority-serving if they provided care to more than

double the number of black patients compared with competing hospitals within a 15-mile radius

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of the facility in 2001.(18) This approach accounts for the distribution of the local population.

Defining Access, Treatment, and Health Outcomes

We evaluated three dimensions of patient care. We defined access as whether a patient

was admitted to a hospital with the following cardiac technology: cardiac care intensive unit,

catheterization lab, and coronary artery bypass graft (CABG) surgery capacity.

We defined treatment received as whether a patient received a given procedure, identified

by the ICD-9 procedure codes on the MedPAR. We examine 3 common treatments for AMI:

percutaneous coronary intervention (PCI), thrombolytic therapy, or CABG.

Finally, we analyzed 2 sets of patient health outcomes: death (whether a patient died

within X days from his ED admission, where X=30, 90, and 365 days) and readmission to the

hospital within 30 days of the index discharge.

Statistical models

We first explored whether racial disparities exist in the absolute amount – e.g., number of

hours - of ambulance diversion. Because diversion is measured at the hospital level, we

compared daily diversion trends between minority serving and non-minority serving hospitals

using the mean daily ambulance diversion hours. We use the nonparamaetric Kolmogorov-

Smirnov tests to test whether the two groups’ diversion trend distributions were the same.(25)

We then implemented a multivariate model to examine the patient outcomes (in terms of

access, treatment received, and health). For all outcomes, we implemented a linear probability

model with fixed effects for each ED that was identified as the closest ED for each patient while

controlling for time-dependent variables. The ED fixed effects eliminate any underlying

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differences across EDs and the communities they serve. Baseline differences might include but

not limited to possible differences in baseline diversion rate, baseline mortality rates, quality of

care, case-mix of the patient population, or other unobserved characteristics that might be

confounded with the outcomes.

For the key variables of interest, we created 3 dichotomous variables based on the

diversion level of the patient’s nearest ED, using previously defined categories of diversion: no

diversion (reference group); <6 hours; 6-12 hours; and ≥12 hours.(18, 20) To also investigate

possible differential outcomes as the result of diversion between black and white patients, we

added the interaction term between indicator for black patients and the three diversion categories.

We controlled for race (black, Hispanic, Asian, other minority), age, gender, as well as 22

comorbid measures based on prior work.(26) For admitting hospital organizational

characteristics, we controlled for hospital ownership, teaching status, size (measured by log

transformed total inpatient discharges), occupancy rate, system membership, and Herfindahl

index to capture the competitiveness of the hospital market within 15-mile radius (0 being

perfectly competitive and 1 being monopoly). Last, we included year indicators to capture the

macro trends.

For treatment outcomes, we estimated an additional model that controlled for cardiac

technology access. Results from these two models allow us to compare whether differences in

treatment received, if any, is the result of lack of technology access. For mortality outcomes, we

estimated a third model accounting for both cardiac technology and actual treatment received.

All models were estimated using Stata 13.(27)

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RESULTS

Figure 2 shows the trend in mean daily diversion hours among hospitals reporting any

diversion hours between non-minority-serving and minority-serving hospitals. Mean daily

diversion hours were higher in minority-serving hospitals than in non-minority-serving hospitals,

with an average difference 2 hours per day between the 2 groups (p<0.001 by nonparametric

Kolmogorov-Smirnov tests). We also examined the percent of patients who experienced

diversion over time if their closest ED was minority-serving compared with other hospitals, and

observed the same pattern.

Our sample included 28,683 patients for all outcomes, except for the readmission analysis

where the sample size was 22,058 patients. The first column of Table 1 shows that among all

patients, 14,628 patients (51%) experienced no diversion at their nearest ED on their day of

admission; for 25%, their nearest ED was on diversion for <6 hours; for 15%, their nearest ED

was on diversion for 6-12 hours; and for 9% their nearest ED was on diversion for >12 hours of

diversion. The next two sets of columns show that a larger percentage of black patients

experienced >12 hours of diversion than white patients (12% vs. 9%). The next panel in Table 1

shows the distribution of dependent variables. In general, blacks had a lower probability of

being admitted to hospitals with cardiac care technology, and a lower probability of receiving

catheterization. The raw mortality outcomes were similar between blacks and whites, but blacks

had a higher probability of being readmitted to hospitals within 30 days of discharge (42 vs.

36%).

Table 1 also reveals underlying demographic and comorbid differences between black

and white patients. Compared to whites, blacks who suffered from acute myocardial infarction

were more likely to be female, younger, and comorbid with either diabetes, renal failure, or

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hypertension.

While informative, the raw rates in Table 1 do not take into account potential differences

across individuals, hospitals and communities. Table 2 shows estimated results from Model 1.

The top panel shows the main effect whereas the bottom panel shows the interactive effect

between diversion and black patients (complete results in Supplementary Table 1). After

controlling for multiple factors, patients exposed to the highest level of diversion (>12 hours)

had worse access to cardiac technology—by -2.75 percentage points for access to cardiac care

intensive unit (95% CI: -4.94, -0.55) compared with patients who were admitted on a day with

no diversion; by -2.56 percentage points for access to catheterization lab (CI -4.33, -.8), and by -

2.28 percentage points for access to CABG facilities (CI -4.02, -0.54). This is equivalent to a

4.6% reduction in CCU access (the base rate for CCU is 60%), and 3.5% reduction in access to

catheterization lab and CABG facilities. In addition, patients exposed to the highest level of

diversion were less likely to receive catheterization, by 2.49 percentage points (CI -4.51, -0.46),

and had a higher 1-year mortality rate, by 2.82 percentage points (CI 0.76, 4.88). In other words,

patients in the highest diversion category had a decreased likelihood of receiving catheterization

by 4.9% (base rate is 15%) and higher 1-year mortality by 9.7% (base rate is 34%).

The bottom panel of Table 2 showed that in general, black and white patients had a

similar experience when facing the same level of diversion. The interaction terms in general

were not statistically significant at the conventional level, with two exceptions. Blacks in the

highest diversion category were less likely to receive thrombolytic therapy relative to whites

facing the same amount of diversion, and blacks in the low diversion category (<6 hours) had a

higher 1-year mortality relative to whites.

Table 3 shows results from the additional model where we controlled for cardiac

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technology access (for both treatment and health outcomes) and treatment received (for health

outcomes only). The notable difference, compared to Table 2, is that once we controlled for

technology access, the probability of receiving PCI was comparable across the diversion levels.

We still observed higher 1-year mortality rates among patients in the highest diversion category,

albeit with a slightly smaller magnitude. The interaction results were similar to those reported in

Table 2.

DISCUSSION

Our study provides a unique perspective into the mechanisms behind ambulance

diversion and health disparities. We hypothesized that ambulance diversion might affect black

and white patients differently through three potential mechanisms: differential amount of

exposure to diversion, differential access to cardiac technology, and differential treatment

received when both experience diversion. While these possibilities are not mutually exclusive

and could happen concurrently, our results mainly support the hypothesis of differential exposure

to ambulance diversion – in other words, blacks with AMI have higher exposure to ambulance

diversion, which is associated with higher long-term mortality. This is in contrast to explanations

where blacks receive differentially less access to technology or treatment compared with whites

when both experience the same diversion condition.

Our findings that minority-serving hospitals are more likely to experience ambulance

diversion than non-minority serving hospitals is concerning. Despite the overall decrease in

ambulance diversion over time, it appears that this decrease has not helped improve the

disparities in diversion. The disparate amount of diversion experienced by minority-serving

hospitals is concordant with previous literature, suggesting that there may be a fundamental

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misalignment in the supply and demand of emergency services at minority-serving hospitals

relative to non minority-serving hospitals.(28-30)

Our findings add support to evidence (18) that policies to reduce ambulance diversion can

improve access, treatment, and outcomes for patients. However, in order to narrow the gap in

disparities between black and white patients, more effort should be made to reduce the amount of

ambulance diversion at minority-serving hospitals. These interventions to target excessive

ambulance diversion in at minority-serving hospitals could also be in keeping with national goals

to decrease disparities at the system level. For example, the Department of Health and Human

Services has stated that one of their strategies to reduce disparities in the quality of care specific

to cardiovascular diseases is to implement policy and health system changes that include

reimbursement incentives.(31) In addition to devoting resources on individually-oriented

initiatives geared at educating physicians about biases in offering cardiac catheterization, then,

our findings suggest that thoughtful reflection about approaches to achieve equitable resource

allocation could be another effective mechanism in the long-term towards decreasing racial

disparities in healthcare.

Our results should be interpreted in light of several limitations. First, our diversion data is

self-reported by LEMSAs, with potential for errors and reporting bias. Second, our patient data

identify date but not time of admission. While we cannot verify with absolute certainty that a

patient was diverted, it is reasonable to assume longer hours of diversion is associated with lower

probability that a patient is admitted to this ED. In addition, the inability to clearly identify the

diverted and the non-diverted patients in our analysis implies that what we observe is the net

effect of ambulance diversion.

Second, our dataset contains the mailing ZIP codes for the patients, which may or may

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not be the same as their ZIP code of residence. There is also a possibility that the patient’s AMI

did not occur at home. With the exclusion criteria we imposed in selecting our sample, we

believe that this limitation should not affect our analyses.

We are aware that using driving distance to determine a patient’s geographical access to

EDs means that our study ignores the availability of the aeromedical transport network. We

believe that this omission does not affect our findings as aeromedical transportation is almost

always limited to inter-hospital (or trauma scene-to-hospital) transport, and is rarely, if ever, an

option for AMI patients in the field, even in remote rural areas.

Third, our dataset was limited to California, which, while a large and diverse state

representing 12% of the U.S. population, is not representative of the nation as a whole. Because

our patient sample was based on the Medicare population, our findings may not be applicable to

non-elderly population. Last, we have relatively small sample of black patients. Future studies

that incorporate non-Medicare populations could also increase the sample size for black patients,

and improve the statistical power of the analysis.

CONCLUSIONS

Our study shows that hospitals treating high volume of black patients experience a

significantly greater amount of ambulance diversion than non minority-serving hospitals. In

addition, patients whose nearest hospital experiences significant diversion have poorer access to

hospitals with cardiac technologies, leading to a lower likelihood of receiving treatment with

revascularization, and lower 1-year survival. We do not find that other downstream

consequences of ambulance diversion, such as decreased access to technology and treatment, are

differentially worse for black patients. Because diversion is asymmetrically experienced by

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hospitals that treat high volume of black patients, targeted efforts to decreased ED crowding and

ambulance diversion in these communities may be able to reduce disparities in quality of care

and, ultimately, outcomes.

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Contributorship:

Dr. Shen had full access to all of the data in the study and takes responsibility for the integrity of

the data and the accuracy of the data analysis.

Study concept and design: Shen, Hsia.

Acquisition of data: Shen, Hsia.

Analysis and interpretation of data: Shen, Hsia.

Drafting of the manuscript: Hsia.

Critical revision of the manuscript for important intellectual content: Shen.

Statistical analysis: Shen.

Obtained funding: Shen, Hsia.

Administrative, technical, or material support: Shen, Hsia.

Study supervision: Shen.

Acknowledgements

We especially thank Harlan Krumholz (Yale University, New Haven CT) for providing

constructive suggestions on the manuscript; Jean Roth (National Bureau of Economic Research,

Cambridge MA) for assisting with obtaining and extracting the patient data; Nandita Sarkar

(NBER) for excellent programming support; Julia Brownell and Sarah Sabbagh (UCSF, San

Francisco CA) for technical assistance. None received additional compensation other than

University salary for their contributions.

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Competing Interest

We have read and understood BMJ policy on declaration of interests and declare that we have no

competing interests.

Funding

This work was supported by the NIH/NHLBI (National Heart, Lung, and Blood Institute) Grant

Number 1R01HL114822 (Shen/Hsia). Its contents are solely the responsibility of the authors and

do not necessarily represent the official views of the NIH.

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overcrowding and ambulance diversions. Jt Comm J Qual Patient Saf. 2006;32(5):239-45.

16. Pham JC, Patel R, Millin MG, et al. The effects of ambulance diversion: a comprehensive

review. Acad Emerg Med. 2006;13(11):1220-7.

17. DeLia D, Cantor J. Emergency department utilization and capacity. Research Syntehsis

Report. Princeton, NJ: The Robert Wood Johnson Foundation; 2009.

18. Shen Y, Hsia RY. Association Between Ambulance Diversion and Survival Among

Patients With Acute Myocardial Infarction. JAMA. 2011;305(23):2440-7.

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19. Yankovic N, Glied S, Green LV, et al. The impact of ambulance diversion on heart attack

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race and site of care. JAMA. 2011;305(7):675-81.

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administrative data. Med Care. 1998;36(1):8-27.

27. StataCorp. Stata Statistical Software: Release 13. College Station, TX: StataCorp LP;

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populations were more likely than others to employ ambulance diversion. Health Aff (Millwood).

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do financial pressure and community characteristics matter? Med Care. 2009;47(9):968-78.

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FIGURE LEGEND:

Figure 1. Conceptualizing stages of ambulance diversion and potential racial disparity

Figure 2. Trend in Ambulance Diversion between Minority-Serving and Non-Minority Serving

Hospitals: 2001-2011

TABLE LEGEND:

Table 1. Descriptive Statistics of Patient Characteristics

Table 2. Association Between Ambulance Diversion of the Nearest ED And Access, Treatment,

and Outcomes, Based on Model 1


and Outcomes, Based on Alternative Models

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Whole Sample White Black

N (%) N (%) N (%)

Nearest ED's exposure to diversion on the day of admission

no diversion 14628 (51%) 11457 (53%) 803 (49%) **

<6 hours 7173 (25%) 5259 (24%) 374 (23%)

[6-12) hours 4207 (15%) 3121 (14%) 260 (16%) *

>=12 hours 2675 (9%) 1933 (9%) 197 (12%) **

Access

admitted to hospital with cardiac care unit 19071 (66%) 14599 (67%) 1070 (65%)

admitted to hospital with cath lab 21368 (74%) 16477 (76%) 1181 (72%) **

admitted to hospital with CABG capacity 19271 (67%) 15140 (70%) 1014 (62%) **

Treatment received

received catheterization 13763 (48%) 10828 (50%) 655 (40%) **

received thrombolytic therapy 443 (2%) 335 (2%) 19 (1%)

received CABG 1667 (6%) 1285 (6%) 72 (4%) *

Health Outcomes

30-day mortality 4833 (17%) 3733 (17%) 246 (15%) *

90-day mortality 6559 (23%) 5038 (23%) 368 (23%)

1-year mortality 8865 (31%) 6826 (31%) 500 (31%)

30-day all cause readmission 7974 (36%) 6027 (36%) 538 (42%) **

Patient demographics

White 21770 (76%)

Black 1634 (6%)

Hispanic 1595 (6%)

Asian 2169 (8%)

Other non-white races 1468 (5%)

Female 14243 (50%) 10770 (49%) 926 (57%) **

Age distribution

65–69 4125 (14%) 3011 (14%) 344 (21%) **

70–74 4620 (16%) 3407 (16%) 305 (19%) **

75–79 5337 (19%) 3891 (18%) 341 (21%) **

80–84 5934 (21%) 4538 (21%) 285 (17%) **

85+ 8667 (30%) 6923 (32%) 359 (22%) **

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Table 1. Descriptive Statistics of Patient Characteristics (Continued)


N (%) N (%) N (%)

Patient comorbid conditions

Peripheral vascular disease 2096 (7%) 1638 (8%) 127 (8%)

Pulmonary Circulation disorders 637 (2%) 481 (2%) 44 (3%)

Diabetes (uncomp+complicated) 7674 (27%) 5183 (24%) 542 (33%) **

Renal failure 3647 (13%) 2521 (12%) 280 (17%) **

Liver disease 223 (1%) 155 (1%) 13 (1%)

Cancer 1056 (4%) 836 (4%) 80 (5%) *

Dementia 1111 (4%) 882 (4%) 70 (4%)

Valvular disease 3848 (13%) 3092 (14%) 185 (11%) **

Hypertension (uncomp+complicated) 17449 (61%) 12807 (59%) 1142 (70%) **

Chronic pulmonary disease 5645 (20%) 4343 (20%) 359 (22%) *

Rheumatoid arthritis/collagen vascular 460 (2%) 378 (2%) 29 (2%)

Coagulation deficiency 955 (3%) 704 (3%) 53 (3%)

Obesity 1032 (4%) 808 (4%) 85 (5%) *

Substance abuse 447 (2%) 350 (2%) 46 (3%) **

Depression 759 (3%) 632 (3%) 35 (2%) *

Psychosis 392 (1%) 306 (1%) 29 (2%)

Hypothyroidism 2356 (8%) 2044 (9%) 60 (4%) **

Paralysis and other neurological disorder 2439 (9%) 1830 (8%) 178 (11%) **

Chronic Peptic ulcer disease 18 (0%) 12 (0%) 1 (0%)

Weight loss 590 (2%) 415 (2%) 49 (3%) **

Fluid and electrolyte disorders 5902 (21%) 4274 (20%) 394 (24%) **

Anemia (blood loss and deficiency) 3810 (13%) 2762 (13%) 266 (16%) **

patient 28683 21770 1634

Note: black and white differences statistically significant at * p<0.05 ** p<0.01

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Table 2. Association Between Ambulance Diversion of the Nearest ED And Access, Treatment, and Outcomes, Based on Model 1

Access (admitting hospital has:) Treatment Outcomes

cardiac care unit cath lab CABG cath/PCI

thrombolytic therapy CABG

30-day mortality

90-day mortality

1-year mortality

30-day readmission

Base rate (among patients in reference group) (60%) (73%) (65%) (51%) (1%) (6%) (16%) (22%) (29%) (34%)

Diversion status (Reference group: nearest ED not on diversion on the day of admission)

Nearest ED's exposure to diversion on the day of admission:

<6 hours -1.25 -1.73** -1.07 -0.89 0.15 -0.46 -0.17 0.19 -0.16 0.19

[-2.64,0.14] [-2.94,-0.52] [-2.25,0.12] [-2.32,0.54] [-0.27,0.58] [-1.24,0.32] [-1.29,0.95] [-1.12,1.51] [-1.64,1.31] [-1.69,2.07]

[6-12) hours -0.40 -0.87 -0.31 -1.36 -0.35 -0.54 0.26 0.38 0.38 0.45

[-2.05,1.25] [-2.47,0.73] [-1.90,1.27] [-3.05,0.33] [-0.82,0.12] [-1.49,0.41] [-1.09,1.61] [-1.12,1.89] [-1.29,2.05] [-1.68,2.58]

>=12 hours -2.75* -2.57** -2.28* -2.49* -0.12 -0.31 1.02 1.71 2.82** 2.02

[-4.94,-0.55] [-4.33,-0.80] [-4.02,-0.54] [-4.51,-0.46] [-0.72,0.48] [-1.34,0.72] [-0.72,2.76] [-0.11,3.54] [0.76,4.88] [-0.69,4.73]

Interaction between black patients and diversion level:

X low diversion -2.63 2.29 3.46 -1.71 -0.93 0.42 0.83 3.88 5.08* 2.11

(<6 hours) [-7.57,2.30] [-2.40,6.98] [-1.56,8.48] [-7.32,3.90] [-2.46,0.59] [-2.29,3.12] [-3.57,5.23] [-0.62,8.38] [0.05,10.11] [-4.45,8.66]

X medium diversion -1.71 -0.33 0.22 -0.01 -0.72 2.88 3.13 5.09 2.11 1.71

[6-12) hours [-7.62,4.20] [-6.37,5.71] [-5.82,6.26] [-6.71,6.69] [-2.22,0.78] [-0.31,6.07] [-2.39,8.65] [-0.35,10.52] [-3.52,7.74] [-6.40,9.81] X high diversion 2.91 -1.99 -0.32 2.08 -2.49** -0.63 2.74 2.51 0.20 -1.56

(>= 12 hours) [-2.83,8.64] [-7.67,3.69] [-5.90,5.27] [-4.14,8.31] [-4.04,-0.94] [-4.08,2.83] [-2.62,8.10] [-3.89,8.90] [-6.54,6.94] [-11.49,8.38]

Control for tech access N/A N/A N/A No No No No No No No

Control for treatment N/A N/A N/A N/A N/A N/A No No No No

N 28683 28683 28683 28683 28683 28683 28683 28683 28683 22058

* Nearest ED Based on Google query of driving distance * p<0.05 ** p<0.01

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Table 3. Association Between Ambulance Diversion of the Nearest ED And Access, Treatment, and Outcomes, Based on Alternative Models

Treatment Outcomes

cath/PCI thrombolytic

therapy CABG 30-day

mortality 90-day

mortality 1-year

mortality 30-day

readmission

Base rate (among patients in reference group) (51%) (1%) (6%) (16%) (22%) (29%) (34%) Diversion status (Reference group: nearest ED not on diversion on the day of admission)


<6 hours -0.48 0.14 -0.41 -0.18 0.18 -0.20 0.20

[-1.88,0.91] [-0.29,0.56] [-1.19,0.38] [-1.29,0.94] [-1.13,1.49] [-1.67,1.27] [-1.68,2.08]

[6-12) hours -1.21 -0.35 -0.53 0.27 0.38 0.37 0.48

[-2.87,0.45] [-0.82,0.12] [-1.49,0.42] [-1.08,1.61] [-1.12,1.89] [-1.30,2.04] [-1.66,2.62]

>=12 hours -1.73 -0.16 -0.18 0.98 1.67 2.73** 1.96

[-3.69,0.23] [-0.76,0.44] [-1.20,0.84] [-0.75,2.71] [-0.14,3.49] [0.68,4.79] [-0.76,4.68]


X low diversion (<6 hours) -2.93 -0.85 0.22 0.91 3.96 5.24* 2.35

[-8.49,2.62] [-2.38,0.68] [-2.52,2.96] [-3.50,5.32] [-0.55,8.48] [0.20,10.29] [-4.22,8.91] X medium diversion [6-12) hours -0.10 -0.71 2.87 3.14 5.10 2.13 1.62

[-6.27,6.08] [-2.20,0.78] [-0.30,6.04] [-2.39,8.68] [-0.34,10.54] [-3.51,7.77] [-6.52,9.77]

X high diversion (>=12 hours) 2.44 -2.50** -0.65 2.76 2.50 0.17 -1.82

[-3.56,8.44] [-4.04,-0.97] [-4.06,2.76] [-2.60,8.13] [-3.89,8.89] [-6.59,6.94] [-11.71,8.08]

Control for tech access Yes Yes Yes Yes Yes Yes Yes

Control for treatment N/A N/A N/A Yes Yes Yes Yes

N 28683 28683 28683 28683 28683 28683 22058


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Figure 1. Conceptualizing stages of ambulance diversion and potential racial disparity

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Figure 2. Trend in Ambulance Diversion between Minority-Serving and Non-

Minority Serving Hospitals: 2001-2011

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Supplementary Table 1. Full Regression Results Based on Model 1


Base rate (among patients in

reference group)

cardiac

unit

care

(60%)

cath lab

(73%)

CABG

(65%)

cath/PCI

(51%)

thrombolytic

therapy

(1%)

CABG

(6%)

30-day

mortality

(16%)

90-day

mortality

(22%)

1-year

mortality

(29%)

30-day

readmission

(34%)

Diversion status (Reference

group: nearest ED not on

diversion on the day of

admission)

<6 hours

-1.25+

-1.73**

-1.07+

-0.89

0.15

-0.46

-0.17

0.18

-0.18

0.19

(0.71)

(0.62) (0.60)

(0.73) (0.22) (0.40)

(0.57) (0.67) (0.76) (0.96)

[6-12) hours -0.40

-0.87 -0.31

-1.36 -0.35 -0.54

0.26 0.39 0.38 0.45

(0.84)

(0.82) (0.80)

(0.86) (0.24) (0.48)

(0.69) (0.77) (0.85) (1.09)

>=12 hours -2.74*

-2.56** -2.28*

-2.49* -0.12 -0.31

1.02 1.73+ 2.84** 2.04

(1.12)

(0.90) (0.89)

(1.03) (0.30) (0.53)

(0.89) (0.93) (1.05) (1.38)

Interaction between black patients

and diversion level: -2.59 2.31 3.50 -1.73 -0.92 0.40 0.88 3.96+ 5.22* 2.16

X low diversion (<6 hours) (2.51) (2.39) (2.56) (2.86) (0.77) (1.37) (2.24) (2.30) (2.56) (3.34)

-1.68 -0.32 0.24 -0.03 -0.71 2.87+ 3.16 5.14+ 2.21 1.75

X medium diversion [6-12) hours (3.01) (3.08) (3.08) (3.41) (0.76) (1.62) (2.81) (2.76) (2.87) (4.13)

2.93 -1.98 -0.30 2.07 -2.49** -0.63 2.77 2.54 0.27 -1.54

X high diversion (>=12 hours) (2.93) (2.89) (2.84) (3.17) (0.79) (1.76) (2.73) (3.25) (3.43) (5.06)

-0.29 -0.45 -0.67+ -5.13** -0.15 -2.73** -0.24 0.25 0.02 3.36**

(0.44) (0.39) (0.38) (0.54) (0.16) (0.30) (0.45) (0.49) (0.52) (0.64)


Female -0.29 -0.45 -0.67+ -5.13** -0.15 -2.73** -0.24 0.25 0.02 3.36**

(0.44) (0.39) (0.38) (0.54) (0.16) (0.30) (0.45) (0.49) (0.52) (0.64)

Black -1.54 -2.99+ -2.42 -6.59** 0.11 -2.02* -2.99* -2.55+ -2.69* 4.33*

(1.66) (1.54) (1.62) (1.95) (0.45) (0.83) (1.16) (1.32) (1.37) (2.10)

hispanic -2.68* -1.79* -2.58** -0.39 -0.49 -0.34 -2.41* -1.65 -2.75* 0.92

(1.10) (0.89) (0.98) (1.25) (0.37) (0.56) (0.97) (1.14) (1.20) (1.65)

other minority race -1.59 -2.28** -2.38** -2.56* 0.15 0.72 -0.32 -1.14 -2.12* -3.53*

(1.02) (0.84) (0.87) (1.14) (0.38) (0.68) (0.94) (1.00) (1.05) (1.40)

age 70-74 -0.01 0.30 0.46 -2.88** -0.77* -1.41* 1.23* 2.17** 3.29** 2.77*

(0.79) (0.68) (0.79) (0.85) (0.32) (0.60) (0.62) (0.68) (0.75) (1.12)

age 75-79 -0.28 0.37 0.24 -6.24** -0.49 -0.87 3.23** 4.88** 6.95** 7.98**

(0.75) (0.66) (0.73) (0.86) (0.33) (0.64) (0.63) (0.72) (0.81) (1.10)

age 80-84 -0.35 0.18 -0.75 -13.72** -0.84** -3.49** 8.02** 11.84** 15.05** 12.42**

(0.77) (0.61) (0.68) (0.91) (0.32) (0.60) (0.73) (0.80) (0.86) (1.17)

age 85+ -1.37+ -0.96 -0.97 -33.70** -1.29** -7.09** 16.21** 22.23** 29.33** 16.56**

(0.75) (0.64) (0.71) (1.02) (0.31) (0.56) (0.65) (0.73) (0.75) (1.11)


Peripheral vascular disease 1.73* 0.75 1.73* 2.06* -0.51* 0.92+ -0.27 1.04 1.34 1.80

(0.81) (0.68) (0.71) (1.01) (0.24) (0.54) (0.82) (0.95) (1.00) (1.28)

Pulmonary Circulation disorders -1.37 -1.04 -0.65 -6.66** 0.12 -0.54 1.75 3.83* 6.88** 2.33

(1.37) (1.20) (1.21) (1.91) (0.48) (0.96) (1.67) (1.87) (1.90) (2.22)

Diabetes -0.43 0.13 -0.61 -4.61** -0.33+ -0.24 -2.01** -1.73** 0.15 1.91**

(0.48) (0.46) (0.48) (0.60) (0.17) (0.29) (0.46) (0.51) (0.58) (0.71)

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Renal failure -1.90** -0.31 -0.63 -11.86** -0.68** -0.58 7.21** 10.61** 14.25** 6.64**

(0.70) (0.53) (0.60) (0.86) (0.17) (0.41) (0.79) (0.85) (0.90) (1.03)

Cancer 0.59 0.60 -0.89 -17.43** -0.58+ -3.54** 15.42** 22.07** 30.46** 5.56**

(1.07) (0.90) (1.01) (1.46) (0.33) (0.62) (1.42) (1.45) (1.42) (1.85)

Dementia -2.41+ -0.12 -0.83 -13.11** 0.06 -0.58 3.40* 4.54** 8.08** 3.21

(1.29) (1.14) (1.07) (1.32) (0.36) (0.47) (1.56) (1.67) (1.70) (2.01)

Valvular disease 1.86** 0.85+ 1.64** -1.61+ -0.48* 3.17** -0.18 1.67* 4.08** 2.02*

(0.63) (0.51) (0.57) (0.82) (0.19) (0.48) (0.66) (0.71) (0.77) (0.96)

Hypertension -1.23** 0.25 -0.20 4.55** -0.21 -0.91** -9.18** -11.56** -12.88** -6.55**

(0.45) (0.37) (0.41) (0.56) (0.15) (0.30) (0.47) (0.49) (0.55) (0.70)

Chronic pulmonary disease 0.29 -0.33 -0.74 -6.02** -0.28 -0.23 0.23 2.69** 5.88** 5.64**

(0.61) (0.51) (0.54) (0.67) (0.18) (0.32) (0.54) (0.59) (0.65) (0.83)

Rheumatoid arthritis/collagen vascu1.14 0.04 -1.58 -2.23 -0.23 -1.81* -2.53 -2.86 -2.60 -2.54

(1.56) (1.38) (1.36) (1.88) (0.53) (0.91) (1.57) (1.76) (2.08) (2.36)

Coagulation deficiency -1.66 1.15 0.80 -0.37 -0.54+ 11.08** 6.94** 9.82** 9.82** 5.76**

(1.20) (0.98) (1.02) (1.46) (0.32) (1.16) (1.43) (1.49) (1.62) (1.81)

Obesity -0.02 -3.25** -2.21+ 1.68 -0.35 -0.33 -3.31** -4.51** -7.28** -3.33*

(1.23) (1.04) (1.15) (1.46) (0.39) (0.78) (0.76) (0.84) (0.95) (1.53)

Substance abuse 0.75 0.49 -2.82 -4.94* -0.35 -0.67 -1.95 -2.38 0.04 6.10*

(1.80) (1.80) (1.73) (2.07) (0.58) (1.25) (1.48) (1.61) (1.88) (2.76)

Depression 0.65 -0.32 0.17 -4.79** -0.26 -0.93 -2.45* -1.60 -1.25 3.15

(1.33) (1.17) (1.20) (1.60) (0.41) (0.66) (1.10) (1.38) (1.58) (2.01)

Hypothyroidism 0.75 0.07 -0.03 -0.41 -0.33 -1.27** -5.66** -6.28** -6.48** -1.85+

(0.84) (0.72) (0.77) (0.91) (0.25) (0.39) (0.69) (0.82) (0.90) (1.09)

Paralysis and other neurological dis -0.28 -1.99* -1.69* -12.51** -0.61* -1.80** 6.96** 8.19** 10.07** 11.46**

(0.90) (0.79) (0.81) (1.03) (0.24) (0.44) (0.91) (0.98) (0.97) (1.33)

Weight loss -2.63 -0.91 -5.07** -12.36** -1.13** 0.95 9.73** 17.60** 18.47** 15.83**

(1.72) (1.55) (1.49) (1.95) (0.28) (1.03) (2.01) (2.11) (2.12) (2.31)

Fluid and electrolyte disorders -0.74 -2.76** -2.47** -12.77** -0.38* 0.47 10.11** 11.86** 12.11** 11.80**

(0.60) (0.53) (0.59) (0.71) (0.18) (0.38) (0.63) (0.68) (0.72) (0.91)

Anemia 0.21 0.29 0.31 -3.65** -0.37+ 1.81** -4.45** -4.01** -2.13** 2.36*

(0.66) (0.53) (0.55) (0.78) (0.20) (0.46) (0.66) (0.73) (0.73) (0.99)

Admitting hospital characteristics

for-profit hospital 9.88** 0.20 -6.38* -2.25 -0.56+ -0.15 0.05 0.79 2.28* 1.37

(2.14) (2.17) (2.68) (2.09) (0.31) (0.47) (0.85) (0.94) (0.98) (1.07)

government hospital -17.57** -2.68 -11.29** -5.08* 0.05 -1.17* 3.90** 4.11** 3.50** -0.07

(3.44) (3.33) (3.56) (2.11) (0.31) (0.59) (1.21) (1.26) (1.33) (1.49)

teaching hospital 9.51** -8.08* -16.66** -11.54** 0.61+ -2.28** 0.12 -0.50 0.17 -0.36

(3.04) (3.73) (3.44) (2.51) (0.37) (0.77) (1.07) (1.14) (1.19) (1.64)

hosopital beds (log transformed) 24.26** 35.14** 39.78** 21.67** -0.85** 3.75** -0.83 -0.62 -0.79 -3.37**

(2.22) (2.38) (2.80) (1.91) (0.22) (0.40) (0.56) (0.67) (0.71) (0.91)

occpuancy rate 52.94** 62.76** 61.48** 45.91** -2.61** 3.48** -6.19** -6.79** -6.88** -10.52**

(5.62) (5.18) (6.62) (4.30) (0.71) (1.20) (1.93) (2.14) (2.30) (2.78)

hospital is part of a system -4.11* 3.84** 8.84** 2.24* -0.13 1.12** -1.52* -2.11** -2.29** -1.12

(1.66) (1.39) (1.74) (0.99) (0.23) (0.36) (0.62) (0.69) (0.72) (0.99)

hospital HHI, based on 15-mi radius 3.71 13.21 11.38 20.75** 1.80 4.09+ -7.19+ -5.08 -3.84 -9.02

N 28,683 28,683 28,683 28,683 28,683 28,683 28,683 28,683 28,683 22,058

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Do Patients Hospitalized in High-Minority Hospitals Experience More Diversion and Poorer Outcomes? A

Retrospective Multivariate Analysis of Medicare Patients in California

Journal: BMJ Open

Manuscript ID bmjopen-2015-010263.R1


Date Submitted by the Author: 20-Jan-2016







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Poorer Outcomes? A Retrospective Multivariate Analysis of Medicare Patients in

California


2






Yu-Chu Shen, PhD


Phone: (831) 656-2951



Monterey, CA 93943


Word count: 3,059

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ABSTRACT

Objective: We investigated the association between crowding as measured by ambulance

diversion and differences in access, treatment, and outcomes between black and white patients.




according to hours in diversion status for their nearest emergency departments on their day of

















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ARTICLE SUMMARY




2011.





• Limitations include: potential reporting bias due to self-reported data by LEMSAs,

diversion status is measured at the hospital level and not for individual patient, lack of

generalizability outside of California, and a small sample of black patients.

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INTRODUCTION























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and can affect patients who have to be diverted elsewhere as well as non-diverted patients within

the overcrowded ED. Ambulance diversion has been used as a proxy for ED crowding.(21-23)

In order to better target potential areas for intervention, it is essential to know exactly where the

disparities occur when a patient experiences ambulance diversion. Figure 1 shows that in the

first stage, a hospital experiences resource constraints, mostly due to overcrowding in the ED,

such that it cannot accept incoming ambulance traffic. At this stage, a potential racial disparity

exists if black patients are more likely to be diverted than white patients because the ED closest

to them is more likely to be on diversion.








resources outstrip the capability of the staff to deploy their technology appropriately. At this

stage, a potential disparity exists if blacks receive inferior treatments compared to white patients,

leading to worse patient outcomes, when both are exposed to the same level of ambulance

diversion.(17)

Our study therefore explored potential differences between black and white patients at

different stages of ambulance diversion, using 100% of Medicare claims and daily ambulance

diversion logs from 26 California counties between 2001 and 2011. We first compared amount

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of ambulance diversion between hospitals serving large share of black patients (henceforth

“minority-serving hospitals”) and others. We then examined whether racial disparities in

ambulance diversion, if any, resulted in differential health outcomes between black and white

patients.

METHODS

Data







(LEMSAs). California has a total 33 LEMSAs, but 10 of them banned diversion for the years of

2001-2011. We excluded counties with diversion ban from our analysis, since they do not

contribute to our understanding of the relationship between diversion and patient outcomes. We

obtained data for 17 out of the 23 LEMSAs that did not ban diversion (actual coverage dates

vary by LEMSA). The 17 LEMSAs together represent 88% of the California population. To

identify the closest ED for a patient, we supplemented our hospital data with longitude and

latitude coordinates of the hospital’s physical address or heliport (if one existed).(24) We

obtained actual driving distance from the patient’s ZIP code centroid to nearest hospital’s

latitude and longitude coordinates based on Google Maps, using automation codes developed in

Stata.(25)

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Patient Population



between 2001 and 2011, and resided in counties for which we had diversion data. We excluded













of the facility in 2001.(18) This approach accounts for the distribution of the local population.





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by the ICD-9 procedure codes on the MedPAR. We examined 3 common treatments for AMI:





Statistical models




using the mean daily ambulance diversion hours. We use the nonparamaetric Kolmogorov-





controlling for time-dependent variables. The ED fixed effects eliminated any underlying







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We controlled for race (black, Hispanic, Asian, other minority, unknown/missing race),

age, gender, as well as 22 comorbid measures based on prior work.(29) For admitting hospital

organizational characteristics, we controlled for hospital ownership, teaching status, size

(measured by log transformed total inpatient discharges), occupancy rate, system membership,

and Herfindahl index to capture the competitiveness of the hospital market within 15-mile radius

(0 being perfectly competitive and 1 being monopoly). Last, we included year indicators to

capture the macro trends.


technology access. Results from these two models allowed us to compare whether differences in

treatment received, if any, are the result of lack of technology access. For mortality outcomes,

we estimated a third model accounting for both cardiac technology and actual treatment received.

All models were estimated using Stata 13.(30) This study was exempted from the Committee on

Human Research at the University of California, San Francisco.

RESULTS






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observed the same pattern. Table 1 shows that the minority-serving and other hospitals are

similar in most dimensions (bed size, cardiac care capacity, occupancy rate, teaching status),

except that a higher share of minority serving hospitals are government-run (22% vs. 12%,

p<0.01) and are located in more concentrated markets as measured by the Herfindahl index (0.2

vs. 0.15, p<0.01).


where the sample size was 22,058 patients. Among all patients, 15,202 patients (51%)

experienced no diversion at their nearest ED on their day of admission; for 25%, their nearest ED

was on diversion for <6 hours; for 15%, their nearest ED was on diversion for 6-12 hours; and

for 10% their nearest ED was on diversion for >12 hours of diversion (Table 1). In addition, a

larger percentage of black patients experienced >12 hours of diversion than white patients (12%

vs. 9%, p<0.01). In general, blacks had a lower probability of being admitted to hospitals with

cardiac care technology, and a lower probability of receiving catheterization. The raw mortality

outcomes were similar between blacks and whites, but blacks had a higher probability of being

readmitted to hospitals within 30 days of discharge (40 vs. 34%, p<0.01).




hypertension.


across individuals, hospitals and communities. Table 3 shows estimated results from Model 1

(complete results in Supplementary Table 1). After controlling for multiple factors, patients

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exposed to the highest level of diversion (>12 hours) had worse access to cardiac technology—

by -2.61 percentage points for access to cardiac care intensive unit (95% CI: -4.81, -0.4)

compared with patients who were admitted on a day with no diversion; by -2.44 percentage

points for access to catheterization lab (CI -4.24, -.65), and by -2.25 percentage points for access

to CABG facilities (CI -4.02, -0.48). This is equivalent to a 4.4% reduction in CCU access (the

base rate for CCU is 60%), and 3.4% reduction in access to catheterization lab and CABG

facilities. In addition, patients exposed to the highest level of diversion were less likely to

receive catheterization, by 2.19 percentage points (CI -4.19, 0.19), and had a higher 1-year

mortality rate, by 2.78 percentage points (CI 0.76, 4.8). In other words, patients in the highest

diversion category had a decreased likelihood of receiving catheterization by 4.3% (base rate is

51%) and higher 1-year mortality by 9.6% (base rate is 29%).

Results from the interaction terms between black patients and diversion status showed

that in general, black and white patients had a similar experience when facing the same level of

diversion. The interaction terms in general were not statistically significant at the conventional

level, with two exceptions. Blacks in the highest diversion category were less likely to receive

thrombolytic therapy relative to whites facing the same amount of diversion, and blacks in the

low diversion category (<6 hours) had a higher 1-year mortality relative to whites.






albeit with a slightly smaller magnitude. The interaction results were similar to those in Table 3.

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DISCUSSION








diversion because a larger share of black patients go to minority-serving hospitals, and longer

exposure to ambulance diversion is associated with higher long-term mortality. This is in

contrast to explanations where blacks receive differentially less access to technology or treatment

compared with whites when both experience the same diversion condition.












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self-reported by LEMSAs, with potential for errors and reporting bias. Second, our diversion

status is identified at the hospital level and not at the individual patient level, and our patient data


patient was diverted, it is reasonable to assume longer hours of diversion is associated with lower

probability that a patient is admitted to this ED. In addition, the inability to clearly identify the

diverted and the non-diverted patients in our analysis implies that what we observe is the net

effect of ambulance diversion.

Third, our dataset contains the mailing ZIP codes for the patients, which may or may not

be the same as their ZIP code of residence. There is also a possibility that the patient’s AMI did

not occur at home. With the exclusion criteria we imposed in selecting our sample, we believe

that this limitation should not affect our analyses.



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Forth, our dataset was limited to California, which, while a large and diverse state


our patient sample was based on the Medicare population, our findings may not be applicable to

non-elderly population. Last, we have a relatively small sample of black patients. Future studies



CONCLUSIONS

Our study showed that hospitals treating high volume of black patients experienced a


addition, patients whose nearest hospital experienced significant diversion had poorer access to


revascularization and lower 1-year survival. We did not find that other downstream

consequences of ambulance diversion, such as decreased access to technology and treatment,

were differentially worse for black patients. Because diversion is asymmetrically experienced by




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Contributorship:












Acknowledgements







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Competing Interest



Funding




Data sharing

No additional data available.

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References


States. Health Aff (Millwood) 2007;26:38-48.

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6. Green A, Carney D, Pallin D, et al. Implicit bias among physicians and its prediction of

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distrust in the United States. Am J Public Health 2007;97:1283-9.

8. Institute of Medicine. Unequal treatment: confronting racial and ethnic disparities in

health care. National Academy of Sciences; 2003.

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(Millwood) 2005;24:325-34.

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review. Acad Emerg Med 2006;13:1220-7.



18. Shen YC, Hsia RY. Association Between Ambulance Diversion and Survival Among

Patients With Acute Myocardial Infarction. JAMA 2011;305:2440-7.

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deaths. Inquiry 2010;47:81-91.

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21. Shenoi RP, Ma L, Jones J, et al. Ambulance diversion as a proxy for emergency

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22. Sun BC, Hsia RY, Weiss RE, et al. Effect of emergency department crowding on

outcomes of admitted patients. Ann Emerg Med 2013;61:605-11.e6.

23. Schull MJ, Lazier K, Vermeulen M, et al. Emergency department contributors to

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effects, and nonprofit objectives. J Health Econ 2009;28:924-37.


distance information. STATA J 2011;11:106-19.






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race and site of care. JAMA 2011;305:675-81.


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do financial pressure and community characteristics matter? Med Care 2009;47:968-78.

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FIGURE LEGEND:

Figure 1. Conceptualizing Stages of Ambulance Diversion and Potential Racial Disparity

Figure 2. Monthly Trend in Ambulance Diversion between Minority-Serving and Non-Minority

Serving Hospitals: 2001-2011

TABLE LEGEND:

Table 1. Descriptive Statistics of Hospital Characteristics






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Mean (SD) All Hospitals

Non-minority serving Hospitals

Minority-serving Hospitals

Minority-serving hospitals 24%

(43%)

Due to definition 1: in the top decile for proportion of all back patients in California 17%

(38%)

Due to definition 2: treat twice as many black patients than other hospitals in their geographic proximity 7%

(25%)

Cardiac care capacity

Has cath lab 60% 60% 59%

(49%) (49%) (49%)

Has cardiac care unit 57% 58% 54%

(50%) (49%) (50%)

Has CABG capacity 48% 49% 48%

(50%) (50%) (50%)

For-profit hospitals 26% 26% 28%

(44%) (44%) (45%)

Government hospitals 14% 12% 22% **

(35%) (32%) (41%)

Teaching hospitals 9% 9% 9%

(29%) (29%) (29%)

Member of a system 68% 69% 64%

(47%) (46%) (48%)

Mean total beds in hospital 232.89 231.39 237.58

(130.69) (130.14) (132.47)

Mean occupancy rate 0.64 0.64 0.65

(0.16) (0.16) (0.15)

Mean HHI index1 0.16 0.15 0.20

**

(0.18) (0.16) (0.22)

Number of hopistal years 1563 1186 377

Note: non-minority serving and minority-serving hospital differences statistically significant at * p<0.05 ** p<0.01 1. The HHI index captures competitiveness of the hospitals' market (defined as within 15-mile radius): the

scale goes from 0 to 1 where 0 represents perfectly competitive market and 1 represents monopoly.

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N (%) N (%) N (%)


no diversion 15202 (51%) 11439 (53%) 798 (50%) **

<6 hours 7514 (25%) 5169 (24%) 374 (23%)

[6-12) hours 4472 (15%) 3006 (14%) 263 (16%) *

>=12 hours 3069 (10%) 1998 (9%) 194 (12%) **

Access




Treatment received



received CABG 1695 (6%) 1216 (6%) 69 (4%) *

Health Outcomes

30-day mortality 4835 (16%) 3507 (16%) 234 (15%) *

90-day mortality 6593 (22%) 4759 (22%) 355 (22%)

1-year mortality 9447 (32%) 6824 (32%) 516 (32%)



White 21414 (72%)

Black 1612 (5%)

Hispanic 1578 (5%)

Asian 2126 (7%)


Unknown/missing race 1818 (6%)

Female 14906 (50%) 10612 (50%) 913 (57%) **

Age distribution

65–69 4231 (14%) 2920 (14%) 335 (21%) **

70–74 4756 (16%) 3292 (15%) 305 (19%) **

75–79 5531 (18%) 3764 (18%) 330 (20%) **

80–84 6197 (21%) 4455 (21%) 271 (17%) **

85+ 9224 (31%) 6983 (33%) 371 (23%) **

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N (%) N (%) N (%)





Renal failure 3965 (13%) 2669 (12%) 301 (19%) **

Liver disease 241 (1%) 155 (1%) 18 (1%)

Cancer 1127 (4%) 837 (4%) 79 (5%) *

Dementia 1171 (4%) 865 (4%) 67 (4%)






Obesity 1062 (4%) 796 (4%) 80 (5%) *

Substance abuse 461 (2%) 343 (2%) 44 (3%) **

Depression 790 (3%) 625 (3%) 34 (2%) *

Psychosis 405 (1%) 298 (1%) 30 (2%)

Hypothyroidism 2462 (8%) 2004 (9%) 58 (4%) **



Weight loss 623 (2%) 416 (2%) 51 (3%) **



patient 29939 21414 1612

Note: black and white differences statistically significant at * p<0.05 ** p<0.01

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30-day mortality

90-day mortality

1-year mortality

30-day readmission




<6 hours -1.40* -1.70** -0.94 -0.86 0.18 -0.42 -0.24 0.11 -0.20 -0.02

[-2.74,-0.05] [-2.91,-0.49] [-2.11,0.23] [-2.23,0.51] [-0.23,0.59] [-1.17,0.34] [-1.31,0.83] [-1.15,1.36] [-1.66,1.25] [-1.86,1.82]

[6-12) hours -0.40 -0.79 -0.25 -1.27 -0.27 -0.50 0.29 0.40 0.21 0.57

[-2.03,1.22] [-2.41,0.82] [-1.83,1.33] [-2.91,0.37] [-0.72,0.18] [-1.42,0.42] [-1.04,1.62] [-1.08,1.89] [-1.43,1.86] [-1.48,2.62]

>=12 hours -2.61* -2.44** -2.25* -2.19* -0.14 -0.29 1.10 1.78* 2.78** 2.12

[-4.81,-0.40] [-4.24,-0.65] [-4.02,-0.48] [-4.81,-0.40] [-0.72,0.43] [-1.29,0.71] [-0.60,2.81] [0.01,3.55] [0.76,4.80] [-0.55,4.79]


X low diversion -0.76 2.51 3.79 -2.16 -1.11 0.12 0.20 2.71 5.56* 2.35

(<6 hours) [-5.62,4.10] [-2.15,7.17] [-1.18,8.77] [-7.85,3.52] [-2.51,0.29] [-2.48,2.72] [-4.16,4.56] [-1.71,7.12] [0.25,10.86] [-4.13,8.83]

X medium diversion -0.12 1.08 3.05 -0.08 -0.93 2.77 3.43 4.94 2.55 0.60

[6-12) hours [-5.73,5.49] [-4.81,6.98] [-2.89,9.00] [-7.13,6.97] [-2.25,0.39] [-0.40,5.94] [-2.28,9.14] [-0.65,10.53] [-3.16,8.26] [-7.81,9.02]

X high diversion 3.37 -2.38 1.41 0.30 -2.70** -0.87 2.58 2.49 1.61 -0.28

(>= 12 hours) [-2.88,9.63] [-8.82,4.05] [-4.54,7.36] [-6.24,6.84] [-4.20,-1.19] [-4.32,2.58] [-3.33,8.50] [-4.29,9.27] [-5.83,9.05] [-11.01,10.45]



N 29939 29939 29939 29939 29939 29939 29939 29939 29939 23199


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26


Treatment Outcomes


therapy CABG 30-day

mortality 90-day

mortality 1-year

mortality 30-day

readmission

Base rate (among patients in reference group) (51%) (1%) (6%) (16%) (22%) (29%) (34%) Diversion status (Reference group: nearest ED not on diversion on the day of admission) Nearest ED's exposure to diversion on the day of admission:

<6 hours -0.51 0.16 -0.37 -0.25 0.09 -0.24 0.00

[-1.85,0.84] [-0.24,0.57] [-1.13,0.38] [-1.31,0.82] [-1.16,1.35] [-1.68,1.21] [-1.84,1.84]

[6-12) hours -1.15 -0.27 -0.49 0.30 0.40 0.21 0.60

[-2.77,0.47] [-0.72,0.18] [-1.41,0.43] [-1.03,1.62] [-1.08,1.89] [-1.43,1.85] [-1.46,2.65]

>=12 hours -1.46 -0.18 -0.17 1.07 1.74 2.70** 2.07

[-3.40,0.48] [-0.75,0.40] [-1.16,0.83] [-0.63,2.76] [-0.02,3.50] [0.69,4.71] [-0.61,4.74]


X low diversion (<6 hours) -3.42 -1.03 -0.10 0.29 2.79 5.73* 2.53


[-7.65,5.64] [-2.18,0.45] [-0.56,5.72] [-2.21,9.24] [-0.59,10.62] [-3.04,8.41] [-7.85,9.12]


[-5.89,6.33] [-4.19,-1.16] [-4.42,2.40] [-3.26,8.60] [-4.22,9.31] [-5.79,9.11] [-11.13,10.26]



N 29939 29939 29939 29939 29939 29939 23199


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Figure 1. Conceptualizing Stages of Ambulance Diversion and Potential Racial Disparity 108x81mm (300 x 300 DPI)

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Figure 2. Monthly Trend in Ambulance Diversion between Minority-Serving and Non-Minority Serving Hospitals: 2001-2011

Note: Each month of every year is plotted from January 2001 to December 2011. 108x78mm (300 x 300 DPI)

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Access (admitting hospital has:)

Treatment Outcomes

cardiac care unit cath lab CABG unit

cath/PCI

thrombolytic

therapy CABG

30-day

mortality

90-day

mortality 1-year mortality

30-day

readmission

Base rate (among

patients in reference group) (60%) (73%) (65%)

(51%) (1%) (6%) (16%) (22%) (29%) (34%)


<6 hours -1.40* -1.70** -0.94

-0.86 0.18 -0.42 -0.24 0.11 -0.20 -0.02

[-2.74,-0.05] [-2.91,-0.49] [-2.11,0.23]

[-2.23,0.51] [-0.23,0.59] [-1.17,0.34] [-1.31,0.83] [-1.15,1.36] [-1.66,1.25] [-1.86,1.82]

[6-12) hours -0.40 -0.79 -0.25

-1.27 -0.27 -0.50 0.29 0.40 0.21 0.57

[-2.03,1.22] [-2.41,0.82] [-1.83,1.33]

[-2.91,0.37] [-0.72,0.18] [-1.42,0.42] [-1.04,1.62] [-1.08,1.89] [-1.43,1.86] [-1.48,2.62]

>=12 hours -2.61* -2.44** -2.25*

-2.19* -0.14 -0.29 1.10 1.78* 2.78** 2.12

[-4.81,-0.40] [-4.24,-0.65] [-4.02,-0.48] [-4.19,-0.19] [-0.72,0.43] [-1.29,0.71] [-0.60,2.81] [0.01,3.55] [0.76,4.80] [-0.55,4.79]


X low diversion -0.76 2.51 3.79

-2.16 -1.11 0.12 0.20 2.71 5.56* 2.35

(<6 hours) [-5.62,4.10] [-2.15,7.17] [-1.18,8.77]

[-7.85,3.52] [-2.51,0.29] [-2.48,2.72] [-4.16,4.56] [-1.71,7.12] [0.25,10.86] [-4.13,8.83]

X medium diversion -0.12 1.08 3.05

-0.08 -0.93 2.77 3.43 4.94 2.55 0.60

[6-12) hours [-5.73,5.49] [-4.81,6.98] [-2.89,9.00]

[-7.13,6.97] [-2.25,0.39] [-0.40,5.94] [-2.28,9.14] [-0.65,10.53] [-3.16,8.26] [-7.81,9.02]

X high diversion 3.37 -2.38 1.41

0.30 -2.70** -0.87 2.58 2.49 1.61 -0.28

(>=12 hours) [-2.88,9.63] [-8.82,4.05] [-4.54,7.36]

[-6.24,6.84] [-4.20,-1.19] [-4.32,2.58] [-3.33,8.50] [-4.29,9.27] [-5.83,9.05]

[-

11.01,10.45]


Female -0.26 -0.35 -0.76*

-5.26** -0.15 -2.67** -0.19 0.26 -0.03 3.29**

[-1.10,0.59] [-1.11,0.41] [-1.48,-0.04]

[-6.29,-4.22] [-0.44,0.13] [-3.22,-2.12] [-1.04,0.65] [-0.67,1.19] [-1.06,1.00] [2.08,4.51]

Black -1.65 -2.39 -1.95

-5.78** 0.16 -1.77* -2.78* -2.10 -2.53 4.17

[-4.71,1.41] [-5.29,0.51] [-4.97,1.06]

[-9.44,-2.12] [-0.69,1.01] [-3.28,-0.26] [-5.03,-0.52] [-4.51,0.31] [-5.24,0.17] [-0.02,8.36]

hispanic -2.70* -1.44 -2.46*

0.34 -0.17 -0.05 -2.42* -1.55 -3.36** 2.00

[-4.87,-0.52] [-3.15,0.27] [-4.36,-0.56]

[-2.13,2.81] [-0.93,0.59] [-1.18,1.08] [-4.37,-0.48] [-3.86,0.77] [-5.69,-1.02] [-1.26,5.26]

asian -0.78 0.25 -0.17

1.29 -0.25 0.41 -1.33 -2.05 -3.44** -0.72

[-2.70,1.14] [-1.54,2.03] [-2.08,1.74]

[-0.84,3.41] [-0.81,0.30] [-0.67,1.49] [-3.30,0.63] [-4.31,0.20] [-5.63,-1.24] [-3.64,2.20]

other minority race -1.27 -2.38* -2.32*

-1.60 0.09 0.96 -0.85 -1.83 -2.77* -3.05*

[-3.54,1.01] [-4.23,-0.52] [-4.15,-0.48]

[-3.95,0.74] [-0.69,0.86] [-0.38,2.30] [-2.70,1.00] [-3.91,0.26] [-4.97,-0.57] [-5.97,-0.13]

unknown/missing -0.62 0.61 -2.00

-1.95 0.60 -5.56** 2.68 -1.22 -3.92 -6.53

race [-6.11,4.87] [-3.45,4.66] [-6.38,2.39]

[-8.58,4.67] [-0.47,1.68] [-9.61,-1.51] [-2.68,8.05] [-7.14,4.70] [-10.33,2.48] [-14.19,1.13]

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cardiac care unit cath lab CABG unit cath/PCI

thrombolytic

therapy CABG

30-day

mortality

90-day


30-day

readmission

age 70-74 0.16 0.44 0.58

-2.76** -0.82* -1.32* 1.18 2.01** 3.44** 2.53*

[-1.37,1.69] [-0.90,1.78] [-0.96,2.12]

[-4.41,-1.11] [-1.46,-0.18] [-2.48,-0.17] [-0.02,2.37] [0.70,3.33] [1.97,4.92] [0.38,4.69]

age 75-79 -0.05 0.37 0.36

-6.20** -0.59 -0.94 3.02** 4.58** 7.28** 7.31**

[-1.49,1.39] [-0.92,1.66] [-1.07,1.79]

[-7.86,-4.53] [-1.23,0.05] [-2.16,0.29] [1.81,4.23] [3.21,5.95] [5.68,8.88] [5.25,9.37]

age 80-84 -0.14 0.17 -0.60

-13.87** -0.93** -3.45** 7.57** 11.30** 15.11** 11.30**

[-1.61,1.32] [-1.01,1.34] [-1.88,0.69]

[-15.64,-12.10] [-1.55,-0.31] [-4.61,-2.30] [6.21,8.94] [9.80,12.79] [13.40,16.81] [9.09,13.51]

age 85+ -0.99 -0.90 -0.67

-33.74** -1.38** -6.98** 14.94** 20.62** 28.96** 14.37**

[-2.44,0.46] [-2.14,0.34] [-2.04,0.69]

[-35.75,-31.73] [-1.97,-0.78] [-8.06,-5.91] [13.70,16.18] [19.24,22.00] [27.45,30.47] [12.28,16.46]


Peripheral vascular 1.67* 0.70 1.62*

2.44* -0.53* 0.98 -0.32 0.96 1.55 1.70

disease [0.16,3.17] [-0.60,1.99] [0.28,2.97]

[0.50,4.39] [-0.98,-0.09] [-0.05,2.01] [-1.87,1.22] [-0.85,2.77] [-0.44,3.53] [-0.73,4.13]

Pulmonary -0.52 -0.76 -0.34

-6.98** 0.08 -0.63 1.31 3.50 6.96** 1.88

Circulation disorders [-3.17,2.14] [-3.09,1.58] [-2.68,1.99]

[-10.63,-3.33] [-0.80,0.96] [-2.39,1.12] [-1.77,4.40] [-0.02,7.03] [3.32,10.61] [-2.21,5.98]

Diabetes -0.45 -0.06 -0.77

-4.78** -0.29 -0.33 -2.00** -1.76** 0.49 1.69*

[-1.38,0.49] [-0.95,0.83] [-1.72,0.18]

[-5.94,-3.63] [-0.61,0.03] [-0.88,0.22] [-2.89,-1.11] [-2.73,-0.79] [-0.65,1.64] [0.36,3.02]

Renal failure -2.01** -0.42 -0.71

-12.42** -0.58** -0.71 6.31** 9.63** 13.86** 5.12**

[-3.30,-0.72] [-1.43,0.59] [-1.87,0.45]

[-14.06,-10.79] [-0.91,-0.26] [-1.47,0.04] [4.86,7.75] [8.06,11.20] [12.18,15.54] [3.22,7.02]

Cancer -0.01 0.26 -0.97

-17.20** -0.60* -3.61** 14.15** 20.47** 29.90** 4.43*

[-2.00,1.99] [-1.49,2.00] [-2.90,0.96]

[-19.94,-14.47] [-1.20,-0.00] [-4.77,-2.44] [11.49,16.82] [17.75,23.20] [27.20,32.59] [0.95,7.90]

Dementia -2.71* 0.19 -0.73

-12.91** 0.06 -0.60 3.15* 4.37** 7.97** 3.10

[-5.26,-0.15] [-1.99,2.38] [-2.83,1.37]

[-15.49,-10.33] [-0.61,0.72] [-1.49,0.29] [0.23,6.07] [1.22,7.53] [4.74,11.20] [-0.67,6.87]

Valvular disease 1.66** 0.83 1.59**

-1.65* -0.45* 3.06** -0.40 1.16 4.05** 1.41

[0.48,2.84] [-0.15,1.82] [0.47,2.71]

[-3.22,-0.07] [-0.81,-0.09] [2.16,3.96] [-1.63,0.83] [-0.18,2.49] [2.53,5.57] [-0.41,3.23]

Hypertension -1.29** 0.27 -0.20

4.35** -0.22 -0.92** -8.69** -10.92** -12.25** -5.83**

[-2.17,-0.41] [-0.45,0.99] [-0.99,0.59]

[3.29,5.42] [-0.51,0.06] [-1.49,-0.35] [-9.59,-7.79] [-11.89,-9.96] [-13.29,-11.20] [-7.15,-4.52]

Chronic pulmonary 0.15 -0.34 -0.70

-6.25** -0.29 -0.37 0.03 2.36** 5.90** 4.84**

disease [-1.02,1.32] [-1.34,0.65] [-1.75,0.35]

[-7.55,-4.94] [-0.64,0.06] [-1.00,0.25] [-0.99,1.05] [1.25,3.47] [4.66,7.14] [3.26,6.42]

Rheumatoid arthritis 0.67 -0.40 -2.00

-2.94 -0.23 -1.66 -2.88 -3.42* -2.81 -3.37

[-2.20,3.54] [-2.90,2.09] [-4.51,0.51]

[-6.57,0.69] [-1.21,0.75] [-3.38,0.06] [-5.82,0.05] [-6.74,-0.09] [-6.74,1.12] [-7.84,1.10]

Coagulation -1.42 0.98 0.67

-0.56 -0.55 10.67** 6.95** 9.97** 9.84** 5.90**

deficiency [-3.71,0.87] [-0.90,2.86] [-1.27,2.61]

[-3.38,2.26] [-1.16,0.06] [8.45,12.89] [4.26,9.64] [7.17,12.78] [6.81,12.87] [2.40,9.40]

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thrombolytic

therapy CABG

30-day

mortality

90-day


30-day

readmission

Obesity 0.14 -3.18** -2.10

2.10 -0.37 -0.31 -3.29** -4.48** -7.90** -3.06*

[-2.27,2.54] [-5.19,-1.18] [-4.32,0.13]

[-0.68,4.89] [-1.12,0.37] [-1.81,1.19] [-4.74,-1.84] [-6.10,-2.86] [-9.75,-6.04] [-5.95,-0.17]

Substance abuse 0.40 -0.00 -3.62*

-5.23* -0.40 -0.71 -1.85 -2.24 0.20 6.38*

[-3.17,3.97] [-3.65,3.64] [-7.19,-0.05]

[-9.28,-1.18] [-1.51,0.70] [-3.13,1.71] [-4.68,0.99] [-5.35,0.86] [-3.53,3.93] [1.07,11.68]

Depression 0.37 -0.59 -0.10

-4.60** -0.30 -0.74 -2.43* -1.69 -1.50 3.29

[-2.12,2.85] [-2.82,1.65] [-2.37,2.17]

[-7.66,-1.55] [-1.09,0.48] [-2.02,0.54] [-4.55,-0.32] [-4.33,0.95] [-4.60,1.61] [-0.60,7.19]

Hypothyroidism 0.90 0.20 0.21

-0.56 -0.29 -1.28** -5.35** -5.84** -5.88** -1.73

[-0.69,2.50] [-1.15,1.54] [-1.22,1.65]

[-2.27,1.15] [-0.75,0.18] [-2.01,-0.55] [-6.66,-4.04] [-7.39,-4.29] [-7.60,-4.16] [-3.78,0.33]

Paralysis and other -0.21 -1.99** -1.68*

-12.61** -0.62** -1.82** 6.47** 7.58** 9.79** 10.28**

neurological disordr [-1.91,1.48] [-3.49,-0.49] [-3.20,-0.15]

[-14.58,-10.64] [-1.07,-0.17] [-2.63,-1.00] [4.77,8.18] [5.75,9.41] [7.87,11.70] [7.76,12.79]

Weight loss -2.28 -1.21 -4.68**

-11.27** -1.09** 1.14 9.06** 16.27** 18.18** 13.94**

[-5.50,0.94] [-4.16,1.75] [-7.47,-1.89]

[-14.95,-7.60] [-1.61,-0.57] [-0.87,3.14] [5.30,12.82] [12.32,20.23] [14.09,22.27] [9.59,18.29]

Fluid and electrolyte -0.73 -2.75** -2.40**

-12.53** -0.38* 0.56 9.78** 11.53** 11.57** 11.27**

disorders [-1.89,0.44] [-3.79,-1.70] [-3.53,-1.27]

[-13.91,-11.15] [-0.72,-0.04] [-0.15,1.27] [8.58,10.98] [10.24,12.81] [10.17,12.96] [9.53,13.01]

Anemia 0.01 0.04 0.06

-3.78** -0.38* 1.61** -4.48** -4.13** -1.80* 1.67

[-1.25,1.27] [-0.96,1.04] [-1.00,1.11]

[-5.28,-2.28] [-0.75,-0.01] [0.77,2.45] [-5.70,-3.26] [-5.51,-2.75] [-3.24,-0.35] [-0.18,3.53]

for-profit hospital 10.47** -0.21 -6.64*

-2.46 -0.56 -0.15 0.01 0.67 2.22* 1.26

[6.26,14.68] [-4.45,4.03] [-11.90,-1.39]

[-6.59,1.67] [-1.15,0.04] [-1.05,0.74] [-1.62,1.64] [-1.13,2.46] [0.29,4.16] [-0.77,3.30]

government hospital -17.10** -3.10 -11.38**

-4.88* 0.04 -1.02 3.70** 3.94** 3.06* -0.23

[-23.95,-10.24] [-9.79,3.60] [-18.36,-4.39]

[-8.86,-0.89] [-0.54,0.61] [-2.13,0.09] [1.42,5.98] [1.52,6.36] [0.41,5.71] [-3.04,2.59]

teaching hospital 9.26** -7.78* -16.37**

-11.77** 0.62 -2.35** 0.23 -0.36 0.20 -0.30

[3.19,15.32] [-15.14,-0.42] [-23.12,-9.62]

[-16.62,-6.91] [-0.08,1.33] [-3.82,-0.87] [-1.82,2.29] [-2.53,1.80] [-2.10,2.50] [-3.39,2.78]

hospital beds 24.28** 34.80** 39.49**

21.60** -0.84** 3.64** -0.82 -0.62 -0.71 -3.09**

(log transformed) [19.88,28.68] [30.05,39.55] [33.90,45.08]

[17.84,25.35] [-1.25,-0.43] [2.85,4.42] [-1.85,0.22] [-1.85,0.62] [-2.05,0.63] [-4.76,-1.42]

occupancy rate 53.15** 63.20** 61.63**

45.02** -2.56** 3.56** -6.52** -7.48** -6.89** -11.13**

[42.08,64.22] [53.15,73.25] [48.65,74.61]

[36.70,53.33] [-3.91,-1.21] [1.25,5.86] [-10.18,-2.87] [-11.57,-3.39] [-11.39,-2.38]

[-16.39,-

5.86]

hospital is part of a -4.07* 3.71** 8.96**

2.42* -0.12 1.15** -1.45* -2.06** -2.38** -1.16

system [-7.35,-0.79] [0.97,6.44] [5.51,12.41]

[0.50,4.33] [-0.55,0.31] [0.44,1.86] [-2.62,-0.28] [-3.38,-0.75] [-3.77,-0.98] [-3.01,0.69]

hospital HHI, based 4.49 15.16 12.62

22.72** 1.72 4.17 -6.30 -3.23 -2.10 -6.78

on 15-mi radius [-18.02,27.01] [-2.02,32.33] [-8.83,34.08]

[7.63,37.81] [-1.04,4.48] [-0.47,8.81] [-14.03,1.44] [-11.70,5.24] [-12.15,7.94] [-18.01,4.45]

N 29939 29939 29939 29939 29939 29939 29939 29939 29939 23199

Year dummies included; Nearest ED fixed effects included

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STROBE 2007 (v4) checklist of items to be included in reports of observational studies in epidemiology*

Checklist for cohort, case-control, and cross-sectional studies (combined)

Section/Topic Item # Recommendation Reported on page #

Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title or the abstract 1, 2

(b) Provide in the abstract an informative and balanced summary of what was done and what was found 2

Introduction

Background/rationale 2 Explain the scientific background and rationale for the investigation being reported 4

Objectives 3 State specific objectives, including any pre-specified hypotheses 5-6

Methods

Study design 4 Present key elements of study design early in the paper 6

Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data

collection 6-7

Participants 6 (a) Cohort study—Give the eligibility criteria, and the sources and methods of selection of participants. Describe

methods of follow-up

Case-control study—Give the eligibility criteria, and the sources and methods of case ascertainment and control

selection. Give the rationale for the choice of cases and controls

Cross-sectional study—Give the eligibility criteria, and the sources and methods of selection of participants

7

(b) Cohort study—For matched studies, give matching criteria and number of exposed and unexposed

Case-control study—For matched studies, give matching criteria and the number of controls per case N/A

Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic

criteria, if applicable 7-8

Data sources/ measurement 8* For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe

comparability of assessment methods if there is more than one group 6-8

Bias 9 Describe any efforts to address potential sources of bias 13-14

Study size 10 Explain how the study size was arrived at 7

Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen

and why 7-8

Statistical methods 12 (a) Describe all statistical methods, including those used to control for confounding 8-9

(b) Describe any methods used to examine subgroups and interactions 9

(c) Explain how missing data were addressed 9

(d) Cohort study—If applicable, explain how loss to follow-up was addressed

Case-control study—If applicable, explain how matching of cases and controls was addressed N/A

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Cross-sectional study—If applicable, describe analytical methods taking account of sampling strategy

(e) Describe any sensitivity analyses 10, 11

Results

Participants 13* (a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility,

confirmed eligible, included in the study, completing follow-up, and analysed 10

(b) Give reasons for non-participation at each stage N/A

(c) Consider use of a flow diagram N/A

Descriptive data 14* (a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and

potential confounders 10, Table 2

(b) Indicate number of participants with missing data for each variable of interest Table 2

(c) Cohort study—Summarise follow-up time (eg, average and total amount) N/A

Outcome data 15* Cohort study—Report numbers of outcome events or summary measures over time N/A

Case-control study—Report numbers in each exposure category, or summary measures of exposure N/A

Cross-sectional study—Report numbers of outcome events or summary measures 11-12, Table 2

Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95%

confidence interval). Make clear which confounders were adjusted for and why they were included 11-12, Tables 2 and 3

(b) Report category boundaries when continuous variables were categorized 10

(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period N/A

Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses 11-12

Discussion

Key results 18 Summarise key results with reference to study objectives 12

Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction

and magnitude of any potential bias 13-14

Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results

from similar studies, and other relevant evidence 12-14

Generalisability 21 Discuss the generalisability (external validity) of the study results 14

Other information

Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on

which the present article is based 16

*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE

checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at

http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.

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Do Patients Hospitalized in High-Minority Hospitals Experience More Diversion and Poorer Outcomes? A

Retrospective Multivariate Analysis of Medicare Patients in California

Journal: BMJ Open

Manuscript ID bmjopen-2015-010263.R2


Date Submitted by the Author: 11-Feb-2016







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Poorer Outcomes? A Retrospective Multivariate Analysis of Medicare Patients in

California


2






Yu-Chu Shen, PhD


Phone: (831) 656-2951



Monterey, CA 93943


Word count: 3,208

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ABSTRACT

Objective: We investigated the association between crowding as measured by ambulance

diversion and differences in access, treatment, and outcomes between black and white patients.




according to hours in diversion status for their nearest emergency departments on their day of

















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ARTICLE SUMMARY




2011.





• Limitations include: potential reporting bias due to self-reported data by LEMSAs,

diversion status is measured at the hospital level and not for individual patient, lack of

generalizability outside of California, and a small sample of black patients.

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INTRODUCTION























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and can affect patients who have to be diverted elsewhere as well as non-diverted patients within

the overcrowded ED. Ambulance diversion has been used as a proxy for ED crowding.(21-23)

In order to better target potential areas for intervention, it is essential to know exactly where the

disparities occur when a patient experiences ambulance diversion. Figure 1 shows that in the

first stage, a hospital experiences resource constraints, mostly due to overcrowding in the ED,

such that it cannot accept incoming ambulance traffic. At this stage, a potential racial disparity

exists if black patients are more likely to be diverted than white patients because the ED closest

to them is more likely to be on diversion.








resources outstrip the capability of the staff to deploy their technology appropriately. At this

stage, a potential disparity exists if blacks receive inferior treatments compared to white patients,

leading to worse patient outcomes, when both are exposed to the same level of ambulance

diversion.(17)

Our study therefore explored potential differences between black and white patients at

different stages of ambulance diversion, using 100% of Medicare claims and daily ambulance

diversion logs from 26 California counties between 2001 and 2011. We first compared amount

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of ambulance diversion between hospitals serving large share of black patients (henceforth

“minority-serving hospitals”) and others. We then examined whether racial disparities in

ambulance diversion, if any, resulted in differential health outcomes between black and white

patients.

METHODS

Data







(LEMSAs). California has a total 33 LEMSAs, but 10 of them banned diversion for the years of

2001-2011. We excluded counties with diversion bans from our analysis, since they did not

contribute to our understanding of the relationship between diversion and patient outcomes.

Those excluded counties tended to be much smaller than counties without diversion bans, but

they had comparable shares of black patients (4.6% among counties with the diversion ban vs.

5.4% without the diversion ban; p=0.80). Figure 2 identifies counties with the diversion ban over

this period and ZIP code communities that had high shares of black population (i.e., those

communities that were in the top tertile of black population distribution in California).

We obtained data for 17 out of the 23 LEMSAs that did not ban diversion (actual

coverage dates vary by LEMSA). The 17 LEMSAs together represented 88% of the California

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population. To identify the closest ED for a patient, we supplemented our hospital data with

longitude and latitude coordinates of the hospital’s physical address or heliport (if one

existed).(24) We obtained actual driving distance from the patient’s ZIP code centroid to nearest

hospital’s latitude and longitude coordinates based on Google Maps, using automation codes

developed in Stata.(25)

Patient Population



between 2001 and 2011, and resided in counties for which we had diversion data. We excluded













of the facility in 2001.(18) This approach accounted for the distribution of the local population.

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by the ICD-9 procedure codes on the MedPAR. We examined 3 common treatments for AMI:





Statistical models




using the mean daily ambulance diversion hours. We used the nonparamaetric Kolmogorov-





controlling for time-dependent variables. The ED fixed effects eliminated any underlying




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We controlled for race (black, Hispanic, Asian, other minority, unknown/missing race),

age, gender, as well as 22 comorbid measures based on prior work.(29) For admitting hospital

organizational characteristics, we controlled for hospital ownership, teaching status, size

(measured by log transformed total inpatient discharges), occupancy rate, system membership,

and Herfindahl index to capture the competitiveness of the hospital market within 15-mile radius

(0 being perfectly competitive and 1 being monopoly). Last, we included year indicators to

capture the macro trends.


technology access. Results from these two models allowed us to compare whether differences in

treatment received, if any, are the result of lack of technology access. For mortality outcomes,

we estimated a third model accounting for both cardiac technology and actual treatment received.

All models were estimated using Stata 13.(30) This study was exempted from the Committee on

Human Research at the University of California, San Francisco.

RESULTS



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observed the same pattern. Table 1 shows that the minority-serving and other hospitals are

similar in most dimensions (bed size, cardiac care capacity, occupancy rate, teaching status),

except that a higher share of minority serving hospitals are government-run (22% vs. 12%,

p<0.01) and are located in more concentrated markets as measured by the Herfindahl index (0.20

vs. 0.15, p<0.01).


where the sample size was 22,058 patients. Among all patients, 15,202 patients (51%)

experienced no diversion at their nearest ED on their day of admission; for 25%, their nearest ED

was on diversion for <6 hours; for 15%, their nearest ED was on diversion for 6-12 hours; and

for 10% their nearest ED was on diversion for >12 hours of diversion (Table 2). In addition, a

larger percentage of black patients experienced >12 hours of diversion than white patients (12%

vs. 9%, p<0.01). In general, blacks had a lower probability of being admitted to hospitals with

cardiac care technology, and a lower probability of receiving catheterization. The raw mortality

outcomes were similar between blacks and whites, but blacks had a higher probability of being

readmitted to hospitals within 30 days of discharge (40 vs. 34%, p<0.01).




hypertension.

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across individuals, hospitals and communities. Table 3 shows estimated results from Model 1

(complete results in Supplementary Table 1). After controlling for multiple factors, patients

exposed to the highest level of diversion (>12 hours) had worse access to cardiac technology—

by -2.61 percentage points for access to cardiac care intensive unit (95% CI: -4.81, -0.40)

compared with patients who were admitted on a day with no diversion; by -2.44 percentage

points for access to catheterization lab (95% CI: -4.24, -0.65), and by -2.25 percentage points for

access to CABG facilities (95% CI: -4.02, -0.48). This is equivalent to a 4.4% reduction in

cardiac care unit (CCU) access (the base rate for CCU is 60%), and 3.4% reduction in access to

catheterization lab and CABG facilities. In addition, patients exposed to the highest level of

diversion were less likely to receive catheterization, by -2.19 percentage points (95% CI: -4.19,

-0.19), and had a higher 1-year mortality rate, by 2.78 percentage points (95% CI: 0.76, 4.80). In

other words, patients in the highest diversion category had a decreased likelihood of receiving

catheterization by 4.3% (base rate is 51%) and higher 1-year mortality by 9.6% (base rate is

29%).

Results from the interaction terms between black patients and diversion status showed

that in general, black and white patients had a similar experience when facing the same level of

diversion. The interaction terms in general were not statistically significant at the conventional

level, with two exceptions. Blacks in the highest diversion category were less likely to receive

thrombolytic therapy relative to whites facing the same amount of diversion, and blacks in the

low diversion category (<6 hours) had a higher 1-year mortality relative to whites.



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albeit with a slightly smaller magnitude. The interaction results were similar to those in Table 3.

DISCUSSION








diversion because a larger share of black patients go to minority-serving hospitals, and longer

exposure to ambulance diversion is associated with higher long-term mortality. This is in

contrast to explanations where blacks receive differentially less access to technology or treatment

compared with whites when both experience the same diversion condition.







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self-reported by LEMSAs, with potential for errors and reporting bias. Second, our diversion

status is identified at the hospital level and not at the individual patient level, and our patient data


patient was diverted, it is reasonable to assume longer hours of diversion is associated with a

lower probability that a patient is admitted to this ED. In addition, the inability to clearly

identify the diverted and the non-diverted patients in our analysis implies that what we observe is

the net effect of ambulance diversion.

Third, our dataset contains the mailing ZIP codes for the patients, which may or may not

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be the same as their ZIP code of residence. There is also a possibility that the patient’s AMI did

not occur at home. With the exclusion criteria we imposed in selecting our sample, we believe

that this limitation should not affect our analyses.






Fourth, our study design necessitates that we exclude counties with a diversion ban.

However, if diversion bans disproportionally favor communities with predominantly white

population, then we did not capture this important source of discrimination. Based on our

comparison, however, the two types of communities had similar shares of black patient

population, so it does not appear to be the case that diversion bans only occurred among mostly

white communities.

Lastly, our dataset is limited to California, which, while a large and diverse state


our patient sample is based on the Medicare population, our findings may not be applicable to

non-elderly population. Last, we have a relatively small sample of black patients. Future studies



CONCLUSIONS

Our study showed that hospitals treating high volume of black patients experienced a

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addition, patients whose nearest hospital experienced significant diversion had poorer access to


revascularization and lower 1-year survival. We did not find that other downstream

consequences of ambulance diversion, such as decreased access to technology and treatment,

were differentially worse for black patients. Because diversion is asymmetrically experienced by




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CONTRIBUTORSHIP












ACKNOWLEDGEMENTS







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COMPETING INTEREST



FUNDING




DATA SHARING

No additional data available.

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REFERENCES


States. Health Aff (Millwood) 2007;26:38-48.

2. National Center for Health Statistics. Health, United States, 2010: with special feature on

death and dying. Hyattsville, MD; 2011.

3. The National Heart Attack Alert Program. Access to timely and optimal care of patients

with acute coronary syndromes - community planning considerations: a report by the national

heart attack alert program. J Thromb Thrombolysis 1998;6:19-46.


myocardial infarction treatment and outcomes. Med Care 2005;43:308-19.

5. Moser DK, Kimble LP, Alberts MJ, et al. Reducing delay in seeking treatment by

patients with acute coronary syndrome and stroke: a scientific statement from the American

Heart Association Council on Cardiovascular Nursing and Stroke Council. Circulation

2006;114:168-82.

6. Green A, Carney D, Pallin D, et al. Implicit bias among physicians and its prediction of

thrombolysis decisions for black and white patients. J Gen Intern Med 2007;22:1231-8.


distrust in the United States. Am J Public Health 2007;97:1283-9.

8. Institute of Medicine. Unequal treatment: confronting racial and ethnic disparities in

health care. National Academy of Sciences; 2003.

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(Millwood) 2005;24:325-34.

10. Epstein AM, Ayanian JZ. Racial disparities in medical care. N Engl J Med

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ambulance diversion. Acad Emerg Med 2006;13:421-6.


point in time. Ann Emerg Med 2003;42:167-72.


defining the problem and eliminating misconceptions. CJEM 2002;4:76-83.


overcrowding and ambulance diversions. Jt Comm J Qual Patient Saf 2006;32:239-45.


review. Acad Emerg Med 2006;13:1220-7.



18. Shen YC, Hsia RY. Association Between Ambulance Diversion and Survival Among

Patients With Acute Myocardial Infarction. JAMA 2011;305:2440-7.

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deaths. Inquiry 2010;47:81-91.

20. Shen YC, Hsia RY. Ambulance diversion is associated with decreased access to

technology and increased one-year mortality. Health Aff (Millwood) 2015;34:1273-80.

21. Shenoi RP, Ma L, Jones J, et al. Ambulance diversion as a proxy for emergency

department crowding: the effect on pediatric mortality in a metropolitan area. Acad Emerg Med

2009;16:116-23.

22. Sun BC, Hsia RY, Weiss RE, et al. Effect of emergency department crowding on

outcomes of admitted patients. Ann Emerg Med 2013;61:605-11.e6.

23. Schull MJ, Lazier K, Vermeulen M, et al. Emergency department contributors to

ambulance diversion: a quantitative analysis. Ann Emerg Med 2003;41:467-76.


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distance information. STATA J 2011;11:106-19.






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race and site of care. JAMA 2011;305:675-81.


2012.


administrative data. Med Care 1998;36:8-27.


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2012;31:1767-76.


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do financial pressure and community characteristics matter? Med Care 2009;47:968-78.

34. Department of Health & Human Serivces. HHS action plan to reduce racial and ethnic

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FIGURE LEGEND

Figure 1. Conceptualizing Stages of Ambulance Diversion and Potential Racial Disparity

Figure 2. California Map Showing Counties with Diversion Ban and Communities with High

Shares of Black Patients

Figure 3. Monthly Trend in Ambulance Diversion between Minority-Serving and Non-Minority

Serving Hospitals: 2001-2011

TABLE LEGEND







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Mean (SD) All Hospitals

Non-minority serving Hospitals

Minority-serving Hospitals

Minority-serving hospitals 24%

(43%)

Due to definition 1: in the top decile for proportion of all back patients in California 17%

(38%)

Due to definition 2: treat twice as many black patients than other hospitals in their geographic proximity 7%

(25%)

Cardiac care capacity

Has cath lab 60% 60% 59%

(49%) (49%) (49%)

Has cardiac care unit 57% 58% 54%

(50%) (49%) (50%)

Has CABG capacity 48% 49% 48%

(50%) (50%) (50%)

For-profit hospitals 26% 26% 28%

(44%) (44%) (45%)

Government hospitals 14% 12% 22% **

(35%) (32%) (41%)

Teaching hospitals 9% 9% 9%

(29%) (29%) (29%)

Member of a system 68% 69% 64%

(47%) (46%) (48%)

Mean total beds in hospital 232.89 231.39 237.58

(130.69) (130.14) (132.47)

Mean occupancy rate 0.64 0.64 0.65

(0.16) (0.16) (0.15)

Mean HHI index1 0.16 0.15 0.20

**

(0.18) (0.16) (0.22)

Number of hospital years 1563 1186 377

Note: non-minority serving and minority-serving hospital differences statistically significant at *p<0.05, **p<0.01 1The HHI index captures competitiveness of the hospitals' market (defined as within 15-mile radius): the scale goes from 0 to 1 where 0 represents perfectly competitive market and 1 represents monopoly.

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N (%) N (%) N (%)


no diversion 15202 (51%) 11439 (53%) 798 (50%) **

<6 hours 7514 (25%) 5169 (24%) 374 (23%)

[6-12) hours 4472 (15%) 3006 (14%) 263 (16%) *

>=12 hours 3069 (10%) 1998 (9%) 194 (12%) **

Access




Treatment received



received CABG 1695 (6%) 1216 (6%) 69 (4%) *

Health Outcomes

30-day mortality 4835 (16%) 3507 (16%) 234 (15%) *

90-day mortality 6593 (22%) 4759 (22%) 355 (22%)

1-year mortality 9447 (32%) 6824 (32%) 516 (32%)



White 21414 (72%)

Black 1612 (5%)

Hispanic 1578 (5%)

Asian 2126 (7%)


Unknown/missing race 1818 (6%)

Female 14906 (50%) 10612 (50%) 913 (57%) **

Age distribution

65–69 4231 (14%) 2920 (14%) 335 (21%) **

70–74 4756 (16%) 3292 (15%) 305 (19%) **

75–79 5531 (18%) 3764 (18%) 330 (20%) **

80–84 6197 (21%) 4455 (21%) 271 (17%) **

85+ 9224 (31%) 6983 (33%) 371 (23%) **

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N (%) N (%) N (%)





Renal failure 3965 (13%) 2669 (12%) 301 (19%) **

Liver disease 241 (1%) 155 (1%) 18 (1%)

Cancer 1127 (4%) 837 (4%) 79 (5%) *

Dementia 1171 (4%) 865 (4%) 67 (4%)






Obesity 1062 (4%) 796 (4%) 80 (5%) *

Substance abuse 461 (2%) 343 (2%) 44 (3%) **

Depression 790 (3%) 625 (3%) 34 (2%) *

Psychosis 405 (1%) 298 (1%) 30 (2%)

Hypothyroidism 2462 (8%) 2004 (9%) 58 (4%) **



Weight loss 623 (2%) 416 (2%) 51 (3%) **



Patient 29939 21414 1612

Note: black and white differences statistically significant at *p<0.05, **p<0.01

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30-day mortality

90-day mortality

1-year mortality

30-day readmission




<6 hours -1.40* -1.70** -0.94 -0.86 0.18 -0.42 -0.24 0.11 -0.20 -0.02

[-2.74,-0.05] [-2.91,-0.49] [-2.11,0.23] [-2.23,0.51] [-0.23,0.59] [-1.17,0.34] [-1.31,0.83] [-1.15,1.36] [-1.66,1.25] [-1.86,1.82]

[6-12) hours -0.40 -0.79 -0.25 -1.27 -0.27 -0.50 0.29 0.40 0.21 0.57

[-2.03,1.22] [-2.41,0.82] [-1.83,1.33] [-2.91,0.37] [-0.72,0.18] [-1.42,0.42] [-1.04,1.62] [-1.08,1.89] [-1.43,1.86] [-1.48,2.62]

>=12 hours -2.61* -2.44** -2.25* -2.19* -0.14 -0.29 1.10 1.78* 2.78** 2.12

[-4.81,-0.40] [-4.24,-0.65] [-4.02,-0.48] [-4.19,-0.19] [-0.72,0.43] [-1.29,0.71] [-0.60,2.81] [0.01,3.55] [0.76,4.80] [-0.55,4.79]


X low diversion -0.76 2.51 3.79 -2.16 -1.11 0.12 0.20 2.71 5.56* 2.35

(<6 hours) [-5.62,4.10] [-2.15,7.17] [-1.18,8.77] [-7.85,3.52] [-2.51,0.29] [-2.48,2.72] [-4.16,4.56] [-1.71,7.12] [0.25,10.86] [-4.13,8.83]

X medium diversion -0.12 1.08 3.05 -0.08 -0.93 2.77 3.43 4.94 2.55 0.60

[6-12) hours [-5.73,5.49] [-4.81,6.98] [-2.89,9.00] [-7.13,6.97] [-2.25,0.39] [-0.40,5.94] [-2.28,9.14] [-0.65,10.53] [-3.16,8.26] [-7.81,9.02]

X high diversion 3.37 -2.38 1.41 0.30 -2.70** -0.87 2.58 2.49 1.61 -0.28

(>= 12 hours) [-2.88,9.63] [-8.82,4.05] [-4.54,7.36] [-6.24,6.84] [-4.20,-1.19] [-4.32,2.58] [-3.33,8.50] [-4.29,9.27] [-5.83,9.05] [-11.01,10.45]



N 29939 29939 29939 29939 29939 29939 29939 29939 29939 23199

Nearest ED Based on Google query of driving distance *p<0.05, **p<0.01

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27


Treatment Outcomes


therapy CABG 30-day

mortality 90-day

mortality 1-year

mortality 30-day

readmission

Base rate (among patients in reference group) (51%) (1%) (6%) (16%) (22%) (29%) (34%) Diversion status (Reference group: nearest ED not on diversion on the day of admission) Nearest ED's exposure to diversion on the day of admission:

<6 hours -0.51 0.16 -0.37 -0.25 0.09 -0.24 0.00

[-1.85,0.84] [-0.24,0.57] [-1.13,0.38] [-1.31,0.82] [-1.16,1.35] [-1.68,1.21] [-1.84,1.84]

[6-12) hours -1.15 -0.27 -0.49 0.30 0.40 0.21 0.60

[-2.77,0.47] [-0.72,0.18] [-1.41,0.43] [-1.03,1.62] [-1.08,1.89] [-1.43,1.85] [-1.46,2.65]

>=12 hours -1.46 -0.18 -0.17 1.07 1.74 2.70** 2.07

[-3.40,0.48] [-0.75,0.40] [-1.16,0.83] [-0.63,2.76] [-0.02,3.50] [0.69,4.71] [-0.61,4.74]


X low diversion (<6 hours) -3.42 -1.03 -0.10 0.29 2.79 5.73* 2.53


[-7.65,5.64] [-2.18,0.45] [-0.56,5.72] [-2.21,9.24] [-0.59,10.62] [-3.04,8.41] [-7.85,9.12]


[-5.89,6.33] [-4.19,-1.16] [-4.42,2.40] [-3.26,8.60] [-4.22,9.31] [-5.79,9.11] [-11.13,10.26]



N 29939 29939 29939 29939 29939 29939 23199

Nearest ED Based on Google query of driving distance *p<0.05, **p<0.01

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Figure 1. Conceptualizing Stages of Ambulance Diversion and Potential Racial Disparity 108x81mm (300 x 300 DPI)

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Figure 2. California Map Showing Counties with Diversion Ban and Communities with High Shares of Black Patients

143x186mm (300 x 300 DPI)

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Figure 3. Monthly Trend in Ambulance Diversion between Minority-Serving and Non-Minority Serving

Hospitals: 2001-2011

108x78mm (300 x 300 DPI)

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Access (admitting hospital has:)

Treatment Outcomes

cardiac care unit cath lab CABG unit

cath/PCI

thrombolytic

therapy CABG

30-day

mortality

90-day


30-day

readmission

Base rate (among

patients in

reference group) (60%) (73%) (65%)

(51%) (1%) (6%) (16%) (22%) (29%) (34%)


<6 hours -1.40* -1.70** -0.94

-0.86 0.18 -0.42 -0.24 0.11 -0.20 -0.02

[-2.74,-0.05] [-2.91,-0.49] [-2.11,0.23]

[-2.23,0.51] [-0.23,0.59] [-1.17,0.34] [-1.31,0.83] [-1.15,1.36] [-1.66,1.25] [-1.86,1.82]

[6-12) hours -0.40 -0.79 -0.25

-1.27 -0.27 -0.50 0.29 0.40 0.21 0.57

[-2.03,1.22] [-2.41,0.82] [-1.83,1.33]

[-2.91,0.37] [-0.72,0.18] [-1.42,0.42] [-1.04,1.62] [-1.08,1.89] [-1.43,1.86] [-1.48,2.62]

>=12 hours -2.61* -2.44** -2.25*

-2.19* -0.14 -0.29 1.10 1.78* 2.78** 2.12

[-4.81,-0.40] [-4.24,-0.65] [-4.02,-0.48] [-4.19,-0.19] [-0.72,0.43] [-1.29,0.71] [-0.60,2.81] [0.01,3.55] [0.76,4.80] [-0.55,4.79]


X low diversion

-0.76 2.51 3.79

-2.16 -1.11 0.12 0.20 2.71 5.56* 2.35

(<6 hours) [-5.62,4.10] [-2.15,7.17] [-1.18,8.77]

[-7.85,3.52] [-2.51,0.29] [-2.48,2.72] [-4.16,4.56] [-1.71,7.12] [0.25,10.86] [-4.13,8.83]

X medium diversion -0.12 1.08 3.05

-0.08 -0.93 2.77 3.43 4.94 2.55 0.60

[6-12) hours [-5.73,5.49] [-4.81,6.98] [-2.89,9.00]

[-7.13,6.97] [-2.25,0.39] [-0.40,5.94] [-2.28,9.14] [-0.65,10.53] [-3.16,8.26] [-7.81,9.02]

X high diversion 3.37 -2.38 1.41

0.30 -2.70** -0.87 2.58 2.49 1.61 -0.28

(>=12 hours) [-2.88,9.63] [-8.82,4.05] [-4.54,7.36]

[-6.24,6.84] [-4.20,-1.19] [-4.32,2.58] [-3.33,8.50] [-4.29,9.27] [-5.83,9.05]

[-

11.01,10.45]


Female -0.26 -0.35 -0.76*

-5.26** -0.15 -2.67** -0.19 0.26 -0.03 3.29**

[-1.10,0.59] [-1.11,0.41] [-1.48,-0.04]

[-6.29,-4.22] [-0.44,0.13] [-3.22,-2.12] [-1.04,0.65] [-0.67,1.19] [-1.06,1.00] [2.08,4.51]

Black -1.65 -2.39 -1.95

-5.78** 0.16 -1.77* -2.78* -2.10 -2.53 4.17

[-4.71,1.41] [-5.29,0.51] [-4.97,1.06]

[-9.44,-2.12] [-0.69,1.01] [-3.28,-0.26] [-5.03,-0.52] [-4.51,0.31] [-5.24,0.17] [-0.02,8.36]

hispanic -2.70* -1.44 -2.46*

0.34 -0.17 -0.05 -2.42* -1.55 -3.36** 2.00

[-4.87,-0.52] [-3.15,0.27] [-4.36,-0.56]

[-2.13,2.81] [-0.93,0.59] [-1.18,1.08] [-4.37,-0.48] [-3.86,0.77] [-5.69,-1.02] [-1.26,5.26]

asian -0.78 0.25 -0.17

1.29 -0.25 0.41 -1.33 -2.05 -3.44** -0.72

[-2.70,1.14] [-1.54,2.03] [-2.08,1.74]

[-0.84,3.41] [-0.81,0.30] [-0.67,1.49] [-3.30,0.63] [-4.31,0.20] [-5.63,-1.24] [-3.64,2.20]

other minority race -1.27 -2.38* -2.32*

-1.60 0.09 0.96 -0.85 -1.83 -2.77* -3.05*

[-3.54,1.01] [-4.23,-0.52] [-4.15,-0.48]

[-3.95,0.74] [-0.69,0.86] [-0.38,2.30] [-2.70,1.00] [-3.91,0.26] [-4.97,-0.57] [-5.97,-0.13]

unknown/missing -0.62 0.61 -2.00

-1.95 0.60 -5.56** 2.68 -1.22 -3.92 -6.53

race [-6.11,4.87] [-3.45,4.66] [-6.38,2.39]

[-8.58,4.67] [-0.47,1.68] [-9.61,-1.51] [-2.68,8.05] [-7.14,4.70] [-10.33,2.48] [-14.19,1.13]

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thrombolytic

therapy CABG

30-day

mortality

90-day


30-day

readmission

age 70-74 0.16 0.44 0.58

-2.76** -0.82* -1.32* 1.18 2.01** 3.44** 2.53*

[-1.37,1.69] [-0.90,1.78] [-0.96,2.12]

[-4.41,-1.11] [-1.46,-0.18] [-2.48,-0.17] [-0.02,2.37] [0.70,3.33] [1.97,4.92] [0.38,4.69]

age 75-79 -0.05 0.37 0.36

-6.20** -0.59 -0.94 3.02** 4.58** 7.28** 7.31**

[-1.49,1.39] [-0.92,1.66] [-1.07,1.79]

[-7.86,-4.53] [-1.23,0.05] [-2.16,0.29] [1.81,4.23] [3.21,5.95] [5.68,8.88] [5.25,9.37]

age 80-84 -0.14 0.17 -0.60

-13.87** -0.93** -3.45** 7.57** 11.30** 15.11** 11.30**

[-1.61,1.32] [-1.01,1.34] [-1.88,0.69]

[-15.64,-12.10] [-1.55,-0.31] [-4.61,-2.30] [6.21,8.94] [9.80,12.79] [13.40,16.81] [9.09,13.51]

age 85+ -0.99 -0.90 -0.67

-33.74** -1.38** -6.98** 14.94** 20.62** 28.96** 14.37**

[-2.44,0.46] [-2.14,0.34] [-2.04,0.69]

[-35.75,-31.73] [-1.97,-0.78] [-8.06,-5.91] [13.70,16.18] [19.24,22.00] [27.45,30.47] [12.28,16.46]


Peripheral vascular 1.67* 0.70 1.62*

2.44* -0.53* 0.98 -0.32 0.96 1.55 1.70

disease [0.16,3.17] [-0.60,1.99] [0.28,2.97]

[0.50,4.39] [-0.98,-0.09] [-0.05,2.01] [-1.87,1.22] [-0.85,2.77] [-0.44,3.53] [-0.73,4.13]

Pulmonary -0.52 -0.76 -0.34

-6.98** 0.08 -0.63 1.31 3.50 6.96** 1.88

Circulation disorders [-3.17,2.14] [-3.09,1.58] [-2.68,1.99]

[-10.63,-3.33] [-0.80,0.96] [-2.39,1.12] [-1.77,4.40] [-0.02,7.03] [3.32,10.61] [-2.21,5.98]

Diabetes -0.45 -0.06 -0.77

-4.78** -0.29 -0.33 -2.00** -1.76** 0.49 1.69*

[-1.38,0.49] [-0.95,0.83] [-1.72,0.18]

[-5.94,-3.63] [-0.61,0.03] [-0.88,0.22] [-2.89,-1.11] [-2.73,-0.79] [-0.65,1.64] [0.36,3.02]

Renal failure -2.01** -0.42 -0.71

-12.42** -0.58** -0.71 6.31** 9.63** 13.86** 5.12**

[-3.30,-0.72] [-1.43,0.59] [-1.87,0.45]

[-14.06,-10.79] [-0.91,-0.26] [-1.47,0.04] [4.86,7.75] [8.06,11.20] [12.18,15.54] [3.22,7.02]

Cancer -0.01 0.26 -0.97

-17.20** -0.60* -3.61** 14.15** 20.47** 29.90** 4.43*

[-2.00,1.99] [-1.49,2.00] [-2.90,0.96]

[-19.94,-14.47] [-1.20,-0.00] [-4.77,-2.44] [11.49,16.82] [17.75,23.20] [27.20,32.59] [0.95,7.90]

Dementia -2.71* 0.19 -0.73

-12.91** 0.06 -0.60 3.15* 4.37** 7.97** 3.10

[-5.26,-0.15] [-1.99,2.38] [-2.83,1.37]

[-15.49,-10.33] [-0.61,0.72] [-1.49,0.29] [0.23,6.07] [1.22,7.53] [4.74,11.20] [-0.67,6.87]

Valvular disease 1.66** 0.83 1.59**

-1.65* -0.45* 3.06** -0.40 1.16 4.05** 1.41

[0.48,2.84] [-0.15,1.82] [0.47,2.71]

[-3.22,-0.07] [-0.81,-0.09] [2.16,3.96] [-1.63,0.83] [-0.18,2.49] [2.53,5.57] [-0.41,3.23]

Hypertension -1.29** 0.27 -0.20

4.35** -0.22 -0.92** -8.69** -10.92** -12.25** -5.83**

[-2.17,-0.41] [-0.45,0.99] [-0.99,0.59]

[3.29,5.42] [-0.51,0.06] [-1.49,-0.35] [-9.59,-7.79] [-11.89,-9.96] [-13.29,-11.20] [-7.15,-4.52]

Chronic pulmonary 0.15 -0.34 -0.70

-6.25** -0.29 -0.37 0.03 2.36** 5.90** 4.84**

disease [-1.02,1.32] [-1.34,0.65] [-1.75,0.35]

[-7.55,-4.94] [-0.64,0.06] [-1.00,0.25] [-0.99,1.05] [1.25,3.47] [4.66,7.14] [3.26,6.42]

Rheumatoid arthritis 0.67 -0.40 -2.00

-2.94 -0.23 -1.66 -2.88 -3.42* -2.81 -3.37

[-2.20,3.54] [-2.90,2.09] [-4.51,0.51]

[-6.57,0.69] [-1.21,0.75] [-3.38,0.06] [-5.82,0.05] [-6.74,-0.09] [-6.74,1.12] [-7.84,1.10]

Coagulation -1.42 0.98 0.67

-0.56 -0.55 10.67** 6.95** 9.97** 9.84** 5.90**

deficiency [-3.71,0.87] [-0.90,2.86] [-1.27,2.61]

[-3.38,2.26] [-1.16,0.06] [8.45,12.89] [4.26,9.64] [7.17,12.78] [6.81,12.87] [2.40,9.40]

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thrombolytic

therapy CABG

30-day

mortality

90-day


30-day

readmission

Obesity 0.14 -3.18** -2.10

2.10 -0.37 -0.31 -3.29** -4.48** -7.90** -3.06*

[-2.27,2.54] [-5.19,-1.18] [-4.32,0.13]

[-0.68,4.89] [-1.12,0.37] [-1.81,1.19] [-4.74,-1.84] [-6.10,-2.86] [-9.75,-6.04] [-5.95,-0.17]

Substance abuse 0.40 -0.00 -3.62*

-5.23* -0.40 -0.71 -1.85 -2.24 0.20 6.38*

[-3.17,3.97] [-3.65,3.64] [-7.19,-0.05]

[-9.28,-1.18] [-1.51,0.70] [-3.13,1.71] [-4.68,0.99] [-5.35,0.86] [-3.53,3.93] [1.07,11.68]

Depression 0.37 -0.59 -0.10

-4.60** -0.30 -0.74 -2.43* -1.69 -1.50 3.29

[-2.12,2.85] [-2.82,1.65] [-2.37,2.17]

[-7.66,-1.55] [-1.09,0.48] [-2.02,0.54] [-4.55,-0.32] [-4.33,0.95] [-4.60,1.61] [-0.60,7.19]

Hypothyroidism 0.90 0.20 0.21

-0.56 -0.29 -1.28** -5.35** -5.84** -5.88** -1.73

[-0.69,2.50] [-1.15,1.54] [-1.22,1.65]

[-2.27,1.15] [-0.75,0.18] [-2.01,-0.55] [-6.66,-4.04] [-7.39,-4.29] [-7.60,-4.16] [-3.78,0.33]

Paralysis and other -0.21 -1.99** -1.68*

-12.61** -0.62** -1.82** 6.47** 7.58** 9.79** 10.28**

neurological disordr [-1.91,1.48] [-3.49,-0.49] [-3.20,-0.15]

[-14.58,-10.64] [-1.07,-0.17] [-2.63,-1.00] [4.77,8.18] [5.75,9.41] [7.87,11.70] [7.76,12.79]

Weight loss -2.28 -1.21 -4.68**

-11.27** -1.09** 1.14 9.06** 16.27** 18.18** 13.94**

[-5.50,0.94] [-4.16,1.75] [-7.47,-1.89]

[-14.95,-7.60] [-1.61,-0.57] [-0.87,3.14] [5.30,12.82] [12.32,20.23] [14.09,22.27] [9.59,18.29]

Fluid and electrolyte -0.73 -2.75** -2.40**

-12.53** -0.38* 0.56 9.78** 11.53** 11.57** 11.27**

disorders [-1.89,0.44] [-3.79,-1.70] [-3.53,-1.27]

[-13.91,-11.15] [-0.72,-0.04] [-0.15,1.27] [8.58,10.98] [10.24,12.81] [10.17,12.96] [9.53,13.01]

Anemia 0.01 0.04 0.06

-3.78** -0.38* 1.61** -4.48** -4.13** -1.80* 1.67

[-1.25,1.27] [-0.96,1.04] [-1.00,1.11]

[-5.28,-2.28] [-0.75,-0.01] [0.77,2.45] [-5.70,-3.26] [-5.51,-2.75] [-3.24,-0.35] [-0.18,3.53]

for-profit hospital 10.47** -0.21 -6.64*

-2.46 -0.56 -0.15 0.01 0.67 2.22* 1.26

[6.26,14.68] [-4.45,4.03] [-11.90,-1.39]

[-6.59,1.67] [-1.15,0.04] [-1.05,0.74] [-1.62,1.64] [-1.13,2.46] [0.29,4.16] [-0.77,3.30]

government hospital -17.10** -3.10 -11.38**

-4.88* 0.04 -1.02 3.70** 3.94** 3.06* -0.23

[-23.95,-10.24] [-9.79,3.60] [-18.36,-4.39]

[-8.86,-0.89] [-0.54,0.61] [-2.13,0.09] [1.42,5.98] [1.52,6.36] [0.41,5.71] [-3.04,2.59]

teaching hospital 9.26** -7.78* -16.37**

-11.77** 0.62 -2.35** 0.23 -0.36 0.20 -0.30

[3.19,15.32] [-15.14,-0.42] [-23.12,-9.62]

[-16.62,-6.91] [-0.08,1.33] [-3.82,-0.87] [-1.82,2.29] [-2.53,1.80] [-2.10,2.50] [-3.39,2.78]

hospital beds 24.28** 34.80** 39.49**

21.60** -0.84** 3.64** -0.82 -0.62 -0.71 -3.09**

(log transformed) [19.88,28.68] [30.05,39.55] [33.90,45.08]

[17.84,25.35] [-1.25,-0.43] [2.85,4.42] [-1.85,0.22] [-1.85,0.62] [-2.05,0.63] [-4.76,-1.42]

occupancy rate 53.15** 63.20** 61.63**

45.02** -2.56** 3.56** -6.52** -7.48** -6.89** -11.13**

[42.08,64.22] [53.15,73.25] [48.65,74.61]

[36.70,53.33] [-3.91,-1.21] [1.25,5.86] [-10.18,-2.87] [-11.57,-3.39] [-11.39,-2.38]

[-16.39,-

5.86]

hospital is part of a -4.07* 3.71** 8.96**

2.42* -0.12 1.15** -1.45* -2.06** -2.38** -1.16

system [-7.35,-0.79] [0.97,6.44] [5.51,12.41]

[0.50,4.33] [-0.55,0.31] [0.44,1.86] [-2.62,-0.28] [-3.38,-0.75] [-3.77,-0.98] [-3.01,0.69]

hospital HHI, based 4.49 15.16 12.62

22.72** 1.72 4.17 -6.30 -3.23 -2.10 -6.78

on 15-mi radius [-18.02,27.01] [-2.02,32.33] [-8.83,34.08]

[7.63,37.81] [-1.04,4.48] [-0.47,8.81] [-14.03,1.44] [-11.70,5.24] [-12.15,7.94] [-18.01,4.45]

N 29939 29939 29939 29939 29939 29939 29939 29939 29939 23199

Year dummies included; Nearest ED fixed effects included

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STROBE 2007 (v4) checklist of items to be included in reports of observational studies in epidemiology*

Checklist for cohort, case-control, and cross-sectional studies (combined)

Section/Topic Item # Recommendation Reported on page #

Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title or the abstract 1, 2

(b) Provide in the abstract an informative and balanced summary of what was done and what was found 2

Introduction

Background/rationale 2 Explain the scientific background and rationale for the investigation being reported 4

Objectives 3 State specific objectives, including any pre-specified hypotheses 5-6

Methods

Study design 4 Present key elements of study design early in the paper 6

Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data

collection 6-7

Participants 6 (a) Cohort study—Give the eligibility criteria, and the sources and methods of selection of participants. Describe

methods of follow-up

Case-control study—Give the eligibility criteria, and the sources and methods of case ascertainment and control

selection. Give the rationale for the choice of cases and controls

Cross-sectional study—Give the eligibility criteria, and the sources and methods of selection of participants

7

(b) Cohort study—For matched studies, give matching criteria and number of exposed and unexposed

Case-control study—For matched studies, give matching criteria and the number of controls per case N/A

Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic

criteria, if applicable 7-8

Data sources/ measurement 8* For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe

comparability of assessment methods if there is more than one group 6-8

Bias 9 Describe any efforts to address potential sources of bias 13-14

Study size 10 Explain how the study size was arrived at 7

Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen

and why 7-8

Statistical methods 12 (a) Describe all statistical methods, including those used to control for confounding 8-9

(b) Describe any methods used to examine subgroups and interactions 9

(c) Explain how missing data were addressed 9

(d) Cohort study—If applicable, explain how loss to follow-up was addressed

Case-control study—If applicable, explain how matching of cases and controls was addressed N/A

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Cross-sectional study—If applicable, describe analytical methods taking account of sampling strategy

(e) Describe any sensitivity analyses 9

Results

Participants 13* (a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility,

confirmed eligible, included in the study, completing follow-up, and analysed 10

(b) Give reasons for non-participation at each stage N/A

(c) Consider use of a flow diagram N/A

Descriptive data 14* (a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and

potential confounders 10, Table 2

(b) Indicate number of participants with missing data for each variable of interest Table 2

(c) Cohort study—Summarise follow-up time (eg, average and total amount) N/A

Outcome data 15* Cohort study—Report numbers of outcome events or summary measures over time N/A

Case-control study—Report numbers in each exposure category, or summary measures of exposure N/A

Cross-sectional study—Report numbers of outcome events or summary measures 11-12, Table 2

Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95%

confidence interval). Make clear which confounders were adjusted for and why they were included 11-12, Tables 3 and 4

(b) Report category boundaries when continuous variables were categorized 10

(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period N/A

Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses 11-12

Discussion

Key results 18 Summarise key results with reference to study objectives 12

Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction

and magnitude of any potential bias 13-14

Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results

from similar studies, and other relevant evidence 12-14

Generalisability 21 Discuss the generalisability (external validity) of the study results 14

Other information

Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on

which the present article is based 17

*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE

checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at

http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.

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Do Patients Hospitalized in High-Minority Hospitals Experience … · INTRODUCTION Racial and ethnic differences in the burden of cardiovascular disease (CVD) contribute significantly

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