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Impact Of Ambulance Diversion: Black Patients With Acute Myocardial Infarction Had Higher Mortality Than Whites

  1. Renee Y. Hsia ( [email protected] ) is a professor in the Department of Emergency Medicine and a core faculty member at the Philip R. Lee Institute for Health Policy Studies, both at the University of California, San Francisco.
  2. Nandita Sarkar is a postdoctoral research analyst at the National Bureau of Economic Research in Cambridge, Massachusetts.
  3. Yu-Chu Shen is a professor at the Graduate School of Business and Public Policy, Naval Postgraduate School, in Monterey, California, and a faculty research fellow at the National Bureau of Economic Research.
PUBLISHED:Free Accesshttps://doi.org/10.1377/hlthaff.2016.0925


This study investigated whether emergency department crowding affects blacks more than their white counterparts and the mechanisms behind which this might occur. Using a nonpublic database of patients in California with acute myocardial infarction between 2001 and 2011 and hospital-level data on ambulance diversion, we found that hospitals treating a high share of black patients with acute myocardial infarction were more likely to experience diversion and that black patients fared worse compared to white patients experiencing the same level of emergency department crowding as measured by ambulance diversion. The ninety-day and one-year mortality rates among blacks exposed to high diversion levels were 2.88 and 3.09 percentage points higher, respectively, relative to whites, representing a relative increase of 19 percent and 14 percent for ninety-day and one-year death, respectively. Interventions that decrease the need for diversion in hospitals serving a high volume of blacks could reduce these disparities.


Racial and ethnic disparities that exist among patients with cardiovascular diseases have been extensively documented in the literature. 1,2 Studies have also shown that emergency department (ED) crowding disproportionately affects minority patients seeking emergency care. 3 This is troubling, given that ED crowding has been linked to decreased access to care, 3 delays in receiving treatment, 46 and increased mortality rates. 4,710

Little is known, however, whether system-level mechanisms, such as ED crowding, specifically affect disparities found in the treatment of cardiovascular diseases in racial and ethnic minorities. A rigorous analysis of this question is critical, given that other literature supports the potential for other explanatory reasons for such disparities. Specifically, ample literature shows that racial and ethnic minorities often are poorly insured, 1113 have more comorbidities, 1416 have decreased access to high-technology hospitals, 17,18 and receive poorer care compared with their white counterparts. 1921 If disparities in outcomes persist after controlling for other social determinants of health, a more compelling case exists for focusing on system-level interventions such as reducing ED crowding to decrease disparities.

A recent study found that hospitals treating large shares of black patients were more likely than other hospitals to be on ambulance diversion (defined as an ambulance being diverted to the next-closest ED because the closest one is saturated), but blacks and whites experienced similar adverse effects when exposed to the same levels of ambulance diversion. 22 However, that study was limited to Medicare patients and had relatively small samples of black patients, whereas ED crowding affects all and can potentially have different effects on non-Medicare patients. In this study we sought to answer whether ED crowding affects blacks more than whites, using nonpublic, all-payer, patient-level data from the State of California, as well as whether or not differences exist between black and white patients experiencing the same levels of diversion. Specifically, we analyzed changes in patient outcomes, as measured by mortality rates at thirty days, ninety days, and one year, between black and white patients who experienced ambulance diversion, while controlling for patient and hospital factors, access to cardiac technology, and receipt of cardiac-related treatment.

Study Data And Methods


We obtained patient-level data from the California Office of Statewide Health Planning and Development (OSHPD) for 2001–11 that contain admissions to every nonfederal, general, acute care hospital in California. These files included patient demographics such as sex, age, insurance, race, comorbidities, and ZIP code of residence, as well as admission date, source of admission, and procedures received (for example, cardiac thrombolysis, cardiac catheterization, or coronary artery bypass). We also obtained a link to each patient’s vital statistics to identify out-of-hospital mortality up to one year.

We obtained hospital-level data, including total beds, teaching status, occupancy rate, and market competition (defined based on the Herfindahl-Hirschman Index, commonly used to measure market concentration and applied to the hospital industry) 23 from annual reports submitted to the Healthcare Cost Report Information System, maintained by the Centers for Medicare and Medicaid Services (CMS). We obtained further hospital-level data from annual surveys from the American Hospital Association, which include ownership, system membership, and availability of cardiac care technology. We used OSHPD utilization files to confirm these characteristics and determine each hospital’s level of cardiac technology, based on the availability of coronary care units (CCUs) and cardiac catheterization, and on the ability to perform coronary artery bypass graft (CABG) procedures, which has been linked to differences in mortality. 24

We identified the nearest ED to each patient based on driving time from Google Maps queries. We first obtained the longitude and latitude of each hospital based on its heliport (if one exists) or physical address 25 and the coordinates for each patient’s ZIP code population centroid. We then determined driving distance between each pair of coordinates using automation codes developed in Stata. 26

To determine a hospital’s daily diversion hours, we obtained ambulance diversion logs from local emergency medical services (EMS) agencies in California, which contain data for seventeen of the twenty-three local EMS agencies that did not ban ambulance diversion during the years studied (2001–11), covering 88 percent of the state’s population.

The Institutional Board of Review of the University of California, San Francisco, approved this study.

Patient Population

We identified patients with acute myocardial infarction (AMI) by extracting records on patients who had 410.x0 or 410.x1 as their primary diagnosis code. 8 Following previous work, we excluded all patients who were not admitted via the ED and patients in counties for which we did not have diversion data. 27 We also excluded patients admitted to hospitals more than 100 miles from their mailing ZIP codes, as they likely did not reside there or were hospitalized while away from home. 10 Last, we excluded patients in counties with diversion bans, since they did not contribute to our understanding of the relationship between ambulance diversion and health outcomes.


We used thirty-day, ninety-day, and one-year mortality rates to evaluate the effects of ambulance diversion on patient outcomes.

Levels Of Ambulance Diversion

As in previous literature, 7,10 we used ambulance diversion as a proxy for ED crowding. We totaled the number of hours a patient’s nearest ED was on diversion the day of the patient’s admission and grouped the patients into four categories based on that number: zero hours, less than six hours, six to less than twelve hours, and twelve or more hours. 8,10

Minority Status

Race/ethnicity categories include non-Hispanic white, black, Hispanic, Asian/Pacific Islander, Native American/Eskimo/Aleut, others, and unknown. White patients served as the reference group, in comparison to black patients, and Hispanic and other racial groups served as control variables.

Because ambulance diversion is measured at the hospital level, we explored whether hospitals treating a large share of black patients (“black-serving hospitals”) were more likely to experience ambulance diversion than non-black-serving hospitals. First, following the previous literature, we ranked each hospital by the proportion of black patients at baseline (2001). Hospitals in the top decile were considered black-serving hospitals. 28 Second, if a hospital was not in the top decile for reasons such as being located in a white community, it was also defined as a black-serving hospital if it provided care to more than twice as many black patients compared to hospitals within a fifteen-mile radius in 2001. 8

Statistical Models

We applied multivariate statistical models using the patient as the unit of analysis. We used a linear probability model that included fixed effects for the ED that was closest in proximity for each patient and also controlled for time-dependent variables. 29 The key variables of interest were three indicators for level of ED diversion (less than six hours, six to less than twelve hours, and twelve or more hours on day of admission) with no diversion as the reference group, and three interaction terms between black indicator and the three diversion indicators. We used the nearest ED operating under normal conditions on the day of admission as the control group, and we used the three diversion indicators to compare patient health outcomes between a given hospital under control status and the same hospital under the three diversion categories. The interaction terms answered our research objective because they captured the black-white patient differences under the same diversion category.

The inclusion of ED fixed effects removed any baseline differences (such as differences in underlying patient population, hospital characteristics, and economic characteristics) across ZIP codes or EMS agencies. We controlled for patient factors such as age, sex, and comorbidity measures based on prior work. 30 To capture macro-level trends, we included time markers and took organizational characteristics of different hospitals into account, such as ownership, teaching status, and market competitiveness.

Given that the literature has shown that blacks often receive treatment at hospitals with less technology, 17,18 we controlled for patients’ access to hospitals with better cardiac technology, defined as hospitals with a CCU, catheterization lab, and the ability to perform CABG. 10 Because blacks are also less likely than whites to receive care, 19,20 we controlled for actual treatment received (specified as thrombolysis, catheterization, or CABG) that could explain any differences in mortality.


Several limitations should be taken into account in interpreting our results. First, our diversion data were self-reported by local EMS agencies, which leaves room for error or reporting bias. However, we believe that any potential bias would have minimal impact because the data were drawn directly from agencies’ online reporting systems, as opposed to using data that had been extracted and then reported to state agencies. In previous work, we aggregated our daily data to compare to the yearly levels of diversion reported by each hospital to the state and found an extremely high degree of concurrence.

A second source of measurement error came from the possibility that a patient’s AMI did not take place in his or her home, as assumed in our analysis. However, 80–85 percent of AMIs are estimated to occur in a patient’s home. 31,32 Because we excluded patients admitted to hospitals far from their home addresses, we do not believe that this affected our results.

Third, we did not capture patients admitted to the ED but not to the inpatient setting. However, it would be very unlikely for an AMI to be documented in the ED and the patient discharged directly home without admission. In addition, we did not include patients who died before they arrived at the hospital in this study, as they were not present in our data. While this measurement error could have introduced a downward bias in estimating the relationship between ambulance diversion and health outcomes, 8 it did not affect the estimated results of the interaction terms (that is, the black-white differences) since we did not expect the proportion of patients in this category to differ between blacks and whites.

Fourth, while the fixed effects removed time-invariant unobserved differences across EDs and we controlled for a wide range of key patient and additional hospital characteristics in our analysis, there might have been unobserved time-varying hospital characteristics associated with ED overcrowding and diversion that we could not capture in the data.

Last, our study was limited to California, and all of the counties in our analysis were considered urban areas. Even though California is a diverse state representing 12 percent of the US population, our results might not be generalizable to the rest of the country, especially not to rural America.

Study Results

Exhibit 1 shows the monthly trend of ambulance diversion separately for black-serving hospitals compared to non-black-serving hospitals. While we observed an overall downward trend in the percentage of patients affected by ambulance diversion, patients in communities where a nearby ED was black-serving were more likely to encounter ambulance diversion across almost all months than patients in communities where the nearest ED was not black-serving. The non-parametric Kolmogorov-Smirnov test confirmed that the two groups’ diversion trend distributions were statistically significantly different ( p<0.01 ). 33

Exhibit 1 Monthly trend in ambulance diversion between black-serving and non-black-serving hospitals, 2001–11

Exhibit 1
SOURCE Authors’ analysis of data from the California Office of Statewide Health Planning and Development, 2001–11.

Our sample included 91,263 patients with AMI admitted to all nonfederal, general, acute care hospitals in California during 2001–11. Exhibit 2 shows patient characteristics by diversion level experienced (full patient characteristics are listed in online Appendix Exhibit 1). 34 Fifty-two percent of whites, compared with 48 percent of blacks, did not experience diversion on day of admission. Sixteen percent of blacks and only 10 percent of whites were admitted when their nearest ED experienced twelve or more hours of diversion ( p<0.01 for black-white difference). Additionally, 53 percent of blacks lived near black-serving hospitals, compared to 21 percent of whites ( p<0.01 ).

Exhibit 2 Descriptive statistics of patient characteristics, access, treatment received, and outcomes, by emergency department (ED) diversion level exposure, 2001–11

Total sample ( N = 91,263)
White ( n = 55,789)
Black ( n = 6,994)
NumberPercentNumberPercentNumberPercentp value
Nearest ED’s exposure to diversion on the day of admission
No diversion46,05650%28,82652%3,33948%***
Less than 6 hours22,3192413,487241,50121***
6 to less than 12 hours12,879147,870141,04215
12 or more hours10,009115,606101,11216***
Nearest ED is black-serving hospital23,3232611,940213,72353***
Admitted to hospital with coronary care unit58,62664%36,56166%4,55365%
Admitted to hospital with catheterization lab64,5287140,518734,66067***
Admitted to hospital with CABG capacity56,3206236,343653,86455***
Treatment received
Received catheterization47,35752%29,78953%3,09344%***
Received thrombolytic therapy3,04831,74131753**
Received CABG5,33363,25162584***
Health outcomes
30-day mortality10,68412%6,86912%67410%***
90-day mortality14,555169,368171,01314***
1-year mortality21,2122313,538241,60923**
30-day all-cause readmission15,680239,557231,37627***

SOURCE Authors’ analysis of data from the California Office of Statewide Health Planning and Development, 2001–11. NOTES Full patient characteristics are listed in Appendix Exhibit 1 (see Note  34 in text). Statistical significance indicates whether the black-white difference is significantly different from zero based on the t-test. CABG is coronary artery bypass graft.



Regarding cardiac technology access, 73 percent of whites were admitted to hospitals with a catheterization lab, compared with 67 percent of blacks ( p<0.01 ). Fifty-three percent of whites received cardiac catheterization, compared with 44 percent of blacks ( p<0.01 ). Higher proportions of blacks than whites were admitted to government (19 percent versus 12 percent, p<0.01 ) and teaching hospitals (18 percent versus 10 percent, p<0.01 ), and were Medicaid-insured (14 percent) or uninsured (5 percent), compared with whites (5 percent and 3 percent, respectively). Blacks tended to be younger, with 47 percent younger than age sixty-five compared with 31 percent of whites. A larger proportion of blacks had diabetes (41 percent versus 29 percent), renal failure (22 percent versus 14 percent), and hypertension (81 percent versus 67 percent) than whites. Without adjustment, blacks had lower mortality at all points in time compared with whites. All black-white differences reported were statistically significant at the 0.05 level. (Further details are available in the Appendix.) 34

In our analysis, we found that longer exposure to ambulance diversion was associated with higher longer-term mortality rates for blacks relative to whites. Exhibit 3 shows the interactive effect between diversion and black patients. Blacks and whites had similar experiences when exposed to low levels of diversion (less than six hours). However, the mortality rate among blacks exposed to medium levels of diversion on days of admission (six to less than twelve hours) was 3.10 percentage points higher (95% confidence interval: 0.65, 5.55) at ninety days and 4.10 (95% CI: 1.58, 6.62) percentage points higher at one year relative to whites. To put this in context, a 4.10-percentage-point increase in one-year mortality given a base rate of 22 percent is a 19 percent increase in the risk of one-year death. Among patients exposed to high levels of diversion (twelve or more hours), the rate was 2.88 percentage points higher (95% CI: 0.64, 5.12) at ninety days and 3.09 percentage points higher (95% CI: 0.31, 5.88) at one year for blacks compared to whites, translating to a relative increase of 19 percent and 14 percent for ninety-day and one-year death, respectively. Full results are available in Appendix Exhibit 2. 34

Exhibit 3 Regression-adjusted mortality rate differences between black and white patients when both experienced ambulance diversion at their nearest emergency department (ED), all patients, 2001–11

30-day mortality90-day mortality1-year mortality
Base rate (among patients in reference group)11%15%22%
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
 Less than 6 hours−
 (95% confidence interval)(−0.63, 0.48)(−0.49, 0.72)(−0.60, 0.76)
 6 to less than 12 hours−0.05−0.07−0.29
 (95% CI)(−0.77, 0.67)(−0.89, 0.74)(−1.16, 0.59)
 12 or more hours−0.32−0.33−0.24
 (95% CI)(−1.24, 0.60)(−1.36, 0.71)(−1.35, 0.86)
Interaction between black patients and diversion level
 Low diversion (less than 6 hours)0.641.361.14
 (95% CI)(−1.14, 2.41)(−0.71, 3.42)(−1.11, 3.38)
 Medium diversion (6 to less than 12 hours) 1.66 * 3.10 ** 4.10 ***
 (95% CI)(−0.30, 3.62)(0.65, 5.55)(1.58, 6.62)
 High diversion (12 or more hours)1.51 2.88 ** 3.09 **
 (95% CI)(−0.63, 3.66)(0.64, 5.12)(0.31, 5.88)
Model specifications
Control for technology accessYesYesYes
Control for treatmentYesYesYes

SOURCE Authors’ analysis of data from the California Office of Statewide Health Planning and Development, 2001–11. NOTES N=91,263 . Nearest ED based on Google Maps query of driving distance. Statistical significance indicates whether the coefficient is significantly different from zero based on the regression model.




Because patients living near black-serving hospitals were more likely than those living near non-black-serving hospitals to experience diversion ( Exhibit 1 ), in a sensitivity analysis we further explored black-white differences by diversion status in those communities only ( n=23,323 in this subanalysis). Exhibit 4 shows that the black-white differences were even more pronounced within communities that were served by black-serving hospitals. Specifically, relative to whites from the same community where the nearby ED was a black-serving hospital, one-year mortality among blacks who were admitted on days with medium diversion was 5.02 percentage points higher (95% CI: 1.43, 8.60), representing a 23 percent increase in one-year mortality. Likewise, ninety-day and one-year mortality among black patients admitted on high diversion days were 3.52 percentage points higher (95% CI: 0.55, 6.49) and 4.97 percentage points higher (95% CI: 1.36, 8.59), or 23 percent higher, respectively, than those of their white counterparts.

Exhibit 4 Regression-adjusted mortality rate differences between black and white patients when both experienced ambulance diversion at their nearest emergency department (ED), only patients nearby black-serving hospitals, 2001–11

30-day mortality90-day mortality1-year mortality
Base rate (among patients in reference group)11%15%22%
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
 Less than 6 hours−0.53−0.290.12
 (95% confidence interval)(−1.98, 0.92)(−1.76, 1.18)(−1.44, 1.67)
 6 to less than 12 hours0.130.090.02
 (95% CI)(−1.61, 1.88)(−1.78, 1.96)(−1.91, 1.95)
 12 or more hours−0.87−0.18−0.18
 (95% CI)(−2.99, 1.24)(−2.32, 1.96)(−2.33, 1.98)
Interaction between black patients and diversion level
 Low diversion (less than 6 hours)−0.01−0.410.00
 (95% CI)(−2.79, 2.77)(−3.44, 2.62)(−3.91, 3.91)
 Medium diversion (6 to less than 12 hours)1.50 3.16 * 5.02 ***
 (95% CI)(−1.38, 4.37)(−0.10, 6.42)(1.43, 8.60)
 High diversion (12 or more hours)2.25 3.52 ** 4.97 ***
 (95% CI)(−0.64, 5.13)(0.55, 6.49)(1.36, 8.59)
Model specifications
Control for technology accessYesYesYes
Control for treatmentYesYesYes

SOURCE Authors’ analysis of data from the California Office of Statewide Health Planning and Development, 2001–11. NOTES N=23,323 . Nearest ED based on Google Maps query of driving distance. Statistical significance indicates whether the coefficient is significantly different from zero based on the regression model.





Our findings suggest that blacks in California experience a “double burden” of ED crowding: Not only are black-serving hospitals more likely to experience ambulance diversion, but black patients with AMI also have worse mortality outcomes relative to whites when exposed to the same diversion levels. Our interaction models show that even under the same levels of diversion, blacks have a 19 percent higher one-year mortality rate compared with whites if the nearby ED experiences six to less than twelve hours of diversion, and 14 percent if the nearby ED experiences twelve hours or more of diversion on day of admission. Black-white differences in mortality rates were even larger in communities where the nearest ED was a black-serving hospital.

While other studies have looked at various predictors of ambulance diversion, 3537 few studies have evaluated the impact of diversion on racial or ethnic disparities when controlling for patient and hospital factors. Our finding that black-serving hospitals were more likely to experience diversion is consistent with prior studies. 3,22 More importantly, our results show that blacks have worse health outcomes than whites when faced with the same diversion level—a contrast to a recent study that found no such differences in an older (Medicare-only) study. 22 Because our patient population was younger and potentially more sensitive to changes in access to care, 38 the contrasting findings could be explained by patient population and sample-size differences.

What possible reasons could explain our findings? Our analyses controlled for access to hospital technology and treatment received, thereby removing the possibility that differences in mortality resulted from variations in treatment. Since blacks have higher mortality rates than whites even after cardiac technology access is controlled for, our findings raise concern that blacks are receiving poorer-quality care. This could be occurring at the individual level or the systems level; the latter is more likely. We found that when both whites and blacks experienced high diversion, 26 percent of whites were admitted to black-serving hospitals, compared with 80 percent of blacks. But for no or low diversion, the difference was much smaller (22 percent of whites versus 44 percent of blacks). It is possible that these differential black-white effects do not arise from deficiencies in care that might lead to in-hospital death but instead could be attributable to delayed care, which happens more often in black-serving hospitals than elsewhere. For example, delayed administration of appropriate medical therapy (such as aspirin or statins) or unmeasured cardiac care quality (such as processes of care) could result in poorer heart function (also known as ejection fraction), which would manifest itself in differences in longer-term mortality. This is supported by our findings showing significant black-white differences at ninety days and one year, but not at thirty days. It is also possible that there are disparities in the availability of noncardiac resources, and while exploration of this is beyond the scope of our study, a substantial literature documents lower quality of care in black-serving hospitals. 3941 The widening gap in the percentage of black and white patients treated at black-serving hospitals during prolonged ED crowding periods may contribute to the widening gap in black-white mortality when both experience ED crowding in the same community. Certainly, a number of factors involving the quality of care following a hospital visit might also affect long-term mortality differences in black and white patients with AMI, such as poor primary care access, lack of cardiac rehabilitation access, and high-stress environments. These reasons reduce any optimism one might have toward diversion bans, especially in these already overcrowded contexts where blacks have much higher ED visit rates 42 and where the ED plays a dual “safety net” and “acute care” role.

While the discussion of ways to improve quality at black-serving hospitals, such as improving hospital governance, is beyond the scope of this article and has been discussed elsewhere, 40 our findings suggest that interventions that decrease ambulance diversion by reducing ED crowding can potentially reduce disparities in outcomes. The reallocation of resources for emergency care in black communities might be another solution to reduce associated racial disparities with diversion, given the potential mismatch in supply of and demand for ED services. 2


Our analysis demonstrated that black patients with AMI differentially experienced higher ninety-day and one-year mortality rates relative to whites when both experienced moderate-to-high levels of ambulance diversion. The black-white differences persisted even after we controlled for a comprehensive set of patient and hospital factors. Our findings suggest that policies that address ED crowding within the context of an interconnected health care system and target efforts in communities with black-serving hospitals might help reduce disparities in quality of care and health outcomes.


The authors thank the California Office of Statewide Health Planning and Development for their assistance in preparing the data sets used in this project. They are grateful to the California Emergency Medical Services (EMS) Authority and the numerous local EMS agency administrators and medical directors who generously provided their data. Additionally, they thank Joanna Guo and Sarah Sabbagh for providing administrative and editorial support. This study was supported by an award from the National Institutes of Health and the National Heart, Lung, and Blood Institute (Grant No. 1R01HL114822) and from the American Heart Association (Grant No. 13CRP14660029). The sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the article. The article’s contents are solely the responsibility of the authors and do not necessarily represent the official views of any of the funding agencies.


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