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Abstract

Objective—Inter-hospital transfer (IHT), a common intervention, may be subject to healthcare disparities. In mechanically ventilated patients with sepsis, we hypothesize that disparities not disease-related would be found between patients who were and were not transferred. Design—Retrospective cohort study. Setting—Nationwide Inpatient Sample, 2006–2012. Patients—Patients over 18 years of age with a primary diagnosis of sepsis who underwent mechanical ventilation. Send Correspondence to: Interventions—none Measurements and Main Results—We obtained age, gender, length of stay, race, insurance coverage, do not resuscitate status, and Elixhauser co-morbidities. The outcome used was inter-hospital transfer from a small-or medium-sized hospital to a larger acute care hospital. Of 55,208,382 hospitalizations, 46,406 patients met inclusion criteria. In the multivariate model, patients were less likely to be transferred if the following were present: older age (OR 0.98, 95% CI 0.978–0.982), black race (OR 0.79, 95% CI 0.70–0.89), Hispanic race (OR 0.79, 95% CI 0.69– 0.90), South region hospital (OR 0.79, 0.72–0.88), teaching hospital (OR 0.31, 95% CI 0.28– 0.33), and DNR status (OR 0.19, 95% CI 0.15–0.25). Conclusions—In mechanically ventilated patients with sepsis, we found significant disparities in race and geographic location not explained by medical diagnoses or illness severity.
Racial and Geographic Disparities in Inter-Hospital Intensive
Care Unit Transfers
Patrick D Tyler, MD1, David J Stone, MD2, Benjamin P Geisler, MD, MPH3, Stuart
McLennan, PhD4, Leo Anthony Celi, MD, MS, MPH5,6, and Barret Rush, MD MPH7
Patrick D Tyler: pdtyler@bidmc.harvard.edu; David J Stone: djs4v@virginia.edu; Benjamin P Geisler:
bgeisler@post.harvard.edu; Stuart McLennan: McLennan.Stuart@mh-hannover.de; Leo Anthony Celi:
lceli@bidmc.harvard.edu; Barret Rush: bar890@mail.harvard.edu
1Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
2Departments of Anesthesiology and Neurosurgery, University of Virginia School of Medicine,
Charlottesville, VA, USA
3Department of Medicine, Massachusetts General Hospital/Harvard Medical School, Boston, MA,
USA
4Institute for History, Ethics and Philosophy of Medicine, Hannover Medical School, Hannover,
Germany
5Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical
Center, Boston, MA, USA
6Institute for Medical Engineering and Science, Massachusetts Institute of Technology,
Cambridge, MA, USA
7Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
Abstract
Objective—Inter-hospital transfer (IHT), a common intervention, may be subject to healthcare
disparities. In mechanically ventilated patients with sepsis, we hypothesize that disparities not
disease-related would be found between patients who were and were not transferred.
Design—Retrospective cohort study.
Setting—Nationwide Inpatient Sample, 2006–2012.
Patients—Patients over 18 years of age with a primary diagnosis of sepsis who underwent
mechanical ventilation.
Send Correspondence to: Patrick D Tyler MD, Department of Emergency Medicine, Beth Israel Deaconess Medical Center,
Rosenberg Building, One Deaconess Road, 2nd Floor, Boston, MA 02215, pdtyler@bidmc.harvard.edu.
Financial Disclosures: No financial disclosures
Conflict of Interest: No authors have any conflicts of interest to disclose
The remaining authors have disclosed that they do not have any potential conflicts of interest.
Author Contributions
Conception and design: PDT, BR, LAC
Statistics: BR
Interpretation: all authors
Drafting the manuscript for important intellectual content: all authors
HHS Public Access
Author manuscript
Crit Care Med. Author manuscript.
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Interventions—none
Measurements and Main Results—We obtained age, gender, length of stay, race, insurance
coverage, do not resuscitate status, and Elixhauser co-morbidities. The outcome used was inter-
hospital transfer from a small- or medium-sized hospital to a larger acute care hospital. Of
55,208,382 hospitalizations, 46,406 patients met inclusion criteria. In the multivariate model,
patients were less likely to be transferred if the following were present: older age (OR 0.98, 95%
CI 0.978–0.982), black race (OR 0.79, 95% CI 0.70–0.89), Hispanic race (OR 0.79, 95% CI 0.69–
0.90), South region hospital (OR 0.79, 0.72–0.88), teaching hospital (OR 0.31, 95% CI 0.28–
0.33), and DNR status (OR 0.19, 95% CI 0.15–0.25).
Conclusions—In mechanically ventilated patients with sepsis, we found significant disparities
in race and geographic location not explained by medical diagnoses or illness severity.
Keywords
inter-hospital transfer; medical transport; healthcare delivery; sepsis; healthcare disparities
BACKGROUND
Patients, their families, and providers in the intensive care unit (ICU) at smaller regional
hospitals commonly face the dilemma of whether the patient should be transferred to a
larger, more specialized medical center. This may occur because the patient needs a service
or procedure not available at the regional hospital, or because one or more of the key
stakeholders (provider, patient, or family members) perceives a mismatch between patient
needs and the available resources at the regional hospital (1). Typically, patients undergo
inter-hospital transfer (IHT) so they can receive care from providers with greater expertise,
and obtain consults or procedures from subspecialists.
The unstated assumption is that patients will experience better outcomes after IHT than if
they remain at the referring hospital (2). Transfer thus represents a kind of intervention, and
may be subject to the same kind of health disparities that exist in other areas of medicine (3).
We compared mechanically ventilated patients with sepsis who underwent IHT and those
who did not. We hypothesized that geographic variation and racial disparities exist that are
not explained by the patient’s diagnosis or medical comorbidities.
METHODS
This study is reported in accordance with the STrengthening the Reporting of OBservational
studies in Epidemiology (STROBE) statement (4). A de-identified dataset was used for this
analysis, for which a waiver of consent was obtained from the University of British
Columbia Institutional Review Board.
Study Population
We used the National (Nationwide) Inpatient Sample (NIS) for 2006–2012. The NIS is a
U.S. Federal all-payer database created by the Agency for Healthcare Research and Quality
(AHRQ) using a complex survey design that captures approximately 20% of all US
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hospitalizations, and allows for the use of weighting to approximate 97% of all inpatient
care delivered across the United States.
Cohort definition
All patients 18 years of age with a primary diagnosis of severe sepsis (International
Classification of Disease 9th edition (ICD9) code 995.92) who also underwent mechanical
ventilation (treatment codes 96.7x, 96.04) were included. The ICD-9 code for severe sepsis
has been prospectively validated to have a sensitivity of 50.4% and a specificity of 96.3%
(5). Only patients initially treated at small and medium sized hospitals (as defined by the
AHRQ) were included in the analysis.
Covariates
Patient characteristics included: age, gender, length of stay, race (categorized as white,
black, Hispanic, or other), insurance coverage (yes vs no), do not resuscitate (DNR) status
(ICD-9 V4986), as well as the Elixhauser co-morbidity index. The Elixhauser co-morbidity
indices are a set of 29 comorbidities validated to adjust for chronic diseases in multi-variate
models. Hospital level characteristics included: size (small, medium, or large, as defined by
the AHRQ), teaching status (yes vs no), and region of the United States (Northeast, South,
Midwest, West).
Statistical Analysis
All analyses were performed in SAS v9.4 (SAS Institute, Cary, NC, USA) employing
complex survey procedures and weights. Independent t-tests were used for continuous
variables; chi-squared was used for ordinal and nominal data. Data are presented with 95%
confidence intervals where appropriate; a p-value of <0.05 was considered significant. All
percentages displayed in Tables are estimates of national projections using proper weights.
Multivariate logistic regression modeling was used to model the likelihood of transferring a
patient to a higher level of care. All models were created a priori. The first model included:
age, gender, insurance coverage, race, hospital region, hospital teaching status, and DNR
status. A second model was created which included all the variables in Model 1 as well as
the 29 Elixhauser co-morbidity index elements.
RESULTS
A total of 55,208,382 hospitalizations from the 2006–2012 NIS samples were analyzed.
There were 46,406 patients who met the inclusion criteria. Of these, 3095 (6.6%) patients
were transferred to a large hospital, assumed to be a more specialized center. There were
13,298 (28.7%) patients treated at small hospitals, whereas there were 33,108 (71.3%)
patients treated at medium sized hospitals. The results of univariate analysis between those
that were transferred and those that were not are displayed in Table 1. Older age, shorter
length of stay, insurance coverage, and DNR status were significantly associated with lower
probabilities of transfer, but female gender was not. There were significant differences
within the categories of race, hospital teaching status, and location that were further
explored in the multivariate model
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Table 2 displays the results of the multivariate regression model; odds ratios reflect
likelihood of transfer. Older patients, black and Hispanic patients, and patients in the South
were significantly less likely to be transferred. Patients from teaching hospitals and those
with DNR status were significantly less likely to be transferred. Insurance coverage was not
associated with transfer likelihood. When the Elixhauser index characteristics were added in
model 2, patients with valvular heath disease were more likely to be transferred; those with
liver failure, HIV infection, fluid and electrolyte disorder, weight loss, neurological
disorders, metastatic cancer, paralysis, and peripheral vascular disease were less likely to be
transferred.
DISCUSSION
In this study comparing mechanically ventilated patients with sepsis who underwent IHT to
those who did not, we found significant differences in patient characteristics unrelated to
diagnosis or illness severity. Factors predictive of non-transfer included older age, minority
status (black or Hispanic), and southern regional location (Table 2). As anticipated in both
cohorts of patients, DNR status and hospitalization at a teaching facility are the identifiable
factors most strongly associated with not being transferred. Our findings satisfied our
hypothesis that geographic variation and racial disparities in IHT may exist that are not
explained by the diagnosis or medical comorbidities. Black and Hispanic patients were
significantly less likely to be transferred compared to white patients, even after adjustment
for comorbidities. The category of Other race did not show a significant association with
risk of transfer, with a wide confidence interval.
Several explanations are possible. First, there may be implicit or, less likely, explicit bias in
medical providers’ choices. Second, minority patients and families may be less likely to
request or consent to being transferred to large, unfamiliar facilities, and may prefer to
receive care from local providers they know and trust. Third, there may be cultural or ethnic
differences with respect to preferences that are unrelated to mistrust; studies of end-of-life
preferences in chronic obstructive pulmonary disease suggest such differences (6). Fourth,
the physicians who treat these patients may be less well-connected with the medical system
at large, and therefore less likely to refer patients to external sources of care. Finally, for
some patients, language problems and concerns about exposing immigration status might
also contribute to this finding.
Healthcare disparities have long been an issue of concern in the US healthcare system.
Subsequent reports by the American Medical Association, the Institute of Medicine (since
renamed the National Academy of Medicine), and others have confirmed the pervasiveness
and persistence of disparities throughout the US healthcare system (3, 7, 8). Our findings are
consistent with previous research, including studies conducted by the Dartmouth Atlas and
the RWJF demonstrating care variations by race and by region (9, 10).
The study has several limitations. First, the NIS contains no information about severity of
illness (SOI). However, even if we assume that transferred patients have greater illness
severity than those remaining at the sending hospital, illness severity should be randomly
assorted between ethnic groups and geographical locations. That is, even if patients in the
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transferred cohort were found to be more severely ill, higher SOI scores would not explain
racial disparity. If anything, higher SOI scores among transferred patients would make our
findings even more concerning, as sepsis appears to be both more common and more severe
in patients of black and other non-white ethnicities (11). Second, as with any administrative
dataset, there could be unmeasured confounding; in this case, some potential confounders
could include patient throughput pressures and bed availability in different transfer regions.
However, if such unmeasured confounding results in systematically lower transfer rates for
minorities, that would still merit further investigation. Third, because of the sensitivity of the
ICD-9 administrative code for sepsis, some patients with sepsis may have been inadvertently
excluded; however, the ethnicity of patients not captured by this administrative approach
should be random, and thus should not affect our findings on disparity. Finally, because the
NIS does not link patient records, we are not able to evaluate the effect of IHT on patient
outcomes.
Further research should be directed towards replicating this analysis, and studying IHT using
more detailed clinical data to evaluate relative contributions of geography and
socioeconomic status as drivers of the findings. We should also study the entire IHT
process, including cost-effectiveness and impact on outcomes; this would require high-
resolution clinical data, including outcomes, from both the transferring and receiving
hospitals. Hospital-level variables such as bed availability, which are not available, would
likely also have an impact on IHT.
CONCLUSIONS
In conclusion, we found that, among mechanically ventilated ICU patients with sepsis
transferred between hospitals, there were significant disparities in patient ethnicity and
location that were unexplained by diagnosis or medical comorbidities.
Acknowledgments
Copyright form disclosure: Dr. Celi received support for article research from the National Institutes of Health.
ABBREVIATION LIST
AHRQ Agency for Healthcare Research and Quality
CI confidence interval
DNR Do Not Resuscitate
HCUP Healthcare Quality Utilization Project. Comprehensive source of hospital
data produced by AHRQ.
ICU Intensive Care Unit
IHT Inter-hospital transfer. Occurs when a patient is moved from one hospital to
another.
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NIS National (Nationwide) Inpatient Sample. One of the databases contained in
HCUP.
RWJF Robert Wood Johnson Foundation
US United States of America
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Table 1
Baseline demographic and patient characteristics for patients transferred to another hospital versus those who
were not.
Variables No Transfer
(n=43 311) Transfer
(n=3 095) p
Age in years, mean (SD) 66.6 (15.5) 61.8 (15.8) <0.01
Length of Stay in days, median (IQR) 10.9 (4.9–19.6) 7.0 (2.1–16.3) <0.01
Female sex, n (%) 20729 (47.8) 1436 (46.4) 0.12
Race
White, n (%) 27103 (65.9) 2013 (69.5)
Black, n (%) 6993 (17.2) 377 (13.0)
Hispanic, n (%) 4113 (10.0) 276 (9.4)
Other, n (%) 2841 (6.9) 236 (8.1) <0.01
Insurance Coverage, n (%) 40651 (93.9) 2863 (92.5) <0.01
Hospital Teaching Status & Location
Rural, n (%) 777 (1.8) 334 (10.7)
Urban Non-teaching, n (%) 17117 (39.1) 1797 (58.1)
Urban Teaching, n (%) 25417 (59.1) 964 (31.2) <0.01
Hospital Region
Northeast, n (%) 10860 (25.9) 707 (23.2)
Midwest, n (%) 7855 (18.1) 539 (17.3)
South, n (%) 15199 (34.8) 981 (32.1)
West, n (%) 9397 (21.3) 868 (27.4) <0.01
DNR Status, n (%) 4435 (10.3) 60 (2.0) <0.01
The p values displayed for race, teaching status, and region apply to the entire category. Note that the significant difference for DNR status
indicates a lower probability of transfer. (DNR = Do Not Resuscitate)
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Table 2
Multivariate models with odds ratios of transfer for patients. Model 2 includes all covariates from model 1,
adding the 29 Elixhauser comorbidity indices. For race, White race serves as the reference category. For
location of the hospital initiating the transfer, Northeast serves as the reference category.
Covariates Odds Ratio Confidence Interval (95%) p
Model 1
Age 0.980 0.978–0.982 <0.01
Female Gender 0.97 0.90–1.04 0.36
Insurance Coverage 1.01 0.88–1.17 0.85
White Race 1 1
Black Race 0.79 0.70–0.89 <0.01
Hispanic Race 0.79 0.69–0.90 <0.01
Other Race 1.08 0.93–1.24 0.31
Northeast Hospital 1 1
Midwest Hospital 0.98 0.87–1.11 0.78
South Hospital 0.79 0.72–0.88 <0.01
West Hospital 0.97 0.87–1.08 0.54
Teaching Hospital 0.31 0.28–0.33 <0.01
DNR Status 0.19 0.15–0.25 <0.01
Model 2
Age 0.979 0.977–0.982 <0.01
Female Gender 0.94 0.87–1.01 0.09
Insurance Coverage 1.04 0.90–1.20 0.62
White Race 1 1
Black Race 0.94 0.75–0.94 <0.01
Hispanic Race 0.80 0.70–0.92 <0.01
Other Race 1.08 0.93–1.25 0.30
Northeast Hospital 1 1
Midwest Hospital 1.02 0.91–1.15 0.72
South Hospital 0.82 0.74–0.91 <0.01
West Hospital 1.02 0.91–1.14 0.74
Teaching Hospital 0.31 0.28–0.33 <0.01
DNR Status 0.20 0.16–0.26 <0.01
Congestive Heart Failure 0.95 0.87–1.03 0.22
Valvular Heart Disease 1.19 1.03–1.38 0.02
Chronic Pulmonary Disease 0.94 0.86–1.02 0.15
Diabetes-Complicated 0.98 0.84–1.14 0.81
Diabetes-Uncomplicated 1.07 0.97–1.16 0.16
Liver Failure 0.81 0.72–0.90 <0.01
Renal Failure 0.98 0.89–1.08 0.72
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Covariates Odds Ratio Confidence Interval (95%) p
HIV Infection 0.36 0.22–0.58 <0.01
Cancer 0.87 0.72–1.04 0.13
Coagulopathy 0.99 0.91–1.09 0.90
Depression 1.07 0.94–1.20 0.30
Alcohol Abuse 0.98 0.85–1.13 0.80
Drug Abuse 0.85 0.71–1.02 0.07
Fluid and Electrolyte Disorder 0.82 0.75–0.89 <0.01
Weight Loss 0.89 0.81–0.96 <0.01
Neurological Disorders 0.76 0.70–0.83 <0.01
Peripheral Vascular Disease 0.81 0.71–0.94 <0.01
Anemia-Iron Deficiency 0.89 0.73–1.10 0.29
Anemia-Blood Loss 0.90 0.67–1.21 0.48
Obesity 1.08 0.97–1.21 0.18
Rheumatoid Arthritis 1.02 0.84–1.25 0.82
Metastatic Cancer 0.43 0.33–0.57 <0.01
Lymphoma 0.87 0.66–1.14 0.31
Peptic Ulcer Disease 0.88 0.59–1.30 0.51
Thyroid Dysfunction 0.90 0.80–1.02 0.11
Paralysis 0.71 0.59–0.86 <0.01
Hypertension 0.96 0.88–1.04 0.30
Arrhythmia 0.93 0.86–1.01 0.09
Pulmonary Circulation Disorder 0.99 0.86–1.15 0.93
Crit Care Med. Author manuscript.
... The out-of-pocket system has resulted in decreased life expectancy, lowered outcomes for men and women, 14 and disparities in quality and care for minorities and individuals in rural or urban settings. 15,16 Further, the system is characterized by employment and income-status, which has led to poor outcomes for populations in lower socioeconomic brackets. 15 ...
... 31 A secondary key difference between Austria and the United States is the differences in care by geographic location in the United States. 16,33,34 For example, Tyler et al 16 emphasized that rural versus urban areas face differential access and quality of care in terms of healthcare in the United States. Research indicates that healthcare failings in the United States include a lack of care that meets the diverse needs of varied geographic regions. ...
... 31 A secondary key difference between Austria and the United States is the differences in care by geographic location in the United States. 16,33,34 For example, Tyler et al 16 emphasized that rural versus urban areas face differential access and quality of care in terms of healthcare in the United States. Research indicates that healthcare failings in the United States include a lack of care that meets the diverse needs of varied geographic regions. ...
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... The results from these studies suggest that Black patients 2,3,13 and Hispanic patients 1,13 were less likely to undergo IHT, although most of these studies did not specifically investigate the association between race/ethnicity and IHT. Moreover, prior studies 5,7,[14][15][16] investigating disparities in IHT have compared Black patients with White patients, whereas disparities between Hispanic patients and White patients are less well understood. Furthermore, while there is some prior research on IHT disparities in the cardiology literature, 5,7,14,15 such disparities have not been thoroughly explored among other medical diagnoses with a potential mortality benefit from IHT. 4 The purpose of this study was to determine if there are racial/ethnicity disparities in IHT practices for diseases for which IHT is associated with a mortality benefit, including AMI, stroke, sepsis, and certain respiratory illness, 4 using a national sample of patients. ...
... Our findings add to prior studies 1-3,13 exploring variables associated with IHT whose results that suggest Black patients are less likely to be transferred for a variety of diseases processes, although this has been minimally explored for specific disease entities. Data from a 2018 study 16 Our findings suggest that there are several notable associations between race/ethnicity and other included variables that are also associated with odds of IHT and may partially, but not completely, explain our observed results. We found that Black patients had more comorbidities (as measured by HCC score 21 ), and HCC score was associated with higher rates of IHT (eTable 1 in the Supplement). ...
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Importance Interhospital transfer (IHT) of patients is a common occurrence in modern health care. Racial/ethnic disparities are prevalent throughout US health care, but their presence in IHT is not well characterized. Objective To determine if there are racial/ethnic disparities in IHT for medical diagnoses for which IHT is associated with a mortality benefit. Design, Setting, and Participants This cross-sectional analysis used 2013 data from the Center for Medicare & Medicaid Services 100% Master Beneficiary Summary and Inpatient Claims merged with 2013 American Hospital Association data. Individuals with Medicare aged 65 years or older continuously enrolled in Medicare Part A and B with an inpatient hospitalization claim in 2013 for primary diagnosis of acute myocardial infarction, stroke, sepsis, or respiratory diseases were included. Data analysis occurred from November 2019 through July 2020. Exposures Race/ethnicity. Main Outcomes and Measures The primary outcome of interest was IHT. For the primary analysis, a series of logistic regression models were created to estimate the adjusted odds of IHT for Black and Hispanic patients compared with White patients, controlling for patient clinical and demographic variables and incorporating hospital fixed effects. In secondary analyses, subgroup analyses were conducted by diagnosis, hospital teaching status, and hospitalization to hospitals in the top decile of Black and Hispanic patient proportion. Results Among 899 557 patients, 734 958 patients were White (81.7%), 84 544 patients were Black (9.4%), and 47 588 patients were Hispanic (5.3%); there were 418 683 men (46.5%), and 306 215 patients (34.0%) were older than 84 years. The mean (SD) age was 76.8 (7.5) years. Among all patients, 20 171 White patients (2.7%), 1913 Black patients (2.3%), and 1062 Hispanic patients (2.2%) underwent IHT. After controlling for patient variables and hospital fixed effects, Black patients had a persistently lower odds of IHT (adjusted odds ratio, 0.87; 95% CI, 0.81-0.92; P < .001), while Hispanic patients had higher odds of IHT (adjusted odds ratio, 1.14; 95% CI, 1.05-1.24; P = .002) compared with White patients. Conclusions and Relevance This national evaluation of IHT among patients hospitalized with diagnoses previously found to have mortality benefit with transfer found that, compared with White patients, Black patients had persistently lower adjusted odds of transfer after accounting for patient and hospital characteristics and measured across various hospital settings. Meanwhile, Hispanic patients had higher adjusted odds of transfer. This research highlights the need for the development of strategies to mitigate disparate transfer practices by patient race/ethnicity.
... Although this still has to be combined with regulations regarding the technical standards of a hospital. On the other hand, from the perspective of basic health services, the utilization of hospital services is also a benchmark for the success of a health care referral system (11). The purpose of this article is to report on the disparity in hospital utilization between regions in Indonesia. ...
... The study also found racial differences as other disparities. The researcher explained that this study did not explain the medical diagnosis or the severity of the patient's disease (11). The study of other disparities was carried out in America to see home care services for geriatrics in America. ...
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Background. The utilization of hospital services is a benchmark for the success of a health care referral system. Indonesia as the largest archipelago in the world encounters challenging conditions along with lack of infrastructure posed economic and health disparity among its regions. Disparities as a result of this development also have an impact on the accessibility of health care facilities. Methods. Chi Square test was used to test dichotomy variables and t-tests were performed for analyzing the difference among continuous variables. These tests were employed to assess the hypothesis that there was significant regional difference in the access of health care in Indonesia. Estimation using multinomial logistic regression test was used to study the disparity between regions in hospital utilization. Results. The results of this study indicate that there were disparities between regions in Indonesia. In the inpatient category all regions have better utilization than the Papua region, except the Sumatra region. The highest disparity occurs between the Nusa Tenggara region and the Papua region. Possibility of utilizing hospital vs. inpatient facilities not using the hospital 1,439 times in adults in the Nusa Tenggara region compared to the Papua region (OR = 1,439; 95% CI = 1,271 - 1,629). In the category of outpatient utilization as well as hospitalization in hospitals, the Papua region has better hospital utilization compared to other regions. The greatest disparity with the Sumatra region (OR = 0.484; 95% CI = 0.392 - 0.597). Conclusion. In conclusion, there were disparities between regions in Indonesia even though the odds ratio for mortality between regions decreased compared to the previous period. Ethical Clearance. The 2013 RISKESDAS survey had ethical clearance that was approved by the national ethical committee in the NIHRD (ethic number: 01.1206.207). Informed consent was used during data collection, which considered aspects of data collection procedure, voluntary, and confidentiality.
... [10][11][12][13][14][15][16][17] Whether racial and ethnic disparities in IHT contribute to gaps in healthcare access is less well studied. Prior IHT research, 1,3,[18][19][20][21][22][23][24][25][26][27][28][29] including registry-based studies exploring factors associated with IHT, 1,4,24 has suggested influence of non-clinical factors, including race/ethnicity, on transfer practices. The most robust body of evidence derives from the cardiology literature, 3,19,20,23,26 in which minority patients have been shown to be less likely to be transferred to hospitals with revascularization capabilities. ...
... 1 Though much of the limited existing literature on IHT and race/ethnicity is focused on Black versus White patients, other studies have also demonstrated lower transfer rates among Hispanic patients for select conditions that often require transfer to receive specialty care. 24,25,39,40 Our findings therefore highlight that such disparities likely persist among a broader population of Hispanic patients with the medical conditions included in this study. Among patients admitted to community hospitals, our results additionally suggest disparity in transfer by race/ ethnicity within select regions in the country, and among patients with select diagnoses. ...
Article
Background Interhospital transfer (IHT) is often performed to provide patients with specialized care. Racial/ethnic disparities in IHT have been suggested but are not well-characterized.Objective To evaluate the association between race/ethnicity and IHT.DesignCross-sectional analysis of 2016 National Inpatient Sample data.PatientsPatients aged ≥ 18 years old with common medical diagnoses at transfer, including acute myocardial infarction, congestive heart failure, arrhythmia, stroke, sepsis, pneumonia, and gastrointestinal bleed.Main MeasuresWe performed a series of logistic regression models to estimate adjusted odds of transfer by race/ethnicity controlling for patient demographics, clinical variables, and hospital characteristics and to identify potential mediators. In secondary analyses, we estimated adjusted odds of transfer among patients at community hospitals (those more likely to transfer patients) and performed subgroup analyses by region and primary medical diagnosis.Key ResultsOf 5,774,175 weighted hospital admissions, 199,015 (4.5%) underwent IHT, including 4.7% of White patients, compared with 3.9% of Black patients and 3.8% of Hispanic patients. Black (OR 0.83, 95% CI 0.78–0.89) and Hispanic (OR 0.81, 95% CI 0.75–0.87) patients had lower crude odds of transfer compared with White patients, but this became non-significant after adjusting for hospital-level characteristics. In secondary analyses among patients hospitalized at community hospitals, Hispanic patients had lower adjusted odds of transfer (aOR 0.89, 95% CI 0.79–0.98). Disparities in IHT by race/ethnicity varied by region and medical diagnosis.Conclusions Black and Hispanic patients had lower odds of IHT, largely explained by a higher likelihood of being hospitalized at urban teaching hospitals. Racial/ethnic disparities in transfer were demonstrated at community hospitals, in certain geographic regions and among patients with specific diseases.
... This condition includes the development of overall public health [15,21]. Other studies in several countries also found the same results [22][23][24]. This study proves that spatially, geographical conditions in an area contribute to creating disparities between regions, including in childbirth services in health care facilities. ...
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Specifically, the characteristics of regions in Indonesia are unique. The situation is because the division of the region refers to the main islands. The study aims to analyze regional disparities of childbirth services in Indonesia. Meanwhile, the analysis in this study uses raw data from the 2017 Indonesian Demographic Health Survey (IDHS). The IDHS used stratification and multistage random sampling. The sample used in this study was 17,769 women aged 15-49 years with live births in the last five years. The study employed all region (seven region) in the analysis, and analyzed data using the Binary Logistic Regression test. The result shows national average of the utilization of healthcare facilities for delivery in Indonesia is 72.0%. The three highest-ranking regions were in the Java-Bali region with 89.5%, Sumatra region 73.5%, and Kalimantan region 69.1%. The study shows a significant disparity between all regions than the Papua region, except for Kalimantan and Sulawesi regions. Sumatra Region has 1.475 times more possibilities to utilize healthcare facilities for delivery than the Papua region. The Java-Bali region has 3.010 times more potential to use healthcare facilities for delivery than the Papua region. The Nusa Tenggara region has 1.891 times more opportunities to use healthcare facilities for delivery than the Papua region. At the same time, the Maluku region has lower utilization than the Papua region. Maluku Region has the possibility of 0.304 times utilizing healthcare facilities for delivery than the Papua region. The study concluded that there were significant disparities between regions in using healthcare facilities for delivery in Indonesia.
... We queried the 2012 -2014 NIS database to identify patients with HRS. The NIS database contains a 20% stratified random sample of all U.S. hospitals discharges, which approximates a total of 8 million discharges yearly [14] . This is a publicly available de-identified dataset and therefore exempted from Institutional Review Board review. ...
Article
COVID‐19 has highlighted a brutal reality known for decades, that Black, Indigenous, and People of Color bear a disproportionate burden of US annual sepsis cases. While plentiful research funds have been spent investigating genetic reasons for racial disparities in sepsis, an abundance of research shows that sepsis incidence and mortality maps to indicators of colonial practices including residential segregation, economic and marginalization sepsis, and denial of care. Here we argue that sepsis risk is an immunological embodiment of racism in colonial states, that the factors contributing to sepsis disparities are insidious and systemic. We show that regardless of causative pathogen, or host ancestry, racialized people get and die of sepsis most frequently in a pattern repeatedly reiterated worldwide. Lastly, we argue that while alleviation of sepsis disparities requires radical, multiscale intervention, biological anthropologists have a responsibility in this crisis. While some of us can harness our expertise to take on the ground action in sepsis prevention, all of us can leverage our positions as the first point of contact for in depth human biology instruction on most college campuses. As a leading cause of death worldwide, and a syndrome that exhibits the interplay between human physiology, race and environment, sepsis is at the nexus of major themes in biological anthropology and is a natural fit for the field's curriculum. In adopting a discussion of race and sepsis in our courses, we not only develop new research areas but increase public awareness of both sepsis and the factors contributing to uneven sepsis burden.
Article
IMPORTANCE:. Studying interhospital transfer of critically ill patients with coronavirus disease 2019 pneumonia in the spring 2020 surge may help inform future pandemic management. OBJECTIVES:. To compare outcomes for mechanically ventilated patients with coronavirus disease 2019 transferred to a tertiary referral center with increased surge capacity with patients admitted from the emergency department. DESIGN, SETTING, PARTICIPANTS:. Observational cohort study of single center urban academic medical center ICUs. All patients admitted and discharged with coronavirus disease 2019 pneumonia who received invasive ventilation between March 17, 2020, and October 14, 2020. MAIN OUTCOME AND MEASURES:. Demographic and clinical variables were obtained from the electronic medical record. Patients were classified as emergency department admits or interhospital transfers. Regression models tested the association between transfer status and survival, adjusting for demographics and presentation severity. RESULTS:. In total, 298 patients with coronavirus disease 2019 pneumonia were admitted to the ICU and received mechanical ventilation. Of these, 117 were transferred from another facility and 181 were admitted through the emergency department. Patients were primarily male (64%) and Black (38%) or Hispanic (45%). Transfer patients differed from emergency department admits in having English as a preferred language (71% vs 56%; p = 0.008) and younger age (median 57 vs 61 yr; p < 0.001). There were no differences in race/ethnicity or primary payor. Transfers were more likely to receive extracorporeal membrane oxygenation (12% vs 3%; p = 0.004). Overall, 50 (43%) transferred patients and 78 (43%) emergency department admits died prior to discharge. There was no significant difference in hospital mortality or days from intubation to discharge between the two groups. CONCLUSIONS AND RELEVANCE:. In a single-center retrospective cohort, no significant differences in hospital mortality or length of stay between interhospital transfers and emergency department admits were found. While more study is needed, this suggests that interhospital transfer of critically ill patients with coronavirus disease 2019 can be done safely and effectively.
Article
Background: Racial and ethnic disparities are a barrier in delivery of healthcare across the USA. Care for minority patients tends to be clustered into a small number of providers at minority hospitals, which has been associated with worse clinical outcomes in several conditions. However, the outcomes of treatment in patients with end-stage liver disease (ESLD) at predominately minority hospitals are unknown. We investigated the burden of the problem. Methods: We utilized the nationwide in-patient sample (NIS) to conduct a retrospective nationwide cohort analysis. All patients >18 years of age admitted with ESLD were included in the analysis. A multivariate logistic regression model was used to study the mortality rate among patients with ESLD treated at minority hospitals compared to nonminority hospitals. Results: A total of 53 281 467 hospitalizations from the 2008 to 2014 NIS were analyzed. There were 163 470 patients with ESLD that met inclusion criteria. In-hospital mortality rates for all races were 8.0 and 8.1% in black and Hispanic minority hospitals, respectively, compared to 7.3% in nonminority hospitals (P < 0.01). On multivariate analysis, treatment of ESLD in black and Hispanic minority hospitals was associated with 11% [odds ratio (OR), 1.11; 95% confidence interval (CI), 1.03-1.20; P < 0.01] and 22% (OR, 1.22; 95% CI, 1.09-1.37; P < 0.01) increased odds of death, respectively, compared to treatment in nonminority hospitals regardless of patient's race. Conclusion: Patients with ESLD treated at minority hospitals are faced with an increased mortality rate regardless of patient's race. This study highlights another quality gap that needs improvement to affect overall survival among patients with ESLD.
Article
Objectives: Treatment in a disproportionately minority-serving hospital has been associated with worse outcomes in a variety of illnesses. We examined the association of treatment in disproportionately minority hospitals on outcomes in patients with sepsis across the United States. Design: Retrospective cohort analysis. Disproportionately minority hospitals were defined as hospitals having twice the relative minority patient population than the surrounding geographical mean. Minority hospitals for Black and Hispanic patient populations were identified based on U.S. Census demographic information. A multivariate model employing a validated algorithm for mortality in sepsis using administrative data was used. Setting: The National Inpatient Sample from 2008 to 2014. Patients: Patients over 18 years of age with sepsis. Interventions: None. Measurements and main results: A total of 4,221,221 patients with sepsis were identified. Of these, 612,217 patients (14.5%) were treated at hospitals disproportionately serving the black community (Black hospitals), whereas 181,141 (4.3%) were treated at hospitals disproportionately serving the Hispanic community (Hispanic hospitals). After multivariate analysis, treatment in a Black hospital was associated with a 4% higher risk of mortality compared to treatment in a nonminority hospital (odds ratio, 1.04; 95% CI, 1.03-1.05; p < 0.01). Treatment in a Hispanic hospital was associated with a 9% higher risk of mortality (odds ratio, 1.09; 95% CI, 1.07-1.11; p < 0.01). Median hospital length of stay was almost 1 day longer at each of the disproportionately minority hospitals (nonminority hospitals: 5.9 d; interquartile range, 3.1-11.0 d vs Hispanic: 6.9 d; interquartile range, 3.6-12.9 d and Black: 6.7 d, interquartile range, 3.4-13.2 d; both p < 0.01). Conclusions: Patients with sepsis regardless of race who were treated in disproportionately high minority hospitals suffered significantly higher rates of in-hospital mortality.
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Background Despite numerous studies of geographic variation in healthcare cost and utilization at the local, regional, and state levels across the U.S., a comprehensive characterization of geographic variation in outcomes has not been published. Our objective was to quantify variation in US health outcomes in an all-payer population before and after risk-adjustment. Methods and Findings We used information from 16 independent data sources, including 22 million all-payer inpatient admissions from the Healthcare Cost and Utilization Project (which covers regions where 50% of the U.S. population lives) to analyze 24 inpatient mortality, inpatient safety, and prevention outcomes. We compared outcome variation at state, hospital referral region, hospital service area, county, and hospital levels. Risk-adjusted outcomes were calculated after adjusting for population factors, co-morbidities, and health system factors. Even after risk-adjustment, there exists large geographical variation in outcomes. The variation in healthcare outcomes exceeds the well publicized variation in US healthcare costs. On average, we observed a 2.1-fold difference in risk-adjusted mortality outcomes between top- and bottom-decile hospitals. For example, we observed a 2.3-fold difference for risk-adjusted acute myocardial infarction inpatient mortality. On average a 10.2-fold difference in risk-adjusted patient safety outcomes exists between top and bottom-decile hospitals, including an 18.3-fold difference for risk-adjusted Central Venous Catheter Bloodstream Infection rates. A 3.0-fold difference in prevention outcomes exists between top- and bottom-decile counties on average; including a 2.2-fold difference for risk-adjusted congestive heart failure admission rates. The population, co-morbidity, and health system factors accounted for a range of R² between 18–64% of variability in mortality outcomes, 3–39% of variability in patient safety outcomes, and 22–70% of variability in prevention outcomes. Conclusion The amount of variability in health outcomes in the U.S. is large even after accounting for differences in population, co-morbidities, and health system factors. These findings suggest that: 1) additional examination of regional and local variation in risk-adjusted outcomes should be a priority; 2) assumptions of uniform hospital quality that underpin rationale for policy choices (such as narrow insurance networks or antitrust enforcement) should be challenged; and 3) there exists substantial opportunity for outcomes improvement in the US healthcare system.
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The PLOS Medicine Editors endorse four measures to ensure transparency in the analysis and reporting of observational studies. Please see later in the article for the Editors' Summary.
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Background: Severe sepsis is a common and costly problem. Although consistently defined clinically by consensus conference since 1991, there have been several different implementations of the severe sepsis definition using ICD-9-CM codes for research. We conducted a single center, patient-level validation of 1 common implementation of the severe sepsis definition, the so-called "Angus" implementation. Methods: Administrative claims for all hospitalizations for patients initially admitted to general medical services from an academic medical center in 2009-2010 were reviewed. On the basis of ICD-9-CM codes, hospitalizations were sampled for review by 3 internal medicine-trained hospitalists. Chart reviews were conducted with a structured instrument, and the gold standard was the hospitalists' summary clinical judgment on whether the patient had severe sepsis. Results: Three thousand one hundred forty-six (13.5%) hospitalizations met ICD-9-CM criteria for severe sepsis by the Angus implementation (Angus-positive) and 20,142 (86.5%) were Angus-negative. Chart reviews were performed for 92 randomly selected Angus-positive and 19 randomly-selected Angus-negative hospitalizations. Reviewers had a κ of 0.70. The Angus implementation's positive predictive value was 70.7% [95% confidence interval (CI): 51.2%, 90.5%]. The negative predictive value was 91.5% (95% CI: 79.0%, 100%). The sensitivity was 50.4% (95% CI: 14.8%, 85.7%). Specificity was 96.3% (95% CI: 92.4%, 100%). Two alternative ICD-9-CM implementations had high positive predictive values but sensitivities of <20%. Conclusions: The Angus implementation of the international consensus conference definition of severe sepsis offers a reasonable but imperfect approach to identifying patients with severe sepsis when compared with a gold standard of structured review of the medical chart by trained hospitalists.
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Higher rates of sepsis have been reported in minorities. To explore racial differences in the incidence and associated case fatality of severe sepsis, accounting for clinical, social, health care service delivery, and geographic characteristics. Retrospective population-based cohort study using hospital discharge and U.S. Census data for all persons (n = 71,102,655) living in 68 hospital referral regions in six states. Measurements and Main Results: Age-, sex- and race-standardized severe sepsis incidence and inpatient case fatality rates, adjusted incidence rate ratios, and adjusted intensive care unit (ICU) admission and case fatality rate differences. Of 8,938,111 nonfederal hospitalizations, 282,292 had severe sepsis. Overall, blacks had the highest age- and sex-standardized population-based incidence (6.08/1,000 vs. 4.06/1,000 for Hispanics and 3.58/1,000 for whites) and ICU case fatality (32.1 vs. 30.4% for Hispanics and 29.3% for whites, P < 0.0001). Adjusting for differences in poverty in their region of residence, blacks still had a higher population-based incidence of severe sepsis (adjusted rate ratio, 1.44 [95% CI, 1.42-1.46]) than whites, but Hispanics had a lower incidence (adjusted rate ratio, 0.91 [0.90-0.92]). Among patients with severe sepsis admitted to the ICU, adjustments for clinical characteristics and the treating hospital fully explained blacks' higher ICU case fatality. Higher adjusted black incidence and the lower Hispanic incidence may reflect residual confounding, or it could signal biologic differences in susceptibility. Focused interventions to improve processes and outcomes of care at the hospitals that disproportionately treat blacks could narrow disparities in overall mortality from severe sepsis.
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This supplement is the second CDC Health Disparities and Inequalities Report (CHDIR). The 2011 CHDIR was the first CDC report to assess disparities across a wide range of diseases, behavioral risk factors, environmental exposures, social determinants, and health-care access (CDC. CDC Health Disparities and Inequalities Report-United States, 2011. MMWR 2011;60[Suppl; January 14, 2011]). The 2013 CHDIR provides new data for 19 of the topics published in 2011 and 10 new topics. When data were available and suitable analyses were possible for the topic area, disparities were examined for population characteristics that included race and ethnicity, sex, sexual orientation, age, disability, socioeconomic status, and geographic location. The purpose of this supplement is to raise awareness of differences among groups regarding selected health outcomes and health determinants and to prompt actions to reduce disparities. The findings in this supplement can be used by practitioners in public health, academia and clinical medicine; the media; the general public; policymakers; program managers; and researchers to address disparities and help all persons in the United States live longer, healthier, and more productive lives.
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Interhospital transfer of critically ill patients is a common part of their care. This article sought to review the data on the current patterns of use of interhospital transfer and identify systematic barriers to optimal integration of transfer as a mechanism for improving patient outcomes and value of care. Narrative review of medical and organizational literature. Interhospital transfer of patients is common, but not optimized to improve patient outcomes. Although there is a wide variability in quality among hospitals of nominally the same capability, patients are not consistently transferred to the highest quality nearby hospital. Instead, transfer destinations are selected by organizational routines or non-patient-centered organizational priorities. Accomplishing a transfer is often quite difficult for sending hospitals. But once a transfer destination is successfully found, the mechanics of interhospital transfer now appear quite safe. Important technological advances now make it possible to identify nearby hospitals best able to help critically ill patients, and to successfully transfer patients to those hospitals. However, organizational structures have not yet developed to insure that patients are optimally routed, resulting in potentially significant excess mortality.
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In an effort to examine national and Chicago, Illinois, progress in meeting the Healthy People 2010 goal of eliminating health disparities, we examined whether disparities between non-Hispanic Black and non-Hispanic White persons widened, narrowed, or stayed the same between 1990 and 2005. We examined 15 health status indicators. We determined whether a disparity widened, narrowed, or remained unchanged between 1990 and 2005 by examining the percentage difference in rates between non-Hispanic Black and non-Hispanic White populations at both time points and at each location. We calculated P values to determine whether changes in percentage difference over time were statistically significant. Disparities between non-Hispanic Black and non-Hispanic White populations widened for 6 of 15 health status indicators examined for the United States (5 significantly), whereas in Chicago the majority of disparities widened (11 of 15, 5 significantly). Overall, progress toward meeting the Healthy People 2010 goal of eliminating health disparities in the United States and in Chicago remains bleak. With more than 15 years of time and effort spent at the national and local level to reduce disparities, the impact remains negligible.
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Medical practice is increasingly informed by the evidence from randomized controlled trials. When such evidence is not available, clinical hypotheses based on pathophysiological reasoning and common sense guide clinical decision making. One commonly utilized general clinical hypothesis is the assumption that normalizing abnormal laboratory values and physiological parameters will lead to improved patient outcomes. We refer to the general use of this clinical hypothesis to guide medical therapeutics as the "normalization heuristic". In this paper, we operationally define this heuristic and discuss its limitations as a rule of thumb for clinical decision making. We review historical and contemporaneous examples of normalization practices as empirical evidence for the normalization heuristic and to highlight its frailty as a guide for clinical decision making.
The normalization heuristic: An untested hypothesis that may misguide medical decisions
  • Sk Aberegg
  • O Brien
Aberegg SK, O'Brien JM. The normalization heuristic: An untested hypothesis that may misguide medical decisions. Medical Hypotheses. 2009; 72(6):745–748. [PubMed: 19231086]