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VA Evidence Synthesis Program Evidence Briefs [Internet]. Washington (DC): Department of Veterans Affairs (US); 2011-.

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Evidence Brief: Update on Prevalence of and Interventions to Reduce Racial and Ethnic Disparities within the VA

, MS, , MPH, and , MS.

Created: .

PREFACE

The VA Evidence-based Synthesis Program (ESP) was established in 2007 to provide timely and accurate syntheses of targeted healthcare topics of particular importance to clinicians, managers, and policymakers as they work to improve the health and healthcare of Veterans. QUERI provides funding for four ESP Centers, and each Center has an active University affiliation. Center Directors are recognized leaders in the field of evidence synthesis with close ties to the AHRQ Evidence-based Practice Centers. The ESP is governed by a Steering Committee comprised of participants from VHA Policy, Program, and Operations Offices, VISN leadership, field-based investigators, and others as designated appropriate by QUERI/HSR&D.

The ESP Centers generate evidence syntheses on important clinical practice topics. These reports help:

  • Develop clinical policies informed by evidence;
  • Implement effective services to improve patient outcomes and to support VA clinical practice guidelines and performance measures; and
  • Set the direction for future research to address gaps in clinical knowledge.

The ESP disseminates these reports throughout VA and in the published literature; some evidence syntheses have informed the clinical guidelines of large professional organizations.

The ESP Coordinating Center (ESP CC), located in Portland, Oregon, was created in 2009 to expand the capacity of QUERI/HSR&D and is charged with oversight of national ESP program operations, program development and evaluation, and dissemination efforts. The ESP CC establishes standard operating procedures for the production of evidence synthesis reports; facilitates a national topic nomination, prioritization, and selection process; manages the research portfolio of each Center; facilitates editorial review processes; ensures methodological consistency and quality of products; produces “rapid response evidence briefs” at the request of VHA senior leadership; collaborates with HSR&D Center for Information Dissemination and Education Resources (CIDER) to develop a national dissemination strategy for all ESP products; and interfaces with stakeholders to effectively engage the program.

Comments on this evidence report are welcome and can be sent to Nicole Floyd, ESP CC Program Manager, at vog.av@dyolF.elociN.

EXECUTIVE SUMMARY

PURPOSE

As part of its mission to champion the advancement of health equity, the Veterans Health Administration (VHA) Office of Health Equity (OHE) is partnering with the Quality Enhancement Research Initiative (QUERI) to evaluate gaps in morbidity and mortality outcomes among vulnerable Veteran populations with major conditions and to examine trends in quality of care across these conditions. To help inform selection of research priorities for the Partnered Evaluation Center (PEC), the OHE requested that the Evidence-based Synthesis Program Coordinating Center (ESP CC) provide an evidence brief update on what research and implementation priorities have emerged since (1) the 2007 ESP publication Racial and Ethnic Disparities in the VA Healthcare System that reviewed in which clinical areas racial and ethnic disparities are prevalent within the VA, and (2) the 2011 ESP review Interventions to Improve Minority Health Care and Racial and Ethnic Disparities.

Because of the shortened timeframe of this evidence brief, we only evaluated studies of race- and ethnicity-based mortality and morbidity differences since these are the OHE's highest priority indicators of health care quality. We did not evaluate studies of the sources of differences (eg, patient, provider, patient-provider, and system factors). To fit the purpose of this report, we defined disparity as any instance of worse mortality or morbidity outcomes for the racial/ethnic minority groups. Figure A illustrates the framework that guided our research recommendations.

Figure A. Research Recommendations Framework.

Figure A

Research Recommendations Framework.

METHODS

To identify relevant citations, we searched MEDLINE® (via PubMed®) and the Cochrane Central Register of Controlled Trials from 10/09/2006 to 2/13/2015 using terms for racial groups and disparities. To rate the internal validity of included studies, we used Cochrane's Risk of Bias Tool for controlled trials and the Drug Effectiveness Review Project's Tool for observational studies. We graded the strength of the overall body of evidence using the AHRQ Methods Guide for Comparative Effectiveness Reviews, based on risk of bias of individual studies (study design and internal validity), consistency, directness, and precision. The complete description of our full methods can be found on the PROSPERO international prospective register of systematic reviews website (http://www.crd.york.ac.uk/PROSPERO/; registration number CRD42015015974).

PREVALENCE OF MORTALITY AND MORBIDITY DISPARITIES

The only mortality/morbidity disparity observed in the 2007 report was higher mortality among African American Veterans diagnosed with HIV between 1999 and 2001. For this update, we identified no new studies of mortality among African American Veterans with HIV.

Since 2007 there has been a steady stream of new research and for this update, we identified 34 new studies of mortality and morbidity outcomes, primarily in cancer (9 studies), heart disease (6 studies) and acute care (6 studies). We found no studies in spinal cord injury, polytrauma, or blast-related injuries. In Table A below, we categorize the findings on prevalence of mortality and morbidity from this update by (1) whether a racial/ethnic mortality or morbidity disparity was found (greater mortality or morbidity or similar or better mortality or morbidity) and (2) the strength of the evidence (high, moderate, or low). We also make recommendations for future research.

Table A. Summary of findings and future research recommendations.

Table A

Summary of findings and future research recommendations.

Moderate strength evidence of a mortality or morbidity disparity (Box 1, Figure A)

African Americans with colon cancer, CKD, or HIV, and Hispanics with hepatitis C are the 4 groups with moderate-strength evidence of a mortality or morbidity disparity. For colon cancer, African Americans had a lower rate of survival after 3 years of follow-up. For CKD, African Americans had a higher rate of end-stage renal disease after 3.7 years of follow-up. For HIV, African Americans with or without comorbid diabetes had a higher rate of end-stage renal disease after 3.7 years of follow-up. For Hepatitis C, there was moderate-strength evidence that Hispanics had higher rates of incident cirrhosis and hepatocellular carcinoma after 5.2 years of follow-up.

In applying these findings, the OHE should consider 2 limitations of the evidence. First, all of these findings are based on VA cohorts from the early 2000s. Over the past 10 years, changes in the delivery system or in diagnostic and treatment approaches may have changed these disparities. OHE can decide whether these findings are still current, or can fund studies to verify them in more recent cohorts. Second, some of these disparities may have more impact on health outcomes than others. The impact depends on the prevalence of each condition and the size of the disparity.

If 10-year-old data is acceptable, or if the mortality and morbidity disparity is verified in a more recent cohort, then new research should examine its sources. The 2007 ESP review reported on the literature assessing the sources of each disparity. A first step would be to update that report with respect to African Americans with colon cancer, CKD, or Hepatitis C. If the 2007 ESP report's findings on decreased medication adherence and increased later-stage diagnosis are well-accepted as causes for the ESRD disparity identified in our report, then an update of the 2007 ESP review may not need to cover African Americans with HIV. However, for Hepatitis C, the source identified in the 2007 report (under treatment with interferon-based regimens, which cause debilitating side effects) is not likely to be useful because the new Direct Acting Antiviral Agents such as sofosbuvir (Sovaldi) have fewer side effects. If an update finds low-strength or no evidence on sources for the colon cancer, CKD, or Hepatitis C disparities, then the PEC should undertake new original research on sources.

Low-strength evidence of a mortality or morbidity disparity (Box 2, Figure A)

African Americans with cancer, diabetes, PTSD, rectal cancer, or venous thromboembolism, and American Indian or Alaska Natives with PTSD or following major non-cardiac surgery all had low-strength evidence of higher mortality or morbidity compared to whites Veterans. Additionally, in Veterans with Alcohol Use Disorders, non-African American minority Veterans had higher injury-related death than African Americans. Each of these low-strength findings is supported by a single retrospective study that only had a medium level of adjustment for potential confounders and had unknown consistency in the magnitude or direction of effect. Because of these limitations, for these groups we need original studies of VA populations to verify the potential disparities. However, for PTSD, because the higher risk of preterm birth was consistently found across 2 minority groups, we recommend examining sources of the disparity as the next step for future research.

Low-strength evidence of similar or better mortality or morbidity for racial/ethnic minorities (Box 4, Figure A)

For many conditions, there is low-strength evidence that African Americans and Hispanics have similar or better mortality or morbidity outcomes than white Veterans. Conditions with low-strength evidence of similar or better mortality or morbidity among minorities compared to whites are much fewer for American Indian/Alaskan Native, Asian, and Hawaiian and Pacific Islanders. Although each of these low-strength findings is supported by a single retrospective study with methodological limitations, there is probably not a need for more research to verify the presence or absence of a disparity. One exception of cause for verification is when there is a large proven disparity outside of the VA.

High or moderate-strength evidence of similar or better mortality or morbidity for racial/ethnic minorities (Box 3, Figure A)

For African American Veterans with stage 4-5 CKD, there is high-strength evidence from two good-quality studies of no mortality disparity compared with whites. There is moderate-strength evidence from multiple fair-quality studies or single good-quality studies that African American Veterans with prostate cancer, lung cancer, hepatitis C, those hospitalized for pneumonia, COPD,CHF, GI bleed, hip fracture, stroke, or AMI, or those classified as in a low-mortality diagnosis related group have similar mortality and morbidity outcomes to white Veterans. Since these findings are likely to be stable, more research is likely not needed.

Evidence Gaps

As most of the mortality and morbidity disparity prevalence studies focused on African Americans or Hispanic minority groups and on cancer, heart disease, or acute care conditions, more work is needed to evaluate prevalence of disparities in other racial/ethnic minority groups and for OHE PEC's other priority conditions, including HIV, hepatitis C, mental illness, spinal cord injury, substance use disorders, polytrauma, and blast-related injuries. To more completely capture the totality of patients' care, future studies should supplement VHA data with Medicare data whenever possible. For morbidity outcomes, to maximize generalizability to the broadest disease populations, studies should examine multiple relevant outcomes, not just a single rare outcome in isolation. For example, future studies of rates of ESRD in HIV should be done in the context of other more common outcomes, such as severe bacterial infections or AIDS events.

Details about the magnitude of effect and the quantity, quality, and nature of the supporting evidence can be found in Tables 1 and 2 in the full report.

Table 1. Magnitude and strength of really evidence of mortality and morbidity disparities found in 2015 evidence brief update, 2007 and 2011 ESP report findings on their potential sources and promising interventions, and inventory results of new process and access measure studies.

Table 1

Magnitude and strength of really evidence of mortality and morbidity disparities found in 2015 evidence brief update, 2007 and 2011 ESP report findings on their potential sources and promising interventions, and inventory results of new process and access (more...)

Table 2. Magnitude and strength of evidence of similar or better mortality or morbidity for racial and ethnic minority groups, organized by racial and ethnic group and clinical area.

Table 2

Magnitude and strength of evidence of similar or better mortality or morbidity for racial and ethnic minority groups, organized by racial and ethnic group and clinical area.

EFFECTS OF VA-BASED INTERVENTIONS TO REDUCE RACIAL OR ETHNIC DISPARITIES

The 2011 ESP review found that care coordination and in-home messaging improved 12-month glycemic control in African Americans, but not in white or Hispanic Veterans. In this update, we found low-strength evidence that (1) among African American Veterans with diabetes, peer mentorship can improve glycemic control and (2) among African American Veterans with knee osteoarthritis, there was no difference in 12-month attendance rates at the orthopedic surgeon consultation appointment between an attention control group who only received an educational booklet versus supplementation with a decision aid, motivational interviewing, or both. We did not identify any studies comparing interventions across Veterans of different minority groups, nor did we identify any intervention studies that measured health outcomes.

CONCLUSION

Our evidence brief update identified several research priorities for OHE's PEC. As the moderate-strength evidence of mortality or morbidity disparities for African American Veterans with colon cancer, HIV, and CKD and for Hispanics with hepatitis C were based on VA cohorts from the early 2000s, and changes are possible in the past 10 years, we recommend considering the need to verify each disparity in a more recent VA cohort. More research is needed to establish the presence or absence of a mortality or morbidity disparity for African Americans with diabetes, stroke, or VTE, American Indians or Alaskan Natives following major non-cardiac surgery, and African American and American Indian or Alaskan Native pregnant women with PTSD. The few interventions that have improved racial/ethnic disparities within the VA have focused only on African Americans and have covered a narrow scope of clinical areas. More research is needed to examine disparities in Hispanic, Asian, Native Hawaiian or other Pacific Islander, and American Indian and Alaska Native groups, and in other priority conditions including HIV, hepatitis C, mental illness, spinal cord injury, substance use disorders, polytrauma, and blast-related injuries. Ideally, future research should be done in the form of prospective studies that address multiple minority groups and supplement VHA data with Medicare data.

ABBREVIATIONS

AA

African American

ACTUR

Automated Central Tumor Registry

AF

Anginal frequency

aHR

Adjusted hazard ratio

AHRQ

Agency for Healthcare Research & Quality

AI/AN

American Indian/Alaskan Native

AMI

Acute myocardial infarction

aOR

Adjusted odds ratio

API

Asian Pacific Islander

AS

Anginal stability

AUD

Alcohol use disorders

B

Black

BE

Barrett's Esophagus

BEST

Beta-Blocker Evaluation of Survival Trial

BMI

Body mass index

BP

Blood pressure

CAD

Coronary artery disease

CEA

Carotid endarterectomy

CHD

Congenital heart defect

CHF

Congestive heart failure

CI

Confidence interval

CKD

Chronic kidney disease

COPD

Chronic obstructive pulmonary disease

CRC

Colorectal cancer

DB

Database

DCS

Direct Care System

DEERS

Defense Enrollment Eligibility Reporting System

DM

Diabetes mellitus

DOD

Department of Defense

DP

Disease perception

DRG

Diagnosis related group

DSS

Decision Support System

eGFR

Estimated glomerular filtration rate

ESP

Evidence-based Synthesis Program

ESP CC

Evidence-based Synthesis Program Coordinating Center

ESRD

End stage renal disease

GI

Gastrointestinal

HbA1c

Hemoglobin A1c

HCC

Hepatocellular carcinoma

HCV

Hepatitis C virus

HIV

Human immunodeficiency virus

HTN

Hypertension

ICU

Intensive care unit

ILEA

Initial lower extremity amputation

LD

Lipid disorders

LVEF

Left ventricular ejection fraction

MCS

Mental component scale

MDCSS

Metropolitan Detroit Cancer Surveillance System

MedPAR

Medicare Provider Analysis and Review

MHS

Military Health System

NHW

Non-Hispanic white

NIH

National Institutes of Health

NLM

National Library of Medicine

NPCD

National Patient Care Database

NSAID

Nonsteroidal anti-inflammatory drug

NSCLC

Non-small cell lung cancer

NSD

No significant difference

NSQIP

National Surgical Quality Improvement Program

NYHA

New York Heart Association

OCF

Outpatient Care File

OPC

Outpatient Care

OPSCC

Oropharyngeal squamous cell carcinoma

PCS

Physical component scale

PL

Physical limitations

PSA

Prostate-specific antigen

PTF

Patient Treatment File

PTSD

Post-traumatic stress disorder

PVD

Peripheral vascular disease

RCT

Randomized controlled trial

RVEF

Right ventricular ejection fraction

SAQ

Seattle Angina Questionnaire

SCLC

Small cell lung cancer

SES

Socioeconomic status

SOE

Strength of evidence

SSI

Surgical site infection

TBI

Traumatic brain injury

TKR

Total knee replacement

TS

Treatment satisfaction

UC

Ulcerative colitis

VA

Veterans Affairs

VACCR

Veterans Affairs Central Cancer Registry

VAMC

Veterans Affairs Medical Center

VTE

Venous thromboembolism

W

White

WBC

White blood cell

WHR

Waist-to-hip ratio

INTRODUCTION

The AHRQ 2013 National Healthcare Disparities Report found little progress since 2000 in most quality and access disparities for racial and ethnic minorities.1 Previous research has shown that racial disparities in health exist in the VA across a wide range of clinical areas2,3 for which almost no promising interventions have been developed.4 The mission of the VHA Office of Health Equity (OHE) (10A6) is to champion the advancement of health equity in the VA health system. In fiscal year 2015, the OHE is partnering with a new evaluation center under the Quality Enhancement Research Initiative (QUERI) to evaluate in which major conditions gaps in morbidity and mortality exist among vulnerable Veteran populations and to examine trends in quality of care across these conditions. To help inform selection of the Partnered Evaluation Center's (PEC) research priorities to better understand and reduce race and ethnicity-related mortality and morbidity disparities, the OHE requested that the Evidence-based Synthesis Program's Coordinating Center (ESP CC) provide an evidence brief on what evidence has emerged since (1) the 2007 ESP review Racial and Ethnic Disparities in the VA Healthcare System2 on which clinical areas racial and ethnic disparities are prevalent within the VA, and (2) the 2011 ESP review Interventions to Improve Minority Health Care and Racial and Ethnic Disparities4 on the effects of interventions implemented within the VA to reduce racial/ethnic disparities or to improve health and health care in minority populations.

An evidence brief differs from a full systematic review in that the scope is narrowly defined and the traditional review methods are streamlined in order to synthesize evidence within a shortened timeframe. An evidence brief does not outline the full context in which the information is to be used and does not present a comprehensive assessment of knowledge on the topic. Brief or rapid review methodology is still developing and there is not yet consensus on what represents best practice.

OVERVIEW OF RESEARCH PLAN

A first step in identifying research priorities for the PEC is to identify differences in health outcomes among racial and ethnic groups within the VA's equal access health care system. The second step is to explore the sources of these differences. This evidence brief focuses on the first step of identifying racial or ethnic differences in mortality and morbidity outcomes. Our exploration of the sources of health outcome disparities was limited to: (1) summarizing evidence from the 2007 ESP review on possible causes related to the identified health outcome differences, (2) noting when studies on health outcome disparities provided insights about causes by conducting additional analyses to assess whether differences in socioeconomic status, facility, access, preferences, needs, etcetera, accounted for the disparities, and (3) providing an inventory and data abstraction of VA studies on process and access measures.

We compared health outcomes of racial/ethnic minority groups to those of white Veterans. The range of potential findings for the comparison of racial/ethnic minority groups to those of white Veterans includes worse outcomes, similar outcomes, better outcomes, or inconclusive evidence for the minority group. To fit the purpose of this report, we defined disparity as any instance of worse mortality or morbidity outcomes for the racial/ethnic minority groups.

Figure 1 illustrates the framework that guided our research recommendations.

Figure 1. Research Recommendations Framework.

Figure 1

Research Recommendations Framework.

SCOPE

The objective of this evidence brief is to update the findings of previous ESP reviews on the prevalence of and interventions for reducing racial and ethnic disparities within the VA. The ESP CC investigators and representatives of the OHE worked together to identify the population, comparator, outcome, timing, setting, and study design characteristics of interest. The OHE approved the following key questions and eligibility criteria to guide this review:

Key Question 1: In which clinical areas are racial and ethnic disparities prevalent within the VA?

  • Key Question 1 inclusion criteria:
    • Population: Any VA population belonging to a racial or ethnic minority group
    • Intervention: N/A
    • Comparison: Minority versus non-minority Veterans
    • Outcomes: Mortality, morbidity, process measures (ie, offer and uptake of care, guideline adherence, etc), access (eg, wait times)
    • Timing: No restrictions
    • Setting: VA
    • Study design: Using a best evidence approach, we will prioritize evidence from systematic reviews and multisite studies that adequately controlled for potential patient-, provider-, and system-level confounding factors. Inferior study designs (eg, single-site, inadequate control for confounding) will only be accepted for particular racial or ethnic minority groups that lack adequate data from preferred study designs.

Key Question 2: What are the effects of interventions implemented within the VA to reduce racial and ethnic disparities?

  • Key Question 2 inclusion criteria:
    • Population: Any VA population belonging to a racial or ethnic minority group
    • Intervention: Any intervention primarily designed to reduce disparities or improve quality of care or outcomes for minority populations
    • Comparison: Head-to-head comparisons of different interventions, comparisons of intervention to usual care, comparison of same intervention in different racial or ethnic groups or in VA versus non-VA population
    • Outcomes: No restrictions
    • Timing: No restrictions
    • Setting: VA
    • Study design: Systematic reviews, controlled studies, interrupted time series, repeated measures studies

METHODS

To identify relevant citations, we searched MEDLINE® (via PubMed®) and the Cochrane Central Register of Controlled Trials from 10/09/2006 to 2/13/2015 using terms for racial groups and disparities. To rate the internal validity of included studies, we used Cochrane's Risk of Bias Tool for controlled trials and the Drug Effectiveness Review Project's Tool for observational studies. We categorized level of adjustment for potential confounders as high, medium or low based on the degree to which studies accounted for (1) demographic, (2) illness severity, and (3) comorbidity variables, and also noted whether SES and treatment facility were included in the models and whether studies presented a conceptual model that explained covariate selection. For SES, we included studies whether or not they adjusted for SES. When studies adjusted for SES, we noted its impact. We graded the strength of the overall body of evidence using the AHRQ Methods Guide for Comparative Effectiveness Reviews, based on risk of bias of individual studies (study design and internal validity), consistency, directness, and precision. The complete description of our full methods can be found on the PROSPERO international prospective register of systematic reviews website (http://www.crd.york.ac.uk/PROSPERO/; registration number CRD42015015974). Six invited peer reviewers provided comments on the draft version of this evidence brief. See the supplemental materials for the peer review disposition table.

SYNTHESIS

LITERATURE FLOW

We screened 1,790 unique records and included 40 articles in this evidence brief (Figure 2): 4 articles on the 2 previous ESP systematic reviews that we were updating,2-5 34 articles that include morbidity or mortality outcomes and describe multi-site studies (Key Question 1),6-39 and 2 articles that describe interventions to reduce disparities (Key Question 2).40,41 Additionally, we included data abstraction of 64 articles that include process measure or access outcomes (see supplemental spreadsheet) and data abstraction of 12 articles that include morbidity or mortality outcomes and describe single-site studies (see supplemental materials).42-53 Among the 76 excluded studies, the majority were excluded for being in non-Veteran populations (N=18), involving an ineligible study design (eg, cross-sectional) (N=16), or having ineligible outcomes (N=26). Types of ineligible outcomes included intermediate clinical outcomes such as glucose or blood pressure control, which could contribute to mortality or morbidity disparities, but which were outside of the scope of this evidence brief.

Figure 2. Literature Flow Chart.

Figure 2

Literature Flow Chart. * Total ≠ 188; many studies included more than one outcome

Of the 34 articles that include morbidity or mortality outcomes and describe multi-site studies, one was prospective.27 Twenty-nine studies were rated fair or good quality for at least one outcome.7,8,10-20,22-27,29-31,33-39 The majority of these studies reflected VA care use only, with only 28% supplemented with Medicare data to more completely capture the totality of care.10,11,22,26,28,31,33,37 Three poor-quality studies failed to adjust for any potential confounders6,9,28 and one study was rated poor quality for the outcome of death for not adjusting for potential confounders, but rated fair quality for the outcome of complications.8 We excluded outcomes rated poor quality from our strength of evidence ratings and review of findings, but data abstraction and quality assessment of these outcomes can be found in the supplemental materials.

Articles addressed morbidity and mortality disparities among only African American Veterans in the clinical areas of cancer (8 studies),15,18-20,30,33,39,45 cardiovascular disease (5 studies),8,9,23,24,27 diabetes (2 studies),11,25 inpatient and acute care (6 studies),16,21,31,32,36,38 and kidney disease (2 studies).10,26 Other studies addressed morbidity and mortality disparities among only American Indian and Alaska Native Veterans in the clinical area of inpatient and acute care (2 studies),6,7 and among multiple minority Veteran groups in the clinical areas of cancer (1 study),12 diabetes (1 study),37 HIV/Hepatitis C (1 study),14 inpatient and acute care (1 study),29 and mental health and substance abuse (4 studies).13,17,22,35

We screened 247 citations from the HSRProj Database and ClinicalTrials.gov to identify potential ongoing or unpublished studies. Of these, 5 were identified as ongoing studies that met our inclusion criteria (see supplemental materials for a complete list). These studies focused on African Americans with Hepatitis C, osteoarthritis, hypertension, and diabetes. Two studies assessed the presence of a disparity (Key Question 1) and 3 studies assessed an intervention to address a disparity (Key Question 2). We did not identify any unpublished studies that met our inclusion criteria.

KEY QUESTION 1. In which clinical areas are racial and ethnic disparities prevalent within the VA?

The 2007 ESP systematic review found a higher risk of mortality for black and Hispanic Veterans compared to white Veterans based on a national sample of HIV-positive Veterans diagnosed between 1999 and 2001. Age-adjusted mortality was higher among black (HR=1.41; 95% CI: 1.19-1.66) and Hispanic Veterans (HR=1.41; 95% CI: 1.06-1.86) than among white Veterans.54 The main limitation of this study is that it did not control for between-groups variability in disease characteristics, comorbidity, treatment, or between-facility effects.

The 2007 ESP review found no differences between African Americans and whites with colorectal cancer in surgery (OR=0.92; 95% CI: 0.74-1.15), chemotherapy (OR=0.99; 95% CI: 0.78-1.24), and radiation (OR=1.10; 95% CI: 0.85-1.43).

2015 EVIDENCE BRIEF FINDINGS

Evidence suggesting the presence of disparities

Table 1 summarizes (1) the magnitude and strength of evidence for each disparity, (2) information from the 2007 and 2011 ESP reports on their potential sources and promising interventions, and (3) results from our 2015 evidence brief update inventory of new process access measure studies that also may provide additional insights about potential sources of disparities.

Moderate-strength evidence of higher mortality or morbidity

African Americans with colon cancer, CKD, HIV, or stroke10,11,33 and Hispanics with hepatitis C14 are the four groups with the strongest evidence of a mortality or morbidity disparity. Each is supported by a single large, good-quality study.

African American Veterans with colon cancer: In a good-quality study of 4,642 Veterans from the VA Central Cancer Registry (VACCR) diagnosed with colon cancer between 2001 and 2004, African American Veterans with colon cancer had a lower rate of 3-year survival than white Veterans (absolute difference -7.9%, -11.5 to -4.3; OR=0.78, 0.64 to 0.96).33 The authors speculated that racial differences in screening, early-stage diagnosis, clinician uncertainty, treatment preferences, and receipt of curative surgery might be factors. Other potential process- and access-related causes include guideline-concordant screening and care,55-57 receipt of wanted care,58 tumor characteristics,51 disease extent,51 and treatment.51,59

African American Veterans with chronic kidney disease: In a good-quality study by Choi and colleagues (2009) of 420,334Veterans that had a serum creatinine level recorded at a VA facility between October 2000 and September 2001, African American Veterans at all stages of CKD were at higher risk of end-stage renal disease (ESRD) at 3.7 years10 The authors said the causes were poorly understood. Potential causes could include: inadequately controlled diabetes, hypertension, proteinuria, and lower achievement of quality of care goals. We did not identify any additional potential causes from the 2007 ESP report or from our scan of access and process measure studies.

African American Veterans with HIV: In one good-quality study by Choi and colleagues of 12,955 Veterans that had a serum creatinine level recorded at a VA facility between October 2000 and September 2001 and who were registered as having HIV in the Immunology Case Registry (ICR), African American Veterans had a much higher risk of developing ESRD than white Veterans (age- and sex-adjusted rate for ESRD per 1000 person-years: African American=7.3 (95% CI: 6.0-8.6) vs white=0.9 (95% CI: 0.4-1.4); adjusted hazard ratio (aHR)=5.97 (95% CI: 3.12-11)).11 This study did not evaluate potential sources of the ESRD disparity, but the authors proposed differences in socioeconomic status (SES), patient preferences, comorbid illnesses, environmental factors, genetics, and access to antiretroviral therapy as potential contributors. Lower HIV medication adherence is another potential cause of the ESRD disparity.2,60 We did not identify any additional potential causes from the 2007 ESP report or from our scan of access and process measure studies.

Hispanic Veterans with hepatitis C: In a good-quality study by El-Serag and colleagues (2014) of 8,925 Hispanic and 84,065 non-Hispanic white Veterans with confirmed viremia between 2000 and 2009 in the VA HCV Clinical Case Registry and at least 1 year of follow-up in the VA, Hispanics had higher incidence rates of cirrhosis (aHR 1.28; 95% CI: 1.21-1.37) and hepatocellular carcinoma (aHR 1.61; 95% CI: 1.44-1.80) after an average follow-up of 5.2 years.14 In terms of potential causes, El-Serag and colleagues ruled out diabetes, body mass index, and treatment, and suggested that future studies should explore the role of racial variation in overall care, rates of fatty liver, prevalence of PNPLA3 polymorphism, insulin resistance in nondiabetics, and adipose tissue amount and distribution.

Low-strength evidence of higher mortality or morbidity

Conditions with low-strength evidence of a mortality or morbidity disparity in outcomes include: (1) African American Veterans with diabetes, post-traumatic stress disorder (PTSD), stroke, and venous thromboembolism (VTE); (2) American Indian or Alaskan Native Veterans with PTSD or following major non-cardiac surgery; and (3) Hispanic Veterans with hepatitis C virus (HCV). Increased risk of pre-term birth in Veterans with PTSD is the only disparity that is present in multiple racial/ethnic minority groups, including African Americans and American Indian or Alaskan Natives. The main limitations of the majority of all other reported disparities included (1) the lack of statistical adjustment for between-facility differences and (2) that consistency was unknown because most were supported by only a single study. A few studies about the sources of disparities in diabetes and stroke have been published since our 2007 review. For diabetes, we identified studies on racial/ethnic differences in medication adherence, medication supply, and time between diagnosis and drug initiation.61,62 For stroke, we identified studies of racial/ethnic differences in provider recommendation for patient to receive carotid endarterectomy (CEA) and receipt of carotid artery imaging.63,64

Evidence suggesting similar or better mortality or morbidity for racial/ethnic minority groups

In 21 studies, mortality and morbidity outcomes were similar or better among racial and ethnic minority groups compared to white Veterans. Table 2 below categorizes these studies by racial and ethnic group and clinical area and provides information about the magnitude and strength of evidence of each finding.

There is high-strength evidence that African American Veterans with stage 4-5 CKD have similar mortality outcomes compared to white Veterans. This finding is supported by 2 good-quality studies. 10,26

There is moderate-strength evidence that African American Veterans with prostate cancer, lung cancer, hepatitis C, those hospitalized for pneumonia, COPD, CHF, GI bleed, hip fracture, stroke, or AMI, or those classified as in a low-mortality diagnosis related group have similar mortality and morbidity outcomes to white Veterans. The finding of similar mortality outcomes among African American and white Veterans with prostate cancer is supported by 4 fair-quality studies.12,15,19,30 The findings of similar mortality or morbidity outcomes among African American and white Veterans with lung cancer,33 stroke,24 hepatitis C,14 hospitalized for pneumonia, CHF, GI bleed, hip fracture, stroke, or AMI,38 admitted to a medical ward or ICU for pneumonia,16 admitted for COPD exacerbation,34 or classified as in a low-mortality diagnosis related group36 are each supported by a single adequately powered, good-quality study that sufficiently controlled for all important confounding variables.

All other findings of similar or better mortality or morbidity for racial/ethnic minority groups are low strength. The primary limitations of the low-strength evidence studies were medium to low levels of adjustment for potential confounders, unknown consistency, and/or imprecision.

Insufficient evidence about the presence of a racial/ethnic difference in mortality/morbidity

For African American Veterans with CKD Stage 3A or 3B, there is insufficient evidence to draw conclusions about the risk of mortality compared to whites because 2 studies had conflicting findings.10,26 Mortality at 4.8 years was higher in a good-quality study by Choi and colleagues of 387,756 individuals treated between 2000-2001 (aHR: 3A=1.32; 95% CI:1.27-1.36 and 3B=1.21, p<.05),10 but was lower at 4.7 years in a good-quality study by Kovesdy and colleagues of 532,542 individuals treated between 2004-2006 (aHR: 3A=0.88; 95% CI: 0.81-0.97 and 3B=0.81; 95% CI: 0.71-0.92).26 In the study by Choi and colleagues, African American Veterans were also at higher risk of end-stage renal disease (ESRD) (3A=2.30; 95% CI: 2.02-2.61 and 3B=3.08; 95% CI: 2.74-3.46), which may have contributed to the higher risk of mortality, but Kovesdy and colleagues did not report ESRD. While there are many methodological differences between the studies, we could not find a satisfactory explanation for the discrepant results.

KEY QUESTION 2. What are the effects of interventions implemented within the VA to reduce race/ethnic disparities?

2011 ESP REVIEW

The 2011 ESP review4 found that care coordination and in-home messaging improved 12-month glycemic control in African Americans, but not in white or Hispanic Veterans.65

2015 EVIDENCE BRIEF UPDATE

Summary

We identified only 2 new studies of interventions involving minority Veterans40,41 published since the 2011 ESP review. For African American Veterans with diabetes, there is low-strength evidence that peer mentorship improves 6-month glucose control compared to usual care (mean HbA1c change, -1.08 vs -0.01; relative change: -1.07; 95% CI: -1.84 to -0.31), but that financial incentives do not (-0.46; relative change: -0.45; 95% CI: -1.23 to 0.32). For African American Veterans with knee osteoarthritis, there is low-strength evidence of no difference in 12-month attendance rates at the orthopedic surgeon between an attention control group who only received an educational booklet versus supplementation with a decision aid, motivational interviewing, or both.

Diabetes

One good-quality RCT from the Philadelphia VAMC compared the 6-month change in HbA1c levels in African American Veterans with diabetes randomized to one of 3 intervention groups: peer mentoring, financial incentives, or control group.41 After enrollment, all 3 groups were notified of their baseline HbA1c level and informed of the HbA1c targets recommended by the American Diabetes Association and VA. Participants in the peer mentoring intervention group were matched to trained mentors by age and gender and participated in monthly calls regarding motivation and HbA1c level goals. Participants in the financial incentives group were offered up to $200 for a 2-point drop in HbA1c level or to 6.5%. Participants in the control group were not offered any additional resources. After 6 months, the mean HbA1c level was significantly lower by 1.07 point (95% CI: -1.84 to -0.31) in the peer mentoring group compared to the control group (p=.006), but not significantly lower in the financial incentives group (-0.45; 95% CI: -1.23 to 0.32).

Arthritis and pain management

There is low-strength evidence from a good-quality RCT of 639 African American Veterans from the Pittsburgh, Cleveland, and Philadelphia VAMCs that, compared to an attention control group who only received an educational booklet, there was no difference in 12-month orthopedic surgeon appointment attendance for a decision aid intervention group who watched a video on the risks and benefits of different treatment options (aOR 1.27; 95% CI: 0.54-3.00), a motivational interviewing intervention group that underwent a counseling session with a trained interventionist (aOR 1.79; 95% CI: 0.78-4.07), or for a decision aid and motivational interviewing group that watched the video before their counseling session (aOR 2.05; 95% CI: 0.90-4.65).40

SUMMARY AND DISCUSSION

Although it would be useful to link interventions with any declines in disparities that were observed since the 2007 report, there was no opportunity to make this link. The only mortality/morbidity disparity observed in the 2007 report was higher mortality among African American Veterans with HIV, and we identified no subsequent studies of mortality among African American Veterans with HIV or of interventions to reduce disparities in African Americans with HIV.

Since 2007, there has been a steady stream of new research emerging, and for this update we identified 34 new studies of mortality and morbidity outcomes, primarily in cancer (9 studies), heart disease (6 studies), and acute care (6 studies). We found no studies in spinal cord injury, polytrauma, or blast-related injuries. In Table 3 below, we categorize the findings on prevalence of mortality and morbidity from this update by (1) whether a racial/ethnic mortality or morbidity disparity was found (greater mortality or morbidity or similar or better mortality or morbidity) and (2) the strength of the evidence (high, moderate, or low). We also make recommendations for future research.

Table 3. Summary of findings and future research recommendations.

Table 3

Summary of findings and future research recommendations.

Moderate strength evidence of a mortality or morbidity disparity (Box 1, Figure 1)

African Americans with colon cancer, CKD, or HIV, and Hispanics with hepatitis C are the 4 groups with moderate-strength evidence of a mortality or morbidity disparity. For colon cancer, African Americans had a lower rate of survival after 3 years of follow-up. For CKD, African Americans had a higher rate of end-stage renal disease after 3.7 years of follow-up. For HIV, African Americans with or without comorbid diabetes had a higher rate of end-stage renal disease after 3.7 years of follow-up. For Hepatitis C, there was moderate-strength evidence that Hispanics had higher rates of incident cirrhosis and hepatocellular carcinoma after 5.2 years of follow-up.

In applying these findings, the OHE should consider 2 limitations of the evidence. First, all of these findings are based on VA cohorts from the early 2000s. Over the past 10 years, changes in the delivery system or in diagnostic and treatment approaches may have changed these disparities. OHE can decide whether these findings are still current, or can fund studies to verify them in more recent cohorts. Second, some of these disparities may have more impact on health outcomes than others. The impact depends on the prevalence of each condition and the size of the disparity.

If 10-year-old data is acceptable, or if the mortality and morbidity disparity is verified in a more recent cohort, then new research should examine its sources. The 2007 ESP review reported on the literature assessing the sources of each disparity. A first step would be to update that report with respect to African Americans with colon cancer, CKD, or Hepatitis C. If the 2007 ESP report's findings on decreased medication adherence and increased later stage diagnosis are well-accepted as causes for the ESRD disparity identified in our report, then an update of the 2007 ESP review may not need to cover African Americans with HIV. However, for Hepatitis C, the source identified in the 2007 report (under treatment with interferon-based regimens, which cause debilitating side effects) is not likely to be useful because the new Direct Acting Antiviral Agents such as sofosbuvir (Sovaldi) have fewer side effects. If an update finds low-strength or no evidence on sources for the colon cancer, CKD, or Hepatitis C disparities, then the PEC should undertake new original research on sources.

Low-strength evidence of a mortality or morbidity disparity (Box 2, Figure 1)

African Americans with cancer, diabetes, PTSD, rectal cancer, or venous thromboembolism, American Indian or Alaska Natives with PTSD or following major non-cardiac surgery all had low-strength evidence of higher mortality or morbidity compared to whites. Additionally, in Veterans with Alcohol Use Disorders, non-African American minority Veterans had higher injury-related death than African Americans. Each of these low-strength findings is supported by a single retrospective study that only had a medium level of adjustment for potential confounders and had unknown consistency in the magnitude or direction of effect. Because of these limitations, for these groups we need original studies of VA populations to verify the potential disparities. However, for PTSD, because the higher risk of preterm birth was consistently found across 2 minority groups, we recommend considering examining sources as the next step for future research.

Low-strength evidence of similar or better mortality or morbidity for racial/ethnic minorities (Box 4, Figure 1)

For many conditions, there is low-strength evidence that African Americans and Hispanics have similar or better mortality or morbidity outcomes than whites. Conditions with low-strength evidence of similar or better mortality or morbidity compared to whites are much fewer for American Indian/Alaskan Native, Asian, and Hawaiian and Pacific Islanders. Although each of these low-strength findings is supported by a single retrospective study with methodological limitations, there is probably not a need for more research to verify the presence or absence of a disparity. One exception of cause for verification is when there is a large proven disparity outside of the VA.

High or moderate-strength evidence of similar or better mortality or morbidity for racial/ethnic minorities (Box 3, Figure 1)

For African American Veterans with stage 4-5 CKD, there is high-strength evidence from 2 good-quality studies of no mortality disparity compared with whites. There is moderate-strength evidence from multiple fair-quality studies or single good-quality studies that African American Veterans with prostate cancer, lung cancer, hepatitis C, those hospitalized for pneumonia, COPD, CHF, GI bleed, hip fracture, stroke, or AMI, or those classified as in a low-mortality diagnosis related group have similar mortality and morbidity outcomes to white Veterans. Since these findings are likely to be stable, more research is likely not needed.

Evidence Gaps

As most of the mortality and morbidity disparity prevalence studies focused on African Americans or Hispanic minority groups and on cancer, heart disease, or acute care conditions, more work is needed to evaluate prevalence of disparities in other racial/ethnic minority groups and for OHE PEC's other priority conditions, including HIV, hepatitis C, mental illness, spinal cord injury, substance use disorders, polytrauma, and blast-related injuries. To more completely capture the totality of patients' care, future studies should supplement VHA data with Medicare data whenever possible. For morbidity outcomes, to maximize generalizability to the broadest disease populations, studies should examine multiple relevant outcomes, not just a single rare outcome in isolation. For example, future studies of rates of ESRD in HIV should be done in the context of other more common outcomes, such as severe bacterial infections or AIDS events.

Regarding interventions to reduce racial and ethnic disparities among Veterans, the 2011 ESP review found that care coordination and in-home messaging improved 12-month glycemic control in African Americans, but not in white or Hispanic Veterans. In this update, we found low-strength evidence that (1) among African American Veterans with diabetes, peer mentorship can improve glycemic control and (2) among African American Veterans with knee osteoarthritis, neither a decision aid, motivational interviewing, nor both a decision aid and motivational interviewing result in a greater 12-month surgical consult rate compared with a control group. We did not identify any studies comparing interventions across Veterans of different minority groups, nor did we identify any intervention studies that measured health outcomes. Although much progress has been made since the 2007 ESP review in conducting studies on the presence of mortality and morbidity disparities, as noted in the 2011 ESP review, still much more work is needed to implement disparities intervention research.

LIMITATIONS

An evidence brief differs from a full systematic review in that the scope is narrowly defined and the traditional review methods are streamlined in order to synthesize evidence within a shortened timeframe. Brief or rapid review methodology is still developing and there is not yet consensus on what represents best practice. The main methodological limitation of this evidence brief within the defined scope is that because of the shortened timeframe, we only evaluated studies of racial and ethnicity-based mortality and morbidity differences since these are the OHE's highest-priority indicators of health care quality. We did not evaluate studies of the sources of differences in health care quality (eg, patient, provider, patient-provider, and system factors). Additionally, because we only synthesized evidence from multicenter studies, we may have missed additional studies of important disparities or interventions. Still, we do not think these exclusions would affect the conclusions of this brief because any findings single-center studies would have very limited generalizability to the broader US Veteran population.

CONCLUSION

Our evidence brief update identified several research priorities for OHE's PEC. As the moderate-strength evidence of mortality or morbidity disparities for African American Veterans with colon cancer, HIV, and CKD, and for Hispanics with hepatitis C was based on VA cohorts from the early 2000s, and changes are possible in the past 10 years, we recommend considering the need to verify each disparity in a more recent VA cohort. More research is needed to establish the presence or absence of a mortality or morbidity disparity for African Americans with diabetes, stroke, or VTE, American Indians or Alaskan Natives following major non-cardiac surgery, and African American and American Indian or Alaskan Native pregnant women with PTSD. The few interventions that have improved racial/ethnic disparities within the VA have focused only on African Americans and have covered a narrow scope of clinical areas. More research is needed to examine disparities in Hispanic, Asian, Native Hawaiian or other Pacific Islander, and American Indian and Alaska Native groups, and in other priority conditions including HIV, hepatitis C, mental illness, spinal cord injury, substance use disorders, polytrauma, and blast-related injuries. Ideally, future research should be done in the form of prospective studies that address multiple minority groups and supplement VHA data with Medicare data.

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Date limits(“2006/10/09”[Date - Entrez] : “3000”[Date -Entrez])Entrez refers to the date the citation was added to the database and is preferable to publication date
N=7309156
Combined with above search
N=28063
VA limits((“Veterans Health”[Mesh])) OR (((VA OR Veteran OR VAMC OR Veterans)) OR (“Veterans”[Mesh] OR “United States Department of Veterans Affairs”[Mesh] OR “Hospitals, Veterans”[Mesh]))N=179429
Combined with above search
N=1481

Cochrane Central Register of Controlled Trials via OVID searched on December 19, 2014 Database: EBM Reviews - Cochrane Central Register of Controlled Trials <November 2014> Search Strategy:

  1. exp Population Groups/ (5103)
  2. exp Race Relations/ (29)
  3. exp Minority Groups/ (203)
  4. (ethnic* or race or racial or black or blacks or hispanic* or minority or minorities or “african american”).mp. [mp=title, original title, abstract, mesh headings, heading words, keyword] (11359)
  5. 1 or 2 or 3 or 4 (13362)
  6. exp Health Services Accessibility/ (608)
  7. exp Healthcare Disparities/ or exp Health Status Disparities/ (121)
  8. (disparity or disparities or equity or difference or discrimination).mp. [mp=title, original title, abstract, mesh headings, heading words, keyword] (108768)
  9. 6 or 7 or 8 (109262)
  10. 5 and 9 (2547)
  11. limit 10 to yr=“2006 -Current” (1624)
  12. exp “United States Department of Veterans Affairs”/ or exp Veterans Health/ or exp Hospitals, Veterans/ or exp Veterans/ (763)
  13. (va or veteran or veterans or VAMC).mp. [mp=title, original title, abstract, mesh headings, heading words, keyword] (3697)
  14. 12 or 13 (3697)
  15. 11 and 14 (17) = VA citations

On January 6, 2015 ClinicalTrials.gov http://www.clinicaltrials.gov was searched.

Search String:

Healthcare Disparities OR Health Status Disparities | received on or after 10/09/2006

145 Results

On January 6, 2015, HSRProj Database http://wwwcf.nlm.nih.gov/hsr_project/home_proj.cfm was searched

Search String:

VA disparity OR disparities OR equity gt_initialYear:2006 status:Completed OR status:Ongoing country:“United States”

Result: 129 Projects

LIST OF EXCLUDED STUDIES

INELIGIBLE COMPARATOR OR NO COMPARISON

  1. Burgess DJ, Grill J, Noorbaloochi S, et al. The effect of perceived racial discrimination on bodily pain among older African American men. Pain Med. 2009;10(8):1341–1352. [PubMed: 20021596]
  2. Hunt KJ, Gebregziabher M, Lynch CP, Echols C, Mauldin PD, Egede LE. Impact of diabetes control on mortality by race in a national cohort of veterans. Ann Epidemiol. 2013;23(2):74–79. [PubMed: 23238350]
  3. Nahleh ZA, Srikantiah R, Safa M, Jazieh AR, Muhleman A, Komrokji R. Male breast cancer in the veterans affairs population: a comparative analysis. Cancer. 2007;109(8):1471–1477. [PubMed: 17342768]
  4. Rabadi MH, Aston C. Complications and urologic risks of neurogenic bladder in veterans with traumatic spinal cord injury. Spinal Cord. 2014 [PMC free article: PMC4436035] [PubMed: 25403501]
  5. Villa VM, Harada ND, Huynh-Hohnbaum AL. Health and ambulatory care use among Native American veterans. Home Health Care Serv Q. 2010;29(4):195–215. [PubMed: 21153998]

DIFFERENTIAL TREATMENT EFFECTS

  1. Allott EH, Howard LE, Cooperberg MR, et al. Postoperative statin use and risk of biochemical recurrence following radical prostatectomy: results from the Shared Equal Access Regional Cancer Hospital (SEARCH) database. BJU Int. 2014;114(5):661–666. [PMC free article: PMC4153797] [PubMed: 24588774]
  2. Govani SM, Higgins PD, Stidham RW, Montain SJ, Waljee AK. Increased Ultraviolet Light Exposure is Associated With Reduced Risk of Inpatient Surgery Among Patients With Crohn's Disease. J Crohns Colitis. 2015;9(1):77–81. [PubMed: 25518047]
  3. Vidal AC, Williams CD, Allott EH, et al. Carbohydrate intake, glycemic index and prostate cancer risk. Prostate. 2015;75(4):430–439. [PMC free article: PMC4293225] [PubMed: 25417840]

INELIGIBLE INTERVENTION

  1. Burgess DJ, Phelan S, Workman M, et al. The effect of cognitive load and patient race on physicians' decisions to prescribe opioids for chronic low back pain: A randomized trial. Pain Medicine (United States). 2014;15(6):965–974. [PubMed: 24506332]
  2. Jackson GL, Oddone EZ, Olsen MK, et al. Racial differences in the effect of a telephone-delivered hypertension disease management program. J Gen Intern Med. 2012;27(12):1682–1689. [PMC free article: PMC3509293] [PubMed: 22865016]

INELIGIBLE OUTCOME

  1. Arora P, Rajagopalan S, Patel N, Nainani N, Venuto RC, Lohr JW. The MDRD equation underestimates the prevalence of CKD among blacks and overestimates the prevalence of CKD among whites compared to the CKD-EPI equation: a retrospective cohort study. BMC Nephrol. 2012;13:4. [PMC free article: PMC3398292] [PubMed: 22264268]
  2. Axon RN, Gebregziabher M, Echols C, Msph GG, Egede LE. Racial and ethnic differences in longitudinal blood pressure control in veterans with type 2 diabetes mellitus. J Gen Intern Med. 2011;26(11):1278–1283. [PMC free article: PMC3208462] [PubMed: 21671132]
  3. Bosworth HB, Dudley T, Olsen MK, et al. Racial differences in blood pressure control: potential explanatory factors. Am J Med. 2006;119(1):70 e79–15. [PubMed: 16431192]
  4. Burgess DJ, Taylor BC, Phelan S, et al. A brief self-affirmation study to improve the experience of minority patients. Appl Psychol Health Well Being. 2014;6(2):135–150. [PubMed: 24124121]
  5. Egede LE, Gebregziabher M, Hunt KJ, et al. Regional, geographic, and racial/ethnic variation in glycemic control in a national sample of veterans with diabetes. Diabetes Care. 2011;34(4):938–943. [PMC free article: PMC3064054] [PubMed: 21335370]
  6. Egede LE, Mueller M, Echols CL, Gebregziabher M. Longitudinal differences in glycemic control by race/ethnicity among veterans with type 2 diabetes. Med Care. 2010;48(6):527–533. [PubMed: 20473215]
  7. Goldstein KM, Melnyk SD, Zullig LL, et al. Heart matters: Gender and racial differences cardiovascular disease risk factor control among veterans. Womens Health Issues. 2014;24(5):477–483. [PubMed: 25213741]
  8. Hamilton NS, Edelman D, Weinberger M, Jackson GL. Concordance between self-reported race/ethnicity and that recorded in a Veteran Affairs electronic medical record. N C Med J. 2009;70(4):296–300. [PubMed: 19835243]
  9. Hausmann LR, Gao S, Mor MK, Schaefer JH Jr., Fine MJ. Patterns of sex and racial/ethnic differences in patient health care experiences in US Veterans Affairs hospitals. Med Care. 2014;52(4):328–335. [PubMed: 24848206]
  10. Hausmann LR, Hannon MJ, Kresevic DM, Hanusa BH, Kwoh CK, Ibrahim SA. Impact of perceived discrimination in healthcare on patient-provider communication. Med Care. 2011;49(7):626–633. [PMC free article: PMC3117903] [PubMed: 21478769]
  11. Hausmann LR, Hanusa BH, Kresevic DM, et al. Orthopedic communication about osteoarthritis treatment: Does patient race matter? Arthritis Care Res (Hoboken). 2011;63(5):635–642. [PMC free article: PMC3092010] [PubMed: 21225676]
  12. Hausmann LR, Jeong K, Bost JE, Kressin NR, Ibrahim SA. Perceived racial discrimination in health care: a comparison of Veterans Affairs and other patients. Am J Public Health. 2009;99 Suppl 3:S718–724. [PMC free article: PMC2774170] [PubMed: 19443818]
  13. Hayes J, Kalantar-Zadeh K, Lu JL, Turban S, Anderson JE, Kovesdy CP. Association of hypo- and hyperkalemia with disease progression and mortality in males with chronic kidney disease: the role of race. Nephron Clin Pract. 2012;120(1):c8–16. [PMC free article: PMC3267990] [PubMed: 22156587]
  14. Hebenstreit C, Madden E, Maguen S. Latent classes of PTSD symptoms in Iraq and Afghanistan female veterans. J Affect Disord. 2014;166:132–138. [PubMed: 25012421]
  15. Humphreys M, Costanzo P, Haynie KL, et al. Racial disparities in diabetes a century ago: evidence from the pension files of US Civil War veterans. Soc Sci Med. 2007;64(8):1766–1775. [PubMed: 17240029]
  16. Ivins BJ, Lange RT, Cole WR, Kane R, Schwab KA, Iverson GL. Using Base Rates of Low Scores to Interpret the ANAM4 TBI-MIL Battery Following Mild Traumatic Brain Injury. Arch Clin Neuropsychol. 2015;30(1):26–38. [PubMed: 25526791]
  17. Kramer BJ, Jouldjian S, Wang M, et al. Do correlates of dual use by American Indian and Alaska Native Veterans operate uniformly across the Veterans Health Administration and the Indian Health Service? J Gen Intern Med. 2011;26 Suppl 2:662–668. [PMC free article: PMC3191227] [PubMed: 21989619]
  18. Luncheon C, Zack M. Health-related quality of life among US veterans and civilians by race and ethnicity. Prev Chronic Dis. 2012;9:E108. [PMC free article: PMC3457754] [PubMed: 22652126]
  19. Noe TD, Kaufman CE, Kaufmann LJ, Brooks E, Shore JH. Providing culturally competent services for American Indian and Alaska Native veterans to reduce health care disparities. Am J Public Health. 2014;104 Suppl 4:S548–554. [PMC free article: PMC4151892] [PubMed: 25100420]
  20. Rao SR, Reisman JI, Kressin NR, et al. Explaining Racial Disparities in Anticoagulation Control: Results From a Study of Patients at the Veterans Administration. Am J Med Qual. 2014 [PubMed: 24642366]
  21. Rose DE, Farmer MM, Yano EM, Washington DL. Racial/ethnic differences in cardiovascular risk factors among women veterans. J Gen Intern Med. 2013;28 Suppl 2:S524–528. [PMC free article: PMC3695277] [PubMed: 23807060]
  22. Rosen MI, Afshartous DR, Nwosu S, et al. Racial differences in veterans' satisfaction with examination of disability from posttraumatic stress disorder. Psychiatr Serv. 2013;64(4):354–359. [PMC free article: PMC3677046] [PubMed: 23318842]
  23. Sohn L, Harada ND. Effects of racial/ethnic discrimination on the health status of minority veterans. Mil Med. 2008;173(4):331–338. [PubMed: 18472621]
  24. Wallin MT, Culpepper WJ, Coffman P, et al. The Gulf War era multiple sclerosis cohort: age and incidence rates by race, sex and service. Brain. 2012;135(Pt 6):1778–1785. [PubMed: 22628389]
  25. Weng HH, Kaplan RM, Boscardin WJ, et al. Development of a decision aid to address racial disparities in utilization of knee replacement surgery. Arthritis Rheum. 2007;57(4):568–575. [PubMed: 17471558]
  26. Wilson SM, Dedert EA, Dennis PA, et al. Do ethnicity and gender moderate the influence of posttraumatic stress disorder on time to smoking lapse? Addict Behav. 2014;39(7):1163–1167. [PMC free article: PMC4064680] [PubMed: 24727113]

INELIGIBLE POPULATION

  1. Bosworth HB, Olsen MK, Grubber JM, Powers BJ, Oddone EZ. Racial differences in two self-management hypertension interventions. Am J Med. 2011;124(5):468 e461–468. [PMC free article: PMC3086723] [PubMed: 21531237]
  2. Carpenter WR, Godley PA, Clark JA, et al. Racial differences in trust and regular source of patient care and the implications for prostate cancer screening use. Cancer. 2009;115(21):5048–5059. [PMC free article: PMC2779840] [PubMed: 19637357]
  3. Clarke SP, Davis BL, Nailon RE. Racial segregation and differential outcomes in hospital care. West J Nurs Res. 2007;29(6):739–757. [PubMed: 17630385]
  4. Crowley MJ, Powers BJ, Olsen MK, et al. The Cholesterol, Hypertension, And Glucose Education (CHANGE) study: results from a randomized controlled trial in African Americans with diabetes. Am Heart J. 2013;166(1):179–186. [PubMed: 23816038]
  5. Fischer SM, Sauaia A, Min SJ, Kutner J. Advance directive discussions: lost in translation or lost opportunities? J Palliat Med. 2012;15(1):86–92. [PMC free article: PMC3264957] [PubMed: 22239609]
  6. Flasar MH, Quezada S, Bijpuria P, Cross RK. Racial differences in disease extent and severity in patients with ulcerative colitis: a retrospective cohort study. Dig Dis Sci. 2008;53(10):2754–2760. [PubMed: 18273704]
  7. Gary KW, Arango-Lasprilla JC, Stevens LF. Do racial/ethnic differences exist in post-injury outcomes after TBI? A comprehensive review of the literature. Brain Inj. 2009;23(10):775–789. [PubMed: 19697166]
  8. Haideri NA, Moormeier JA. Impact of patient navigation from diagnosis to treatment in an urban safety net breast cancer population. J Cancer. 2011;2:467–473. [PMC free article: PMC3171897] [PubMed: 21915191]
  9. Hausmann LR, Ibrahim SA, Mehrotra A, et al. Racial and ethnic disparities in pneumonia treatment and mortality. Med Care. 2009;47(9):1009–1017. [PMC free article: PMC6521966] [PubMed: 19648832]
  10. Houston TK, Allison JJ, Sussman M, et al. Culturally appropriate storytelling to improve blood pressure: a randomized trial. Ann Intern Med. 2011;154(2):77–84. [PubMed: 21242364]
  11. Jayadevappa R, Johnson JC, Chhatre S, Wein AJ, Malkowicz SB. Ethnic variation in return to baseline values of patient-reported outcomes in older prostate cancer patients. Cancer. 2007;109(11):2229–2238. [PubMed: 17443664]
  12. Penrod JD, Litke A, Hawkes WG, et al. The association of race, gender, and comorbidity with mortality and function after hip fracture. J Gerontol A Biol Sci Med Sci. 2008;63(8):867–872. [PMC free article: PMC3807236] [PubMed: 18772476]
  13. Rafie C, Ayers A, Cadet D, Quillin J, Hackney MH. Reaching Hard to Reach Populations with Hard to Communicate Messages: Efficacy of a Breast Health Research Champion Training Program. J Cancer Educ. 2014 [PMC free article: PMC4345135] [PubMed: 25171905]
  14. Thoma MN, Jimenez Cantisano BG, Hernandez AV, Perez A, Castro F. Comparison of adenoma detection rate in Hispanics and whites undergoing first screening colonoscopy: a retrospective chart review. Gastrointest Endosc. 2013;77(3):430–435. [PubMed: 23317579]
  15. Weisbord SD, Fried LF, Mor MK, et al. Associations of race and ethnicity with anemia management among patients initiating renal replacement therapy. J Natl Med Assoc. 2007;99(11):1218–1226. [PMC free article: PMC2574315] [PubMed: 18020096]
  16. Williams AE, Smith WR, Starr AJ, et al. Ethnic differences in posttraumatic stress disorder after musculoskeletal trauma. J Trauma. 2008;65(5):1054–1065. [PubMed: 19001973]
  17. Wilson DB, McClish D, Tracy K, Quillin J, Jones R, Bodurtha J. Variations in breast cancer screening and health behaviors by age and race among attendees of women's health clinics. J Natl Med Assoc. 2009;101(6):528–535. [PubMed: 19585920]
  18. Zoellner JM, Connell CC, Madson MB, et al. H.U.B city steps: methods and early findings from a community-based participatory research trial to reduce blood pressure among African Americans. Int J Behav Nutr Phys Act. 2011;8:59. [PMC free article: PMC3127969] [PubMed: 21663652]

EXAMINING RACE AS A MEDIATOR

  1. Alele JD, Luttrell LM, Hollis BW, Luttrell DK, Hunt KJ. Relationship between vitamin D status and incidence of vascular events in the Veterans Affairs Diabetes Trial. Atherosclerosis. 2013;228(2):502–507. [PubMed: 23608249]
  2. Kokkinos P, Myers J, Faselis C, Doumas M, Kheirbek R, Nylen E. BMI-mortality paradox and fitness in African American and Caucasian men with type 2 diabetes. Diabetes Care. 2012;35(5):1021–1027. [PMC free article: PMC3329828] [PubMed: 22399701]
  3. Williams EC, Bradley KA, Gupta S, Harris AH. Association between alcohol screening scores and mortality in black, Hispanic, and white male veterans. Alcohol Clin Exp Res. 2012;36(12):2132–2140. [PMC free article: PMC3443543] [PubMed: 22676340]

INELIGIBLE SETTING

  1. Blumberg SN, Warren SM. Disparities in initial presentation and treatment outcomes of diabetic foot ulcers in a public, private, and Veterans Administration hospital. J Diabetes. 2014;6(1):68–75. [PubMed: 23551696]
  2. Bottonari KA, Stepleman LM. Improving access to mental health services via a clinic-wide mental health intervention in a Southeastern US infectious disease clinic. AIDS Care. 2010;22(2):133–136. [PubMed: 20390491]
  3. Bromley EG, May FP, Federer L, Spiegel BM, van Oijen MG. Explaining persistent under-use of colonoscopic cancer screening in African Americans: A systematic review. Prev Med. 2014 [PMC free article: PMC4329030] [PubMed: 25481094]

INELIGIBLE STUDY DESIGN

  1. Alexander DD, Waterbor J, Hughes T, Funkhouser E, Grizzle W, Manne U. African-American and Caucasian disparities in colorectal cancer mortality and survival by data source: an epidemiologic review. Cancer Biomark. 2007;3(6):301–313. [PMC free article: PMC2667694] [PubMed: 18048968]
  2. Becerra BJ, Becerra MB. Association between asthma and serious psychological distress among male veterans compared to civilian counterparts. Prev Med. 2014;71C:8–11. [PubMed: 25482421]
  3. Borzecki AM, Bridgers DK, Liebschutz JM, Kader B, Kazis LE, Berlowitz DR. Racial differences in the prevalence of atrial fibrillation among males. J Natl Med Assoc. 2008;100(2):237–245. [PubMed: 18300541]
  4. El-Serag H, McGlynn KA, Graham GN, et al. Achieving health equity to eliminate racial, ethnic, and socioeconomic disparities in HBV- and HCV-associated liver disease. J Fam Pract. 2010;59(4 Suppl):S37–42. [PMC free article: PMC4550292] [PubMed: 20398589]
  5. Grubaugh AL, Slagle DM, Long M, Frueh BC, Magruder KM. Racial disparities in trauma exposure, psychiatric symptoms, and service use among female patients in Veterans Affairs primary care clinics. Womens Health Issues. 2008;18(6):433–441. [PubMed: 19041595]
  6. Halanych JH, Wang F, Miller DR, et al. Racial/ethnic differences in diabetes care for older veterans: accounting for dual health system use changes conclusions. Med Care. 2006;44(5):439–445. [PubMed: 16641662]
  7. Harris GL. Reducing healthcare disparities in the military through cultural competence. J Health Hum Serv Adm. 2011;34(2):145–181. [PubMed: 22106545]
  8. Ibrahim SA. Racial and ethnic disparities in hip and knee joint replacement: a review of research in the Veterans Affairs Health Care System. J Am Acad Orthop Surg. 2007;15 Suppl 1:S87–94. [PubMed: 17766799]
  9. Kressin NR, Raymond KL, Manze M. Perceptions of race/ethnicity-based discrimination: a review of measures and evaluation of their usefulness for the health care setting. J Health Care Poor Underserved. 2008;19(3):697–730. [PMC free article: PMC2914305] [PubMed: 18677066]
  10. Long JA, Jahnle E, Richardson D, Volpp K. A randomized controlled trial of peer mentoring and financial incentive to improve glucose control in African American Veterans. Journal of general internal medicine. 2011;26(4)
  11. Loo CM, Ueda SS, Morton RK. Group treatment for race-related stresses among minority Vietnam veterans. Transcult Psychiatry. 2007;44(1):115–135. [PubMed: 17379613]
  12. Nayback AM. Health disparities in military veterans with PTSD: influential sociocultural factors. J Psychosoc Nurs Ment Health Serv. 2008;46(6):41–51. [PubMed: 18595458]
  13. Nguyen TH, Thrift AP, Ramsey D, et al. Risk Factors for Barrett's Esophagus Compared Between African Americans and Non-Hispanic Whites. Am J Gastroenterol. 2014;109(12):1870–1880. [PubMed: 25420546]
  14. Nonzee NJ, McKoy JM, Rademaker AW, et al. Design of a prostate cancer patient navigation intervention for a Veterans Affairs hospital. BMC Health Serv Res. 2012;12:340. [PMC free article: PMC3517303] [PubMed: 23009152]
  15. Rowley DL, Jenkins BC, Frazier E. Utilization of joint arthroplasty: racial and ethnic disparities in the Veterans Affairs Health Care System. J Am Acad Orthop Surg. 2007;15 Suppl 1:S43–48. [PubMed: 17766789]
  16. Singh JA. Can racial disparities in optimal gout treatment be reduced? Evidence from a randomized trial. BMC Med. 2012;10:15. [PMC free article: PMC3337326] [PubMed: 22316088]

EVIDENCE TABLES

ONGOING STUDIES

Download PDF (31K)

DATA ABSTRACTION: MORBIDITY/ MORTALITY MULTISITE STUDIES ADDRESSING KQ1

Download PDF (76K)

DATA ABSTRACTION: MORBIDITY/MORTALITY SINGLE SITE STUDIES ADDRESSING KQ1

Download PDF (63K)

DATA ABSTRACTION: INTERVENTION STUDIES ADDRESSING KQ2

Download PDF (30K)

QUALITY ASSESSMENT: INCLUDED MORBIDITY/MORTALITY STUDIES ADDRESSING KQ1

Download PDF (122K)

QUALITY ASSESSMENT: INCLUDED INTERVENTION STUDIES ADDRESSING KQ2

Download PDF (25K)

STRENGTH OF EVIDENCE: MORBIDITY/MORTALITY STUDIES ADDRESSING KQ1

Download PDF (80K)

STRENGTH OF EVIDENCE: INTERVENTION STUDIES ADDRESSING KQ2

Download PDF (25K)

PEER REVIEW DISPOSITION TABLE

Comment
#
Reviewer
#
CommentAuthor response
1. Are the objectives, scope, and methods for this review clearly described?
12YesNo response
23No - The methods are not described in sufficient detail to understand what was done. Though a weblink is provided, a brief description of the methods should be included so that this evidence brief can stand on its own. At a minimum, the search criteria and the methods for rating study quality need to be described.Added a description of methods for searching, internal validity assessment, and strength of evidence rating.
33In the literature flow section, there are numerous discrepancies in the article count within and between the text and the figure (e.g., 105 vs 108 articles included, 174 vs 176 full text articles assessed, the number of articles addressing key question 1, to name a few). It is also not clear which articles are included in the review, e.g., (page 7, lines 22-29), were there 43 articles included for key question 1, or only the 32 fair or better quality articles.We corrected the literature flow figure and text overview.
44YesNo response
55No - See reviewer comments belowSee responses below
66YesNo response
78YesNo response
2. Is there any indication of bias in our synthesis of the evidence?
82NoNo response
93NoNo response
104NoNo response
115NoNo response
126NoNo response
138NoNo response
3. Are there any published or unpublished studies that we may have overlooked?
142No
153Yes - The search strategies for published studies and the methods for identifying unpublished studies are not described, so the completeness of the methods cannot be assessed. Across the classifications, there are three broad areas that appear to have been omitted from the evidence that is reported on, and therefore these categories should be removed from the classifications. These categories are: studies providing a rationale for verify disparity in more recent cohort (A1); studies on the magnitude and strength of evidence of the disparity outside the VA (C1); and studies supporting whether there is reason to believe that decreasing the causes would reduce the disparity (C3). Omission of studies on the disparity outside the VA should be reported as a limitation, so that the recommendations of this evidence brief can be considered in the proper context.Added brief description of search strategy and methods for assessing internal validity of individual studies and for rating the strength of the body of evidence.

The three areas you are referring to are indeed outside of the scope of this review, and are being suggested as the next logical steps for future research. We added a figure to better illustrate the framework that guided our research recommendations and added more detail about the rationale behind our recommendations.

We focused on studies of disparities within the VA as the most applicable to the national VA population.
164NoNo response
175Yes - See review belowSee responses below
186Yes -
  1. Axon RN, Gebregziabher M, Echols C, Msph GG, Egede LE. Racial and ethnic differences in longitudinal blood pressure control in veterans with type 2 diabetes mellitus. J Gen Intern Med. 2011;26(11):1278-1283.
  2. Ayotte BJ, Hausmann LR, Whittle J, Kressin NR. The relationship between perceived discrimination and coronary artery obstruction. Am Heart J. 2012;163(4):677-683.
  3. Bosworth HB, Olsen MK, Grubber JM, Powers BJ, Oddone EZ. Racial differences in two self-management hypertension interventions. Am J Med. 2011;124(5):468 e461-468.
  4. Burgess DJ, Gravely AA, Nelson DB, et al. A national study of racial differences in pain screening rates in the VA health care system. Clin J Pain. 2013;29(2):118-123.
  5. Burgess DJ, Nelson DB, Gravely AA, et al. Racial differences in prescription of opioid analgesics for chronic noncancer pain in a national sample of veterans. J Pain. 2014;15(4):447-455.
  6. Burgess DJ, van Ryn M, Grill J, et al. Presence and correlates of racial disparities in adherence to colorectal cancer screening guidelines. J Gen Intern Med. 2011;26(3):251-258.
  7. Egede LE, Dismuke C, Echols C. Racial/Ethnic disparities in mortality risk among US veterans with traumatic brain injury. Am J Public Health. 2012;102 Suppl 2:S266-271.
  8. Egede LE, Gebregziabher M, Hunt KJ, et al. Regional, geographic, and racial/ethnic variation in glycemic control in a national sample of veterans with diabetes. Diabetes Care. 2011;34(4):938-943.
  9. Egede LE, Mueller M, Echols CL, Gebregziabher M. Longitudinal differences in glycemic control by race/ethnicity among veterans with type 2 diabetes. Med Care. 2010;48(6):527-533.
  10. Hausmann LR, Hannon MJ, Kresevic DM, Hanusa BH, Kwoh CK, Ibrahim SA. Impact of perceived discrimination in healthcare on patient-provider communication. Med Care. 2011;49(7):626-633.
  11. Hausmann LR, Hanusa BH, Kresevic DM, et al. Orthopedic communication about osteoarthritis treatment: Does patient race matter? Arthritis Care Res (Hoboken). 2011;63(5):635-642.
  12. Hausmann LR, Mor M, Hanusa BH, et al. The effect of patient race on total joint replacement recommendations and utilization in the orthopedic setting. J Gen Intern Med. 2010;25(9):982-988.
  13. Jackson GL, Oddone EZ, Olsen MK, et al. Racial differences in the effect of a telephone-delivered hypertension disease management program. J Gen Intern Med. 2012;27(12):1682-1689.
  14. May FP, Bromley EG, Reid MW, et al. Low uptake of colorectal cancer screening among African Americans in an integrated Veterans Affairs health care network. Gastrointest Endosc. 2014;80(2):291-298.
  15. Myaskovsky L, Almario Doebler D, Posluszny DM, et al. Perceived discrimination predicts longer time to be accepted for kidney transplant. Transplantation. 2012;93(4):423-429.
  16. Rao SR, Reisman JI, Kressin NR, et al. Explaining Racial Disparities in Anticoagulation Control: Results From a Study of Patients at the Veterans Administration. Am J Med Qual. 2014.
  17. Rosen MI, Afshartous DR, Nwosu S, et al. Racial differences in veterans' satisfaction with examination of disability from posttraumatic stress disorder. Psychiatr Serv. 2013;64(4):354-359.
  18. Spoont MR, Nelson DB, Murdoch M, et al. ARE THERE RACIAL/ETHNIC DISPARITIES IN VA PTSD TREATMENT RETENTION? Depress Anxiety. 2014.
  19. Williams CD, Stechuchak KM, Zullig LL, Provenzale D, Kelley MJ. Influence of comorbidity on racial differences in receipt of surgery among US veterans with early-stage non-small-cell lung cancer. J Clin Oncol. 2013;31(4):475-481.
Axon et al, 2011: We excluded this study since it does not report on any outcomes of interest for this review.

Ayotte et al, 2012: We included this study in our synthesis.

Bosworth et al, 2011: We excluded this study since it does not report on Veterans, the population of interest for this review.

Burgess et al, 2013: We included this study in our process measure and access supplemental spreadsheet.

Burgess et al, 2011: We included this study in our process measure and access supplemental spreadsheet.

Burgess et al, 2014: We included this study in our process measure and access supplemental spreadsheet.

Egede et al, 2012: We included this study in our synthesis.

Egede et al, 2011: We excluded this study since it does not report on any outcomes of interest for this review.

Egede et al, 2010: We excluded this study since it does not report on any outcomes of interest for this review.

Hausman et al, 2010: We included this study in our process measure and access supplemental spreadsheet.

Hausmann et al, 2011a: We excluded this study since it does not report on any outcomes of interest for this review.

Hausmann et al, 2011b: We excluded this study since it does not report on any outcomes of interest for this review.

Jackson et al, 2012: We excluded this study since the intervention was not specifically designed to reduce racial/ethnic disparities.

May et al, 2014: We included this study in our process measure and access supplemental spreadsheet.

Myaskovsky et al, 2012: We added this study to our process measure and access supplemental spreadsheet.

Rao et al, 2014: We excluded this study since it does not report on any outcomes of interest for this review.

Rosen et al, 2013: We excluded this study since it does not report on any outcomes of interest for this review.

Spoont et al, 2014: We included this study in our process measure and access supplemental spreadsheet.

Williams et al, 2013: We included this study in our process measure and access supplemental spreadsheet.
198NoNo response
4. Additional suggestions or comments can be provided below. If applicable, please indicate the page and line numbers from the draft report.
202The purpose of this evidence brief is to identify the research and implementation priorities in racial and ethnic disparities that have emerged since previous ESPs examined clinical areas in which disparities exist and intervention to improve health care among minorities were reviewed. Racial and ethnic minorities in the Veterans Health Administration (VHA) are important indicators nationally and in the VHA because it is an equal access health care system. Disparities that are observed in this setting are important because it is a context in which other mitigating factors related to racial and ethnic background (e.g., education, income, health insurance) are better controlled. For this reason, disparities in the VHA may be a more accurate reflection of the effects of racial and ethnic backgrounds on health care and outcomes. Comments about the scope, methods, and conclusions of the brief are provided below.

The authors should clearly describe the inclusion date for articles that were eligible for inclusion in the report. It is implied that the inclusion dates were 2007 through 2011, but this is not clearly stated. While this information may be provided in the protocol that is registered with PROSPERO, it should also be listed in the Executive Summary and the Methods section of the report. The authors should also make sure that the registration number identifies the protocol for the review in PROSPERO. I searched the database using the registration number provided in the methods and was unable to find the review.
Added search details to Executive Summary and Methods (To identify relevant citations, we searched MEDLINE® (via PubMed®) and the Cochrane Central Register of Controlled Trials from 10/09/2006 to 2/13/2015). Verified protocol listing in PROSPERO, which can be a little slow to upload.
212If studies had to be published between 2007 through 2011 in order to update findings from previous ESP reviews, then it seems that there would have been some restrictions put in place to address Key Questions 1 and 2. This should be clarified.Added search details to Executive Summary and Methods (To identify relevant citations, we searched MEDLINE® (via PubMed®) and the Cochrane Central Register of Controlled Trials from 10/09/2006 to 2/13/2015).
222The authors describe three categories that were used to group findings about morbidity and mortality outcomes in racial and ethnic minority groups. Caution should be used in the descriptions used in these categories. In particular, the text used to describe Category (B) suggests that the available evidence indicated that it is uncertain if a disparity even exists. For instance, diabetes has a low rating in terms of strength of evidence related to the disparities, but it appears that the evidence is based on only two studies. The rating given to diabetes implies that additional research is needed to determine if there are actually disparities in diabetes between African Americans and whites. But, one of the two studies that are cited as being as the basis for this category is from a study that compared end-stage renal disease rates among patients with HIV to rates of diabetes. The primary purpose of this research was not to compare diabetes outcomes between African Americans and whites. Rather, the purpose of this study was to compare rates of end-stage renal disease between Veterans who have HIV to those who have diabetes. Similarly, the other study related to diabetes also had a different focus and only included men. Concluding that more research is needed to determine if a disparity exists based on studies that were not designed to address the disease specifically is inaccurate. This point may be relevant to other conditions. Relatedly, drawing conclusions about the strength of the evidence about disparities results reported in other systematic reviews seems like a big leap to make.We agree there are limitations to using studies that are not specifically designed to answer our questions. Most often the limitations are in the form of imprecision and inadequate adjustment for important confounders. Because of such limitations for the examples you noted, we downgraded the strength of the evidence to low to reflect our limited confidence in their findings

We did not rate strength of evidence of the results from the 2007 and 2011 ESP reviews.
232The connection between the strength of the evidence about the presence of disparities and interventions that were developed and evaluated them is not explicitly made. This may be beyond the scope of the report, but it could be a missed opportunity to link interventions with any declines in disparities that were observed since the 2007 report.Added to summary: “Although it would be useful to link interventions with any declines in disparities that were observed since the 2007 report, there was no opportunity to make such a this link. The only mortality/morbidity disparity observed in the 2007 report was higher mortality among African American Veterans with HIV and we identified no subsequent studies of mortality among African American Veterans with HIV or of interventions to reduce disparities in African Americans with HIV.”
242Although the authors provide a strong rationale for limiting the scope of this review to the time period following the 2007 and 2011 ESP, it seems that this aspect of their review limits the overall strength of the evidence. That is, making a determination that the strength of evidence is limited or that additional research is not needed based on a relatively small number of studies (the absolute number of studies) that were published during a 4-year period may be a premature conclusion.We also included findings from the 2007 and 2011 ESP reports in addition to any new studies that had subsequently emerged. The 2007 and 2011 ESP reports identified very few studies that evaluated mortality or morbidity outcomes or interventions to reduce disparities to begin with. The 34 multi-site studies that evaluate mortality or mortality outcomes that were published since the 2011 ESP report represents a huge increase in disparities research.
252The most significant finding of this review may be that during a relatively limited period of time, a small number of studies have been conducted to examine or address racial and ethnic disparities in health care and outcomes in the VHA. This point should be emphasized and future directions for how to address this finding should be included in the conclusions.Added to Future Research section: “Although much progress has been made since the 2007 ESP review in conducting studies on the presence of mortality and morbidity disparities, as noted in the 2011 ESP review, still much more work is needed to implement disparities intervention research.”
263Page 1-2, prevalence and associated research priorities section, and page 19 Summary: These sections are confusing as written because the metrics are not described before they are used, and the type of information presented is not uniform across categories. It would be clearer to introduce the classifications as categorizing the findings by racial and ethnic minority group and by four classifications based on the (1) the morbidity and mortality difference between minority and majority groups (greater versus similar/better), and (2) the strength of the evidence (moderate or better versus low). A clear way to present these classifications would be as a 2 × 2 table, with the research needs for each of the classifications appearing within their respective cells.We added a figure to the Executive Summary and Overview of Research Plan sections to better introduce and illustrate the framework that guided our research recommendations. The figure accomplishes the same objective as you recommended with the 2×2 table of visually illustrating how each research need category links to the direction of the finding (worse or similar/better mortality for racial/ethnic minorities) and its strength (moderate or better vs low).
273Classification A appears to be studies with good evidence for a morbidity or mortality difference, and therefore, the next research priorities in this category would be those listed as describing the category. By reframing the category definition in this manner, the rationale for the research recommendations becomes more apparent. I suggest moving the listing of the studies out of this section, so that the definition of the categories is reported on separately from the findings. When discussing the studies for this category, it would be useful to clarify which type of research is being recommended (e.g., verifying the disparity, identifying the source of the disparity, evaluating interventions), and provide a supporting rationale for the recommendation.We kept the findings and future research recommendations together, but reorganized the table horizontally to reframe as you've suggested: 1) a column with the 4 categories of direction and strength of evidence, along with a reference to which part of the new figure each category links to, 2) a column with the racial/ethnic minority groups, conditions and outcomes that link to each evidence category, and 3) a column with the research recommendations. We added clarification about which type of research we are recommending and provided more supporting rationale.
283Page 3, Table 1: Why are Native Americans listed as a category separate from American Indian or Alaska Native?Moved the Native American category into the American Indian or Alaska Native category.
293Page 4, Conclusion: It is not clear from the evidence why a recommendation is being made to verify the disparities listed in a more recent cohort. An evidence gap analysis informed by the non-VA scientific literature was beyond the scope of this review. Specifically, for many racial/ethnic groups and conditions there were no VA studies. That is an important finding which should be emphasized because this is a potential area for future research.As colon cancer, HIV and CKD findings were based on VA cohorts from the early 2000's, and changes are possible in the past 10 years, we are suggesting considering the need to verify the disparity in a more recent VA cohort.

Added to Future Research section: “As most of the mortality and morbidity disparity prevalence studies focused on African Americans or Hispanic minority groups and on cancer, heart disease or acute care conditions, more work is needed to evaluate prevalence of disparities in other racial/ethnic minority groups and for OHE PEC's other priority conditions including HIV, hepatitis C, mental illness, spinal cord injury, substance use disorders, polytrauma and blast-related injuries.”
303Page 8, Key Question 1: Since the focus of KQ1 is to provide a 2015 update of evidence on prevalence, it is unclear why isolated findings from the 2007 review are highlighted.The focus of KQ1 is to provide a synthesis of all evidence to-date on mortality/morbidity disparities, including those from the 2007 review. The only mortality/moribidity disparity from the 2007 review was the higher mortality among African American Veterans with HIV.
313Page 9, ESRD in African-American Veterans with HIV: The sample size appears to be for the parent study, not for the study of the subset of Veterans registering as having HIV (not likely to be 2 million of the 5.5 million VA users).
Along those lines, throughout the tables, the sample sizes should be corrected to reflect the manuscript being reported on.
We confirmed and corrected sample sizes throughout the document.
323Page 18, Summary: Statements such as “increased risk of pre-term birth in Veterans with PTSD is the only disparity that is present in multiple racial/ethnic minority groups” are misleading in the absence of a large number of studies to examine disparities in multiple racial/ethnic groups.Changed to: “However, for PTSD, because the higher risk of preterm birth was consistently found across two minority groups, we recommend considering examining sources as the next step for future research.”
334This evidence brief addresses the important and timely topic of health and health care disparities that affect Veterans being cared for by the Veterans Affairs (VA) Healthcare System. The report offers an informative snapshot of current state of research on disparities in the VA. I offer several comments for the authors to consider as they finalize this worthwhile report:

The classification categories in the executive summary were not intuitive to me. Were they supposed to range from strongest to weakest evidence, along with the recommended action based on the level of evidence? That did not come through in the way they were written. I was particularly confused by Category C from the executive summary.
Yes, the classification categories were based on direction (worse or similar/better mortality for racial/ethnic minorities) and strength (moderate or better vs low) of evidence. We added a figure to the Executive Summary and Overview of Research Plan sections to better introduce and illustrate how each research need category links to the direction of the finding (worse or similar/better mortality for racial/ethnic minorities) and its strength (moderate or better vs low).
344Minor point: There are some stray or missing punctuation marks throughout the text and table that need to be corrected prior to finalization of the report. I did not line-item edit these marks, but first noticed them in the colon cancer section on p. 9.We copy-edited the evidence brief and removed all stray punctuation marks.
354Page 10, paragraph 1: Why is adjustment for between-facility differences being emphasized as a major limitation of studies? Is the VA only interested in within-facility racial differences? Identifying whether disparities observed at the national level are happening because patients receive different treatment within the same facilities and/or because minority patients receive care at lower-performing facilities is moving into the “understanding” phase of disparities. That is, a study focused on identifying the presence of disparities shouldn't necessarily be faulted for not taking into account facility-level variation in outcomes. Once a disparity is recognized as being present (regardless of where patients receive care), determining whether the disparity is due, in part or in full, to differences in where patients get care would be one of many possible avenues to pursue in trying to understand why the disparity is happening, which will then guide intervention strategies to reduce the disparities.We take your point that facility-level variation overlaps with the understanding phase of disparities. Although we still note whether treatment facility were included in the models, we corrected instances where we downgraded evidence due to lack of treatment facility adjustment. This resulted in three instances where we changed the strength of evidence rating from low to moderate: increased risk of incidence cirrhosis and hepatocellular carcinoma in Hispanics with Hepatitis C, reduced risk of incident cirrhosis and incident hepatocellular carcinoma in blacks with Hepatitis C, and reduced risk of death in blacks one-year after a stroke.
364In Table 1 a 3-star system is used to designate high, moderate, and low strength of evidence. How does that map onto the 4 categories of studies presented in the executive summary? Also, in the second column of the table, terms like “fair”, “good” “unknown consistency” and “imprecision” are used, and I'm not sure how those fit into the 3-star system or the A-D categories of evidence from the executive summary. I understand you are trying to summarize a lot of different things in a single table, which is a challenge, but I think the take-home message would be more powerful and clear if you had a more straightforward and consistent way of communicating all the different components you evaluated for each study.We added a figure to the Executive Summary and Overview of Research Plan sections to better introduce and illustrate how each research need category links to the direction of the finding (worse or similar/better mortality for racial/ethnic minorities) and its strength (moderate or better vs low).

To clarify how the concepts “fair”, “good” “unknown consistency” and “imprecision” were used to determine strength of evidence, we added a brief description of the rating system to the Methods section.
374P. 18, Arthritis and pain management section: The explanation of study 64 is a little strange, in that it does not give the comparison group (i.e., attention control), and the conclusion is not clear from the way the findings are described. My understanding of that study is that none of the intervention arms resulted in significantly different 12-month attendance rates at orthopedic clinics compared to an attention control arm. So, is the intervention effective or not? This ambiguity is also present in the summary section, where it says the 3 interventions all result in similar consult rate (similar to what? an attention control study arm).Yes, no intervention resulted in different 12-month attendance rates at an orthopedic clinic compared to the attention control arm. We changed the explanation of this study: “There is low-strength evidence from a good-quality RCT of 639 African American Veterans from the Pittsburgh, Cleveland, and Philadelphia VAMCs that, compared to an attention control group who only received an educational booklet, there was no difference in 12-month orthopedic surgeon appointment attendance for a decision aid intervention group who watched a video on the risks and benefits of different treatment options (aOR 1.27, 95% CI, 0.54-3.00), a motivational interviewing intervention group that underwent a counseling session with a trained interventionist (aOR 1.79, 95% CI, 0.78-4.07), or for a decision aid and motivational interviewing group that watched the video before their counseling session (aOR 2.05, 95% CI, 0.90-4.65).”
384As noted in my comment on the executive summary, I find the A-D categories not very clear or particularly useful as currently presented. I think the groupings are probably fine, but the way the categories are described does not really drive home the recommendations that follow from each category. Consider providing more explanation for each category and the logical next steps for research/action for each.

For example, the 3rd part of Category C needs to be elaborated (i.e., “whether there is reason to believe that decreasing the causes would reduce the disparity). What do the authors mean by that? A category that includes “may or may not be needed” is really not very useful. How are people supposed to act (or not) on that?

Taking Category B as another example, I think it would be much more clear if was relabeled something like, “More research is needed to establish the presence/absence of disparity.”
We added a figure to the Executive Summary and Overview of Research Plan sections to better introduce and illustrate how each research need category links to the direction of the finding (worse or similar/better mortality for racial/ethnic minorities) and its strength (moderate or better vs low) and added more explanation of each category and logical next steps. .

Also, we changed Category B as suggested and simplified Category C to: “More research to establish the presence/absence of disparity is probably not needed unless there is a large proven disparity outside of the VA.”
394Limitations: I don't think the exclusion of Non-English studies needs to be mentioned as a limitation given that there are unlikely to be many, if any, non-English studies published using VA data.Removed.
404I disagree with excluding all single-center studies, given that the HSR&D budget caps and the 3-year project timeline makes large multi-site trials difficult to conduct in the VA. Consider including at least the high-quality single-center studies.We focused on multi-center studies because their findings have the broadest generalizability to the national US Veteran population
415This evidence brief seeks to provide an update on the prevalence of racial/ethnic health disparities in VA and interventions to address these disparities. Comments:

page 1: I was unclear about the different classifications. “A” refers to “need for research to … verify disparity in more recent cohort or … identify sources of disparity.” These seem like separate concepts. For example, for the lower 3-years survival for colon cancer, is there a need to verify this disparity using more recent data, identify the sources of the disparity, or implement interventions to address the disparity.
Added: “In applying these findings, the OHE should consider that all are based on VA cohorts from the early 2000's. Over the past 10 years, changes in the delivery system or in diagnostic and treatment approaches may have changed these disparities. OHE can decide whether these findings are still current, or can fund studies to verify them in more recent cohorts. If 10-year-old data is acceptable, or if the mortality and morbidity disparity is verified in a more recent cohort, then new research should examine its sources.”
425page 1: what is “3.7-year end stage renal disease”. Seems like there is a word or phrase missing.Added details about when timeframe started or whether it was average follow-up for a mixed cohort.
435page 5: the section labeled conceptual framework does not present a conceptual framework. This appears to be an overview of the research plan. The reason this would be important is that the report presents differences that adjust for socioeconomic factors, while the IOM framework would consider SES to be a mediator of disparities rather than a “confounder”. A conceptual model would be helpful to orient the reader about the types of studies included, identify how the authors defined the terms “dispartity” and “differences” and the estimates that are abstracted from each study.We changed title of section to “Overview of Research Plan”.

We did not use or refer to the IOM framework because its objective of evaluating healthcare inequities differs to our objective of evaluating health inequities.

We added to the ‘Overview of Research Plan’ this sentence about how we defined disparity: “To fit the purpose of this report, we defined disparity as any instance of worse mortality or morbidity outcomes for the racial/ethnic minority groups. “

For SES, we added to Methods:” For SES, we included studies whether or not they adjusted for SES. When studies adjusted for SES, we noted its impact.” Only 5 studies adjusted for SES and 4 of them found disparities despite that adjustment. We do not believe that Volpp 2007's findings of no disparity are due to their adjustment for SES as the additional analyses in Jha 2010 and Polsky 2007 did not adjust for SES and also found no disparity
445Page 6: Given the scope outlined, there appear to be missing studies. Here are 7:
  1. Rehman SU, Hutchison FN, Hendrix K, Okonofua EC, Egan BM. Ethnic differences in blood pressure control among men at Veterans Affairs clinics and other health care sites. Arch Intern Med 2005;165(9):1041-1047.
  2. Heisler M, Smith DM, Hayward RA, Krein SL, Kerr EA. Racial disparities in diabetes care processes, outcomes, and treatment intensity. Med Care 2003;41(11):1221-1232.
  3. Safford M, Eaton L, Hawley G, et al. Disparities in use of lipid-lowering medications among people with type 2 diabetes mellitus. Arch Intern Med 2003;163(8):922-928.
  4. Etzioni DA, Yano EM, Rubenstein LV, et al. Measuring the quality of colorectal cancer screening: the importance of follow-up. Dis Colon Rectum 2006;49(7):1002-1010.
  5. Bosworth HB, Dudley T, Olsen MK, et al. Racial differences in blood pressure control: potential explanatory factors. Am J Med 2006;119(1):70 e79-15.
  6. Trivedi AN, Grebla R, Wright SM, Washington DL. Despite improved quality of care in the Veterans Affairs health care system, racial disparity persists for important clinical outcomes. Health Affairs 2011.
  7. Halanych JH, Wang F, Miller DR, Pogach LM, Lin H, Berlowitz DR, Frayne SM. Racial/ethnic differences in diabetes care for older veterans: accounting for dual health system use changes conclusions. Med Care. 2006 May;44(5):439-45. PubMed PMID: 16641662.
There is a table of ongoing projects, which does not include a VA-funded Merit study (PI Donna Washington) that examines facility-level determinants of racial/ethnic disparities.

This suggests that either the search strategy was incomplete, or there were other inclusion and exclusion criteria that are not described in the review. A more complete description of the seach strategy including search terms would be helpful.
Thank you for these suggestions.

Rehman et al, 2005: We added this study to our process measure and access supplemental spreadsheet.

Heisler et al, 2003: We added this study to our process measure and access supplemental spreadsheet.

Safford et al, 2003: We added this study to our process measure and access supplemental spreadsheet.

Etsioni et al, 2006: We added this study to our process measure and access supplemental spreadsheet.

Bosworth et al, 2006: We excluded this study since it does not report on any outcomes of interest for this review.

Trivedi et al, 2011: We included this study in our process measure and access supplemental spreadsheet.

Halanych et al, 2006: We excluded this study since it is cross-sectional.

We did identify Donna Washington's ongoing VA-funded merit study, but it does not meet the inclusion criteria of this review.
465The tables in the review issue grades and an overall quality rating without a clear rubric for how these assignments are made. What were the criteria for assigning studies to “fair” or “good”? What does “non-biased” selection mean? How did the reviewers determine that missing data were handled “adequately”? What is an “adequate” duration of follow-up? None of these ratings are defined nor is there a description of the interrater reliability of these classifcations.

It appears that every article included “outcomes that were pre-specified and defined. Does this column need to be included given the ‘yes’ for all of the studies? It is unclear how an article could be included or published without specifying an outcome.
The level of adjustment for potential confounders and multilevel modeling column is particularly problematic, since the IOM does not consider SES to be a confounder of racial/ethnic disparities. Further, the choice of a multilevel model (vs. a fixed effect or GEE) is not a marker of the quality of a study but rather reflects the objective of a particular analysis. If including a facility fixed effect eliminates the racial/ethnic disparity, that does not necessarily mean that the study found no disparity within the VA. A conceptual model that describes how the authors treat clinical factors, SES, and providers in the relationship between race/ethnicity and otucomes would be helpful. The IOM model might provide a helpful orientation.
Although described in detail in our PROSPERO-registered protocol, we added details to our Methods section that we rated internal validity of included studies based on Cochrane's Risk of Bias Tool for controlled trials and the Drug Effectiveness Review Project's Tool for observational studies. It is not standard to formally assess interrater agreement using kappa statistics because the goal of dual review is not to achieve high agreement, but to identify, explore and resolve reasons for disagreement.

For the item, ‘included “outcomes that were pre-specified and defined’, yes we often find that publications' methods sections do not prespecify the total number of planned outcomes and lack clear definitions for outcomes. For example, a publication could report that they assessed mortality, but omit details as to at which timepoint(s) and when the counting began (e.g., after diagnosis? after initiation of treatment?).

We did not use or refer to the IOM framework because its objective of evaluating healthcare inequities differs to our objective of evaluating health inequities. For clarification of how we assessed level of adjustment for potential confounders, we added the following details from our protocol to our Methods: “We categorized level of adjustment for potential confounders as high, medium or low based on the degree to which studies accounted (1) demographic, (2) illness severity, and (3) comorbidity variable and, to add insight, noted whether SES and treatment facility were included in the models and whether studies presented a conceptual model that explained covariate selection.” We removed the multi-level modeling specification. We had meant that specification to capture whether treatment facility was included as a covariate, regardless of the choice of multi-level modeling. .
476Executive Summary
Question if Category B or C has evidence from earlier studies, and also for Category A. Or were the results from the Systematic Reviews included as well? (I wasn't sure from what was said under Methods, Literature Flow which references systematic reviews.) If it includes the Systematic Reviews also, I think that some description of this would be helpful in the Executive Summary.
Yes, our future research recommendations include evidence from the previous ESP reviews. We added clarification to the Executive Summary as to which findings were from the previous reviews.
486Executive Summary
Purpose in Exec Summary gives timeframe. Maybe should include number of studies part of this analysis. I know it's in the full report. I would be interested in seeing someplace the number of studies by year – would be interesting to see quantity of research findings – if seeing more or less research. This is all context.
Added to Executive Summary that the 2007 ESP review only identified 1 mortality/morbidity study and that, since 2007, there has been a steady stream of new research emerging and for this update, we identified 34 new studies of mortality and morbidity outcomes.
496Executive Summary
VA based interventions to reduce disparities. Bosworth found a multi-pronged intervention did result in differences for AA in hypertension. Not sure where that is.
Bosworth et al, 2011: We excluded this study since it does not report on Veterans, the population of interest for this review.
506My concern here is that by not at all mentioning that some areas may be addressed in earlier studies and systematic reviews, this leaves the impression that we need to investigate the prevalence of disparities for all kinds of conditions, and also reasons, when we may know some of these already. If as it seems as I read the full report, these are included, I think stating up front in Ex Sum would be helpful for clarification also.

Also, this is probably in the body of the document, but I will look to see the dates of study for when there was documentation of disparities. If it's 10 years ago or 5 years ago…

Not to say update is not needed, but it does have relevance. Maybe there needs to be more explicit attention to what update means. For example, in body when I see evidence about disparities in color cancer, it does say that one study based on 2001-2004. Clearly, update is needed since changes possible in 10 years. CKD also old data, etc.
Yes, we included evidence from the previous ESP reviews and better clarified this up front in the Executive Summary.

Yes, we agree that the findings on African American Veterans with colon cancer, HIV and CKD are based on old data from the early 2000's and that changes are possible in the past 10 years and that the next step is to consider the need to verify the disparity in a more recent VA cohort. We better clarified this in the Future Research Recommendations section.
516P. 10 - for which almost no promising interventions have been developed.4 I'd have to think about more whether this statement is accurate.

In looking at conditions – I was thinking that some listing – not sure how you'd get that – maybe in some order – either body system or alphabetical – could be helpful to think systematically where we know something or not. Obviously, there are conditions where we know nothing (or maybe where there might not be any disparities). I'm just thinking in terms of going forward, what we need to do. At any rate, some systematic way for listing/ordering the conditions could be helpful in looking from one category or section to another. So what I'm thinking is: Cancer, CVD, diabetes, mental health, etc. As I'm looking, sometimes the different conditions are discussed alphabetically, but not always.
Revised to consistently list conditions in alphabetical order.
526Scope
For comparison, does this mean you didn't look at studies that focused on a single population
(African American but not in relationship to whites)?
For prevalence, we required a comparison group. But, for interventions, we included studies that compared before and after an intervention in a single minority group.
536Methods
Nine single center studies39-47 were not assessed for quality or included in our synthesis but are abstracted in the supplemental materials. Just curious why – something about single center???
Later saw reference to only multicenter studies included.
We focused on multi-center studies because their findings have the broadest generalizability to the national US Veteran population
546Table 1
I like this table a lot. Again, not sure of logic of order.
What is RR on last column?
So for African-American vs. white – nothing on mental health other than 1 PTSD and preterm birth? Nothing in what other categories? I thought there was a recent PTSD study – Spoont.
RR=rapid review, but this was changed to ‘brief’

Yes, Spoont and colleagues published a PTSF study in Depression and Anxiety in 2014 showing that African Americans and Latino Veterans were less likely to receive treatments. We provided data abstraction of this process measure study in an appendix, but in the report only synthesized findings of mortality/morbidity outcomes.
566In Table 1, are these the same studies that look at different racial/ethnic groups compared to whites? I guess I'd like to know that at some point in the text narrative.Yes, Table 1 reflects comparisons of minority groups to white groups. Added clarification to the table.
576When you say # of new process/access measures studies and types of outcomes identified in RR – I understand the process measures, not sure what you're looking at in terms of access (I'm not sure if all of this is defined somewhere) and by types of outcomes – you mean treatment, time between diagnosis and drug initiation, etc.Added examples of both process measures (i.e., offer and uptake of care, guideline adherence, etc.) and access (e.g., wait times)
586Table 2
There's no discussion/column for 2007 report, intervention etc. because these are low-strength evidence studies?
We felt that evidence about potential causes and interventions were not relevant because Table 2 reflects evidence suggesting similar or better mortality for minority groups.
596Question
Except for pre-term birth – there are no studies that address racial/ethnic disparities in females?
Yes.
606Maybe Category E – no studies on disparities. Like if really none on PTSD, other mental health, other than stroke, nothing on CVD? Breast cancer?Added clear statement to results that we found no studies in spinal cord injury, polytrauma and blast-related injuries and added recommendation for more studies in less well-researched racial/ethnic minority groups and for OHE PEC's other priority conditions including HIV, hepatitis C, mental illness, spinal cord injury, substance use disorders, polytrauma and blast-related injuries
616Limitations
Additionally, due to the exclusion of studies published in languages other than English, and because we only synthesized evidence from multicenter studies, we may have missed additional studies of important disparities or interventions. I guess this is why the single center studies were not included.
We did not evaluate studies of the sources of differences in health care quality (eg, patient, provider, patient-provider, and system factors). You list studies not included. Maybe there should be a few sentences that describe what types of studies these are – since they could examine factors that may impact morbidity and mortality.
We focused on multi-center studies because their findings have the broadest generalizability to the national US Veteran population.

Regarding excluded studies, added: “Among the 76 excluded studies, the majority were excluded for being in non-Veteran populations (N=18), involving an ineligible study design (e.g., cross-sectional) (N=16), or having ineligible outcomes (N=26). Types of ineligible outcomes included intermediate clinical outcomes such as glucose or blood pressure control, which could contribute to mortality or morbidity disparities, but which were outside of the scope of this brief.”
626Evidence Tables
Split of conducting….
Corrected.
636Chapko study is officially completed. Not sure if publications.Thank you for your comment. We contacted Dr. Chapko and he confirmed that they are still analyzing their data. This is captured in the status listed in the supplemental materials.
646Supplemental materials
This is likely the format you always use, but I find it cumbersome to have to consult end list of references to see study. It would be helpful to me to have first author and date in the table. This is Page 34 on. I see earlier ones do have author and year.
Added Author and Year to tables.
658Good report- some major comments:
I would distinguish mortality studies by users of VA care versus Veteran non-VA care users (it was not clear whether study populations only constituted VA users, which differ than the general Veteran population). Were there disparities in HIV -realated outcomes apparent beyond ESRD? This conclusion seems too specific to generalize to the larger HIV population. This also points to the need for fewer disease-specific studies and more population-based analyses that do not parse out by diagnosis.
Added clarification that the majority of studies reflected VA care use only, with only 24% supplemented with Medicare data to more completely capture the totality of care across the general VA population. Also added recommendation that future studies should supplement VHA data with Medicare data whenever possible to more completely capture the totality of patients' care.
For HIV, yes, the 2007 ESP review found higher mortality for African Americans with HIV.

Regarding the limitation of evaluating rarer morbidities such as ESRD in HIV, added to the Future Research Recommendations: “For morbidity outcomes, to maximize generalizability to the broadest disease populations, studies should examine multiple relevant outcomes, not just a single rare outcome in isolation. For example, future studies of rates of ESRD in HIV should be done in the context of other more common outcomes, such as severe bacterial infections or AIDS events..

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Prepared for: Department of Veterans Affairs, Veterans Health Administration, Quality Enhancement Research Initiative, Health Services Research & Development Service, Washington, DC 20420

Prepared by: Evidence-based Synthesis Program (ESP), Coordinating Center, Portland VA Medical Center, Portland, OR, Mark Helfand, MD, MPH, MS, Director

Recommended citation: Peterson K, McCleery E, Waldrip K. Evidence brief: Update on prevalence of and interventions to reduce racial and ethnic disparities within the VA. VA ESP Project #09-199; 2014.

This report is based on research conducted by the Evidence-based Synthesis Program (ESP) Coordinating Center located at the Portland VA Health Care System, Portland, OR, funded by the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, Quality Enhancement Research Initiative. The findings and conclusions in this document are those of the author(s) who are responsible for its contents; the findings and conclusions do not necessarily represent the views of the Department of Veterans Affairs or the United States government. Therefore, no statement in this article should be construed as an official position of the Department of Veterans Affairs. No investigators have any affiliations or financial involvement (eg, employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties) that conflict with material presented in the report.

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