

November 2020

Prepared for:

Department of Veterans Affairs

Veterans Health Administration

Health Services Research & Development Service

Washington, DC 20420

Prepared by:

Evidence Synthesis Program (ESP) Center

Portland VA Health Care System

Portland, OR

Devan Kansagara, MD, MCR, Director 

Health Inequalities in Infectious 

Disease Epidemics Pre-dating 

COVID-19 in the United States: A 

Rapid Review 

Authors:

Karli Kondo, PhD

Chelsea K. Ayers, MPH

Beth E. Williams, MSN, MPH

Devan Kansagara, MD, MCR

Mia Smith, MPH

Katherine M. Mackey, MD

Shailesh Advani, MD, PhD

Somnath Saha, MD, MPH

Evidence Synthesis Program
Health Inequalities in Infectious Disease Epidemics Evidence Synthesis Program

Pre-dating COVID-19 in the United States

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PREFACE 

The VA Evidence Synthesis Program (ESP) was established in 2007 to provide timely and accurate 

syntheses of targeted healthcare topics of importance to clinicians, managers, and policymakers as they 

work to improve the health and healthcare of Veterans. 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 program comprises three ESP Centers across the US and a Coordinating Center located in Portland, 

Oregon. Center Directors are VA clinicians and recognized leaders in the field of evidence synthesis 

with close ties to the AHRQ Evidence-based Practice Center Program and Cochrane. The Coordinating 

Center was created to manage program operations, ensure methodological consistency and quality of 

products, and interface with stakeholders. To ensure responsiveness to the needs of decision-makers, the 

program is governed by a Steering Committee composed of health system leadership and researchers.

The program solicits nominations for review topics several times a year via the program website. 

Comments on this report are welcome and can be sent to Nicole Floyd, Deputy Director, ESP 

Coordinating Center at Nicole.Floyd@va.gov. 

Recommended citation: Kondo KK, Ayers CK, Williams BE, Kansagara D, Smith M, Mackey KM, 

Advani S, Saha S. Health Inequalities in Infectious Disease Epidemics Predating COVID-19 in the 

United States. Washington, DC: Evidence Synthesis Program, Health Services Research and 

Development Service, Office of Research and Development, Department of Veterans Affairs. VA ESP 

Project #05-225; 2020. 

This report is based on research conducted by the Evidence Synthesis Program (ESP) Center located at the

Portland VA Health Care System, Portland, OR, funded by the Department of Veterans Affairs, Veterans Health 

Administration, Health Services Research and Development. 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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TABLE OF CONTENTS 

Executive Summary ...................................................................................................................... 1

Evidence Report ............................................................................................................................ 6

Background ................................................................................................................................... 6

Key Questions ................................................................................................................................ 7

Search Strategy ............................................................................................................................. 8

Methods .......................................................................................................................................... 9

Inclusion/Exclusion Criteria ....................................................................................................... 9

Screening Process ....................................................................................................................... 9

Outcomes Selected ...................................................................................................................... 9

Risk of Bias Assessment ............................................................................................................. 9

Synthesis ................................................................................................................................... 12

Assessing the Overall Body of Evidence .................................................................................. 12

Results .......................................................................................................................................... 13

KQ1: What factors contribute to disparate infection rates and health-related outcomes 

among different segments of the population during infectious disease epidemics or 

pandemics? ................................................................................................................................ 25

Exposure ............................................................................................................................... 25

Susceptibility......................................................................................................................... 27

Access to Care ....................................................................................................................... 28

Discrimination and Trust ...................................................................................................... 29

Information and Knowledge ................................................................................................. 29

KQ2. What interventions or intervention components have been used to reduce health 

inequalities (or identified in preliminary studies) in infectious disease transmission or 

health outcomes in disasters, infectious disease epidemics or pandemics 

in the United States? ................................................................................................................. 39

Individual-Focused Interventions and Programs .................................................................. 39

System-Focused Interventions and Programs ....................................................................... 40

Expert Panel Papers .............................................................................................................. 48

What were the H1N1 vaccination rates among different segments of the population, what 

factors contributed to disparate vaccination, and what were the benefits and harms of 

interventions designed to mitigate inequities in vaccine uptake? ............................................. 52

H1N1 Vaccine uptake ........................................................................................................... 52

Factors Associated with H1N1 Vaccine Uptake ................................................................... 57

Interventions to Reduce Disparities in Vaccine Uptake ....................................................... 67
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Discussion..................................................................................................................................... 68

Key Question 1 ..................................................................................................................... 68

Key Question 2 ..................................................................................................................... 80

H1N1 Vaccine ....................................................................................................................... 81

Applicability ......................................................................................................................... 82

Limitations ............................................................................................................................ 82

Conclusion ................................................................................................................................... 83

References .................................................................................................................................... 84

TABLES

Table 1. PICOTS ....................................................................................................................... 10

Table 2. Studies of Risk Factors by Population ........................................................................ 14

Table 3. Studies Examining Measures of Exposure, Susceptibility, Access to Care, 

Trust and Discrimination, and Information and Knowledge .................................................... 31

Table 4. Relevant Findings from Infectious Disease and Emergency Response Literature ..... 42

Table 5. Relevant Findings from CDC Expert Panel Meetings on Impact of Influenza in 

Vulnerable Populations ............................................................................................................. 50

Table 6. H1N1 Vaccine Uptake by Group ................................................................................ 54

Table 7. Factors Associated with H1N1 Vaccine Uptake ......................................................... 60

Table 8. Interventions for Vaccination-related Disparities ....................................................... 67

Table 9: Health Inequity Mediators by Group: Exposure ......................................................... 70

Table 10: Health Inequity Mediators by Group: Susceptibility, Access to Care, 

Discrimination/Trust ................................................................................................................. 74

Table 11. Strength of the Evidence for Studies of H1N1 Vaccine Uptake ............................... 81

FIGURES

Figure 1. Organizational Framework Developed by Quinn, Kumar, and Colleagues ................ 8

Figure 2. Literature Flow Chart ................................................................................................ 13

Appendix A. Search Strategies .................................................................................................. 89

Appendix B. Study Selection ...................................................................................................... 96

Appendix C. Quality Assessment .............................................................................................. 99

Table. Quality Ratings for Cross-sectional Studies* ................................................................ 99

Table. Quality Ratings for Cohort Studies* ............................................................................ 101

Table. Quality Ratings for Qualitative Studies* ..................................................................... 102

Table. Quality Ratings for Case Control Studies* .................................................................. 103

Table. Risk of Bias in Randomized Controlled Trials of Interventions to Reduce 

Pandemic/Epidemic-related Health Disparities ...................................................................... 104
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EXECUTIVE SUMMARY 

AIMS 

We conducted a systematic review of infectious disease epidemics predating COVID-19 to better 

understand the potentially modifiable factors that may have contributed to differential infection 

rates and health outcomes, as well as the interventions and programs implemented to mitigate 

them. 

METHODS 

We searched electronic databases and reference lists from database inception through May 20, 

2020 for studies of adult populations examining health inequalities by race/ethnicity, SES, 

disability, or geographic location related to infectious disease epidemics predating COVID-19 or 

disasters (KQ2 only) in the United States. We abstracted data on study design, factors, 

interventions, and outcomes. Dual assessment of studies’ full text, quality, and strength of 

evidence (where applicable) was agreed upon by consensus using published criteria. 

RESULTS

We identified 50 articles relevant to health inequalities in infectious disease epidemics in the 

United States predating COVID-19. We found 14 studies (16 articles) that examined potential 

mediating factors associated with health inequities, and 12 studies (3 articles) and 5 CDC expert 

panel reports that provide examples of interventions and lessons learned from previous 

epidemics and disasters to guide us forward in mitigating health inequalities during the COVID19 pandemic and beyond. We also identified 20 studies that examined inequalities in H1N1 

vaccine uptake and mediating factors, and 1 vaccine-related intervention study. 

To our knowledge, this is the first review of studies aimed at identifying both the factors that 

mediate health disparities in infectious disease epidemics and also potential avenues for 

mitigating then. Our conceptual framework was guided by the work of Quinn and Kumar, who 

considered the potential causes of epidemic influenza based on measures of exposure, 

susceptibility, and access to care as they applied to data collected in 2009-2010 during the H1N1 

pandemic. The framework points to proximal and distal determinants of disease burden with the 

ultimate goal of identifying potential points of policy and programmatic intervention. 

Across Key Questions, studies were heterogeneous in their operationalization of potentially 

mediating factors, populations, programs and interventions. Studies of potentially mediating 

factors generally, and those related specifically to H1N1 vaccine uptake specifically, were 

largely cross-sectional. Several included studies did not control for confounding variables or 

their methods were unclear; however, many were well conducted and adequately reported. A few 

of the qualitative studies clearly reported their methodology and/or findings. However, more did 

not. We identified very few interventions or program evaluation studies specific to infectious 

disease epidemics; most were focused on post-disaster needs or disaster preparedness. 

KEY QUESTION 1. What factors contribute to disparate infection rates 

and health-related outcomes among different segments of the 
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population during infectious disease epidemics or pandemics in the 

United States? 

Our findings related to mediators associated with differential exposure or the transmission of 

infectious disease were unsurprising. Despite looking across racial/ethnic groups and 

socioeconomic status independently, the findings that disparities were related to societal-based 

structural and work-related factors, rather than individual factors such as hygiene and cleaning, 

were consistent across studies. We identified few significant differences in social distancing 

attitudes and intentions between groups. Instead, it was clear that the meaningful differences lay 

in the ability or inability to social distance. Only 1 study examining variables related to exposure 

to illness disaggregated the Latino population by language proficiency, and 1 additional study 

provided qualitative input in the form of stakeholder interviews. In contrast with other 

populations we examined, compared to either English-proficient Latinos or Whites, limited 

English-proficient Latinos (and/or migrant and seasonal farm workers) were at higher risk across 

both structural and work-related variables measured (see Table 9). 

Susceptibility to illness played a major role in H1N1 severity and mortality; however, access to 

care (ie, having a primary care provider or health insurance) did not. Significantly greater 

proportions of every racial and ethnic minority group reported having experienced discrimination 

while seeking healthcare, and many reported being less informed or were less prepared. Much of 

the literature guiding communication is dated due to advances in technology, and findings of 

proportionally high rates of trust in the federal governmental, particularly in AA/Black and 

Latino adults, may be out of date. 

KEY QUESTION 2. What interventions or intervention components 

have been used to reduce health inequalities (or identified in 

preliminary studies) in infectious disease transmission or health 

outcomes in disasters, or infectious disease epidemics or pandemics 

in the United States?

Of the 12 studies that described interventions or intervention components, only 1 study examined

effectiveness outcomes, while the majority described acceptability and feasibility of the studied 

interventions. We identified only 1 randomized controlled trial and 1 longitudinal cohort study. 

While many focus on disaster preparedness and response, the interventions in this review often 

represent real world applications of CDC and expert panel recommendations. For instance, the 

positive effect that interpersonal, culturally appropriate education delivered by a community 

health worker can have on disaster preparedness in vulnerable communities validates the 

recommendation for using lay promotoras in delivery of health services, goods, and messaging 

in the case of a pandemic. These results could be translated into an intervention to reach 

vulnerable populations during the current COVID-19 pandemic. 

The importance of community engagement and partnership with community leaders was 

repeated often in expert-based recommendations, and was empirically grounded in some studies 

we examined. For example, 1 study found that African American clergy could be integral as 

community liaisons in facilitating the delivery of mental health services after Hurricane Katrina, 

and that churches could serve as sites for delivery of community-based services. These findings 
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mirror the recommendations of partnering with faith-based organizations and community leaders 

in order to “allay distrust and ensure successful implementation of mitigation interventions in 

minority communities” in the setting of an influenza pandemic. These recommendations support 

the use of such partnerships to lessen the disproportionate burden of COVID-19 in racial and 

ethnic minorities. While it is too early in the pandemic to expect a rigorous evaluation of the 

effect of faith-based partnerships on COVID-19 disparities, the popular media has already 

chronicled that such interventions are underway. 

The interventions described here emphasize that preparedness efforts must be prioritized and that 

marginalized communities must be included before disaster hits. Nevertheless, some of the 

lessons learned may be relevant to the current pandemic phase: months into the trajectory of 

pandemic response but prior to a safe and widely available vaccine or treatment. Proven 

preparedness interventions could also be considered for implementation now given the potential 

for future waves of COVID-19 or new epidemics to emerge.

What remains missing from the studies in this review are examples of successful systems-level 

interventions that target the distal determinants of worse outcomes of influenza illness in 

vulnerable populations. This is despite evidence from the H1N1 pandemic that variables of 

exposure that occur at higher rates among these vulnerable groups, such as inability to take sick 

leave, can drastically affect disease rates. In key stakeholder reports, we find multiple systemslevel recommendations, such as liberal workplace leave and teleworking policies, wage freezes 

and childcare vouchers, and creating an ethical and equitable system for ensuring access to 

treatment and vaccination, particularly among the uninsured. These interventions may already be 

underway, and researchers and policymakers should actively test their impact on health 

disparities so that lessons learned may be applied to our current and possible future disease 

epidemics. 

INEQUITIES IN H1N1 VACCINE UPTAKE, CONTRIBUTING FACTORS, 

AND INTERVENTIONS 

We identified 10 studies examining disparities in H1N1 vaccine uptake in the US during the 

2009-2010 flu season. We found moderate-strength evidence that vaccine uptake was lower in 

AA/Black than White populations from 4 of 6 studies presenting unadjusted data. We also found 

low-strength evidence of lower vaccine uptake for Latino populations, although there was some 

inconsistency in results. The evidence for Asian, AI/AN, and Pacific Islander populations 

compared to Whites was insufficient. 

Three studies looked at disparities by socioeconomic status (SES) and provide low-strength 

evidence that lower-SES individuals were less likely to have been vaccinated. Lastly, a very 

small study of rural versus urban participants provided insufficient evidence for H1N1 vaccine 

uptake between those populations. There was no evidence by disability status (see Table i).
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Table i. Strength of the Evidence for Studies of H1N1 Vaccine Uptake

Population of 

interest

Comparator 

population

# of 

studies

Vaccine 

uptake 

likelihood Strength SOE justification

AA/Black

White

6 Less likely Moderate

Latino 7 Less likely Low Inconsistency

Asian 2 Unclear Insufficient Inconsistency, imprecision, 

indirectness

AI/AN 2 Unclear Insufficient Indirectness, imprecision

Pacific 

Islander 1 Unclear Insufficient Indirectness

Asian/Pacific 

Islander 1 Unclear Insufficient Indirectness, imprecision

Lower SES 

(education 

and/or 

income)

Higher SES 3 Less likely Low Inconsistency

Rural Urban 1 Unclear Insufficient Single, small study with 

multiple limitations

With 

Disabilities

Without 

Disabilities 0 No evidence --- ---

Abbreviations: AA = African American, AI = American Indian, AN = Alaska Native, SES = socioeconomic status

Studies examining H1N1 vaccines explored a wide range of factors that were either proximal or 

distal to H1N1 vaccine uptake. Health insurance coverage and availability of/access to vaccines 

were both important factors. Also important were receipt of the seasonal influenza vaccine, 

vaccine-related safety and effectiveness beliefs, and perceived susceptibility to H1N1. Across all 

factors AA/Black adults, and often Latino and low SES adults as well, were at higher risk.

We did not identify any studies of interventions specifically targeting disparities in H1N1 

vaccine uptake. However, the single vaccine intervention study we did find demonstrated greater 

Hepatitis A vaccine uptake in an ED setting after the implementation of an EHR alert system that 

informed providers of the patient’s homeless status and prompted them to recommend 

vaccination during a regional outbreak. This suggests that EHR notification systems may be 

useful in increasing vaccination, and potentially could be used in the COVID-19 vaccination 

campaign to prompt providers to recommend vaccination, especially for vulnerable groups. 

CONCLUSION

The literature examining health disparities associated with previous infectious disease epidemics, 

and in some cases disasters, may provide some guidance for the current COVID-19 response. 

Evidence consistently pointed to disparities in structural and work-related exposure to infection 

as underlying disparities, with the impact of comorbid conditions on susceptibility for more 

severe infection and higher rates of mortality playing a less certain role. Discrimination was 

reported more frequently by all racial and ethnic minorities. However, its impact on disparities 

during infectious disease epidemics is uncertain. African American/Black and Latino adults 

generally were disproportionately affected. However, Latinos with limited English proficiency 

were at especially high risk. There is moderate-strength evidence that AA/Black adults were less 
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likely to receive a H1N1 vaccine, and low-strength evidence of lower vaccination rates for 

Latinos adults of lower SES. Advances in technology, and sociopolitical shifts over the past 

decade call into question the applicability of findings. Interventions and programs from the 

disaster literature bring to light recommendations for infectious disease response by the CDC and 

other experts. In order to better prevent widespread health disparities that emerge in the wake of 

the current and future disease epidemics, more research is needed on policy- and systems-level 

interventions and their effect on the distal determinants of poor health outcomes among 

vulnerable groups. 
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EVIDENCE REPORT

BACKGROUND

Health inequalities in the United States generally, and in Veteran populations specifically, are 

well documented.1,2 Infectious disease-related outbreaks create unique challenges, as they 

involve rapid transmission and may necessitate public health measures (eg, social distancing, 

school, business and facility closures) that could inadvertently differentially impact 

disadvantaged populations, and potentially contribute to higher burdens of morbidity and 

mortality among certain populations. 

Since the first cases of severe acute respiratory syndrome (SARS) Coronavirus Disease 2019 

(COVID-19) were detected in the US in early 2020, the effects of the disease have varied greatly 

between and across regions and communities. Recent studies have shown poorer health 

outcomes for those of lower socioeconomic status and racial/ethnic minorities and higher rates of 

hospitalization among African Americans/Blacks.3-5 A study of Veterans Health Administration

(VHA) data examined COVID-19 testing patterns and results from early February to early May, 

and found that African American/Black and Latino Veterans were more likely to test positive, 

regardless of underlying medical conditions and geographic location.6

 

Similar disparities in health outcomes have been observed in past infectious disease outbreaks. In 

the 2009 H1N1 pandemic, African Americans/Blacks and Hispanics had higher rates of 

hospitalizations and mortality in Illinois.7 Patients of low socioeconomic status (SES) were also 

found to have higher odds of hospitalization in New York City.8 While it is early in the pandemic 

to know the true impact of the novel SARS- CoV2 virus on vulnerable populations, it is likely 

the US will be feeling the ripple effects of these unequal health outcomes for years. For this 

reason, research on the root causes and potential mitigating strategies of these disparities is 

crucial.

The VHA places a high priority applying lessons learned from past experience to develop and 

target evidence-based interventions to mitigate health inequalities in the current COVID-19 

pandemic and future epidemics and pandemics. 

THE CURRENT STUDY

The scope of this review was refined through a process that included a preliminary review of 

published peer-reviewed literature and iterative discussions with our operational partners. Our 

approach is guided by existing frameworks used to describe: 1) health inequalities specifically 

related to infectious disease epidemics9,10 and 2) health inequality research generally.11-13

The conceptual framework we adapted was initially developed in 2008 by Blumenshine et al14 to 

describe the mechanisms through which inequalities in influenza health outcomes occur, and was 

later adapted by Quinn, Kumar, et al to describe evidence from the H1N1 pandemic.9,10 Factors 

contributing to inequalities are categorized into the following domains: 1) exposure (eg, 

structural factors such as working and living conditions; work-related factors such as the 

inability to work from home or fear of job loss; and other factors related to childcare or public 

transportation), 2) susceptibility (eg, existing chronic conditions), 3) access to care (eg, lack of a 
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regular healthcare provider, insurance, or ability to pay co-insurance or copays), and 4) 

experiencing health-care related discrimination (see Figure 1). We expanded the model to add 

hygiene and health-related behaviors to the exposure category, broadened discrimination to go 

beyond experiences of interpersonal mistreatment and include community discrimination, as well 

as trust in healthcare systems and government, and added a category to take into consideration 

information and knowledge. 

We categorized studies based on the framework initially developed by Kilbourne in 2006,11 and 

later refined by Saha13 and Thomas.12 This framework describes 3 types of health inequality 

research. First generation studies are those that identify health inequalities, second generation 

studies examine mediating and moderating factors that may contribute to inequalities for any 

given group/population, and third generation studies examine interventions to mitigate or reduce 

inequalities. 

The aims of this evidence synthesis are to: 1) examine existing literature of infectious disease 

pandemics or epidemics predating COVID-19 to better understand the potentially modifiable 

factors that may have contributed to differential infection rates and health outcomes; and 2) 

identify interventions that have been used to mitigate and reduce health inequalities in past 

infectious disease pandemics, epidemics, or disasters. 

KEY QUESTIONS 

The Key Questions (KQs) for this study are:

KQ1: What factors contribute to disparate infection rates and health-related outcomes among 

different segments of the population during infectious disease epidemics or pandemics in the 

United States?

KQ2: What interventions or intervention components have been used to reduce health 

inequalities (or identified in preliminary studies) in infectious disease transmission or health 

outcomes in disasters, or infectious disease epidemics or pandemics in the United States?
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Figure 1. Organizational Framework Developed by Quinn, Kumar, and Colleagues

Note. Framework was developed by Quinn, Kumar, et al.

9,10

SEARCH STRATEGY

We searched MEDLINE ALL, PsycINFO, Cochrane Database of Systematic Reviews, and 

Cochrane Central Register of Controlled Trials from database inception through May 20, 2020. 

Searches included controlled vocabulary terms (eg, MeSH), along with free-text words, related 

to previous epidemics, pandemics, disasters, and health disparities. We reviewed the 

bibliographies of relevant articles and contacted experts to identify additional studies. Search 

strategies were designed and conducted by an experienced systematic review/medical reference 

librarian with input from the investigators (Appendix 1). 

We further refined search results by performing keyword searches in EndNote (X9.3.3) to 

exclude articles that are not studies (ie, errata, comments, replies, proposals), basic science 

studies and studies of animals, studies that are not of infectious disease pandemics or epidemics 

relevant to the US, and studies of only non-US state or territory populations. Titles and abstracts 

excluded via keyword search were confirmed by an investigator. 

LANGUAGES

We searched for English-language publications.
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METHODS 

INCLUSION/EXCLUSION CRITERIA

Criteria for population, interventions, comparators, outcomes, timing, and setting (PICOTS) 

were developed in collaboration with our operational partners (see Table 1). We included studies 

of adult populations examining health inequalities by race/ethnicity, SES, disability, or 

geographic location related to infectious disease epidemics predating COVID-19 or disasters 

(KQ2 only) in the United States. To examine the effectiveness of interventions, we included 

studies with a comparison group (pre-, other-, or no intervention). We also included studies that 

would help to identify potentially important components and strategies to target in future 

interventions. In preliminary searches we found very little intervention effectiveness literature. 

Given the immediacy of the pandemic and the need to identify even potentially relevant 

information that might help guide intervention development we were more inclusive. For these 

studies (eg, cross-sectional surveys, qualitative), no comparator was required. 

SCREENING PROCESS

A single reviewer screened titles and abstracts, and 2 reviewers independently assessed the full 

text of studies for inclusion based on pre-specified criteria. All discordant results were resolved 

through consensus or consultation with a third reviewer. Articles meeting eligibility criteria were 

included for data abstraction. 

OUTCOMES SELECTED

For all key questions, we examined outcomes related to the utilization and the quality of 

healthcare, health outcomes, and outcomes that were a result of measures implemented during an 

infectious disease pandemic/epidemic that may have impacted a health outcome (eg, job loss due 

to social distancing mandates).

RISK OF BIAS ASSESSMENT 

Two reviewers independently assessed the methodological quality of each study using 

established methods for each study design. For trials, we adapted criteria established by the 

Cochrane Collaboration.15 The Newcastle-Ottawa scale16 was used for observational studies, and 

we used the Critical Appraisal Skills Programme (CASP) Qualitative Checklist for qualitative 

studies.17 Disagreements were resolved by consensus or a third reviewer.
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Table 1. PICOTS 

KQ1: What factors contribute to disparate infection rates 

and health-related outcomes among different segments 

of the population during infectious disease epidemics or 

pandemics? 

KQ2: What interventions or intervention components have 

been used to reduce health inequalities (or identified in 

preliminary studies) in infectious disease transmission or 

health outcomes in disasters, infectious disease epidemics or 

pandemics in the United States?

Populations Adult Subgroups: race or ethnicity, socioeconomic status, disability, geographic location (eg, urban/rural, high density 

neighborhoods)

Intervention/

Mediating 

and 

Moderating 

Factors 

Risk of exposure:

• Structural (employment, urban/rural, living arrangement, 

crowding)

• Work-related inability to social distance

• Other measures of inability to social distance (childcare 

access, need for public transport, language or cultural 

barriers)

• Access to clean water and sanitation

• Hygiene and health-related behaviors

Susceptibility:

• Comorbid chronic diseases 

• Immunosuppression

• Psychologic and nutritional stress

Access to care:

• Regular health care provider

• Insurance

• Quality of health care

Discrimination and trust

• Interpersonal mistreatment

• Community discrimination

• Trust in healthcare systems and government

Information/Knowledge

• Emergency preparedness 

• Messaging and communication 

• Employment, telework

• Childcare

• Health care access 

Comparator • Comparison group within the same group

• Comparison to other groups relevant to the population

• Standard public health response

• No intervention or pre-intervention

• Other interventions 

• No comparator necessary for pre-intervention studies

Outcomes • Mortality

• Health care utilization and access

• Infectious-disease-related hospitalizations
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KQ1: What factors contribute to disparate infection rates 

and health-related outcomes among different segments 

of the population during infectious disease epidemics or 

pandemics? 

KQ2: What interventions or intervention components have 

been used to reduce health inequalities (or identified in 

preliminary studies) in infectious disease transmission or 

health outcomes in disasters, infectious disease epidemics or 

pandemics in the United States?

• Burden of illness

• Severity of illness

• Loss of job due to epidemic/pandemic/disaster

Timing Related to an infectious disease pandemic or epidemic Related to an infectious disease pandemic or epidemic, or disaster

Setting United States and Territories

Study design Trials, quasi-experimental, observational, descriptive, case series (depending on search yield), qualitative. Systematic reviews will 

be included if they directly address key questions. If not, reference lists will be pearled.
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SYNTHESIS 

For studies examining proximal and distal factors that may contribute to health inequalities during 

infectious disease epidemics predating COVID-19, we qualitatively synthesized the evidence and 

present it in tables organized by the framework we adapted from Quinn, Kumar, and colleagues.9,10

We organized studies examining interventions designed to mitigate health inequalities according to 

whether they were an individual or system focused intervention. We summarized findings from 

expert panel interviews and present them in tabular form. We identified a number of studies 

examining H1N1 vaccine uptake, factors associated with differential rates, and interventions to 

mitigate inequities in vaccination. We qualitatively synthesized the evidence from these studies 

and present them after Key Question 2.

ASSESSING THE OVERALL BODY OF EVIDENCE 

For studies examining H1N1 vaccine uptake we assessed the overall strength of evidence for each 

population using a method developed for the Agency for Healthcare Research and Quality’s 

(AHRQ) Evidence-based Practice Centers (EPCs).18 The AHRQ EPC method considers study 

limitations, directness, consistency, precision, and reporting bias to classify the strength of 

evidence for individual outcomes independently for randomized controlled trials (RCTs) and 

observational studies, with supplemental domains of dose-response association, plausible 

confounding that would decrease the observed effect, and strength of association, as well as 

separate guidance for applicability.19 Ratings were based on the following criteria: 

• High = Very confident that the estimate of effect lies close to the true effect for this 

outcome. The body of evidence has few or no deficiencies, the findings are stable, and 

another study would not change the conclusions.

• Moderate = Moderately confident that the estimate of effect lies close to the true effect for 

this outcome. The body of evidence has some deficiencies and the findings are likely to be 

stable, but some doubt remains. 

• Low = Limited confidence that the estimate of effect lies close to the true effect for this 

outcome. The body of evidence has major or numerous deficiencies (or both). Additional 

evidence is needed before concluding either that the findings are stable or that the estimate 

of effect is close to the true effect.

• Insufficient = No evidence, unable to estimate an effect, or no confidence in the estimate of 

effect for this outcome. No evidence is available, or the body of evidence has unacceptable 

deficiencies, precluding reaching a conclusion. 

We did not assess the strength of evidence for factors related to disparate infection rates, healthrelated outcomes (KQ1), or vaccine uptake due to heterogeneity, nor did we assess SOE for 

interventions (KQ2), as our approach to the intervention literature was exploratory. 
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RESULTS 

We reviewed a total of 9,071 articles. After title and abstract review, 163 met inclusion criteria. 

Upon full text review, we included a total of 41 studies (in 45 articles) and 5 expert panel papers. 

14 studies (16 articles) were included in Key Question 1, 12 studies (13 articles) and the 5 expert 

panel papers were included in Key Question 2, and 20 studies examined H1N1 vaccines. Seven

studies included in Key Question 1 also examined H1N1 vaccines (see Table 2 and Figure 2; the 

complete quality assessment is presented in Appendices C and D). 

Figure 2. Literature Flow Chart 

163 Potentially relevant 

articles for full text review

113 Excluded publications: 

4 Excluded population

6 Excluded subpopulation

5 Excluded setting

1 Excluded timing

24 Excluded study design/type

12 Excluded outcome

4 Excluded comparator

57 Excluded factor/intervention

25 Citations identified from reference 

lists of relevant articles and reviews, 

key experts, and other sources

9,096 Citations compiled for 

review of titles and abstracts

50 Total included 

articles

9,071 Citations identified from electronic database searches: 

7,090 from PubMed/Ovid MEDLINE May 1, 2020

1,946 from PsycINFO May 8, 2020

35 from Ovid EBM Reviews (CDSR, CCRCT) May 11, 2020

8,933 Titles and abstracts 

excluded for lack of relevance

KQ2: 18

12 studies 

(13 articles)

5 reports

KQ1: 16 

*14 studies

(16 articles)

Vaccines:

*20 studies

*7 studies were included in both KQ1 and Vaccines
Health Inequalities in Infectious Disease Epidemics Evidence Synthesis Program

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Table 2. Studies of Risk Factors by Population

Study

Author, year

N participants

Setting

Dates

Study design

Study timing

Focus

Demographics 

% female

Age (SD)

Race/Ethnicity

Education 

Unemployed 

Other

Key Question

African 

American/Black

Latino

Asian and/or Pacific IslanderAmerican Indian or Alaska Native

Limited English 

ProficientLow Socioeconomic StatusRuralDisabilityApplicability

Study Quality 

Ratings and 

Concerns 

Andrulis, 201120

N = 17

California statewide + 4 regions 

(Central Coast, Bay Area, Central 

Valley, and Los Angeles)

June and Aug 2008

Qualitative: Key informant interviews

Disaster Planning

6 Nonprofit agencies; 3 County; 

PH Depts; 3 CBOs; 2 Local 

EMR orgs; 2 State agencies; 1 

Academic 

2       Fair

Generally good, 

lacks some 

methods 

reporting.

Aten, 201021

Aten, 201122

N = 41

Southern Mississippi (Hancock,

Harrison, and Forrest Counties) 

~1 year after Hurricane Katrina

Qualitative: semi-structured 

interviews with pastors of AA 

churches

Post Disaster Mental Health 

Disparities

7% Female

Age: 51.2(10.81)

1 Catholic; 2 African Methodist 

Episcopal; 38 Missionary 

Ministry experience: 

16.91(10.41) 

2  Fair

No issues. Good 

qualitative 

methods 

described.

Bouye, 200923

N = 26

CDC Stakeholder meeting 

May 1-2, 2008

Influenza Pandemic: Low-SES, Public 

Housing Residents, Single-Parent 

Families

Federal, State, and Local HUD 

Depts.; State and Local 

agencies; CBOs and FBOs; 

Academics; Community 

Members 

2  Good NA: CDC 

Stakeholders

Boyd, 201324

N = 56 (30 rural, 26 urban)

Atlanta GA

June-Aug 2010

Clients:

Rural (n = 30) vs urban (n = 26)

Median age 23 vs 25

AA/Black: 100% vs 88%

Latino: 0 vs 4%

V  Fair ---
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Study

Author, year

N participants

Setting

Dates

Study design

Study timing

Focus

Demographics 

% female

Age (SD)

Race/Ethnicity

Education 

Unemployed 

Other

Key Question

African 

American/Black

Latino

Asian and/or Pacific IslanderAmerican Indian or Alaska Native

Limited English 

ProficientLow Socioeconomic StatusRuralDisabilityApplicability

Study Quality 

Ratings and 

Concerns 

Qualitative interviews of WIC clients 

and staff

Low-SES women

H1N1 Vaccine

< HS: 7% vs 12%

HS: 55% vs 31%

Not working: 47% vs 35%

Staff:

39.5 Median age

AA/Black: 80%

White: 20%

HS or less: 0%

Burger, 201825

N = 11,834

National Health Interview Survey 

2010

Cross-sectional Survey

H1N1 Vaccine

White: 74.2%*

Latino, US born: 9.6%*

Latino, foreign born: 16.2%*

White vs US-born Latino vs 

Latino immigrant:

Female: 51% vs 52% vs 46%

18-34yrs: 27% vs 52% vs 37%; 

35-64 yrs: 53% vs 41% vs 54%; 

65+ yrs: 19% vs 7% vs 9%

Uninsured: 13% vs 25% vs 47%

<$20K: 14% vs 16% vs 26%

<HS: 10% vs 18% vs 48%

V   Good No reporting on 

non-respondents

Cassady, 201226

N = 90

California

2010

Qualitative focus groups

H1N1 Vaccine

66% Female

Age (mean): 36

Mean yrs in US: 16

8 focus groups: Central Valley 

and Central Coast; 2 in suburbs 

of a large city in Southern CA

V   Good Good quality 

Castillo, 201827

N = 1,131

ED, San Diego, CA

Retrospective pre- postAug 2016 - Jan 2018 

Hepatitis A Vaccine for the Homeless 

population

NR V  Fair Good quality
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Study

Author, year

N participants

Setting

Dates

Study design

Study timing

Focus

Demographics 

% female

Age (SD)

Race/Ethnicity

Education 

Unemployed 

Other

Key Question

African 

American/Black

Latino

Asian and/or Pacific IslanderAmerican Indian or Alaska Native

Limited English 

ProficientLow Socioeconomic StatusRuralDisabilityApplicability

Study Quality 

Ratings and 

Concerns 

Eisenman, 200928 and Glik, 201429

N = 187

Los Angeles County

2006-2007

Randomized longitudinal cohort

Disaster Preparedness Intervention

I vs C

% Female: 66.67% vs 68%

Age: 37.08 vs 36.97

≤HS: 77% vs 75%

HS+: 23% vs 25%

Below FPL: 66.67% vs 71%

Not working: 24.14% vs 29%

2   Fair Fair quality

Etingen 201230

LaVela31

N = 3,384

Hines, Illinois

Veterans with Spinal Cord Disabilities

August 2010

Cross-sectional Survey

H1N1 and H1N1 Vaccine

3% Female

Age: 61.82(11.70)

AA/Black: 14.71%

Latino: 8.42%

White: 73.05%

< HS: 6.39%

HS: 20.21%

1 

V     Fair Veteran sample, 

low response rate

Freimuth, 201432

N = 1,543

Nationally Represented 

June-July 2009

Cross-sectional Survey

H1N1 

51.8% Female

Age: 46.3(0.54)

AA/Black: 11.4%

Latino:13.7%

White: 68.8%

<HS: 13.6%

HS: 31.7%

1 

V   Good --

Frew, 201233

N = 503

Atlanta, GA

Sept-Dec 2009

Cross-sectional Survey

H1N1 Vaccine

50.9% female

18-24: 22.1%; 25-63; 70.4%; 64-

70: 2.4%

Latino: 6.2%

AA/Black: 79.3%

Multiracial: 5.6%

Asian: 2.4%

AI/AN: 1.2%

< HS: 16.7%

Unemployed: 48.7%

≤$40K/year: 81.9%

V   Fair

Multivariable 

regression on 

some, but not all

relevant factors.
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Study

Author, year

N participants

Setting

Dates

Study design

Study timing

Focus

Demographics 

% female

Age (SD)

Race/Ethnicity

Education 

Unemployed 

Other

Key Question

African 

American/Black

Latino

Asian and/or Pacific IslanderAmerican Indian or Alaska Native

Limited English 

ProficientLow Socioeconomic StatusRuralDisabilityApplicability

Study Quality 

Ratings and 

Concerns 

Galarce, 201134

N = 1,569

Nationally Representative 

March 2010

Cross-sectional Survey

H1N1 Vaccine

Female: 50.9%*

Age: 18-29: 21.4%*; 30-44: 

27.9%*; 45-59: 27.9%*; 60+: 

22.9%*

White: 66.6%*

AA/Black: 10.6%*

Latino: 14.5%*

<HS: 13.9%*

V     Good ---

Gargano, 201135

N = 102

Two rural counties in GA

September 2009

Cross-sectional Survey

H1N1 Vaccine

Female: 69%

Age range: 23-67 

White: 72%

AA/Black: 25%

Latino: 2%

V   Fair

High 

nonresponse 

rate, small 

sample

Goodman, 200936

N = 6 

New Orleans, LA

post-Hurricane Katrina

Culturally Competent Disaster 

Response mental health students

100% Female

Age: 31

1 Haitian American

1 Indian American

4 European Americans

2  Fair NA: Program 

Evaluation

Hennessy, 201537

N = 381

AI/AN N = 72

AK, AZ, NM, OK, and WY

2009

Case Control 

H1N1 Mortality

Cases vs Controls

Female: 50% vs 54%

≤ 20: 19% vs 50%; 21-60: 59% 

vs 47%; ≥ 61: 22% vs 3%

AI/AN: 17% vs 11%

AA/Black: 4% vs 1%

Asian: 0% vs 6%

White: 76% vs 72%

1  Fair

Issues with 

method of 

ascertainment 

and comparability 

of cases and 

controls.

Hernandez, 201938

N = 225

Pregnant women in 2 large

midwestern cities

2009-2010

100% Female

Age: 29.9(5.3)

White: 67.7%

AA/Black: 5.4%

Latino: 5.8%

V  Fair

Response rate 

low, and unclear 

if non-responders 

were different
Health Inequalities in Infectious Disease Epidemics Evidence Synthesis Program

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Study

Author, year

N participants

Setting

Dates

Study design

Study timing

Focus

Demographics 

% female

Age (SD)

Race/Ethnicity

Education 

Unemployed 

Other

Key Question

African 

American/Black

Latino

Asian and/or Pacific IslanderAmerican Indian or Alaska Native

Limited English 

ProficientLow Socioeconomic StatusRuralDisabilityApplicability

Study Quality 

Ratings and 

Concerns 

Cross-sectional Survey

H1N1 Vaccine

Other race: 21.5%

≤HS: 8.9%

Some college: 12.5%

College graduate: 38.8%

Hutchins, 200939

N = NR

CDC Stakeholder Meeting

May 1-2 2008

Influenza Pandemic: Racial and 

Ethnic Minorities

State and Local PH officials; 

health care providers, State and 

Local EMR professionals, 

Academics, CBOs, FBOs, 

advocacy organizations, racial 

and ethnic minorities

2      Good NA: CDC 

Stakeholder

Kumar, 201240

Kumar, 201241

N = 2042

Nationally Representative 

Jan 2010

Cross-sectional Survey

H1N1 and H1N1 Vaccine

Women: 51.7%

Age: 44.9 (SE = 0.4)

AA/Black: 28.94%

Latino: 29.48%

White: 41.58%

<$25K: 30.5%

< HS: 18.8%

1 

V    Good --

Levy8

N = 374

New York City

Oct 2009-Feb 2010

Case Control

H1N1 Hospitalization

Cases vs Controls

58% vs 57% Female

Age: 47 vs 42

AA/Black: 22% vs 9%

Latino: 24% vs 12%

White: 22% vs 46%

<HS: 35% vs 5%; HS: 41% vs 

44%

1  Fair

Expected uneven 

response rate, so 

controls were 

oversampled. 

Matched 2:1 as 

planned.

Lin, 201442

Lin, 201843

N = 1,569

Nationally Representative 

Feb-March 2010

Cross-sectional Survey

H1N1 and H1N1 Vaccine

51% Female

Age: 49% were ≤44

AA/Black: 11%

Latino: 14%

White: 68%

<HS 14%

Unemployed: 21%

1 

V  Good --
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Study

Author, year

N participants

Setting

Dates

Study design

Study timing

Focus

Demographics 

% female

Age (SD)

Race/Ethnicity

Education 

Unemployed 

Other

Key Question

African 

American/Black

Latino

Asian and/or Pacific IslanderAmerican Indian or Alaska Native

Limited English 

ProficientLow Socioeconomic StatusRuralDisabilityApplicability

Study Quality 

Ratings and 

Concerns 

Lin, 201744

N = 627

Nationally Representative 

December 2013

Cross-sectional Survey

MERS and Previous Pandemics

51% Female

18-29: 19%; 30-39: 15%; 40-49:

20%; 50-59: 20%; ≥60: 26%

AA/Black 13%

Latino 17%

White 69%

< HS 12%

1    Fair

Unclear whether 

confounding 

factors were 

controlled.

Liu, 201745

N =148

Washington, DC

Dec 2015- June 2016

Qualitative: Intercept interviews

Medical Countermeasures

Female: 35%

Age: 32 (15.47)

Caucasian: 33.8%‡

African American: 35.8%‡

Hispanic/Latino: 22.3%‡

Unreported race: 8.1%‡

V   Fair

Generally good, 

lacks some 

methods 

reporting.

McCabe, 201346

N = 178

Maryland

Date: NR

Program Evaluation

Community-level Disaster 

Preparedness 

Faith-based Participants:

73% Female

AA/Black: 31%

Latino: 1%

Biracial: 2fx% 

2  Fair NA: Program 

Evaluation

McCauley47

N = 46

Four Communities in New England

Focus Groups

H1N1

City A Only:

57.7% Female

Age: 45

AA/Black: 81.25% 

Latino: 12.5% 

White: 6.25% 

< HS: 43.75%; HS: 43.75% 

< Poverty: 50% 

1  Poor

Qualitative 

synthesis not 

very more robust.

Mesch, 2014

N = 968

National

October 2009

Cross-sectional Survey 

51% Female

Age: 45.80(17.84)

AA/Black: 11%

Latino: 7.6%

< HS: 14.2%

1 

V    Good

Unclear if 

respondents 

similar to nonrespondents.
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Study

Author, year

N participants

Setting

Dates

Study design

Study timing

Focus

Demographics 

% female

Age (SD)

Race/Ethnicity

Education 

Unemployed 

Other

Key Question

African 

American/Black

Latino

Asian and/or Pacific IslanderAmerican Indian or Alaska Native

Limited English 

ProficientLow Socioeconomic StatusRuralDisabilityApplicability

Study Quality 

Ratings and 

Concerns 

H1N1 and H1N1 Vaccine HS: 30.9%

Nassar, 2014 48

N = 50

Washington DC

2010

Feasibility RCT

Monitoring H1N1 Symptoms

100% women

I vs C

Age: 24.1(6.3) vs 23.5(5.2)

AA/Black: 87% vs 84.6%

Medicaid Eligible: 87% vs 80.7%

2  Fair

Poor quality, 

unblinded, small 

feasibility trial

Obaid, 201749

N = 667 participants

Rural Nebraska

2010-2013

Program evaluation

Rural Infectious Disease Disaster 

Preparedness

83 agencies across 3 medical 

response systems/coalitions 

Also: EMS, Fire, Emergency 

Management, county officials, 

health care and public health 

staff

2  Fair NA: Program 

Evaluation

Person, 201450

N = 70+ people, 50 agencies/orgs

April 2003

NCID/CDC Response Focus Groups 

(11)

SARS-related Stigma

Chamber of Commerce, trade 

associations, school officials, 

public health, mental health 

professionals, academics

2  Good NA: NCID/CDC

Plough, 201151

N =163,087 

Oct-Dec 2009

Cross-sectional

H1N1 Vaccine

Black: 3.0%

Asian: 28.5%

White: 20.5%

Latino: 47.0%

AI/AN: 0.3%

Pacific Islander: 0.7%

V     Fair

No control for 

confounders; 

Inappropriate 

denominator; 

Sample not 

representative

Price, 201352

N = 1,180

2 South Carolina counties (Galveston 

and Chambers) 

~1 yr post-Hurricane Ike

Pre-post feasibility study, qualitative 

follow-up

50.7% Female

Age: 47(17)

AA/Black: 13.7%

Latino: 6.3%

White: 80%

HS: 21.4%

Some college: 36.1%

2   Fair --
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Study

Author, year

N participants

Setting

Dates

Study design

Study timing

Focus

Demographics 

% female

Age (SD)

Race/Ethnicity

Education 

Unemployed 

Other

Key Question

African 

American/Black

Latino

Asian and/or Pacific IslanderAmerican Indian or Alaska Native

Limited English 

ProficientLow Socioeconomic StatusRuralDisabilityApplicability

Study Quality 

Ratings and 

Concerns 

Post-Disaster Mental Health 

Intervention

<$40K: 21.4%

Quinn, 200953

Quinn, 201110

2009 N = 1,543

2011 N = 1,479

Nationally Representative 

June 2009

Cross-sectional Study

H1N1 and H1N1 Vaccine

2009

51.8% Female

Age: 46.3

AA/Black: 11.4%

Latino: 13.7%

< HS: 13.6%; HS: 31.7%

2011

51.2% Female

18-34: 27.9%; 35-64: 57%; ≥65: 

15.1%

AA/Black: 13.1%

Latino (LEP): 15.5%

English-speaking Latino: 4.4%

<$25K: 25.5%

<HS: 14%

1 

V    Good --

Redelings, 201254

N =1,541

Los Angeles County public health 

clinics

June-August 2010

Cross-sectional Survey

H1N1 Vaccine

Female: 48%

18-24 yrs: 24%

25-34 yrs: 29%

35-44 yrs: 18%

45-54 yrs: 15%

55-64 yrs: 8%

60+ yrs: 2%

Asian/PI: 11%

Black: 33%

V    Fair ---

Rosenbaum, 201855

N = 22 (completed training)

Oceana County, MI

NR 2   Fair NA: Program 

Evaluation
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Study

Author, year

N participants

Setting

Dates

Study design

Study timing

Focus

Demographics 

% female

Age (SD)

Race/Ethnicity

Education 

Unemployed 

Other

Key Question

African 

American/Black

Latino

Asian and/or Pacific IslanderAmerican Indian or Alaska Native

Limited English 

ProficientLow Socioeconomic StatusRuralDisabilityApplicability

Study Quality 

Ratings and 

Concerns 

June & October 2016

Program Evaluation

Disaster Preparedness Training for 

MSFW

Santibanez,201356

N = 55,850

2009 National H1N1 Flu Survey 

(CDC-sponsored) 

Nov 2009-June 2010

Cross-sectional Survey

H1N1 Vaccine

50.8% Female

Age: 46.9 (0.6)

White: 69.3% 

Black: 11.5% 

Latino:14.3% 

Uninsured: 12.9%

<HS: 13.9%/ HS: 31.1%

V    Good ---

Schoch-Spana, 201057

N = 33

Multiple sites nationally

July - October 2009

Qualitative stakeholder interviews

H1N1

18 Community clinic executives

6 Government agencies

7 MSFW advocacy groups

2 Industry and academic 

contacts

1   Good

Stakeholder 

interviews, 

qualitative 

methods not 

adequately 

described.

Steege, 200958

Briefing report from National 

Farmworker Health Conference and 

Western Migrant Stream Forum 

organizers

May 2008

Influenza Pandemic: Farmworkers

NR 2   Good NA: Report

SteelFisher, 201559

N = 2,355

Nationally Representative March-April 

2010

Cross-sectional Survey

H1N1 and H1N1 Vaccine

Female: NR

AA/Black: 14.3%

Latino: 13.8%

Asian: 11.7%

AI/AN: 11.4%

White: 48.8%

1 

V      Good --

Truman, 200960

CDC Stakeholder meeting 

May 1-2, 2008

Public health scientists

Service program managers 2     Good NA: CDC
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Study

Author, year

N participants

Setting

Dates

Study design

Study timing

Focus

Demographics 

% female

Age (SD)

Race/Ethnicity

Education 

Unemployed 

Other

Key Question

African 

American/Black

Latino

Asian and/or Pacific IslanderAmerican Indian or Alaska Native

Limited English 

ProficientLow Socioeconomic StatusRuralDisabilityApplicability

Study Quality 

Ratings and 

Concerns 

Influenza Pandemic: Immigrants and 

Refugees

Uscher-Pines, 201161

N = 4,040

Nationally Representative 

March 2010

Cross-sectional Survey

H1N1 Vaccine

White vs AA/Black vs Latino 

(unweighted):

Female: 49% vs 63% vs 51%

18-49: 24% vs 24% vs 32%; 50-

64: 37% vs 50% vs 44%; ≥65: 

39% vs 26% vs 24%

HS graduate: 28% vs 22% vs 

26%; College graduate: 33% vs 

32% vs 27%

Uninsured: 9% vs 15% vs 17%

Income <$25K: 18% vs 28% vs 

17%

V   Good ---

Vaughan, 200962

CDC Stakeholder meeting 

May 1-2, 2008

Influenza Pandemic: Risk

Communication and Vulnerable 

Populations

Public health experts

Program managers 2        Good NA: CDC

Witrago, 201163

N = 209

Fresno County, CA

Cross-sectional Survey

Influenza Pandemic Preparedness: 

Rural Latinos

71% Female

18-34: 35%; 35-54: 57%; 55-64: 

5%; 65 older: 1%

Born in Mexico: 89%

<HS: 53%

1    Fair

No control for 

confounders, 

methods poorly 

reported.

Wyte-Lake, 201464

N = 7 

Single urban VHA HBPC program 

Qualitative: semi-structured 

interviews 

Disaster Preparedness

Associated chief of staff

Program manager

HBPC practitioners (nursing, 

OT, social work, psychology)

2  Fair Small study, 

poorly reported
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Study

Author, year

N participants

Setting

Dates

Study design

Study timing

Focus

Demographics 

% female

Age (SD)

Race/Ethnicity

Education 

Unemployed 

Other

Key Question

African 

American/Black

Latino

Asian and/or Pacific IslanderAmerican Indian or Alaska Native

Limited English 

ProficientLow Socioeconomic StatusRuralDisabilityApplicability

Study Quality 

Ratings and 

Concerns 

Wyte-Lake, 201965

N = 754 patients

10 VA HBPC sites in 8 states

April-October 2017

Cross-sectional survey evaluating a 

patient assessment tool

Disaster Preparedness

16% high risk; 44% medium risk; 

40% low risk

25% on oxygen

30% chair-bound

55% assistive device

33% cognitive impairment

20% communication limitation

2  Poor

No control for 

confounders, 

methods poorly 

reported

Yip, 200966

N = 100

Seattle, WA

April - June 2009

Cross-sectional Survey

H1N1

73% Female

Age: Median 47.5

Years in US: 

0-5: 39%; 6-10: 27%; >10: 35%

1  Fair

Pilot study, no 

control for 

confounders, 

methods poorly 

reported.

Abbreviations: AA = African American; AI/AN = American Indian/Alaska Native; C = control; CBO = community based organization; CDC = Centers for Disease Control; ED = 

emergency department; EMR = electronic medical record; HBPC = Home-based Primary Care; HS = high school; I = intervention; LEP = limited English proficiency; MSFW = 

migrant and seasonal farmworkers; NCID = National Center for Infectious Disease; OT = Occupational Therapy; PH = public health; PI = Pacific Islander; RCT = randomized 

controlled trial; SD = standard deviation; SES = socioeconomic status; US = United States; V=Vaccine-related study; VHA = Veterans Health Administration
Health Inequalities in Infectious Disease Epidemics Evidence Synthesis Program

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KQ1: What factors contribute to disparate infection rates and healthrelated outcomes among different segments of the population during 

infectious disease epidemics or pandemics?

We identified 14 studies (16 articles) that met criteria for Key Question 1. All but 2 studies 

focused on factors that potentially contributed to health disparities during the 2009 H1N1 

influenza pandemic. Other studies examined the 2012 Middle East respiratory syndrome 

(MERS) outbreak,44 or planning for future infectious disease epidemics.63 Six studies examined 

both African American/Black and Latino populations.10,30,32,40,44,59 Two studies examined Asian 

populations,50,59 and 2 focused on American Indian/Alaska Natives.37,59 Five studies examined 

participants with limited English proficiency (LEP), 4 were of Latino populations,10,47,57,63 and 1 

of Chinese immigrants.66 Five studies (6 articles) examined persons with low SES,8,30,42-44,59 and 

1 study was of Veterans with spinal cord disorders30 (see Table 2).

We have organized findings in this section to report: 1) associations between demographic 

characteristics (eg, race/ethnicity, SES) and hypothesized mediating risk factors (eg, exposure, 

susceptibility); 2) associations between risk factors and outcomes (eg, influenza-like illness, 

hospitalization); 3) evidence of mediation of associations between demographic characteristics 

and outcomes by hypothesized risk factors; and 4) findings from qualitative studies. 

Exposure

Structural

Three studies examined structural factors of exposure.10,40,57 Two nationally representative crosssectional studies examined measures of density by race and ethnicity. After controlling for 

sociodemographics, the studies by Kumar et al (N = 2,042)40 and Quinn and colleagues (N = 

1479)10 both found that compared to Whites, AA/Black and Latino (both English-speaking and 

those with Limited English Proficiency [LEP]) participants were more likely to live in 

apartments and metropolitan areas. Both studies also found that AA/Black participants had a 

similar number or fewer children per household than Whites, and Kumar and colleagues found 

that Latino families reported significantly more children per household than Whites.40 Quinn et 

al disaggregated Latinos by language and found that while English-speaking Latino participants 

reported fewer children per household, Latino participants with LEP reported more.10

Kumar et al also examined the relationship between structural factors and the likelihood of a 

self-reported influenza like illness (ILI) in both the participant and their household. They found 

the number of children in a household was positively related to the likelihood of ILI in the 

household (OR = 1.10, 95% CI NR). Neither apartment nor metropolitan living were related.40 

No studies directly addressed the role of structural factors of exposure in mediating disparities in 

epidemic-related outcomes. 

The third study, by Schoch-Spana et al, was a qualitative analysis of interviews with stakeholders

from government agencies, community health clinics, advocacy groups, and academia engaged 

in work related to H1N1 and Latino migrant and seasonal farmworkers (N = 33). Study methods 

were not well described. Stakeholders acknowledged that labor camp living conditions, with 10-
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12 people or 2-3 families sharing small cabins, inhibited compliance with official H1N1 

containment guidance (see Tables 2 and 3 for more detail).57 

Work-Related

The same 3 studies also looked at work-related exposure factors. 

The studies by both Quinn and colleagues10 and by Kumar et al40 used a work-related social 

distancing index that assessed the ease or difficulty of staying home from work if needed (eg, 

paid sick leave, ability to work from home), and found that compared to Whites, AA/Black 

participants were similarly40 or better able to social distance.10 Kumar and colleagues found that 

Latinos were less able to social distance.40 Examining the role of LEP, Quinn and colleagues 

found that there was no difference between English-speaking Latinos and Whites, but that 

Latinos with LEP reported more work-related barriers to social distancing.10

Kumar and colleagues40 also examined the relationship between work-related factors and selfreported ILI and found an 8% increase in the likelihood of an ILI in the participant and a 6% 

increase for each unit increase in the social distancing index. 

No studies directly addressed the role of work-related exposure factors in mediating disparities in 

epidemic-related outcomes. 

Findings from Schoch-Spana and colleagues’ qualitative study suggest that Latino migrant and 

seasonal farmworkers have numerous barriers to staying home from work. As a result it is not 

uncommon for them to go to work when sick and also to take their (sick or well) children with 

them (see Tables 2 and 3 for more detail).57 

Social Distancing

Four studies (5 articles) examined measures of social distancing by race, ethnicity, and 

SES.10,40,42,43,59 Across studies, findings comparing AA/Black and Latino to White participants 

were inconsistent. Two studies found that AA/Black participants were similar to Whites or were

more likely to social distance (eg, avoid public transportation, air travel, social gatherings, 

people with flu-like symptoms).40,59 However, Quinn et al found that AA/Blacks reported being 

more dependent on public transportation.10 One study found that Latinos were better able to 

social distance.59 However, both Quinn et al10 and Kumar and colleagues40 found that both 

English-speaking Latinos and those with LEP reported more barriers to social distancing than 

Whites.

Quinn et al found that AA/Black and Latino participants with LEP reported more difficulty than 

Whites in securing childcare that wasn’t with a group of children. However, English-speaking 

Latinos and Whites were similar.10

Only 1 study (N = 2,355) examined Asian and American Indian/Alaska Native (AI/AN)

populations and found that both groups were as or more likely than Whites to avoid social 

gatherings, air travel, and public transportation, and to avoid people with flu-like symptoms

during the H1N1 epidemic.59 
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Two studies (3 articles) examined the relationship between SES and social distancing and also 

reported conflicting findings.42,43,59 One was a cross-sectional study (reported in 2 articles) of the 

H1N1 epidemic (N = 1569) that found no association between educational attainment and 

“staying home,” social distancing (including using public transportation), and the reduction of 

contact with non-household members.42,43 The other found that participants with less than a high 

school education were more likely to avoid social gatherings, air travel, and public transportation 

but less likely to avoid people with flu-like symptoms.59

Kumar and colleagues examined the relationship between dependence on public transportation 

and self-reported ILI in the self and household. No differences were reported (see Tables 2 and 

3). 40 

No studies directly addressed the role of social distancing in mediating disparities in epidemicrelated outcomes. 

Hygiene-Related Behaviors

Two cross-sectional studies (3 articles) examined hygiene-related behaviors during the 2009 

H1N1 pandemic.42,43,59 Findings were consistent that participants with lower education 

attainment were as or more likely than those with some college or college graduates to report 

adhering to recommended cleaning and hygiene practices during H1N1 (eg, frequent 

handwashing, hand sanitizer, coughing with mouth covered, cleaning more frequently). One 

study compared White to AA/Black, Latino, Asian, and AI/AN participants. Racial and ethnic 

minority participants were not significantly less likely to report following recommended cleaning 

and personal hygiene practices (see Tables 2 and 3 for detail).59

No studies examined associations of hygiene-related behaviors with epidemic-related outcomes, 

or the role of hygiene-related behaviors as mediators of racial or socioeconomic disparities in 

outcomes. 

Susceptibility

One cross-sectional study examined comorbid conditions associated with susceptibility to H1N1 

complications and found that AA/Black, English-speaking Latino participants, and Latino 

participants with LEP all had similar or fewer comorbid conditions than Whites.10 

Two case control studies examined the relationship between comorbid conditions associated with 

susceptibility to H1N1 complications and patient outcomes. One study (N = 374) was conducted 

in New York City, and looked at the impact of both education and neighborhood poverty on 

hospitalization for H1N1. It found that overall, adults with 1 or more comorbid conditions 

associated with susceptibility to H1N1 complications were significantly more likely to be 

hospitalized. (OR = 12.83, 95% CI [4.99-32.97]). In a model adjusted for education and access to 

care, adults with 1 or more comorbid conditions remained more likely to be hospitalized (AOR = 

7.61, 95% CI [2.68-21.65]). Comorbid conditions (and access to care) only partially mediated the 

relationship between education and hospitalization. After adjustment, compared to adults with 

some college or more, both adults with less than or equal to a high school education (AOR = 

21.21, 95% CI [5.32-84.53]) and high school graduates (AOR = 3.82, 95% CI [1.64-8.90]) were 

still more likely to be hospitalized.8

 
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The findings were similar for the relationship between neighborhood poverty and hospitalization. 

After controlling for access to care and the percentage of residents below the federal poverty 

level (FPL) in a neighborhood, adults with 1 or more comorbid conditions were 10 times more 

likely to be hospitalized (AOR = 10.05, 95% CI [3.65-27.64]). After adjusting for comorbid 

conditions (and access to care) adults from neighborhoods with 30% or more residents under the 

FPL remained 5 times more likely to be hospitalized (AOR = 5.02, 95% CI [1.83-13.89]).8

The second case control study (N = 381) found that although American Indian/Alaska Natives 

were nearly twice as likely to die from H1N1 (OR = 1.95, 95% CI [1.03–3.68]), comorbid 

conditions and age mediated the relationship and AI/AN race was not an independent risk factor 

for H1N1 mortality.37

Access to Care

Findings across a disparate array of studies were mixed in terms of the association among 

race/ethnicity, SES, access to care variables, and health outcomes. Three cross-sectional 

studies10,59,63 and a qualitative study57 examined factors related to access to care during H1N1. 

Findings indicated that AA/Black and English-speaking Latino participants were no different 

from Whites on an access to care index measure (see Table 3). However, Latinos with LEP 

scored significantly lower.10 In addition, controlling for demographics, access to healthcare, and 

H1N1-related attitudes, AA/Black, Latino, and AI/AN participants were more likely than Whites 

to have spoken to a doctor or other healthcare professional about how to protect themselves or 

their families from H1N1. There was no difference for Asians or low SES participants.

59 Other 

studies found that among LEP Latinos, a primary reason for not having medication on hand in 

case of an influenza pandemic was a lack of health insurance.63 (see Tables 2 and 3).

One additional study examined the relationship between measures of access to care and 

hospitalization for H1N1 in New York City. Findings indicated no difference in hospitalization 

when comparing adults with and without health insurance (OR = 0.42, 95% CI [0.12-1.49]). 

However, people with private (versus public) insurance were less likely to be hospitalized (OR = 

0.15, 95% CI [0.07-0.32]). There was no significant relationship between having a primary care 

provider and hospitalization for H1N1 (OR = 0.88, 95% CI [0.35-2.18]). In a model adjusted for 

participant education and comorbid conditions, the relationships between having a primary care 

provider (AOR = 1.88, 95% CI [0.50-7.05]) and having health insurance (AOR = 0.73, 95% CI 

[0.14-3.70]) and hospitalization for H1N1 remained nonsignificant. When adjusting for access to 

care (and comorbid conditions), compared to adults with some college or more, both adults with 

less than or equal to a high school education (AOR = 21.21, 95% CI [5.32-84.53]) and high 

school graduates (AOR = 3.82, 95% CI [1.64-8.90]) were more likely to be hospitalized.

The findings were similar for the relationship between neighborhood poverty and hospitalization. 

After controlling for comorbid conditions and the percentage of residents below the federal 

poverty level (FPL) in a neighborhood, neither having a primary care provider (AOR = 1.50, 

95% CI [0.42-5.30]) nor health insurance (AOR = 0.42, 95% CI [0.09-2.04]) were significantly 

associated with hospitalization. After adjusting for access to care (and comorbid conditions) 

adults from neighborhoods with 30% or more residents under the FPL were 5 times more likely 

to be hospitalized (AOR = 5.02, 95% CI [1.83-13.89]).8

 
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A qualitative study of migrant and seasonal farmworkers found that ingrained barriers for low 

healthcare utilization such as lack of money for care, lack of insurance or ability to access public 

assistance, lack of knowledge of migrant health clinics, lack of Spanish and indigenous language 

materials and support at health centers, lack of transportation, and fear of deportation, would 

likely impede treatment for H1N1.57

Discrimination and Trust

One cross-sectional study10 and 1 qualitative study57 examined experiences of discrimination in 

health care settings. Findings indicated that compared to Whites, AA/Black, and Latino 

participants (both English-speaking and LEP) were more likely to have ever experienced 

discrimination when seeking health care.10 

Three cross-sectional studies32,53,67 and 1 qualitative study47 examined trust in the government 

and government agencies regarding the handling of H1N1. Findings indicated similar or higher 

government trust scores among AA/Black and Latino participants,53,67 and that AA/Black and 

Latino participants were more likely than Whites to trust the Federal Government specifically, 

including President Obama specifically, with no difference for the Centers for Disease Control 

(CDC), or state or local governments.32 There was no difference in trust by SES (see Tables 2 

and 3).32 

No studies examined associations of discrimination or trust with epidemic-related outcomes, or 

the role of discrimination and trust as mediators of racial or socioeconomic disparities in 

outcomes.

Findings from a qualitative study suggests that AA/Black participants were unsure who to trust 

with regard to H1N1 due to mixed messages in the media and by government officials.47 Another 

found that providers-stakeholders reported knowledge of stigmatization directed towards Latino 

migrant and seasonal farmworkers by other providers. For example, 1 participant reported 

overhearing a colleague say, “People [are] coming from Mexico and they’re bringing in the 

swine flu” (see Tables 2 and 3).57 

Information and Knowledge

Five cross-sectional studies examined factors related to information and knowledge of 

pandemics predating COVID-19. A study of Veterans with spinal cord injuries (N = 3,384) 

found that, compared to Whites, fewer AA/Black and Latino Veterans, and fewer low SES

participants reported receiving “adequate” information about H1N1.30 Another study asked 

respondents about the MERS outbreak and previous pandemics. AA/Black and Latino 

participants were similar to Whites in their awareness of previous pandemics; however, 

participants with lower SES (education) had less awareness. Both AA/Blacks and Latino 

participants were less likely than Whites to have heard of MERS, and AA/Black participants 

were less likely to have accurate knowledge about MERS. There was no difference by SES for 

having heard of or having accurate knowledge of MERS (see Table 3).44 

Two studies examined information and knowledge by English proficiency. A small crosssectional survey of Latinos in a rural setting in California (N=209) compared English-speaking 

Latinos to LEP Latinos and found that those with LEP scored lower on an influenza pandemic 
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preparedness scale.63 The second was a study of Chinese residents in Seattle. It found that 

compared to those with better English skills, LEP participants were less likely to feel well 

informed about H1N1. Commonly used channels for information among LEP participants were 

TV (including Chinese-language channels; 81%), Chinese-language newspapers (69%), and 

community-based organizations (30%; see Table 3 for more detail). The study did not control for 

confounding variables.66

A study (N = 1,569) examined H1N1 knowledge and misconceptions by SES (education), and 

found that after controlling for sociodemographics and communication behaviors, there were no 

differences on an H1N1 transmission knowledge and misconception index. However, even after 

controlling for confounders, participants with less than a high school education were more likely 

than those with a college degree to avoid eating pork products.42,43

No studies examined associations of information and knowledge with epidemic-related 

outcomes, or the role of information and knowledge as mediators of racial or socioeconomic 

disparities in outcomes. 
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Table 3. Studies Examining Measures of Exposure, Susceptibility, Access to Care, Trust and Discrimination, and Information 

and Knowledge

Author, Year

N Participants

Focus 

Population(s) of Interest Mediating Factors

Exposure

Structural

Kumar, 201240

N = 2,042

H1N1

• AA/Black

• Latino

Children per Household:

• Controlling for age, gender, and SES, Latinos had significantly more children per household (b = 0.43; p < 

0.001.). 

• There was no difference between AA/Blacks and Whites.

• Each additional child resulted in a 10% increase in the likelihood of an influenza like illness in the household (b = 

0.10; p < 0.05). 

Density: 

• Controlling for age, gender, and SES, Latinos (b = 0.95; p < 0.01) and AA/Blacks (b = 0.66; p < .05) were more

likely to live in an apartment building. 

• Latinos (b = 1.08; p < 0.001) and AA/Blacks (b = 0.64; p < 0.001) were also more likely to live in a metropolitan 

area. 

• There was no relationship between density and likelihood of an influenza like illness.

Levy 20138

N = 374

H1N1 Hospitalization

• Low SES: Neighborhood 

Poverty

Hospitalization:

• Multivariate analysis that also included SES (education), having a primary care provider, health insurance, and 

1+ comorbid condition found that those living in neighborhoods with 30+% poverty were more likely to be 

hospitalized (AOR = 5.02 [95% CI 1.82-13.89])

Quinn 201110

N = 1,479

H1N1

• AA/Black

• Latino (English and LEP)

Number of People in a Household:

• Controlling for age, gender, and SES, Spanish-speaking Latinos had more adults (b = 0.55, p < 0.001) and 

children (b = 0.33, p < 0.001) per households than Whites. 

• AA/Blacks had fewer adults per household (b = -0.22, p = 0.01). 

• There was no difference between English-speaking Latinos and Whites.

Apartment and Metropolitan Living: 

• Controlling for age, gender, and SES, AA/Blacks (AOR = 2.4, 95% CI [1.3-4.3]); AOR = 3.1, 95% CI [1.9-5.1]), 

English-speaking Latinos (AOR = 5.1, 95% CI [1.4-19.7]); AOR = 2.1, 95% CI [1.0-4.4]), and Spanish-speaking 

Latinos (AOR = 3.9, 95% CI [2.1-7.1]); AOR = 3.2, 95% CI [1.8-5.7]) were more likely to live in a metro area and 

an apartment, respectively.

Schoch-Spana, 201057 People per Household:
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Author, Year

N Participants

Focus 

Population(s) of Interest Mediating Factors

N = 33

H1N1 

• Latino (LEP)

• Participants reported 10-12 people or 2-3 families sharing a small cabin.

Work-related

Kumar, 201240

N = 2,042

H1N1

• AA/Black

• Latino

Work-Related Inability to Social Distance:

• Controlling for age, gender, and SES, compared to Whites, Latinos were less able to social distance due to work 

(b = 0.37; p < 0.001). 

• There was no significant difference between AA/Blacks and Whites. increase in the household (b = 0.06; p < 

0.05).

• For each unit increase in the work-related social distancing index there was an 8% increase in the likelihood of 

an ILI in the participant and a 6% increase in the likelihood of an ILI in their household.

Index included: ability to work at home, sick leave (general), sick leave for the flu, fired for no-show, job can only be 

done in workplace.

Quinn 201110

N = 1,479

H1N1

• AA/Black

• Latino (English and LEP)

Work-Related Inability to Social Distance:

• Controlling for age, gender, and SES, compared to Whites, Spanish-speaking Latinos were less able to social 

distance due to work (b = 0.89; p < 0.001). 

• AA/Blacks were better able to social distance (b = -0.57, p = 0.01). 

• There was no difference between English-speaking Latinos and Whites.

Index included: ability to work at home, sick leave (general), sick leave for the flu, fired for no-show, job can only be 

done in workplace.

Schoch-Spana, 201057

N = 33

H1N1 

• Latino (LEP)

Limited Work Benefits

• Dependence on jobs with poor benefits (eg, sick leave, low wages) – unable to stay home when sick

School Closures and Sick Children:

• Limited ability to stay home from work when schools close or children are sick – lack of consistent childcare, 

children may go to work with parents.

Social Distancing

Kumar, 201240

N = 2,042

H1N1

• AA/Black

• Latino

Public Transportation:

• Controlling for age, gender, and SES, Latinos were significantly more dependent on public transportation than 

Whites (b = 0.35; p < .01). 

• There was no significant difference between AA/Blacks and Whites. 

• There was no relationship between public transportation and self-reported ILI in the participant or household.
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Author, Year

N Participants

Focus 

Population(s) of Interest Mediating Factors

Lin, 2014,

42 201843

N = 1,569

H1N1

• Low SES: Education s

Social Distancing (Including Public Transportation):

• There was no difference in social distancing, staying home, or the reduction of human contact outside the 

household by education

Quinn 201110

N = 1,479

H1N1

• AA/Black

• Latino (English and LEP)

Public Transportation:

• Controlling for age, gender, and SES, AA/Blacks (AOR = 3.0, 95% CI [1.9-4.6]), English-speaking Latinos (AOR 

= 2.8, 95% CI [1.5-5.3]), and Spanish-speaking Latinos (AOR = 4.0, 95% CI [2.5-6.5]) were more dependent on 

public transportation than Whites.

Group Childcare:

• Controlling for age, gender, and SES, AA/Blacks (AOR = 3.0, 95% CI [1.6-5.6]) and Spanish-speaking Latinos 

(AOR = 10.3, 95% CI [5.9-18.2]) had difficulty obtaining childcare that wasn’t with a group of children. 

• There was no difference between English-speaking Latinos and Whites.

SteelFisher 201559

N = 2,355

H1N1 

• AA/Black

• Latino

• Asian

• AI/AN

• Low SES: Education s

Social Distancing:

• Controlling for covariates, healthcare, and attitude, AA/Blacks, Latinos, Asians, and AI/ANs were more likely 

than Whites to avoid both air travel and public transportation. 

• Latinos and Asians, but not AA/Blacks were more likely to avoid social gatherings (no difference for AA/Blacks). 

• There was no difference in avoiding people with flu-like symptoms by race/ethnicity.

• Controlling for covariates, healthcare, and attitude, adults with a HS diploma or less were less likely to avoid air 

travel and public transportation, social gatherings, and people with flu-like symptoms.

Hygiene-related Behaviors

Lin, 2014,42 201843

N = 1,569

H1N1

• Low SES: Education d

Personal Hygiene

• There was no difference in the frequency of handwashing or hand sanitizer, and no difference in coughing with 

mouth covered by education. 

SteelFisher 201559

N = 2,355

H1N1 

• AA/Black

• Latino

• Asian

Personal Hygiene

• Controlling for covariates, healthcare, and attitude racial/ethnic minorities were similar to or more likely than 

Whites to adopt hygiene-related behaviors (eg, covering nose and mouth, cleaning more frequently).

• Adults with a HS education or less were similar to or more likely than higher educated adults to adopt hygienerelated behaviors (eg, covering nose and mouth, cleaning more frequently).
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Author, Year

N Participants

Focus 

Population(s) of Interest Mediating Factors

• AI/AN

• Low SES: Education s

Susceptibility

Hennessey, 201537

N = 381

H1N1 Mortality

D

• AI/AN

Preexisting Conditions

• AI/ANs had higher rates of H1N1 mortality (OR = 1.95, 95% CI [1.03-3.68]). 

• However, preexisting conditions associated with susceptibility to H1N1 complications mediated the relationship 

between AI/AN race and H1N1 mortality (no data provided)

Levy 20138

N = 374

H1N1 Hospitalization

• Low SES: Education

• Low SES: Neighborhood 

Poverty

Comorbid Conditions:

• Having 1+ comorbid conditions associated with susceptibility to H1N1 complications significantly increased the 

odds of hospitalization in adults (OR = 12.83, 95% CI [4.99-32.97]). 

Comorbid Conditions adjusted for education and access to care:

• In multivariate analysis also including education, having a primary care provider, and health insurance, having 

1+ comorbid condition significantly increased the odds of hospitalization (AOR = 7.61, 95% CI [2.68-21.65]).

• After adjusting for access to care and comorbid conditions, adults with ≤ HS education (AOR = 21.21, 95% CI 

[5.32-84.53]) and HS graduates (vs some college or more; AOR = 3.82, 95% CI [1.64-8.90]) remained more 

likely to be hospitalized.

Comorbid Conditions adjusted for % neighborhood residents below FPL and access to care:

• In multivariate analysis also including having a primary care provider, health insurance, and % neighborhood 

below FPL, those with 1+ comorbid condition (AOR = 10.05, 95% CI [3.65-27.64]) were more likely to be 

hospitalized.

• After adjusting for access to care and comorbid conditions, adults living in neighborhoods with 30%+ below the 

FPL remained more likely to be hospitalized (AOR = 5.02, 95% CI [1.83-13.89]).

Quinn 201110

N = 1,479

H1N1

• AA/Black

• Latino (English and LEP) s

Comorbid Conditions:

• Controlling for confounders, Spanish-speaking Latinos had fewer comorbidities associated with susceptibility to 

H1N1 complications than Whites (b = -0.37; p < 0.001). 

• There was no difference from Whites for English-speaking Latinos or AA/Blacks.

Access to Healthcare

Levy 20138

N = 374

H1N1 Hospitalization

• Low SES: Education

Public vs Private Insurance

• Adults with private insurance were less likely to be hospitalized (OR = 0.15, 95% CI [0.07-0.32]). 

Primary Care Provider:

• Having a primary care provider was not significantly related to the odds of hospitalization in adults (OR = 0.88, 

95% CI [0.35-2.18]). 
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Author, Year

N Participants

Focus 

Population(s) of Interest Mediating Factors

• Low SES: Neighborhood 

Poverty

Health Insurance:

• Having health insurance was not significantly related to the odds of hospitalization in adults (OR = 0.42, 95% CI 

[0.12-1.49]).

Primary Care and Health Insurance adjusted for education and comorbidities:

• In multivariate analysis that also included education, and 1+ comorbid condition, neither having a primary care 

provider (AOR = 1.88, 95% CI [0.50-7.05]) nor having health insurance (AOR = 0.73, 95% CI [0.14-3.70]) were 

related to hospitalization. ]). 

• Controlling for comorbidity and access to care, adults with ≤ HS education (AOR = 21.21, 95% CI [5.32-84.53]) 

and being a HS graduate (vs some college or more; AOR = 3.82, 95% CI [1.64-8.90]) remained more likely to be 

hospitalized.

Primary Care Provider and Health Insurance adjusted for % neighborhood residents below FPL and comorbidities:

• In multivariate analysis that also included % neighborhood residents below FPL and having 1+ comorbid 

condition, neither having a primary care provider (AOR = 1.50, 95% CI [0.42-5.30]) nor health insurance (AOR = 

0.42, 95% CI [0.09-2.04]) were significantly associated with hospitalization. 

• Controlling for comorbidity and access to care, adults living in neighborhoods with 30%+ of residents below the 

FPL remained more likely to be hospitalized (AOR = 5.02, 95% CI [1.83-13.89]).

Quinn 201110

N = 1,479

H1N1

• AA/Black

• Latino (English and LEP)

Access to care:

• Controlling for gender, age, and SES, Spanish-speaking Latinos had a harder time accessing care than Whites 

(b = 0.85, p < 0.001). 

• There was no significant difference between AA/Blacks and English-speaking Latinos and Whites. 

Index included: health insurance, regular provider, and the perception that lack of insurance or money would make 

it difficult to receive a flu shot

Schoch-Spana, 201057

N = 33

H1N1 

• Latino (LEP)

Health Centers:

• MSFW are not always aware of migrant health centers, lack transportation from rural locations 

Insurance/Cost:

• Lack of money for healthcare costs, no insurance, can’t access public assistance

Limited English Proficiency

• Participants discussed the lack of Spanish and Indigenous language materials and support at health centers

Fear:

• May not seek care for fear of deportation

SteelFisher 201559

N = 2,355

H1N1 

Spoke to Provider about H1N1

• Controlling for covariates, healthcare, and attitude, AA/Blacks, Latinos, and AI/ANs were more likely to have 

spoken to a doctor or other healthcare professional about how to protect their selves or families from H1N1. 
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Author, Year

N Participants

Focus 

Population(s) of Interest Mediating Factors

• AA/Black

• Latino

• Asian

• AI/AN

• Low SES: Education s

• There was no difference for Asians or low SES.

Witrago, 201163

N = 209

Influenza Pandemic 

Preparedness 

• Latino (LEP) S

Lack of health insurance:

• The top reason (25%) for not storing/keeping medication on hand was due to lack of money or health insurance. 

Discrimination and Trust

Freimuth 201432

N = 1,543

H1N1

• AA/Black

• Latino

Trust in the government’s ability to cope with H1N1:

• AA/Blacks and Latinos were more likely than Whites to trust the federal government (ie, President Obama, the 

HHS). 

• There was no difference in trust of the CDC and state and local governments.

Discrimination when Seeking Health Care

• There was no difference in overall trust in government information sources among those who had experienced 

discrimination in health care (M = 2.18 [SE = 0.02] trust score) vs those who had not (M = 2.30 [SE = 0.07] trust 

score).

McCauley 201347

N = 46

H1N1

• AA/Black s

Trust in the Government and Media:

• AA/Black

• Participants in a largely AA//Black focus group expressed concerns that they didn’t feel that health experts, the 

government, and the media provided consistent adequate information and that they could believe.

Mesch, 201467

N = 968

H1N1

• Latino

• Low SES: Education s

Confidence in the government’s ability to cope with H1N1:

• Latinos were more likely than Whites to trust the government’s ability to deal with H1N1 (OR = 2.19, 95% CI 

[1.03-4.69]). 

• There was no difference by education (OR = 1.11, 95% CI [0.98-1.25]).

Schoch-Spana, 201057

N = 33

H1N1 

• Latino (LEP)

Community stigmatization:

• Misinformation by the media about community health centers serving migrant workers as “hotspots,” 

• During the H1N1 pandemic, MSFW felt they were shunned and bullied, 1 family was denied school admission 

despite no symptoms. 
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Author, Year

N Participants

Focus 

Population(s) of Interest Mediating Factors

Stigmatization by Providers:

• Example: Provider overheard to say, “people [are] coming from Mexico and bringing the swine flu.”

Quinn 2009,53 201110

2009 N = 1,543

2011 N = 1,479

H1N1

• AA/Black

• Latino (English and LEP) x

Experienced Discrimination when Seeking Healthcare:

• Controlling for age, gender, and SES, AA/Blacks (AOR = 3.9, 95% CI [2.2-7.0]), English-speaking Latinos (AOR 

= 2.8, 95% CI [1.2-6.9]), and Spanish-speaking Latinos (AOR = 6.1, 95% CI [3.2-11.5]) were more likely than 

Whites to have experienced discrimination when seeking healthcare. 

Trust in the government’s ability to cope with H1N1:

• Both AA/Black and Latino participants scored higher on a scale related to government trust and H1N1 (p < 

0.001).

Information and Knowledge

Etingen 201230

N = 3,384

H1N1

• AA/Black

• Latino

• Low SES: Education s

Receipt of H1N1 Information:

• Among Veterans with spinal cord injuries and disorders, report of the receipt of adequate information was more 

likely for Whites (vs non-Whites; OR = 1.67 [95 % CI 1.39-2.01]) and higher SES (college graduates; (OR 1.28, 

95% CI [1.05-1.56]). 

Lin, 2014,42 201843

N = 1,569

H1N1

• Low SES: Education c

H1N1 Transmission Knowledge

• Participants with less education were less knowledgeable about H1N1 transmission and were more likely to 

avoid eating pork products. 

• However, both became non-significant when information barriers were considered. 

• There was no difference in misconceptions about H1N1 by education.

Lin, 201744

N = 627

MERS and Previous 

Epidemics

• AA/Black

• Latino

• Low SES: Education

Pandemic Awareness:

• Individuals with lower education (AOR = 3.67, 95% CI 1.44-9.36) were more likely to have a low awareness of 

pandemics compared with other groups. 

• There was no difference by race/ethnicity.

MERS Awareness: 

• Compared to Whites, AA/Blacks (OR = 0.36, 95% CI [0.19–0.70], p < 0.005) and Latinos (OR = 0.46 [95% CI 

0.23–0.91], p = 0.03]) were less likely to have heard of MERS. 

• There was no difference by SES (education).

MERS Knowledge: 

• Compared to Whites, AA/Blacks (OR = 0.13, 95% CI [0.03–0.57]) were less likely to have accurate knowledge of 

MERS. 

• There was no difference between Latinos and Whites. 
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Author, Year

N Participants

Focus 

Population(s) of Interest Mediating Factors

• There was no difference by SES.

Witrago, 201163

N = 209

Influenza Pandemic 

Preparedness 

• Rural Latinos (LEP) v

Preparation:

• Regardless of SES, years in US, and demographics, rural Spanish-speaking Latinos were not prepared for an 

influenza pandemic

Index included: available food, water, flashlight, medical supplies etc., prep plan

Yip, 200966

N = 100

H1N1 

• Chinese (LEP) g

Informed about H1N1:

• Compared to individuals who did not speak English, those who reported not speaking English “well” were more 

likely to feel that they were informed about H1N1 (OR = 2.65, 95% CI [1.04-7.01], p < 0.05).

Abbreviations: AA = African American; AI/AN = American Indian/Alaska Native; AOR = adjusted odds ratio; C = control; CDC = Centers for Disease Control; CI 

= confidence interval; ED = emergency department; EMR = electronic medical record; HS = high school; LEP = limited English proficiency; MERS = Middle East 

Respiratory Syndrome; MSFW = migrant and seasonal farmworkers; OR = odds ratio; OT = Occupational Therapy; PI = Pacific Islander; SES = socioeconomic 

status; US = United States
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KQ2. What interventions or intervention components have been used 

to reduce health inequalities (or identified in preliminary studies) in 

infectious disease transmission or health outcomes in disasters, 

infectious disease epidemics or pandemics in the United States? 

We included 12 studies that described interventions or intervention components to reduce health 

inequalities or health outcomes related to infectious disease epidemics pre-dating COVID-19 or 

relevant disasters. The 12 studies included 4 studies (5 publications) of individual-focused 

interventions 28,29,48,52,55 and 8 studies (9 publications) of systems-focused interventions.20-

22,36,46,49,64,65,50 Only 1 study28 describes intervention effectiveness outcomes, while the majority 

described acceptability and feasibility of the studied interventions. We found only 1 randomized 

controlled trial and 1 longitudinal cohort study among those included. The remainder of the 

papers reviewed were program evaluations or studies to inform future interventions with 

resulting recommendations. Table 4 provides study details. 

We additionally included 5 papers presenting expert recommendations from CDC key 

stakeholder meetings on pandemic influenza preparedness in vulnerable populations convened in 

2008 (see Table 5).

23,58,60,62

Individual-Focused Interventions and Programs

Four studies (5 papers) featured interventions focused at the individual level. One longitudinal 

cohort study aimed to improve emergency preparedness among a low-income Latino community. 

Participants (N=231) were randomized to receive either culturally tailored information sent by 

mail or to participate in discussion groups led by a community health worker. The discussion 

groups arm reported better disaster preparation post-intervention than the mailer-only group.28 In 

a subsequent publication, the authors attributed the success of the emergency preparedness 

discussion groups to the use of targeted outreach provided by community-based organizations 

and the use of clear, consistent, culturally appropriate messaging.29 

A poor-quality, small (N = 50), unblinded randomized controlled trial evaluated the feasibility of 

an automated call-monitoring system to detect H1N1 symptoms among low-income pregnant 

women. In addition to an individualized health education session, the intervention group received 

daily calls with prompts for yes or no responses for H1N1 symptoms. Participants who 

responded “yes” were transferred to a nurse midwife, and a same-day appointment was 

scheduled. Findings indicated that the intervention was feasible. There was no difference 

between groups in prenatal care visit attendance. One participant in the intervention group 

experienced H1N1 symptoms and received immediate intervention. Nearly all (93.3%) women in 

the intervention group recommended a similar system in future health crises.48 

Another paper describing a program evaluation analyzed differences in completion rates by 

race/ethnicity of a post-disaster web-based mental health intervention. The intervention included 

a baseline interview and mental health screen, after which participants (N=1180) were directed to 

relevant online modules addressing PTSD, depression, generalized anxiety disorder, panic 

disorder, marijuana abuse, alcohol abuse, and smoking. The evaluation found that rates of access, 

use, and completion of the intervention did not differ between African-Americans, Latinos, and 

Whites. 52
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Finally, a program evaluation examined an intervention to train migrant and seasonal 

farmworkers in disaster preparedness through 2 workshops utilizing the Community Emergency 

Response Team curriculum.55 Results showed that the intervention was feasible and the 

workshops were highly rated by participants (N=22), but challenges existed with reaching 

recruitment goals. An outcomes evaluation revealed lessons learned including the need for 

partnering with stakeholders and accounting for participants’ work schedules and language needs 

in planning the training (see Table 4). 

System-Focused Interventions and Programs

Eight studies (9 publications) focused on system-level interventions to improve emergency 

preparedness and response. These are categorized below as either program evaluations 36,46,49 or 

studies to inform future interventions 20-22,50,64,68. 

Program Evaluations

A qualitative evaluation of a program that aimed to increase efficacy of counseling provided to 

ethnically and culturally diverse populations after a natural disaster used a critical consciousness 

approach to train psychology graduate students (N = 6).36 The authors found that the experience 

increased participants’ cultural competence and social-justice oriented perspective. While effects 

on the care the students provided were not directly evaluated, this training potentially increased 

their efficacy at serving a majority African American community.

In a paper by McCabe et al,46 the authors describe a disaster/emergency preparedness training for 

lay communities in a rural region of Maryland with the goal of enhancing capacity of rural 

emergency response through a systems-based partnership between faith-based organizations, 

local health departments, and academic institutions. A post-intervention assessment 

demonstrated an increased understanding among participants (N = 178) of community disaster 

and mental health plans and increased self-efficacy to execute these plans. It also showed the 

feasibility of recruiting from local health departments and faith-based organizations, and 

potential for scale-up of the training program.

Another publication49 described a program evaluation of a University of Nebraska Medical 

Center cross-agency rural health system response simulation exercise for communities in rural 

Nebraska. The exercise highlighted system weaknesses that included difficulties with backup 

communication, lack of knowledge around how to request additional medical staff and assets or 

to make patient transfer requests, the need to develop coordinated public messaging, and deficits 

in intra-agency coordination, for example not initiating a report of a notifiable disease. Overall, 

the program demonstrated the utility of functional exercises for testing regional disaster response 

coordination. 

Studies to Inform Future Interventions

A qualitative study consisting of 41 interviews with pastors of African American churches in 

Southern Mississippi recommended that mental health professionals build relationships with 

churches prior to disasters and work to engage and empower pastors and congregants, building 

trust. Through these partnerships, mental health services could potentially be brought to the 

communities during disasters, increasing access.21,22
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In a different study, a situational analysis was completed of emergency preparedness of diverse 

communities in California. The analysis was conducted via a literature review, an environmental 

scan of organizational websites, and key informant interviews (N = 17). It showed that there is a 

need to better engage diverse communities in all stages of disaster preparedness and response, 

mitigate stigma and fear, build cultural competence, and better coordinate information and 

resources when planning for community disaster preparedness.20

In Person et al (2004), the study team performed a situational analysis of stigmatization of Asian 

communities in the United States during the SARS pandemic in 2003. The analysis included 

group discussions with key informants (N = 70) from 50 different organizations, monitoring of 

the CDC public response service hotline, and a scan of Asian-language information services. 

Results demonstrated significant stigmatization and misinformation related to SARS. Important 

recommendations gleaned from these findings included the need to develop simple, tailored 

SARS prevention messages and materials in various Asian languages and to disseminate SARS 

information through multiple and culturally appropriate channels, including (but not limited to) 

community visits and town hall meetings.

In Wyte-Lake,

64 7 interviews were conducted with Home-based Primary Care (HBPC) providers 

to explore issues regarding emergency management planning for homebound patients. The 

qualitative analysis showed that a lack of standardized policies and procedures and unclear 

designation of provider responsibility resulted in inconsistent preparedness among HBPC 

Veterans. Recommendations included better training of providers to assist their patients in 

disaster preparedness, and formalization of the preparedness evaluation and intervention process.

One additional study evaluated use of a disaster preparedness assessment tool among homebound 

Veterans enrolled in HBPC.65 The assessment tool was deployed with patients at 10 HBPC sites 

in 8 states (N = 754) over a 3-week period. Results showed that in general, providers were 

teaching basic skills of disaster preparedness to their most vulnerable patients. Evacuation 

planning was the most commonly covered topic, and Veterans in the high- or medium-risk 

categories were more likely to receive preparedness information than those in the low-risk 

category (see Table 4). 
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Table 4. Relevant Findings from Infectious Disease and Emergency Response Literature

Author, Year

N Participants

Focus 

Population

Intervention or Program 

Description; Comparator

Recommendations or Findings

Relevance for COVID-19

Individual-Focused Interventions and Programs

Eisenman, 200928 and 

Glik, 201429

N = 187

Disaster Preparedness 

Intervention

• Latino (English and

LEP) Intervention: Emergency 

preparedness program (2 groups: 

high-intensity [platicas = small 

group discussions with community 

health worker]; and low-intensity 

[culturally appropriate mailers])

Comparator: Culturally appropriate 

mailing

Findings:

• Of those susceptible at pre-intervention, both groups improved on communication plan and

some disaster supplies (eg, stockpiling water, food, and blankets)

• Platica group improved significantly more on most self-reported preparedness indicators at

3-month follow-up including stockpiling of emergency water (media 66.7% vs. platicas

93.3%; P = 0.003) and food (media 60.6% vs platica 91.7%; P = 0.013) and creation of a

family communication plan (media 42.3% vs platica 70.4%; P = 0.002)

Implications for research and practice:

• Clear consistent messages delivered through a community-based organization led to

increased preparedness among households that were resource constrained

• Importance of working with trusted community-based organizations to help translate

disaster preparedness messages for disadvantaged households

• More focused community-based outreach than current standard practice is needed;

• Reliance on mass-media campaigns to disseminate messages may be inconsistent and

they are not necessarily understood, recalled or interpreted in ways that lead to action at

the level of households

Delivery of information and services related to COVID-19 to vulnerable populations will likely 

be more effective if delivered via trusted community intermediaries and targeted community 

outreach efforts rather than via print media alone.

Goodman, 200936

N = 6 

Culturally Competent 

Disaster Response mental 

health students

• AA/Black

Cultural competence program; 8-

day outreach experience providing 

disaster response counseling 

services, accompanied by journal 

and processing with peers and 

faculty supervisor

Results highlighted:

• Participants demonstrated increased critical consciousness and social-justice oriented

perspective. Also, increased cultural competence and understanding of its importance in

counseling.

• They integrated this understanding into their personal and professional identities.

• Developing cultural competence of disaster response counselors can be achieved through

outreach experience with processing using a critical consciousness lens.

A critical consciousness-based approach could be useful in training counselors to provide 

culturally competent counseling to vulnerable individuals who experience trauma related to 

COVID-19. 

Nassar, 2014 48

N = 50

Monitoring H1N1 

Symptoms

Intervention: Daily automated calls 

re: flu symptoms. If yes, they were 

transferred to nurse midwife for 

triage and next day visit. If they 

• Automated call participants received and interacted with the daily automated telephone

calls 45.1 % (SD = 3.2%) of the time, and 65.1 % (SD = 3.1%) received and interacted with

the daily automated telephone calls at least once every 3 days.
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Author, Year

N Participants

Focus 

Population

Intervention or Program 

Description; Comparator

Recommendations or Findings

Relevance for COVID-19

• Low SES didn't respond to automatic calls for 

3 days, they were called. 

Comparator: Health education

• Two (8%) participants never participated, and 5 (21%) participated in more than 58 % of

calls.

• There was no statistical difference in attendance rates for receiving prenatal care between

the automated call group and the health information group. 

• One participant in intervention group developed influenza symptoms

An automated system for triage of symptoms and referral to care could help reach 

disadvantaged populations affected by COVID-19

Price, 201352

N = 1,180

Post-Disaster Mental 

Health Intervention

• AA/Black

• Latino

Intervention: Brief, web-based 

disaster mental health intervention

carried out 1 year after hurricane 

Ike. Participants completed a 

baseline telephone interview and, 

based on survey results, were 

directed to a series of online 

modules including depression, 

PTSD, generalized anxiety 

disorder, panic disorder, alcohol 

abuse, marijuana abuse, and 

cigarette smoking. Engagement 

was assessed based on 3 types of 

attrition. 

Results highlighted:

• Non-use attrition of the internet intervention (completed baseline telephone interview but

did not access website): No difference between Whites and AAs (OR = 0.96; 95% CI: 0.64

to 1.43) or Whites and Latinos (OR = 1.31; 95% CI: 0.76 to 2.29)

• Dropout attrition for access of intervention modules (accessed website but did not

complete modules): No difference between Whites and AAs (OR = 1.45; 95% CI: 0.63 to

3.29) or Whites and Latinos (OR = 0.74; 95% CI: 0.32–1.74)

• Intervention module completion attrition (failing to complete a module after having

accessed a module): No difference between Whites and AAs (OR = 1.10; 95% CI: 0.89 to

1.36) or Whites and Latinos (OR = 1.22; 95% CI: 0.86–1.67).

• Bottom line: Rates of non-use attrition, access attrition, and completion attrition did not

differ between AAs, Latinos and Whites.

Web-based interventions related to COVID-19 could be useful to reach AAs, Latinos, and 

Whites at similar rates. 

Rosenbaum, 201855

N = 22 

Disaster Preparedness 

Training for MSFW

• Latino (LEP)

Two disaster preparedness 

workshops were conducted with 

migrant and seasonal farm workers 

using the Community Emergency 

Response Team curriculum that 

includes basic disaster response 

skills such as fire safety, light 

search and rescue, team 

organization, incident command, 

and disaster medical operations.

Results highlighted/lessons learned

• Participants improved emergency preparedness and first aid, CPR, and AED

competencies.

• Community benefit of having certified participants in their midst.

• Participants rated the training highly and expressed interest in continuing emergency

response training among participants.

• Partnerships with the university and the relevant local stakeholders were important to

project planning and implementation.

• Needs of participants such as work/school schedules, transportation, and childcare

needs must be considered

• Bilingual trainer and training materials are important

• Curriculum needs to be culturally relevant
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Author, Year

N Participants

Focus 

Population

Intervention or Program 

Description; Comparator

Recommendations or Findings

Relevance for COVID-19

In person COVID-19-related training of migrant and seasonal farm workers is important, 

needs to include culturally appropriate trainers and materials, and needs to accommodate 

work and personal needs of participants to allow for participation.

System-Focused Interventions and Programs

Andrulis, 201120

N = 17

Disaster Planning

• AA/Black

• Latino (English and

LEP)

• Asian/PI

• AI/AN

Through literature review, 

environmental scan of 

organizational websites, and 17 

key informant interviews with public 

health and emergency 

management personnel, 

researchers identified barriers and 

disaster preparedness needs of 

racially/ethnically diverse 

communities.

Results highlighted:

• Barriers to preparedness include socioeconomic factors, trust in perceived fairness of

government, cultural and linguistic factors, lack of funding for diversity initiatives, limited

knowledge about and collaboration with diverse communities

• Program and policy priorities: enhance collaboration; increase flexibility for program

development and allocation of funds; improving organizational capacity

• Intervention priorities: engage diverse communities; mitigate stigma and fear; build

cultural competence; coordinate information and resources

Many of the barriers to reaching racially and ethnically diverse communities that were 

identified in this study also apply to COVID-19. Outreach efforts must employ cultural 

competence, enhance collaboration, and leverage resources to enhance organization 

capacity. 

Aten, 201021

Aten, 201122

N = 41

Post Disaster Mental 

Health Disparities

• AA/Black Pastors of churches in South 

Mississippi affected by hurricane 

Katrina participated in semistructured interviews 1 year after 

the storm. Results were 

synthesized to provide 

recommendations for fostering 

collaboration between African 

American religious leaders and 

mental health professionals 

towards better serving minority 

communities.

Results highlighted:

• Need for pastor education on effects of disasters on mental health, referral services,

stigma towards receiving mental health services and providers

• Need for collaboration between AA churches and mental health professionals to perform

mental health assessments

• Need for collaboration to provide consultation activities to help churches better prepare for

disasters (eg, evacuation plans)

• Need for clinically focused activities offered by mental health professionals in the

aftermath of a disaster;

• Possibility through collaboration for bidirectional referrals; rather than churches only

referring to providers, providers could also refer to spiritual resources.

Recommendations:

• Establish working relationships prior to disasters

• Empower AA churches through participation, both empowering AA faith communities to

utilize spiritual resources, but also providing leadership opportunities for pastors and

congregation members

• Utilize AA churches for community-based services: bringing services to the community

can increase access and utilization.

AA pastors and churches could be an essential ally when considering interventions to mitigate 

disproportionate effects of COVID-19 on AA communities.
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Author, Year

N Participants

Focus 

Population

Intervention or Program 

Description; Comparator

Recommendations or Findings

Relevance for COVID-19

McCabe, 201346

N = 178

Community-level Disaster 

Preparedness

• Rural

Disaster/emergency preparedness 

intervention with 1-day didactic 

session and 2-day technical 

workshop focused around disaster 

preparedness and partnerships 

between faith-based organizations 

and local health departments:

Results highlighted:

• Increased understanding of community disaster mental health plans and increased selfefficacy to execute these plans

• Proof of feasibility of the intervention and potential for scale-up

• Intervention model showed effectiveness of partnerships between faith-based

organizations, local health departments, and academic institutions.

• Written follow-up after in-person presentations were more effective for recruitment than

postal mailings or radio spots

Providing training for FBOs through established partnerships could be 1 method of reaching 

vulnerable communities with information and resources related to COVID-19.

Obaid, 201749

N = 667 participants

Rural Infectious Disease

Disaster Preparedness

• Rural

Functional infectious disease 

disaster response exercises, 

developed by Center for 

Preparedness Education at the 

University of Nebraska Medical 

Center 

Six exercises evaluated hospitals 

and public health departments in 

rural Nebraska health care 

coalitions. Each facility had an 

evaluator and controller on-site for 

assessment. Exercises were 

debriefed, and there was an afteraction conference after each 

functional exercise. Improvement 

plans were developed for each 

coalition.

Results highlighted:

• Feasibility of disaster response exercises including quantitative evaluation of disaster

preparedness

• Areas needing improvement including: difficulties with backup communications, not

activating incident command, lack of knowledge on whom to contact for patient

transfer requests, agencies not fulfilling requests for incident actions plans.

• Functional exercises that require the real exchange of information and communication

across participating agencies are best suited for testing regional disaster response

communication.

Provides a model for assessing preparedness of medical and public health systems prior to 

onset of an infectious disease disaster such as COVID-19. Could be applied in anticipation of 

future disease outbreaks.

Person, 201450

N = 70+ people, 50 

agencies/orgs

SARS-related Stigma

• Asian

NICD/CDC SARS Community 

Outreach Team Activities:

1) advised other SARS emergency

response teams on how to

minimize the risk of stigmatizing

groups in their own

communications by focusing

messages on the virus and the

relevant behavioral risk factors; 2)

Results Highlighted: 

• The need to develop simple, tailored infectious disease prevention messages and

materials in various Asian languages

• Disseminate information through multiple and culturally appropriate channels, including

(but not limited to) community visits and town hall meetings
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Author, Year

N Participants

Focus 

Population

Intervention or Program 

Description; Comparator

Recommendations or Findings

Relevance for COVID-19

assisted with developing culturally 

tailored health education materials; 

and 3) conducted community visits, 

panel discussions, and media 

interviews to positively influence 

negative behaviors occurring in 

communities

Wyte-Lake, 201464

N = 7 

Disaster Preparedness

• Disability

Seven interviews were conducted 

with HBPC providers to explore 

issues regarding emergency 

management planning for 

homebound patients. 

Results Highlighted: 

• Individual HBPC programs are often tasked with developing their own policy for disaster

preparedness assessment as part of routine patient assessment

• Tools used in initial preparedness assessment are insufficient

• Provider comprehension for how to assign patients to risk categories based on acuity

varied

• Providers identified cognitive impairments, patient willingness, and limited resources as

barriers to patient engagement in preparedness activities

• Providers received limited formal training on how to prepare their patients for disaster.

• Provider recommendations: (1) training to focus on better strategies to get patients to

participate, (2) more consistent time spent on patient education, (3) formalizing the initial

assessment to actually evaluate how prepared patients are, and (4) having emergency

preparedness be formally addressed on a more consistent basis, possibly in the annual

interdisciplinary review of each patient.

HBPC providers are uniquely positioned to provide education and intervention around disaster 

preparedness to vulnerable patients. This could include provision of education about COVID19. Efforts should be made to standardize COVID-19 preparedness assessment among

HBPC providers.

Wyte-Lake, 201965

N = 754 patients

Disaster Preparedness

• Disability

Evaluation of the HBPC Patient 

Assessment Tool – tool to assess 

disaster preparedness among 

homebound vets. The rates at 

which education was provided on 

various items was assessed based 

on patient risk categorization to 

observe patterns in how providers 

communicated this information.

Results Highlighted:

• The most frequent education topic covered with all patients was how to evacuate their

home

• Provision of education about advanced directives/POLST and reminder about the HBPC

handbook were not associated with the level of patient risk.

• Veterans in high/medium risk categories were more likely to receive preparedness

education for 6/9 items listed in the tool

• Those with less social support were more likely to receive disaster preparedness

information
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Author, Year

N Participants

Focus 

Population

Intervention or Program 

Description; Comparator

Recommendations or Findings

Relevance for COVID-19

• Information on how to put together an emergency kit and emergency shelter

registration/emergency transport were less likely to be discussed than other preparedness

topics

• Home health agencies can play an important role in educating home-bound adults about

disaster preparedness. These results indicate that providers are giving basic education on

disaster preparedness to their most vulnerable patients, but opportunities exist for

improvement

Home health agencies may be an important partner in disseminating COVID-related education 

to vulnerable home-bound adults.

Abbreviations: AA = African American; AED = Automated external defibrillator; CI = Confidence interval; CPR = cardiopulmonary resuscitation; HBPC = Home-based Primary 

Care; LA = Los Angeles; OR = Odds ratio; SES = Socioeconomic status; VHA = Veterans Health Administration
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Expert Panel Papers

Five papers presented expert recommendations resulting from CDC key stakeholder meetings on 

pandemic influenza preparedness in vulnerable populations. These meetings were held in 2008, 

with reports published in an American Journal of Public Health supplement in 2009. Experts 

were convened around the following vulnerable groups: publicly housed, single parent families, 

low income families, racial and ethnic minorities, migrant farm workers, and immigrants and 

refugees. One paper addressed communication strategies to vulnerable populations in general 

(see Table 5). 

In Bouye et al,23 the expert group highlighted the ways in which poverty makes those who are 

publicly housed, part of single parent households, or low wage earners more susceptible to a 

pandemic outbreak. Early implementation of community mitigation strategies were considered 

paramount to halt the spread of infection. The ways in which these strategies are disseminated 

was also considered important, as was the need for education and communication strategies to 

account for the distinction in cultures, lifestyles, and behaviors of these groups. The panel 

recommended engaging community liaisons in devising education campaigns as being essential 

to their success. Many of the strategies discussed hinge on preparedness planning prior to the 

onset of a pandemic, which would build community partnerships, establish an emergency 

communication plan, initiate appropriate education and training programs, and plan for advocacy 

for policy and program change to better enable mitigation strategies (eg, flexible work policies, 

home delivery services).

Another expert group meeting report39 looked at the effect of influenza on racial and ethnic 

minorities. The experts pointed to evidence for an increased rate of comorbid conditions, 

increased rate of complications, inferior vaccine coverage, and higher rates of poverty as factors 

that may have contributed to racial/ethnic minorities having been disproportionately affected 

during past influenza pandemics. Similarly to Bouye et al,

23 possible solutions they proposed 

hinge on tailored educational strategies and strengthening of public health infrastructure. The 

group emphasized the importance of involving racial and ethnic minorities at all stages of 

planning and prevention in order to address the socioeconomic, cultural, linguistic, and 

educational barriers that may prevent community mitigation and vaccine delivery. 

Steege et al58 argued for the need for a distinct approach to protecting migrant farm workers 

against pandemic influenza. Migrant workers constitute a unique group in that they risk 

contracting disease due to their exposure to animals. Furthermore, working conditions and 

cultural or linguistic barriers may make an influenza outbreak more likely among this vulnerable 

group. Mitigation strategies focused on delivery of timely prevention and treatment of influenza. 

Two other recommended strategies included the use of mobile clinics and lay community health 

workers. Given that workers may delay seeking care due to fear of legal repercussions, 

establishing trusting relationships with service providers to enable care delivery was considered 

important.

Immigrants and refugees share important risk factors with the aforementioned vulnerable 

groups.60 This group may be more at risk for pandemic influenza due to factors including higher 

rates of chronic conditions, lower seasonal influenza vaccine rates, and linguistic or cultural 

barriers. In the case of undocumented immigrants, they may be reluctant to seek care due to fear 
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of detention or deportation, similar to migrant farm workers. The authors proposed a multi-level 

approach to risk mitigation, with strategies that are household-focused (eg, remaining home 

while ill), provider-supervised (eg, vaccine provision), and agency-driven (eg, effective 

communication). This expert group also emphasized involvement of the target group in all 

preparedness planning and communication. 

One final paper62 focused on general communication strategies to vulnerable groups during an 

influenza pandemic. They recommended that communication strategies be tailored to the phase 

of the pandemic and that they be situation specific. For example, schools, workplaces, and public 

gatherings all require different communication strategies. Like the other expert panels, they 

emphasized the importance of linguistically and culturally appropriate communication. A 

participatory approach was considered important to the development of communication, with 

dissemination to be best done in partnership with community organizations, faith-based 

organizations, or other trusted intermediaries (see Table 5). 
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Table 5. Relevant Findings from CDC Expert Panel Meetings on Impact of Influenza in Vulnerable Populations 

Author, Year

N Participants

Topic General Recommendations/Findings

Strategic Partnership 

Recommendations

Bouye, 200923

N = 26

Influenza Pandemic: 

Low-SES, Public 

Housing Residents, 

Single-Parent 

Families

• Use culturally specific communication to impart messages related to vaccines and hygiene

• Engage strategic partnerships to relay public health messaging

• Create defined school policies, provide childcare vouchers, and stockpile supplies at 

churches and community centers

• Government support for workers – aid packages and wage freezes.

• Workplace flexibility and competitive compensation

• Preparation for delivery of goods and services via home delivery, mobile clinics

• Education around signs and symptoms of pandemic influenza

Engage faith-based 

organizations, CBOs, and 

neighborhood planning units

Hutchins, 200939

Influenza Pandemic: 

Racial and Ethnic 

Minorities

• Participatory approach to planning and preparedness process, engaging racial and ethnic 

minorities in every step of the process and allotting funding to do so.

• Social safety net policies and procedures are needed to meet survival needs, including 

access to clean water, sufficient food, shelter, and utilities. 

• Education materials that are culturally appropriate and adapted to low-literacy populations

• Educating early about use of PPE

• Systems for equitable access to scarce resources including antiviral medications and 

vaccines

Steege, 200958

Influenza Pandemic: 

Farmworkers

• Collaboration between federal, state and local public and animal health and agriculture 

authorities

• Seasonal influenza vaccination

• Training on reduction of risk of infection

• Sufficient PPE

• Sanitary facilities

• Surveillance and early detection of disease in workers and animals

• Linguistically and culturally appropriate information about vaccination

• Emergency messaging via multiple media

• Two-way information system to reach farm worker in remote encampments

Federal, state, and local 

public and animal health and 

agriculture authorities 

should collaborate with farm 

employers, farmworker 

health and social service 

organizations, agricultural 

extension agencies, and 

farmworker advocacy 

groups

Truman, 200960

Influenza Pandemic: 

Immigrants and 

Refugees

• Provision of information re. importance of staying at home while ill

• Liberal workplace leave and teleworking policies

• Engage faith-based and community organizations in how to best work with families to avoid 

social stigma in case of needed quarantine

• Work with providers of services to immigrants and refugees on appropriate use, distribution, 

and barriers to use of PPE

Faith-based organizations, 

community organizations 

and leaders, service 

providers
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Author, Year

N Participants

Topic General Recommendations/Findings

Strategic Partnership 

Recommendations

• Consider means to provide vulnerable children with lunch when schools are closed

• Vaccine distribution in states where immigrants/refugees are overrepresented and along 

the southern border

• Locate vaccination spots in easily accessible locations and provide vaccination without 

asking about immigration status

Vaughan, 200962

Influenza Pandemic: 

Risk Communication 

and Vulnerable 

Populations

Communication strategy for during pandemic:

• Phased – public health information should track with phase of pandemic (mild, moderate, 

severe outbreak)

• Communications should be situation-specific; schools and childcare centers, workplaces, 

and public and social gatherings all require different communication strategies

• Use of multiple communication strategies

• Using communication intermediaries when mistrust exists (community leaders, faith-based 

leaders, etc) 

• Focus on understanding factors that affect subgroup differences in response to infectious 

disease and to develop and aggregate communication strategies that strengthen rather 

than diminish the

• value of community beliefs and traditions. 

• All messages, materials, and documents should be culturally sensitive, match the language 

proficiency of targeted individuals, and be responsive to the changing conditions and needs 

of the audience as the crisis unfolds.

• Vaccines: Communications that address concerns arising from values, beliefs, and cultural 

traditions, and that feature spokespersons who are credible from the perspectives of 

targeted populations, will be more persuasive in disseminating information about vaccine 

effectiveness and safety.

• Social distancing: Pre-pandemic planning and communications need to identify ways to 

make actions feasible and communications credible.

• PPE: Affordability, accessibility, availability, and appropriateness are real considerations for 

vulnerable populations. Equally important are language considerations and ensuring that 

instructions on how and when to use PPE (eg, masks, gloves) are clear and workable.

Partnerships with 

community organizations, 

faith-based organizations, 

and trusted communication 

intermediaries

Abbreviations: CDC = Centers for Disease Control and Prevention, CBOs = Community Based Organizations, PPE = Personal Protective Equipment
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What were the H1N1 vaccination rates among different segments of 

the population, what factors contributed to disparate vaccination, and 

what were the benefits and harms of interventions designed to 

mitigate inequities in vaccine uptake?

We identified 20 studies examining health inequalities in H1N1 vaccine uptake, factors 

associated with vaccine uptake, and intervention to address disparities in vaccine uptake.

H1N1 Vaccine uptake

There were 10 studies of H1N1 vaccine uptake.24,25,31,34,38,51,54,56,59,61 Racial and ethnic 

differences, comparing minority groups to Whites, were examined in the majority of studies. 

Nine studies reported on AA/Black participants, 8 on Latinos, 2 of Asians, and 1 each of Pacific 

Islanders, and Asians and Pacific Islanders as a combined group. Differences in vaccine uptake 

by participants with lower versus higher socioeconomic status were examined in 4 studies, and 2 

studies looked at participants’ uptake based on rural versus urban status. All but 1 study51 used 

self-reported vaccination status, as opposed to status obtained from health records. Eight of the 

10 provided data on H1N1 vaccine uptake that was unadjusted. Studies that reported adjusted 

rates controlled for a variety of factors, not consistent across studies. As such, we rated our 

strength of evidence for H1N1 vaccine uptake based only on unadjusted data. Afterwards we 

report the findings of adjusted models where applicable. 

By Racial/Ethnic Group Affiliation

Seven studies31,34,51,54,56,59,61 provide evidence examining vaccine uptake in AA/Black versus 

White participants. Of these, six31,51,54,56,59,61 reported unadjusted data and contribute to 

moderate-strength evidence of lower H1N1 vaccine uptake in AA/Blacks. Four of the 6 studies 

found that AA/Black participants were less likely than Whites to be vaccinated with the H1N1 

vaccine.51,54,56,61 In a large (N = 55,850), national cross-sectional survey study, 17% of AA/Black 

participants reported receiving the H1N1 vaccine compared to 26% of Whites (P<0.05).56

Another (N = 4,040) national study showed similar results (14% vs 20%; P = 0.02),61 as did a 

Los Angeles (LA)-area survey study (N = 1,541; OR = 0.7 [95% CI 0.6 to 1.0]).54 In LA county, 

Plough and colleagues conducted a study during a vaccination campaign consisting of free public 

mass vaccination sites throughout the county (distributing 20% of LA County’s total H1N1 

vaccine supply). AA/Black residents were half as likely as Whites to receive the H1N1 vaccine 

at 1 of the clinic sites despite targeted outreach efforts specific to that population.51 A study of 

Veterans with spinal cord conditions (N = 3384) found no difference in vaccine uptake by race.31

One other national study by Steelfisher and colleagues (N = 2,355)59 found no difference 

between AA/Black and White participants for H1N1 vaccine uptake (RR = 0.92 [95% CI: 0.73 to 

1.55]; See Table 6). Despite these 2 studies showing no racial differences, given the size and 

quality of the majority of studies finding less vaccine uptake for AA/Black populations, our 

strength in that finding is moderate. 

One additional national cross-sectional study also examined vaccine uptake for AA/Blacks 

compared to Whites, but only reported adjusted data. It found that after controlling for 

sociodemographic factors, there was no difference in vaccine uptake (N = 1,569; OR = 0.78 

[95% CI: 0.49 to 1.24]; Table 6).34 Additionally, after controlling for sociodemographics, 

comorbidities, healthcare access, and attitudes towards the vaccine, the study by Steelfisher and 
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colleagues still found no difference (OR = 1.06 [95% CI: 0.75 to 1.50]; See Table 6).59 These

adjusted data were not reported in a manner consistent with other studies, so we did not include it 

in our determining our strength of evidence.

Eight studies provide evidence examining vaccine uptake in Latino versus White

participants.25,31,34,51,54,56,59,61 Seven of these studies reported unadjusted data and contribute to 

low-strength evidence of lower H1N1 vaccine uptake among Latinos.25,31,51,54,56,59,61 Three 

studies found that Latino participants were less likely to report being vaccinated than 

Whites.25,54,56 One of these was a large (N = 55,850), national cross-sectional survey study in 

which 21% of Latino participants reported vaccination versus 26% of Whites (P<0.05),56 and 

another substantially sized study by Burger and colleagues (N = 11,834) that examined US-born 

and immigrant (foreign-born) Latino populations separately found similar results for both groups 

compared to Whites.25 An LA-area survey also found Latino participants less likely to be 

vaccinated than Whites.54 The Plough study actually found that Latino participants were more 

likely to have been vaccinated for H1N1 (RR = 1.45 [95% CI: 1.43 to 1.47]), but this was among 

LA County residents who received the H1N1 vaccine at free clinic sites.51 A study of Veterans 

with spinal cord conditions (N = 3384) found no difference in vaccine uptake by race,31 as did 

the remaining 2 national cross-sectional studies (N = 2,355 and 4,040).59,61

Adjusted analyses provide some clues about factors associated with vaccine uptake for Latinos. 

Burger and colleagues unadjusted findings were that Latinos (US-born and immigrants) were 

less likely to be vaccinated. Controlling for demographics and comorbidities accounted for the 

disparity for US-born Latinos. Further modeling found that after controlling for demographics 

and comorbidities, SES accounted for the disparity for foreign-born Latinos.25 Another study 

controlling for confounders found that Latinos were more likely to be vaccinated when SES and 

demographic factors were accounted for.34 However, after controlling for sociodemographics, 

comorbidities, healthcare access, and attitudes towards the vaccine, the study by Steelfisher and 

colleagues still found no difference for H1N1 vaccine uptake between Latino and White

participants.59

Other racial groups examined included Asian, American Indian/Alaska Native (AI/AN), and 

Pacific Islander populations. Two studies examined H1N1 vaccine uptake for Asian participants

compared to Whites. The Plough study found that Asian participants in LA County were more 

likely be vaccinated than Whites (RR = 3.16 [95% CI: 3.12 to 3.21]),51 but a national survey 

study found no significant difference.59 We considered these studies’ findings insufficient to 

determine strength of evidence, due to inconsistency, imprecision, and indirectness of the 

evidence. Two studies examined differences in H1N1 vaccine uptake between AI/AN 

participants and Whites and found AI/AN participants were more likely to be vaccinated (RR = 

1.87 [95% CI: 1.72 to 2.03]51; OR = 1.36 [95% CI 1.01 to 1.84]59), but we determined the 

evidence for this finding to be insufficient. Pacific Islanders in LA County were over 4 times 

more likely to be vaccinated than Whites.51 Finally, an LA-area survey combining Asian and 

Pacific Islander identified participants into a single category found no difference for this group 

versus White participants.54 The evidence for these groups was insufficient. 

By Socioeconomic Status

Two of 3 studies provided low-strength evidence that low-SES participants were less likely to be 

vaccinated. A large (N = 55,850), national, cross-sectional survey study found that a significantly 
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lower percentage of low-SES adults reported receiving the H1N1 vaccine.56 In a small crosssectional study (N = 225) of pregnant women in 2 large, Midwestern cities, those with less than a 

bachelor’s degree were also less likely to have been vaccinated.38 A study of Veterans with 

spinal cord conditions (N = 3384) found no difference in vaccine uptake by education.31 A study 

that controlled for demographic confounders found no difference in vaccine uptake by 

educational attainment, aside from lower uptake among those with a high school education (see 

Table 6).34

By Rural/Urban Status

One small, mixed-methods study (N = 56) of Women, Infants, and Children (WIC) clients and 

staff in Georgia found that rural clients were less likely than urban ones to vaccinate themselves 

(3% vs 15%) or their children (23% vs 35%). Among staff participants however, the rural 

location staff were more likely to have vaccinated themselves and their children.24 The evidence 

from these unadjusted results was insufficient to draw conclusions. A study that controlled for 

sociodemographic factors also found that urban participants were still more likely to be 

vaccinated (see Table 6).

34

Table 6. H1N1 Vaccine Uptake by Group 

Author, Year

Total N 

Participants

Unadjusted H1N1 Vaccine Uptake Results†

H1N1 Vaccine Uptake Results

Adjusted for sociodemographic 

factors

By race/ethnicity Compared to White participants

AA/Black populations

Galarce, 201134

N = 1,569 ---

No difference when controlling for 

SES and demographic factors: OR = 

0.78 (95% CI: 0.49 to 1.24)

LaVela 201231

N = 3,384 No difference by race (data not provided) ---

Plough, 201151

N = 163,087 

Less likely to be vaccinated: RR = 0.49 (95% 

CI: 0.48 to 0.50)* ---

Redelings 201154

N = 1,541

Less likely to report having received the H1N1 

vaccine: OR = 0.7 (95% CI: 0.6 to 1.0) ---

Santibanez, 201356

N = 55,850

Less likely to report being vaccinated: RR = 0.63 

(95% CI: 0.59 to 0.67)* ---

SteelFisher 201559

N = 2,355

No difference in reported vaccination: RR = 0.92 

(95% CI: 0.73 to 1.55) ---

Uscher-Pines, 

201161

N = 4,040

Less likely to report: RR = 0.67 (95% CI: 0.57 to 

0.80)* ---

Latino

Burger, 201725

N = 11,834 Less likely

US-born Latinos: RR = 0.86 (95% CI: 0.76 to 

0.98)*

Latino immigrants (foreign-born): RR = 0.74 (95% 

CI: 0.66 to 0.83)*

• US-born Latinos no longer 

significantly different after 

controlling for demographics and 

comorbid conditions.

• SES accounts for the disparity for 

foreign-born Latinos.

• Before SES AOR = 0.77 (95% CI: 

0.65 to 0.90)‡
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Author, Year

Total N 

Participants

Unadjusted H1N1 Vaccine Uptake Results†

H1N1 Vaccine Uptake Results

Adjusted for sociodemographic 

factors

Among foreign-born Latinos, no difference by: 

language, nativity, time in the US, citizenship, 

region of origin

• With SES (household income, 

homeownership, and education): 

AOR = 1.04 (95% CI: 0.87 to 1.23)

Galarce, 201134

N = 1,569 ---

More likely when controlling for age 

gender, urbanicity, and SES OR = 

1.59 (95% CI: 1.11 to 2.28)

LaVela 201231

N = 3,384 No difference by race (data not provided) ---

Plough, 201151

N = 163,087 

More likely to be vaccinated: 

RR = 1.45 (95% CI: 1.43 to 1.47)* ---

Redelings 201154

N = 1,541 Less likely to report being vaccinated. ---

Santibanez, 201356

N = 55,850

Less likely to report being vaccinated:

21.2% vs 26.4% (P<0.05)

RR = 0.80 (95% CI: 0.75 to 0.86)*

---

SteelFisher 201559

N = 2,355

No difference in reported vaccination: RR = 0.92 

(95% CI: 0.73 to 1.16) ---

Uscher-Pines, 

201161

N = 4,040

No difference in reported vaccination: RR = 0.91 

(95% CI: 0.75 to 1.10) ---

Asian and/or Pacific Islander

Plough, 201151

N = 163,087 

• Asians: more likely to be vaccinated: RR = 

3.16 (95% CI: 3.12 to 3.21)

• PI: More likely to be vaccinated: RR = 4.34 

(95% CI: 4.09 to 4.60)*

---

Redelings 201154

N = 1,541 Asian/PI: No difference in reported vaccinations. ---

SteelFisher 201559

N = 2,355

• Asians: No difference: RR = 1.16 (95% CI: 0.94 

to 1.45)*

• PI: More likely be vaccinated: RR = 1.25 

(95% CI: 1.02 to 1.55)*

---

AI/AN

Plough, 201151

N = 163,087 

More likely to be vaccinated: RR = 1.87 (95% 

CI: 1.72 to 2.03)* ---

SteelFisher 201559

N = 2,355

More likely to report being vaccinated: RR = 1.25 

(95% CI: 1.02 to 1.55)* ---

By SES

Galarce, 201134

N = 1,569

---

Controlling for other 

sociodemographic factors, no 

difference by education, except for 

those with a high school education 

having lower uptake than those 

without: OR = 0.65 (0.42–0.99)*

Hernandez, 201938

N = 225

Low SES less likely. Participants with less than a 

bachelor’s degree were less likely to have been 

vaccinated than those with a bachelor’s degree or 

higher.

---
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Author, Year

Total N 

Participants

Unadjusted H1N1 Vaccine Uptake Results†

H1N1 Vaccine Uptake Results

Adjusted for sociodemographic 

factors

LaVela 201231

N = 3,384 No difference by SES (data not provided) ---

Santibanez, 201356

N = 55,850

Low SES less likely. Participants with < college 

education and making <$75k were significantly 

less likely to be vaccinated than those with higher 

education and income.

---

By rural/urban status

Boyd, 201324

N = 56 (30 rural, 26 

urban)

Rural less likely A higher percentage of low-SES 

urban participants were vaccinated (rural 3% vs 

urban 15%) and had their children vaccinated 

(rural 23% vs urban 35%). 

In contrast, among WIC staff, all rural employees 

were vaccinated, while none were at the urban 

location. A higher percentage of rural staff with 

children also vaccinated their children than urban 

staff (rural 50% vs urban 33%). 

---

Galarce, 201134

N = 1,569 ---

No difference when controlling for 

SES and demographic factors: 

Urban OR = 1.15 (95% CI 0.80 to 

1.64)

*Calculated from study data by reviewers. RRs were calculated using MedCalc.

69

†Strength of Evidence based on unadjusted results

‡Adjusted for sex, age, marital status, and preexisting conditions.

Abbreviations: AA = African American; AI/AN = American Indian/Alaska Native; AP = Adjusted prevalence; APR

= Adjusted prevalence ratio; CDC = Centers for Disease Control and Prevention; CI = Confidence interval; HBPC = 

Home-based Primary Care; HS = High school; LA = Los Angeles; NR = Not reported; OR = Odds ratio; RR = 

Relative risk; SES = Socioeconomic status VHA = Veterans Health Administration; WIC = Women, Infants, and 

Children
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Factors Associated with H1N1 Vaccine Uptake

Eighteen studies examined factors that may have been associated with H1N1 vaccine uptake. 

Some studies provided evidence of disparities mediation (or lack thereof) by analyzing whether 

the association between group status and H1N1 vaccine uptake was changed when a given factor 

was incorporated in a model. Others, particularly the qualitative studies, provided factors 

potentially contributing to lower or higher vaccine uptake for particular groups. The factors fell 

into 5 main categories: health insurance and healthcare access, information and knowledge about 

the H1N1 vaccine, vaccination intentions, seasonal influenza vaccination, and trust and beliefs 

(Table 7 provides details by each of these categories).

Health Insurance and Access 

Four studies examined the association between health insurance coverage and H1N1 vaccine 

uptake.25,40,59,61 Health insurance coverage was associated with greater H1N1 vaccine uptake in 2 

studies, and further, these studies found that minority racial/ethnic and lower-SES groups were 

less likely to have insurance coverage.25,59 In 1 study with multivariable modeling, health 

insurance status of foreign-born Latino participants was a significant factor in whether they 

reported receiving the H1N1 vaccine.25 In the nationwide survey study by Uscher-Pines and 

colleagues (N = 4,040),61 AA/Black participants remained less likely to be vaccinated after 

controlling for a number of sociodemographic factors and insurance status. However, another 

study did not find racial or ethnic differences in H1N1 vaccine uptake by insurance coverage.40

Besides health insurance, other issues of H1N1 vaccine accessibility identified in studies 

included work-related issues, such as inflexible work schedules to take time off for vaccination, 

lack of availability of the vaccine, concerns about its cost, and inconvenient locations/settings for 

vaccination. 

Information/Knowledge About Vaccine 

No broadly representative studies reported on information or knowledge of H1N1 as it directly 

pertained vaccine uptake. Two studies in specific populations offer some insights. In 1 study 

among Veterans with spinal-cord related disabilities (N = 3,384), those with adequate and 

accurate information about H1N1 were more likely to be vaccinated, and AA/Black, Latino, and 

less educated participants were more likely to report not receiving enough information.30 A small 

study (N = 225) among pregnant women in the American Midwest, found that while lower 

educational attainment predicted lower vaccination rates, the relationship was mediated by the 

proportion of social network connections who were college educated H1N1 vaccine supporters.38

Intention to Vaccinate/Vaccine-seeking Behavior

Vaccine seeking, and willingness and intention to vaccinate, did not differ by SES in 3 

studies.33,43,67 In the national survey study by Lin and colleagues (N = 1,569) 43 this was true for 

income and education; however, unemployed participants were more likely to vaccinated with 

the other factors controlled. Latino participants were more likely to be willing to get the H1N1 

vaccine than White participants in a nationwide survey.33 Similarly, a study conducted prior to 

the approval of the H1N1 vaccine asked about willingness to receive a new, unapproved vaccine, 

and found that Latinos were more willing in all analyses.53 AA/Black participants were no 

different from White participants in their willingness or intention to be vaccinated in studies 

conducted during vaccination efforts35,67 and before the vaccine was approved.53
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Receipt of Seasonal Influenza Vaccine

Two studies provide evidence of an overall association between receipt of the seasonal influenza 

vaccine and H1N1 vaccine uptake,33,34 with 1 specifically among minority participants.33 In this 

small study (N = 503) in Atlanta, Georgia, participants who were more likely to report that they 

were likely to get a seasonal influenza vaccine were also more likely to report intention to get an 

H1N1 vaccine.33 The other study, in a nationally representative sample (N = 1,569), found an 

overall association between seasonal influenza vaccination and H1N1 vaccine uptake, but there 

was no difference in seasonal vaccination for AA/Blacks or Latinos compared to Whites, nor by 

participant educational status.34

Trust/Beliefs

Trust (or lack thereof) in the government and healthcare providers, as well as beliefs about 

vaccines and government were examined in several studies. A small study (N = 503) of racial 

and ethnic minority adults in Atlanta, Georgia found that acceptance of the H1N1 vaccine was 

associated with a group of survey items indicating higher trust in the US government and greater 

disagreement with vaccine conspiracy beliefs.33 It also found that participants with negative 

attitudes towards the H1N1 vaccine were less likely to accept the vaccine.33 In another study of 

low-SES participants in Los Angeles (N = 1,750), those with negative beliefs about vaccines 

generally were also less likely to receive the H1N1 vaccination.54 

In regards to trust in government, a study utilizing intercept interviews in Washington, DC found 

that AA/Black and Latino participants were less likely than Whites to trust the US government 

and its agencies when it came to medical countermeasure messages in a hypothetical scenario of 

an infectious disease outbreak.45 In another national survey study (N = 1,543), within group 

associations were not found for government trust and H1N1 vaccine uptake among AA/Black or 

Latino participants, while a positive relationship existed for Whites.32

Multiple studies noted participants’ concerns over the novelty of the vaccine and whether it had 

been adequately tested. In 1 study, an overall analysis of all participants found that belief in 

H1N1 vaccine safety was highly predictive of vaccination (OR = 5.11 [3.50–7.45] P<0.001), and 

low-SES and urban participants were less likely to believe the vaccine was safe.34 In another 

survey study in which safety beliefs were associated with vaccination in the overall sample, 

H1N1 vaccine safety beliefs differed between racial and ethnic groups, with AA/Black 

participants in particular less likely to believe the vaccine was safe (Latino and AI/AN 

participants had more mixed views of vaccine safety, and Asian participants were no different 

than Whites).59 Another study found that AA/Black participants were more concerned about 

safety and side effects of the H1N1 vaccine than other racial and ethnic groups.51 A large 

national survey study found more fear that the vaccine was unsafe among Latino participants, 

followed by AA/Black participants, with White participants least likely to perceive the vaccine 

as unsafe.56 However, fear of getting sick from the vaccine was counterintuitively associated 

with higher vaccine uptake in bivariate analysis, but after adjusting for other factors, there was 

no association; fear of getting sick from the vaccine did not influence vaccine uptake.56 Another 

smaller national survey study found no racial differences in perceptions of vaccine safety, but in 

all groups the percentage who believed it was safe was less than 45%.61 Fears about vaccine 

novelty and safety were also highlighted in qualitative studies. For instance, among Spanish-
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speaking Latino participants of 1 qualitative study, fear of vaccine side effects including “urban 

legends” of vaccine adverse events were common themes.26

Relatedly, belief in the effectiveness of the vaccine may also be associated with uptake. The 

largest survey study found that AA/Black and low-SES participants were less likely to believe 

the vaccine was effective in preventing H1N1, and in the overall sample belief in vaccine 

effectiveness was associated with uptake.56 

Perception of the risk posed by H1N1 was another factor associated with vaccination. The largest 

survey study found that Latinos were more likely to believe they would get sick if not vaccinated 

than AA/Black and White participants.56 Concern that someone in their family could get H1N1 

was higher for all surveyed racial and ethnic minority groups than for Whites in another study, 

and the same study found that those who were very concerned were more likely to be vaccinated 

than those who were less concerned in the overall sample.59 In 2 qualitative studies, perception 

of susceptibility to H1N1 was low among minority participants.24,26
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Table 7. Factors Associated with H1N1 Vaccine Uptake

Author, Year

N Participants

Focus 

Population(s) of Interest Mediating Factors

Health insurance and access

Boyd, 201324

N = 56 

H1N1 Vaccine

• Low SES women 1

Cost and availability: Cited as reasons for non-vaccination.

Burger, 201825

N = 11,834

H1N1 Vaccine

• Latino (US and Foreign-born)

• Low SES: Education

Health insurance (overall):

• In a model with demographics, SES, and insurance coverage, participants with health insurance (AOR = 1.91 [95% CI 1.57 

to 2.33], p < 0.001) were significantly more likely to have received the H1N1 vaccine.

• In unadjusted comparison, more Latinos than Whites had no healthcare coverage.

• In adjusted models vaccination remains less likely for those with less than college education – health insurance coverage 

doesn’t account for the disparity.

• Controlling for demographics and SES, foreign-born Latinos with health insurance were more likely to be vaccinated (OR = 

1.53; P<0.05).

Cassady, 201226

N = 90

H1N1 Vaccine

• Latinos with LEP

Healthcare Utilization: 

• Men, in particular, did not utilize healthcare services unless they were already very sick – prevention not a priority.

Inflexible Work Environment: 

• The use of preventive services and time off for vaccines are challenging for male farm laborers with inflexible work 

schedules.

Knowledge of Vaccine Availability: 

• Most subjects were able to list a location where the vaccine was available for free or at low cost (eg, clinics, flea markets, 

church, and schools).

Kumar, 201241

N = 2,079

H1N1 Vaccine

• AA/Black

• Latino 

Health Insurance: 

• Controlling for demographics and priority status, there was no difference in vaccine uptake by health insurance in the overall 

sample (AOR = 1.28, 95% CI 0.63-2.63), or among AA/Blacks (AOR = 2.97, 95% CI 0.92-9.62), Latinos (AOR = 1.39 [95% 

CI NR; P = 0.27]), or Whites (AOR = 1.03 [95% CI NR; P = 0.95]). 

Galarce, 201134

N = 1,569

H1N1 Vaccine

• AA/Black

• Latino

• Low SES: Education

s

Ability to get Vaccine: 

• Of those who tried to obtain the vaccine, significantly more AA/Blacks, Latinos, and less educated adults reported it being 

unavailable. 
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Author, Year

N Participants

Focus 

Population(s) of Interest Mediating Factors

SteelFisher, 201559

N = 2,355

H1N1 and H1N1 Vaccine

• AA/Black

• Latino

• Asian

• AI/AN

• Low SES: Education w

Health Insurance: 

• In the overall sample, those with health insurance were more likely to receive H1N1 vaccine than uninsured: OR = 1.51 

(95% CI: 1.10 to 2.09).

• AA/Black, Latino, and AI/AN (but not Asian) pts were less likely to have health insurance coverage than Whites. 

• After controlling for healthcare access, and attitude-related variables in addition to sociodemographics, AI/AN participants 

remained more likely than Whites to be vaccinated. 

• Asian pts were also more likely to be vaccinated after these factors were incorporated. 

• There were no differences by SES.

Uscher-Pines, 201161

N = 4,040

H1N1 Vaccine 

• AA/Black

• Latino

Health Insurance:

• Controlling for demographics, SES, comorbidities and insurance status AA/Black pts were still less likely to vaccinate: AOR

= 0.67, P = 0.05.

Provider Recommendation to Vaccinate: 

• No difference by race in receipt of provider recommendation to vaccinate.

Vaccination Settings:

• Fewer AA/Blacks (2.9%) than Whites (12.4%) reported receiving a vaccine in a retail setting (p = 0.001), or in health 

department flu clinics (7.7% vs 22.1%, p = 0.002). 

• Latinos were more likely to be vaccinated in a physician’s office than Whites (42% vs 23.5%, p = 0.04). 

• There were no other differences by setting (ie, physician’s office, health department, medical clinic).

Vaccination in the Workplace:

• Controlling for confounders, Latinos were less likely to have been vaccinated in the workplace (6.2% vs 18.8%; P = 0.01). 

• There was no difference between AA/Blacks and Whites.

Information/Knowledge about vaccine

Etingen, 201230

N = 3,384

H1N1 and H1N1 Vaccine

• Veterans with Spinal Cord 

Injuries/Disorders

Adequacy and accuracy of information 

• In the overall sample, those reporting that they received adequate information about H1N1 were more likely to have 

received an H1N1 vaccine (OR = 1.80 [95% CI 1.49 to 2.17]); likewise for those with accurate information (OR = 1.99; 95% 

CI [1.67 to 2.37]). 

Hernandez, 201938

N = 225

H1N1 Vaccine

• Low SES Pregnant Women: 

Education

Social network connections: 

• Association between educational attainment (<bachelor’s degree vs bachelor’s degree+) and vaccination was mediated by 

social network connections, specifically proportion of college-educated individuals in network:

• Proportion of college educated people with whom participants discussed H1N1 partially mediated the relationship.

• Proportion of college-educated H1N1 vaccine supporters in personal network substantially mediated the relationship.
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Author, Year

N Participants

Focus 

Population(s) of Interest Mediating Factors

Intention to vaccinate/Vaccine seeking behavior

Frew, 201233

N = 503

H1N1 Vaccine

• Low SES Racial/Ethnic 

Minorities: Education; Income S

Vaccine Acceptance/Intentions:

• SES had no impact on vaccine acceptability (ie, reported likelihood of getting H1N1 vaccine in next year): 

• Education: AOR = 1.05, 95% CI [0.81 to 1.36])

• Income: OR = 0.82 (95% CI: 0.59 to 1.14)

Gargano, 201135

N = 102

H1N1 Vaccine

• Rural AA/Black s

Intention to be Vaccinated

• There was no difference between AA/Black and White participants (AOR = 0.56, 95% CI [0.17-1.85]).

Lin, 201442

Lin, 201843

N = 1,569

H1N1 and H1N1 Vaccine

• Low SES: Education; 

Income; Employment s

Sought Vaccine for self or loved ones: 

• No difference by household income or education, but more likely if unemployed (vs employed). 

• These relationships were unchanged by adding interpersonal networks (health-related social networks and talking to a 

doctor about H1N1) and intrapersonal factors (concerned about self or family getting sick and knowledge of H1N1 

transmission) to the model.

Mesch, 201467

N = 968

H1N1 and H1N1 Vaccine

• AA/Black

• Latino

• Low SES: Education s

Willingness to take H1N1 Vaccine: 

• Latinos were more likely to be willing to take the H1N1 vaccine than Whites (P<0.01). 

• No difference for AA/Blacks. 

• No differences based on education.

Quinn, 200953

N = 1,543

H1N1 Vaccine

• AA/Black

• Latino

• Low SES: Education

Willing to take H1N1 Vaccine under FDA EUA: 

• Significantly fewer Latinos than Whites and AA/Blacks would refuse the vaccine. 

• After controlling for covariates this relationship remained (Latino [vs White] acceptance OR = 3.27 [95% CI: 1.40 to 7.63]; 

p < 0.003).

• Undecided: AA/Black and Latino race/ethnicity (p < 0.001), lower income (p = 0.002), and a lower education level 

(p < 0.001) were associated with being undecided about accepting the vaccine.

Confidence in vaccine decision: 

• Latinos were less confident in their decision than Whites and AA/Blacks (p = 0.007). 

• SES: 35.5% of pts with < high school were confident in their decision compared to 57.7% of those with college+ 

(p = 0.001).

Receipt of seasonal influenza vaccine

Frew, 201233

N = 503

• Among racial/ethnic minority adults, H1N1 vaccine acceptance was associated with having had the flu shot in the past 5 

years (OR = 2.50, 95% CI [1.52, 4.10])
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Author, Year

N Participants

Focus 

Population(s) of Interest Mediating Factors

H1N1 Vaccine

• Low SES Racial/Ethnic 

Minorities: Education; Income s

Galarce, 201134

N = 1,569

H1N1 Vaccine

• AA/Black

• Latino

• Low SES: Education

• Rural

• Participants (in overall sample) who had received the seasonal influenza vaccine were more likely to have been vaccinated 

for H1N1 (AOR = 21.46 [95% CI 14.30 to 32.21]), P<0.001.

• Seasonal flu vaccine receipt didn’t differ by race/ethnicity or SES.

• In a sequential model controlling for SES and demographic factors, then adding H1N1 vaccine-related beliefs and seasonal 

influenza vaccination status, there was no difference in H1N1 vaccine uptake for AA/Blacks vs Whites in either model. 

• However, the addition of beliefs and seasonal vaccine status eliminated the significantly higher odds for Latinos and lower 

odds for those with a high school degree (vs < HS) and resulted in significantly higher odds for urban vs rural participants.

Trust/Beliefs

Boyd, 201324

N = 56 

H1N1 Vaccine

• Low SES women s

Trust: 

• Both clients and staff expressed lack of clear information/what information sources to trust, perception of inadequate 

research/‘‘newness’’ of the vaccine, and fears related to potential side effects of vaccination.

Risk perception: 

• Belief that they were personally at risk for H1N1 was especially low among rural participants.

Cassady, 201226

N = 90

H1N1 Vaccine

• Latinos with LEP s

Reasons for not getting vaccinated: 

• Novelty of vaccine created fear that vaccine had not been sufficiently tested for side effects (vaccine waivers contributed to 

fear); urban legends about vaccine dangers; and perception that H1N1 was not serious. 

• Lack of trust in government cited by a minority of participants.

Trusted vaccine information sources: 

• Parents trusted information from their children’s schools; male farm workers cited doctors as most trusted source of health 

information.

Freimuth, 201432

N = 1,543

H1N1 Vaccine

• AA/Black

• Latino s

Trust in government: 

• There was no relationship between trust in the government and vaccine uptake within AA/Blacks or Latinos. 

• There was a significant positive relationship for Whites (Cohen’s d = 0.218; P = 0.034). 

Frew, 201233

N = 503

H1N1 Vaccine

• Low SES Racial/Ethnic 

Minorities: Education; Income s

• Among minority adults, H1N1 vaccine acceptance was associated with lower mistrust and greater disagreement with 

vaccine conspiracy beliefs [OR = 2.15, CI (1.57, 2.95)]. 

• Respondents who had a general negative opinion about the H1N1 vaccine-related benefits were less likely to accept 

vaccination [OR = 0.23, CI (0.16, 0.33)].
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Author, Year

N Participants

Focus 

Population(s) of Interest Mediating Factors

Galarce, 201134

N = 1,569

H1N1 Vaccine

• AA/Black

• Latino

• Low SES: Education

• Rural

Vaccine-Related Beliefs:

• In a sequential model controlling for SES and demographic factors, then adding H1N1 vaccine-related beliefs and seasonal

influenza vaccination status, there was no difference in H1N1 vaccine uptake for AA/Blacks vs Whites in either model. 

• However, the addition of beliefs and seasonal vaccine status eliminated the significantly higher odds for Latinos and lower

odds for those with a high school degree (vs < HS).

• The addition resulted in significantly higher odds for urban vs rural participants.

Perceived vaccine safety

• Overall, pts more likely to be vaccinated if believe vaccine is safe (vs unsafe) OR = 5.11 (3.50–7.45) P<0.001.

• There were no racial/ethnic differences in belief in vaccine safety.

• College graduate vs <high school were more likely to believe vaccine safe (OR = 1.69, 95% CI [1.17-2.44]).

• Urban participants were less likely to perceive the H1N1 vaccine as safe (OR = 0.75, 95% CI [0.56-0.99]).

Liu, 201745

N = 148

H1N1 Vaccine

• AA/Black

• Latino s

Trust in the US Government:

• Whites reported significantly more trust in the US government (P = 0.047) and the CDC (P = 0.017) related to medical

countermeasure messages than AA/Blacks and Latinos.

Plough, 201151

N = 1,750

H1N1 Vaccine

• AA/Black

• Latino

• Asian s

Trust in the Vaccine: 

• Concerns regarding the safety and side effects of vaccination were more prevalent among AA/Blacks than among other

racial/ethnic groups.

• Among those who reported being unlikely to get vaccinated, 44% of AA/Blacks felt that the vaccine may have adverse

effects versus 29%, 25%, and 33% of Whites, Latinos, and Asians, respectively.

Redelings, 201154

N = 1541

H1N1 Vaccine

• AA/Black s

Trust in the Vaccine and Providers Recommendations: 

• AA/Blacks were less likely to report that they trust providers who recommend vaccines (OR = 0.5 [95% CI 0.4–0.7]), that

they believe vaccines can prevent disease (OR = 0.4 [95% CI 0.3–0.5]), and that vaccines are safe (OR = 0.5 [95% CI 0.4–

0.6]).

• Among all participants, beliefs about vaccine safety and efficacy predicted receipt of H1N1 vaccine.

Santibanez, 201356

N = 55,850

H1N1 Vaccine

• AA/Black

• Latino

• Low SES: Education; Income

Vaccine Beliefs:

• In overall sample, those who believed H1N1 vaccines were effective were more likely to get H1N1 vaccine (P<0.001).

• AA/Blacks (69.2%) were less likely to believe it effective compared to Whites (74.9%) and Latinos (73.5%); both

P<0.05.

No difference between Whites and Latinos.

Low SES (income and education) were less likely to believe in effectiveness.

• Belief that 1 will get sick if not vaccinated was associated with more H1N1 vaccine uptake overall (P<0.001).

• Latinos more likely to believe this (39.3%) than Whites (23.4%) or AA/Blacks (25.5%); both P<0.05.

• No difference between Whites and AA/Blacks.
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Author, Year

N Participants

Focus 

Population(s) of Interest Mediating Factors

• Less educated more likely to believe than more educated, and lowest incomes more likely to believe than higher 

incomes.

• Worry about getting sick from vaccine was associated with more H1N1 vaccine uptake (P<0.001). 

• Latinos (45.9%) were more likely to be worried than Whites (26.6%) and AA/Blacks (36.8%), and AA/Blacks 

were more likely than Whites; all P<0.05. 

• Lower SES (income and education) were more worried than higher SES.

After adjusting for sociodemographic factors* and opinion: 

• White pts were still more likely to be vaccinated than Latinos

• Unadjusted vs adjusted prevalence of vaccination:

• Latino: 21.2 vs 16.5%

• AA/Black: 16.6 vs 17.9%

• White: 26.4 vs 25.6%

• Lower SES remained less likely to vaccinate after adjustment

SteelFisher, 201559

N = 2,355

H1N1 Vaccine

• AA/Black

• Latino

• Asian

• AI/AN

• Low SES: Education

After controlling for healthcare access, attitude-related variables, and sociodemographics: 

• AI/AN participants remained more likely than Whites to be vaccinated. 

• Asian pts were also more likely to be vaccinated after these factors were incorporated. 

• There were no differences by SES.

Attitude-related variables:

• Those very concerned about self/family getting H1N1: More likely to be vaccinated than less concerned. OR = 2.13 (95% 

CI: 1.66 to 2.74)† 

• All racial/ethnic minorities (AA/Black, Latino, Asian, and AI/AN) were significantly more likely to be very or somewhat 

concerned than Whites.

Perception of safety: 

• Those who believed it was very safe were more likely to vaccinate than those with lesser safety beliefs: OR = 5.81 (95% 

CI: 4.66 to 7.24)†

• Compared to Whites:

• AA/Blacks: Less likely to believe vaccine is “very safe” and more likely to believe it is “not at all safe”

• Latinos: More likely to believe vaccine is “not very safe” 

• Asians: No differences

• AI/ANs: Less likely to believe vaccine is “very” safe, but more likely to believe it is “somewhat safe,” and less likely to 

believe is “not very safe”

Uscher-Pines, 201161

N = 4,040

H1N1 Vaccine

• AA/Black

• Latino s

Perception of Safety

• AA/Blacks (40.6%), Latinos (39.6%), and Whites (44.7%) were similar in their perception that the H1N1 vaccine was safe.
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* adjusted for income, education, sex, and priority status

† controlled for race and SES

‡ proportions calculated by reviewers

Abbreviations: AA = African American; AI/AN = American Indian/Alaska Native; AP = Adjusted prevalence; APR = Adjusted prevalence ratio; CDC = Centers for Disease 

Control and Prevention; CI = Confidence interval; EUA = Emergency Use Authorization; FDA = Food and Drug Administration; HBPC = Home-based Primary Care; HS = High 

school; LA = Los Angeles; NR = Not reported; OR = Odds ratio; RR = Relative risk; SES = Socioeconomic status; VHA = Veterans Health Administration; WIC = Women, 

Infants, and Children
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Interventions to Reduce Disparities in Vaccine Uptake

No studies examined interventions to address H1N1 vaccine-related disparities. We identified 

only 1 intervention study, aimed at increasing Hepatitis A vaccine uptake among those 

experiencing homelessness in San Diego, California during a regional outbreak. The intervention 

was a computerized alert system embedded in the electronic health record (EHR) that notified 

emergency department (ED) providers of the participants houseless status and suggested offering 

the Hepatitis A vaccine. Vaccination significantly increased after the alert was implemented (see 

Table 8).27

Table 8. Interventions for Vaccination-related Disparities

Author, Year

N Participants

Setting

Study Design 

Dates

Focus and Population

Intervention Findings

Relevance to COVID-19

Castillo, 201827

N = 1,131

ED, San Diego, CA

Retrospective pre- postAug 2016 - Jan 2018 

Hepatitis A Vaccine for the 

Homeless population

ED-based computerized 

alert system embedded 

within the EHR to identify 

patients experiencing 

homelessness and prompt 

providers to consider 

offering the Hepatitis A 

vaccine during regional 

outbreak.

Vaccinations given:

Historical period: none (0/1,000 visits)

Pre-intervention period: 23 (8/1,000 visits)

Intervention period: 465 (184/1,000 visits)

Intervention period:

Visits with receipt of vaccine in ED: 77.5%

An automated system identifying

disadvantaged populations can help to 

mitigate potential disparities in COVID-19

vaccinations

Abbreviations: CA = California; ED = Emergency department; EHR = Electronic health record
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DISCUSSION

We identified 50 articles relevant to health inequalities in infectious disease epidemics in the 

United States predating COVID-19. We found 14 studies (16 articles) that examined potential 

mediating factors associated with health inequities, and 12 studies (3 articles) and 5 expert panel 

reports that provide examples of interventions and lessons learned from previous epidemics and 

disasters to guide us forward in mitigating health inequalities during the COVID-19 pandemic 

and beyond. We also identified 20 studies that examined inequalities in H1N1 vaccine uptake 

and mediating factors, and 1 vaccine-related intervention study. 

To our knowledge, this is the first review of studies aimed at identifying both the factors that 

mediate health disparities in infectious disease epidemics and also potential avenues for 

mitigating then. Our conceptual framework was guided by the work of Quinn and Kumar, who 

considered the potential causes of epidemic influenza based on measures of exposure, 

susceptibility, and access to care as they applied to data collected in 2009-2010 during the H1N1 

pandemic (see Figure 1). The framework points to proximal and distal determinants of disease 

burden with the ultimate goal of identifying potential points of policy and programmatic 

intervention. 

Across Key Questions, studies were heterogeneous in their operationalization of potentially 

mediating factors, populations, programs, and interventions. Studies of potentially mediating 

factors generally, and those related to H1N1 vaccine uptake specifically, were largely crosssectional. Several included studies did not control for confounding variables or their methods 

were unclear44,51,63,65,66; however, many were well conducted and adequately reported. A few of 

the qualitative studies clearly reported their methodology and/or findings.21,22,26 However, more 

did not.20,45,47,57,64 We identified very few interventions or program evaluation studies specific to 

infectious disease epidemics; most were focused on post-disaster needs or disaster preparedness. 

Key Question 1

Our findings related to mediators associated with differential exposure or the transmission of 

infectious disease were unsurprising. Despite looking across racial/ethnic groups and 

socioeconomic status independently, the findings that disparities were related to societal-based 

structural and work-related factors, rather than individual factors such as hygiene and cleaning 

were consistent across studies. We identified few significant differences in social distancing 

attitudes and intentions between groups. Instead, it was clear that the meaningful differences lay 

in the ability or inability to social distance. Only 1 study examining variables related to exposure 

to illness disaggregated the Latino population by language proficiency, and 1 additional study 

provided qualitative input in the form of stakeholder interviews. In contrast with other 

populations we examined, compared to either English-proficient Latinos or Whites, limited 

English-proficient Latinos (and/or migrant and seasonal farm workers) were at higher risk across 

both structural and work-related variables measured (see Table 9). 

Susceptibility to illness played a major role in H1N1 severity and mortality; however, access to 

care (ie, having a primary care provider or health insurance) did not. Significantly greater 

proportions of every racial and ethnic minority group reported having experienced discrimination 

while seeking healthcare, and many reported being less informed or were less prepared. Much of 

the literature guiding communication is dated due to advances in technology, and findings of 
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proportionally high rates of trust in the federal governmental, particularly in AA/Black and 

Latino adults, may be out of date (see Table 10). 
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Table 9: Health Inequity Mediators by Group: Exposure

Study 

Timing N 

Exposure

Structural Work-related Social Distancing Hygiene-related 

Behaviors

African American/Black compared to non-Hispanic White

Kumar 201240

H1N1 2042

†More likely to live in an 

apartment

†No difference in ability to 

social distance due to work

†No difference in ability to 

social distance --

†More likely to live in a 

metro area

†No difference in the 

number of children per 

household 

SteelFisher 

201559

H1N1

2355 -- --

‡No difference in avoiding 

people with flu-like 

symptoms

‡More likely to cover nose 

and mouth with a tissue, to 

use hand sanitizer more 

frequently, and not touch 

face. 

‡No difference in avoiding 

social gatherings

‡No difference in hand 

washing, sneezing or 

coughing into elbow

‡More likely to have cleaned 

more frequently

‡More likely to avoid air 

travel and public 

transportation 

‡More likely to have used 

stronger cleaners or 

disinfectants than usual

Quinn 201110

H1N1 1479

†Fewer adults per 

household

†Better able to social 

distance at work

†More dependent on public 

transportation

--

†No difference in the 

number of children

†More likely to live in an 

apartment †Less able to obtain 

childcare that wasn’t with a 

group of children †More likely to live in a 

metro area
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Study 

Timing N 

Exposure

Structural Work-related Social Distancing Hygiene-related 

Behaviors

Hispanic/Latino compared to non-Hispanic White (general)

Kumar 201240

H1N1 2042

†More children per 

household 

†Less able to social 

distance due to work 

†Less able to social 

distance (including public 

transportation)

-- †More likely to live in an apt 

†More likely to live in a 

metro area

SteelFisher 

201559

H1N1

2355 -- --

‡No difference in avoiding 

people with flu-like 

symptoms

‡More likely to cover nose 

and mouth with a tissue. 

‡No difference in other 

personal hygienebehaviors.

‡More likely to avoid social 

gatherings

‡More likely to have cleaned 

more frequently

‡More likely to avoid air 

travel and public 

transportation

‡More likely to have used 

stronger cleaners or 

disinfectants than usual

Quinn 201110

H1N1 1479

†No difference in adults or 

children per household

†No difference in ability to 

social distance due to work

†More dependent on public 

transportation

-- †More likely to live in an 

apartment †No difference in ability to 

obtain childcare that wasn’t 

with a group of children †More likely to live in a 

metro area

American Indian/Alaska Native compared to non-Hispanic White

SteelFisher 

201559

H1N1

2355 -- --

‡No difference in avoiding 

people with flu-like 

symptoms

‡More likely to cover nose 

and mouth with a tissue, 

and to cough or sneeze into 

elbow. 

‡No difference in other 

personal hygienebehaviors.

‡No difference in avoiding 

social gatherings

‡More likely to have cleaned 

more frequently
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Study 

Timing N 

Exposure

Structural Work-related Social Distancing Hygiene-related 

Behaviors

‡More likely to avoid air 

travel and public 

transportation

‡More likely to have used 

stronger cleaners or 

disinfectants than usual

Asian compared to non-Hispanic White

SteelFisher 

201559

H1N1

2355 -- --

‡No difference in avoiding 

people with flu-like 

symptoms

‡More likely to cover nose 

and mouth with a tissue. 

‡More likely to avoid social 

gatherings

‡No difference in other 

personal hygienebehaviors.

‡No difference in cleaning 

more frequently

‡More likely to avoid air 

travel and public 

transportation

‡More likely to have used 

stronger cleaners or 

disinfectants than usual

Limited English Proficiency

Latino Spanish or Indigenous language compared to Latino English speakers or non-Hispanic White

Quinn 201110

H1N1 1479

†More likely to live in an 

apartment

†Less able to social 

distance due to work

†More dependent on public 

transportation

--

†More likely to live in a 

metro area

†More adults and children 

per household

†Less able to obtain 

childcare that wasn’t with a 

group of children

Schoch-Spana, 

201057

H1N1

33 10-12 people or 2-3 families 

in a small cabin

Unable to stay home when 

schools close or children 

are sick 

Limited work benefits and -- --

low wages but dependence 

on job – may not stay home 

when sick
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Study 

Timing N 

Exposure

Structural Work-related Social Distancing Hygiene-related 

Behaviors

Lack of consistent 

childcare. Children may 

accompany parents to work

Lower SES compared to Higher SES

Levy 20138

H1N1 374

†‡ Residents of highpoverty neighborhoods 

were more likely to have 

been hospitalized for H1N1

-- -- --

Lin 201442

Lin 201843

H1N1

1569 -- --

†‡ No difference in social 

distancing (Including public 

transportation) (education)

†‡ No difference in wearing 

a face mask (education)

†‡ No difference in “staying 

home” (education)

†‡ No difference in the 

frequency of hand-washing 

or hand sanitizer use 

(education) †‡ No difference in the 

reduction of human contact 

with people outside of 

household (education)

†‡ No difference in coughing 

with mouth covered 

(education)

SteelFisher 

201559

H1N1

2355 -- --

‡≤ HS were less likely to 

have avoided people with 

flu-like symptoms

‡≤ HS were more likely to 

cover nose and mouth with 

a tissue. 

‡No difference in other 

personal hygiene-behaviors 

by education

‡≤ HS were more likely to 

avoid social gatherings

‡≤ HS were more likely to 

have cleaned more 

frequently

‡≤ HS were more likely to 

avoid air travel and public 

transportation

‡≤ HS were more likely to 

have used stronger 

cleaners or disinfectants 

than usual

1

Veteran population †Controlled for demographics and SES; ‡Controlled for demographics and additional confounders 

Higher risk No difference in risk Lower risk 
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Table 10: Health Inequity Mediators by Group: Susceptibility, Access to Care, Discrimination/Trust

Study N Susceptibility Access to Care Discrimination and Trust

Information and 

Knowledge

African American/Black compared to non-Hispanic White

1Etingen 201230

H1N1 3384 -- -- --

‡ Less likely to report 

receiving adequate H1N1 

information (patients with a 

disability)

Freimuth, 201432

H1N1 1543 -- --

§More likely to trust the 

federal government (ie, 

President Obama, HHS).

--

§No difference in trust of 

the CDC and state and 

local governments.

--

§No difference in overall 

government trust by 

previous experience of 

discrimination in health 

care

--

Lin 201744

MERS 627 -- -- --

†No difference in awareness 

of pandemics in the past 10 

yrs

†Less likely to have heard of 

MERS

†Less likely to have 

accurate knowledge about 

MERS

McCauley, 

201347

H1N1

46 -- --

Not sure who and what to 

trust re: H1N1 due to 

mixed messages in the 

media and by government 

officials. 

--

Quinn 2009

H1N1 1543 -- -- Higher trust in government re: H1N1 --
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Study N Susceptibility Access to Care Discrimination and Trust

Information and 

Knowledge

Quinn 201110

H1N1 1479

†No difference in 

number of comorbid 

conditions

†No difference in access to care 

(ie, insurance, primary provider, 

ability to pay)

†More likely to have 

experienced discrimination 

when seeking health care

--

SteelFisher 

201559

H1N1

2355 --

‡More likely to have spoken to 

a provider about H1N1 

protection

-- --

Hispanic/Latino compared to non-Hispanic White

1Etingen 201230

H1N1 3384 -- -- --

‡ Less likely to report 

receiving adequate H1N1 

information (patients with a 

disability)

Freimuth, 201432

H1N1 1543 -- --

§More likely to trust the 

federal government (ie, 

President Obama, HHS).

--

§No difference in trust of 

the CDC and state and 

local governments.

--

§No difference in overall 

government trust by 

previous experience of 

discrimination in health 

care

--

Lin 201744

MERS 627 -- -- --

†No difference in awareness 

of previous pandemics

†Less likely to have heard of 

MERS

†No difference in accurate 

knowledge about MERS

Mesch, 201467

H1N1 1000 -- --

‡More trust in the 

government to handle 

H1N1

--

Quinn 2009

H1N1 1543 -- -- Higher trust in government re: H1N1 --
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Study N Susceptibility Access to Care Discrimination and Trust

Information and 

Knowledge

Quinn 201110

H1N1

1479 †No difference in 

number of comorbid 

conditions

†No difference in access to care 

(composite)

†More likely to have 

experienced discrimination 

when seeking health care

--

SteelFisher 

201559

H1N1

2355 --

‡More likely to have spoken to 

a provider about H1N1 

protection

-- --

American Indian/Alaska Native compared to non-Hispanic White

Hennessy 2015

H1N137

H1N1

381 †Preexisting conditions 

mediated the 

relationship between 

AI/AN and H1N1 

mortality

-- -- --

SteelFisher 

201559

H1N1

2355 --

‡More likely to have spoken to 

a provider about H1N1 

protection

-- --

Asian compared to non-Hispanic White

SteelFisher 

201559

H1N1

2355 --

No difference in having spoken 

to a provider about H1N1 

protection

-- --

Limited English Proficiency

Hispanic/Latino Spanish or Indigenous language compared to Hispanic/Latino English speakers or non-Hispanic White

Quinn 201110

H1N1 1479

†Fewer comorbid 

conditions

†Poorer access to care 

(composite)

†More likely to have 

experienced discrimination 

when seeking health care

--

Schoch-Spana, 

201057

H1N1

33

--

Not aware of migrant health 

centers

Lack of transportation from 

rural locations

Lack of Spanish and 

Indigenous language materials 

and support

Stigmatization by 

providers, “(they are) 

coming from Mexico and 

they’re bringing in the 

swine flu.”

--
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Study N Susceptibility Access to Care Discrimination and Trust

Information and 

Knowledge

Low utilization of care for 

prevention and early stages of 

illnesses

Don’t seek care due to fear of 

deportation 

Lack of money for healthcare 

costs No insurance, can’t 

access public assistance

Witrago 201163

Influenza 

Pandemic 

Preparedness

209 --

The primary reason reported for 

not having medication at home 

was lack of health insurance 

and money

-- Less prepared for an 

influenza pandemic (scale)

Chinese ethnicity regardless of language compared to Chinese ethnicity English speakers

Yip 200966

H1N2 100 -- -- --

Less likely than English 

proficient to feel well 

informed about H1N1

Low SES compared to Higher SES

1Etingen 201230

H1N1 3384 -- -- -- 

‡ Less than a college 

degree were less likely to 

report receiving adequate 

H1N1 information 

(participants with a 

disability)

Lin 201442

Lin 201843

H1N1

1569 -- -- -- 

† Lower knowledge about 

H1N1 transmission 

(education)

‡ No difference in 

knowledge about H1N1 

transmission (education)

†‡ No difference in 

misconceptions about 

H1N1 transmission 

(education)
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Study N Susceptibility Access to Care Discrimination and Trust

Information and 

Knowledge

† No difference in pork 

avoidance behaviors

‡ More likely to avoid eating 

pork products

Lin 201744

MERS 627 -- -- -- 

†Less likely to be aware of 

previous pandemics

†No difference in having 

heard of MERS

†No difference in accurate 

knowledge about MERS

Levy 20138

H1N1 374

†‡ Adults with 1+ 

comorbidity were more 

likely to be hospitalized 

for H1N1. 

Comorbidities did not 

explain all of the 

association between 

hospitalization and low 

SES (education) or 

neighborhood SES 

(FPL)

Participants with public (vs 

private) insurance were more 

likely to be hospitalized with 

H1N1

-- -- 

†‡No relationship associated 

with having a primary care 

provider and hospitalization for 

H1N1

†‡No relationship associated 

with having insurance and 

hospitalization for H1N1

Freimuth, 201432

H1N1 1543 -- --

§No difference in trust in 

the federal government (ie, 

President Obama, HHS).

--

§No difference in trust of 

the CDC and state and 

local governments.

§No difference in overall 

government trust by 

previous experience of 

discrimination in health 

care
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Study N Susceptibility Access to Care Discrimination and Trust

Information and 

Knowledge

Mesch, 201467

H1N1 1000 -- --

No difference in trust in the 

government to handle 

H1N1

--

SteelFisher 

201559

H1N1

2355 --

‡No difference in having spoken 

to a provider about H1N1 

protection by SES (level of 

education)

-- --

1

Veteran population

†

Controlled for demographics and SES

‡

Controlled for demographics and additional variables

§

Weighted by demographics, SES and additional variables

Abbreviations: AA = African American; AI = American Indian; AN = Alaska Native; CDC = Centers for Disease Control; FPL = federal poverty level; HHS = 

Health and Human Services; MERS = Middle East Respiratory Syndrome; SES = socioeconomic status

Note:

Higher-risk No difference in risk Lower-risk 
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Key Question 2

We identified 5 population-focused papers summarizing key stakeholder recommendations for 

pandemic influenza preparedness. While the recommendations generated from CDC stakeholder 

meetings preceded Quinn and Kumar’s 2014 publication, many of them can be classified 

according to their framework. Collectively their recommendations address proximal causes that 

influence differential exposure (eg, hand-washing and social distancing),23 disease susceptibility

(ie, vaccinations),39 and differential consequences (eg, mobile clinics).

58 They also provide 

recommendations for distal determinants of disease exposure (eg, liberal leave policies and job 

security guarantees),60 and disease severity (eg, migrant health clinics).58 

Of the 12 studies that described interventions or intervention components, only 1 study28

examined effectiveness outcomes, while the majority described acceptability and feasibility of 

the studied interventions. We identified only 1 randomized controlled trial and 1 longitudinal 

cohort study. 

While many focus on disaster preparedness and response, the interventions in this review often 

represent real world applications of the recommendations outlined in the 2008 CDC stakeholder 

meetings. For instance, the intervention studied in Eisenman28 demonstrates the positive effect 

that interpersonal, culturally appropriate education delivered by a community health worker can 

have on disaster preparedness in vulnerable communities. This validates the recommendation for 

using lay promotoras in delivery of health services, goods, and messaging in the case of a 

pandemic.58 These results could be translated into an intervention to reach vulnerable 

populations during the current COVID-19 pandemic. 

The importance of community engagement and partnership with community leaders was 

repeated often in expert-based recommendations,39 and was empirically grounded in some 

studies we examined. In Aten et al, the authors found that African American clergy could be 

integral as community liaisons in facilitating the delivery of mental health services after 

Hurricane Katrina. They also found that churches could serve as sites for delivery of communitybased services.21,22 These findings mirror the recommendations of partnering with faith-based 

organizations and community leaders in order to “allay distrust and ensure successful 

implementation of mitigation interventions in minority communities” in the setting of an 

influenza pandemic.39 These recommendations support the use of such partnerships to lessen the 

disproportionate burden of COVID-19 in racial and ethnic minorities. While it is too early in the 

pandemic to expect a rigorous evaluation of the effect of faith-based partnerships on COVID-19 

disparities, the popular media has already chronicled that such interventions are underway.70

The interventions described here emphasize that preparedness efforts must be prioritized and 

marginalized communities must be included before disaster hits. Nevertheless, some of the 

lessons learned may be relevant to the current pandemic phase: months into the trajectory of 

pandemic response but prior to a safe and widely available vaccine or treatment. Proven 

preparedness interventions could also be considered for implementation now given the potential 

for future waves of COVID-19 or new epidemics to emerge.

What remains missing from the studies in this review are examples of successful systems-level 

interventions that target the distal determinants of worse outcomes of influenza illness in 

vulnerable populations. This is despite evidence from the H1N1 pandemic that variables of 
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exposure that occur at higher rates among these vulnerable groups, such as inability to take sick 

leave, can drastically affect disease rates.40 In the key stakeholder reports, we find multiple 

systems-level recommendations; for instance liberal workplace leave and teleworking policies,

60

wage freezes and childcare vouchers,

23 and creating an ethical and equitable system for ensuring 

access to treatment and vaccination, particularly among the uninsured.

39 These interventions may 

already be underway, and researchers and policy makers should actively test their impact on 

health disparities so that lessons learned may be applied to our current and possible future 

disease epidemics. 

H1N1 Vaccine

We identified 10 studies examining disparities in H1N1 vaccine uptake in the US during the 

2009-2010 flu season. We found moderate-strength evidence that vaccine uptake was lower in 

AA/Black than White populations from 4 of 6 studies presenting unadjusted data. We also found 

low-strength evidence of lower vaccine uptake for Latino populations, although there was some 

inconsistency in results. The evidence for Asian, AI/AN, and Pacific Islander populations 

compared to Whites was insufficient. 

Three studies looked at disparities by SES and provide low-strength evidence that lower-SES 

individuals were less likely to have been vaccinated. Lastly, a very small study of rural versus 

urban participants provided insufficient evidence for H1N1 vaccine uptake between those 

populations. There was no evidence by disability status (see Table 11).

Table 11. Strength of the Evidence for Studies of H1N1 Vaccine Uptake

Population of 

interest

Comparator 

population

# of 

studies

vaccine 

uptake 

likelihood Strength SOE justification

AA/Black

White

6 Less likely Moderate

Latino 7 Less likely Low Inconsistency

Asian 2 Unclear Insufficient Inconsistency, imprecision, 

indirectness

AI/AN 2 Unclear Insufficient Indirectness, imprecision

Pacific 

Islander 1 Unclear Insufficient Indirectness

Asian/Pacific 

Islander 1 Unclear Insufficient Indirectness, imprecision

Lower SES 

(education 

and/or 

income)

Higher SES 3 Less likely Low Inconsistency

Rural Urban 1 Unclear Insufficient Single, small study with 

multiple limitations

With 

Disabilities

Without 

Disabilities 0 No evidence --- ---

Abbreviations: AA = African American, AI = American Indian, AN = Alaska Native, SES = socioeconomic status

Studies examining H1N1 vaccines explored a wide range of factors that were either proximal or 

distal to H1N1 vaccine uptake. Health insurance coverage, and availability of/access to vaccines 
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were both important factors. Also important were receipt of the seasonal influenza vaccine, 

vaccine-related safety and effectiveness beliefs, and perceived susceptibility to H1N1. Across all 

factors AA/Black adults, and often Latino and low SES adults as well, were at higher risk.

We did not identify any studies of interventions specifically targeting disparities in H1N1 

vaccine uptake. However, the single vaccine intervention study we did find demonstrated greater 

Hepatitis A vaccine uptake in an ED setting after the implementation of an EHR alert system that 

informed providers of the patient’s homeless status and prompted them to recommend

vaccination during a regional outbreak. This suggests that EHR notification systems may be 

useful in increasing vaccination, and potentially could be used in the COVID-19 vaccination 

campaign to prompt providers to recommend vaccination, especially for vulnerable groups. 

Applicability 

Many of the studies we identified were more than 10 years old and examined the H1N1 

epidemic. Much has changed over the last decade, including advances in technology that affect 

the ways that we communicate, access information, and interact with health care providers. In 

recent years our country has shifted socio-politically, affecting factors related to discrimination 

and government trust. Similarly, beliefs and theories about vaccine safety may have also shifted, 

bringing into question the applicability of H1N1 vaccine data. In addition, the COVID-19 

pandemic is very different than H1N1. Not only is SARS-CoV-2 more infectious and more 

widely spread in United States, it has affected the way that we live, work, and even socialize in 

more pervasive ways that the H1N1 epidemic did. 

However, despite a decade of change, some things have remained stagnant. The societal factors 

that placed vulnerable populations at higher risk for health disparities are largely unchanged. 

Groups that were vulnerable a decade ago are similarly or more so today. Social and institutional 

barriers remain. 

Only 3 studies were of Veterans, all of which were narrowly focused on specific populations (ie, 

Veterans who are homebound and Veterans with spinal injuries and conditions). More research is 

needed to elucidate pandemic-related disparities and their mitigation for the Veteran population. 

Limitations 

There are a number of limitations of this evidence base. Studies were largely cross-sectional, and 

we identified only 1 RCT, which was of poor quality. The operationalization of potential 

mediating factors was heterogeneous, and we identified very few studies that examined whether 

risk factors mediated the associations between population characteristics and outcomes. Many of 

the studies are a decade old, and findings may no longer be applicable. The categorization of 

racial and ethnic groups was not consistent across studies (eg, Latinos as a group or stratified by 

nativity or language), and very few studies examined disparities among Asian, Pacific Islander, 

and American Indian/Alaska Native populations, or among rural residents and adults with 

disabilities. Very few studies examined interventions or programs to mitigate disparities during 

infectious disease epidemics. The strength of H1N1 vaccine uptake was limited by imprecision 

and poor reporting, and vaccine uptake was measured by self-report in all but 1 study. 
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CONCLUSION

The literature examining health disparities associated with previous infectious disease epidemics, 

and in some cases disasters, may provide some guidance for the current COVID-19 response. 

Evidence consistently pointed to disparities in structural and work-related exposure to infection

as underlying disparities, with the impact of comorbid conditions on susceptibility for more 

severe infection and higher rates of mortality playing a less certain role. Discrimination was 

reported more frequently by all racial and ethnic minorities. However, its impact on disparities 

during infectious disease epidemics is uncertain. African American/Black and Latino adults 

generally were disproportionately affected. However, Latinos with limited English proficiency 

were at especially high risk. There is moderate-strength evidence that AA/Black adults were less 

likely to receive a H1N1 vaccine, and low-strength evidence of lower vaccination rates for 

Latinos adults of lower SES. Advances in technology, and sociopolitical shifts over the past 

decade, call into question the applicability of findings. Interventions and programs from the 

disaster literature bring to light recommendations for infectious disease response by the CDC and 

other experts. In order to better prevent widespread health disparities that emerge in the wake of 

the current and future disease epidemics, more research is needed on policy- and systems-level 

interventions and their effect on the distal determinants of poor health outcomes among 

vulnerable groups. 
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2009 H1N1 influenza pandemic: how to protect Latino farmworkers in future health 

emergencies. Biosecurity & Bioterrorism. 2010;8(3):243-254. 

58. Steege AL, Baron S, Davis S, Torres-Kilgore J, Sweeney MH. Pandemic influenza and 

farmworkers: the effects of employment, social, and economic factors. American Journal 

of Public Health. 2009;99 Suppl 2:S308-315. 

59. SteelFisher GK, Blendon RJ, Kang M, et al. Adoption of preventive behaviors in 

response to the 2009 H1N1 influenza pandemic: a multiethnic perspective. Influenza & 

Other Respiratory Viruses. 2015;9(3):131-142. 

60. Truman BI, Tinker T, Vaughan E, et al. Pandemic influenza preparedness and response 

among immigrants and refugees. American Journal of Public Health. 2009;99 Suppl 

2:S278-286. 

61. Uscher-Pines L, Maurer J, Harris KM. Racial and ethnic disparities in uptake and 

location of vaccination for 2009-H1N1 and seasonal influenza. American Journal of 

Public Health. 2011;101(7):1252-1255. 
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88

62. Vaughan E, Tinker T. Effective health risk communication about pandemic influenza for 

vulnerable populations. 2009;1.

63. Witrago E, Perez MA. Preparing for an influenza pandemic: policy implications for rural 

Latino populations. Journal of Health Care for the Poor & Underserved. 2011;22(3 

Suppl):58-71.

64. Wyte-Lake T, Claver M, Griffin A, Dobalian A. The role of the home-based provider in 

disaster preparedness of a vulnerable population. 2014;1. 

65. Wyte-Lake T, Der-Martirosian C, Claver M, Davis D, Dobalian A. Provider Delivery of 

Emergency Preparedness Education in Home-Based Primary Care. Disaster Medicine & 

Public Health Preparedness. 2019;13(3):547-554. 

66. Yip MP, Ong B, Painter I, Meischke H, Calhoun B, Tu SP. Information-seeking 

behaviors and response to the H1N1 outbreak in Chinese limited-English proficient 

individuals living in King County, Washington. American Journal of Disaster Medicine. 

2009;4(6):353-360. 

67. Mesch GS, Schwirian KP. Social and political determinants of vaccine hesitancy: 

Lessons learned from the H1N1 pandemic of 2009-2010. American Journal of Infection 

Control. 2015;43(11):1161-1165. 

68. Der-Martirosian C, Schmitz S, Wyte-Lake T, Dobalian A. US Military Veterans: An 

Untapped Resource as Disaster Volunteers. American Journal of Public Health. 

2019;109(S4):S263-S264. 

69. MedCalc. Free statistical calculators: Relative risk calculator. 

https://www.medcalc.org/calc/relative_risk.php. Accessed 2 October 2020.

70. Blitzer J. The renegade priest helping undocumented people survive the pandemic. The 

New Yorker. 2020. Published August 17, 2020. Accessed August 26, 2020. 

71. U.S. Preventive Services Task Force. Appendix VII. Criteria for Assessing External 

Validity (Generalizability) of Individual Studies. 

https://www.uspreventiveservicestaskforce.org/Page/Name/appendix-vii-criteria-forassessing-external-validity-generalizability-of-individual-studies. Published 2017. 

Accessed.

72. Tsai V, Khan NM, Shi J, Rainey J, Gao H, Zheteyeva Y. Evaluation of Unintended 

Social and Economic Consequences of an Unplanned School Closure in Rural Illinois. 

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Health Inequalities in Infectious Disease Epidemics Evidence Synthesis Program

Pre-dating COVID-19 in the United States

89

APPENDIX A. SEARCH STRATEGIES

Ovid MEDLINE ALL 1946 to May 01, 2020

Date searched: May 4, 2020

1 Coronavirus Infections/ or COVID-19.rs. (6243) 

2 ((("Corona virinae" or "corona virus" or Coronavirinae or coronavirus or COVID or nCoV) 

adj4 ("19" or "2019" or novel or new)) or (("Corona virinae" or "corona virus" or Coronavirinae 

or coronavirus or COVID or nCoV) and (wuhan or china or chinese)) or "Corona virinae19" or 

"Corona virinae2019" or "corona virus19" or "corona virus2019" or Coronavirinae19 or 

Coronavirinae-19 or Coronavirinae2019 or Coronavirinae-2019 or coronavirus19 or coronavirus19 or coronavirus2019 or coronavirus-2019 or COVID19 or COVID-19 or COVID2019 or 

COVID-2019 or nCOV19 or nCOV-19 or nCOV2019 or nCOV-2019 or SARS-CoV-2 or SARSCoV2 or SARS-CoV or "severe acute respiratory").ti,ab,hw,kw. (16846) 

3 or/1-2 (20340)

4 Healthcare Disparities/ or Health Equity/ or Health Status Disparities/ or Culturally Competent 

Care/ or Social Determinants of Health/ or Sociology, Medical/ (35656) 

5 (disadvantaged or discriminat* or disparat* or disparit* or disproportion* or inequal* or 

inequit* or unequal or underserved or under-served or (cultural* adj3 compet*) or (social* adj3 

determin*)).ti,ab,kf. or (difference or different).ti. (679237) 

6 Ethnic Groups/ or Minority Groups/ or African Americans/ or Arabs/ or Asian Americans/ or 

Hispanic Americans/ or Mexican Americans/ or Indigenous Peoples/ or exp Alaska Natives/ 

(152741) 

7 (ethnic* or race* or racial* or minority or minorities or "people of color" or AfricanAmerican* or Black or Blacks or Hispanic* or Chican* or Latino* or Latina* or Latinx or 

Mexican-American* or Asian-American* or Chinese-American or Filipino* or Japanese or 

Korean or Vietnamese or Native American* or Indian or Indians or indigenous).ti,ab,kf. 

(711222) 

8 Socioeconomic Factors/ or Economic Status/ or exp Employment/ or Homeless Persons/ or 

Medicaid/ or Medically Uninsured/ or exp Medicare/ or Poverty/ or Poverty Areas/ or Public 

Assistance/ or Public Housing/ or Social Class/ or "Transients and Migrants"/ or Undocumented 

Immigrants/ or Veterans/ or Working Poor/ (388391)

9 ("blue collar" or impoverish* or homeless* or immigrant* or indigent or low-income or lowwage or lower-income or Medicaid or Medicare or migrant* or poverty or (public adj (assistance 

or housing)) or social or socio* or SES or undocumented or uninsured or veteran* or (working 

adj2 (class or poor))).ti,ab,kf. (876545) 

10 Disabled Persons/ or Health Services for Persons with Disabilities/ or Persons With Hearing 

Impairments/ or Visually Impaired Persons/ or Vulnerable Populations/ (56412)

11 (disabilit* or disabled or blind or deaf or handicapped or ((visual* or hearing or physical*) adj 

impair*) or vulnerab*).ti,ab,kf. (557881) 

12 Rural Health/ or Rural Health Services/ or Rural Population/ or Urban Health/ or Urban 

Health Services/ or Urban Population/ 146477) 

13 (inner-city or metropol* or municipal* or neighborhood* or rural-urban or rural or urban or 

"New York" or "Los Angeles" or Chicago or Houston or Phoenix of Philadelphia or "San 

Antonio" or "San Diego" or Dallas or "San Jose" or "San Francisco" or Austin or Jacksonville or 

"Fort Worth" or Columbus or Charlotte or Indianapolis or Seattle or Denver or Washington or 

Boston or "El Paso" or Detroit or Nashville or Portland or Memphis or "Oklahoma City" or "Las 
Health Inequalities in Infectious Disease Epidemics Evidence Synthesis Program

Pre-dating COVID-19 in the United States

90

Vegas" or Louisville or Baltimore or Milwaukee or Albuquerque or Tucson or Fresno or Mesa or 

Sacramento or Atlanta or "Kansas City" or "Colorado Springs" or Miami or Raleigh or Omaha or 

"Long Beach" or "Virginia Beach" or Oakland or Minneapolis or Tulsa or Arlington or Tampa or 

"New Orleans").ti,ab,kf. or (city* or cities or county).ti,kf. (530985) 

14 or/4-13 (3076764)

15 3 and 14 (1963) 

16 15 not (Beijing or "Hong Kong" or Huangshi or Hubei or Jiangsu or London or Paris or 

Qingdao or Shanghai or Shenzhen or Tianjin or "Wuhan city" or Zhuhai or Caribbean or 

Europe* or "South America" or "South Asia" or "Southeast Asia" or "East Asia" or "North 

Africa" or "East Africa" or "West Africa" or "Southern Africa" or Afghanistan or Albania* or 

Algeria* or Andorra or Angola or Antigua or Argentin* or Armenia* or Australia* or Austria* 

or Azerbaijan or Bahamas or Bahrain or Bangladesh* or Barbados or Belarus or Belgium or 

Belize or Benin or Bhutan* or Bolivia* or Bosnia* or Botswana or Brazil* or Britain* or Brunei 

or Bulgaria* or "Burkina Faso" or Burundi or "Cabo Verde" or Cambodia* or Cameroon* or 

Canada or Canadian* or "Central African Republic" or Chad or Chile* or China or Colombia* or 

Comoros or Congo* or "Costa Rica" or "Cote d'Ivoire" or Croatia* or Cuba or Cyprus or 

Czechia or Denmark or Djibouti or Dominica or "Dominican Republic" or Ecuador or Egypt* or 

"El Salvador" or England or "Equatorial Guinea" or Eritrea* or Estonia* or Eswatini or 

Ethiopia* or Fiji* or Finland or France or French or Gabon or Gambia* or Germany or German* 

or Ghana or Greece or Grenada or Guatemala* or Guinea or Guinea-Bissau or Guyana or Haiti* 

or Honduras or Hungary or Iceland or India or Indonesia* or Iran* or Iraq* or Ireland or Israel* 

or Italy or Jamaica* or Japan or Jordan* or Kazakhstan or Kenya* or Kiribati or Korea or 

Kosovo or Kuwait or Kyrgyzstan or Laos or Latvia* or Lebanon or Lesotho or Liberia* or 

Libya* or Liechtenstein or Lithuania* or Luxembourg or Madagascar or Malawi or Malaysia* or 

Maldives or Mali or Malta or "Marshall Islands" or Mauritania* or Mauritius or Mexico or 

Micronesia* or Moldova or Monaco or Mongolia* or Montenegro or Morocco or Mozambique 

or Myanmar or Namibia* or Nauru or Nepal* or Netherlands or "New Zealand" or Nicaragua* or 

Niger or Nigeria* or Macedonia* or Norway or Oman or Pakistan or Palau or Palestine or 

Panama or "Papua New Guinea" or Paraguay or Peru or Philippines or Poland or Portugal or 

Qatar or Romania* or Russia* or Rwanda* or "Saint Kitts" or "Saint Lucia" or "Saint Vincent" 

or "San Marino" or "Sao Tome" or Saudi* or Scotland or Senegal* or Serbia* or Seychelles or 

"Sierra Leone" or Singapore* or Slovakia* or Slovenia* or "Solomon Islands" or Somalia* or 

"South Africa" or "South Sudan" or Spain or "Sri Lanka" or Sudan* or Suriname or Sweden or 

Switzerland or Syria* or Taiwan* or Tajikistan or Tanzania* or Thailand or "Timor-Leste" or 

"East Timor" or Togo or Tonga* or Trinidad or Tunisia* or Turkey or Turkmenistan or Tuvalu 

or Uganda* or Ukraine or "United Arab Emirates" or "United Kingdom" or UK or Uruguay or 

Uzbekistan or Vanuatu or Venezuela* or Vietnam or Yemen or Zambia* or Zimbabwe*).ti. 

(1391) 

17 limit 16 to english language (1295) 

18 limit 17 to yr="2020 -Current" (637) 

Ovid PsycINFO 1806 to May Week 1 2020 

Date searched: May 8, 2020

1 *Pandemics/ or *Epidemics/ or *Disasters/ or *Natural Disasters/ or Emergency Preparedness/ 

(10503) 
Health Inequalities in Infectious Disease Epidemics Evidence Synthesis Program

Pre-dating COVID-19 in the United States

91

2 (disaster* or earthquake* or epidemic* or hurricane* or pandemic* or postdisaster or postdisaster or (public adj3 emergenc*) or H1N1 or SARS or Zika or "severe acute respiratory").ti. 

(9540) 

3 or/1-2 (13615)

4 *Health Disparities/ (6152) 

5 (difference* or disadvantaged or discriminat* or disparat* or disparit* or disproportion* or 

inequal* or inequit* or unequal or underserved or under-served or (cultural* adj3 compet*) or 

(social* adj3 determin*)).ti. (116879) 

6 *"Racial and Ethnic Differences"/ or *"Race and Ethnic Discrimination"/ or *Ethnic Groups/ 

or *Minority Groups/ or *Alaska Natives/ or *American Indians/ or *Arabs/ or exp *Asians/ or 

*Blacks/ or *Hawaii Natives/ or *Jews/ or exp *"Latinos/Latinos"/ or *Pacific Islanders/ (92858)

7 (ethnic* or race* or racial* or minority or minorities or "people of color" or AfricanAmerican* or Black or Blacks or Hispanic* or Chican* or Latino* or Latina* or Latinx or 

Mexican-American* or Asian-American* or Chinese-American or Filipino* or Japanese or 

Korean or Vietnamese or Native American* or Indian or Indians or indigenous).ti. (130827) 

8 *Disadvantaged/ or *Socioeconomic Status/ or *Lower Class/ or *Lower Income Level/ or 

*Poverty/ or *Social Class/ or exp *Homeless/ or *Shelters/ or *Social Services/ or *"Uninsured 

(Health Insurance)"/ or *"Welfare Services (Government)"/ or *Immigration/ or *Migrant Farm 

Workers/ or *Refugees/ (78171)

9 ("blue collar" or impoverish* or homeless* or immigrant* or indigent or low-income or lowwage or lower-income or Medicaid or Medicare or migrant* or immigrant* or poverty or "public 

assistance" or "public housing" or socio* or SES or undocumented or uninsured or veteran* or 

"working class" or "working poor").ti. (75944) 

10 exp *Disabilities/ or *Disability Discrimination/ or *"At Risk Populations"/ or *Blind/ or 

*Deaf/ or *Partially Hearing Impaired/ (84942) 

11 (disabilit* or disabled or blind or deaf or handicapped or ((visual* or hearing or physical*) adj 

impair*) or vulnerab*).ti. (88840) 

12 exp *Neighborhoods/ or *Rural Environments/ or *Urban Environments/ (28918) 

13 (city or cities or county or inner-city or metropol* or municipal* or neighborhood* or ruralurban or rural or urban or "New York" or "Los Angeles" or Chicago or Houston or Phoenix of 

Philadelphia or "San Antonio" or "San Diego" or Dallas or "San Jose" or "San Francisco" or 

Austin or Jacksonville or "Fort Worth" or Columbus or (Charlotte and (SC or Carolina*)) or 

Indianapolis or Seattle or Denver or Washington or Boston or "El Paso" or Detroit or Nashville 

or Portland or Memphis or "Oklahoma City" or "Las Vegas" or Louisville or Baltimore or

Milwaukee or Albuquerque or Tucson or Fresno or Mesa or Sacramento or Atlanta or "Kansas 

City" or "Colorado Springs" or Miami or Raleigh or Omaha or "Long Beach" or "Virginia 

Beach" or Oakland or Minneapolis or Tulsa or Arlington or Tampa or "New Orleans").ti. 

(59491) 

14 or/4-13 (543361)

15 3 and 14 (1823) 

16 15 not (Beijing or "Hong Kong" or Huangshi or Hubei or Jiangsu or London or Paris or 

Qingdao or Shanghai or Shenzhen or Tianjin or "Wuhan city" or Zhuhai or Kathmandu).ti. 

(1816) 

17 limit 16 to english language (1751)

18 17 not (epidemic* adj2 (cigarette or cocaine or crack or diabetes or heroin or obesity or 

opioid or opioids or smoking or tobacco)).ti,ab.(1701) 
Health Inequalities in Infectious Disease Epidemics Evidence Synthesis Program

Pre-dating COVID-19 in the United States

92

19 18 not (cat or cats or dog or dogs or mice or mouse or rat or rats or rodent).ti.(1700) 

20 19 not (Caribbean or Europe or "South America" or "South Asia" or "Southeast Asia" or 

"East Asia" or "North Africa" or "East Africa" or "West Africa" or "Southern Africa" or 

Afghanistan or Albania or Algeria or Andorra or Angola or Antigua or Argentina Armenia or 

Australia or Austria or Azerbaijan or Bahamas or Bahrain or Bangladesh or Barbados or Belarus 

or Belgium or Belize or Benin or Bhutan or Bolivia or Bosnia or Botswana or Brazil or Brunei or 

Bulgaria or "Burkina Faso" or Burundi or "Cabo Verde" or Cambodia or Cameroon or Canada or 

"Central African Republic" or Chad or Chile or China or Colombia or Comoros or Congo or 

"Costa Rica" or "Cote d'Ivoire" or Croatia or Cuba or Cyprus or Czechia or Denmark or Djibouti 

or Dominica or "Dominican Republic" or Ecuador or Egypt or "El Salvador" or "Equatorial 

Guinea" or Eritrea or Estonia or Eswatini or Ethiopia or Fiji or Finland or France or Gabon or 

Gambia or Georgia or Germany or Ghana or Greece or Grenada or Guatemala or Guinea or 

Guinea-Bissau or Guyana or Haiti or Honduras or Hungary or Iceland or India or Indonesia or 

Iran or Iraq or Ireland or Israel or Italy or Jamaica or Japan or Jordan or Kazakhstan or Kenya or 

Kiribati or Korea or Kosovo or Kuwait or Kyrgyzstan or Laos or Latvia or Lebanon or Lesotho 

or Liberia or Libya or Liechtenstein or Lithuania or Luxembourg or Madagascar or Malawi or 

Malaysia or Maldives or Mali or Malta or "Marshall Islands" or Mauritania or Mauritius or 

Mexico or Micronesia or Moldova or Monaco or Mongolia or Montenegro or Morocco or 

Mozambique or Myanmar or Namibia or Nauru or Nepal or Netherlands or "New Zealand" or 

Nicaragua or Niger or Nigeria or Macedonia or Norway or Oman or Pakistan or Palau or 

Palestine or Panama or "Papua New Guinea" or Paraguay or Peru or Philippines or Poland or 

Portugal or Qatar or Romania or Russia or Rwanda or "Saint Kitts" or "Saint Lucia" or "Saint 

Vincent" or "San Marino" or "Sao Tome" or Saudi or Senegal or Serbia or Seychelles or Sierra 

Leone or Singapore or Slovakia or Slovenia or "Solomon Islands" or Somalia or "South Africa" 

or "South Sudan" or Spain or "Sri Lanka" or Sudan or Suriname or Sweden or Switzerland or 

Syria or Taiwan or Tajikistan or Tanzania or Thailand or "Timor-Leste" or "East Timore" or 

Togo or Tonga or Trinidad or Tunisia or Turkey or Turkmenistan or Tuvalu or Uganda or 

Ukraine or "United Arab Emirates" or "United Kingdom" or UK or Uruguay or Uzbekistan or 

Vanuatu or Venezuela or Vietnam or Yemen or Zambia or Zimbabwe).ti,lo. (1165) 

EBM Reviews - Cochrane Central Register of Controlled Trials April 2020

Date searched: May 11, 2020 

1 (disaster* or earthquake* or epidemic* or hurricane* or pandemic* or post-disaster or (public 

adj3 emergenc*) or H1N1 or SARS or Zika or "severe acute respiratory").ti. (1455) 

2 (difference* or disadvantaged or discriminat* or disparat* or disparit* or disproportion* or 

inequal* or inequit* or unequal or underserved or under-served or (cultural* adj3 compet*) or 

(social* adj3 determin*)).ti. (8858) 

3 (ethnic* or race* or racial* or minority or minorities or "people of color" or AfricanAmerican* or Black or Blacks or Hispanic* or Chican* or Latino* or Latina* or Latinx or 

Mexican-American* or Asian-American* or Chinese-American or Filipino* or Japanese or 

Korean or Vietnamese or Native American* or Indian or Indians or indigenous).ti. (18233) 

4 ("blue collar" or impoverish* or homeless* or immigrant* or indigent or low-income or lowwage or lower-income or Medicaid or Medicare or migrant* or poverty or (public adj (assistance 

or housing)) or social or socio* or SES or undocumented or uninsured or veteran* or (working 

adj2 (class or poor))).ti. (13475) 
Health Inequalities in Infectious Disease Epidemics Evidence Synthesis Program

Pre-dating COVID-19 in the United States

93

5 (disabilit* or disabled or blind or deaf or handicapped or ((visual* or hearing or physical*) adj 

impair*) or vulnerab*).ti. (87945) 

6 (city or cities or county or inner-city or metropol* or municipal* or neighborhood* or ruralurban or rural or urban or "New York" or "Los Angeles" or Chicago or Houston or Phoenix of 

Philadelphia or "San Antonio" or "San Diego" or Dallas or "San Jose" or "San Francisco" or 

Austin or Jacksonville or "Fort Worth" or Columbus or (Charlotte and (SC or Carolina*)) or 

Indianapolis or Seattle or Denver or Washington or Boston or "El Paso" or Detroit or Nashville 

or Portland or Memphis or "Oklahoma City" or "Las Vegas" or Louisville or Baltimore or 

Milwaukee or Albuquerque or Tucson or Fresno or Mesa or Sacramento or Atlanta or "Kansas 

City" or "Colorado Springs" or Miami or Raleigh or Omaha or "Long Beach" or "Virginia 

Beach" or Oakland or Minneapolis or Tulsa or Arlington or Tampa or "New Orleans").ti. (8380)

7 or/2-6 (132248)

8 and/1,7 (112) 

9 8 not (Beijing or "Hong Kong" or Huangshi or Hubei or Jiangsu or London or Paris or Qingdao 

or Shanghai or Shenzhen or Tianjin or "Wuhan city" or Zhuhai or Australia* or Bangladesh or 

Britain or Canada or China or Europe* or England or France or India or Iran or Ireland or Italy 

or Japan or Korea or Pakistan or Singapore or Scotland or "South Korea" or Thailand or Turkey 

or "United Kingdom" or UK).ti. (104)

10 9 not (epidemic* adj2 (cigarette or cocaine or crack or diabetes or heroin or obesity or opioid 

or opioids or smoking or tobacco)).ti,ab. (104) 

11 10 not (cat or cats or dog or dogs or mice or mouse or rat or rats or rodent).ti. (104) 

12 11 not (Caribbean or Europe or "South America" or "South Asia" or "Southeast Asia" or 

"East Asia" or "North Africa" or "East Africa" or "West Africa" or "Southern Africa" or 

Afghanistan or Albania or Algeria or Andorra or Angola or Antigua or Argentina Armenia or 

Australia or Austria or Azerbaijan or Bahamas or Bahrain or Bangladesh or Barbados or Belarus 

or Belgium or Belize or Benin or Bhutan or Bolivia or Bosnia or Botswana or Brazil or Brunei or 

Bulgaria or "Burkina Faso" or Burundi or "Cabo Verde" or Cambodia or Cameroon or Canada or 

"Central African Republic" or Chad or Chile or China or Colombia or Comoros or Congo or 

"Costa Rica" or "Cote d'Ivoire" or Croatia or Cuba or Cyprus or Czechia or Denmark or Djibouti 

or Dominica or "Dominican Republic" or Ecuador or Egypt or "El Salvador" or "Equatorial 

Guinea" or Eritrea or Estonia or Eswatini or Ethiopia or Fiji or Finland or France or Gabon or 

Gambia or Georgia or Germany or Ghana or Greece or Grenada or Guatemala or Guinea or 

Guinea-Bissau or Guyana or Haiti or Honduras or Hungary or Iceland or India or Indonesia or 

Iran or Iraq or Ireland or Israel or Italy or Jamaica or Japan or Jordan or Kazakhstan or Kenya or 

Kiribati or Korea or Kosovo or Kuwait or Kyrgyzstan or Laos or Latvia or Lebanon or Lesotho 

or Liberia or Libya or Liechtenstein or Lithuania or Luxembourg or Madagascar or Malawi or 

Malaysia or Maldives or Mali or Malta or "Marshall Islands" or Mauritania or Mauritius or 

Mexico or Micronesia or Moldova or Monaco or Mongolia or Montenegro or Morocco or 

Mozambique or Myanmar or Namibia or Nauru or Nepal or Netherlands or "New Zealand" or 

Nicaragua or Niger or Nigeria or Macedonia or Norway or Oman or Pakistan or Palau or 

Palestine or Panama or "Papua New Guinea" or Paraguay or Peru or Philippines or Poland or 

Portugal or Qatar or Romania or Russia or Rwanda or "Saint Kitts" or "Saint Lucia" or "Saint 

Vincent" or "San Marino" or "Sao Tome" or Saudi or Senegal or Serbia or Seychelles or Sierra 

Leone or Singapore or Slovakia or Slovenia or "Solomon Islands" or Somalia or "South Africa" 

or "South Sudan" or Spain or "Sri Lanka" or Sudan or Suriname or Sweden or Switzerland or 

Syria or Taiwan or Tajikistan or Tanzania or Thailand or "Timor-Leste" or "East Timore" or 
Health Inequalities in Infectious Disease Epidemics Evidence Synthesis Program

Pre-dating COVID-19 in the United States

94

Togo or Tonga or Trinidad or Tunisia or Turkey or Turkmenistan or Tuvalu or Uganda or 

Ukraine or "United Arab Emirates" or "United Kingdom" or UK or Uruguay or Uzbekistan or 

Vanuatu or Venezuela or Vietnam or Yemen or Zambia or Zimbabwe).ti. (95) 

EBM Reviews - Cochrane Central Register of Controlled Trials April 2020

Date searched: May 11, 2020 

1 (disaster* or earthquake* or epidemic* or hurricane* or pandemic* or post-disaster or (public 

adj3 emergenc*) or H1N1 or SARS or Zika or "severe acute respiratory").ti. (1455) 

2 (difference* or disadvantaged or discriminat* or disparat* or disparit* or disproportion* or 

inequal* or inequit* or unequal or underserved or under-served or (cultural* adj3 compet*) or 

(social* adj3 determin*)).ti. (8858) 

3 (ethnic* or race* or racial* or minority or minorities or "people of color" or AfricanAmerican* or Black or Blacks or Hispanic* or Chican* or Latino* or Latina* or Latinx or 

Mexican-American* or Asian-American* or Chinese-American or Filipino* or Japanese or 

Korean or Vietnamese or Native American* or Indian or Indians or indigenous).ti. (18233)

4 ("blue collar" or impoverish* or homeless* or immigrant* or indigent or low-income or lowwage or lower-income or Medicaid or Medicare or migrant* or poverty or (public adj (assistance 

or housing)) or social or socio* or SES or undocumented or uninsured or veteran* or (working 

adj2 (class or poor))).ti. (13475) 

5 (disabilit* or disabled or blind or deaf or handicapped or ((visual* or hearing or physical*) adj 

impair*) or PTSD or post-traumatic stress or posttraumatic stress or vulnerab* or ((severe* or 

serious* or chronic* or persistent*) adj mental* ill*)).ti. (91871)

6 (city or cities or county or inner-city or metropol* or municipal* or neighborhood* or ruralurban or rural or urban or "New York" or "Los Angeles" or Chicago or Houston or Phoenix of 

Philadelphia or "San Antonio" or "San Diego" or Dallas or "San Jose" or "San Francisco" or 

Austin or Jacksonville or "Fort Worth" or Columbus or (Charlotte and (SC or Carolina*)) or 

Indianapolis or Seattle or Denver or Washington or Boston or "El Paso" or Detroit or Nashville 

or Portland or Memphis or "Oklahoma City" or "Las Vegas" or Louisville or Baltimore or 

Milwaukee or Albuquerque or Tucson or Fresno or Mesa or Sacramento or Atlanta or "Kansas 

City" or "Colorado Springs" or Miami or Raleigh or Omaha or "Long Beach" or "Virginia 

Beach" or Oakland or Minneapolis or Tulsa or Arlington or Tampa or "New Orleans").ti. (8380)

7 or/2-6 (135518)

8 and/1,7 (138) 

9 8 not (Beijing or "Hong Kong" or Huangshi or Hubei or Jiangsu or London or Paris or Qingdao 

or Shanghai or Shenzhen or Tianjin or "Wuhan city" or Zhuhai or Australia* or Bangladesh or 

Britain or Canada or China or Europe* or England or France or India or Iran or Ireland or Italy 

or Japan or Korea or Pakistan or Singapore or Scotland or "South Korea" or Thailand or Turkey 

or "United Kingdom" or UK).ti. (126)

10 9 not (epidemic* adj2 (cigarette or cocaine or crack or diabetes or heroin or obesity or opioid 

or opioids or smoking or tobacco)).ti,ab. (126) 

11 10 not (cat or cats or dog or dogs or mice or mouse or rat or rats or rodent).ti. (126) 

12 11 not (Caribbean or Europe or "South America" or "South Asia" or "Southeast Asia" or 

"East Asia" or "North Africa" or "East Africa" or "West Africa" or "Southern Africa" or 

Afghanistan or Albania or Algeria or Andorra or Angola or Antigua or Argentina Armenia or 

Australia or Austria or Azerbaijan or Bahamas or Bahrain or Bangladesh or Barbados or Belarus 

or Belgium or Belize or Benin or Bhutan or Bolivia or Bosnia or Botswana or Brazil or Brunei or 
Health Inequalities in Infectious Disease Epidemics Evidence Synthesis Program

Pre-dating COVID-19 in the United States

95

Bulgaria or "Burkina Faso" or Burundi or "Cabo Verde" or Cambodia or Cameroon or Canada or 

"Central African Republic" or Chad or Chile or China or Colombia or Comoros or Congo or 

"Costa Rica" or "Cote d'Ivoire" or Croatia or Cuba or Cyprus or Czechia or Denmark or Djibouti 

or Dominica or "Dominican Republic" or Ecuador or Egypt or "El Salvador" or "Equatorial 

Guinea" or Eritrea or Estonia or Eswatini or Ethiopia or Fiji or Finland or France or Gabon or 

Gambia or Georgia or Germany or Ghana or Greece or Grenada or Guatemala or Guinea or 

Guinea-Bissau or Guyana or Haiti or Honduras or Hungary or Iceland or India or Indonesia or 

Iran or Iraq or Ireland or Israel or Italy or Jamaica or Japan or Jordan or Kazakhstan or Kenya or 

Kiribati or Korea or Kosovo or Kuwait or Kyrgyzstan or Laos or Latvia or Lebanon or Lesotho 

or Liberia or Libya or Liechtenstein or Lithuania or Luxembourg or Madagascar or Malawi or 

Malaysia or Maldives or Mali or Malta or "Marshall Islands" or Mauritania or Mauritius or 

Mexico or Micronesia or Moldova or Monaco or Mongolia or Montenegro or Morocco or 

Mozambique or Myanmar or Namibia or Nauru or Nepal or Netherlands or "New Zealand" or 

Nicaragua or Niger or Nigeria or Macedonia or Norway or Oman or Pakistan or Palau or 

Palestine or Panama or "Papua New Guinea" or Paraguay or Peru or Philippines or Poland or 

Portugal or Qatar or Romania or Russia or Rwanda or "Saint Kitts" or "Saint Lucia" or "Saint 

Vincent" or "San Marino" or "Sao Tome" or Saudi or Senegal or Serbia or Seychelles or Sierra 

Leone or Singapore or Slovakia or Slovenia or "Solomon Islands" or Somalia or "South Africa" 

or "South Sudan" or Spain or "Sri Lanka" or Sudan or Suriname or Sweden or Switzerland or 

Syria or Taiwan or Tajikistan or Tanzania or Thailand or "Timor-Leste" or "East Timore" or 

Togo or Tonga or Trinidad or Tunisia or Turkey or Turkmenistan or Tuvalu or Uganda or 

Ukraine or "United Arab Emirates" or "United Kingdom" or UK or Uruguay or Uzbekistan or 

Vanuatu or Venezuela or Vietnam or Yemen or Zambia or Zimbabwe).ti. (117) 

 

 
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APPENDIX B. STUDY SELECTION

1. Language: Is the full text of the article in English?

Yes  Proceed to #2

No  Code X1 (NA Language). STOP

2. Population: Are participants Adults?

In mixed-age studies, findings must be reported separately for adults.

Yes  Proceed to #3

No  Code X2 (NA population). Add code B if retaining for 

background/discussion. STOP

3. Population: Does the study provide data specific to or stratified by 1 or more of 

the following participant populations: racial/ethnic minorities or persons a) with 

disabilities; b) of low socioeconomic status; c) living in rural communities; d) 

living in population dense neighborhoods; e) living in high-poverty 

neighborhoods.

Note: For SES, proxies may include occupation, education, neighborhoods or 

geographic regions, income, insurance-status, Medicare/Medicaid, SNAP, 

TANF, households with children qualifying for free or reduced school lunch, 

Yes  Proceed to #4

No  Code X3 (NA subpopulation). Add code B if retaining for 

background/discussion. STOP

4. Setting: Is this a US-only population (including U.S territories)?

Yes  Proceed to #5

No  Code X4 (NA setting). Add code B if retaining for background/discussion.

STOP

5. Timing: Is the study performed during or in preparation or response to an 

infectious disease pandemic or epidemic, or a disaster? 

Yes  Proceed to #6

No  Code X5 (NA timing). Add code B if retaining for background/discussion.

STOP

6. Study design and type: Is the article an original research study, or a systematic 

review/meta-analysis? 

Excluded: Dissertations, non-systematic reviews, conference abstracts, 

protocols, erratum, comments, non-research letters

Yes  Proceed to #7

No  Code X6 (NA study design/type). Add code B if retaining for 

background/discussion. STOP

7. Outcomes: Is the outcome health-related?

Examples: Utilization or access, infection, burden of illness, severity, mortality, 

morbidity
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Note: Outcomes may also be factors that contribute to disparities in healthrelated outcome (eg, trust)

Yes  Proceed to #9

No  Proceed to #8

8. Outcomes: Is the outcome a result of measures implemented during a 

pandemic, epidemic, or disaster (eg, social distancing, school closure, etc.) that 

impact a health-related outcome?

Examples: Employment loss or reduction, income reduction

Yes  Proceed to #9

No  Code X7 (NA outcome). Add code B if retaining for 

background/discussion. STOP

9. Comparator: Does the study include a comparison (within the same population 

or to a relevant comparator); or, if an intervention study, does it compare results 

to no intervention, pre-intervention, or another intervention or public health 

response?

Yes  Proceed to #11

No  Proceed to #10

10.Pre-intervention studies: Does the study inform a future intervention (eg,

survey, qualitative study)

Examples: Preferred forms of communication, emergency preparedness, social 

distancing, access to care

Yes  Proceed to #11

No  Code X8 (NA comparator). Add code B if retaining for 

background/discussion. STOP

11.KQ1: Does the study examine the COVID-19 Pandemic?

Yes  Code KQ1. If the study is a systematic review or meta-analysis, Code 

KQ1SR. 

STOP

No  Proceed to #12

12.Timing: Is the study performed during, or in preparation or response to, a 

disaster? 

Yes  Proceed to #14

No  Proceed to #13

13.KQ2 Contributing Factors: Does the study examine factors that contribute to

health inequalities?

Examples: Risk of exposure, susceptibility/risk of poor outcomes, access to care, 

trust in healthcare system, discrimination.

Note: These are not 1st Generation/Phase I studies that simply find that a 

disparity exists. These are 2nd Generation/Phase II studies that help us to 

understand why (see Kilbourne et. al, 2006 – uploaded to Slack).
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Yes  Code KQ2. If the study is a systematic review or meta-analysis, Code 

KQ2SR. Proceed to #14

No  proceed to #14

14.KQ3: Is the study a 1) program evaluation, pre-intervention study (eg, survey, 

qualitative), or an intervention study 2) designed to mitigate health inequalities?

Select “Yes” only if both 1) and 2) are true

Yes  If the study is not also included in KQ2, Code KQ3. If the study is a 

systematic review or meta-analysis, Code KQ3SR. 

Yes  If the study is also included in KQ2, Code KQ2&3. If the study is a 

systematic review or meta-analysis, Code KQ2&3SR. 

No  Code X9 (NA factors or intervention). Add code B if retaining for 

background/discussion. STOP

Note: B codes can be added for any excluded study that we should retain/reference 

for background or discussion.

Key Questions

KQ1: In the COVID-19 pandemic:

a. What health inequalities have been described?

b. What factors have contributed to health inequalities?

c. What is the effectiveness of interventions used to address health inequalities? 

KQ2: What factors contribute to disparate infection rates and health-related outcomes 

among different segments of the population during infectious disease epidemics or 

pandemics?

KQ3: What interventions have been used to reduce health inequalities in infectious 

disease transmission or health outcomes in disasters, epidemics or pandemics? 

Codes Key:

X1: NA Language

X2: NA Population

X3: NA Subpopulation

X4: NA Setting

X5: NA Timing 

X6: NA Study design/type 

X7: NA Outcome 

X8: NA Comparator 

X9: NA factor/intervention
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APPENDIX C. QUALITY ASSESSMENT

Table. Quality Ratings for Cross-sectional Studies* 

Author 1 2 3 4 5 6 7 Quality concerns Applicability71

Burger, 201825 Y Y U Y Y, A&B P Y No reporting on non-respondents. Good

Etingen, 201330 Y Y N Y Y, A&B P Y Veteran sample. Low response rate. Fair

Freimuth, 201432 Y Y Y Y Y, A&B P Y --- Good

Frew, 201233 Y Y U Y Y, A&B P Y Multivariable regression on some, but not all, relevant factors. Fair

Galarce, 201134 Y Y Y Y Y, A&B P Y --- Good

Gargano, 201135 Y N N Y Y, A&B P Y High nonresponse rate, small sample Fair

Hernandez, 201938 Y Y U Y Y, A&B P Y Response rate low, and unclear if non-responders were 

different Fair

Kumar, 201240,41 Y Y Y Y Y, A&B P Y --- Good

Lin, 201442 and 201843 Y Y Y Y Y, A&B P Y --- Good

Lin, 201744 Y N Y Y U P Y Unclear whether confounding factors were controlled. Fair

Mesch, 201567 Y Y U Y Y, A&B P Y Unclear if respondents similar to non-respondents. Good

Plough, 201151 N Y N Y N Y N No control for confounders. Inappropriate denominator. Not 

representative. Fair

Price, 201352 Y Y Y Y Y, A&B Y Y -- Fair

Quinn, 200953

Quinn, 201110 Y Y Y Y Y, A&B P Y --- Good

Redelings, 201254 Y Y Y Y Y, A&B P Y --- Fair

Santibanez,201356 Y Y Y Y Y, A&B P Y --- Good

SteelFisher, 201559 Y Y Y Y Y, A&B P Y --- Good

Tsai, 201772 Y N N Y N P Y Small sample size, no control for confounders, methods poorly 

reported. Poor

Uscher-Pines, 201161 Y Y Y Y Y, A&B P Y --- Good

Wyte-Lake, 201965 U Y NA U N P N No control for confounders, methods poorly reported Poor

Witrago, 201163 N N NR Y N P N No control for confounders, methods poorly reported. Fair

Yip, 200966 N U U Y N P N Pilot study, no control for confounders, methods poorly 

reported. Fair

Abbreviations: N=No; NA=Not applicable; NR=Not reported; P=Partial; U=Unclear; Y=Yes

*Criteria (Adapted Newcastle-Ottawa16): 
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Selection

1. Sample representative?

yes = Truly representative of the average in the target population (all subjects or random sampling); or, somewhat representative of the average in the 

target population. (non-random sampling)

no = Selected group of users

unclear = No description of the sampling strategy.

2. Sample size justified and satisfactory?

Yes/no

3. Non-respondents comparable to respondents?

yes = Comparability between respondents and non-respondents characteristics is established, and the response rate is satisfactory.

no = The response rate is unsatisfactory, or the comparability between respondents and non-respondents is unsatisfactory.

unclear = No description of the response rate or the characteristics of the responders and the non-responders.

4. Ascertainment of the exposure (risk factor) appropriate?

yes = Adequately described

unclear = Not adequate description of the measurement tool.

Comparability

5. The subjects in different outcome groups are comparable, based on the study design or analysis. Confounding factors are controlled. 

yes = specify a, b, or a&b

a) The study controls for the most important factor (age).

b) The study control for any additional factor.

no = no adjustment for potential confounders

Outcome

6. Assessment of the outcome appropriate?

Yes = Independent blind assessment or Record linkage. 

Partial = Self report.

unclear = No description.

7. Statistical test described and appropriate (including measurement of the association, including CIs and probability level [p- value])?

Yes/no/unclear
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Table. Quality Ratings for Cohort Studies* 

Study 1 2 3 4 5 6 7 8 9 10 11 12 13 14

Funding 

source Quality Applicability71

Eisenman, 200928 and Glik, 

201429 Y Y Y Y Y Y Y Y Y Y N N N N CDC Fair Fair

Castillo, 201827 Y NA Y Y NA Y NA N Y Y Y NA Y N NR Good Fair

Abbreviations: CDC=Centers for Disease Control and Prevention; N=No; NA=Not applicable; Y=Yes

*Criteria (Adapted Newcastle-Ottawa16): 

Selection

1. Was the exposed cohort representative? 

2. Was the non-exposed systematically selected?

3. Ascertainment of exposure reported and valid?

4. Eligibility criteria specified?

Comparability

5. Were the groups comparable at baseline?

Outcome

6. Did the study use accurate methods for ascertaining exposures, potential confounders, and outcomes?

7. Outcome assessors masked?

8. Reporting of attrition, adherence, and contamination?

9. Were outcomes pre-specified and defined, and ascertained using accurate methods? 

10. Follow-up long enough for outcomes to occur?

11. Appropriate statistical analyses on potential confounders? 

12. Important differential loss to follow-up or overall high loss to follow-up?

13. Appropriate Handling of Missing Data?

14. Evidence of Selective Outcome Reporting? 
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Table. Quality Ratings for Qualitative Studies* 

Author, Year

Section A Section B Section C

1 2 3 4 5 6 7 How valuable is the research? Overall Quality Notes Applicability

Andrulis, 201120 U Y Y NR NR Y U 

Valuable - provides a good framework 

from which to address barriers in 

California.

Generally good, lacks some 

methods reporting. Fair

Aten, 201021 Y Y Y Y Y Y Y Valuable and likely generalizable to 

other topics for this community. 

No issues. Good qualitative 

methods described. Fair

Aten, 201122 Y Y Y U Y Y U Valuable - research resulted in action. Good quality study, except for

snowball sampling. Fair

Boyd, 201324 U Y Y NR Y Y Y Valuable, but unclear the value of staff 

interviews. No other issues. Fair

Cassady, 201226 U Y Y Y Y Y Y Valuable - well conducted, high value 

research. Good quality study. Good

Liu, 201745 Y Y Y NR Y U Y Valuable, although analytic methods 

could have been clearer.

Generally good, lacks some 

methods reporting. Poor

McCauley, 201347 Y Y Y Y U Y Y Valuable well conducted study. Qualitative synthesis could have 

been more robust. Poor 

Schoch-Spana, 

201057 U Y U NR NR N U Valuable, this work can help guide 

policy. 

Stakeholder interviews, 

qualitative methods not 

adequately described.

Good

Wyte-Lake, 201464 U U Y NR NR Y U Valuable, but suggests more research 

needed. Small study, poorly reported Poor

Abbreviations: N=No; NR=Not reported; U=Unclear; Y=Yes

*Criteria (CASP17):

Section A: Are the results valid?

1. Was the research design appropriate to address the aims of the research?

2. Was the recruitment strategy appropriate to the aims of the research?

3. Was the data collected in a way that addressed the research issue?

4. Has the relationship between researcher and participants been adequately considered?

Section B: What are the results?

5. Have ethical issues been taken into consideration?

6. Was the data analysis sufficiently rigorous?

7. Is there a clear statement of findings?

Section C: Will the results help locally?
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Table. Quality Ratings for Case Control Studies*

Study 1 2 3 4 5 6 7 8 Overall Quality Applicability71

Hennessy, 

201637 Y Y Y Y N Y N NA Issues with method of ascertainment and comparability of cases and controls. Fair

Levy, 20138 Y Y Y Y Y, A&B Y Y U Expected uneven response rate, so controls were oversampled. Matched 2:1 

as planned. Fair

Abbreviations: N=No; NA=Not applicable; U=Unclear; Y=Yes

*Criteria (Newcastle-Ottawa16): 

Selection

1. Is the case definition adequate?

Yes = with independent validation

No = record linkage or based on self-reports

Unclear = no description

2. Representativeness of the cases?

Yes = consecutive or obviously representative series of cases

No= potential for selection biases 

Unclear = not stated

3. Selection of Controls appropriate?

Yes = community controls

No = hospital controls

Unclear = no description

4. Definition of Controls?

Yes = no history of disease (endpoint)

No/Unclear

Comparability

5. Comparability of cases and controls on the basis of the design or analysis

Yes = specify a or a&b in response

a) study controls for the most important factor.

b) study controls for any additional factor. 

No = neither of the above

Exposure

6. Ascertainment of exposure

Yes = secure record (eg, surgical records) or structured interview where blind to case/control status 

No= interview not blinded to case/control status or written self-report or medical record only

Unclear = no description

7. Same method of ascertainment for cases and controls? Y/N

8. Non-Response rate adequate

Yes = same rate for both groups

No = non respondents just described or rate different and no designation
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Table. Risk of Bias in Randomized Controlled Trials of Interventions to Reduce Pandemic/Epidemic-related Health Disparities

Study

Randomization/

sequence 

generation? 

Was the 

allocation 

sequence 

adequately 

generated?

Allocation

concealment: 

Was allocation 

adequately 

concealed?

Blinding:

Was 

knowledge of 

the allocated 

intervention 

adequately 

prevented 

during the 

study?

Missing 

data: 

How was 

incomplete 

data 

addressed?

Selective 

outcome 

reporting: 

Are reports of 

the study free of 

suggestion of 

selective 

outcome 

reporting?

Other sources 

of bias: 

Was the study 

apparently free of 

other problems 

that could put it at 

a high risk of bias 

(ROB)?

Overall 

ROB Applicability71

Nassar, 201348 Y Y N N/A no 

missing data

Y No High Fair

Cochrane Risk of Bias