PATIENT-CENTERED OUTCOMES RESEARCH INSTITUTE …6 1.2; P = .124]) increased at the South Side...

PATIENT-CENTERED OUTCOMES RESEARCH INSTITUTE

FINAL RESEARCH REPORT

Does a Community Education Program Help Increase Early Hospital Arrival and Ambulance Use for Patients Who Experience Stroke?—The CEERIAS Study

Shyam Prabhakaran, MD, MS1; Neelum T. Aggarwal, MD2; Knitasha V. Washington, FACHE3

AFFILIATIONS:

1Northwestern University, Chicago, Illinois 2Rush University Medical Center, Chicago, Illinois 3Governors State University, University Park, Illinois

Institution Receiving the Award: Northwestern University Original Project Title: Community Engagement for Early Recognition and Immediate Action in Stroke (CEERIAS) PCORI ID: AD-1310-07237 HSRProj ID: HSRP20152035 ClinicalTrials.gov ID: NCT02301299 _______________________________ To cite this document, please use: Prabhakaran S, Aggarwal N, Washington KV. (2020). Does a Community Education Program Help Increase Early Hospital Arrival and Ambulance Use for Patients Who Experience Stroke?—The CEERIAS Study. Patient-Centered Outcomes Research Institute (PCORI). https://doi.org/10.25302/04.2020.AD.131007237

https://hsrproject.nlm.nih.gov/view_hsrproj_record/20152035https://doi.org/10.25302/04.2020.AD.131007237

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TABLE OF CONTENTS

ABSTRACT ............................................................................................................................. 5

BACKGROUND ....................................................................................................................... 7

Disparities in Stroke Care ........................................................................................................... 7

Previous Stroke Preparedness Studies....................................................................................... 8

Community Engagement for Early Recognition and Immediate Action in Stroke (CEERIAS) Study Aims ............................................................................................................... 11

PARTICIPATION OF PATIENTS AND OTHER STAKEHOLDERS .................................................. 13

Community Advisory Board ..................................................................................................... 13

Table 1. Research Team and Community Advisory Board Members, With Their Affiliation and Roles .......................................................................................................... 14

METHODS ........................................................................................................................... 18

Study Overview ........................................................................................................................ 18

Figure 1. Map of study sites in CEERIAS ............................................................................ 19

Study Design ............................................................................................................................. 18

Study Setting ............................................................................................................................ 18

Figure 2. Stroke hospitalization rates in Chicago at the time of CEERIAS inception ........ 20

Table 2. Population and Racial/Ethnic Demographics of the 3 Regions Compared in the CEERIAS Study ......................................................................................................... 21

Table 3. Characteristics of the Intervention and Comparison Hospitals Included in the Study ........................................................................................................................... 22

Figure 3a. Theory of planned behavior ............................................................................. 23

Figure 3b. Integration of the Bandura theoretical self-efficacy model (red) with the Kolb experiential learning theory (green) during stroke promoter training sessions ................................................................................. Error! Bookmark not defined.

Aim 1: To Develop a Stroke Preparedness Intervention With Community Stakeholder Input ......................................................................................................................................... 26

Aim 2: To Implement a Culturally Adapted Stroke Preparedness Intervention ...................... 30

Figure 4. Intervention development and implementation from pilot projects (orange), the adapted MIP-Pact program and training of stroke promoters (red), and promoter activities (blue) .......................................................................................... 31

Aim 3: To Assess Change in Early Hospital Arrival and EMS Use for Confirmed Ischemic Stroke Before and After the Intervention ................................................................. 35

Figure 5. Implementation timeline ................................................................................... 38

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Secondary Aim: To Evaluate Change in EMS Use for Suspected Stroke Before and After the Intervention Using GIS Analysis ................................................................................ 42

Exploratory Aim: To Assess Change in Knowledge, Self-efficacy, Trust, and Stress Before and After the Intervention ........................................................................................... 44

Table 4. Survey Instruments Used to Assess Knowledge, Self-efficacy, Trust, and Stress ................................................................................................................................. 45

RESULTS .............................................................................................................................. 48

Aim 1: Development ................................................................................................................ 48

Table 5. Characteristics of Focus Group Participants ....................................................... 48

Aim 2: Implementation ............................................................................................................ 51

Figure 6. Pact pledges received by Chicago zip code........................................................ 53

Aim 3: Evaluation ..................................................................................................................... 53

Table 6. Patient Characteristics at the Intervention, North Side, and St. Louis Hospitals ............................................................................................................................ 54

Table 7. Interrupted Time Series Regression Model for the Primary Outcomes at the Intervention South Side Chicago Hospital and Comparison With North Side Chicago Hospitals and St. Louis Hospitals ......................................................................... 55

Table 8. Subgroup Analysis for Early Arrival at the Intervention Hospital by Age, Sex, and Race .................................................................................................................... 57

Secondary Aim: EMS Calls ........................................................................................................ 58

Figure 7. GIS analysis of overall EMS use in Chicago before and after CEERIAS intervention ...................................................................................................................... 59

Figure 8. GIS analysis of EMS use for suspected stroke before and after CEERIAS interventiona .................................................................................................................... 60

Table 9. EMS Use for Suspected Stroke in Specific Regions of Chicago Before and After CEERIAS Interventiona .............................................................................................. 61

Table 10. Participants’ Demographics on Pre- and Postintervention Survey Assessments ...................................................................................................................... 62

Table 11. Reasons for Not Calling 911 by Location (P

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Implementation of Study Results ............................................................................................. 71

Subpopulations ........................................................................................................................ 73

Study Limitations ...................................................................................................................... 74

Future Research ....................................................................................................................... 76

CONCLUSIONS ..................................................................................................................... 78

REFERENCES ........................................................................................................................ 79

RELATED PUBLICATIONS ...................................................................................................... 88

ACKNOWLEDGMENTS .......................................................................................................... 89

APPENDICES ........................................................................................................................ 90

A. Pact to Act FAST Cards ......................................................................................................... 90

B. FAST Magnets ....................................................................................................................... 91

C. Chat Rooms and Forums ...................................................................................................... 92

D. Calendar for Event Planning and Tracking ........................................................................... 93

E. Leadership Dashboard.......................................................................................................... 94

F. Pact to Act FAST Forms (online) ........................................................................................... 95

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ABSTRACT

Background: Early hospital arrival after stroke onset increases access to proven treatments and improves outcomes. In addition to knowledge and recognition of stroke symptoms, misperceptions, mistrust, fears, cultural norms, and financial concerns may influence early hospital arrival after stroke symptom onset. Previous stroke education campaigns have focused on improving knowledge but ignored these other decision factors. We hypothesized that a community-engaged stroke preparedness intervention to overcome these barriers would increase early hospital arrival and use of emergency medical services (EMS; ie, calling 911 followed by paramedic evaluation, treatment, and ambulance transport to the hospital) after stroke symptom onset in areas at high risk for stroke in Chicago, Illinois.

Objectives: Our 3 primary aims were to (1) examine barriers to early hospital arrival after stroke and develop a community-engaged stroke preparedness intervention to overcome them; (2) implement the intervention in a large, multiethnic community in the South Side of Chicago; and (3) assess change in early hospital arrival and EMS use in patients experiencing stroke before and after the intervention.

Methods: For aim 1, we identified areas in the South Side of Chicago that have high stroke incidence and mortality and relatively low EMS use for stroke. Based on focus group and stakeholder input, we developed a community stroke preparedness intervention for implementation within selected South Side communities. For aim 2, laypeople and health workers from the intervention community whom we trained and designated stroke promoters delivered the intervention. They focused on face-to-face messaging with South Side Chicago residents to discuss and overcome barriers and obtain stroke preparedness pledges, measured by Pact to Act FAST (Face, Arm, Speech, Time). For aim 3, we applied an interrupted time series (ITS) analysis at the intervention hospital to study the effects of the intervention on our primary outcomes: early hospital arrival (within 3 hours of symptom onset) and EMS use for confirmed ischemic stroke. We also compared these results with results from 6 North Side Chicago hospitals and 17 St. Louis, Missouri, hospitals as concurrent comparison cohorts. The secondary aim was EMS use for suspected stroke before and after our intervention using geographic information system (GIS) analysis of EMS transports in specific geographic locations within Chicago.

Results: For aim 1, we conducted 6 focus groups consisting of 51 participants to assess knowledge of treatments, attitudes, and specific barriers to acute stroke care in minority and lower-income communities in Chicago; this assessment informed our adapted community intervention for stroke preparedness. We trained 242 stroke promoters on how to conduct this intervention during 21 training sessions between October 2015 and May 2016. For aim 2, during a 12-month period, stroke promoters distributed more than 110 000 educational materials, including FAST cards and magnets; participated in 167 community events; and registered 39 975 Pact to Act FAST kits. Registered pacts had the greatest community penetration (19.4% of residents) in the area adjacent to the intervention hospital. For aim 3, early arrival (0.5% per month increase in rate of early arrival postintervention [95% CI, –0.2 to

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1.2; P = .124]) increased at the South Side Chicago intervention hospital, but this change was not significantly different from changes in North Side Chicago hospitals (a difference of –0.3% per month [95% CI, –0.12 to 0.06; P = .560]). By contrast, we observed a nonsignificant increase in early arrival at the South Side Chicago intervention hospital compared with changes at St. Louis hospitals (a difference of 0.7% per month [95% CI, –0.1 to 1.4; P = .072]). In subgroup analyses, the effect on early arrival at the intervention hospital was strongest among patients aged

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BACKGROUND

Stroke is a leading cause of death and disability in the United States.1 Among patients

who experience stroke and survive, the majority (56%) do not return to work because of

disability.2 The economic burden in stroke-related costs and disability exceeds $70 billion

annually.1 In ischemic stroke, cells in the brain begin to die within seconds of the blockage of

blood flow, resulting in the permanent loss of 120 million neurons within 1 hour.3 Thus,

guidelines recommend early treatment to reverse the effects of ischemic stroke.

The only medication approved by the US Food and Drug Administration for acute

ischemic stroke is tissue plasminogen activator (tPA), a thrombolytic or “clot-busting” drug

given intravenously within the 4.5-hour window after stroke symptom onset.4 Even within this

window, for every 10 minutes that passes without tPA treatment, 1 fewer patient (of 100

patients) benefits from the drug, and more patients are left disabled.5 Furthermore, recent

studies of endovascular reperfusion strategies6-10 have unequivocally demonstrated time-

dependent clinical benefit of tPA within 6 hours of symptom onset.

Despite these proven treatments, delayed patient arrival to the hospital remains the

primary reason for low tPA and endovascular treatment rates nationwide.11-13 Only 25% of

patients arrive at the hospital within 3 hours of stroke symptom onset.14 An important

predictor of early arrival is use of emergency medical services (EMS), which is defined as calling

911 to activate paramedic response and ambulance transport to the hospital. Use of EMS can

then translate into an increased likelihood of guideline-based tPA treatment in the hospital and

improved long-term outcomes (eg, less disability).15 However, in the United States, fewer than

two-thirds of patients who experience stroke arrive by EMS to the hospital.15 Thus, early arrival

and arrival by EMS after stroke symptom onset are suitable targets and meaningful patient-

centered outcomes for community intervention studies.

Disparities in Stroke Care

Racial and ethnic disparities have been observed in all aspects of stroke. Disparities in

the prevalence of risk factors, awareness and health care–related beliefs, access to stroke care

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and treatments, stroke incidence, severity and mortality, and acute treatment with tPA and

preventive medications are well established.16 In acute stroke, black and Hispanic patients are

less likely to use EMS than non-Hispanic white patients.15 Thus, delay in hospital arrival may be

an important driver of disparities in tPA use and poststroke outcomes among minority groups.16

Given the burden of stroke and poor outcomes among minority populations, the need is critical

for effective interventions that emphasize (1) early arrival to the hospital after stroke onset and

(2) immediate activation of EMS.

Previous Stroke Preparedness Studies

Modifying layperson behavior (eg, calling 911) is complex and challenging. The theory of

planned behavior17,18 emphasizes that knowledge and attitudes, cultural and social norms, and

self-efficacy determine behavioral intent and precede behaviors or actions. Several key

challenges pose barriers to appropriate behaviors and acute stroke care.

Poor public awareness of stroke warning signs is a significant barrier to appropriate

early action. Knowledge of stroke warning signs is universally suboptimal, but it is particularly

low among minority groups, who are at the highest risk for stroke.19-26 Minority populations

may be less likely to receive stroke information because of literacy, education, and language

barriers; thus, they are less likely to recognize the warning signs of a stroke.27,28 Furthermore,

knowledge of stroke warning signs has not improved in the past decade despite educational

campaigns, especially among minority groups.29,30 Another critical step is preparedness.

Although up to 53% of Americans are familiar with stroke through personal, family, or friend

experience, only 7% worry about stroke, and nearly 60% do not know if they are at risk.31 Other

barriers include perceived lack of severity,15,31,32 misperceptions about treatment (eg, tPA) risks

and benefits, reluctance to call 911 because of poor knowledge about tPA,33 mistrust of health

care,16,34-36 and low self-efficacy.37,38

Previous educational campaigns in stroke have focused largely on improving public

knowledge of stroke by using the FAST (Face, Arm, Speech, Time) message. The FAST message

emphasizes 3 common signs (ie, facial droop, arm weakness, speech change) and the

importance of “time to call 911.”16-18 Although poor public awareness of the stroke warning

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signs is a barrier to appropriate early action (eg, calling 911),19-26 mass-media public education

campaigns improve stroke knowledge only temporarily and, in some instances, only improve

the intention to call 911.28,29,31,39 They have not, however, delivered meaningful changes in

actual EMS use for stroke.15,31,32

Some observational studies have noted an effect from education interventions on

hospital arrival time or tPA use for stroke,40-43 but the proportion of patients with stroke

presenting within 3 hours of symptom onset decreased following the intervention in 1 study.44

Most studies included both public and professional (eg, physicians, nurses, paramedics)

education and were not controlled trials. Indeed, a recent systematic review of stroke

preparedness interventions found that 10 of 13 studies decreased prehospital delay, but only 1

was a prospective cluster randomized clinical trial (RCT). This study showed a reduction in

prehospital delay among women but not men in Berlin, Germany.45 Three other studies, all

from the United Kingdom, used prospective ITS designs; they produced mixed results.46-48 In the

only controlled US study that employed both public and professional education, both the

control and the intervention groups demonstrated decreased time to hospital arrival; however,

tPA use increased only in the intervention group, suggesting that professional education played

a more significant role than public education.49

Recent US studies have produced similar mixed results. The Stroke Warning Information

and Faster Treatment (SWIFT) trial developed a culturally tailored stroke education program for

stroke survivors in New York City.50 The investigators randomly assigned 1193 stroke survivors

and family members to bilingual enhanced educational materials with or without in-hospital

interactive intervention. Their intervention increased stroke knowledge and behavioral capacity

but did not increase the number of patients arriving within 3 hours of stroke symptom onset

(Bernadette Boden-Albala, MS, MPH, Department of Epidemiology, New York University,

personal communication; 2018). However, the SWIFT program focused on stroke survivors in a

hospital-based setting rather than the lay public in a community setting. Another project, the

Acute Stroke Program of Interventions Addressing Racial and Ethnic Disparities (ASPIRE) study,

developed community partnerships to deliver public education, which included systematic

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professional education of hospital staff and paramedics in the Baltimore, Maryland, and

Washington, DC, areas.51 These investigators conducted 531 community interventions, reaching

10 256 participants; 3289 intervention evaluations were performed, and 19 000 preparedness

bracelets and 14 000 stroke warning magnets were distributed. The proportion of patients

arriving within 3 hours of stroke symptom onset and of tPA use doubled.52 However, because

this intervention included paramedic, nurse, physician, and public education in addition to

implementation of optimized EMS routing and hospital protocols, it was unclear which aspects

of the mixed community-, hospital-, and policy-level interventions were responsible for the

increased timeliness in seeking medical care.

Other studies of stroke preparedness have used community-based participatory

research (CBPR) approaches, defined as involving collaboration between researchers and

community members in all phases of the research process, with the goal of achieving social or

behavioral change, reducing disparities, and improving health outcomes. To date, these stroke

preparedness studies have not evaluated the effects on behavioral outcomes (eg, EMS use).53,54

Using CBPR principles, the Stroke Ready program implemented a peer-based intervention that

included role play, interactive demonstration, and a music video to increase stroke awareness

and preparedness among black patients in Flint, Michigan. The investigators noted an increase

in behavioral intent up to 1 month after the intervention, but they did not evaluate any changes

in actual behavior.55 Similar studies using peer- and investigator-led workshops have been

conducted in China and Australia.56,57 These studies have shown increases in knowledge and

preparedness in follow-up assessments, but the investigators did not evaluate change in early

arrival or EMS use.

A novel, culturally tailored approach in New York City used film-based stroke education.

After 250 Hispanic and black residents recruited from 14 churches watched the film, behavioral

intent to activate EMS increased among these participants.58,59 A cluster RCT using this

approach is under way to study the effect of this intervention on early hospital arrival and EMS

use in New York City.

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Given the proportion of multigenerational families in the United States, youth-tailored

interventions hold promise.60-62 Several groups have implemented animated cartoons, video

games, puppets, and other popular culture items to target youth, but they have not studied

clinical outcomes.63-65

Community Engagement for Early Recognition and Immediate Action in Stroke (CEERIAS) Study Aims

Based on preliminary data, we identified key Chicago areas in which stroke incidence

and mortality were high and EMS use was low. These areas served as sites for our community-

partnered intervention, CEERIAS (Community Engagement for Early Recognition and Immediate

Action in Stroke). Given the gaps in current evidence and significant disparities, we

hypothesized that a community-informed and community-delivered stroke preparedness

intervention would increase early hospital arrival and EMS use in the South Side of Chicago.

We applied a CBPR approach to developing a novel, face-to-face intervention that

overcomes racial and ethnic disparities in recognizing the symptoms of and taking early action

after acute stroke onset. These strategies were based on Albert Bandura’s theoretical self-

efficacy model as a framework to promote decision-making capacity among bystanders and

family members and on David A. Kolb’s experiential learning theory.37,66,67 Before this study

started, we engaged communities to pilot 2 components of our intervention: the Pact to Act

FAST program and the Primary Stroke Center (PSC) Mini-Internship Program (MIP). We

presented these components to focus groups and community advisory board (CAB) members to

further refine them and integrate them into the final combined intervention (MIP-Pact).

Our study was conducted between October 1, 2014, and May 31, 2018. It had 3 primary

specific aims, 1 secondary aim, and 1 exploratory aim.

Specific Aim 1: Development

In aim 1, we set out to (1) examine and identify personal, community, and cultural

barriers to EMS use after stroke symptom onset in focus groups of blacks, Hispanics, and non-

Hispanic whites; (2) test and adapt our proposed intervention model for implementation in

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these focus groups; and (3) using qualitative methods (focus groups and content analysis),

retest and refine the intervention prior to implementation.

Specific Aim 2: Implementation

In aim 2, we set out to (1) implement a culturally adapted stroke preparedness

intervention delivered by trained stroke promoters and (2) monitor its penetration and

adoption using the RE-AIM (Reach, Evaluate, Adoption, Implementation, Maintenance)

framework68 in multiethnic, disadvantaged areas of Chicago’s South Side.

Specific Aim 3: Evaluation

In aim 3, we set out to (1) assess change in early hospital arrival and EMS use among

patients with confirmed stroke at an intervention hospital in the South Side of Chicago using an

ITS analysis and (2) compare these results with those obtained contemporaneously from North

Side Chicago hospitals and St. Louis hospitals; the intervention did not occur in North Side

Chicago or St. Louis.

Secondary Aim

Our secondary aim was to evaluate the change in EMS use overall and for suspected

stroke using geographic information system (GIS) mapping of the intervention hospitals in

South Side and North Side Chicago before and after implementing the intervention.

Exploratory Aim

Our exploratory aim was to assess the change in knowledge, self-efficacy, trust, and

stress in a sample of community residents of the intervention areas on the South Side of

Chicago compared with results from residents of nonintervention areas on the North Side of

Chicago.

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PARTICIPATION OF PATIENTS AND OTHER STAKEHOLDERS

As part of the Regional Stroke Advisory Subcommittee of Chicago, we formed the

Community Outreach Working Group in early 2012 to monitor trends in EMS use in Chicago.

We held 5 town hall–type events with key community leaders and local health care

professionals in South Side and West Side Chicago, where hospital data suggested low EMS use.

Three themes emerged from the discussions: (1) The communities mistrusted the health care

system; (2) residents doubted that appropriate action and care would “make a difference”; and

(3) they expressed financial concerns about ambulance costs.

We assembled multiple organizations and patient advocacy groups from the Chicago

area, including patients and caregivers, to join us in tackling these barriers and to work on the

PCORI proposal. Leading up to and during the CEERIAS study, we established partnerships with

more than 80 organizations, including the Chicago Fire Department, Chicago Department of

Public Health, Chicago Public Schools, Chicago Medical Society, National Association of Health

Services Executives, American Heart Association (AHA)/American Stroke Association (ASA)

Midwest Affiliate, the Center for Faith and Community Health Transformation, Stroke Survivors

Empowering Each Other, the Coalition of Limited English Speaking Elderly (CLESE), A Safe

Haven, and the Chicago Hispanic Health Coalition. Some members of these organizations served

on the CAB, directly providing input on the conduct of the study; other community stakeholders

assisted the research team with focus group, stroke promoter, and survey participant

recruitment.

Community Advisory Board

Based on referrals from community partners, recruitment from existing community

projects led by our research team members, and stroke survivors with stated interest in

participation, we recruited 5 community stakeholders—Esther Sciammarella from the Chicago

Hispanic Health Coalition, Kirsten Peachy from the Center for Faith and Community Health

Transformation, Marta Pereyra from CLESE, and Kathleen O’Neill and Amanda Kelley from the

AHA/ASA—and 3 patients (Kimberly Rodgers, Tom Behrens, Lisa Bartlett) to participate in the

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CEERIAS CAB and complement and collaborate with the CEERIAS research team. Table 1 shows

the research team and CAB members, with their affiliations and roles in CEERIAS.

Table 1. Research Team and Community Advisory Board Members, With Their Affiliation and

Roles

Research team member Institution Role

Shyam Prabhakaran, MD, MS Northwestern University PI

Neelum Aggarwal, MD Rush University Medical Center

Co-PI

Knitasha Washington, FACHE Independent contractor Community PI

Heather Beckstrom Mt. Sinai Hospital Community navigator

Amy Eisenstein, PhD CJE Senior Life Qualitative researcher in aging

Jen Brown, MPH Northwestern University Community-engaged researcher

Namratha Kandula, MD Northwestern University Internist and disparities researcher

Peggy Jones Independent contractor Community advocate/stroke survivor

Christopher Richards, MD Northwestern University Emergency medicine physician

Erin Wymore, MS Northwestern University Research coordinator

Sarah Song, MD, MPH Rush University Medical Center

Neurologist

Soyang Kwon, PhD Lurie Children’s Hospital Biostatistician

Maryann Mason, PhD Lurie Children’s Hospital Qualitative researcher

CAB Member Affiliation Role

Amanda Kelley, MAT AHA/ASA Community advocate

Kathleen O’Neil, MHA AHA/ASA Health care and quality advocate

Esther Sciammarella, MS Chicago Hispanic Health Coalition

Hispanic community advocate

Marta Pereyra CLESE Elderly and Hispanic community advocate

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Research team member Institution Role

Kirsten Peachey Center for Faith and Community Health Transformation

Faith-based community advocate

Tom Behrens Stroke survivor Patient advocate

Lisa Bartlett Stroke survivor Patient advocate

Kimberly Rodgers Stroke survivor Patient advocate

Abbreviations: AHA/ASA, American Heart Association/American Stroke Association; CJE, Council for Jewish Elderly; CLESE, Coalition of Limited English Speaking Elderly; PI, principal investigator.

With regular CAB meetings and conference calls beginning in September 2013, we

introduced CAB members to the gaps and disparities in stroke symptom recognition and EMS

use after symptom onset and solicited their input on various aspects of intervention design and

outcome measurement. Although the CAB agreed that the overarching goal was to improve

stroke-related disability and mortality, these long-term outcomes are influenced by many

factors, such as medical and surgical treatments, complications, rehabilitation, and community

reintegration.

In contrast, early stroke symptom recognition and activation of EMS are initiated

entirely by patients or bystanders and can be directly affected by community intervention. Our

CAB and research team collectively decided that a community-based approach would be most

successful in overcoming barriers and promoting trust and behavioral change in the South Side

of Chicago. Further attempts to deliver mass-media educational programming were considered

less likely to succeed based on previous studies showing limited effect on behavioral change,

limited sustainability, and concerns that cultural factors would be overlooked. Our stroke

patient survivors on the CAB (Kimberly Rodgers, Tom Behrens, and Lisa Bartlett) and research

team (Peggy Jones) emphasized patient partnerships to better understand the problem and the

importance of the patient journey from onset to recovery. Minority community leaders on the

research team (Knitasha Washington) and CAB (Esther Sciammarella, Marta Pereyra)

emphasized the need for simple messages that overcome barriers to immediate action after

stroke.

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The research team and CAB members assembled in person on December 20, 2013, to

devise a final version of our approach. At this meeting, the research team, with CAB input,

settled on the target areas of Chicago, implementation plans, and timelines for the proposed

study.

Following the award of our PCORI funding in August 2014, conduct of the CEERIAS study

relied heavily on CAB input. We shared governance of the study with our CAB and remunerated

members ($100 per meeting) for their effort in the study. We provided remuneration to CAB

members and covered travel expenses to 4 in-person meetings per year in years 1 and 2 and

monthly web conference calls throughout the study period. The research team, led by the

principal investigator (PI), the co-PI, and the community PI, also included a patient as a co-

investigator (Peggy Jones) and solicited input from the CAB during each stage of the study.

In aim 1, the research team sought input from the CAB on developing the focus group

guide, recruiting and selecting participants for focus groups, and conducting analyses and

interpreting focus group results. For example, 2 CAB members provided heavy edits to the

focus group guide and suggested questions that we had not thought of; others provided

opportunities for dissemination of flyers for participant recruitment.

In aim 2, we worked with the CAB to (1) develop the intervention (specifically, the

manual for stroke promoter training) and the website, (2) modify the community survey tool,

(3) adapt the intervention, (4) devise strategies for recruiting and selecting stroke promoters

and tracking their performance, and (5) set study milestones. For example, the research team

and CAB members made weekly calls during summer 2015 (>10 hours); following the

completion of aim 1, they discussed content ideas and the readability of the training manual for

the intended recipients (promoters) and subsequently provided troubleshooting strategies for

promoter retention and sustained engagement.

In aim 3, CAB members were included in discussions about primary, secondary, and

exploratory aim results and interpretation of all findings. They also helped the research team

disseminate results locally among their constituents and develop a broader dissemination plan.

17

All results were presented in a transparent fashion, with full data distributed to all team

members for independent, unbiased review. The research team sought feedback, elicited

questions, and often performed further analyses based on CAB input. During our final CAB

teleconference call for the study period, several members expressed an interest in remaining

engaged in the post-award period and for dissemination projects.

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METHODS

Study Overview

In aim 1 of the CEERIAS study, we integrated 2 pilot programs and used focus groups to

inform and adapt this combined approach for implementation in the South Side of Chicago. In

aim 2, we then implemented the intervention in October 2015 and monitored its reach,

penetration, and adoption until November 2016. Finally, in aim 3, we evaluated the

effectiveness of the intervention on rates of EMS use and hospital arrival within 3 hours of

stroke symptom onset at an intervention hospital located in the South Side of Chicago vs

concurrent comparison with 6 stroke center hospitals on the North Side of Chicago and 17

stroke center hospitals in St. Louis (Figure 1). We selected concurrent comparison hospitals to

provide contemporaneous reference data to compare with the intervention hospital’s early

arrival and EMS arrival data, as well as to account for potential unintended diffusion of the

intervention outside the selected areas in the South Side of Chicago.

Study Design

This study used mixed methods—specifically, qualitative approaches for aim 1 and aim 2

and quantitative approaches for aim 3. We developed a formal study protocol (ClinicalTrials.gov

Registry No. NCT02301299). We submitted it to the ethics boards of all participating hospitals

for approval, which all hospitals granted. We detail the approach for each aim separately in the

sections that follow.

Study Setting

We chose an urban, underserved setting on the South Side of Chicago for the

intervention based on baseline data on stroke incidence and EMS use, field observations, and

community town halls. The 2 hospitals selected for intervention were the only PSCs in the

South Side of Chicago (red crosses in Figure 2). Northwestern University Feinberg School of

Medicine, located in downtown Chicago, served as the study headquarters.

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Figure 1. Map of study sites in CEERIAS

Abbreviation: CEERIAS, Community Engagement for Early Recognition and Immediate Action in Stroke.

20

Figure 2. Stroke hospitalization rates in Chicago at the time of CEERIAS inception


21

The city of Chicago has a population of 2.7 million and an area of 247 square miles. A

single municipal fire-based EMS agency responds to all 911 calls. EMS system protocols require

paramedics to screen patients with suspected stroke using the Cincinnati Prehospital Stroke

Scale and transport them to the closest stroke center hospital. At the time of study inception,

Chicago had 16 stroke centers, but only 2 PSCs and 1 comprehensive stroke center (CSC) were

in the South Side.

Although the South Side of Chicago is predominantly black, substantial proportions of

the residents are Hispanic and non-Hispanic white (Table 2). Therefore, we intended to develop

an intervention tailored to all 3 major racial/ethnic groups of the South Side of Chicago and

compare its effectiveness on the primary outcomes among patients with confirmed ischemic

stroke from the intervention South Side Chicago hospital, 6 North Side Chicago hospitals, and

17 St. Louis hospitals. The hospital characteristics are shown in Table 3.

Table 2. Population and Racial/Ethnic Demographics of the 3 Regions Compared in the

CEERIAS Study

City, state

Catchment population,

no.

Non-Hispanic white, no. (%)

Black, no. (%)

Hispanic, no. (%)

Asian, no. (%)

South Side Chicago, IL

797 127 88 181 (11.1)

493 320 (61.9)

203 102 (25.6)

5281 (0.6)

North Side Chicago, IL

933 954 474 176 (50.8) 47 696 (5.1)

312 609 (33.5)

52 240 (5.6 )

St. Louis, MO 319 394 144 226 (45.2) 151 372 (47.4)

11 130 (3.5)

9660 (3.0)


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Table 3. Characteristics of the Intervention and Comparison Hospitals Included in the Study

Hospitals and site

Certification Bed size Academic hospital

Ownership

South Side Chicago, IL

Hospital 1 PSC 203 No Nonprofit


North Side Chicago, IL

Hospital 1 CSC 894 Yes Nonprofit




Hospital 5 CSC 572 No Nonprofit


St. Louis, MO

Hospital 1 CSC 1163 Yes Nonprofit





Hospital 6 None 122 No Nonprofit




Hospital 10 CSC 460 No Nonprofit

Hospital 11 PSC 356 Yes Nonprofit





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Hospitals and site

Certification Bed size Academic hospital

Ownership

Hospital 16 None 167 No Proprietary


Abbreviations: CSC, comprehensive stroke center; PSC, primary stroke center.

We developed and adapted our community intervention based on the theory of planned

behavior,17 Bandura’s theoretical self-efficacy model as a framework to promote decision-

making capacity in stroke bystanders, and Kolb’s experiential learning theory as the framework

that guided the interactive stroke promoter training program (Figure 3).37,67 We combined 2

prototypes we had previously developed into a combined and adapted intervention for

CEERIAS. These were presented to participants in focus groups in aim 1 to adapt and refine the

intervention to suit the local context and address cultural factors relevant to the South Side of

Chicago.

24

Figure 3a. Theory of planned behavior

Figure 3b. Integration of the Bandura theoretical self-efficacy model (red)

with the Kolb experiential learning theory (green) during stroke promoter

training sessions

25

The first prototype intervention, the PSC MIP, had been piloted at 5 Chicago PSCs in

2011-2012, before the CEERIAS study’s inception. Using a hands-on experiential training

approach that included hospital emergency department (ED) tours and direct interactions with

patients and health care professionals, we educated and trained community stroke promoters

on the barriers to and benefits of early action after stroke symptom onset. We also deliberately

held the events at PSCs because they can provide concrete experiences to community leaders

and were also part of the message we hoped to spread: “Rapid access to stroke centers in your

own communities saves lives.” Community-based leaders were introduced to stroke team

members at the PSCs in their communities. This program not only emphasized the benefits of

calling 911 but also visually demonstrated the resources and treatments available at the PSC. At

the conclusion of the MIP, the organizers, led by Drs. Prabhakaran and Aggarwal, distributed a

stroke information packet of educational materials, services, and community events to

participants.

Our collaborator, Peggy Jones, had developed a second prototype intervention, the Pact

to Act FAST, in 2011 for rural Illinois communities, also before the CEERIAS study began. Ms.

Jones designed the Pact to Act FAST initiative to increase self-efficacy in 2 ways: (1) by

developing an action plan before witnessing a stroke and (2) by developing a social contract

(“Pact”) that required a pledge or pact from individuals in a community to act on the behalf of

loved ones, neighbors, and coworkers if they witness a possible stroke. The Pact to Act FAST

initiative used a collaborative approach to deliver educational programs and associated

resources with partnering county health agencies, firehouses, health clinics, parishes, and

schools to reach out to residents in rural Illinois. Specifically, this program developed materials

for schools, churches, and community events, which we included in the final prototype of the

CEERIAS intervention.

26

Aim 1: To Develop a Stroke Preparedness Intervention With Community Stakeholder Input

Approach

We used a qualitative approach to understand levels of community knowledge about

stroke, barriers to and facilitators of EMS use, and proposed solutions, as well as to develop a

culturally relevant stroke preparedness intervention. Qualitative methods are particularly well

suited to exploring the range of opinions and norms in particular populations because they

allow for unexpected findings and for participants to convey concepts in their own words.69 The

interplay among participants allows for a range of opinions to be heard; this approach also

permits collecting information about participants’ endorsement of the opinions offered.70,71

We designed the focus groups to explore and probe barriers to calling EMS for stroke

patients and to elicit responses and feedback on existing strategies to decrease time-to-hospital

arrival after stroke symptom onset. We then used the themes and considerations from the

focus groups to form the basis of developing the community intervention best suited to the

selected areas of the South Side of Chicago. We applied Consolidated Criteria for Reporting

Qualitative Research (COREQ) when possible to report our results.72

Participant Recruitment, Selection, and Composition

Beginning in January 2015, we screened and recruited adults (aged >18 years) residing

in neighborhoods surrounding the 2 potential intervention hospitals on the South Side of

Chicago to participate in focus groups. Because bystanders call 911 for stroke in more than 90%

of calls,39 it is important to understand the perspectives of younger generations, not only those

likely to have a stroke. However, children were not included in the focus groups because the

primary target of our intervention was not school-aged children and because of ethics review

board concerns. Because laypeople (eg, adult residents of the selected communities) and

stakeholders (eg, key leaders and community organizers) have equally valid yet different points

of view, we conducted focus groups for these 2 groups separately. Similarly, because

viewpoints may differ by race or ethnicity, we conducted separate focus groups for each group.

27

We recruited interested participants via flyers in English and Spanish distributed at

community events and in common spaces of partner organizations. We also attended local

events and performed on-site recruitment directly with community members. Interested

participants were interviewed by phone (or, rarely, in person). Finally, we identified key

informants and stakeholders in collaboration with community organizations and the CAB; we

interviewed them to validate findings from the layperson focus groups and probe ideas for

intervention messaging, delivery modes, and overcoming barriers until we reached thematic

saturation.

Before participation, we obtained informed consent from the recruited focus group

participants, which included providing information about the rationale for the study and the

risks and benefits of participation. At the start of each focus group, the facilitator, Dr. Amy

Eisenstein, introduced the study again, reviewed the informed consent document, and

disclosed her background and research interests as well as her role on the study team.

Focus Group Guide and Procedures

Research team members initially developed the focus group guide based on a review of

the extant literature and pilot data from previous discussions with CAB members. The guide

was refined and expanded through further conversations with the research team and CAB

members. We designed the guide to include a set of probing, open-ended questions that would

encourage focus group participants to share a range of perspectives, beliefs, and experiences

regarding stroke.

The guide also included short presentations of current stroke messaging and education

promotion programs and case vignettes, including 3 responses to stroke symptoms. We

designed these vignettes to elicit discussion regarding agreement or disagreement with the

actions taken and participants’ previous experiences with stroke and knowledge of stroke

symptoms. We pilot-tested the final focus group guide with a group of nurses, research

coordinators, and several CEERIAS research team members before using it in community focus

groups.

28

Data Collection

The focus groups occurred between January and April 2015 in community settings in the

South Side of Chicago. Each focus group meeting lasted approximately 90 minutes and included

refreshments. An experienced focus group facilitator (Amy Eisenstein, PhD, assistant professor

of medical social sciences, Northwestern University, with more than 10 years of experience),

with assistance from other team members as note takers, conducted the focus groups. The

exception was for the 2 Spanish-speaking groups, which were conducted by a bilingual

facilitator trained and observed by Dr. Eisenstein. Dr. Eisenstein’s prior work focused on

qualitative research and patient-reported outcomes in elderly patients, although never in a

stroke population. She had had no previous independent training or expertise in stroke care. A

stroke health care professional attended the focus group meetings to provide clarification and

additional information at the conclusion of the session. We digitally recorded each focus group

and transcribed responses verbatim for analysis. Transcripts were not returned to participants

for comments or corrections.

Adaptation and Refinement of the Prototype

Our goal was to combine the MIP (experiential tour) and Pact to Act FAST (social

contract) prototypes into a single intervention (MIP-Pact) to be taught to stroke promoters

during training. To achieve this, we incorporated ideas arising from the focus groups, including

suggestions for changes in content, context, and delivery of the community intervention. Using

an adapted Delphi approach,73 we reviewed our findings with a representative from each of 6

focus groups from aim 1, using an iterative process, before implementation.

Based on messages, content, teaching styles, and learning techniques suggested in the

focus groups and confirmed by focus group representatives, we developed the final adapted

MIP-Pact intervention for implementation in the South Side of Chicago. We made edits to the

original Pact to Act FAST materials, developed for use in rural Illinois, to reflect a more culturally

sensitive and specific message suitable for urban, minority communities. Once the prototypes

were modified and adapted, we pilot-tested the MIP-Pact intervention with a small sample of

additional stakeholders and lay community members, and then conducted further interviews

29

with this sample to evaluate the intervention. Based on these cognitive interviews, we made

additional modifications to the MIP-Pact intervention before implementation. Finally, we

created a training manual as a guide for stroke promoters during the 4-hour training session

and for reference afterwards.

Statistical Analysis

We loaded transcript documents into ATLAS.ti, release 7.077 (Scientific Software

Development GmbH) for analysis. A qualitative statistician (Maryann Mason, PhD) performed

and led the analysis; 6 research team members participated in the initial coding and coding

debriefings (Christopher Richards, MD; Amy Eisenstein, PhD; Namratha Kandula, MD; Sarah

Song, MD, MPH; Neelum Aggarwal, MD; Shyam Prabhakaran, MD, MS).

The analysts applied the constant comparative method. In this approach, coded

segments are continually compared with one another to identify overlap, emerging codes, and

themes. We collaboratively developed the initial codes based on the focus group protocol and a

review of the literature by 1 team member (Shyam Prabhakaran, MD, MS). We then grouped

the codes into 2 a priori thematic areas of interest: (1) calling (or not calling) EMS for stroke or

other medical emergencies and (2) how to educate community members regarding stroke.

We applied the initial codes to the data (transcripts), and the coding team reviewed

them for fit. In assessing fit, we evaluated how well the segments coded with the same code

reflected a unified concept rather than an array of concepts. If the coded segments fit, we

found that the segments were consistent in the concept they reflected. If we did not find any

fit, coders concluded that the segments did not represent a unified concept. Based on results

from the initial coding, we further refined the codes and applied them to the data. After 3

revision cycles, we agreed on a final codebook and shared it with the CAB. We applied the final

coding scheme to the data.

We analyzed the 2 themes separately and grouped the first theme into subthemes

based on the reasons for calling or not calling EMS. To augment the analyses, the research

team, led by Dr Mason completed matrices comparing themes across the 2 potential

30

intervention hospital community or catchment areas and racial/ethnic groups to identify

similarities and differences among them. We performed the analysis in intervals and stopped

when thematic saturation was achieved with the sixth focus group. We discussed findings with

the research team and CAB members for interpretation; we did not share the findings with

focus group participants.

Aim 2: To Implement a Culturally Adapted Stroke Preparedness Intervention

Approach

Using the final prototype of the intervention, we sought to identify and recruit

laypeople and trusted community members to serve as stroke promoters. We developed

materials and aids to facilitate our training program. We then trained the recruited stroke

promoters on the adapted MIP-Pact program and gave them tools for tracking activity in the

community, event planning, and logging of obtained Pacts (Figure 4).

31

Figure 4. Intervention development and implementation from pilot projects (orange), the

adapted MIP-Pact program and training of stroke promoters (red), and promoter activities

(blue)

Abbreviations: FAST, Face, Arm, Speech, Time; MIP, Mini-Internship Program.

32

Stroke Promoter Recruitment, Selection, and Retention

The CEERIAS study maintained a range of community partnerships with large

multiethnic outreach potential and drew on 5 core community groups: (1) faith-based

organizations (FBOs), (2) hospitals and clinics, (3) public and private schools, (4) local

businesses, and (5) advocacy groups. These groups were present in all areas of the intervention

and had previous experience in community health initiatives.

Stroke promoters were identified by partner community organizations, the community

PI, subsequent stroke promoter referrals, and interested participants who contacted research

team members at community events. Stroke promoters were required to be adults aged

>18 years who had strong connections in the target areas of Chicago based on the review of

their roles, previous activities, and organizational contacts. Based on interest and community

outreach potential, the research team and CAB members selected final candidates for training.

We recruited stroke promoters concurrently in 12 waves (~20 stroke promoters per wave) for

training ($732 total per promoter). We explicitly asked stroke promoters to incorporate stroke

preparedness discussions into their regularly planned community activities over 6 months from

the date of their training.

Promoter Training

We held the MIP-Pact training program at the 2 intervention hospitals; each session

lasted approximately 4 hours and was facilitated by the PI, co-PI, community PI, community

navigator, and study research coordinator. We provided stroke promoters with (1) training on

the benefits of early recognition of and EMS use for stroke (eg, stroke centers, tPA), (2)

culturally adapted solutions to current barriers (eg, misperceptions of vulnerability, severity,

mistrust, costs), and (3) cues to aid in stroke recognition and immediate action. Stroke

promoters engaged in interactive discussions with community leaders and health care

professionals regarding strategies to enhance patient and bystander self-efficacy and increase

public knowledge of stroke warning signs, treatments, and expected outcomes.

33

The training program included lectures, a mix of didactic material on stroke statistics

relevant to South Side communities, hospital-based tours of the “stroke patient journey,” case

examples, multimedia aids, role-playing activities, and storytelling of shared experiences and

feelings to enhance the learning process. We distributed training manuals and presented slides

on stroke demographics, disparities, and local data from the hospitals on EMS use, arrival times,

treatment rates, and outcomes. Following the didactic portion, we conducted hospital-based

tours at the 2 planned intervention hospitals on Chicago’s South Side with assistance from the

local stroke program coordinator, to minimize disruption in the clinical setting. We encouraged

stroke promoters to ask questions throughout the training about patient throughput, physician-

patient discussions of the risks and benefits of administering tPA, and required tests in the ED.

Following the tour, we discussed barriers to early hospital arrival and EMS use and solicited and

provided solutions using role-playing and small group workshops. The groups also discussed

approaches that incorporated their experiences and techniques learned during the training for

use in their interactions with their constituents in the home, school, and workplace. At the

completion of training, we provided every stroke promoter with website sign-in credentials and

instructions as well as distribution materials, including magnets, bookmarks, Pact to Act FAST

cards, and suggested community educational activities (Appendices A and B).

Implementation

We tasked each trained stroke promoter with disseminating the educational materials

to their constituents (eg, parishioners, school-aged children, customers) over a 6-month period.

Adults were the primary target of the intervention to disseminate knowledge of stroke

prevention and treatment, although school-aged children were not excluded. We asked stroke

promoters to present the program at least twice monthly for 6 months as part of their

interactions in the community and to document activities using the CEERIAS website

(www.ceerias.com; link no longer active). We required stroke promoters to obtain Pact cards in

person, with zip code verification of the individual making the pledge, and to log the Pact cards

on the website. We defined the number of Pact cards collected as the objective measure of

total individuals whose behavioral intent to call 911 for stroke could be verified. If Pact cards

34

were collected on paper, we required that these responses be entered online or faxed to our

central coordinating office at Northwestern University for manual entry on the website.

Personnel in concurrent comparison settings performed stroke education activities in a

nonprescribed way; these activities therefore provided contemporaneous comparisons with

those in the intervention community. These nonstandardized approaches included ongoing

community education led by regional hospitals and their staff members in the form of health

fairs, distribution of materials to patients and families in the hospitals and clinics, and local

news media interviews and stories about stroke that occurred at both intervention and

nonintervention settings. None of the North Side Chicago and St. Louis comparison hospitals or

their regional partners participated in stroke promoter training or similar community health

worker programs to improve stroke recognition and early EMS activation after stroke onset.

The investigators did not have contact with personnel at control sites nor access to any

systematic gathering of information about stroke prevention or treatment programs at control

sites.

Monitoring and Evaluating Intervention Implementation

We evaluated the intervention using the RE-AIM framework.68 In partnership with

EdgeOne Medical in Chicago, we created online forms using the CEERIAS website for stroke

promoters to log their activities, document Pact cards recorded, and communicate with fellow

stroke promoters using chat rooms and forums. Appendices C-F show representative online

calendars, forms, and chat forums. EdgeOne Medical staff pilot-tested the website’s

functionality among 5 stroke promoters from the first training session, deployed it to

subsequent stroke promoters, and maintained the website throughout the study period.

Immediately following the training, stroke promoters completed surveys on the content,

speakers, hospital-based tours, and distribution materials. In addition, the community PI and

community navigator contacted each stroke promoter to monitor activity, reinforce

performance goals, provide post-training advice on high-yield activities and events, disseminate

successful tips and strategies for overcoming resistance, and continue follow-up regularly to

35

ensure ongoing community engagement and intervention implementation. In addition to

forums created on the website to generate conversations among stroke promoters, we held

webinars and phone conferences with stroke promoters to evaluate adoption of the tools and

strategies discussed in training.

Members of the research team attended some stroke promoter events to ensure that

high-fidelity adoption of the intended intervention was taking place. For example, we assessed

whether stroke promoters were using materials as instructed, engaging in face-to-face

discussions with the goal of overcoming resistance and barriers, and obtaining Pact cards from

attendees. We also boosted and maintained the intervention through ongoing presentations by

research team members at community fairs and events, local radio interviews, and community

newsletters in South Side Chicago. Other than tallying the number of Pact cards signed, we did

not systematically track the delivery of stroke prevention and treatment knowledge in the

target community. In an exploratory study aim, we tried to measure knowledge in a sample of

residents.

Aim 3: To Assess Change in Early Hospital Arrival and EMS Use for Confirmed Ischemic Stroke Before and After the Intervention

Approach

For the primary analysis, we applied an ITS analysis at the intervention hospital to study

the effects of our community intervention on the primary outcomes of interest. An ITS study

design is used to evaluate public health interventions, particularly for interventions introduced

at a population level over a clearly defined time period that target population-level health

outcomes. A time series is a continuous sequence of observations on a population, taken

repeatedly at equal intervals over time. In an ITS study, a time series of an outcome of interest

is used to establish an underlying trend, which is interrupted by an intervention at a known

time point, and the unit of analysis is time (eg, months) rather than patients.74

In the secondary analysis, we used concurrent comparison cohorts from hospitals

outside the intervention community (North Side of Chicago and St. Louis) to account for

36

unmeasurable temporal trends and diffusion of our intervention beyond our intended

community. We hypothesized that some diffusion would occur into the North Side of Chicago

but little if any into St. Louis settings. We selected St. Louis because it is another Midwestern

urban setting with a substantial population of black individuals. We did not, however, match

hospitals by demographics or other features.

Primary Outcomes

The 2 primary outcomes for this aim were (1) early hospital arrival and (2) EMS use. We

evaluated them in separate models for each outcome and did not combine them. We defined

early hospital arrival as a patient with confirmed ischemic stroke arriving at a hospital within

3 hours of symptom onset, a definition that is standard stroke care center practice because the

duration of stroke symptoms is the basis for deciding whether to use thrombolytic and

endovascular interventions. We defined EMS use as a patient with confirmed ischemic stroke

arriving at the ED by EMS rather than by private transport, taxi, or other form of transportation

from either home or the scene of the stroke.

Data Collection

We collected hospital data by using the Get With The Guidelines–Stroke (GWTG-Stroke)

registry, which includes demographic, clinical, and hospital outcome data.12 GWTG-Stroke is a

quality improvement program for stroke care in the United States that promotes consistent

adherence to the latest scientific management guidelines. Hospitals enter clinical and outcomes

data by using a web-based patient management tool (powered by Quintiles Real-World & Late-

Phase Research). Trained hospital personnel at participating hospitals identify patients

admitted with stroke by using prospective clinical identification. All PSCs in Chicago and in St.

Louis used GWTG-Stroke for data collection. With a waiver of informed consent under the

common rule for data collected for quality improvement, we accessed and downloaded

deidentified GWTG-Stroke records from all participating hospitals between January 2013 and

December 2017 for review and analysis.

37

From the GWTG-Stroke database, we extracted data for patients whose stroke type was

ischemic stroke, whose stroke occurred before hospitalization, and whose arrival mode was

either EMS or private transport, taxi, or other means from either the home or the scene.

Admissions whose arrival mode was transfer or unknown were excluded. Hemorrhagic stroke

was not included in the analyses because (1) PSC hospitals in the target intervention community

are not required to collect data on this subset of patients experiencing stroke; (2) these patients

are often transferred emergently from the hospital ED to specialized stroke centers with

neurosurgical services; and (3) unlike ischemic stroke, no proven, specific, time-dependent

intervention exists for hemorrhagic stroke. Extracted data included demographics (age, sex,

race/ethnicity), mode of hospital arrival, symptom onset time, hospital arrival time, tPA

administration (if applicable), and hospital discharge outcomes. Race and ethnicity were

combined into 4 groups for analysis: non-Hispanic white, non-Hispanic black, Hispanic, and

other.

We created an early arrival variable by subtracting symptom onset time from hospital

arrival time. When symptom onset time was unknown or missing, we used the last known well

time (ie, the last time the patient was known to be neurologically at baseline, as provided by

corroborating witnesses, such as family members) as symptom onset time. When both

symptom onset time and last known well time were unknown or missing, we treated that

admission as a late arrival (>3 hours from symptom onset) because it is conventional in stroke

care and research. We performed frequency analyses of admissions by years and months to

ensure the stability of the frequency of the patient population over years and months and

descriptive analysis of patients’ characteristics, such as age, sex, and race/ethnicity.


We conducted all quantitative data analyses by using SAS statistical software,

version 9.4 (SAS Institute Inc). For ITS analysis,74 we considered December 1, 2015, through

March 31, 2016, as the intervention implementation period; we thus excluded this 4-month

period and resulting data from data analysis. We defined this period of implementation as the

time from start of stroke promoter activity, as measured by Pact cards collected (December

38

2015) until approximately half of the number of total Pact cards (March 2016) had been

collected (Figure 5).

Figure 5. Implementation timeline

For data analysis, we specified an impact model before the analysis based on our

hypothesis that the change in the outcome would be both a gradual change in the gradient of

the trend and a change in the level. In addition, we assumed a natural change over time before

the intervention and that autocorrelation would take place between consecutive observations.

To test the hypothesis, we used a regression model that included level change after

intervention in April 2016 and slope change after intervention as predictors. To account for the

gradient of the trend before intervention, we included the time variable to indicate the time

point of the outcome assessment in months as a predictor in the regression model. To correct

the autocorrelation, we selected the order of the autoregressive error model by using stepwise

autoregression.75 The stepwise autoregression method initially fits a high-order model with

many autoregressive lags, and then sequentially removes autoregressive parameters until all

remaining autoregressive parameters have significant t tests.

39

The regression models of the ITS data for monthly early hospital arrival rates and

monthly EMS arrival rates included the following 4 predictors: intercept, time point in months,

change in level after intervention, and change in slope after intervention. Because we

aggregated individual patient data monthly and the regression model regressed monthly early

hospital arrival rates and monthly EMS arrival rates on time in months, the sample unit of the

regression model was a time point (month), not an individual. Therefore, the sample size was

the number of months, not the number of patients per month or the number of hospitals in the

analysis.

We aggregated individual admission data into monthly time series data to calculate a

monthly early hospital arrival rate and a monthly EMS arrival rate. In this procedure, we also

generated seasonally adjusted time series data to account for a seasonal pattern. Using the

seasonally adjusted time series data, we conducted linear regression analysis for time series

data. The statistical hypothesis of the regression model was that there would be a level change

and a slope change after the intervention. In the regression model, in addition to level change

and slope change parameters, we included a time variable to account for a natural trend before

intervention introduction (or in the absence of the intervention). We assumed that the patient

population was stable during the 5-year period and that no factors other than the intervention

would affect the outcome. Therefore, no other potential confounding factors were considered.

A back-step approach (backward elimination) was used to correct for autocorrelation. A

maximum likelihood method was used to estimate parameters.

For power calculation, we assumed that the postintervention period would be one-third

of the total months of data available from the hospitals. We had 5-year (60-month) data from

January 2013 to December 2017, of which 4 months were considered implementation

(December 2015 to March 2016); thus, we had 35 months of preintervention data

(January 2013 to November 2016) and 21 months of postintervention data (April 2016 to

December 2017). We defined the effect size as the sum of the expected change in slope and the

expected change in level (in percentages) over the standard deviation in the outcome (early

hospital arrival or EMS use). Assuming an autocorrelation level of 0.3, an effect size of 0.5

40

would translate to a 4% level change and 1% change per month (SD, 10%) in the outcome. This

approach provides 90% power at a significance level of α = 0.05. Such an effect size means an

absolute increase in the outcome >10% over 1 year of observation postintervention, which is

considered clinically meaningful.76

We dropped 1 of the 2 potential intervention hospitals in the South Side of Chicago used

for hospital-level data collection in aim 3. Based on data quality assessment, we determined

that GWTG-Stroke data for this hospital were unreliable for 4 reasons: (1) 2013 was the first

year the GWTG-Stroke program had been implemented in this hospital; (2) total stroke

admissions in 2013 were significantly lower than in subsequent years (160 admissions in 2013

vs 284 in 2014 and 283 in 2015); (3) rates of missing information regarding symptom onset and

last known well times were high (38% vs 2% at the other hospital); and (4) rates of EMS use at

baseline and throughout the study period were higher than reported during study planning

(85% vs 40%), suggesting that a sampling methodology was being used. In addition, the

penetration of the intervention around the excluded hospital’s geographic catchment area was

lower based on Pact cards collected (estimated 4.3% of 140 855 households based on 2010

census data) than penetration for the included hospital (estimated 19.4% penetration of 58 427

households based on 2010 census data). Discussions with the hospital indicated that a change

in stroke coordinators, which occurred in 2015, had resulted in a change in data collection such

that only a subset of patients arriving in the ED with stroke were being tracked and with

minimal data entry. Because of resource limitations at this hospital, we were unable to improve

the quality of data collection to suit the study requirements. We did not include this hospital’s

data in sensitivity analysis, therefore, given that we deemed such information highly unreliable

and flawed. A replacement hospital was not available because these 2 hospitals are the only

PSCs in the South Side of Chicago. Moreover, because patients with suspected stroke who

activate EMS are transported to PSCs, non–stroke center hospitals in the South Side would not

be appropriate replacement hospitals.

Because ITS analysis provides a quasi-experiment at a single center and may still

underestimate temporal trends, we sought to compare the primary outcomes at our

41

intervention hospital with those from concurrent nonintervention hospitals in 2 other areas: (1)

6 hospitals in the North Side of Chicago, where the diffusion of our intervention would be

possible but lower than in the target community, and (2) 17 St. Louis hospitals, where the

diffusion of our intervention would be unlikely. Thus, we conducted 3 ITS analyses comparing

data from 3 different samples: (1) the intervention hospital alone, (2) the intervention hospital

and 6 North Side Chicago comparison hospitals, and (3) the intervention hospital and 17 St.

Louis comparison hospitals. For comparisons of change in level and slope between intervention

and comparison hospitals, a negative value indicates that the intervention hospital was worse

than the comparison hospitals, while a positive value indicates that it was better

(eg, intervention – comparison).

To explore the heterogeneity of the intervention effect by demographic background, we

conducted subgroup analyses for those aged

42

Secondary Aim: To Evaluate Change in EMS Use for Suspected Stroke Before and After the Intervention Using GIS Analysis

Approach

We sought to identify hot spots for EMS use as well as stroke recognition and awareness

in Chicago using GIS analyses of transports by the Chicago Fire Department, the sole EMS

agency that responds to 911 calls in the city of Chicago. Specifically, we aimed to assess

whether ambulance runs for suspected stroke increased after the intervention compared with

before the intervention in selected communities around the intervention hospitals, the South

Side of Chicago (south of Interstate 290), and the North Side of Chicago (north of Interstate

290).

Outcomes

The frequency of EMS runs for paramedic-suspected stroke was the primary outcome.

We defined suspected stroke as any EMS patient encounter in which the paramedic included

“suspected stroke” and “rule-out stroke” as the prehospital paramedic impression of the nature

of the encounter. In addition, we included as suspected stroke any patient whom the

paramedics transported to a PSC based on their clinical assessment. The default destination

hospital for EMS transports by the Chicago Fire Department is the closest hospital. However, for

patients who activate 911, including those with suspected stroke, the closest appropriate

hospital (ie, stroke center) may be farther away than the closest hospital. When EMS transports

a patient to a more distant hospital because the paramedics suspect that the patient is having a

stroke, the paramedics convey this impression in the Destination Hospital Reason field. In

addition to examining geographic clustering of suspected stroke, we analyzed the overall

frequency of EMS runs (for any condition) to assess contemporaneous EMS use trends and

potential unintended consequences (eg, overuse of EMS beyond suspected stroke) of the

intervention. Because our intervention aimed to increase EMS use for suspected stroke,

nonstroke uses of EMS (eg, severe hypoglycemic episode with focal neurologic symptoms)

would be false-positive stroke if paramedic impression was stroke and therefore an appropriate

use and within our intended effect. However, if the paramedic determined it was a

43

hypoglycemic attack by glucose measurement and documented it as such, then it would not be

included in our definition of suspected stroke. Likewise, if a patient called 911 for knee pain or

chest pain, this would be an unintended use of EMS (ie, not specific to the intent of the

intervention).

Data Collection

We obtained EMS records from the electronic medical record system (SafetyPad, ESO

Solutions) of the Chicago Fire Department. EMS records included the paramedic impression, the

destination hospital reason, and the address of the patient contact.


We used ArcGIS and ArcGIS Pro mapping and analysis software (Esri) for GIS analysis.

We geolocated EMS run locations by using addresses provided in the record. Optimized Hot

Spot Analysis facilitated hot spot analysis of all EMS runs, irrespective of incident type or

paramedic impression. Using Getis-Ord Gi* analysis, we identified hot spots for paramedic-

suspected stroke runs. Statistical hot and cold spots are defined as areas that have a

44

Exploratory Aim: To Assess Change in Knowledge, Self-efficacy, Trust, and Stress Before and After the Intervention

Approach

We recruited residents from the North Side and South Side of Chicago (defined as being

north or south of Interstate 290 and living in Chicago, respectively). We compared knowledge,

self-efficacy, trust, and stress before and after the intervention because these decision factors

are relevant to EMS activation for stroke.

Outcomes

Specified outcomes were (1) knowledge, (2) self-efficacy, (3) trust, and (4) stress

(Table 4).

The standardized test for assessing knowledge and behavioral intent was the Stroke

Action Test (STAT), a validated assessment tool for assessing emergency responses to various

stroke and nonstroke scenarios.79 STAT has excellent reliability; it takes, on average, 5 minutes

to complete. For self-efficacy, we used a Likert scale ranging from 1 (strongly agree) to 4

(strongly disagree) on the following 2 questions based on a previous study:38 “I would not be

able to tell if someone is having a stroke” and “If I saw someone having a stroke, I would not

know what to do.” For trust, we adapted 9 questions based on the Health Care Trust Survey,80 a

validated tool among multiethnic communities. For stress, we applied the 6-item Perceived

Stress Scale, a validated instrument for measuring stress in the community.81

45

Table 4. Survey Instruments Used to Assess Knowledge, Self-efficacy, Trust, and Stress

Construct Instrument No. of items Time to complete, min

Knowledge STAT 28 5

Self-efficacy Self-efficacy rating (Likert scale)

2 1

Trust Health Care Trust Survey (adapted)

9 3

Stress Perceived Stress Scale

6 3

Abbreviation: STAT, Stroke Action Test.

Participants

To be eligible for participation in this survey, subjects were required to be adults (aged

≥18 years), to speak English or Spanish, and to have resided for the past ≥1 year in a Chicago zip

code. We used purposive sampling in attempts to achieve equal representation from each

racial/ethnic group (black, Hispanic, and non-Hispanic white individuals) from the North Side

and the South Side for a total of 6 subgroups. Owing to difficulty following up on participants

who completed the baseline survey (preintervention) for the 1-year postintervention survey,

we changed the study protocol (see “Changes to Study Protocol”) to recruit an independent

sample for the pre- and postintervention assessments. We employed a purposive recruitment

approach to ensure that the postintervention sample matched the preintervention sample

based on the distribution of racial/ethnic group, education, and sex.

Data Collection

We recruited subjects at community events, offered financial incentives ($25) to

participate, and permitted subjects to complete the survey in person on paper, online, or by

telephone. In addition to collecting addresses to enable us to separate participants into North

Side and South Side groups, we collected information about age, sex, race/ethnicity, education

level, and residence type. In a standardized survey, we asked the questions in the same order

each time.

46


We selected a threshold of 10% improvement in knowledge (STAT) scores for sample

size and power calculation. In an earlier study, those with stroke experiences had a 10% higher

STAT score than those without stroke experience (41% vs 31%, respectively). In addition, a 31%

absolute difference was noted among medical students before and after stroke-specific

training.79 We could not anticipate such a large improvement in the lay community, but we

estimated that a 10% absolute improvement in STAT score would be significant and in line with

the difference noted among those with and without stroke experience.

In sample size calculation, to detect a 10% absolute change in knowledge using the STAT

score (ie, 30% to 40% [SD, 10%]; effect size of 1.0) for each racial/ethnic category in the North

or South Side, at a power of 80% and a significance level of α = 0.05 (2-tailed), we required 396

people total from pre- and postintervention assessments. That goal meant 198 participants

(33 × 3 racial/ethnic categories × 2 geographic areas) at preintervention assessment and 198

participants at postintervention assessment.

In calculating a STAT score, each correct response received 1 point, and incorrect

responses received 0 points. The total score is reported as a percentage of correct responses.79

We averaged the responses to 2 self-efficacy questions with 6 response options (1 = strongly

disagree to 6 = strongly agree) to create a self-efficacy score; a lower score indicated higher

self-efficacy. We maintained this scoring method for comparability with the earlier study using

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