Design Alternatives and Opportunities for Studies in Cardiovascular Disease

Epidemiology

Virginia J. Howard, PhD

School of Public Health University of Alabama at Birmingham

Spectrum of Evidence(and progression of understanding)

• Initial observations (clinical observations & ecological studies)

• Observational epidemiology– Case/control studies

• Advantage: Efficient study of “rare” diseases• Disadvantage: Subject to many biases

– Cross-sectional studies• Advantage: Relatively inexpensive and fast• Disadvantage: Direction of causality

– Longitudinal cohort studies• Advantage: Solves direction of causality• Disadvantage: Requires follow-up of a sufficient number of events

• Randomized clinical trials: the “gold standard”

The Beginning of “Modern” Cardiovascular Epidemiology: 1948

Framingham Heart Study• In 1948 NHLBI established clinic and invited ≈5,200 town

residents to come to the clinic to be evaluated • Kept clinic active, and re-evaluated cohort at roughly 3-year

intervals to identify:– Incident cardiovascular disease (CVD) including heart disease and

stroke– Incident risk factors and/or change in risk factors

• The paradigm for the longitudinal cohort study– Identified and/or refined description of most risk factors (lipids,

hypertension, diabetes, smoking, etc.) – Developed risk prediction models for both stroke and heart disease– Actually coined the term “risk factor”

• Subsequently gave rise to the Framingham Family Study (early genetics work)

• Became the model for most CVD epidemiological studies for the National Heart Lung and Blood Institute (NHLBI)

“Children” of the Framingham StudyExamples of Observational Cohorts Stratified by Age

• CARDIA (Coronary Artery Risk Development in Young Adults)

– ≈5,000 B/W participants aged 18-30 recruited 1985-1986 in 4 sites, stratified by race, sex, education

• ARIC (Atherosclerosis Risk in Communities)

– ≈16,000 B/W participants aged 45-64 recruited between 1986 – 1990 from 4 sites, blacks primarily from one site

• CHS (Cardiovascular Health Study)

– ≈6,000 primarily white participants aged 65+ recruited 1985

“Children” of the Framingham StudyExamples of Studies of Specific Populations

• Honolulu Heart Study– American men of Japanese ancestry born 1900-

1919, leaving on island of Oahu in 1965– Ages 28-62, N=8000, 5 exams through 1996

• Strong Heart Study – American Indians in 13 communities in Arizona,

Oklahoma, South/North Dakota ,1988-2000• MESA (Multiethnic Study of Atherosclerosis)– Ages 45-84, N=6500, W/B, Hispanics, Chinese-A– Subclinical markers of atherosclerosis

“Children” of the Framingham StudyExamples of Studies of Specific Populations

• JHS (Jackson Heart Study)

– 6500 black participants recruited as part of ARIC supplemented by additional participants

– Ages 35-84, enrolled 1999-2005– All recruited in Jackson, Mississippi

• HCHS/SOL (Hispanic Community Study / Study of Latinos) – 16,000 Hispanic Participants in 4 US sites– Completed enrollment 2012– Cuban, Mexican, Puerto Rican, Central/Southern

American

Summary of Framingham and its “Children” Studies

• Whether done by age, race/ethnic group– Selected a community-based cohort– Brought the participants into a limited number of

central clinics (generally 4-6) for evaluation– Followed cohort with subsequent telephone and

revaluation in the clinic (frequently at 3-4 year intervals)

Selected advantages of the “Framingham Paradigm”

• Longitudinal cohort study: powerful approach for both predictors of incident events and incident risk factors

• Participant’s identification with the study can enhance retention

• Allows incorporation of complex evaluations of subclinical disease (e.g., intimal medial thickness, coronary calcium, etc.) to reduce sample size

• Control of clinic allows collection of data with high quality control

Selected disadvantages of the “Framingham Paradigm”

• Cost limits size of the cohort (clinics are expensive!)– Requires reliance on subclinical measures of

disease as an outcome (that may not be related to clinical events)

– Limits ability to address novel (non main-effect) hypotheses such as interaction and mediation

• Geographic restrictions – Limits generalizability to national population– Cannot address geographic –specific hypotheses

Example of Geographic Challenges• Hispanics are the fasting growing racial/ethnic group

in the US• There is high interest in assessing disparities between

race/ethnic groups• For stroke mortality in 2009 (white reference group)

Black Hispanic Asian/PI Native Am0.00.51.01.52.02.53.03.5

45-54 55-64 65-74 75-84 85+

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• But still … are there differences in stroke incidence?• Question has given rise to two population-based

surveillance studies– Northern Manhattan Study (NOMAS) in New York City,

studying whites, Hispanics and blacks– Brain Attack Surveillance in Corpus Christi (BASIC) in

southern Texas, studying whites and Hispanics

Stroke Incidence Differences for Hispanics (and Blacks)(compared to white reference)

45-54 55-64 65-74 75-84 85+0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Black Hispanic

Age

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45-59 60-74 75+0.0

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1.0

1.5

2.0

2.5

3.0

3.5

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Hispanic

Age

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Northern Manhattan Study (NOMAS)

Brain Attack Surveillance in Corpus Christi (BASIC)

Morgenstern LB. AJE 2004;160:376-383

IRH:W = 1.24 (1.12, 1.37)IRH:W = 2.11IRB:W = 2.40

Sacco RL. AJE 1998;147:259268.

… but wait a minute• Nationally, stroke mortality in Hispanics is

consistently lower than in whites• Two well-done stroke studies show stroke

incidence higher in Hispanics (albeit by a considerably different amount)

• How can this be happening?– Hispanic subpopulations?– Issues with vital statistics for the Hispanic

population?– Confounding of racial/ethnic differences with

geographic disparities?

Are Race Differences Confounded with Geography?

• Is it possible that a higher stroke incidence in Hispanics compared to whites is simply the confounding of race with geography?– In Manhattan, Hispanic stroke incidence is higher than

white… but white stroke mortality in New York is literally the lowest in the nation for whites

– In Nueces County (Texas), Hispanic stroke incidence is also higher than white

… but Hispanic stroke mortality in Nueces County is among the highest in the nation for Hispanics

• There is no way to evaluate this possibility using the clinic-based Framingham paradigm

Attempts to Break the “Framingham Paradigm” (#1)

• Provide ability to move clinics• Example: National Health and Nutritional

Examination Survey (NHANES)– Multi-stage sample (random communities, random

neighborhoods, random homes)– Clinic goes to the community (clinic on wheels)

• Advantages/Disadvantages– Advantages: can dramatically increase the number of sites

… reducing the problem of generalizability– Disadvantages: at the end of the day … you can only

partially address the issue (NHANES is in only 82 communities of the US) – and expensive

Attempts to Break the “Framingham Paradigm” (#2)

• Eliminate clinics and use remote assessment• Example: Behavioral Risk Factor Surveillance System

(BRFSS) (also Iowa Women’s Health Study)– Obtained information from telephone interviews conducted

by each state, choice of modules of interest– Cross-sectional assessment of health status, risk factors

• Advantage/Disadvantage– Advantage: can perform a huge sample of the entire US– Disadvantage:

• Self-reported conditions only• Cross-sectional in nature (BRFSS only, follow-up is possible)

Attempts to Break the “Framingham Paradigm” (#3)

• Avoid clinics by the use of special pre-established cohorts

• Examples: Physicians Health Study (PHS) and Nurses Health Study (NHS)– Baseline self evaluation and follow-up of physicians and

nurses

• Advantages/Disadvantages– Advantages: Large geographically dispersed cohort can

be very efficiently evaluated– Disadvantages: Problematic generalizability to other

populations

Our Attempts to Break the “Framingham Paradigm”

• There are immense geographic and racial (black versus white) disparities in stroke mortality

• Understanding of causes is poor• Goal was to develop a national cohort:– Of sufficient size to allow assessment of “novel”

hypotheses (interaction and mediation)• Requires ≈ 1000+ stroke events• That in turn requires 30,000+ participants• That requires remarkable study efficiency

– To provide geographic heterogeneity with oversampling of the southeastern “Stroke Belt” (goal 50%)

– To provide oversampling of blacks (goal 50%)

REasons for Geographic And Racial Differences in Stroke (REGARDS)

Study Population

• Longitudinal population-based cohort of 30,239 volunteers age 45 and older (goal was 30,000)

• Simple random sampling with geographic representation– 56% from the stroke belt (goal was 50%)– 44% from the rest of the contiguous US (goal 50%)

• Racial representation: 42% African American / 58% white (goal was 50/50%)

• Sex representation: 45% male / 55% female (goal was 50/50%)

WhiteAfrican American

N = 30,239

REGARDS Participants

REGARDSOverview of a Novel Study Design to Meet Study Needs

• Central participant recruitment using mail and telephone

• Central computer-assisted telephone interview• In-home evaluation for physical, venipuncture and ECG,

signed consent form, self-administered forms:EMSI: Examination Management Services, Inc

• Central facilitiesLaboratory and repository for serum and urine samples: University of VermontECG Reading Center: Wake Forest University

• Central follow-up for detection of suspected stroke events plus cognitive change(+ MI, VTE and fractures)

• Physician adjudication of stroke events

Types of Data From REGARDS• Examples of “Expected” data

– Questionnaire data on major CVD risk factors, HRQoL, depressive sx, stress, etc.

– Lab data: lipids, glucose, insulin, CRP, creatinine, albumin, cystatin C, etc.

– ECG for MI, atrial fibrillation, LVH

– Self administered data for diet, family history of CVD

– Follow-up data for incident stroke/MI events

• Examples of “Novel” data– Remarkable geographic

heterogeneity– Longitudinal cognitive

assessments– Residential history– Medication inventory– Links to satellite data on

pollution weather– Direct measures of physical

activity– Childhood assessment of SES

• Examples of data not present– Some interview data– Anything requiring complex

equipment (carotid ultrasound, coronary calcium, etc)

Stroke Events in Major Studies

Study Sample Follow-up Stroke Events

ARIC (Ohira T, et al. Am J Epidemiol 2009;169:1437-1444)

≈ 12,096 black and white age 45-65 15 years 577

CHS (Wong ND, et al, Am J Hypertension 2010;23:161-167)

≈ 5000 age 65+ 15 years 437

NHANES (Giles, et al. Arch Intern Med 1995;155:1319-1324)

≈ 9,565 aged 45 – 74 12 years 660

Framingham (Salaycik KJ, et al. Stroke 2007;38:16-21)

≈ 5,800 8 years 144

REGARDS (Current) ≈ 30,000 age 45+ 5 years 842

REGARDS (funding through 7/17) ≈ 30,000 age 45+ 8 years ≈1,300

(projected)

Age Distribution of REGARDS Participants

45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84 85-89 90+0

1000

2000

3000

4000

5000

6000

7000

1526

2261

5664 58755479

4207

3032

1548

490101

591

5,171

14,857

Conclusions to REGARDS• Met goals:– National cohort with participants from 1,833 of the 3,033

counties in the US– Sample size of 30,239, sufficient to address “novel”

hypotheses (cost of $832/participant for first 5-year period)– Oversampling of blacks and “stroke belt” residents

• Advantages/Disadvantages– Advantages: well-positioned to provide (and are currently

doing so) insights to the causes of disparities– Powered to use clinical endpoints as the outcome– Disadvantages

• Trades tight clinic-based quality control for national sample• Cannot perform complex assessments

Central laboratory: University of Vermont; Drs. Mary Cushman, Neil Zakai, Nancy Jenny

ECG Reading Center: Wake Forest University; Drs. Prineas, Soliman

Migration Center: MUSC; Dr. Daniel Lackland

Operations Center: UAB; Drs. George Howard, Virginia Howard, Leslie McClure, Virginia Wadley, Suzanne Judd, Monika Safford, Ella Temple,

Neuroepidemiology Center: University of Cincinnati; Drs. Brett Kissela, Dawn Kleindorfer

Psychology Center: University of Indiana; Dr. Fredrick Unverzagt

Home Visit Center: Examination Medical Services, Inc.; Ms. Andra Graham

REGARDS Functional Units

Survey Design: University of Arkansas; Dr. LeaVonne Pulley

Funding Agency: NINDS: Dr. Claudia Moy

NIH/NINDS U01 NS41588