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Frederick L. Brancati, MD, MHS Professor of Medicine & Epidemiology Director, Division of General Internal Medicine Osler Journal Club 2006 Visit Hopkins.

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Presentation on theme: "Frederick L. Brancati, MD, MHS Professor of Medicine & Epidemiology Director, Division of General Internal Medicine Osler Journal Club 2006 Visit Hopkins."— Presentation transcript:

1 Frederick L. Brancati, MD, MHS Professor of Medicine & Epidemiology Director, Division of General Internal Medicine Osler Journal Club 2006 Visit Hopkins GIM at Prospective Cohort Studies


3 Background Physical activity  lower CVD risk DHHS recommends life-long pursuits Sports differ in sustainability CVD benefits of individual sports uncertain

4 The Johns Hopkins Precursors Study Over 1300 students (mainly white men) from the JHUSOM Classes of 1948-64. Baseline data collected in person in medical school. Follow- up data collected by yearly mailed questionnaires thereafter.

5 Caroline Thomas, MD The Johns Hopkins Precursors Study

6 Hypothesis: Tennis ability in youth predicts lower CVD risk in middle age Design: Prospective cohort study Setting: Johns Hopkins Precursors Study Participants: 1019 male medical students Data Collection: Extensive interview and physical assessment at baseline (early 20s); annual mailed follow-up questionnaires Outcome: Incident CVD, including MI, CHD, CABG or PTCA, hypertensive heart disease, heart failure, & cerebrovascular disease Analysis: Kaplan-Meier, Cox models Outline

7 Assessment of Sports Ability How would you rate your overall ability in tennis (golf, football, baseball, basketball) during and before medical school? –No ability –Poor or fair ability –Good or excellent ability No data on frequency, intensity, or subsequent participation

8 Results






14 Conclusions / Implications Self-described tennis ability in young adulthood predicts lower CVD risk in middle age Association of tennis to lower risk is –Graded (i.e. dose-response) –Independent of many possible confounders –Specific to tennis (as hypothesized) Suggests promotion of tennis as a means to reduce CVD risk

15 Strengths Prospective design Long-term follow-up Multiavariate analysis Blinded assessment of CVD

16 Weaknesses Observational studies can’t prove causality Residual confounding is likely Assessment of exposure was suboptimal –Ability, not activity –Single point, not repeated measures –Self-assessed, not objective Sample limits generalizability

17 Discussion Points What’s special about a cohort study? What are common obstacles? Can it be used for housestaff research? Can it ever be sufficient to change practice? How do cohort studies relate to outcomes research?

18 Taxonomy of Designs Randomized Controlled Trial Prospective Cohort Study Case-Control Study Cross-Sectional Study Other Designs –Quasi-Experimental –Ecologic –Case Report

19 The basic fighting unit was a cohort, composed of six centuries (480 men plus 6 centurions). The legion itself was composed of ten cohorts, and the first cohort had many extra men—the clerks, engineers, and other specialists who did not usually fight—and the senior centurion of the legion, the primipilus, or “number one javelin.”cohort

20 pro·spec·tive Pronunciation: pr&-'spek-tiv also 'prä-", prO-', prä-' Function: adjective Date: circa 1699 1 : relating to or effective in the future 2 a : likely to come about : EXPECTED b : likely to be or become EXPECTED

21 “Prospective” in Epidemiology Clearly defined cohort (group, sample) of persons at risk followed through time Data regarding exposures (risk factors, predictors) collected prior to data on outcomes (endpoints) Research-grade data collection methods used for purpose of testing hypothesis (?)

22 Diagram of Hypothetical 6-Year Cohort Study to Identify Risk Factors for Facial Acne in Teenagers 1000 12-year-olds without acne 500 18-year-olds without acne 900 15-year-olds without acne 50 with Acne 300 with Acne 5 moved 10 no answer 35 refused 10 moved 40 no answer 48 refused 2 deaths 350 incident cases of acne over 6 years 6-yr Follow-up Rate = 850/1000 = 85% Incidence Rate of Acne = 350/5475 PY = 63.9 per 1000 PY

23 Why Do A Cohort Study? Get incidence data Study a range of possible risk factors Establish temporal sequence Get representative data Prepare for randomized controlled trial Establish a research empire

24 Types of Cohorts Occupational (e.g. Asbestos workers) Convenience (e.g. Precursors, Nurses) Geographic (e.g. Framingham, ARIC) Disease or Procedure –Natural History (e.g. Syncope, Lupus) –Outcomes Research (e.g. Dialysis, Cataracts)

25 Sources of Cohort Data Clinic Visits –Laboratory Assays –Interview –Physical Examination –Imaging –Physiologic tests Home visits Mailed materials Telephone Interview Medical Records Administrative Data –Medicare –Medicaid –Managed Care –Veterans Admin Birth Records Death Certificates Specimen Bank

26 William Castelli, MD The Framingham Heart Study

27 Recently Published Studies from the Johns Hopkins Precursors Study Coronary Disease -Anger, Depression, Gout, -Sports Ability Type 2 Diabetes -Blood pressure, Adiposity Hypertension -Coffee Knee Osteoarthritis -Knee injury Depression -Insomnia OutcomeExposure

28 What Might Explain Observed Relationship of Tennis Ability to Heart Disease Risk? Tennis protects against heart disease Men who like to play tennis are different –Thinner –Healthier Lifestyles –Higher Socioeconomic Status Men who play tennis well are different –Taller, Thinner –Greater Cardiovascular Fitness Chance (type I error) – Needs confirmation

29 Plays Tennis Plays Tennis Well Sustained Activity Thru Midlife Lower adiposity, Greater Fitness Lower BP, Lower LDL, Higher HDL Lower Risk of CHD Hypothetical Causal Pathway Healthier Men Choose Tennis Healthier Men Play Tennis Well Potential Confounders

30 Grey Hair Higher Risk of CHD Hypothetical Causal Pathway Older Age Potential Confounders

31 Challenges in Cohort Studies Possibly long duration Possibly large sample size Need to recruit people “at risk” Drop outs, Deaths, Other losses Concern about residual confounding Multiple comparisons  Type I error

32 How to Exploit Cohort Design When Time is Short & Money is Scarce Analyze existing data from another study Piggy-back onto on-going study Choose hospital-based cohort Choose short-term outcome Consider administrative data Consider public-use data Consider non-concurrent design



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