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Cigarette Yield and the Risk of Myocardial Infarction in Smokers
William H. Sauer, MD;
Jesse A. Berlin, ScD;
Brian L. Strom, MD, MPH;
Carolyn Miles, MPH;
Jeffrey L. Carson, MD;
Stephen E. Kimmel, MD, MSCE
Arch Intern Med. 2002;162:300-306.
ABSTRACT
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Background Although cigarette smoking is a major risk factor for acute myocardial
infarction (MI), cigarette tar yield has not been clearly demonstrated to
affect MI risk.
Methods A case-control study of first MI in smokers aged 30 through 65 years
was conducted among 68 hospitals in an 8-county area during a 28-month period.
Case subjects were smokers hospitalized at any of the area hospitals with
a first MI. Approximately 4 community control smokers per case subject were
randomly selected from the same geographic area using random digit dialing.
Detailed data on smoking history and cigarette brand were collected.
Results We identified 587 case subjects and 2685 controls who smoked cigarettes
with known tar yields. After adjustment using multivariable logistic regression,
the odds ratios (ORs) for subjects smoking medium- and high compared with
low-tar-yield cigarettes were 1.86 (95% confidence interval [CI], 1.21-2.87)
and 2.21 (95% CI, 1.47-3.34), respectively. The adjusted OR increased as tar
per day intake increased (P<.001 for the trend);
compared with the lowest category of tar per day, the ORs (95% CIs) for increasing
tar per day were 1.16 (0.83-1.62), 1.85 (1.35-2.52), 2.42 (1.54-3.78), and
2.50 (1.78-3.52). There was a similar trend of increasing ORs as tar per day
increased in smokers of lower-yield cigarettes (P<.001
for the trend) and when low-yield cigarette smokers were excluded (P<.001 for the trend).
Conclusions Smoking higher-yield cigarettes is associated with an increased risk
of MI, and there is a dose-response relationship between total tar consumption
per day and MI.
INTRODUCTION
SINCE THE FIRST observational study linking tobacco and heart disease
in 1940,1 numerous studies have confirmed the
association between cigarette smoking and an increased risk of myocardial
infarction (MI).2-4
Smoking cessation dramatically decreases this risk to the level of nonsmokers
within 3 years.5-6 Despite these
compelling data and great efforts by public health officials to educate smokers,
an estimated 1.1 billion people worldwide continue to smoke.7
Regulatory efforts to limit the tar and nicotine content of cigarettes have
been proposed in many countries,8 based primarily
on the effects of higher cigarette yield on the risk of malignancy9-12 and
mortality from some smoking-related diseases.13-14
Several countries have recently adopted or proposed legislation that limits
the amount of tar and nicotine in cigarettes,8, 15-16
and the European Union, which already has a 12-mg tar limit, recently passed
legislation that will reduce the upper limit of tar to 10 mg.17
Similar regulations have not been enacted in the United States.
While reductions in cigarette yield (eg, reductions in nicotine, carbon
monoxide, and tar) may reduce the risk of some smoking-related malignancies,9-14
the effects on MI are unknown. Because the absolute increase in risk of MI
from cigarette smoking is greater than that for lung cancer,2
a better understanding of the effects of cigarette yield on MI risk is critically
important to worldwide regulatory efforts. Although prior investigations have
failed to identify a clear difference in MI risk by cigarette yield,12, 18-21
most of these studies were performed more than a decade ago, before low-tar
cigarettes became popular, and therefore may have had limited ability to detect
an effect of higher vs lower-yield cigarettes.18-20
The only study to suggest an increase in the occurrence of nonfatal MI from
higher tar-yield cigarettes did not specifically include data for smokers
of the lowest-yield cigarettes.21 Therefore,
the specific aims of this case-control study were to determine if smoking
higher-yield cigarettes is associated with an increased risk of first nonfatal
MI and to examine the contribution of tar yield to MI risk.
SUBJECTS AND METHODS
SOURCE POPULATION
We performed a case-control study of MI in smokers from an 8-county
region of eastern Pennsylvania (Philadelphia, Montgomery, Bucks, Chester,
Delaware, Camden, Gloucester, and Burlington counties). The primary objective
of the study was to examine the effect of nicotine patch exposure and the
risk of MI in smokers.22 This study also collected
detailed information on smoking habits and therefore permitted a secondary
post hoc evaluation of the role of tar yield in MI.
IDENTIFICATION AND DEFINITION OF CASE SUBJECTS
Case subjects were between the ages of 30 and 65 years with a first
MI who were hospitalized at any of the 68 acute care hospitals in the 8-county
region from September 1995 through December 31, 1997. To maximize the completeness
of case subject identification, hospital-specific systems of case subject
ascertainment were developed, and the person responsible for case subject
ascertainment at each hospital was contacted on at least a monthly basis.
Acute MI was defined using the criteria from the Minnesota Heart Survey.23 Of potentially eligible subjects (N = 778), 84% had
their medical records reviewed for confirmation of their MI, and 85% had MIs
that met the study criteria. Given this high rate of confirmation, the 140
eligible subjects for whom charts were not available are included in the primary
analyses; a separate analysis excluded these subjects.
Subjects were excluded if (1) they were not current smokers (defined
as abstinence from cigarettes for at least 1 week prior to their MI); (2)
they had the MI as a complication of a hospitalization for a different condition
(eg, postoperatively); (3) they had a prior MI; (4) they were pregnant or
currently nursing (an exclusion criterion used in the primary data set22); (5) they did not have telephones or did not speak
English; (6) they did not live in 1 of the 8 counties; or (7) the brand of
cigarette smoked by the participant was not tested by the Federal Trade Commission.
The participation rate among eligible case subjects was 68%; among all
potential case subjects (known eligible and potentially eligible), participation
was estimated to be 61%.24 The charts of 349
nonparticipant eligible case subjects (79% of the known eligible nonparticipants)
were reviewed to collect basic demographic information (age, sex, and insurance).
The only difference between participants and nonparticipants was insurance
status (P<.01), with nonparticipants more likely
to be receiving medical assistance (13.1% vs 5.1%).22
IDENTIFICATION AND SELECTION OF CONTROLS
Approximately 4 community control subjects were selected for each case
subject using a modification of the Waksberg random digit dialing method.25 Each randomly derived telephone number was dialed
up to 9 times (3 attempts each during the day, evening, and weekend) to maximize
participation and avoid the bias of using daytime only calls. Any household
with a subject who refused to participate received up to 2 follow-up "conversion"
telephone calls. If there was more than 1 eligible person living in a single
household, one was chosen at random. Controls were between the ages of 30
and 65 years and were subject to the same exclusion criteria as case subjects.
The participation rate among known eligible controls was 51%. A study
was performed to estimate the use of nicotine patches (one marker of trying
to quit smoking) among nonparticipants. Of the 214 subjects who refused to
participate, 85 agreed to answer 2 questions, which were not specified until
the subject agreed, about patch use. Two (2.4%; 95% confidence interval [CI],
0.3%-8.2%) of the 85 had used a nicotine patch within the prior week compared
with 1.0% (95% CI, 0.7%-1.4%) of participant controls.22
DATA COLLECTION
Exposure and covariate data were collected using a structured telephone
interview for both case subjects and controls. The study hypothesis was not
revealed to subjects at any time. To maximize the validity of exposure information,
case subjects were interviewed only if they could be reached within 6 months
of their MI. Controls were also interviewed only within 6 months of being
identified to prevent the potential selection bias that could result if subjects
who could not be reached within this time frame differed from those who could.
Detailed information was obtained regarding tobacco use (including most recent
brand of cigarette smoked, frequency and duration of smoking, and prior attempts
to quit) and other clinical and demographic characteristics. All data were
collected relative to the index date: the date of MI for case subjects and
the date of the telephone interview for controls.
CIGARETTE YIELD CLASSIFICATION
Tar yield was used as a measure of cigarette yield because (1) it is
directly proportional to the amount of nicotine, carbon monoxide, and other
potentially toxic substances produced by a cigarette; (2) it is the measure
being used for regulatory limitations in many countries8, 15-17;
and (3) it is the basis for the labeling of cigarettes as ultralight, light, or regular. The tar yield of each brand of cigarettes smoked by patients was
determined from data published by the Federal Trade Commission.26
From these data, 3 categories of cigarettes (low tar, medium tar, and high
tar) were derived, which correspond to ultralight ( 6 mg of tar), light
(7-12 mg), and regular (>12 mg). A measure of tar consumed per day also was
calculated for each participant by multiplying the tar yield of the cigarette
smoked by the quantity of cigarettes smoked per day during the week prior
to the index date. Quintiles were created to ensure an equal number of control
group participants in each category.
STATISTICAL ANALYSIS
The odds ratio (OR) was used to estimate the relative risk of MI from
smoking higher-yield cigarettes vs lowest-yield cigarettes. Multivariable
logistic regression analysis was performed to control for possible confounding.
The method of Hosmer and Lemeshow27 demonstrated
good fit for all models (P>.05). The multivariable
model included variables that are known risk factors for MI and any potential
confounding variable that changed the crude OR by more than 10% after adjustment.28 These covariates included sociodemographic and lifestyle
traits (age, sex, race, any degree of exercise within the past year, vitamin
use, education, years smoking, and number of cigarettes smoked per day during
the index week) and clinical characteristics (body mass index; history of
coronary disease, hypertension, diabetes mellitus, or hypercholesterolemia;
and any family history of coronary disease). Other potential confounding variables
tested (total household income, marital status, caffeine and alcohol consumption,
type of insurance, prior attempts to quit, use of any nicotine replacement
therapy, aspirin or  -blocker use, patient concerns for MI, and a validated
physical activity score29) did not significantly
affect any of the tar-yield ORs and were therefore not included.
Dose-response relationships were tested by including the tar variables,
both as continuous and categorical variables, in multivariable models. Additional
quadratic terms were included to test for nonlinearity. Separate analyses
using nicotine or carbon monoxide instead of tar as a marker for cigarette
yield produced similar results. Analyses including any subject who smoked
within the last year, using the lifetime average smoking frequency as a covariate
and excluding the 140 case subjects with unverified MI, were performed with
no meaningful change in the results. In addition, interactions were tested
between each variable and tar yields; none was significant (P>.10). Statistical analyses were performed using the SPSS statistical
program (version 9.0, SPSS Inc, Chicago, Ill), and statistical significance
was defined as a 2-sided P value lower than .05.
RESULTS
CHARACTERISTICS OF STUDY PARTICIPANTS
A total of 609 eligible case subjects and 2739 eligible controls were
identified. Of these, 22 case subjects and 54 controls were excluded because
they smoked cigarettes with unknown tar yields. The characteristics of smokers
in the control group, listed by type of cigarette smoked, are given in Table 1.
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Table 1. Characteristics of Smokers in the Control Group*
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ASSOCIATION BETWEEN CIGARETTE TYPE AND MI
In the unadjusted analysis, smokers of medium- and high-yield cigarettes
had a higher OR for MI than low-yield smokers (Table 2). The confounding variables that increased the ORs after
adjustment were age, quantity smoked per day, and history of diabetes, coronary
artery disease, hypertension, or hypercholesterolemia. The confounders that
decreased the ORs after adjustment were vitamin use, education, and exercise.
After adjustment for all confounders, the ORs increased for smokers of medium
and high-yield cigarettes, and the associations remained significant (Table 2). When we controlled for all sociodemographic
and lifestyle factors, the ORs for smokers of medium- and high-yield cigarettes
increased relative to the unadjusted results: 1.61 (95% CI, 1.09-2.39) and
2.00 (95% CI, 1.38-2.91), respectively. When we controlled for all clinical
factors, the ORs also increased (medium-yield OR, 1.95; 95% CI, 1.29-2.95;
high-yield OR, 2.67; 95% CI, 1.81-3.92).
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Table 2. Association Between Cigarette Type and Myocardial Infarction
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DOSE-RESPONSE RELATIONSHIP BETWEEN TAR AND MI
Tar Yield per Cigarette
The association between tar yield per cigarette (using quintiles) and
MI is given in Table 3. Compared
with the lowest group (5 mg tar), the multivariable-adjusted ORs for each
of the categories of tar were sequentially higher (P<.001
for the trend). When tar yield was treated as a continuous variable, the estimated
risk for MI increased by 4% for each 1-mg increase in tar yield (adjusted
OR, 1.04; 95% CI, 1.01-1.06; P = .002). In addition
to this linear association, there was a nonlinear relationship for continuous
tar (adjusted for quadratic term, = -0.003; SE = 0.0015; P = .05).
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Table 3. Association Between Tar Yield per Cigarette and Myocardial
Infarction
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Tar Dose Per Day
There was a significant increase in ORs with increasing tar consumption
per day within each subgroup of cigarette type (Figure 1). This trend was also seen in the unadjusted analysis for
each individual subgroup of smokers (low, P = .002;
medium, P<.001; high, P<.001).
Multivariable adjustment did not alter the results for smokers of medium-tar
(P = .005) or high-tar (P
= .01) cigarettes; however, we could not fit reliable multivariable models
for smokers of low-tar cigarettes because of the small number of exposed individuals
in the higher-tar-per-day categories within this group.
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Multivariable odds ratios were adjusted for the same confounding
variables listed in Table 2, excluding
cigarettes smoked per day. The reference group comprises those subjects who
consume less than 150 mg of tar per day. P = .001
for the trend: P = .001 for all tar levels; P <.001 for the category excluding low-tar cigarettes; P<.001 for the category excluding high-tar cigarettes;
and P<.001 for high-tar cigarettes only. Brackets
indicate 95% confidence intervals (CIs). The asterisk indicates that the upper
limit of the 95% CI for the group consuming more than 450 mg of tar per day
in the category excluding high-tar cigarette smokers is 17.9.
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COMMENT
STUDY RESULTS
Using tar as a marker for cigarette yield, the results of this study
show that smoking higher-yield cigarettes is associated with an increase in
the odds of MI. In addition, increasing amounts of tar inhaled per day was
associated with increased risk. Although a dose-response relationship between
smoking and risk for MI has been clearly demonstrated in previous studies,2, 4 this relationship was only explained
in terms of the number of cigarettes smoked per day. The results of our study
demonstrate that, among people smoking the same number of cigarettes per day,
tar yield is an independent risk factor for MI, and that people who consume
more tar, regardless of cigarette type, have an increased risk for MI.
PRIOR INVESTIGATIONS OF TAR YIELD AND MI
There are relatively few, and somewhat contradictory, epidemiological
data on the association between tar and MI. One large case-control study conducted
in England concluded that smokers of medium-tar cigarettes ( 10 mg, the
highest tar yield studied) had modestly increased (10%) odds for MI when compared
with smokers of low-tar cigarettes (10 mg).21
Our study demonstrates not only a greater increase in risk from greater than
10-mg tar yield, but also an increased risk from even a 6- to 10-mg tar yield.
The lesser OR in the previous study21 may have
resulted from the selection of controls who were relatives of the case subjects.
These controls may have been more likely to smoke similar tar-yield cigarettes
because of their relationship, potentially biasing the results toward the
null. In addition, because this study,21 as
well as other European studies,12, 18
did not specifically include data on the lowest-yield cigarettes (<6 mg),
the effects of lower-yield cigarettes could not be studied.
Two prior studies examining yield of American cigarettes did not demonstrate
an association between increasing yields of nicotine or carbon monoxide and
MI19-20 relative to nonsmokers.
Although tar was not specifically studied, the results for tar yield would
be expected to be related to nicotine and carbon monoxide, as the tar yield
is proportional to these compounds in all cigarettes. The apparent discrepancy
with our study could be explained by the use of hospital-based controls and
of nonsmokers as the reference group in the prior studies.19-20
Hospitalized patients may not accurately reflect the general population from
which our case subjects were drawn, and the comparison with those who have
never smoked may have diminished any relative dose-response relationship that
cigarette yield has among smokers. In addition, these studies were conducted
more than 10 years ago, when low-tar cigarettes were only beginning to gain
popularity. Most of the smokers of the lowest-yield cigarettes were likely
to have recently switched to these brands, perhaps preventing a clear distinction
from smokers of higher-yield cigarettes.
POTENTIAL LIMITATIONS
The potential limitations of observational research and secondary post
hoc analyses must be considered in interpreting the results of this study.
Because this study only included patients with nonfatal MI, we cannot draw
conclusions regarding fatal MIs. A false association could be created if smokers
of lower-yield cigarettes were more likely to develop silent MI or sudden
death after an MI. However, there are no data to suggest that lower-yield
cigarettes would increase the likelihood of developing either of these clinical
outcomes, and thus this potential bias is unlikely.
A low participation rate could have created a false association if nonparticipant
controls were more likely to smoke higher-yield cigarettes, or if nonparticipant
case subjects were more likely to smoke lower-yield cigarettes, than participants.
Although response bias is difficult to assess, the information that was obtained
from nonparticipant controls and case subjects suggests that this bias is
unlikely. Because the prevalence of nicotine patch use among nonparticipant
controls seemed to be higher than that of participants (although this could
be a chance finding), nonparticipant controls may have been more likely to
attempt to quit, a characteristic associated with smokers of lower-yield cigarettes
in our data. In addition, although insurance status of nonparticipant controls
is unknown, nonparticipant case subjects were more likely to be receiving
medical assistance, a characteristic that was strongly associated with smoking
higher-yield cigarettes in our study. These characteristics of nonparticipants
would falsely diminish an association between tar yield and MI.
Uncontrolled confounding (eg, lifestyle factors and depression) is another
potential limitation of our study. It has been postulated that the low-yield-cigarette
smoking population is likely to choose this type of cigarette as a way to
minimize the damaging health consequences of smoking30
and that the marketing of low-tar cigarettes targets more educated and health-conscious
smokers.31 However, adjustment for numerous
markers of low-risk individuals (eg, vitamin use, education, exercise) did
not alter the study results. In addition, low-yield smokers may have been
at higher, rather than lower, risk because they tended to have more traditional
risk factors for MI. In fact, adjustment for all measured potential confounders
increased, rather than decreased, the ORs for smokers of medium-tar and high-tar
cigarettes. In addition, several subanalyses, including those that excluded
smokers of low- and medium-yield cigarettes, continued to demonstrate a clear
association between increasing tar and MI. Therefore, uncontrolled confounding
is unlikely to have explained the study results.
The inability to accurately measure the amount of tar exposure of an
individual smoker could have affected our results in several ways. First,
we only collected information on the most recent brand of cigarette. However,
we believe it is more likely that high-yield smokers would have switched to
low-yield brands, diminishing the association between tar and MI. Second,
individual smoking behavior can alter the delivery of the proposed dose of
tar,32 especially among those who switch to
lower-yield cigarettes but titrate the amount of tar delivered through "vent-blocking"
and other smoking behavior modifications.33-35
This may be of greater importance right after switching than with longer-term
use.35-39
Regardless, individual smoking behavior that increased actual exposure to
tar in the lower-yield groups, relative to what was predicted, would have
biased our results toward the null. Finally, smokers may have changed their
quantity of smoking in the index week; however, calculations of tar per day
using lifetime averages did not alter the results.
CONCLUSIONS
This study demonstrated a significant association between smoking higher-yield
cigarettes and first nonfatal MI, independent of the quantity of cigarettes
smoked, and a consistent dose-response relationship between tar intake per
day and MI, regardless of the type of cigarette smoked. Tar yields above 10
mg per cigarette, and even above 6 mg, were associated with a significant
increase in MI. Therefore, legislation aimed at reducing the amount of tar
in cigarettes could have additional benefits, above and beyond reducing smoking-related
cancers and other morbidities. Of course, smoking cessation should remain
the goal of all smokers, as it is the only way to abolish the increased risk
of MI from smoking,5-6 even among
smokers of low-yield cigarettes.19-20
AUTHOR INFORMATION
Accepted for publication April 14, 2001.
This study was supported by grants from Aventis Pharmaceuticals (formerly
Hoechst Marion Roussel Inc), Parsippany, NJ; Novartis Consumer Health, Summit,
NJ; and McNeil Consumer Products Co, Fort Washington, Pa.
| Study Participants
Advisory Board Members
Robert Wallace, MD, chair: The University of
Iowa, Iowa City. Neal L. Benowitz, MD: University
of California, San Francisco. Michael Criqui, MD, MPH:
University of California San Diego School of Medicine, La Jolla. Paul D. Stolley, MD, MPH: University of Maryland School of Medicine,
Baltimore. Stephen Walter, PhD: McMaster University
Health Sciences Center, Hamilton, Ontario.
Participating Hospitals and Sponsors
Abington Memorial Hospital, Abington, Pa: James
Robertson, MD. Albert Einstein Medical Center, Philadelphia,
Pa: Morris N. Kotler, MD. Allegheny University Hospitals,
Bucks County, Warminster, Pa: David Waldstein, MD. Allegheny University Hospitals, City Ave, Philadelphia, Pa: Albert
F. D'Alonzo, DO. Allegheny University Hospitals, Elkins
Park, Jenkintown, Pa: Gilbert Grossman, MD. Allegheny
University Hospitals, Graduate, Philadelphia, Pa: Robert Lester, MD. Allegheny University Hospitals, Hahnemann, Philadelphia:
William G. Kussmaul, MD. Allegheny University Hospitals,
Medical College of Pennsylvania, Philadelphia: Steven Meister, MD. Allegheny University Hospitals, Parkview, Philadelphia:
David Masiak, DO. Brandywine Hospital, Thorndale, Pa:
Arthur B. Hodess, MD. Bryn Mawr Hospital, Bryn Mawr, Pa: Jack Martin, MD. Chester County Hospital, West Chester,
Pa: Azam Husain, MD. Chestnut Hill Hospital, Philadelphia: Raymond Rodriguez, MD. Cooper Hospital–University
Medical Center, Camden, NJ: William H. Matthai, Jr, MD. Crozer Chester Medical Center, Upland, Pa: R. David Mishalove, MD. Deborah Heart and Lung Center, Browns Mills, NJ: Charles
Dennis, MD. Delaware County Memorial Hospital, Drexel Hill,
Pa: William Beckwith, MD. Delaware Valley Medical
Center, Langhorne, Pa: Morris I. Rossman, DO. Doylestown
Hospital, Doylestown, Pa: James J. Kmetzo, MD. Episcopal
Heart Institute, Philadelphia: Nirmal De, MD. Frankford
Hospital,Torresdale and Frankford Campus, Philadelphia: Robert Krause,
MD. Germantown Hospital and Medical Center, Philadelphia: Frank S. James, MD. Grand View Hospital, Sellersville,
Pa: Paul Hermany, MD. Holy Redeemer Hospital, Meadowbrook,
Pa: William Haaz, MD. Hospital of the University
of Pennsylvania, Philadelphia: Evan Loh, MD. Jeanes
Hospital, Philadelphia: Richard A. Narvaez, MD. John F. Kennedy Memorial Hospital, Jenkintown, Pa: Gilbert Grossman,
MD. Kennedy Memorial Hospital–Stratford, Voorhees,
NJ: Louis Papa, DO. Kennedy Memorial Hospital–Washington
Township, Sewell, NJ: Mario Maiese, DO. Kennedy Memorial
Hospital–Cherry Hill, Cherry Hill, NJ: Norman P. Silvers, MD. Lankenau Hospital, Wynnewood, Pa: Peter R. Kowey, MD. Lower Bucks Hospital, Langhorne, Pa: Jonathan Gold, MD. Memorial Hospital of Burlington County, Mt Laurel, NJ:
Steven Lederman, MD. Mercy Catholic Medical Centers Fitzgerald/Misericordia,
Philadelphia: Clifford E. Schott, Jr, MD. Mercy Haverford,
Broomall, Pa: Julian L. Gladstone, MD. Methodist
Hospital, Philadelphia: David Elbaum, DO. Montgomery
Hospital, Norristown, Pa: Edward Buonocore, MD. Nazareth Hospital, Philadelphia: Richard Vassallo, MD. Neumann Medical Center, Philadelphia: Nirmal De, MD. North Penn Hospital, Lansdale, Pa: Joseph Kraynak, MD. North Philadelphia Health System–St Joseph's Hospital, Philadelphia: David Knox, MD. Northeastern Hospital, Philadelphia: Donald L. Kahn, MD. Our Lady of Lourdes Medical
Center, Voorhees, NJ: Donald W. Orth, MD. Paoli Memorial
Hospital, Paoli, Pa: Elliot M. Gerber, MD. Pennsylvania
Hospital, Philadelphia: John U. Doherty, MD. Phoenixville
Hospital, Phoenixville, Pa: Kathleen E. Magness, MD. Pottstown Memorial Medical Center, Pottstown, Pa: Joseph Krantzler,
MD. Presbyterian Medical Center of Philadelphia, Philadelphia: Stephen E. Kimmel, MD. Quakertown Community Hospital,
Quakertown, Pa: Ric Baxter, MD. Rancocas Hospital,
Willingboro, NJ: Ivan Rudolph, MD. Riddle Memorial
Hospital, Media, Pa: Vsevolod Kohutiak, MD. Roxborough
Memorial Hospital, Philadelphia: Michael DeAngelis, MD. Southern Chester County Medical Center, West Grove, Pa: David Callahan,
DO. Springfield Hospital, Springfield, Pa: Dominic
Pisano, DO. St Agnes Medical Center, Philadelphia:
Pasquale Procacci, MD. St Mary's Medical Center, Levittown,
Pa: Rajnikant Shah, MD. Suburban General Hospital,
Norristown, Pa: John Fornace, DO. Taylor Hospital,
Ridley Park, Pa: Roger Weiner, MD. Temple University
Hospital, Philadelphia: David Wiener, MD. Thomas
Jefferson University Hospital, Philadelphia: Perry Weinstock, MD. Underwood–Memorial Hospital, Woodbury, NJ: John S.
Owens, MD. Veterans Affairs Medical Center, Philadelphia: Lawrence H. Frame, MD. West Jersey Hospital–Berlin,
Berlin, NJ: Richard Perlman, MD, PhD. West Jersey
Hospital–Camden, Camden, NJ: Richard Perlman, MD, PhD. West Jersey Hospital–Marlton, Marlton, NJ: Richard
Perlman, MD, PhD. West Jersey Hospital–Voorhees, Voorhees,
NJ: Richard Perlman, MD, PhD.
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Corresponding author: Stephen E. Kimmel, MD, MSCE, University of
Pennsylvania School of Medicine, Center for Clinical Epidemiology and Biostatistics,
717 Blockley Hall, 423 Guardian Dr, Philadelphia, PA 19104-6021 (e-mail: skimmel{at}cceb.med.upenn.edu).
From the Department of Medicine, Cardiovascular Division (Drs Sauer
and Kimmel), Division of General Internal Medicine (Dr Strom), and the Center
for Clinical Epidemiology and Biostatistics and Department of Biostatistics
and Epidemiology (Drs Berlin, Strom, and Kimmel and Ms Miles), University
of Pennsylvania School of Medicine, Philadelphia; Department of Medicine,
Division of General Internal Medicine, University of Medicine and Dentistry
of New Jersey, New Brunswick (Dr Carson).
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