maintained cessation rates were significantly greater in the special intervention than in
the usual care group, to date the difference has not been large enough to provide
adequate statistical power to assess the effect of smoking cessation alone on differences
in morbidity and mortality between the intervention and control groups (Chapter 3).
However, MRFIT was designed as a multifactor trial and did not assess the impact of
smoking cessation alone. Because MRFIT results indicated the greatest difference in
smoking cessation between special intervention and usual care subjects compared with
any other clinical trial and still lacked the power to detect outcome differences from
smoking cessation. it is unlikely that smaller trials would have sufficient power to
demonstrate an effect of cessation on morbidity and mortality (Chapter 3) (US DHHS
198.3).

Compared with observational studies which place few demands directly on subjects.
the use of interventions for smoking cessation in clinical trials increases the probability
of misreporting smoking status at postintervention followup because of the expectations
of the participants and the investigators. Typical periodic followup in clinical trials.
however, reduces the chances of misclassification related to relapses or to delayed
action to quit smoking-phenomena that are often not adequately recorded in observa-
tional studies. Routine followup also allows for more accurate measurements of the
duration of prolonged or continuous abstinence and the opportunity to validate with
biochemical testing.

Intervention trials other than clinical trials also provide information on the health
consequences of smoking cessation. A number of studies are in progress involving
interventions of varying intensity within a community. The North Karelia project
conducted in Finland is such a community trial: a comprehensive, community-based
intervention program was conducted to reduce cardiovascular disease (CVD)
(Tuomilehto et al. 1986). Mortality rates in North Karelia were compared with those
in other areas of Finland.

Methodologic Issues

Introduction

Epidemiologic studies have been the principal source of information on the health
benefits of smoking cessation.  Although the resulting data have provided strong
evidence for the benefits of cessation, the data need to be interpreted with consideration
of potential sources of bias and of other methodologic issues. This Section considers
the methodologic issues potentially affecting interpretation of studies of the health
consequences of smoking ceshation. The criteria for causality have served as a basis
for evaluating all of the evidence relevant to a particular association (US PHS 1963:
US DHHS 1981. 1989). However. associations found in individual studies must also
be assessed carefully. In any epidemiologic or clinical study. association may result
by chance, as the result of bias. or through a causal mechanism. Thus. this Section
presents an overview of statistical considerations relevant to studies of smoking
cessation and the most prominent sources of bias in such studies-information bias and


confounding hia\. It also considers the potentially complex problem ofanal!~ing data
on the effects of smohing cessation.

Statistical Considerations

Statistical significance testing addresses the likelihood that an observed association
has occurred by chance if. in fact. exposure and disease are unassociated (the null
hypothesis). By convention. probability (p) L alues less than 0.05 are generally accepted
as "statistically significant"; that is. chance is considered an unlikely explanation for
the association. For example. if the p value is less than 0.05. the probability that chance
explains the association is less than 5 percent. Confidence intervals describe the range
of effects compatible with the data at some specified level of probability. for example
95 percent.

Some studies find associations that do not attain statistical significance. "Negative"
investigations must be interpreted in the context of an investigation's sample size: a
small sample size may not provide sufficient information to test associations in the
range of interest. Such small sample sizes often provide inadequate statistical power
to test for the anticipated effects of smoking cessation. and such studies are uninforma-
tive as a result. In interpreting associations not achieving statistical significance.
confidence limits describe the range of effect compatible with the data.

Bias

In an)' epidemiologic study. associations may be affected h> bias. Biases from
misclassification and from confounding need to he considered in interpreting the
findings of studies of the consequences of smoking cessation. This Section focuses on
the effects of these biases in studies of smohing cessation.

Categorizing the dynamic process ofsmohing cessation poses ;I substantial challenge
to epidemiologic researchers (Chapter 2. Part I ). hlorrovcr. subject<niq not accurateI>
report their o\\ n sniokin, 17 beha\,ior. and reliance on surrogate sources of information
on smohing. LIS ma\ bt~ nc:ccssar!. in casc`+control studIt`\. ma\ also introduce error.

The c~~scqucnces of misclassi~c~~tion in obser\ ation studies ha\,c recei\ 4 substall-
tialcon~ideratic,n in the rpidcmiolog~c litt'rature (Copeland et al. 1977: Greenland 19X0:
Fleiss 1% I: Klcinhaum. Kuppcr. I\lor~enstcrn 19X2: Schlc~sclman 19X7: Kothm;r~~
19X6). Misclassiticatiorl c;m oc`c~ir in classif! in; either e\pc)surc` or outcome. Onl!
exposure inisclllssific~ltic~il. that is smohing \t;ltus. will he considered in this Section
(Chapter 2, Part I ).

Miscl~!s\it`ic~ltiorl nl;~> be cla\sified ;I\ nondifferential (or random) or 215 differential:
both types of miscl~rssit'ic~ltion ;!I-e potentialI> relet ant to studies of \mohing cessation.
~0ndit`ferentiA misclasslfic~ition occurs r:uidonil\ In relation to disease or ourcome
status. \rhercas diffcrcntial iiiiscl3\sificati(,n al`fects exposure information in a pattern
that varies u ith outcome status.  For c\;unple, differential ini\classification \roulJ
occur in a case+control stud! of lung cancer if cast`s tended to minimize the extent of
past smohing in compari\on u ith the information 5 "ii en h\ controls: elderI\ cases and
                                          _


controls might introduce nondifferential misclassification from errors in recall of past
smoking.
The consequences of nondifferential and differential misclassification have been
addressed in the epidemiologic literature. Brass ( 1954) is credited with demonstrating
that random misclassification in a 2x2 contingency table diminishes an association that
exists between two variables: in general for such cross-classified data. nondifferential
misclassification of exposure biases toward the null value. indicating no effect of
eposure (Rothman 1986). For exposures classified into three or more levels. the
consequencs of nondifferential misclassification are not exclusively directed toward
reducing the degree of association. Differential misclassification may either strengthen
or weaken associations. depending on the direction of the bias in reporting exposure
(Kleinbaum, Kupper, Morgenstern 1982: Rothman 1986).
The information presented in prior sections of this Chapter describes the directions
that bias may take and allows some generalizations. First, some degree of nondifferen-
tial misclassification may affect studies of active smoking and of smohing cessation:
the extent of misclassification depends on the type of information collected. the choice
of respondents (index subject or surrogate). and the health and age of the respondents.
Second. because disease is present at the time of interview. nondifferential mis-
classification is particularly likely to affect exposure information collected in cross-
sectional studies and case<ontrol studies. but little empirical evidence is available.
Third, because of the dynamic nature of smohing cessation. some current and former
smokers will be misclassified in cohort studies and clinical trials unless smoking
behaviors are measured with sufficient frequency during followup.
For example. MRFIT data illustrate the potential for misclassification of current and
former smokers as smoking status changes over time if smoking status is not longi-
tudinally assessed (Ockene et al. 1990). The usual care group included 3.09 I smohers
at baseline with 13.7 percent reporting quitting by the first annual folloclup visit. Of
those first-year quitters. only about half or 6.3 percent of all usual care smokers
maintained abstinence for the entire &year followup period ("continued stoppers").
However in each year of followup. additional smohers quit ("new stoppers") at a
maximum rate of 7.5 percent between the first and second years. decreasing to the
lowest rate of 4.3, percent between the fifth and sixth years. Simultaneously. smokers
who quit and relapsed during the trial succeeded in quitting in subsequent followup
periods ("recycled stoppers"). Recycled stoppers increased from 5.3 percent of the
usual care baseline smokers in the third year to IS.3 percent at the end of the sixth year.
By the sixth year of the study. 25.X percent of the usual care group were classified as
former smokers: 6.3 percent stopped during the first year and maintained abstinence
for the remaining &year followup period: 15.3 percent stopped. relapsed. and stopped
again: and 4.2 percent stopped for the first time in the last year of followup. Although
the usual care group is not representative of adult malt smohers. these data illurtmte
the dynamics of smoking behavior and the potential for misclassification.
Incorrect categorization of some current smokers as former smokers and of some
former smokers as current smokers. if nondifferential. would tend to reduce the apparent
benefit of smoking cessation. as disease occurrence is reduced in the category of
apparent current smohers by the inclusion of former smokers and is increased in the


category of apparent former smokers by the inclusion of current smokers. Stratification
by the duration of abstinence may prov,ide some control of this type of misclassification,

The category of never smokers in an epidemiologic study may include some persons
who smoked in the past (Britten 1988: Persson and Norell 1%`)). In general. former
smokers who reported themselves as never smokers consumed fewer cigarettes than
those correctly categorizing themselves as former smokers. Nevertheless. the bias
resulting from the inclusion of some former smokers in the category of never smokers
would tend to reduce the apparent benefit of cessation when former smokers are
compared with never smokers.

The consequences of misclassification must be considered in the context of the
disease under investigation.  For example. in studying lung cancer and smoking
cessation, the failure of long-term former smokers to report a brief period of relapse has
little relevance. In contrast. unreported periods of relapse would be relevant in
assessing smoking cessation and occurrence of myocardial infarction or of respiratory
symptoms, conditions for which cessation has some short-term benefit.

Bias from confounding is also of concern in studies of the health consequences of
smoking cessation. Former smokers tend to differ from continuing smokers in the
earlier intensity of cigarette smoking and in other aspects of lifestyle that may determine
disease risk. Former smokers tend to have smoked fewer cigarettes per day and to have
started smoking at an older age than continuing smokers (Friedman et al. 1979 Garvey
et al. 1983; Myers et al. 1987; Volume Appendix). Thus. at any age. former smokers
have had less cumulative exposure to cigarette smoke. on average. than continuing
smokers. Failure to account appropriately for differences in cumulative exposure
between former smokers and continuing smokers may exaggerate the benefits of
cessation. Misclassification of smoking measures may limit the degree to which
confounding can be controlled (Greenland 1980: Rothman 1986).

Other differences between former smokers and current smokers may also influence
disease risk. Former smokers are more likely to be of higher socioeconomic status than
continuing smokers and tend to follow a healthier lifestyle than persistent smokers
(Chapter I I and Volume Appendix). Former smokers generally drink less alcohol and
less coffee. are more physically activ,e, and experience less stress. although their relative
body weight tends to be greater (Friedman et al. 1979: Kaprio and Ko\henvuo 1988:
Chapters IO and I I ). However. some persons may' stop smoking because a personal
combination of risk factors places them at increased risk for disease. In the British
Regional Heart Study. former smohers had higher blood pressure and total serum
cholesterol at entry than current or never smokers (Cook et al. 1986).

In fact, observed mortality rates for many diseases have been higher for former
smokers than current smokers during the first few years following cessation. Persons
with symptoms of incipient illness or with newly diagnosed illness may stop smoking
(Hammond and Garfinkel 1966). Consequently. mortality rates for former smokers
immediately following cessation may exceed those for current smokers.
In studies of the effect ofcessation on the course of established disease. consideration
must be given to the severity of the underlying disease in former smokers and persistent
smokers. For example. in a study of mortality following myocardial infarction, persons

54


who quit smoking were at greater risk for death than those who did not quit because of
more severe underlying disease (Vlietstra et al. 1986: Hermanson et al. 1988).

Analytic Issues in Observation Studies

Complex associations among disease risk. age. and duration of active smoking and
abstinence further complicate assessment of the health consequence\ of cessation.
Analytic approaches should represent these relationships in a biologically appropriate
fashion. The risks of many cigarette-related diseases (e.g.. cancer. CVD. and chronic
obstructive pulmonary disease) increase with age (Figure 2). Following cessation.
disease risk may change in diverse patterns. depending on the disease-specific
mechanisms through which cessation alters disease occurrence. Disease rish may be
unaltered (Curve A). decline quickly or slowly compared with that for never smokers
(Curve C). or decline to a level between that of nevjer and persistent smokers (Curve B)
(Figure 2). Comparing the disease risk for former smokers with the rish for persistent

FIGURE 2.-Hypothetical examples of disease incidence rates for current,
      former, and never smokers, by age


smokers describes the disease burden removed by cessation: whenever possible. this
Report provides this comparison. For many diseases. risks for former smokers do not
revert to those for never smokers. Relative rirhs for former smokers compared with
never smokers describe the persisting consequences of past active smoking.
Thus. in studies concerning the consequences of smoking cessation. the analytic focus
is on describing disease incidence after cessation in relation to either the incidence of
disease in never smohers or in smohers who do not stop smohing. Interest centers on
addressing several questions: In a population that started smoking at a given age.
smoked at the same rate. and then quit at a given age. how does the disease rate evo1v.e
as a function of time since quitting? In particular. how does the disease rate compare
with that of a population of lifelong nonsmokers of the same age or with that of a
population of smokers who continue to smoke at the same rate'? How does the disease
rate after cessation depend on such factors as duration of smoking. number of cigarettes
smoked daily, age at starting. or other factors? These analytic questions are generally
addressed by estimating either the attributable risk (the difference between the risks for
exposed and nonexposed) or the relative risk (the ratio of the risks in exposed and
nonexposed) and comparing former smokers with either never smokers or current
smokers.
A cohort study that observed subjects from birth to death could supply the data
requisite for meeting these analytic goals. Observations could be made concerning the
age at starting smoking. the amount smoked. the age at stopping smoking. the duration
of time since stopping smoking. and the occurrence of disease. Incidence rates could
be calculated and the attributable risk or relative risk considered as a function of time
since quitting. To assess the effects of such factors as duration or amount of smoking.
smoking cohorts with different durations and rates could be analyzed.
Typically, however. cohort studies enroll subjects at various ages. and the smoking
histories of the subjects span a broad range of ages at starting smoking. durations of
smoking. amounts of smoking. ages at stopping smohing. and ages at observation. In
analyzing data from a cohort study. stratification and multi\,ariate modeling are used
to describe the disease occurrence in former smohers in relation to the time interval
since cessation. New statistical methods have fdcilitated the analysis of longitudinal
data on cancer and other diseases (Breslow and Day 1987: Thomas 198X). The analytic
approach should pro\,ide control for the effect of changing disease risk Mith increasing
age: as duration of smoking abstinence increases. age and disease risk should be
compared with that of never or current smohers in the same age stratum.
HowevJer. some analytic approaches may introduce overadjustment for the timr-
related dimensions of smohing history and of age and obscure the benefits of cessation.
Age at starting smohing. age at observation. duration of smoking. and duration ot
abstinence are interdependent: specification of any three of these v,ariables fixes the
fourth. Assuming that current and former smokers of a given attained age started
smoking at about the same age. the duration of smoking among fomrer smokers must
be less than for current smohers. Thus. adjustment for duration of smoking in compar-
ing current and fonner smokers is incorrect. Methods that attempt to allow each ot
these four time-dependent factors to vary freely are inappropriate and provide biased
descriptions of the variation in risk folIoKing cessation (Brown and Chu lYX7).


Data from case-control studies can be used for the same analytic objectives. Infor-
mation on age at starting to smoke, duration of smoking. duration of abstinence. and
number of cigarettes smoked can be obtained retrospectively. Conventional analytic
methods enable calculation of odds ratios by time since quitting. which estimate the
ratios of incidence rates: the reference group for former smokers can be either never
smokers or current smokers.

Risk of disease for former smokers changes because exposure to active smoking
ceases: for some diseases, the exposure of interest in assessing the health consequences
of cessation is the subsequent tobacco exposure experienced by continuing users but
avoided by former smokers. Some analytic methods may not address adequately this
avoided exposure. For example, using variables for cumulative exposure combines the
additional exposure for the continuing smoker with the consumption to the point of
cessation for the abstinent smoker. If repair processes affect disease risk after cessation.
then the interval of abstinence is also a relevant exposure parameter. Thus. regardless
of the type of data analyzed, the method of analysi\ should properly represent the
underlying biologic process.

SUMMARY

Correct classification of smoking status is important to determine accurately the
effects of cessation. Smoking cessation is a dynamic process in which smokers progress
through a series of stages in an effort to quit smoking. These stages have been labeled
differently by various investigators. The model generating the most research refers to
the stages as precontemplation, contemplation. action, and maintenance and/or relapse.
Very few smokers progress through these stages linearly. because most smokers relapse
and recycle through the stages three or four times before attaining long-term main-
tenance.

Four common types of studies for assessing the health consequences of smoking
cessation are vulnerable to various sources of information bias leading to misclaasifica-
tion of smoking status. Cross-sectional surveys have a relatively low frequency of
misreporting: however. recall of duration of abstinence is vulnerable to error. A
case<ontrol study. because of its retrospective nature. is possibly more likely to have
misreporting of smoking status in diseased cases than in nondiseased controls. Cohort
studies are likely to have low rates of misreporting of initial smoking status but high
rates of misclassification due to changes in smoking status over time. Clinical trials
are likely to have high rates of misreporting for subjects receiving intensive clinical
interventions. However, such trials should have relatively little misclassification of
smoking status over time and provide more accurate assessment of duration of
abstinence when regular followups are maintained.

Misclassification of smokers as former smokers will have the effect of under-
estimating the benefits of smoking cessation when a true effect exists. The extent of
the bias is proportional to the degree of misclassification. Any specificity added to
measurement by validation mea\urej will diminish the r-ni~classificatiorl bias.


CONCLUSIONS

I. Most former smokers havte cycled several times through the process of smoking
cessation and relapse before attaining long-term abstinence. Any static measure of
smoking status is thus a simplification of a dynamic process.

2. In studies of the health effects of smoking cessation. persons classified as fomler
smokers may include some current smokers. Consequently. the health benefits ot
smoking cessation are likely to be underestimated.

3. In contexts other than intervention trials. \elf-reported smoking status at the time of
measurement and concurrent biochemical assessment are highly concordant. This
high concordance supports self-report as a valid measure of smoking status in
observational studies of the health effects of smoking cessation.


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69


        CHAPTER 3
SMOKING CESSATION AND OVERALL
  MORTALITY AND MORBIDITY


CONTENTS

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...75

Smoking Cessation and Overall Mortality in Cohort Studies . . . . . . . 75

Smoking Cessation and Overall Mortality in Intervention Studies .............. 8-I

Smoking Cessation and Medical Care Utilization ........................... X7

Population Projections  .............................................. X7
Observational Studies ............................................... X7

Smoking Cessation and Health Status .................................... X7

Conclusions  ........................................................ 92

Chapter3 Appendix  .................................................. 93

References . . . . . .._................................................. 90

73


INTRODUCTION

The overall risk of mortality among smokers has been discussed in several prior
reports of the Surgeon General (US PHS 1964. 1969; US DHEW 1979: US DHHS
I989 ). The 1989 Report estimated that approximately 390.000 Americans died in I985
from diseases attributable to smoking (US DHHS 1989). Another source (Mattson.
Pollack. Cullen 1987) estimated that 36 percent of heavy smokers aged 35 will die
before age 85. and 2X percent before age 75. from a disease caused by smoking. Prior
reports of the Surgeon General (L'S PHS 196X; US DHEW 1979: US DHHS 19X9) have
reviewed the association of smoking with overall morbidity. concluding that ov,erall
morbidity is increased among smokers.  Quantitative estimates of the amount of
morbidity attributable to smoking vary because of differences in the measures of
morbidity used.

Data from the aggregate of studies of overall mortality and morbidity among \mohers
and former smokers show that smoking causes increased risk of morbidity and mor-
tality. However. the temporal pattern of the reduced all-cause mortality after quitting
and the effects on mortality risk of quitting at variou\ ages have not been fully described.
In addition, questions about the benefits of smoking cessation for mortality have arisen
because of the results of studies involving interventions to promote smoking cessation.
The association of smoking with medical care utilization is a topic that has not been
addressed in detail in previous reports of the Surgeon General.

This Chapter reviews studies of overall mortality among former smokers, with
particular attention to the temporal pattern of decline in mortality after quitting and the
association of age at quitting with decline in mortality. Overall mortality in intervention
studies that include smoking cessation is discussed with attention to problems of
inferring the benefits of smoking cessation for the individual from these studies. Studie\
of medical care utilization by and health status of former smokers are described.

SMOKING CESSATION AND OVERALL MORTALITY
          IN COHORT STUDIES

Table I summarizes the results of major cohort studies comparing overall mortality
among never, current, and former smokers. The studies consistently showed a substan-
tially lower risk of mortality among former smokers in comparison with continuing
smokers. Compared with continuing smokers. former smokers had a progressive
decline in mortality risk as duration of abstinence increased. although risk in some
studies was increased for I to 3 years after cessation, almost certainly because some
people quit due to ill health (Chapter 2).

The durations of abstinence required for former smokers to reach the mortality risk
of never smokers differ among studies. The American Cancer Society (ACS) study of
I million American volunteers (Hammond 1966). also known as the 2S-State Study and
as the Cancer Prevention Study I (ACS CPS-I). found that after IO years, mortality rates
among former smokers of fewer than 20 cigarettes per day reached levels equivalent to
those of never smokers. Among former smokers of 30 cigarettes or more per day.

7s