EPIDEMIOLOGY 377
COHORT ANALYSIS OF MORTALITYA similar breakdown of age-specifi c mortality rates can be
made, in order to reveal different patterns of relationship to
the passage of time. Figure 1, for instance, shows mortality
rates by sex and age in a single calendar year—the age in
which death took place. Mortality rates are given for 5-year
age groups, which is the usual practice, so that if a similar
curve were to be drawn on the same graph for the calendar
year 5 years earlier, you could join together the point rep-
resenting, say, the age group 60–64 on the original curve
to the point for 55–59 5 years earlier. This line would then
represent a short segment of the cohort age-specifi c mortal-
ity curve born in the period 60–64 years before the date of
the fi rst curve. By repeating the process, it is clearly pos-
sible to extend the cohort curves spaced 5 years apart in
their birth years. Figure 6 shows how the cohort mortality
makes clear the rising impact of cigarette smoking in the
causation of lung cancer, since successive later-born cohorts
show increases in the rates, until those of 1916 and 1926,
which begin to show diminishing rates. The cohort method
is thus of particular relevance where there have been secular
changes similar to that of cigarette smoking.MEASUREMENT OF SICKNESS (MORBIDITY)If, instead of death, you look for ways of measuring sickness
in the population, once again you are confronted by several
major differences in both interpretation and presentation. In
the fi rst place, illness has a duration in a sense that is absentfrom death. Secondly, the same illness can repeat in the same
individual, either in a chronic form or by recurrence after
complete remission or cure. And thirdly, there are grades of
illness or of its severity, which at one extremity may make its
recognition by sign or symptom almost impossible without
the occurrence of the individual. The tolerance of pain or dis-
ability, or their threshold, differ widely between people, and
therefore complicate its measurement. In the case of absence
from work, where a certifi cate specifying a cause may (or
may not) be required, various measures have been used.
A single period of absence is known as a “spell,” and thus
the number of spells per employee in a year, for instance,
can be quoted, as well as the mean length of spell, again per
employee, or perhaps more usefully, by diagnosis. Inception
rate, being the proportion of new absences in a given period
(1 year, or perhaps less) is another measure, which again
would be broken down into diagnostic groups. Prevalence is
yet another measure, intended to quantify the proportion of
work by sickness (perhaps by separate diagnostic groups) at
a particular time. This may be, for instance, on one particular
day, when it is known as “point prevalence,” or in a certain
length of time (e.g., 1 month), which is known as “period
prevalence.” Most prevalence rates are given for a year, and
the defi nition often referred to is the number of cases that
exist within that time frame. On the other hand, incidence is
the number of cases that arose in the time period of interest,
again usually a year. When sickness-absence certifi cates are
collected for the purpose of paying sickness benefi ts, they
have been analyzed to present rates and measures such as
those discussed here, often against a time base, which can
show the effect of epidemics or extremes of weather—or
may indicate the occurrence of popular sports events! But
such tabulations are either prepared for restricted circulation
only, or if published are accompanied by a number of cave-
ats concerning their too-literal interpretation.
Incidence and prevalence rates are related to each other,
and it is not unusual to have both reported in a single study
(Mayeux et al., 1995). An example of prevalence and inci-
dence for Parkinson’s disease for the total population and
different ethnic groups is shown in Tables 2 and 3. For
prevalence, the study identifi ed 228 cases of the diseases
(Parkinson’s) for the time period 1988–1989, with the fi nal
date of inclusion being December 31, 1989. Not included
in the table is the mean age of cases (prevalence) (73.7
years, standard deviation 9.8) for patients having ages 40 to
96 years. Mayeux also reported that the mean age of occur-
rence (symptoms) was 65.7 (standard deviation 11.3), with
differing ages for men (64.6, standard deviation 12.7) and
women (67.4, standard deviation 10.6), with these differ-
ences having a p value of 0.06, or 6%. It should be noted
that if a statistical signifi cance of 5% is used for establish-
ing a difference, the age difference in years between men
and women when symptoms of Parkinson’s disease were
fi rst observed (occurrence or onset of diseases), thus, is not
different. However, this raises an important issue that using
a cutoff value, say 5%, does not provide a defi nitive deter-
mination for evaluating data, in this case the importance of35 40 45 50 55 60 65 70 75 80 85
Age0100200300400500600700Mortality rate per 100,0001926(^19161886)
1911
1891
1901
FIGURE 6 Lung-cancer incidence in birth cohorts.
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