* "Suppose
that a 45-year-old woman, advised by her physician to stop smoking,
to exercise regularly, and to get a mammogram, makes getting a mammogram
her priority because she believes that early cancer detection is most
likely to prevent her untimely death. As shown in Table
2 [Ed. Note - this table contains reference 26.,
which is by Paffenbarger RS Jr, Hyde RT, Wing AL, et al. "The association
of changes in physical-activity level and other lifestyle characteristics
with mortality among men." N Engl J Med.1993;328:538-545],
the patient faces a 1.8% probability of dying from breast cancer before
age 75 years. The chance that mammography will prevent her death during
that time is 0.5% (1 chance in 205), and the probability that other
screening tests will do so is even lower. Her life is much more likely
to be saved by primary prevention. Stopping smoking and becoming physically
active would reduce her 30-year risk of dying by 10.9% (1 in 9) and
6.1% (1 in 16), respectively. Lipid and blood pressure control would
offer similar benefits. Compared with these lifestyle changes, disease
treatments offer far less benefit."
Furthermore,
in these studies, the statistics are surrounded by wide confidence intervals,
and differ depending on the report. While most data come from randomized
trials, some are from observational studies. "The rates are incidence-based
but are used cross-sectionally. The RRR is applied equally to all persons
who die of the disease, although all deaths are not equally preventable.
The analysis for the 45-year-old woman presumes that interventions are
delivered and prevent death at the same rate for 30 years. Some rates
are for all-cause mortality, while others are for disease-specific deaths.
The model is binary, but health effects are continuous. Table
1 assumes, for example, that exercise prevents deaths only for sedentary
persons, yet all persons benefit to some degree from more intense activity.59"
Also, it
important to note that estimates assume "complete adherence to study
conditions, and the projections for primary prevention assume that the
population is 100% successful in changing behavior… . Projections based
on such optimistic assumptions give policy makers and individuals an
upper boundary of what is possible but are unrealistic unless bounded
by estimates using current compliance rates. Optimal trial conditions
(efficacy) misrepresent the real world (effectiveness), where variations
in clinician skills, the intensity and duration of interventions, patient
adherence, and local resources influence outcomes."
Woolf
SH The Need for Perspective in Evidence-Based Medicine JAMA.
1999;282(24):2358-2365