Transcript Chapter 4 All-Cause Mortality

Chapter 4
All-Cause Mortality
“A N D I W I L L S T R I K E D O W N U P O N T H E E W I T H G R E AT V E N G E A N C E A N D F U R I O U S A N G E R
T H O S E W H O W O U L D AT T E M P T TO P O I S O N A N D D E S T R OY M Y B R OT H E R S . A N D Y O U W I L L
K N O W M Y N A M E I S T H E LO R D W H E N I L AY M Y V E N G E A N C E U P O N T H E E .”
EZEKIEL 25:17
The average life expectancy of a U.S. citizen is about 75 for a male & 80 for a female. This ranks
among the lowest among industrialized nations.
Physical inactivity is a burden to the U.S. causing an estimated 191,000 deaths a year from all
causes.
2 leading causes of death are: (1) CHD (2) stroke & other cerebrovascular diseases—these
account for 22% of all deaths worldwide and 26% of deaths in high-income countries.
Physical activity protects against premature death from all causes and against the development of
coronary heart disease and stroke.
 Physical inactivity (or low fitness levels) increase the risk of all-cause mortality.
Women tend to live longer than men: 75 for a male & 80 for a female (in the US).
Why? Sex hormones—Testosterone is linked with hazardous behavior & undesirable cholesterol
levels, while estrogen is an antioxidant (protecting against cell damage) and appears to regulate
enzymes that favorably effect cholesterol metabolism. Long life in women may also be linked to a
genetic advantage for childbirth and care of the young.
Living longer, does not, however, mean more years of good health.
The leading causes of death (mortality) are: (1) heart disease (2) cancer (3) cerebrovascular
disease. These account for more than half of all deaths (in US). Heart disease and cancer are 1 &
2 for both men and women. Men—the 3rd cause is accidents & for women it is cerebrovascular
disease.
There is strong evidence showing that PA or fitness is inversely associated with the risk of
developing each of these diseases.
Highly active men had a 22% lower risk of all-cause mortality compared with low-active men, while highly active
women had a 31% lower risk. Moderately active men and women had a 19% and 24% lower risks, respectively.
Early studies of PA and all-cause mortality tended to focus on occupational PA, later studies—in particular, those
conducted in the 1980’s and later—increasingly focused on leisure-time PA because PA in the workplace had
declined in Western developed nations during the late 1950’s to 1960’s as industry moved from manual to
mechanized labor.
Studies (of physical activity)--P-80-84. Primarily measured using self-reports (Subjectivity).
Physical Fitness & Physical Activity: Physical fitness is a concept different form but related to physical activity.
Physical fitness represents a physical condition while physical activity represents a behavior.
While physical fitness has a genetic component, regular PA can improve cardiorespiratory fitness in most people.
Studies (of physical fitness)—P-85-87. Tends to be measured objectively (treadmill, etc.).
ALL of these studies (so far) have been observational epidemiologic studies, not establishing cause and effect.
•Sedentary behavior, or sitting, may represent an independent risk factor for heart disease separate from
PA—AND being physically active all day is different than exercise!
•Physical fitness (PF) is a concept different from but related to physical activity (PA). PF represents a
physiologic condition; PA represents a behavior. (P-85) While PF has a genetic component, regular PA
can improve cardiorespiratory fitness in most people.
•PA usually measured via self-reports (subjective) while PF tends to be objectively measured
(treadmill).
•Heart disease is the leading cause of death in the USA and in most high-income countries.
•Sedentary behavior is a risk factor for all-cause mortality.
•Even among people active enough to meet PA recommendations, those spending more time sitting
were at increased risk of all-cause mortality compared with those sitting less.
•The Canada Fitness Survey showed that more sitting time is associated with higher mortality rates,
regardless of how much subjects exercised during their leisure time.
On average—investigations indicate a 31% reduction in all-cause mortality rates
among the most active compared with the least-active individuals.
Changes in Physical Activity or Fitness and All-Cause Mortality
Changing from low to high physical activity or fitness levels is associated with lower mortality
rates compared with remaining at low levels.
Conversely, changing from high to low levels is associated with mortality rates similar to those
associated with remaining at low levels—as with the following studies.
Preceding studies were Observational Epidemiologic Studies—studies of this design cannot
prove cause and effect—but they can provide info that strengthens the premise of a causal link. P87.
Harvard Alumni Health Study.
Study of Osteoporotic Fractures.
Aerobic Center Longitudinal Study.
Norwegian Men.
Are the Associations Real?
Chance? P-89.
Bias? [reverse causation]
Misclassification?
Strength of the Evidence. P-90.
Temporal Sequence.
Strength of Association.
Consistency of results.
Biological Plausibility.
Dose Response
Chance?
As a first step toward determining that the observed association is causal, we have to ensure that the observed
associations are valid and not the result of some other factor such as chance, bias, or confounding.
We usually accept that chance is unlikely if we obtain a p-value of <0.05 for the findings.
When you perform a hypothesis test in statistics, a p-value helps you determine the significance of your results.
Hypothesis tests are used to test the validity of a claim that is made about a population. This claim that’s on trial, in
essence, is called the null hypothesis.
The alternative hypothesis is the one you would believe if the null hypothesis is concluded to be untrue. The evidence in
the trial is your data and the statistics that go along with it. All hypothesis tests ultimately use a p-value to weigh the
strength of the evidence (what the data are telling you about the population). Thep-value is a number between 0 and 1
and interpreted in the following way:
A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis.
A large p-value (> 0.05) indicates weak evidence against the null hypothesis, so you fail to reject the null hypothesis.
p-values very close to the cutoff (0.05) are considered to be marginal (could go either way). Always report the p-value so
your readers can draw their own conclusions.
p-value example
For example, suppose a pizza place claims their delivery times are 30 minutes or less on average
but you think it’s more than that. You conduct a hypothesis test because you believe the null
hypothesis, Ho, that the mean delivery time is 30 minutes max, is incorrect. Your alternative
hypothesis (Ha) is that the mean time is greater than 30 minutes. You randomly sample some
delivery times and run the data through the hypothesis test, and your p-value turns out to be
0.001, which is much less than 0.05. In real terms, there is a probability of 0.001 that you will
mistakenly reject the pizza place’s claim that their delivery time is less than or equal to 30
minutes. Since typically we are willing to reject the null hypothesis when this probability is less
than 0.05, you conclude that the pizza place is wrong; their delivery times are in fact more than
30 minutes on average, and you want to know what they’re gonna do about it! (Of course, you
could be wrong by having sampled an unusually high number of late pizza deliveries just by
chance.)
Bias?
Selection bias is the selection of individuals, groups or data for analysis in such a way that
proper randomization is not achieved, thereby ensuring that the sample obtained is not
representative of the population intended to be analyzed. It is sometimes referred to as
the selection effect.
Another bias is reverse causation--Reverse causation (also called reverse causality) refers either to
a direction of cause-and-effect contrary to a common presumption or to a two-way causal
relationship in, as it were, a loop. Reverse causation can occur when people change their diet or
other lifestyle habit after developing a disease or perhaps after having a close family member
suffer an event like a heart attack.
Example--When lifelong smokers are told they have lung cancer or emphysema, many may then
quit smoking. This change of behavior after the disease develops can make it seem as if exsmokers are actually more likely to die of emphysema or lung cancer than current smokers.
Confounding?
Confounding occurs when the experimental controls do not allow the experimenter to reasonably
eliminate plausible alternative explanations for an observed relationship between independent and
dependent variables.
Consider this example. A drug manufacturer tests a new cold medicine with 200 volunteer subjects 100 men and 100 women. The men receive the drug, and the women do not. At the end of the test
period, the men report fewer colds.
This experiment implements no controls at all! As a result, many variables are confounded, and it is
impossible to say whether the drug was effective. For example, gender is confounded with drug use.
Perhaps, men are less vulnerable to the particular cold virus circulating during the experiment, and the
new medicine had no effect at all. Or perhaps the men experienced a placebo effect.
This experiment could be strengthened with a few controls. Women and men could be randomly
assigned to treatments. One treatment could receive a placebo, with blinding. Then, if the treatment
group (i.e., the group getting the medicine) had sufficiently fewer colds than the control group, it
would be reasonable to conclude that the medicine was effective in preventing colds.
Strength of Evidence?
While we conclude that the association of PA with lower mortality is real—is the association one
of cause and effect? Only well-designed and well-conducted randomized controlled trials can
provide data supportive of a causal relationship. Only observational data in the past.
Temporal Sequence?
If an association were causal, the exposure (PA or PF)—would have to precede the outcome—
mortality.
Strength of Association?
The degree of relationship between a causal factor and the occurrence of a disease, usually expres
sed in terms of a relative risk ratio.
Strength of association is the magnitude or size of a relative risk (RR). An RR of 1.0 means that
there is no statistical association between EXPOSURE and DISEASE (case-control studies and
cohort studies) or TREATMENT and DISEASE (clinical trials).
Consistency of Results?
Data on the association of PA or PF with all-cause mortality have been consistently reported
across many studies, in both men and women.
Biological Plausibility?
One of the most important criteria for helping to establish a causal relation is evidence for the
biological plausibility of the observed association.
Biological plausibility is one component of a method of reasoning that can establish a cause-andeffect relationship between a biological factor and a particular disease or adverse event.
It is generally agreed that to be considered “causal”, the association between a biological factor
and a disease (or other bad outcome) should be biologically coherent. That is to say, it should be
plausible and explicable biologically according to the known facts of the natural history and
biology of the disease in question.
Dose Response?
The dose–response relationship, or exposure–response relationship, describes the change in
effect on an organism caused by differing levels of exposure (or doses) to a stressor (usually
a chemical) after a certain exposure time.[1] This may apply to individuals (e.g.: a small amount
has no significant effect, a large amount is fatal), or to populations (e.g.: how many people or
organisms are affected at different levels of exposure).
How Much Physical Activity is Needed to Decrease Risk of Premature Mortality?
How Much?—Can refer to: total volume of energy expended, intensity, duration, frequency. Most
studies have used volume of energy expended.
2 to 2.5 hours per week of moderate-intensity physical activity is sufficient to significantly
decrease all-cause mortality rates.
Walking 2 or more hours per week.
“Some is good; more is better.”
What about vigorous vs. lower intensity—with the same energy expenditure for each?
Weekend Warriors. (frequency).
Current guidelines recommend 150 min/week of moderate intensity aerobic activity or 75
min/week of vigorous intensity physical activity—work, leisure, sports…whatever..
Law of Diminishing Returns