the reader who is not familiar with public health and epidemiology terminology,
brief explanations are given here. A basic textbook on epidemiology will
(see controlling for)
This is the feedback loop in primates
and other mammals, in which the adrenal (next to the kidney) gland secretes
a hormone ( cortisol) from its outer shell (cortex), which initiates a
feedback loop in the hypothalamus (part of the brain), to suppress further
secretion of that hormone. The axis refers to this loop.
This is a summarizing procedure
to make comparisons of populations with different age distributions. Age
adjusted mortality rates allow comparison of death rates between populations
in which one such as Florida in the US which has a large percentage of
older people, with another such as Illinois, where the proportion of older
people is less. Age adjusted or age standardized mortality rates are summary
measures that assume the population has a standard age distribution.
In epidemiology, this refers to
a statistical relationship between one or two events or variables. In our
material, this usually refers to a relationship between a measure such
as life expectancy, and another, usually income distribution. The relationship
in this case is negative, that is higher life expectancies are found in
populations with less inequality of income distribution. No causality is
implied in using the term association. The term relationship is sometimes
used instead of association.
This refers to deviation of results
or inferences from the truth, or to the processes leading to such deviation.
In other words, because of a certain bias in the study, the findings did
not come out correctly, and an erroneous conclusion was drawn. A simplistic
example would be if you compared the death rates in two populations, one
young, and the other old, in a rich country, and found the older one had
more deaths, then concluded that it was less healthy than the younger one.
This would be a poor analysis, unless you recognized the bias that the
different age structure produced in coming to the conclusion. In a poorer
country, just the opposite might be true, death rates for a population
of people in their 20s may be lower than for a population of children up
to age 10. Good investigators attempt to describe possible bias in their
studies, and correct for these whenever possible.
between two variables.
This is a population born during
a particular historical period, that is usually followed over time. It
can also mean any population, not necessarily born during the same time
period, that is followed over time.
This refers to a situation where
two separate processes are going on that must be looked at individually.
In our example for bias, age would be a confounder, that is, the investigators
should have looked at the ages of the populations and separated them before
drawing conclusions. An example could be the finding that people drinking
coffee are more likely to have heart attacks. But people who drink coffee
are more likely to smoke, so it may be that this association is just that
between smoking and heart attacks. Smoking would be said to confound the
relationship between coffee drinking and heart attacks.
Adjusting for is another term with
the same meaning, namely that in the statistical analysis, account is taken
of a factor. That is, differences in the factor are corrected for in the
analysis, so that they do not account for the findings. If you correct
or control or adjust for socioeconomic status in a study of lung cancer
in a population, then the effect cannot be due to the poor having more
lung cancer. In the studies showing that income distribution was associated
with mortality by states, the relationship held true after controlling
for different poverty levels, or absolute incomes, or smoking levels, in
the different states. Hence these factors were not responsible for the
This number, between plus and minus
one (-1.0 to 1.0)measures the amount of agreement between two variables,
meaning how close the graph drawn linking to two is to a straight line.
IT is also called the Pearson Correlation Coefficient.
A hormone secreted by the adrenal
gland in the body that has numerous functions, and is elevated in stress
A variable that might be possibly
predictive of the outcome under study. If life expectancy is the outcome,
income distribution can be considered a covariate.
A study looking at a defined population
at one point in time. For example among the fifth US states, looking at
their mortality rates in 1990 to see what relationship there is with a
measure of income distribution by state represents a cross sectional design.
An investigation in which populations
or groups of people, rather than individuals are looked at. Most of the
studies referred to in this web page are such. An individual does not have
a life expectancy, nor an income distribution, but a population, a city,
state or country, does. Ecologic studies do not allow statements
to be made about individuals, just about the population. See ecologic
fallacy in the Overview and Making Causal Inferences
A term referring to a gland that
secretes its hormone directly into the bloodstream that flows through it,
rather than through an opening into the intestine, as say the gallbladder,
or pancreas does with digestive enzymes.
An (endocrine) hormone secreted
by the adrenal gland in its interior (medulla), that activates the acute
stress, flight or fright reaction. Adrenaline is another term for it.
A measure of inequality, usually
applied to income. It is derived from a Lorentz Curve which plots the cumulative
percent of income against the cumulative percent of income recipients.
It is twice the area of the curve between what would be perfect equality
and the existing distribution is the Gini coefficient. A coefficient of
0 means perfect equality, while that of 1 means one unit of the population
has everything and there is none for the rest. It is more sensitive to
differences in the middle of the distribution, than to the ends.
The cover term for the hormones
made in the adrenal cortex, an example of which is cortisol.
inequalities (or inequalities in health)
This is the term commonly used in
Europe to indicate the virtually universal phenomenon of variation of health
by socioeconomic status, that is poorer people have poorer health. In the
US, there is no single such term, and instead it is referred to as the
socioeconomic status and health relationship.
Incidence refers to new events,
within a specific time period. An incidence rate is the number of occurrences
of something, such as an illness, per unit of time (usually a year), per
person, or per 1000 or per 100,000 people in that population.
A general term referring to the
variation of income in a population, that is some people have more than
others. The Gini coefficient is one measure of this.
mortality rate (IMR)
The IMR is the number of deaths
occurring in a population per year among infants in their first year of
expectancy (also termed the expectation of life)
This number, for a population ,
is the average number of years an individual, born today, would be expected
to life if current mortality rates continued to apply. To calculate it,
you need to know the mortality pattern of the population, that is the death
rates in different age intervals.
This refers to using a statistical
model for a process that contains an exponential factor, and seeing what
is the best fit of the data.
The median of a measure in a population
is the number which divides the population into two equal groups, those
above, and those below. Median income would then be the income value that
separates the population as above
This is a kind of logistic regression
in which there are many variables, including several exponential factors.
This refers to a study in which
there are several variables being considered simultaneously.
Refers to the probability that the
result obtained could have happened by chance. Usually refers to a number
derived from a calculation in the study and is displayed as p_ 0.05 or
p_ 0.01 or such. This means the likelihood of such a result by chance is
less than one in twenty or one in a hundred. The custom is to consider
p values of 0.05 or less to signify a significant result, one highly unlikely
to happen by chance. A p value .1 or higher is more likely to be a chance
event, and is accorded less significance.
The prevalence counts the number
of events in a specific population, not just new occurrences, as in incidence.
The incidence of malnutrition would be all the new cases that occurred
that year, while the prevalence would count the total number of people
with malnutrition in that population.
Originally used to find the best
straight line that fits the data representing two variables under question.
Regression analysis can also be used to indicate the process of trying
to fit a mathematical relationship to the data
capital or social cohesion
Terms, that relate to the features
of social organization and community life, such as civic participation,
norms of reciprocity and trust in others that facilitate cooperation for
A descriptive term for a person's
position in society, usually expressed in terms of income, education, occupation,
but it could also be represented by net worth, ownership of assets such
as a home, automobile, yacht, etc.
mortality rate (SMR)
The ratio (times 100) of the number
of deaths observed in a population to the number that would be expected
if the study population had the same specific rates as the standard population.
The standard population is specified. This is different than the age adjusted
mortality rate, in that former is a ratio (comparison of two rates, one
to a standard population), while the latter is a rate (number per unit
time), assuming the population had a specific age distribution.
Some estimate in a study is said
to be statistically significant if it is unlikely to happen by chance.
Usually it is described as a number, or a curve fit, with a p value that
is sufficiently low. Usually p=0.05 or less.