Epidemiology is the science that studies the
patterns, causes,
and effects of health
and disease
conditions in defined populations. It is the cornerstone of public
health, and informs policy decisions and evidence-based practice by identifying risk
factors for disease and targets for preventive healthcare. Epidemiologists help
with study design, collection, and statistical analysis of data, and
interpretation and dissemination of results (including peer review
and occasional systematic review). Epidemiology has helped
develop methodology
used in clinical research, public
health studies, and, to a lesser extent, basic
research in the biological sciences.
Major areas of epidemiological study include disease etiology, transmission, outbreak
investigation, disease surveillance and screening, biomonitoring,
and comparisons of treatment effects such as in clinical
trials. Epidemiologists rely on other scientific disciplines like biology to better
understand disease processes, statistics to make efficient use of the data and draw
appropriate conclusions, social sciences to better understand proximate and
distal causes, and engineering for exposure assessment.
Etymology
Epidemiology, literally meaning "the study of
what is upon the people", is derived from Greek
epi, meaning "upon, among", demos, meaning "people,
district", and logos,
meaning "study, word, discourse", suggesting that it applies only to
human populations. However, the term is widely used in studies of zoological
populations (veterinary epidemiology), although the term "epizoology"
is available, and it has also been applied to studies of plant populations
(botanical or plant disease epidemiology).
The distinction between "epidemic" and
"endemic" was first drawn by Hippocrates,
to distinguish between diseases that are "visited upon" a population
(epidemic) from those that "reside within" a population (endemic).
The term "epidemiology" appears to have first been used to describe
the study of epidemics in 1802 by the Spanish physician Villalba in Epidemiología
Española. Epidemiologists also study the interaction of diseases in a
population, a condition known as a syndemic.
The term epidemiology is now widely applied to cover the
description and causation of not only epidemic disease, but of disease in
general, and even many non-disease health-related conditions, such as high
blood pressure and obesity. Therefore, this epidemiology is based upon how the
pattern of the disease cause changes in the function of everyone.
History
"History of epidemiology" redirects here.
The Greek physician Hippocrates,
known as the father of medicine, sought a logic to sickness; he is the first
person known to have examined the relationships between the occurrence of
disease and environmental influences. Hippocrates believed sickness of the
human body to be caused by an imbalance of the four Humors (air,
fire, water and earth “atoms”). The cure to the sickness was to remove or add
the humor in question to balance the body. This belief led to the application
of bloodletting and dieting in medicine. He coined the terms endemic (for diseases usually found
in some places but not in others) and epidemic
(for diseases that are seen at some times but not others).
In ancient India, Ayurveda
considered disease to be a manifestation of imbalance in 3 bodily humors,
called Doshas.
Around this theory, systems of diagnosis
were based.
One of the earliest theories on the origin of disease was
that it was primarily the fault of human luxury. This was expressed by
philosophers such as Plato
and Rousseau,
and social critics like Jonathan
Swift.
In the middle of the 16th century, a doctor from Verona named Girolamo Fracastoro was the first to propose a
theory that these very small, unseeable, particles that cause disease were
alive. They were considered to be able to spread by air, multiply by themselves
and to be destroyable by fire. In this way he refuted Galen's miasma theory (poison gas in sick people).
In 1543 he wrote a book De
contagione et contagiosis morbis, in which he was the first to promote
personal and environmental hygiene to prevent disease. The development of a sufficiently
powerful microscope by Anton van Leeuwenhoek in 1675 provided visual
evidence of living particles consistent with a germ theory of disease.
Another pioneer, Thomas
Sydenham (1624–1689), was the first to distinguish the fevers of Londoners
in the later 1600s. His theories on cures of fevers met with much resistance
from traditional physicians at the time. He was not able to find the initial
cause of the smallpox
fever he researched and treated.
John Graunt, a haberdasher
and amateur statistician, published Natural and Political Observations ...
upon the Bills of Mortality in 1662. In it, he analysed the mortality rolls
in London before
the Great Plague, presented one of the first life tables,
and report time trends for many diseases, new and old. He provided statistical
evidence for many theories on disease, and also refuted some widespread ideas
on them.
Modern era
John Snow is famous for his investigations
into the causes of the 19th century cholera epidemics, and is also known as the
father of (modern) epidemiology. He began with noticing the significantly
higher death rates in two areas supplied by Southwark Company. His
identification of the Broad Street pump as the cause of the Soho
epidemic is considered the classic example of epidemiology. Snow used chlorine
in an attempt to clean the water and removed the handle; this ended the
outbreak. This has been perceived as a major event in the history of public
health and regarded as the founding event of the science of epidemiology,
having helped shape public health policies around the world. However, Snow’s
research and preventive measures to avoid further outbreaks were not fully
accepted or put into practice until after his death.
Other pioneers include Danish physician Peter Anton Schleisner,
who in 1849 related his work on the prevention of the epidemic of neonatal
tetanus on the Vestmanna Islands in Iceland. Another
important pioneer was Hungarian physician Ignaz
Semmelweis, who in 1847 brought down infant mortality at a Vienna hospital
by instituting a disinfection procedure. His findings were published in 1850,
but his work was ill received by his colleagues, who discontinued the
procedure. Disinfection did not become widely practiced until British surgeon Joseph Lister 'discovered' antiseptics
in 1865 in light of the work of Louis
Pasteur.
In the early 20th century, mathematical methods were
introduced into epidemiology by Ronald Ross,
Janet Lane-Claypon, Anderson Gray McKendrick and others.
Another breakthrough was the 1954 publication of the results
of a British Doctors Study, led by Richard
Doll and Austin Bradford Hill, which lent very strong
statistical support to the suspicion that tobacco
smoking was linked to lung cancer.
In the late 20th century, with advancement of biomedical
sciences, a number of molecular markers in blood, other biospecimens and
environment were identified as predictors of development or risk of a certain
disease. Epidemiology research to examine the relationship between these biomarkers
analyzed at the molecular level and disease was broadly named “molecular epidemiology”. Specifically,
"genetic epidemiology" has been used for
epidemiology of germline genetic variation and disease. Genetic variation is
typically determined using DNA from peripheral blood leukocytes. Since the
2000s, genome-wide association studies
(GWAS) have been commonly performed to identify genetic risk factors for many
diseases and health conditions.
While most molecular epidemiology studies are still using
conventional disease
diagnosis
and classification
system, it is
increasingly recognized that disease evolution
represents inherently heterogeneous process differing
from person to person. Conceptually, each individual has a unique disease
process different from any other individual (“the unique disease principle”),
considering uniqueness of the exposome (a totality of endogenous and exogenous / environmental
exposures) and its unique influence on molecular pathologic process in each
individual. Studies to examine the relationship between an exposure and
molecular pathologic signature of disease (particularly, cancer) became
increasingly common throughout the 2000s. However, the use of molecular pathology in epidemiology posed
unique challenges including lack of research guidelines
and standardized statistical
methodologies,
and paucity of interdisciplinary experts and training
programs. Furthermore, the concept of disease heterogeneity appears to conflict
with the long-standing premise in epidemiology that individuals with the same
disease name have similar etiologies and disease processes. To resolve these
issues and advance population health science in the era of molecular precision medicine, “molecular pathology” and “epidemiology” was
integrated to create a new interdisciplinary field of “molecular pathological epidemiology”
(MPE), defined as “epidemiology of molecular pathology and heterogeneity of
disease”. In MPE, investigators analyze the relationships between; (A)
environmental, dietary, lifestyle and genetic factors; (B) alterations in
cellular or extracellular molecules; and (C) evolution and progression of
disease. A better understanding of heterogeneity of disease pathogenesis
will further contribute to elucidate etiologies of
disease. The MPE approach can be applied to not only neoplastic diseases but
also non-neoplastic diseases. The concept and paradigm of MPE have become
widespread in the 2010s.
The profession
To date, few universities
offer epidemiology as a course of study at the undergraduate level. Many
epidemiologists are physicians, or hold graduate degrees such as a Master of Public Health (MPH), Master
of Science of Epidemiology (MSc.). Doctorates
include the Doctor of Public Health (DrPH), Doctor of Pharmacy (PharmD), Doctor of Philosophy (PhD), Doctor
of Science (ScD), Doctor of Social Work (DSW), Doctor of Clinical
Practice (DClinP), Doctor of Podiatric Medicine (DPM), Doctor of Veterinary Medicine (DVM), Doctor of Nursing Practice (DNP), Doctor of Physical Therapy (DPT), or for
clinically trained physicians, Doctor of Medicine (MD) and Doctor of Osteopathic Medicine (DO).
In the United Kingdom, the title of 'doctor' is by long custom used to refer to
general medical practitioners, whose professional degrees are usually those of Bachelor of Medicine and Surgery
(MBBS or MBChB).
As public health/health protection practitioners,
epidemiologists work in a number of different settings. Some epidemiologists
work 'in the field'; i.e., in the community, commonly in a public health/health
protection service and are often at the forefront of investigating and
combating disease outbreaks. Others work for non-profit organizations,
universities, hospitals and larger government entities such as the Centers for Disease Control
and Prevention (CDC), the Health Protection Agency, the World Health Organization (WHO), or the Public Health Agency of Canada. Epidemiologists
can also work in for-profit organizations such as pharmaceutical and medical
device companies in groups such as market research or clinical development.
The practice
Epidemiologists employ a range of study designs from the
observational to experimental and generally categorized as descriptive,
analytic (aiming to further examine known associations or hypothesized
relationships), and experimental (a term often equated with clinical or
community trials of treatments and other interventions). In observational
studies, nature is allowed to “take its course”, as epidemiologists observe
from the sidelines. Conversely, in experimental studies, the epidemiologist is
the one in control of all of the factors entering a certain case study.
Epidemiological studies are aimed, where possible, at revealing unbiased
relationships between exposures such as alcohol or smoking, biological
agents, stress, or chemicals
to mortality or morbidity.
The identification of causal relationships between these exposures and outcomes
is an important aspect of epidemiology. Modern epidemiologists use informatics as a tool.
Observational studies have two components: descriptive, or
analytical. Descriptive observations pertain to the “who, what, where and when
of health-related state occurrence”. However, analytical observations deal more
with the ‘how’ of a health-related event.
Experimental epidemiology contains three case types:
randomized control trial (often used for new medicine or drug testing), field
trial (conducted on those at a high risk of conducting a disease), and
community trial (research on social originating diseases).
Unfortunately, many epidemiology studies conducted cause
false or misinterpreted information to circulate the public. According to a
class taught by professor Madhukar Pai MD, PhD at McGill, “...optimism bias is
pervasive, most studies biased or inconclusive or false, most discovered true
associations are inflated, fear and panic inducing rather than helpful;
media-induced panic, cannot detect small effects; big effects are not to be
found anymore”.
The term 'epidemiologic triad' is used to describe the
intersection of Host, Agent, and Environment in analyzing
an outbreak.
As causal inference
Although epidemiology is sometimes viewed as a collection of
statistical tools used to elucidate the associations of exposures to health
outcomes, a deeper understanding of this science is that of discovering causal
relationships.
"Correlation does not imply
causation" is a common theme for much of the epidemiological
literature. For epidemiologists, the key is in the term inference.
Epidemiologists use gathered data and a broad range of biomedical and
psychosocial theories in an iterative way to generate or expand theory, to test
hypotheses, and to make educated, informed assertions about which relationships
are causal, and about exactly how they are causal.
Epidemiologists Rothman and Greenland emphasize that the
"one cause – one effect" understanding is a simplistic
mis-belief. Most outcomes, whether disease or death, are caused by a chain or
web consisting of many component causes. Causes can be distinguished as
necessary, sufficient or probabilistic conditions. If a necessary condition can
be identified and controlled (e.g., antibodies to a disease agent), the harmful
outcome can be avoided.
Bradford Hill criteria
In 1965 Austin Bradford Hill proposed a series of
considerations to help assess evidence of causation, which have come to be
commonly known as the "Bradford Hill criteria". In contrast to
the explicit intentions of their author, Hill's considerations are now
sometimes taught as a checklist to be implemented for assessing causality. Hill
himself said "None of my nine viewpoints can bring indisputable evidence
for or against the cause-and-effect hypothesis and none can be required sine
qua non."
- Strength: A small association does not mean that there is not a causal effect, though the larger the association, the more likely that it is causal.
- Consistency: Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.
- Specificity: Causation is likely if a very specific population at a specific site and disease with no other likely explanation. The more specific an association between a factor and an effect is, the bigger the probability of a causal relationship.
- Temporality: The effect has to occur after the cause (and if there is an expected delay between the cause and expected effect, then the effect must occur after that delay).
- Biological gradient: Greater exposure should generally lead to greater incidence of the effect. However, in some cases, the mere presence of the factor can trigger the effect. In other cases, an inverse proportion is observed: greater exposure leads to lower incidence.
- Plausibility: A plausible mechanism between cause and effect is helpful (but Hill noted that knowledge of the mechanism is limited by current knowledge).
- Coherence: Coherence between epidemiological and laboratory findings increases the likelihood of an effect. However, Hill noted that "... lack of such [laboratory] evidence cannot nullify the epidemiological effect on associations".
- Experiment: "Occasionally it is possible to appeal to experimental evidence".
- Analogy: The effect of similar factors may be considered.
Legal interpretation
Epidemiological studies can only go to prove
that an agent could have caused, but not that it did cause, an effect in any
particular case:
"Epidemiology is concerned with the incidence of disease in populations and
does not address the question of the cause of an individual's disease. This
question, sometimes referred to as specific causation, is beyond the domain of
the science of epidemiology. Epidemiology has its limits at the point where an
inference is made that the relationship between an agent and a disease is
causal (general causation) and where the magnitude of excess risk attributed to
the agent has been determined; that is, epidemiology addresses whether an agent
can cause a disease, not whether an agent did cause a specific plaintiff's
disease."
In United States law, epidemiology alone cannot prove that a
causal association does not exist in general. Conversely, it can be (and is in
some circumstances) taken by US courts, in an individual case, to justify an
inference that a causal association does exist, based upon a balance of probability.
The subdiscipline of forensic epidemiology is directed at
the investigation of specific causation of disease or injury in individuals or
groups of individuals in instances in which causation is disputed or is
unclear, for presentation in legal settings.
Advocacy
As a public health discipline, epidemiologic evidence is
often used to advocate
both personal measures like diet change and corporate measures like removal of junk food
advertising, with study findings disseminated to the general public to help
people to make informed decisions about their health. Often the uncertainties
about these findings are not communicated well; news articles often prominently
report the latest result of one study with little mention of its limitations,
caveats, or context. Epidemiological tools have proved effective in
establishing major causes of diseases like cholera and lung cancer,[38]
but experience difficulty in regards to more subtle health issues where
causation is more complex. Notably, conclusions drawn from observational
studies may be reconsidered as later data from randomized controlled trials becomes
available, as was the case with the association between the use of hormone replacement therapy
and cardiac risk.
Population-based health management
Epidemiological practice and the results of epidemiological
analysis make a significant contribution to emerging population-based health
management frameworks.
Population-based health management encompasses the ability
to:
- Assess the health states and health needs of a target population;
- Implement and evaluate interventions that are designed to improve the health of that population; and
- Efficiently and effectively provide care for members of that population in a way that is consistent with the community's cultural, policy and health resource values.
Modern population-based health management is complex,
requiring a multiple set of skills (medical, political, technological,
mathematical etc.) of which epidemiological practice and analysis is a core
component, that is unified with management science to provide efficient and
effective health care and health guidance to a population. This task requires
the forward looking ability of modern risk management approaches that transform
health risk factors, incidence, prevalence and mortality statistics (derived
from epidemiological analysis) into management metrics that not only guide how
a health system responds to current population health issues, but also how a
health system can be managed to better respond to future potential population
health issues.
Examples of organizations that use population-based health
management that leverage the work and results of epidemiological practice
include Canadian Strategy for Cancer Control, Health Canada Tobacco Control
Programs, Rick Hansen Foundation, Canadian Tobacco Control Research Initiative.
Each of these organizations use a population-based health
management framework called Life at Risk that combines epidemiological
quantitative analysis with demographics, health agency operational research and
economics to perform:
- Population Life Impacts Simulations: Measurement of the future potential impact of disease upon the population with respect to new disease cases, prevalence, premature death as well as potential years of life lost from disability and death;
- Labour Force Life Impacts Simulations: Measurement of the future potential impact of disease upon the labour force with respect to new disease cases, prevalence, premature death and potential years of life lost from disability and death;
- Economic Impacts of Disease Simulations: Measurement of the future potential impact of disease upon private sector disposable income impacts (wages, corporate profits, private health care costs) and public sector disposable income impacts (personal income tax, corporate income tax, consumption taxes, publicly funded health care costs).
Types of studies
Case series
Case-series may refer to the qualitative study of the
experience of a single patient, or small group of patients with a similar
diagnosis, or to a statistical technique comparing periods during which
patients are exposed to some factor with the potential to produce illness with
periods when they are unexposed.
The former type of study is purely descriptive and cannot be
used to make inferences about the general population of patients with that
disease. These types of studies, in which an astute clinician identifies an
unusual feature of a disease or a patient's history, may lead to formulation of
a new hypothesis. Using the data from the series, analytic studies could be
done to investigate possible causal factors. These can include case control studies
or prospective studies. A case control study would involve matching comparable
controls without the disease to the cases in the series. A prospective study
would involve following the case series over time to evaluate the disease's
natural history.
The latter type, more formally described as self-controlled
case-series studies, divide individual patient follow-up time into exposed and
unexposed periods and use fixed-effects Poisson regression processes to compare
the incidence rate of a given outcome between exposed and unexposed periods.
This technique has been extensively used in the study of adverse reactions to
vaccination, and has been shown in some circumstances to provide statistical
power comparable to that available in cohort studies.
Case control studies
Case control studies select subjects based on
their disease status. A group of individuals that are disease positive (the
"case" group) is compared with a group of disease negative
individuals (the "control" group). The control group should ideally
come from the same population that gave rise to the cases. The case control
study looks back through time at potential exposures that both groups (cases
and controls) may have encountered. A 2×2 table is constructed, displaying
exposed cases (A), exposed controls (B), unexposed cases (C) and unexposed
controls (D). The statistic generated to measure association is the odds ratio
(OR), which is the ratio of the odds of exposure in the cases (A/C) to the odds
of exposure in the controls (B/D), i.e. OR = (AD/BC).
|
.....
|
Cases
|
Controls
|
|
Exposed
|
A
|
B
|
|
Unexposed
|
C
|
D
|
If the OR is clearly greater than 1, then the conclusion is
"those with the disease are more likely to have been exposed,"
whereas if it is close to 1 then the exposure and disease are not likely
associated. If the OR is far less than one, then this suggests that the
exposure is a protective factor in the causation of the disease. Case control
studies are usually faster and more cost effective than cohort
studies, but are sensitive to bias (such as recall bias
and selection
bias). The main challenge is to identify the appropriate control group; the
distribution of exposure among the control group should be representative of
the distribution in the population that gave rise to the cases. This can be
achieved by drawing a random sample from the original population at risk. This
has as a consequence that the control group can contain people with the disease
under study when the disease has a high attack rate in a population.
A major drawback for case control studies is that, in order
to be considered to be statistically significant, the minimum number of cases
required at the 95% confidence interval is related to the odds ratio by the
equation:
total cases = (a+c) =
(1.96)^2×(1+N)×(1÷ln(OR))^2×((OR+2√OR+1)÷√OR)≈15.5×(1+N)×(1÷ln(OR))^2
where N = the ratio of cases to controls. As the odds ratio
approached 1, approaches 0; rendering case control studies all but useless for
low odds ratios. For instance, for an odds ratio of 1.5 and cases = controls,
the table shown above would look like this:
|
.....
|
Cases
|
Controls
|
|
Exposed
|
103
|
84
|
|
Unexposed
|
84
|
103
|
For an odds ratio of 1.1:
|
.....
|
Cases
|
Controls
|
|
Exposed
|
1732
|
1652
|
|
Unexposed
|
1652
|
1732
|
Cohort studies
Cohort studies select subjects based on their
exposure status. The study subjects should be at risk of the outcome under
investigation at the beginning of the cohort study; this usually means that
they should be disease free when the cohort study starts. The cohort is
followed through time to assess their later outcome status. An example of a
cohort study would be the investigation of a cohort of smokers and non-smokers
over time to estimate the incidence of lung cancer. The same 2×2 table is
constructed as with the case control study. However, the point estimate
generated is the relative risk (RR), which is the probability of
disease for a person in the exposed group, Pe = A / (A + B)
over the probability of disease for a person in the unexposed group, Pu = C / (C + D),
i.e. RR = Pe / Pu.
|
.....
|
Case
|
Non-case
|
Total
|
|
Exposed
|
A
|
B
|
(A + B)
|
|
Unexposed
|
C
|
D
|
(C + D)
|
As with the OR, a RR greater than 1 shows association, where
the conclusion can be read "those with the exposure were more likely to
develop disease."
Prospective studies have many benefits over case control
studies. The RR is a more powerful effect measure than the OR, as the OR is
just an estimation of the RR, since true incidence cannot be calculated in a
case control study where subjects are selected based on disease status.
Temporality can be established in a prospective study, and confounders are more
easily controlled for. However, they are more costly, and there is a greater
chance of losing subjects to follow-up based on the long time period over which
the cohort is followed.
Cohort studies also are limited by the same equation for
number of cases as for cohort studies, but, if the base incidence rate in the
study population is very low, the number of cases required is reduced
by ½.
Outbreak investigation
For information on investigation of infectious disease outbreaks, please
see outbreak investigation.
Validity: precision and bias
Different fields in epidemiology have different levels of
validity. One way to assess the validity of findings is the ratio of
false-positives (claimed effects that are not correct) to false-negatives
(studies which fail to support a true effect). To take the field of genetic
epidemiology, candidate-gene studies produced over 100 false-positive findings
for each false-negative. By contrast genome-wide association appear close to
the reverse, with only one false positive for every 100 or more
false-negatives. This ratio has improved over time in genetic epidemiology as
the field has adopted stringent criteria. By contrast other epidemiological
fields have not required such rigorous reporting and are much less reliable as
a result.
Random error
Random error is the result of fluctuations around a true
value because of sampling variability. Random error is just that: random. It
can occur during data collection, coding, transfer, or analysis. Examples of
random error include: poorly worded questions, a misunderstanding in
interpreting an individual answer from a particular respondent, or a
typographical error during coding. Random error affects measurement in a
transient, inconsistent manner and it is impossible to correct for random
error.
There is random error in all sampling procedures. This is
called sampling error.
Precision in epidemiological variables is a measure of
random error. Precision is also inversely related to random error, so that to
reduce random error is to increase precision. Confidence intervals are computed
to demonstrate the precision of relative risk estimates. The narrower the confidence
interval, the more precise the relative risk estimate.
There are two basic ways to reduce random error in an epidemiological study. The first is to
increase the sample size of the study. In other words, add more subjects to
your study. The second is to reduce the variability in measurement in the
study. This might be accomplished by using a more precise measuring device or
by increasing the number of measurements.
Note, that if sample size or number of measurements are
increased, or a more precise measuring tool is purchased, the costs of the
study are usually increased. There is usually an uneasy balance between the
need for adequate precision and the practical issue of study cost.
Systematic error
A systematic error or bias occurs when there is a difference
between the true value (in the population) and the observed value (in the
study) from any cause other than sampling variability. An example of systematic
error is if, unknown to you, the pulse
oximeter you are using is set incorrectly and adds two points to the true
value each time a measurement is taken. The measuring device could be precise but not accurate. Because the error
happens in every instance, it is systematic. Conclusions you draw based on that
data will still be incorrect. But the error can be reproduced in the future
(e.g., by using the same mis-set instrument).
A mistake in coding that affects all responses for
that particular question is another example of a systematic error.
The validity of a study is dependent on the degree of
systematic error. Validity is usually separated into two components:
- Internal validity is dependent on the amount of error in measurements, including exposure, disease, and the associations between these variables. Good internal validity implies a lack of error in measurement and suggests that inferences may be drawn at least as they pertain to the subjects under study.
- External validity pertains to the process of generalizing the findings of the study to the population from which the sample was drawn (or even beyond that population to a more universal statement). This requires an understanding of which conditions are relevant (or irrelevant) to the generalization. Internal validity is clearly a prerequisite for external validity.
Three types of bias
Selection bias
Selection bias is one of three types of bias that
can threaten the validity of a study. Selection bias occurs when study subjects
are selected or become part of the study as a result of a third, unmeasured
variable which is associated with both the exposure and outcome of interest.
For instance, it has repeatedly been noted that cigarette smokers and non
smokers tend to differ in their study participation rates. (Sackett D cites the
example of Seltzer et al., in which 85% of non smokers and 67% of smokers
returned mailed questionnaires.) It is important to note that such a difference
in response will not lead to bias if it is not also associated with a
systematic difference in outcome between the two response groups.
Information bias
Information bias is bias arising
from systematic error in the assessment of a variable. An example of this is
recall bias. A typical example is again provided by Sackett in his discussion
of a study examining the effect of specific exposures on fetal health: "in
questioning mothers whose recent pregnancies had ended in fetal death or
malformation (cases) and a matched group of mothers whose pregnancies ended
normally (controls) it was found that 28% of the former, but only 20% of the
latter, reported exposure to drugs which could not be substantiated either in
earlier prospective interviews or in other health records". In this
example, recall bias probably occurred as a result of women who had had
miscarriages having an apparent tendency to better recall and therefore report
previous exposures.
Confounding
Confounding has traditionally been defined as bias
arising from the co-occurrence or mixing of effects of extraneous factors,
referred to as confounders, with the main effect(s) of interest. A more recent
definition of confounding invokes the notion of counterfactual effects.
According to this view, when one observes an outcome of interest, say Y=1 (as
opposed to Y=0), in a given population A which is entirely exposed (i.e.
exposure X = 1 for every unit of the population) the risk of
this event will be RA1. The counterfactual or unobserved risk
RA0 corresponds to the risk which would have been observed if
these same individuals had been unexposed (i.e. X = 0 for
every unit of the population). The true effect of exposure therefore is: RA1 − RA0
(if one is interested in risk differences) or RA1/RA0
(if one is interested in relative risk). Since the counterfactual risk RA0
is unobservable we approximate it using a second population B and we actually
measure the following relations: RA1 − RB0
or RA1/RB0. In this situation, confounding
occurs when RA0 ≠ RB0. (NB:
Example assumes binary outcome and exposure variables.)
Some epidemiologists prefer to think of confounding
separately from common categorizations of bias since, unlike selection and
information bias, confounding stems from real causal effects.
Journals
A list of journals:
Areas
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By physiology/disease:
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By methodological approach:
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