ABSTRACT
The paper reviews the strengths and weaknesses of the epidemiologic
methodology using examples from observational studies and experimental studies (natural and true or man-made) The epidemiological
methodology uses the scientific method in the generation and testing of hypotheses. Epidemiologic reasoning is empirical,
inductive, and refutative. Epidemiological studies are much cheaper in terms of material or human resources and provide generalizable
results in a shorter time than comparable laboratory-based studies. The biggest strength of the epidemiologic methodology
is ability to offer viable practical interventions that solve problems even before detailed causal mechanisms are known. Unlike
laboratory experiments, epidemiological studies can not control extraneous or confounding factors perfectly. Epidemiological
findings are rarely conclusive.
NATURE OF THE EPIDEMIOLOGIC METHODOLOGY
Epidemiology uses the procedures of the scientific method: stating a hypothesis, collecting and analyzing
data to test the hypothesis, and reaching conclusions about the hypothesis. The epidemiologic methodology ensures continuous
improvement of knowledge by generating and testing new hypotheses. An epidemiological hypothesis is formulated to relate two
phenomena: the disease and the putative cause of the disease (the exposure or risk factor). Hypotheses must be specific and testable. Empirical data is from experimentation and observation.
The conclusions from testing a hypothesis can be rejection/non-rejection and never acceptance. A new hypothesis is generated
from the conclusion and the process is repeated. Use of the scientific method implies among other things that epidemiological
knowledge is never stable. It keeps on changing and getting nearer the truth as new information is discovered.
Epidemiological methodology is empirical, inductive, and refutative. Empiricism refers
to reliance on physical proof. Epidemiology relies on and respects only empirical findings. There is no room for preventive
action being based on pure reasoning, rationalism, not subjected to empirical verification by collection and analysis of data.
Both inductive and deductive logic are used in epidemiological reasoning. The inductive is used more because it is more in
line with empirical experimental verification. Induction is a type of reasoning that starts from one observation and generalizes.
Refutation is a type of scientific reasoning introduced by the philosopher Karl Popper (1) that emphasizes rejection of suppositions
rather than accepting them. Rejecting an idea opens the way to test other ideas. Epidemiology can refute a finding but can
never offer conclusive proof. This is in line with the spirit of empirical inquiry that knowledge and understanding grow continuously
and facts accepted and interpreted in one way today may be rejected and interpreted in another way tomorrow.
NATURAL EXPERIMENTS
Natural experiments are not deliberately designed by humans.
They can be analysed to provide insight at little cost to humans because they just involve observation of events. They however
are rarely conclusive. Natural experiments can be divided into 2 types. Some involve no human agency at all like earth-quakes,
floods, and cosmic radiation. Others involve human action which is not deliberate or planned in any systematic way.
The following are examples of natural experiments:
- Smog
episode in Danora, PA. In 1948 (2)
- The
London fog of 1952 that killed 4000 persons in 10 days (3)
- Snow's
study of the relation between polluted water and cholera in London (4)
- Occurrence
of polio after tonsillectomy (5)
- Cancer
in accidental chemical exposure ( 6)
- Cancer
in survivors of atomic explosions in Hiroshima and Nagasaki (7, 8)
- Heart
attacks and asthma following the Athens earthquake of 1981 (9).
- Thalidomide disaster (10)
- En epidemic of deafness in Australia (11)
TRUE EXPERIMENTS
True experiments involve deliberate human action or intervention whose outcome is then observed. Their
objective is to establish the definitive causal relation. True experiments are more involved and expensive that observational studies or natural experiments but are still cheaper
and easier than laboratory-based research. The main strength of experimental studies
is good control of extraneous or confounding factors that might make interpretation of study results difficult. The main weaknesses
is that well controlled experiments on humans are difficult. It is difficult to put humans under full experimental conditions
where they can be observed for 24 hours. Ethical controversies and violation of human rights always arise in such studies.
The following are examples of true experiments
- Lind's
trial of fruit juice for scurvy in 1747 (12)
- Jenner's
cowpox vaccination in 1796 (13)
- Induction
of pellagra by Goldberger and Wheeler in 1915 (14)
- Discovery
of the relation between rice and beri-beri by Fletcher in Kuala Lumpur in 1905 (15)
COMMUNITY INTERVENTION STUDIES
A community intervention study is designed to test whether a certain public health intervention
such as health education or water fluoridation has an effect on a given outcome measure. Two or more similar communities are
randomly allocated to receive different interventions and the outcome is then measured. Random allocation ensures comparability.
The strength of the community intervention study is that it can evaluate a public health intervention is natural field circumstances.
The weakness is that people in the control community may receive the intervention under study on their own because tight control
as occurs in laboratory experimental or animal studies is not possible with humans.
Examples of community intervention studies
- Tests
of the prevention of dental caries by fluoridation carried out by the United States Public Health Service (USPHS) in 1970
(16).
- Trials
of vaccination efficacy
OBSERVATIONAL STUDIES
Observational studies, descriptive or analytic, allow nature to take its course with no
human interference. They usually precede and prepare for definitive experimental studies. Cross-sectional (prevalence), case-control,
and follow-up studies are the main types of observational studies in epidemiology. The advantages of observational studies
are their low cost and lack of ethical controversies. A cheap study is made of a wide variety of human experiences by just
observing and recording information. This is much cheaper than experimental studies in which people must be subjected to various
treatments and exposures at the experimenter's cost. Ethical problems in observational studies are much less than those in
experimental studies because the human subject is not exposed to any major physical risk. Observational studies have three
main disadvantages. It is not possible to study etiology directly because the investigator does not manipulate the exposures.
Etiology is studied only indirectly by comparing disease experience in the group exposed to a putative risk factor with the
group that was not exposed. Information on the variable of interest may not be available recourse being made to surrogate
variables. Several unplanned co-factors (giving rise to confounding, interaction, or effect modification) are involved making
interpretation difficult. Experimental studies, unlike observational studies, collect systematic information on these co-factors
rendering study interpretation easier.
Most of epidemiological study is observational. The following are a few examples
- Hippocrates and observations on the relation
between disease and environment (17)
- Observations by Graunt on the London bills of mortality (18)
- Observations by William Farr based on
vital statistics of England and Wales (19)
- Semmelweiss's observation of the relation
between washing hands and child-bed fever (20)
- Relation between working in the dye industry
and bladder tumors (21)
- Cancer mortality after irradiation for
ankylosing spondylitis (22, 23)
- Smoking and lung cancer (24, 25, 26,
27, 28, 29, 30)
- Cancer of the cervix and circumcision
(31)
- Social class and mental illness (32)
- Mortality of American radiologists (33)
- Neoplasia in children treated with x-rays
(34)
- Cancer in uranium miners (35)
PREVENTIVE ACTION BEFORE FULL UNDERSTANDING
It is a strength of the epidemiological methodology that effective preventive action against
disease can be undertaken even before the complete mechanism of etiology is known as shown in the following examples
- Smoking and lung cancer
- High lipid diet and cardio-vascular disease
- Low fiber diet and colon cancer
- HBV vaccination and hepatocellular carcinoma
THE PROBLEM OF CONFOUNDING BIAS
Miettinen has dealt with the theoretical basis of confounding
(36). Confounding bias arises when the disease-exposure relationship is disturbed by an extraneous factor called the confounding
variable. The confounding variable is not actually involved in the exposure-disease relationship. It is however predictive
of disease but is unequally distributed between exposure groups. Being related both to the disease and the risk factor, the
confounding variable could lead to a spurious apparent relation between disease and exposure.
Confounding can be understood by the following example. Alcohol
consumption confounds the relation between smoking and lung cancer. There is an indirect relation between alcohol consumption
and cancer of the lung. We observe that those who have lung cancer also consume alcohol. This is because of the non-causal
relation between alcohol consumption and cigarette smoking. The two are part of the same lifestyle and tend to occur together.
The direct causal relationship between cigarette smoking and lung cancer could be distorted in a study in which alcohol consumption
is not balanced between the smoking and non-smoking exposure groups. A negative relationship between cigarette smoking and
lung cancer will be seen if study subjects are selected predominantly from the non-smoking population.
Confounding can be prevented or minimized. Prevention of confounding
at the design stage by eliminating the effect of the confounding factor can be achieved using 3 strategies are used: pair-matching,
stratification, and randomisation. Multivariate techniques can be used to adjust for the effects of confounding at the analysis
stage of the study.
BALANCE OF STRENGTHS AND WEAKNESSES:
In a 1979 seminal discussion of the strengths and weaknesses of epidemiological methodology,
Brian McMahon, Professor of epidemiology at Harvard, summarized the strengths as obtaining observational data cheaply and
with a wide wide range of human exposures. The humans both choose and pay for the exposures and what the epidemiologist does
is to collect the data. He identified the major weakness as inability to offer conclusive proof of the disease-exposure relationship.
In this sense epidemiological findings open the way for further work and verification by laboratory science. As more epidemiologic
studies indicate the same etiology, increasing convincing evidence is obtained. However the final proof will have to come
from the laboratory. In some cases the final proof is never obtained. Using the limited epidemiological knowledge on causation,
public health interventions may be undertaken and they result in elimination of the disease before the laboratory workers
have a chance to say their word.
TABLE #1: MORTALITY FROM CHOLERA IN THE DISTRICTS OF LONDON SUPPLIED BY SOUTHWALK & VAUXALL COMPANY and the
LAMBETH COMPANY, July 8 to August 26, 1854
Districts with water supplied by |
Population in 1851 |
Deaths from cholera |
Cholera deaths per 1000 population |
Southwalk and Vauxhall Company only |
167, 654 |
844 |
5.0 |
Lambeth Company only |
19,133 |
18 |
0.9 |
Both companies |
300,149 |
652 |
2.2 |
Source: MacMahon, B. et al. Epidemiology:
Principles and Methods. 2nd Edition. Little, Brown, and Company, Boston 1996.
TABLE #2: MORTALITY FROM CHOLERA IN LONDON, July 8 to August 26, 1854 Related to the Water
Supply of Individual Houses in Districts Served by Both the SOUTHWALK & VAUXALL COMPANY and the LAMBETH COMPANY, July 8 to August 26, 1854
Water supply of individual houses |
Population in 1851 |
Deaths from cholera |
Cholera deaths per 1000 population |
Southwalk and Vauxhall Company only |
98, 862 |
419 |
4.2 |
Lambeth Company only |
154, 615 |
80 |
0.5 |
Source: MacMahon, B. et al. Epidemiology:
Principles and Methods. 2nd Edition. Little, Brown, and Company, Boston 1996.
TABLE #3: RELATION BETWEEN CURED RICE AND BERIBERI
|
Uncured* (siamese) rice |
Cured* (bengali) rice |
Beriberi cases |
34 (18 deaths) |
2 (0 deaths) |
Healthy |
186 |
121 |
Total |
120 |
123 |
Source: MacMahon, B. et al. Epidemiology:
Principles and Methods. 2nd Edition. Little, Brown, and Company, Boston 1996.
* Cured rice is parboiled in its husks before milling which allows thiamine to diffuse
and stay in the rice and is not removed by subsequent milling.
TABLE #4: RELATIVE and ATTRIBUTABLE MORTALITY FROM SELECTED CAUSES ASSOCIATED WITH HEAVY
CIGARETTE SMOKING BY BRITISH MALE PHYSICIANS 1951-1961
Cause of death |
Death rate among non-smokers |
Death rate among heavy smokers |
Death rate ratio |
Attributable death rate |
Lung cancer |
0.07 |
2.27 |
32.4 |
2.20 |
Other cancers |
1.91 |
2.59 |
1.4 |
0.68 |
Chronic bronchitis |
0.05 |
1.06 |
21.2 |
1.01 |
Cardiovascular diseases |
7.32 |
9.93 |
1.4 |
2.61 |
All causes |
12.06 |
19.67 |
1.6 |
7.61 |
TABLE #5: DEATH RATES FROM LUNG CANCER ACCORDING TO SMOKING HABITS OF BRITISH MALE PHYSICIANS
AGE 35 and OVER, 1951-1956
Age group (years) |
Non-smokers |
Light smokers |
Moderate smokers |
Heavy smokers |
35-54 |
0.00 |
0.09 |
0.17 |
0.26 |
55-64 |
0.00 |
0.32 |
0.92 |
3.10 |
65-75 |
0.00 |
1.35 |
3.34 |
4.80 |
75+ |
1.17 |
2.78 |
2.07 |
4.16 |
Total |
0.07 |
- |
- |
1.66 |