Report from the Sub-comittee on the environment and health

Analytical studies: case
control studies
Analytical studies: cohorts
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exposure to <strong>the</strong>m in large, limited population groups (e.g. farmers) can
be linked to an increased occurrence of cancer. However, such a
relationship says nothing at all about causal relationships, i.e. that a high
exposure to a pesticide cannot, <strong>from</strong> <strong>the</strong> above data, be said to be <strong>the</strong>
cause of cancer but can give rise to a suspicion concerning causal
relationships, which must <strong>the</strong>n be examined more closely.
Case control studies are <strong>the</strong> most common type of analytical
epidemiological studies. In <strong>the</strong>se, one compares causal factors, e.g.
lifestyle, in individuals with a given disease or o<strong>the</strong>r factor that one
wishes to investigate, with healthy persons. The individual in <strong>the</strong> control
group should be chosen at random, and with <strong>the</strong> same inclusion and
exclusion criteria, <strong>from</strong> <strong>the</strong> same population base as cases. Case control
studies are retrospective. Memory <strong>the</strong>refore plays a vital role. It is also
<strong>the</strong> case that precisely <strong>the</strong> memory of a sick person is often seriously
affected by <strong>the</strong> disease and can give rise to systematic errors (bias). It is
also often uncertain which period of <strong>the</strong> sick person’s life is most
relevant for <strong>the</strong> study. The longer <strong>the</strong> time elapsing between a harmful
effect and <strong>the</strong> occurrence of recognised disease, <strong>the</strong> greater this problem
becomes. A study that compares <strong>the</strong> pesticide intake through diet in
women that have borne a child with a congenital defect with <strong>the</strong> intake in
women that have born healthy children is an example of a case control
study that is encumbered with <strong>the</strong>se possibilities of error.
Ano<strong>the</strong>r frequently used analytical epidemiological study – <strong>the</strong>
prospective cohort study – has fewer weaknesses than case control
studies. In <strong>the</strong> prospective cohort study, relevant information is ga<strong>the</strong>red
<strong>from</strong> healthy persons, who are <strong>the</strong>n monitored for a period – often a
number of years – during which <strong>the</strong> morbidity of <strong>the</strong> cohort is recorded.
This means that when a disease occurs one has <strong>the</strong> possibility of going
back and finding <strong>the</strong> factors that characterised persons that later became
ill for comparison with persons that did not do so. In cohort studies one
also has <strong>the</strong> possibility of extracting and storing biological material for
later analysis. For example, analyses of blood samples, fatty tissue
samples and milk <strong>from</strong> nursing mo<strong>the</strong>rs provide information about <strong>the</strong>
exposure to pesticides.
Cohort studies have <strong>the</strong> major drawback that <strong>the</strong>y require many
participants because <strong>the</strong> incidence (number of new cases per year) of
ordinary diseases (e.g. cancer) is relatively low. Ano<strong>the</strong>r factor is that it
can take a long time for a disease to develop. In <strong>the</strong> case of cancer, 20
years or more can elapse <strong>from</strong> <strong>the</strong> initiation phase until clinical diagnosis
(see later). These factors mean that cohort studies are considerably more
costly and more difficult to handle than case control studies.
However, in <strong>the</strong> case of cohort studies as well, it is not possible to
determine whe<strong>the</strong>r <strong>the</strong>re is a causal relationship. Persons with a high
exposure to a pesticide probably differ in o<strong>the</strong>r ways <strong>from</strong> persons
characterised by a low exposure. In studies, one often adjusts statistically
for factors that can confound <strong>the</strong> result, e.g. socioeconomic status,
smoking habits, alcohol consumption, physical activity, use of hormones,
etc. These factors are called “confounders” or confounding factors.
However, it is important to be aware that differences in, say, lifestyle and