Causality And Association Lecture Notes PDF

CAUSALITY AND ASSOCIATION By D r. ASH RAF HUSSAI N  In The Usual Epidemiological work Descriptive studies help in the identification of the disease problem in the community; and relating disease to host, agent and environmental factors, it endeavors to suggest an aetiological hypoth...

CAUSALITY AND ASSOCIATION By D r. ASH RAF HUSSAI N  In The Usual Epidemiological work Descriptive studies help in the identification of the disease problem in the community; and relating disease to host, agent and environmental factors, it endeavors to suggest an aetiological hypothesis. Analytical and Experimental studies test the hypotheses derived from descriptive studies and confirm or refute the observed association between suspected causes and disease.  ASSOCIATION The term Association (or "relationship“) may be defined as the concurrence of two variables more often than would be expected by chance. Events are said to be associated when they occur more frequently together than one would expect by chance. BUT Association does not necessarily imply a causal relationship  The measures of association compare disease occurrence among one group with disease occurrence in another group. Examples of measures of association include risk ratio (relative risk), odds ratio, and proportionate mortality ratio.  Risk ratio also called relative risk, compares the risk of a health event (disease, injury or death) among one group with the risk among another group. The two groups are typically differentiated by such demographic factors as sex (e.g., males versus females) or by exposure to a suspected risk factor. Then the two groups are compared by the risk of developing the health outcome of interest.  Odds ratio (OR) is another measure of association that quantifies the relationship between an exposure with two categories and health outcome. is based on enrolling a group of persons with disease (“case-patients”) and a comparable group without disease (“controls”). It reflects the comparison of the probability of exposure among the two groups (diseased and non diseased)  Association can be broadly grouped under three headings : a. Spurious association b. Indirect association c. Direct (causal) association (i) one-to-one causal association (ii) multifactorial causation.  SPURIOUS ASSOCIATION Refer to a non real relationship between a factor and an outcome For example, a study in UK of 5174 births at home and 11,156 births in hospitals showed perinatal mortality rates of 5.4 per 1000 in the home births, and 27.8 per 1000 in the hospital births. Apparently, the perinatal mortality was higher in hospital births than in the home births. It might be concluded that homes are a safer place for delivery of births than hospitals. Such a conclusion. is spurious or artifactual, because in general, hospitals attract women at high risk for delivery because of their special equipment and expertise, whereas this is not the case with home deliveries. The high perinatal mortality rate in hospitals might be due to this fact alone, and not because the quality of care was inferior.  B. INDIRECT ASSOCIATION Associations appeared at first to be causal but found on further study to be due to indirect association. The indirect association is a statistical association between a characteristic (or variable) of interest and a disease due to the presence of another factor, known or unknown, that is common to both the characteristic and the disease. This third factor (i.e., the common factor) is also known as the "confounding" variable. Such confounding variables (e.g., age, sex, social class) represent a formidable obstacle to overcome in trying to assess the causal nature of the relationship.  EXAMPLE The indirect association between high coffee drinking and the occurrence of ischemic heart disease. Further studies found that heavy smokers are at the same time present as high coffee drinkers and this explain the present apparent association between the two factors  EXAMPLE Altitude and endemic goitre Endemic goitre is generally found in high altitudes, showing thereby an association between altitude and endemic goitre. We know, that endemic goitre is not due to altitude but due to environmental deficiency of iodine at high altitude illustrates how a common factor {i.e., iodine deficiency) can result in an apparent association between two variables, when no association exists. C. DIRECT (CAUSAL) ASSOCIATION (i) One-to-one causal relationship Two variables are stated to be causally related (AB) if a change in A is followed by a change in B. If it does not, then their relationship cannot be causal. This is known as one to-one" causal relationship. This model suggests that when the factor A is present, the disease B must result. Conversely, when the disease is present, the factor must also be present. Measles may be one disease in which such a relation exists. The concept of one-to-one causal relationship is complicated by 2 things 1. In most cases, the cause alone is not sufficient to cause the disease and the presence of other factors as person susceptibility, socioeconomic state and environmental factors appear to be crucial to complete the circle. 2. Sometimes, a single cause or factor may lead to more than one outcome as in the streptococcal example.  Streptococcal tonsillitis Haemolytic Scarlet fever streptococci Erysipelas C. DIRECT (CAUSAL) ASSOCIATION ii. Multifactorial causation In noncommunicable diseases or conditions (e.g., CHO) the aetiology is always multifactorial. In one model, there are alternative causal factors (Factors (each acting independently) to cause the same disease. This situation is exemplified in lung cancer where more than one aetiological factor (e.g., smoking, air pollution, exposure to asbestos) can produce the disease independently. In the second model, the causal factors act cumulatively to produce the disease. (simply when an individual is exposed to 2 or more factors, there may be a synergistic effect).  When the disease is multifactorial (e.g., coronary heart disease) numerous factors or variables become implicated in the web of causation, and the notion of "cause" becomes confused. The epidemiologist must establish a "cause and effect“ relationship and has to shift from demonstration of statistical association to demonstration that the association is causal. CRITERIA FOR JUDGING CAUSALITY 1. Temporal association 2. Strength of association 3. Specificity of the association 4. Consistency of the association 5. Biological plausibility 6. Coherence of the association  1. TEMPORAL ASSOCIATION This requirement is basic to the causal concept. Causal association requires that exposure to a putative cause must precede temporarily the onset of a disease to allow for any necessary period of induction and latency.  2. STRENGTH OF ASSOCIATION In general, The strength of association is based the size of the Relative risk and the dose-response relationship. the larger the relative risk, the greater the likelihood of a causal association. Furthermore, the likelihood that there is a causal relationship is strengthened if there is a biological gradient or dose-response relationship - i.e., with increasing levels of exposure to the risk factor, an increasing rise in incidence of the disease is found. 3. SPECIFICITY OF THE ASSOCIATION The concept of specificity implies a "one-to-one“ relationship between the cause and effect. There is an argument against this point to the above mentioned multifactorial association between factors and diseases. Further studies about the risk factors might explain more the specificity criteria as in smoking which more than 80 chemical substance and each one may be more specifically involved in the causation of its own disease. 4. CONSISTENCY OF ASSOCIATION The association said to be consistent if the results are replicated when studied in different settings and by different methods. A consistent association has been found between cigarette smoking and lung cancer. More than 50 retrospective studies and at least nine prospective studies in different countries had shown a consistent association between cigarette smoking and subsequent development of lung cancer, lending support to a causal association.  5. BIOLOGICAL PLAUSIBILITY Causal association is supported if there is biological credibility to the association, that is, the association agrees with current biological knowledge of the response of cells, tissues, organs, and systems to stimuli. For example, the notion that food intake and cancer are interrelated is an old one. The positive association of intestine, rectum and breast cancers is biologically logical, whereas the positive association of food and skin cancer makes no biological sense and require further studying to support such assumption. 6. COHERENCE OF THE ASSOCIATION A final criterion for the appraisal of causal significance of an association is its coherence with known facts that are thought to be relevant. For example, the historical evidence of the rising consumption of tobacco in the form of cigarettes and the rising incidence of lung cancer are coherent. Male and female differences in trends of lung cancer death rates are also coherent with the more recent adoption of cigarette smoking by women. Here death rates rose first in males and are now increasing relatively more rapidly in females.