Prognosis, Causation, Factors of Causation, Risk Factors

Prognosis

Prognosis is the prediction of the course of a particular event that will occur in the future. Prognostic factors present in persons already known to have a disease, and are associated with an outcome of the disease. For prognosis, a variety of consequences of diseases are counted including death, complications, suffering etc.

Knowledge of the likely prognosis is helpful in determining the most useful treatment. Epidemiological information is necessary to provide sound predictions on prognosis and outcome. Clinical experience alone is inadequate for this purpose since it is often based on a limited set of patient and inadequate follow-up. For these reasons, epidemiological studies are required to describe accurately the natural history and prognosis of thedisease. For the prognosis of the disease, cohort studies are used. For the description of the prognosis, rates such as five-year survival (survival analysis), case fatality, remission percentage, recurrencepercentages are used. During analysis, stratification, standardization, multivariate adjustment, and sensitivity are the strategies.

(From: Bates IJ and Winder AE. An Introduction to Health Education, Mayfield 1984)

Fig. Natural history of a disease process

Causation

Cause

An understanding of the causes of the diseaseis important in the health field not only for prevention but also in diagnosis and the application of correct treatments. A cause of a disease is an event, condition, and characteristic of a combination of these factors. A cause is termed sufficient when it inevitably produces or initiates a disease. The cause is termed necessary if a disease cannot develop in its absence e.g. in food borne infection, chicken salad is sufficient causes of salmonellae diarrhea whereas occurrence ofSalmonellais necessary cause. In case of malaria,Anopheles fulviatilisis a sufficient cause whereasPlasmodiumparasite is a necessary cause.

Conditions of causality

Factor (X) is necessary Factor (X) id sufficient Examples

+ + Trisomy 21 and Down's syndrome

+ _ Mycobacterium tuberculosis and clinical tuberculosis

_ + Smoking and lung cancer

_ _ Some additional factors may be needed, Phenylpropanolamine

and hemorrhage stroke

The cause of disease may be single or multiple. According to Koch's germ theory of disease:

1. The organisms must be present in every case of the disease.
2. The organisms must be able to isolate and grown in pure culture.
3. The organisms must cause the specific diseas when inoculated into a susceptible animal.
4. The organisms must then be recovered from the animal and identified.

Factors in Causation

Four types of factors play a part in the causation of the disease:

1. Predisposing factors such as age, sex and previous illness, which may create a state of susceptibility to a disease agent.
2. Enabling factors such as low income, poor nutirtion, bad housing and inadequate medical care may favor the development od disease.
3. Precipitating factors such as exposure to a specific disease agent.
4. Reinforcing factors such as exposure and unduly hard work may aggravate an established disease or state.

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Fig. Causal model of tuberculosis

Risk Factors

The factors which are positively associated with the risk of developing a disease but that are not sufficient to cause the disease are called risk factors. Same disease are associated with several risk factors. There are several kinds of risk factors such as physical environment related toxins, infectious agents, drugs, and social environment related disruption of family, culture etc. Other risk factors are behavioral such as smoking, inactivity etc. Some risk factors are genetic and are inherited to offspring. Exposure to risk factors can take place a a single point in time e.g. when acommunity is exposed to radiation during a nuclear accident or over a period of time e.g. cigarette smoking. It is difficult to establish a relationship between exposure to risk factors and disease because of long latency period between exposure and disease, frequent exposure to risk factor, low incidence of disease, small risk from exposure, common disease, and multiple causess and effects of disease. Information about risk factors of the disease has following uses:

1. Prediction of the occurrence of the disease- the first use of risk factors of disease is to predict the occurrenec of the disease if it is present. However, it does not essentially mean that if someone exposed to risk facto of a disease, he gets the disease. It signifies that the person at exposure is at high risk of getting the disease in comparison to those who are not exposed.
2. To find the cause of the disease- Some risk factors may not be the cause of the disease. In such case, these factors are termed as markers. Removing such risk factors might not remove the excess risk assoiated with it.
3. Diagnosis of the disease- Risk factors in fact are very useful in diagnosing the disease together with the help of clinical and laboratory diagnosis. The presence of risk factors usually increases the probability of occurrence of disease e.g. age and sex are strong risk factors for coronary artery disease.
4. Prevention of disease- The removal of risk factors can be used to prevent disease. Cholera can be prevented by cutting the polluted water supplies. HIV/AIDS can be prevented by removing the risk behaviors of the disease.

Interaction

The effect of two or more causes acting together is often greater or less than would be expected on the basis of summing the individual effect. This phenomenon is called interaction. There are two types of interaction in epidemiological data:

Synergism-The phenomenon that the joint effect is greater than the sum of the effects of the individual causes is synergism. For example, combined effects of asbestos and cigarettes on lung function.

Antagonism-The phenomenon that the joint effect is less than the sum of the effects of the individual causes is antagonism. For example, combination effects of alcohol and folic acid on tumor incidence.

Establishing the Cause of a Disease

Casual inference is the process of determining whether observed associations are likely to be causal. The following concepts are used for guidelines of causation:

1. Temporal relationship-It is used to explain whether the cause precedes the effect.
2. Plausibility-It answers whether the associations are consistent with other knowledge.
3. Consistency-It is achieved it similar results have been shown in other studies.
4. Strength-What is the strength of the association between the cause and the effect (relative risk)?
5. Dose response relationship-Is increasing exposure to the possible cause associated with increased effect?
6. reversibility-Does the removal of a possible cause lead to reduction of disease risk?
7. Study design-Is the evidence based on a strong study design?
8. Judging the evidence- How many lines of evidencelead to theconclusion?

If the above seven points are strong enough between cause and effect, it suggests to conclude that the specific effect is due to that specific cause.

References

Gordis, L. Epidemiology. third edition. 2004.

Joshi, Banjara. Fundamentals of Epidemiology. Kathmandu: Quality Printing Press, 2007

Park, K. Park's Text Book of social and prevention Medicine. 18th edition. 2008.