Some Terminology

1. Some Terminology • Congenital diseases: present at birth • Chronic diseases: these present or recur over a long period of time • Acute diseases: symptoms are severe and their course is short

2. Some Terminology … • Infectious / transmissible / contagious / communicable. • Diseases that can be transmitted from person to person (or between species). Usually involve a causal agent (e.g. bacteria or virus), but transmission may be genetic. • Degenerative / non-transmissible / non-communicable. • Traditionally assumed to be associated with the ageing process (i.e. risks increase as body degenerates with age).

3. Some Terminology … • Subclinical condition: an infectious agent may enter the body, multiply, stimulate the production of antibodies, and be eliminated from the body without the person being consciously aware of the illness • Usually the only way subclinical conditions can detected is through serology, or the identification of antibodies and other immune reactions in the blood

4. Some Terminology … • Latency or Incubation: between the time the infection occurs and the appearance of clinical symptoms • Specific Etiology - one cause (germ) that is both necessary and sufficient for each disease • Endemic: when a disease is constantly present in an area • Hypoendemic: occur at low levels, occasionally poping up here or there – e.g., typhoid in US • Hyperendemic: occur with intense transmission, e.g., malaria in Africa

5. symptoms death acute infection subclinical Clinical Full health latent Infectious Impaired health Figure 1: Source Meade & Earickson, 2000 The Health Continuum • Figure 1 illustrates the terminology and stages of ill health between full health and death

6. Incidence and Prevalence • Two other commonly employed concepts are those of incidence and prevalence. • Incidence refers to the number of new cases of a specific health disorder occurring within a given population during a stated period of time.

7. Incidence and Prevalence … • The incidence of AIDS during a particular month would be the proportion of persons within a population who are reported as having developed the illness during the month in question.

8. Disease Prevalence • Prevalence, is the total number of cases of a health disorder that exist at any given time. • Prevalence would include new cases as well as all previously existing cases. • Prevalence rates are sometimes expressed as point or period or lifetime prevalence

9. Point/Period Prevalence • Point prevalence - the number of cases at a certain point in time, usually a particular day or week. • Period prevalence - the total number of cases during a specified period of time, usually a month or year. • Lifetime prevalence - the number of people who have had the health problem at least once during their lifetime.

10. Point/Period Prevalence… • Distinguishing between incidence and prevalence is to regard incidence as the rate at which cases first appear, while prevalence is the rate at which all cases exist. • For example: Consider that the incidence of influenza in a community might be low because no new cases had developed. • Yet a measure of the disease's prevalence could be a larger figure because it would represent all persons who are currently sick from the illness.

11. Causation in Health Research • A fundamental task of health research is to investigate causation • Searching for associations between some aspects of the physical and social environment and the disease under study. • An association has to be elaborated to discover why it is happening and under what conditions it occurs.

12. Causation in Health Research cont.. • Once an association is established key questions are asked. • Is the relationship true and real or somehow artificial and false? • What is the nature of the relationship, is it positive or negative, weak or strong?

13. Causation in Health Research cont.. • Is the result brought about by chance or does it have any substantive meaning? • How consistent is the relationship and does it generalize to other situations? • In short, does the 'explanatory' variable cause the dependent variable?

14. Causation Defined • Causation is defined as a relationship between two variables such that a change in the level of the explanatory or independent variable (X) produces a change in the level of the dependent variable (Y). • If an increase in X produces an increase in Y, there is a positive causal relationship; if an increase in X produces a decrease in Y the relationship is a negative causal one.

15. Multiple Causality • Unlike the biomedical approach, medical geographers and disease ecologists hold a particular causal view of the world that can be best described as probabilistic and multicausal. • According to the probabilistic version of causality, no effect is completely determined by a cause. • While not all smokers will die of lung cancer, the smoking of cigarettes can lead to an increased chance of dying from this disease.

16. Multiple Causality cont.. • The concept of multicausality: • Each effect has a number of causes and each cause can produce a number of effects.

17. Multiple Causality cont..

18. Multiple Causality cont.. • For example, consider the causal model in the diagram below Here there are multiple causes and multiple effects; and effects in one causal model can be causes in another.

19. Conditions for a causal relationship • Co-variation • That is X and Yare associated over space and time. • For example, if air pollution is a cause of bronchitis, an increase in bronchitis should be associated with an increase in air pollution.

20. Conditions for a causal relationship … • Temporal precedence; if X is causally prior to Y, changes in X produce a subsequent change in Y in the course of time. • Unfortunately, temporal precedence is often plagued by time-lags and possible reciprocal causation (X causing Y, and Y causing X). • For example, a person may be caused to be depressed by losing a job (X Y) or the loss of a job may be caused by the inability to work effectively due to depression (Y X)

21. Conditions for a causal relationship … • Eliminate other possible explanations of the cause and effect • The researcher seeks other 'lurking' variables that are causally prior to both X and Y which, when controlled or removed, result in the disappearance of the original spurious association.

22. Conditions for a causal relationship … • No investigator can know when all possible important variables have been controlled and identified and, therefore, it is never possible to prove any relationship to be causal. • In practice researchers can be increasingly confident of their provisional causal interpretations as they fail to find them spurious after controlling several possible variables.

23. Causal Models - Extraneous variables • Consider a simple model in which two variables, X and Y, have been found to occur in association. It may be postulated that X →Y, or overcrowding causes bronchitis. • Such an association can occur for one of three reasons: there may be a true causal relationship, the association may occur simply by chance, or X may co-vary with Y because of some third variable Z

24. Firstly, there is the possibility that: Causal Models cont.. • In this case, living at high densities and suffering air pollution are both independent causes of bronchitis.

25. The alternative model is: Causal Models cont.. • In this case only air pollution is truly a cause of bronchitis. • Theoriginal association between overcrowding and bronchitis is,therefore, a spurious one produced by the association of airpollution and overcrowding.

26. Causal Models cont.. • For example: • Sub-areas of a city may have both high air pollution and a great deal of overcrowding, but it may be only the former variable that is causally linkedto chest disease. • If overcrowding is reduced but air pollution • remains at previous levels, this model predicts that bronchitis deaths would not decrease. • It is essential, therefore, that if there is deliberate intervention to lower death-rates, decisions are not made on the basis of such false, spurious relationships.

27. Cigarette consumption Cancer Causal Models – Searching for Spurious Relationships An example: • Cigarette smoking and cancer • Reading the government warning that is printed on cigarette packets may lead one tobelieve that the simple causal relationship (below) is undisputed.

28. Searching for Spurious Relationships … • While the majority of researchers do believe in the • essential validity of this model, others continue to argue that alternative models are more appropriate. • Doll and Peto (1981) estimated that smoking accounts for some 30 per cent of all cancers, occupational cancers cause less than 5 per cent of all cancers and pollution only 2 per cent

29. Searching for Spurious Relationships … • Epstein (1979) argues that occupational carcinogens produce anything between 20% and 40% of all cancers and the contribution of smoking has been overestimated. • His fundamental model is that there is a synergistic interaction between smoking and occupation whichcan be conceptualized as follows:

30. Synergistic Interaction Between Smoking and Occupation • Both smoking and occupation are independent causes of cancer, but they also interact • The people most likely to develop cancer work in a carcinogenic environment and are heavy smokers.

31. Synergistic Interaction Between Smoking and Occupation … • According to this view, studies that have considered smoking without controlling for occupation are bound to overestimate the smoking effect, for heavy smokers tend to be manual workers who work in high-risk occupations.

32. Synergistic Interaction Between Smoking and Occupation … • Other researchers have contended that the relationship between smoking and cancer may have been misunderstood because the role of genetics and the behavioural environment have been ignored. • Research has shown that smoking is related to personality factors (Eysenck, 1980) and so it is possible to suggest a whole series of alternative models.

33. For example, in the following model: Synergistic Interaction Between Smoking and Occupation … • genetic and behavioural factors are related to cancer through the intervening variables of extroversion and smoking; lower smoking would lower cancer.

34. In the following model: Synergistic Interaction Between Smoking and Occupation … • genetic and behavioural background variables are related to smoking and cancer through the intervening variable, extroversion. • In this model, the link between smoking and cancer is spurious; lowering smoking levels would not alter the risks of getting cancer.

35. Suppressor Variables … • Suppression usually occurs when only a relatively small part of the independent variable has an effect on the dependent variable. • In any causal analysis, the research must, therefore, attempt to 'refine' the causal variable and to determine which aspects of the independent variable are truly causal.

36. Suppressor Variables .. • For example, the association between smoking and lung cancer deaths was considerably increased when, instead of just comparing those who did and did not smoke, the smokers were refined into different groups. • Thus, cigarette smokers had a much greater risk of developing lung cancer than pipe and cigar smokers (Susser, 1973).

37. Distorter Variables • A distorter variable is exemplified by the apparent theory that more married people commit suicide than single people. • However, when the population is segmented by age, it is found that in each age group, the single suicides outnumber the married. Thus, this actually supports the thesis that marriage reduces suicide. • A distorter variable – in this case age – reveals that the correct interpretation is exactly the opposite of what was suggested by the original data.

38. Controlling for Other Variables • Any relationship between two variables (be it positive or negative, weak, strong or non-existent) should not be taken at face value. • The presence of uncontrolled extraneous, suppressor or distorter variables may lead to a totally spurious interpretation.

39. Techniques used for control • Control by design • Experimental control • Clinical trials • Control by Analysis