(10) Evaluating data, statistics, graphs and correlation for diseases e.g. risk factors

A correlation is a relationship between two random sets of data e.g. set A and set B for two variables.

A correlation does not automatically mean that A may affect B, B may affect A or both A and B are affected by a third variable.

Medical research scientists have to decide:

(i) whether links between possible causes and effects are valid, and

(ii) whether the link between treatments and cures is actually valid,

and this all about real correlation and just pure coincidence or a third unknown factor.

There is also the question of data collection and its evaluation and analysis.

(1) How reliable is the data and compared with an appropriate control group e.g. as in drug testing.

(2) How valid is the data - was it correctly measured and collected without bias? Where the results repeatable?

(3) How big was the sample size? Was it big enough for clear statistical significance?

(4) If repeated, how could a survey or set of experiment trials be improved?

(5) Where there any anomalies in the data? Can they be explained? Are they significant i.e. is the some unknown factor influencing the results in some way? Was sufficient time allowed for other possible effects to be clearly seen?

(6) What is the confidence of the conclusion? Have you proved the relationship between two variables?

(7) Could there be a bias in the interpretation of the results - vested personal or economic interests? I'm afraid this can happen in the pharmaceutical industry.

(ii)

Graph 2 A negative correlation graph

Again, despite the scatter of points (X), quite clearly, on average, an increase in variable B follows an decrease in variable A. The graph has a negative gradient - don't take the word negative to mean no correlation!

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Graph 3 A no correlation graph

No particular pattern or correlation - just a scattered set of points (X).

There is no clear trend of A influencing B or B influencing A.

Graph 4 examples

(i) If variable A is time, years of the 20th century, the variable B could be quantity of cigarettes smoked (blue line) and the number of lung cancer cases (purple line). Lung cancer takes many years to develop and there is a 20 year lag between the two sets of data. This is good evidence for the connection between the non-communicable disease of lung cancer and smoking.

Graph 5 examples

Linear relationships, but one (blue line) increasing proportionately greater than the other (purple line) - steeper gradient observed.

Graph 6 examples

(i) Correlation of smoking and lung cancer. A = time, B blue = amount of smoking and B purple = lung cancer deaths.

This has some similarities with graph 4, but in this case, although there is a lag, some other factor has intervened to bring both trends down.

Hopefully, this is what will happen in the future. At the moment the two curves have just dropped a bit from their peak values.

There has been a world-wide health campaign against smoking and some countries ban the advertising of cigarettes and it does seem to be having some effect.

Apparently during the Covid-19 corona flue pandemic in 2020. large numbers of people stopped smoking for fear it would make the more susceptible to this virulent and potentially deadly virus.

They hope, and quite correctly, it will help their lings to healthier, they are also more reluctant to go out to shops to buy cigarettes, others have lost their jobs and need to cut down on expenses.

Doctors in China have evidence that survival rates of non-smokers exceed that of smokers.

All in all, 2020 is a good time to stop smoking! and hope the two curves continue to fall.

Graph 7 examples

(i) Variable A could represent time in years. The purple B line could represent the number of people vaccinated in a population - increasing coverage. The blue B line could represent the fall in cases of the disease being vaccinated against - increasing immunisation of a population.

Graph 8 examples

Graph 8 is a typical graph for when a new vaccine is introduced to combat some disease.

A represents time in years. B represents the incidence of the disease (infection) in a given population.

Once the vaccine is developed and the immunisation programme instigated, the effect can be quite dramatic.

This happened in1940s with the development of a vaccine against diphtheria, producing a rapid decline in the number of cases.

Diphtheria is an acute and highly contagious bacterial disease causing inflammation of the mucous membranes, formation of a false membrane in the throat which hinders breathing and swallowing, and potentially fatal heart and nerve damage by a bacterial toxin in the blood. It is now rare in developed countries owing to immunization.

There other similar patterns for communicable diseases e.g. measles is an infectious viral disease causing fever and a red rash, typically occurring in childhood. This has been greatly minimised by the MMR vaccination of young children which is very effective at protecting people against measles, mumps, and rubella, and preventing the complications caused by these diseases.

Graph 9 examples

Graph 9 represents a trend that rises, reaches a peak and falls again to a similar start value.

If variable A represents a year in time, the blue graph line could represent the peak of some infection cases e.g. salmonella food poisoning tends reach a peak in the summer months. Warmer temperatures encourage the growth of salmonella bacteria in food causing the disease. Careless cooking and food storage add to the effect.

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