10. Medical data - statistics and graphs about medical
conditions, correlations of possible cause and effects of illness
Doc Brown's biology exam revision study notes
There are various sections to work through, after 1 they can be read and studied in any order.
INDEX of notes on non-communicable diseases
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
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
Graph 1 A positive correlation
Despite the scatter of points (X),
quite clearly, on average, an increase in variable B leads to an
increase in variable A. The graph has a positive gradient.
(i) A plot of blood pressure versus weight
shows this kind of correlation.
Obesity, is a problem for very overweight
people because it can lead artery damage as the walls become
thicker and stiffer and less flexible and elastic - anything
that restricts blood flow will raise blood pressure.
Cardiovascular disease is a
non-communicable medical condition.
The data points would be more scattered
than graph 1, but the general trend would show a strong
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!
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.
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
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
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 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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