As a result, I will use the following method which is much less intrusive, therefore ensuring that the best interests of everyone involved are respected:
- I will identify the areas of the cemetery which contain gravestones from the time periods I am interested in.
- I will walk along the paths which surround these areas and will record data from every other gravestone which is situated next to the path.
Results
Summary Table
Data Analysis
I am going to use a student t-test to determine whether or not there is a significant difference between two mean values which have been calculated from my results. In order to carry out a t-test I will formulate a null hypothesis, stating that there is no significant difference between the two mean values that are being tested. If the null hypothesis is rejected, then it can be said that there is a significant difference between the two different data sets. A null hypothesis can be rejected using the results of the t-test. If the calculated t value is greater than the critical t-value, the null hypothesis is rejected.
Null Hypothesis: There is no significant difference between the life expectancy of males and females between 1900 and 1920.
Calculated t-value: 16.01
Critical t-value: 1.99
The calculated t-value is greater than the critical t-value and therefore I can reject the null hypothesis. This shows that there is a significant difference between life expectancies of males and females between 1900 and 1920, with females (who have a mean life expectancy of 53) having a higher life expectancy than males (who have a mean life expectancy of 46). This is at 5% significance, 95% confidence.
Null Hypothesis: There is no significant difference between life expectancy of males and females between 1980 and 2000.
Calculated t-value: 38.61
Critical t-value: 1.99
The calculated t-value is greater than the critical t-value and therefore I can reject the null hypothesis. This shows that there is a significant difference between life expectancies of males and females between 1980 and 2000, with females (who have a mean life expectancy of 76) having a higher life expectancy than males (who have a mean life expectancy of 62). This is at 5% significance, 95% confidence.
Null Hypothesis: There is no significant difference between life expectancy of males between 1900 and 1920 and males between 1980 and 2000.
Calculated t-value: 42.29
Critical t-value: 1.99
The calculated t-value is greater than the critical t-value and therefore I can reject the null hypothesis. This shows that there is a significant difference between life expectancies of males between 1900 and 1920 and males between 1980 and 2000, with males between 1980 and 2000 (who have a mean life expectancy of 62) having a higher life expectancy than males between 1900 and 1920 (who have a mean life expectancy of 46). This is at 5% significance, 95% confidence.
Null Hypothesis: There is no significant difference between life expectancy of females between 1900 and 1920 and females between 1980 and 2000.
Calculated t-value: 53.40
Critical t-value: 1.99
The calculated t-value is greater than the critical t-value and therefore I can reject the null hypothesis. This shows that there is a significant difference between life expectancies of females between 1900 and 1920 and females between 1980 and 2000, with females between 1980 and 2000 (who have a mean life expectancy of 76) having a higher life expectancy than females between 1900 and 1920 (who have a mean life expectancy of 53). This is at 5% significance, 95% confidence.
Conclusion
This data supports my first prediction that the average life expectancy for both males and females between 1900 and 1920, will be lower than the average life expectancy for both males and females between 1980 and 2000 in so far as I found the average life expectancy of males and females between 1900 and 1920 to be significantly lower than the average life expectancy of both males and females between 1980 and 2000.
However this data does not support my prediction that the average life expectancy of men would be greater than that of women in both time periods. It can be concluded from the data that in fact, men were found to have a significantly lower life expectancy than women in both time frames.
Discussion
The increase in life expectancy of both sexes between the earlier and later time frames could be attributed to a number of different factors. Medical discoveries which occurred in the 1900s would have been a major cause of the increase in life expectancy. A number of vaccines, which provide artificial immunity against diseases, were discovered in the nineteenth century. A vaccination works by releasing antigens into the blood stream which are taken in by macrophages. When the antigens are taken in, they are processed by the macrophage before being presented on a membrane protein. This antigen is then detected by a receptor on a helper T-cell, which binds to the macrophage. The macrophage then passes a signal to the T-cell, causing it to become active. Once the helper T-cell is activated, the antigen binds to a B-cell, which has antibodies in its plasma membrane. These antibodies recognize the antigen, which results in the binding of the antigen and the B-cell. The activated helper T-cell, which has receptors for the same antigen, binds to the B-cell. The T-cell then sends a signal to the inactive B-cell which causes it to become activated. Once activated, the B-cell starts to divide by mitosis to form many identical plasma cells. Plasma cells are active B-cells which contain a very large network of Rough Endoplasmic Reticulum, which synthesis large amounts of antibody, which is secreted by exocytosis in order to destroy the antigens which have infected the body. At the same time that activated helper T-cells are formed, memory cells, which are T-cells and B-cells which persist once the activated cells and antibodies which were produced to fight the disease that triggered the immune response have disappeared. These memory cells allow a rapid immune response the next time the same antigen is detected in the body, resulting in immunity from the disease. This immunity would result in a decrease in deaths from these diseases and therefore an increase in life expectancy for both men and women.
Another hugely significant medical discovery which emerged between the two timeframes is the discovery of penicillin and its development as an antibiotic. Sir Alexander Fleming discovered penicillin accidentally in 1929 when he forgot about a sample of staphylococci bacteria he was growing. He later returned to find that a mould had developed on the set of culture dishes he was using to grow the bacteria. The mould had reduced the spread of the bacteria, resulting in a bacteria free circle around itself. After further experimentation, Fleming named the substance which resulted in the reduced spread of the bacteria which was produced by the mould, penicillin.[7] However it was not until the 1940s that the antibiotic could be used to treat disease. The development of penicillin as an antibiotic was carried out by Howard Florey and Ernst Chain. This development began by purifying and concentrating the penicillin. This was carried out by repeatedly freeze drying the substance produced by the penicillium mould. Two mice were then injected with the new concentrated penicillin, and although the injection of penicillin produced was a much higher dose than that given to mice by Alexander Fleming, the two mice survived seemingly unharmed, and the concentrated penicillin passed its first toxicity test. Later eight mice were injected with haemolytic streptococci bacteria. Four of these mice were later injected with measured and timed doses of penicillin and after sixteen and a half hours, the four mice treated with penicillin had survived, whereas the four mice left untreated had died. This result showed that penicillin was indeed effective against bacterial infections. Further testing was then carried out on hundreds of mice in order to confirm this conclusion and further test the toxicity of the penicillin. In January of 1941, penicillin was first tested on a human, however the test was unsuccessful as the woman in question consequently experienced trembling and a sharply rising fever. The impurities were then removed from the penicillin using paper chromatography, before the new purified penicillin was again tested on a human in February 1941. The test subject was suffering from an invasive infection, and when treated with penicillin initially improved, however the stores of penicillin had almost run out and as a result he relapsed, and there was not enough penicillin to save him.[8] The last problem that needed to be overcome was how to increase the production and yield of penicillin. By the early 1940s, penicillin was being widely produced on a large scale as an antibiotic and could be used to treat many otherwise fatal infections, consequently greatly increasing life expectancy by reducing the number of deaths caused by infections.
Other medical developments that occurred in the years in between the two time frames used in this investigation include the discovery by George Papanicolaou in 1941[4] of the significance of cervical smears in the diagnosis of cervical cancer, which is the fifth most deadly cancer in women. This discovery would result in an increased life expectancy for women in the later time frame, as a smear test can be used to detect abnormal cervical cells and treat them before they become cancerous, and possibly fatal. The discovery of kidney dialysis by Willem Klaff in 1944[4] and the increased success of transplants for organs of the human body such as the kidney in 1954 and the heart in 1967[1], as well as the discovery of magnetic resonance imaging (MRI) by Lauterbur, and the development of the first MRI scanner for medical use by Damadian in 1973[9] increased life expectancy of both men and women in the later time period.
The welfare state was introduced in Britain between 1944 and 1951. This would have had a great effect on the life expectancy of people living in Britain. The welfare state was introduced to combat the five evils of society; want, disease, ignorance, squalor and idleness [10]. The welfare state entitled people in Britain to free medical treatment, regardless of their circumstances. The welfare state also provided people with means to help themselves, by nationalising industries to create more employment opportunities, providing pensions and providing council houses for those who were living in poor conditions. The modern welfare state also provides benefits for those unable to work. By improving the quality of living environment for many people, the Welfare State would also have been instrumental in preventing the spread of disease, as living in squalor, and in very close proximity with other people, unable to afford decent housing, heating, or sufficient food and drink, not only results in a higher chance of becoming ill, but also provides optimum conditions for the spread of disease. The welfare state also resulted in those who were previously unable to afford to receive even basic medical care, being able to receive free medical treatment, which would not only have resulted in a decreased mortality rate as a result of disease, but also an increase in the number of people who were vaccinated against a number of killer diseases such as tuberculosis.
The World Health Organisation (WHO) was established by the United Nations in 1948 and is the main authority for health within the United Nations. The main responsibilities of the world health organisation are to provide leadership on global health issues, setting out the agenda for research into health matters, providing technical support to countries within the UN and monitoring and assessing global health trends. The establishment of the WHO resulted in universal aims for the improvement of global health, and in setting universal standards which must be met in regards to the preservation of the health of the world’s population. This in turn would result in the fair distribution of health care amongst everyone in a population, not just those who can afford to pay for treatment and technology [11].
The WHO was also instrumental in the eradication of smallpox, one of the biggest killers of the 20th century, killing over 300 million people worldwide, in the 20th century alone. Smallpox was declared to have been eradicated in 1980, at the beginning of the second time frame in this investigation, as a result of successful immunisation programmes. The eradication of such a deadly disease would have resulted in a significant increase in life expectancy for both men and women between the 1900-1920 timeframe and the 1980-2000 timeframe.
Another possible cause of the reduced life expectancy of both men and women in the earlier time frame is the influenza pandemic of 1918. An influenza pandemic occurs when a new form of the flu virus to which no one has immunity, emerges and spreads as easily as normal influenza. One such pandemic occurred in 1918, with a particularly severe and deadly strain of the influenza virus which infected about half of the worlds population at the time and killed over 200 000 people in the UK alone [12]. This variation of the flu virus killed more people than any other outbreak of disease, and healthy adults (between 20 and 40 years) made up the largest proportion of the mortality rate. Although it is not possible to determine where in the UK most of these deaths occurred, it is reasonable to say that such a large number of premature deaths would have caused a decrease in life expectancy for both men and women during the earlier time period, however the casualties may have been greater amongst those fighting in the trenches in the first world war due to the poor living conditions and sanitation which would result in easier spreading of the influenza virus [13].
The life expectancy of men in the first time period may be significantly lower than that of men in the second time period as a result of the fact that the First World War was taking place during the first time period. There were 908,371 British deaths as a result of the First World War [14], and as a large proportion of these deaths would have been significantly premature as a result of the large proportion of young men serving in the armed forces during the war, this would decrease the life expectancy of men in the earlier time period of my investigation.
A lower life expectancy for men than for women in the earlier time frame may also have been a result of the significant difference in the risks posed by jobs for men and women in this period. At the beginning of the 20th Century, a second period of industrialisation occurred in Britain. This second industrialisation was based, not on coalfields and iron ore as with the first industrialisation, but on new manufacturing industries and new forms of transport [15]. These new industrial jobs did not come without risk, and there were often unsafe working conditions, which may have resulted in a decreased life expectancy for people in the earlier time period when compared to the later time period as well as resulting in the lower life expectancy for men, as men were more involved in the jobs related to the industrial growth of Britain, and it was not until the second world war began in 1939 that women had to take over the roles and jobs of the men in society who were away fighting in the war.
The lower life expectancy of men than women in the later time period may be caused by the fact that men have a higher risk of developing heart disease than women pre-menopause. As men are more likely to develop heart disease or have strokes earlier in life than women, a further decrease of male life expectancy in this period may occur as male deaths as a result of heart disease or stroke are more likely to occur at a younger age than deaths resulting from the same causes in women.
Evaluation
I am going to number each piece of data starting with 1 for the first piece, and then plot it against the running mean for each set of data in order to determine whether my results are reliable and representative of the entire population. If the running mean becomes constant for the later data pieces, I can say that my data sample is representative of the population.
As the running mean is not constant, this data is not representative of the population.
As the running mean is not constant, this data is not representative of the population.
As the running mean is constant, it can be said that this data is representative of the population.
As the running mean is constant, it can be said that this data is representative of the population.
Limitations
Abstract
This report investigates
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[11] World Health Organization – Site last visited on 31/12/07
[12] Health Protection Agency – Site last visited on 31/12/07
[13] BBC News – Site last visited on 31/12/07
[14] Spartacus Educational – Site last visited on 31/12/07
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[16] The University of Edinburgh: Health Matters –Site last visited on 31/12/07