* Government: These provide a wide range of current statistics; The Office of National Statistics provides this. They have a hard and electrical copy of such things as GP appointments from surgeries, infant mortality rates, hospital admissions, suicide rates and many other statistics. They analyse this data by age, social class, gender and location of where the data is from and often make a comparison and study if there is a trend.
* Academic researchers and other authors: Often from a university, people research contributing to evidence and debate a wide range of issues regarding health and social care.
Charitable organisations and pressure groups: Special interest and charitable groups also produce and publish statistics regarding their area of concern: this information is on-going and up-to-date. The government also produces statistics for mortality rates, death rates, and morbidity rates, disease of a given period of time. These rates are then compared over a period of time and studied as to whether they have increased or decreased, analysed by social class, age, sex and location.
Specific morbidity rates are measured in the terms of its prevalence.
Either disease prevalence, number of cases of a disease in a population during a given period of time, or disease incidence, number of new cases of specific disease occurring in a population during a given period of time. The data for these statistics are collected from appointments from GPs and hospitals. Mortality rates and the causes of the death are collected from the official and required registration of deaths.
Infant mortality rate are especially studied to work out the health and well-being of a society. If the infant ortality rises this indicates that this given location has a poor standard of health and well-being where as if it decreases this is showing it is improving. The reasons for the increases or decreases may lie in the economic or social environment, maybe due to inadequate services of these forms may be available to the expecting mothers. Difficulties in measuring health Many problems occur when measuring health due to the statistics involved. Arising problems when collecting data which is secondary data is the reliability of this.
If the data is from the internet, which nowadays is so often used, a number of questions are asked such as: is this data real? How was the data collected? Has it been altered to suite what the person wanted to find? Who collected the data? And also where and when was the data collected? All these left unanswered cause for unrealisable statistics. As well as this, primary data can also be a cause for problem as the official sources may not provide an accurate representation of the pattern regarding health and illness.
This has been said due to different opinions on illness, some people do not go to seek medical advice when they are ill and some people go when they are in fact not ill at all; this causes for incorrect data collection and incorrect statistics on health and illness. Doctors can also have different ideas, such as two people which have the same symptoms may be diagnosed by two different doctors with two different illnesses. This will distort the figures of the people with specific illness.
Regarding this Ken Browne explained in 2006 a framework involving at least four stages these are: Stage 1: individuals must first realise that they are they have a form of problem either physically or mentally Stage 2: they must decide whether their problem is serious enough to seek advice from a health professional Stage 3: they will go seek this advice Stage 4: the health professional must then decide whether this person’s mental or medical problem has a label of illness that requires treatment.
The name ‘Clinical iceberg’ is often given to official statistics to do with health and illness as the true level of illness are thought to be largely concealed; this is for such reasons as many people who are ill do not go and see a doctor. Another difficulty for measuring health is that the reasons for death which are stated on a death certificate may not be true. As with the illnesses, doctors may not but the ‘real’ cause of death and may just put a ‘sugar coated’ version to avoid causing further distress to the family and friends of the deceased.
An example of this could be a homeless person living on the street, the cause of death according to the death certificate could be hypothermia but the doctors may not take in to consideration that it could have been years of substance abuse, malnutrition and the lack of shelter or none at all. As well as this a doctor may interpret the cause of death from symptoms they find; this could only be only one of the reasons they put down but the ‘real’ cause could have been many. Social class and patterns of health and illness.
Although statistics have to be approached with an open mind, evidence has been released which shows overwhelmingly that good health and life-expectancy is dictated by social class, with the lower classes being subject to poorer health and lower life expectancy. In 1980 The Black Report followed by the Acheson Report in 1998 are the most influential reports on this topic. In each report they provide an in-depth explanation of the relationship between the various social and the environmental matters regarding health and illness.
The findings in The Black Report shocked the government who stopped the publication of the results of the reports, but a few circulated. The two reports are still extremely influential to sociologist today. Those who did the research were persuaded that the reasons for the findings of the difference in health and life expectancy was due to the level of the peoples income, the environment they live in and the quality of their housing. The Black Report considered four explanations for the results of the research. These are: * The statistical artefact explanation: This was that the black report statistics were in fact biased.
People argued that the lower classes have higher amount of older people working and in more dangerous jobs; therefore have a lower life expectancy and a higher risk to poor health to those who are younger and work in office based jobs. * Natural or social selection: This idea of that in fact it is not the low wages, poor housing nor poverty causing the higher infant mortality rates, illness and lower life expectancy; but in fact it’s the poor health, absence from work and lack of energy needed for promotion and success that places them in the lower class.
Researches evidence proved that in fact poverty and poor living conditions were responsible for the increase in ill-health * Cultural or behavioural explanations: this explanation focused on the fact that lower social classes seemed to smoke and drink more alcohol, and there was evidence of this along with the higher likelihood of eating junk food and not a sufficient amount of exercise. These lifestyle choices were proved to link with the probability of illnesses such as forms of cancer, heart disease, diabetes and bronchitis.
Allow these theories were proved it is also though that in fact people in the lower social classes uses alcohol and smoking as a way of dealing with their circumstances. * Material or structural explanations: this explanation was focused on that the lower social groups who have the increased amount of illness, lower life expectancy and a higher infant mortality rate. It is based on that those with a lower wage and in poverty are linked with having a poorer diet, poorer housing which are situated in poorer environments.
Those who are in these situations often have more insecure and more dangerous employment. This teamed together can lead to differences in health and well-being. Gender and patterns of health and illness A harsh reality in society is that women’s life expectancy is around 5 years higher on average to men, along with infant mortality being higher within boys compared to girls. Although, statistics have shown on one or more occasions that women have a higher tendency to suffer with ill-health.
It is thought that three different social factors contribute to these things, they are: * Risk factors: the fact that men are more likely to drink alcohol and smoke maybe linked with the higher death rate, also this could be due to the more dangerous and risky sports, work and other activities they participate in. The particularly high numbers of death is within the age group of 17 to 24; this linked with risk-taking attitudes in young men as well as deaths associated with road accidents. Economic inequalities: many statistics show that women earn less that men. If two people with the same qualification and of different sexes, on average the male will earn on average ?1000 than the female classmate within 3 years of leaving university. 40 % of men and only 26% of women are likely to earn over ?25,000 a year 3 years after graduating. Compared to men, women are more likely to be in low wage and part time jobs. They are also more likely to be the lone-parent in one parent families and more probable to be on the means tested state benefit.
Women also have a higher prospect to e in poverty in old age due to the fact they may not have an employers pension due to family commitments; this may also affect a full state pension. Due to poverty this maybe the link with the higher statistics of ill-health in women along with stress. * Impact of the female role: Often in family homes women still take on the role of cleaning and it is thought that the dull nature of cleaning may increase the likelihood of depression. In 1989, Popay and Bartley did research in to the hours spent by women on cleaning in 1700 different households.
The results of this shown that women spent up to 87 hours and those with children spent up to 64, even if they had full time jobs. Some women manage on a little budget, work long hours and have limited time they can spend to themselves. Although this may be the increased cause for the higher rates of stress-related illness, in actual fact it could be that it happens to be diagnosed more due to the willingness women take in to discussing mental health issues with a GP. Ethnicity and patterns of health and illness The categorisation of a person race or ethnicity is hard, especially in the increasing population of those who are of multi race.
Also the minority of ethnic groups live in deprived inner-city areas within poor housing, pollution and where there are high unemployment levels. Due to this it makes and research inconclusive as the questions arise of is it down to the poor living conditions that they have poor health. Although some statistics are available compared to the white majority * Rickets has a higher incidence in children of the sub continent; this is due to lack of vitamin-D * Most minority ethnic groups have a higher number of infant mortality * Shorter life expectancy also occurs in minority ethnic groups
As well as this, language and other cultural barriers oppose a problem to the access of health and social care; this affecting the levels of poor health. Such as women of the Asian minority may feel uncomfortable seeing a male doctor also translators for those with little English are available but are limited although this is undergoing improvement. Racism or the fear of racism is a daunting situation for people. If health and care professionals do not fully understand the religious or cultural beliefs of those they are working with it is unlikely that their needs will be truly met.
Age and patterns of health and illness Although the majority of those past retirement age are fit, healthy and contribute to society there is a high level of illness within this age group, particularly in people aged over 75. Within 3 months of 2003, it was statistically revealed on General Household Survey that 24% of people that had attended accident and emergency or out-patients department were over 75, compared with only 14% being the other ages. In 2007 the Alzheimer’s society revealed that 1 in 20 people over the age of 65 and 1 in 5 people over the age of 80 suffered from dementia.
Locality and health and illness Trends also apply with the locality people are within. Mortality and morbidity rates vary across parts of the UK and between towns and cities. Studies have shown, with no surprise, that within the poorer regions and poorer areas of cities that there is an increase in the levels of illness. An example of this in that within research of the trends regarding lung cancer within the England the north-west, northern and Yorkshire regions the rates are higher than average; where as the south-western, southern and eastern regions the rates of Lung cancer are below average.
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