Gardiner V., Matthews H. The changing geography of the United Kingdom
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FIGURE 12.8 Mortality rates for males and females aged 15–44, 1990–2FIGURE 12.7 Mortality rates for males and females aged 15–44, 1950–3
FIGURE 12.10 Mortality rates for males and females aged 45–64, 1990–2FIGURE 12.9 Mortality rates for males and females aged 45–64, 1950–3
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likely to die in the 1950s became the adults least likely to die before retirement. This is
because of the effects of the living conditions in the places in which they grew up upon their
subsequent lives and health. Those children who survived the first years of life in areas with
the highest rates of mortality were still more likely to die young as adults.
To conclude this description of the geography of mortality in Britain Figures 12.11 and
12.12 show the age standardised mortality ratios in each area at both dates. We describe the
process of age standardisation below, but basically these are the areas in which mortality in
general is most and least likely for men and women, irrespective of their ages. In the 1950s the
overall pattern of mortality is made most clear by combining the six age groups. Mortality
rates were very high in Scottish towns, in South Wales and for men in the north-west of
England. Rates were low across the south of England. The two aberrant areas of Nairn county
in Scotland and the City in London are both very small districts containing only a few thousand
people. By the early 1990s the overall pattern had changed little. The highest rates of mortality
still clustered in Scottish towns and in the north-west of England, and rates were still low
across the South. The most significant change over this second half of the twentieth century is
that three London Boroughs now rank amongst the areas with the highest standardised mortality
ratios and we know—from having looked at the individual age groups—that this is due largely
to the concentration of relatively higher rates of mortality among young adults in particular
London Boroughs. To sum up, the North-South divide in Britain epitomises the geography of
mortality over the period 1950–92. In recent years there has been a renewed concentration of
mortality within large urban areas and this has led to parts of London now joining the North in
the top ten of the mortality league table of Britain.
The rise of inequality
One way of measuring geographical changes in health is to calculate what proportion of the
population are living in places where Standardised Mortality Ratios (SMRs) are either very
high or very low. SMRs are the chances of dying for people living in a particular area,
having taken account of the age and sex structure of the population of that area. They are
presented in terms of a ratio as compared to a national average of 100 (in this case we use
the contemporary mortality rates at each period in England and Wales as these are used in
official statistics). Thus people living in an area with an SMR of 125 are 25 per cent more
likely to die at any given time than is the population more generally. Table 12.1 on page 240
shows those changing proportions of the population for each of the time periods being
considered here for the 292 areas in Britain, used in the maps described previously (which
were referring to 1950–3 and 1990–2). The time periods shown in this table are those for
which Registrar General’s Reports were available or for which comparable statistics could
be calculated.
Table 12.1 can be interpreted in different ways. On the one hand it shows a process of
convergence. In the first period, 57.8 per cent of the population lived in areas with close to
average SMRs of between 95 and 114. This proportion has risen slowly but steadily every
decade such that it has reached 65.5 per cent of the population of Britain by the latest
period. More and more people are living in areas where their aggregate chances of dying
are near the national average. The proportion of people living in areas with low ratios (under
95) rose to almost a third of the population (32.8 per cent) over the first three periods but
has now fallen to almost a quarter (26.4 per cent).
FIGURE 12.12 Age standardised mortality ratios (under 65) males and
females, 1990–2
FIGURE 12.11 Age standardised mortality ratios (under 65) for males
and females, 1950–3
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However, the opposite trend is seen for the proportion of the population living in
areas with high SMRs which fell from 10.3 per cent to 6.5 per cent over the first three
periods, but which has risen steadily since then. And, as the table shows, there is even more
fluctuation at the extremes. The problem with measuring changes in geographical inequality
using this technique is that the results can depend too much on what SMR threshold is used
to calculate them.
An alternative and more robust measure is to consider the number of deaths which
occur in each area above the number that would be expected given the age and sex of the
population of each area. This statistic, when expressed as a proportion of all deaths, is
labelled excess mortality and fluctuates far less than do figures based on simple thresholds.
Table 12.2 below presents the figures for excess mortality for the whole population and for
the selected age and sex groups which contain the majority of early deaths in Britain.
Table 12.2 shows that just after the Second World War 4.3 per cent of deaths in Britain
were accounted for by excess mortality (i.e. deaths above those expected in areas where the
SMR was greater than 100). This proportion fell slightly during the 1950s, then rose again
to 4.3 per cent in the late 1980s to settle in the contemporary period again at 4.1 per cent. As
Table 12.2 shows, the figures are more dramatic when smaller groups of the population are
considered. For men aged 15 to 44 and 45 to 64, respectively 5.2 per cent and 9.6 per cent
of all deaths are now accounted for by excess mortality. While for women of the same ages
excess mortality now accounts for 3.9 per cent and 8.9 per cent of all deaths. Most premature
deaths occur to people in the older of these two age groups, and excesses there were highest
in the late 1980s for men and early 1990s for women.
Geographical inequality in mortality in Britain is rising for older adults and has been
fairly stable for the population as a whole. It is thus likely that this country will fail to meet its
commitment to the reduction of inequality as laid out in Target One of the World Health
Organisation (WHO 1985). Indeed, for particular groups of the population there will probably
be a rise in inequality, rather than a reduction of the specified 25 per cent. For the population
aged under 65 there is a significant rise in area inequality over the period. Table 12.3 shows
the SMRs for the population aged under 65 when Britain is divided into ten areas each containing
approximately the same number of people. In the early 1950s the worse off 10 per cent of the
population had a mortality rate below the age of 65 which was 31 per cent higher than the
average. By the early 1990s this had risen to 42 per cent—the highest recorded.
TABLE 12.1 People in Britain living in local authority areas by SMR (figures are per
thousand people)
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Table 12.3 provides the most robust single summary we have of how geographical
inequalities in mortality in Britain have risen over time. This rise is not dependent on the
decile divisions we have used, as both the first and second decile of the population have
seen their position deteriorate since 1981 (in both cases SMRs have risen since 1973, rising
quickly during the 1980s). Similarly there has been a steady improvement in the mortality
rates of the two deciles of the population which had the best life chances after 1973. The
best off 10 per cent of the population have seen their mortality ratio fall from being 83 per
cent of the national average between 1969–73 to 79 per cent, 78 per cent and now 76 per
cent of the national average. If we compare this with the worse off 10 per cent of the
population’s current standardised mortality ratio of 142 we see that people living in these
areas are almost twice as likely to die at any given time under the age of sixty-five when
compared to the best-off decile of the population. The gap between the best and worse off in
Britain has never been wider.
Underlying this rise in geographical inequality is the changing geographical
concentration of people with different individual chances of dying in different parts of
Britain. This could have happened in two ways and both probably occurred to some extent.
TABLE 12.2 Excess mortality in Britain by local authority areas (figures are percentages
of all deaths in the age group) of the whole population, of men aged 15–44 and 45–64, and
of women aged 15–44 and 45–64
TABLE 12.3 SMRs under 65 for population deciles of equal size
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First, the populations within an area can change in terms of their aggregate behavioural
patterns and social circumstances (for instance, people in Surrey may start smoking less).
Second, different groups of people may have different likelihoods of moving to different
parts of the country at different times (for instance, Liverpool may have seen an exodus of
its more qualified population). It is difficult to unravel the varying importance of these two
types of change, but first we need to know how the relative importance of the different
choices people make and the constraints under which they operate in determining their life
chances. Only after that can we begin to explain the changing geography of health in Britain.
Life expectancy: demographic basics
Age and sex
There are many factors which influence how long you will live. One of the most important
of these is chance, and chance by its very nature is unpredictable. We cannot predict, for
example, the likelihood of an individual being killed in a car accident, by a bolt of lightning
or by a crazed madman. An individual may also have a specific medical condition which
dramatically alters their life chances, but this is also very unpredictable. Although different
people have different chances some factors are more certain than others and some affect
much larger groups of people than others.
Here, we concentrate on those factors which have the most predictable effect on your
life expectancy and which do not require a doctor to diagnose. These include five things
which you cannot change: your age, your sex, your father’s occupation when you were 14
years old, your country of birth and your height. Also included are five factors which you
may be able to influence to some degree: your occupation, whether you are unemployed,
where you now live, whether you own or rent your home and whether you have a partner.
Finally there are seven factors over which you have the most control: smoking, alcohol
consumption, injecting drug use, diet, your weight, exercise, and sexual activity.
All of the factors we consider here have a geography to them, some more than others,
and just living in a particular place can have an effect on your health after all other factors
have been taken into account. You have no control over some of the factors and do not have
total control over any of them. You can only choose to smoke, for instance, if someone is
prepared to sell you cigarettes; although it is far easier to smoke than it is to inject illegal
drugs—and it could be argued that it is far easier to drink alcohol in excess than either of
these. It is society as a whole that proclaims what behaviour is acceptable, and thus society—
as much as the individual—is responsible for people’s behaviour.
The geographical aspects to health will be discussed as we explain the impact, in
terms of increasing or reducing years of total life expectancy, of the various factors. In order
to estimate your life expectancy, you will need a starting point, which refers to the average
life expectancy for your age and sex group. Choose your starting point from Table 12.4. For
instance, if you are a 20-year-old female you start at 80.
The first thing to see from Table 12.4 is that the younger you are the lower is your life
expectancy. All else being equal your parents and grandparents are likely to live longer than
you. At first this may appear counterintuitive, but there are two reasons why this is so. Most
importantly this is a table for survivors. The life expectancies refer only to people who have
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already managed to live to a certain age. Someone who is currently 85 years old has to have
a life expectancy greater than 85 (our Table stops at 85 as we envisage few readers will be
older than this). Second, Table 12.4 is based on the life expectancies of the population
today. It is possible that there will be improvements in general nutrition, conditions of work
and in medicine that will improve life chances in the future, as they have improved in the
past. So the life expectancies in Table 12.4 may well be an underestimate. It is also possible
that there could be general catastrophes in the future, as there have been in the past. In this
case these figures could be an overestimate. If, for instance, we were producing this table a
hundred years ago we would not have known of the future toll of the Great War of 1914–18,
or of the influenza epidemic of 1918–19 (which killed more people world-wide than the
Great War).
From Table 12.4 note how, on average, women begin with an expectation of five more
years of life than men, but that this advantage diminishes with age. It is still not known with
any degree of certainty why women now tend to live longer than men (they have not always
had this ‘advantage’). Several factors are likely to matter. First, it is well known that there
are gender differences in socialisation—in the way that men and women are taught and
expected to behave in society. Little boys, for example, are expected to be boisterous and
adventurous and they learn to be ambitious and assertive. Girls, on the other hand, are
encouraged to be more passive and co-operative, careful and gentle. It may be that behaviour
that is seen as appropriate for males and females results in very different risks for those two
groups in society. For example, women have much lower rates of death from accidents than
do men. Second, biological explanations may also play a part in the sex differences in death
TABLE 12.4 Life expectancy starting points: men and women in Britain
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rates. Fewer women die of heart and circulatory diseases in middle age—hormones seem to
protect women from heart attacks until the menopause around the age of 50. After the
menopause, female rates of heart disease gradually rise and by the age of 75 more women
than men die from this cause of death. Medical advances may also be a factor—until the last
century childbirth was a very dangerous activity; death as a result of childbirth is now
extremely rare.
From the influences of age and sex alone we can see that there will be a geography of
life expectancy in Britain because in some parts of the country there are more elderly people
(for example, coastal retirement towns such as Bournemouth and Worthing). The ratio of
men to women also varies across Britain. It is important that this distribution of age and
gender is not seen as a map of health in Britain. In fact it is the opposite of that, as elderly
people are much more likely to be ill than are younger people and there is some evidence
that women are more likely to be ill than men (Miles 1991). Most studies of the medical
geography of Britain first standardise mortality rates to ‘take into account’ the differing
age-sex profiles of different areas, but it is important to understand that age and sex alone
produce the largest variations in our risks of mortality.
As you calculate your own life expectancy we will use two imaginary students
throughout this chapter as examples to illustrate some extremes. These fictitious students
are Gary and Victoria. Gary is a 24-year-old mature student at Teesside University and
Victoria is 21-year-old undergraduate at Bristol University. From Table 12.4 their respective
starting life expectancies are 75 and 80.
Lifecourse factors
Father’s social class
A map of life expectancies based only on age and sex would not show the full range of
geographical differences in life chances in Britain. In recent years there has been a rapid
divergence in the life chances of people both living in different social groups (Drever and
Whitehead 1997) and between people living in different parts of the country (Dorling 1997).
This growing division of mortality within Britain is largely accounted for by the polarising
social, cultural and economic geographies of Britain. In the 1980s and 1990s where you
were brought up and where you live had a greater influence on your chances of success in
the labour market and in other areas of life than was the case at any time since the 1930s.
Evidence has recently been emerging that health and the risk of premature death are
strongly effected by socio-economic factors acting throughout life (Bartley et al. 1997).
There is evidence that widening inequalities in mortality in adulthood reflect the accumulating
influence of poor socio-economic circumstances throughout life. Studies have shown that
there is an association between cardiovascular risk and socio-economic position during
adulthood and childhood, and that both past and present socio-economic circumstances
affect mortality.
Davey Smith et al. (1997a) reported an analysis referring to respondents’ fathers’
social class, first social class and current social class. The effect of fathers’ social class
(taking other factors including your own social class into account) is shown below. You
should modify your life expectancy according to the rule in the Question 1 box.
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Question 1: What was your Father’s occupational class when you
were 14 years old?
If your father was in a manual occupation or not working when you were 14 years old
subtract one year from your life expectancy, otherwise add two years.
To return to our hypothetical examples, Gary’s father was a shipbuilder when he was
a child and so his life expectancy falls to 74, whereas Victoria’s father was an accountant
and so her life expectancy rises to 82. We use the father’s social class as the best simple
indicator of material conditions in childhood.
Why should your father’s social class matter and what are the geographical
implications of differing material conditions in childhood on health? One reason for these
differing chances is that people whose father was in a manual occupation or unemployed
when they were born are less likely to have been well nourished when they were children.
Food makes up a much higher proportion of the expenditure of the poor than of the rich
and access to good food is worse in poorer areas of Britain (Blackburn 1991; Sooman and
MacIntyre 1995). Another reason is that children whose father is in a manual occupation
are many times more likely to suffer from accidents and illness in childhood. Today children
of parents in the lowest social classes are five times more likely to be killed by a car than
those of parents in the highest classes. The cumulative effect of all the disadvantages of
poor income and status result in the average three-year gap in life expectancies given in
the question above. Geographically this means that parts of the country where adults
were more likely to be in manual occupations in the past will have higher mortality rates
today. Although here we have only referred to one previous generation, the geography of
the great economic depression of the 1930s can still be seen reflected in the pattern of
mortality in Britain in the 1990s.
Current social class
Next we refer to your current social class as this also has a large effect on your life
expectancy. This relationship has been known since the Registrar General first devised
social class categorisations based on occupation in 1911. The differences observed are
due to the material and social advantages of people in particular classes. The effects are
greater for men than women because, traditionally, the gap in incomes has been greater
between men than between women. However, there has been a rapid growth in the
proportions of people in higher social classes, particularly for women, and this growth
has not been evenly spread across the country. Part of the explanation of the diverging
trends in mortality are diverging geographies of class. It is also possible that occupational
status has a greater effect on the health of men than on women because men spend a
greater proportion of their lives in paid work. It may also be that the classification
groups men more effectively than it does women —that an occupational stratification
is not as sensitive to the range of socio-economic differences between women as it is
for men. The Question 2 box shows how your life expectancy should be adjusted given
your current social class.