Objective To examine the prevalence of major chronic diseases and their risk factors in different socioeconomic groups in the Australian population, in order to highlight the need for public policy initiatives to reduce socioeconomic inequalities in health.
Objective
To examine the prevalence of major chronic diseases and their risk factors in different socioeconomic groups in the Australian population, in order to highlight the need for public policy initiatives to reduce socioeconomic inequalities in health.Methods Data were provided by the Australian Bureau of Statistics (ABS) from the 2001 National Health Survey (NHS) for selected chronic diseases and associated risk factors. Conditions selected were those, which form the National Health Priority Area (NHPA) conditions (other than injury, which has not been included in this paper, with its focus on chronic disease); plus other 'serious' chronic conditions, in line with the classification developed by Mathers; and for which sufficient cases were available for analysis by socioeconomic status. Indirectly age-standardised prevalence rates were calculated by broad age group for Australia and for five groups of socioeconomic status; rate ratios were calculated to show variations in prevalence between these groups.
Methods
Data were provided by the Australian Bureau of Statistics (ABS) from the 2001 National Health Survey (NHS) for selected chronic diseases and associated risk factors. Conditions selected were those, which form the National Health Priority Area (NHPA) conditions (other than injury, which has not been included in this paper, with its focus on chronic disease); plus other 'serious' chronic conditions, in line with the classification developed by Mathers; and for which sufficient cases were available for analysis by socioeconomic status. Indirectly age-standardised prevalence rates were calculated by broad age group for Australia and for five groups of socioeconomic status; rate ratios were calculated to show variations in prevalence between these groups.Results Significant socioeconomic inequalities were evident for many of the major chronic diseases; the largest was for diabetes mellitus (at ages 25 to 64 years); and for many diseases, there was also a strong, continuous socioeconomic gradient in the rates. Circulatory system diseases (in particular, hypertensive disease) and digestive system diseases also exhibited a strong differential in the 25 to 64 year age group. In the 65 years and over age group, the strongest inequalities were evident for mental and behavioural problems, diabetes (with a continuous socioeconomic gradient in rates) and respiratory system diseases. A number of risk factors for chronic diseases, namely self-reported smoking, alcohol misuse, physical inactivity and excess weight showed a striking association with socioeconomic status, in particular for people who were smokers and those who did not exercise.
Results
Significant socioeconomic inequalities were evident for many of the major chronic diseases; the largest was for diabetes mellitus (at ages 25 to 64 years); and for many diseases, there was also a strong, continuous socioeconomic gradient in the rates.Circulatory system diseases (in particular, hypertensive disease) and digestive system diseases also exhibited a strong differential in the 25 to 64 year age group.In the 65 years and over age group, the strongest inequalities were evident for mental and behavioural problems, diabetes (with a continuous socioeconomic gradient in rates) and respiratory system diseases.A number of risk factors for chronic diseases, namely self-reported smoking, alcohol misuse, physical inactivity and excess weight showed a striking association with socioeconomic status, in particular for people who were smokers and those who did not exercise.Conclusion This analysis shows that the prevalence of chronic disease varies across the socioeconomic gradient for a number of specific diseases, as well as for important disease risk factors. Therefore, any policy interventions to address the impact of chronic disease, at a population level, need to take into account these socioeconomic inequalities.
Conclusion
This analysis shows that the prevalence of chronic disease varies across the socioeconomic gradient for a number of specific diseases, as well as for important disease risk factors. Therefore, any policy interventions to address the impact of chronic disease, at a population level, need to take into account these socioeconomic inequalities.

Background As in other developed countries, chronic diseases in Australia are major contributors to the extent of illness, disability and premature mortality in the population. They are estimated to make up the greatest proportion of the burden of disease, mental problems and injury for the population as a whole (about 80%), and for particular sub-population groups [ 1 ]. Chronic diseases are exemplified by having multifactorial aetiologies, including common disease risk factors and determinants; significant latency periods and protracted clinical courses; and are seldom cured completely [ 2 , 3 ]. Causal factors interact together at an individual and at a population level to determine the degree of disease burden and illness, and unhealthy risks can be passed on through families, communities, and populations following demographic gradients [ 4 ]. At different life stages, common risk factors and determinants include poor intra-uterine conditions; stress, violence and traumatic experiences; educational disadvantage; inadequate living environments that fail to promote healthy lifestyles; poor diet and lack of exercise; alcohol misuse and tobacco smoking [ 5 , 30 ]. Risk factors are also increasingly more prevalent in areas of low socioeconomic status and in communities characterised by low levels of educational attainment; high levels of unemployment; substantial levels of discrimination, interpersonal violence and exclusion; and poverty. There is a higher prevalence of such factors among Indigenous communities, and other socioeconomically disadvantaged Australians [ 5 , 6 ]. The inequalities in health observed across populations are many – some of them are inevitable and others, unnecessary and unfair. Those inequalities that are potentially avoidable are deemed 'inequitable' [ 7 ]. Despite significant medical advances and improved public health in recent decades, socioeconomically disadvantaged communities continue to suffer an unequal burden of illness, premature death and disability. Therefore, the study of socioeconomic inequalities in chronic diseases and conditions and in risk factors is important and necessary. This is particularly so, if we wish to develop more effective policy mechanisms for preventing and intervening earlier in the progression of chronic diseases and their associated risk factors across the diverse Australian population, and to reduce some of the existing health inequities. Our approach There have been a number of studies published in Australia on socioeconomic inequalities in mortality from various chronic diseases and conditions. The earlier ones were analysed using information on occupation recorded on the death certificate [ 8 - 10 ]. An alternative approach has been to examine variations in mortality rates by grouping residential locations according to socioeconomic criteria. A number of such studies have documented substantial variations in mortality for different age groups [ 11 - 17 ]. However, to date, there have been fewer studies that have examined socioeconomic inequalities in chronic disease prevalence in those still living (analyses of hospital admissions for chronic conditions have been published, but not of prevalence) [ 15 ]. One of the earliest was undertaken by Broadhead, who analysed data on morbidity and social status from the 1977–78 Australian Health Survey (ABS), and found that men in lower status occupations tended to suffer a higher age-standardised rate of self-reported chronic conditions and days of reduced activity; the picture for women appeared less clear [ 18 ]. Lee et al found that low income males were more likely to report mental health problems, chronic symptoms and acute symptoms than their high income counterparts [ 19 ]. Similar findings were reported in other studies, and risk factors associated with chronic diseases also were also associated with low income [ 15 , 19 - 21 ]. The work of Mathers is significant for its systematic documentation of health inequalities among working aged Australians (25 to 64 years) in the late 1980s. He examined mortality, disability, disease groups, specific diseases, self-perceived health, risk factors, health service use and use of preventive screening services, using data from the 1989–90 National Health Survey (NHS) [ 15 ]. Mathers found that there were no clear gradients of chronic, recent or minor illness with level of socioeconomic disadvantage of area, although there were some specific health status indicators (self-reported health, reduced activity, unhappiness) and certain risk factors (inactivity, smoking and alcohol use) that were reported more frequently by those in the more disadvantaged quintiles [ 15 ]. Current information on inequalities in health other than mortality is limited in Australia because of a dearth of suitable data collections. However, the release of data from the 2001 NHS provides an opportunity to examine the prevalence of self-reported chronic disease in Australia and the way in which this impacts on different socioeconomic groupings within the population.
Background
As in other developed countries, chronic diseases in Australia are major contributors to the extent of illness, disability and premature mortality in the population. They are estimated to make up the greatest proportion of the burden of disease, mental problems and injury for the population as a whole (about 80%), and for particular sub-population groups [ 1 ].Chronic diseases are exemplified by having multifactorial aetiologies, including common disease risk factors and determinants; significant latency periods and protracted clinical courses; and are seldom cured completely [ 2 , 3 ]. Causal factors interact together at an individual and at a population level to determine the degree of disease burden and illness, and unhealthy risks can be passed on through families, communities, and populations following demographic gradients [ 4 ]. At different life stages, common risk factors and determinants include poor intra-uterine conditions; stress, violence and traumatic experiences; educational disadvantage; inadequate living environments that fail to promote healthy lifestyles; poor diet and lack of exercise; alcohol misuse and tobacco smoking [ 5 , 30 ]. Risk factors are also increasingly more prevalent in areas of low socioeconomic status and in communities characterised by low levels of educational attainment; high levels of unemployment; substantial levels of discrimination, interpersonal violence and exclusion; and poverty. There is a higher prevalence of such factors among Indigenous communities, and other socioeconomically disadvantaged Australians [ 5 , 6 ].The inequalities in health observed across populations are many – some of them are inevitable and others, unnecessary and unfair. Those inequalities that are potentially avoidable are deemed 'inequitable' [ 7 ]. Despite significant medical advances and improved public health in recent decades, socioeconomically disadvantaged communities continue to suffer an unequal burden of illness, premature death and disability. Therefore, the study of socioeconomic inequalities in chronic diseases and conditions and in risk factors is important and necessary. This is particularly so, if we wish to develop more effective policy mechanisms for preventing and intervening earlier in the progression of chronic diseases and their associated risk factors across the diverse Australian population, and to reduce some of the existing health inequities.Our approach There have been a number of studies published in Australia on socioeconomic inequalities in mortality from various chronic diseases and conditions. The earlier ones were analysed using information on occupation recorded on the death certificate [ 8 - 10 ]. An alternative approach has been to examine variations in mortality rates by grouping residential locations according to socioeconomic criteria. A number of such studies have documented substantial variations in mortality for different age groups [ 11 - 17 ]. However, to date, there have been fewer studies that have examined socioeconomic inequalities in chronic disease prevalence in those still living (analyses of hospital admissions for chronic conditions have been published, but not of prevalence) [ 15 ]. One of the earliest was undertaken by Broadhead, who analysed data on morbidity and social status from the 1977–78 Australian Health Survey (ABS), and found that men in lower status occupations tended to suffer a higher age-standardised rate of self-reported chronic conditions and days of reduced activity; the picture for women appeared less clear [ 18 ]. Lee et al found that low income males were more likely to report mental health problems, chronic symptoms and acute symptoms than their high income counterparts [ 19 ]. Similar findings were reported in other studies, and risk factors associated with chronic diseases also were also associated with low income [ 15 , 19 - 21 ]. The work of Mathers is significant for its systematic documentation of health inequalities among working aged Australians (25 to 64 years) in the late 1980s. He examined mortality, disability, disease groups, specific diseases, self-perceived health, risk factors, health service use and use of preventive screening services, using data from the 1989–90 National Health Survey (NHS) [ 15 ]. Mathers found that there were no clear gradients of chronic, recent or minor illness with level of socioeconomic disadvantage of area, although there were some specific health status indicators (self-reported health, reduced activity, unhappiness) and certain risk factors (inactivity, smoking and alcohol use) that were reported more frequently by those in the more disadvantaged quintiles [ 15 ]. Current information on inequalities in health other than mortality is limited in Australia because of a dearth of suitable data collections. However, the release of data from the 2001 NHS provides an opportunity to examine the prevalence of self-reported chronic disease in Australia and the way in which this impacts on different socioeconomic groupings within the population.
Our approach
There have been a number of studies published in Australia on socioeconomic inequalities in mortality from various chronic diseases and conditions. The earlier ones were analysed using information on occupation recorded on the death certificate [ 8 - 10 ]. An alternative approach has been to examine variations in mortality rates by grouping residential locations according to socioeconomic criteria. A number of such studies have documented substantial variations in mortality for different age groups [ 11 - 17 ].However, to date, there have been fewer studies that have examined socioeconomic inequalities in chronic disease prevalence in those still living (analyses of hospital admissions for chronic conditions have been published, but not of prevalence) [ 15 ]. One of the earliest was undertaken by Broadhead, who analysed data on morbidity and social status from the 1977–78 Australian Health Survey (ABS), and found that men in lower status occupations tended to suffer a higher age-standardised rate of self-reported chronic conditions and days of reduced activity; the picture for women appeared less clear [ 18 ]. Lee et al found that low income males were more likely to report mental health problems, chronic symptoms and acute symptoms than their high income counterparts [ 19 ]. Similar findings were reported in other studies, and risk factors associated with chronic diseases also were also associated with low income [ 15 , 19 - 21 ].The work of Mathers is significant for its systematic documentation of health inequalities among working aged Australians (25 to 64 years) in the late 1980s. He examined mortality, disability, disease groups, specific diseases, self-perceived health, risk factors, health service use and use of preventive screening services, using data from the 1989–90 National Health Survey (NHS) [ 15 ]. Mathers found that there were no clear gradients of chronic, recent or minor illness with level of socioeconomic disadvantage of area, although there were some specific health status indicators (self-reported health, reduced activity, unhappiness) and certain risk factors (inactivity, smoking and alcohol use) that were reported more frequently by those in the more disadvantaged quintiles [ 15 ].Current information on inequalities in health other than mortality is limited in Australia because of a dearth of suitable data collections. However, the release of data from the 2001 NHS provides an opportunity to examine the prevalence of self-reported chronic disease in Australia and the way in which this impacts on different socioeconomic groupings within the population.Results Information for a selection of chronic diseases is shown in Table  1 . Diseases were included on the basis of either high prevalence or their contribution to the burden of disease. Table 1 Inequality in prevalence of selected chronic diseases 1 , 2001 Age group (years) and chronic disease Rate 2 Rate ratio by quintile of socioeconomic disadvantage of area 3 First Second Third Fourth Fifth 0–14 4 Mental and behavioural problems 5 6 596 1.00 1.04 1.10 1.12 1.52** Respiratory system 21 807 1.00 1.07 1.05 1.11 0.99  Asthma 13 363 1.00 1.10 1.12 1.25* 1.12 15–24 Mental and behavioural problems 5 10 284 1.00 1.02 0.97 1.08 1.28 Respiratory system 33 373 1.00 1.04 1.12 1.09 1.00  Asthma 16 263 1.00 0.82 1.14 1.02 1.00  Bronchitis/emphysema 1 701 1.00 6 1.32 6 1.66 6 1.94 6 1.97 6 Musculoskeletal system 7 19 088 1.00 1.11 1.00 1.08 0.94 25–64 Diabetes mellitus 2 234 1.00 1.37 1.67* 1.72* 2.28*** Mental and behavioural problems 5 11 093 1.00 1.05 1.20* 1.36*** 1.67*** Circulatory system 17 491 1.00 1.04 0.97 1.15* 1.28***  Hypertensive disease 9 751 1.00 1.12 1.01 1.24* 1.54*** Respiratory system 32 964 1.00 1.00 0.99 0.99 1.01  Asthma 10 393 1.00 1.10 0.99 1.19* 1.14  Bronchitis/emphysema 3 429 1.00 0.97 1.14 1.55** 1.70*** Digestive system 8 074 1.00 1.03 1.07 1.12 1.37*** Musculoskeletal system 7 39 840 1.00 1.10* 1.16*** 1.16*** 1.22*** 65 & over Diabetes mellitus 8 981 1.00 1.13 1.14 1.52* 1.56* Mental and behavioural problems 5 7 222 1.00 1.21 1.62* 1.67* 1.56* Circulatory system 56 592 1.00 1.09 1.06 1.10 1.19* Respiratory system 31 442 1.00 1.03 0.87 0.95 1.22* Musculoskeletal system 7 63 669 1.00 1.02 1.06 1.03 1.08 1 Survey respondents can report more than one disease. 2 Rate is the number of persons per 100,000 population reporting the disease. 3 The extent of any inequality is shown by the rate ratio, which expresses the ratio of the rate in each quintile to the rate in Quintile 1 (the most advantaged areas, with a rate ratio of 1.00); rate ratios differing significantly from 1.0 are shown with * p < 0.05; ** p < 0.01; *** p < 0.001. 4 Information about these age groups were collected by proxy, using parental report. 5 Information may be based on self-diagnosis, rather than diagnosis by a health practitioner. 6 Indicates rate ratio based on estimates with a Relative Standard Error of between 25% and 50% and should be used with caution. 7 Includes diseases of the connective tissue. Source: National Health Survey, ABS 2002 The main findings are: The largest differential between those in the most well off and those in the most disadvantaged areas was for diabetes mellitus at ages 25 to 64 years, with the prevalence in the most disadvantaged areas being just over two and a quarter times (a rate ratio of 2.28) the prevalence for the least disadvantaged; there is also a strong, continuous gradient in the rates, with the rate ratios in each of the third to fifth quintiles statistically significant. There was a statistically significant differential of 67% at ages 25 to 64 years, with a strong, continuous gradient, in the prevalence of self-reported mental and behavioural problems across the socioeconomic gradient; differentials (also statistically significant) in the 0 to 14 year and 65 years and over age groups were 52% and 56%, respectively. Circulatory system diseases (in particular, hypertensive disease) and digestive system diseases also exhibit a strong differential in the 25 to 64 year age group (statistically significant differentials of 28% and 54%, respectively). In the 65 years and over age group, the strongest differentials were evident for mental and behavioural problems (a statistically significant 56%), diabetes (with a continuous gradient in rates, statistically significant in quintile3 four and five) and respiratory system disease (a statistically significant 22%). Asthma accounted for almost two thirds of the rate of reporting of respiratory system disease in the 0 to 14 year age group, almost half in the 15 to 24 year age group, and for about a third of the rate in the 25 to 64 year age group. The NHS also included data on a number of risk factors for chronic diseases, namely self-reported smoking, alcohol misuse, physical inactivity and excess weight. A number of these risk factors show a striking association with socioeconomic status, in particular for people who are smokers and those who did not exercise, with continuous gradients and highly elevated rates of statistical significance (Table  2 ). The differences in male and female rates are also of interest. It was only for underweight females, and for the risk factor of high-risk alcohol consumption by females, that the socioeconomic gradient was reversed. Table 2 Inequality in prevalence of selected health risk factors, 18–64 years, 2001 1 Health risk factors Rate 2 Rate ratio by quintile of socioeconomic disadvantage of area 3 First Second Third Fourth Fifth Current smokers - Male 30 582 1.00 1.40*** 1.55*** 1.71*** 1.95***   - Female 24 009 1.00 1.29*** 1.34*** 1.48*** 2.00***   - Persons 27 275 1.00 1.35*** 1.45*** 1.61*** 1.96*** Alcohol – High risk - Males 6 976 1.00 1.09 1.26* 1.26* 1.45***   - Females 2 127 1.00 0.59* 0.94 0.76 0.87   - Persons 4 537 1.00 0.93 1.16 1.12 1.22* Did not exercise - Males 28 772 1.00 1.20** 1.36*** 1.52*** 1.68***   - Females 28 220 1.00 1.19** 1.29*** 1.35*** 1.65***   - Persons 28 494 1.00 1.20*** 1.32*** 1.43*** 1.66*** Underweight females 12 675 1.00 0.89 0.83* 0.72*** 0.91 Overweight/obese - Males 54 701 1.00 1.09* 1.11* 1.04*** 1.00   - Females 37 004 1.00 1.09 1.21*** 1.16** 1.17**   - Persons 45 798 1.00 1.09** 1.15*** 1.09** 1.06 1 Survey respondents can be shown under more than one type of risk factor. 2 Rate is the number of persons per 100,000 population estimated to be at risk from the health risk factor. 3 The extent of any inequality is shown by the rate ratio, which expresses the ratio of the rate in each quintile to the rate in Quintile 1 (the most advantaged areas, with a rate ratio of 1.00); rate ratios differing significantly from 1.0 are shown with * p < 0.05; ** p < 0.01; *** p < 0.001. Source: National Health Survey, ABS 2002 It is important to note that the inequalities reported above relate to the health of those people living in a geographic area and to the overall level of socioeconomic disadvantage of that area. Most areas will contain varying levels of individual socioeconomic disadvantage and, to the extent that the poorer health is associated with individual economic circumstances and living conditions rather than communal environment, the inequalities will understate the true differences in health status according to socioeconomic disadvantage [ 15 ]. Furthermore, there are limitations to the use of area-based measures of SES. Due to misclassification error (i.e. ascribing area-SES to individuals), estimates of difference across the quintiles will be smaller than if data on individual-level measures of SES were used [ 28 ]. Thus, chronic disease inequalities in the wider population by SES are likely to be larger than those reported in this study. In addition, the exclusion of the 'sparsely settled' areas of Australia in NHS data collection results in the omission of data from a high percentage of Indigenous people, who are the population group with the poorest health.
Results
Information for a selection of chronic diseases is shown in Table  1 . Diseases were included on the basis of either high prevalence or their contribution to the burden of disease.Inequality in prevalence of selected chronic diseases 1 , 20010–1415–2425–6465 & over1 Survey respondents can report more than one disease.2 Rate is the number of persons per 100,000 population reporting the disease.3 The extent of any inequality is shown by the rate ratio, which expresses the ratio of the rate in each quintile to the rate in Quintile 1 (the most advantaged areas, with a rate ratio of 1.00); rate ratios differing significantly from 1.0 are shown with * p < 0.05; ** p < 0.01; *** p < 0.001.4 Information about these age groups were collected by proxy, using parental report.5 Information may be based on self-diagnosis, rather than diagnosis by a health practitioner.6 Indicates rate ratio based on estimates with a Relative Standard Error of between 25% and 50% and should be used with caution.7 Includes diseases of the connective tissue.Source: National Health Survey, ABS 2002The main findings are:The largest differential between those in the most well off and those in the most disadvantaged areas was for diabetes mellitus at ages 25 to 64 years, with the prevalence in the most disadvantaged areas being just over two and a quarter times (a rate ratio of 2.28) the prevalence for the least disadvantaged; there is also a strong, continuous gradient in the rates, with the rate ratios in each of the third to fifth quintiles statistically significant.There was a statistically significant differential of 67% at ages 25 to 64 years, with a strong, continuous gradient, in the prevalence of self-reported mental and behavioural problems across the socioeconomic gradient; differentials (also statistically significant) in the 0 to 14 year and 65 years and over age groups were 52% and 56%, respectively.Circulatory system diseases (in particular, hypertensive disease) and digestive system diseases also exhibit a strong differential in the 25 to 64 year age group (statistically significant differentials of 28% and 54%, respectively).In the 65 years and over age group, the strongest differentials were evident for mental and behavioural problems (a statistically significant 56%), diabetes (with a continuous gradient in rates, statistically significant in quintile3 four and five) and respiratory system disease (a statistically significant 22%).Asthma accounted for almost two thirds of the rate of reporting of respiratory system disease in the 0 to 14 year age group, almost half in the 15 to 24 year age group, and for about a third of the rate in the 25 to 64 year age group.The NHS also included data on a number of risk factors for chronic diseases, namely self-reported smoking, alcohol misuse, physical inactivity and excess weight. A number of these risk factors show a striking association with socioeconomic status, in particular for people who are smokers and those who did not exercise, with continuous gradients and highly elevated rates of statistical significance (Table  2 ). The differences in male and female rates are also of interest. It was only for underweight females, and for the risk factor of high-risk alcohol consumption by females, that the socioeconomic gradient was reversed.Inequality in prevalence of selected health risk factors, 18–64 years, 2001 11 Survey respondents can be shown under more than one type of risk factor.2 Rate is the number of persons per 100,000 population estimated to be at risk from the health risk factor.3 The extent of any inequality is shown by the rate ratio, which expresses the ratio of the rate in each quintile to the rate in Quintile 1 (the most advantaged areas, with a rate ratio of 1.00); rate ratios differing significantly from 1.0 are shown with * p < 0.05; ** p < 0.01; *** p < 0.001.Source: National Health Survey, ABS 2002It is important to note that the inequalities reported above relate to the health of those people living in a geographic area and to the overall level of socioeconomic disadvantage of that area. Most areas will contain varying levels of individual socioeconomic disadvantage and, to the extent that the poorer health is associated with individual economic circumstances and living conditions rather than communal environment, the inequalities will understate the true differences in health status according to socioeconomic disadvantage [ 15 ].Furthermore, there are limitations to the use of area-based measures of SES. Due to misclassification error (i.e. ascribing area-SES to individuals), estimates of difference across the quintiles will be smaller than if data on individual-level measures of SES were used [ 28 ]. Thus, chronic disease inequalities in the wider population by SES are likely to be larger than those reported in this study. In addition, the exclusion of the 'sparsely settled' areas of Australia in NHS data collection results in the omission of data from a high percentage of Indigenous people, who are the population group with the poorest health.Discussion Our analysis indicates that socioeconomic inequalities in the prevalence of chronic diseases and their concomitant risk factors are evident across the Australian population. However, the diseases with substantial disparities across the socioeconomic quintiles are different, for different stages in the life course. Although these results cannot be directly compared with those of previous studies, because of definitional and methodological differences, the recurring finding of inequalities for chronic disease morbidity and risk factor prevalence across the socioeconomic gradient remains a significant concern. The burden in the Australian population attributable to socioeconomic inequality is large, and has far-reaching implications in terms of unnecessary disability and suffering, the loss of potentially economically productive members of society, and increased costs for the health and social care systems [ 35 ]. Despite the expenditure of millions of dollars to prevent and reduce the prevalence of chronic diseases and their risk factors, these inequities have persisted. However, the situation in Australia is by no means unique, for inequalities in these diseases and their risk factors have been observed for most of the developed countries in which they have been studied [ 26 ]. What should we be doing differently? There is a growing body of knowledge that will help to provide direction for developing policies to reduce inequities across the population. The socioeconomic environment is a powerful and potentially modifiable factor, and public policy is a key instrument to improve this environment, particularly in areas such as housing, taxation and social security, work environments, urban design, pollution control, educational achievement, and early childhood development [ 34 ]. However, attention must be paid to the nature of any action that is taken, to ensure that social and economic inequalities are not increased. Some programs, by their very success, can increase inequality by widening the gap between groups in the population; for example, such programs may be more attractive to those who are already healthier, or not as effective for certain groups with poorer health, less education or more stressful lives. In one smoking cessation initiative, it was found that the prevalence of smoking decreased predominately in those adults with higher education, thus increasing the existing difference with those who were more disadvantaged [ 37 ]. While smoking prevalence in Australia has reduced considerably over the last 20 years, attributes such as lower education and occupational status, unemployment, rented housing, and living in disadvantaged areas reflect a higher probability of reporting tobacco expenditure [ 32 ]. As a result, the tax revenue from the sale of tobacco products is being disproportionately drawn from the poorest households and represents a greater proportion of their household budget [ 32 ]. It is also evident that the ways in which systems such as education and health are delivered and structured can increase existing inequality. For example, schooling can be a way of addressing inequality and also a way of reproducing it. It has been suggested that there are two goals for a social justice program in education: to work to eliminate the contribution that the education system makes to the production over time of social inequality in general; and to maximise the positive contributions that the education system makes to reducing social inequality [ 33 ]. Therefore, different approaches and mixes of policies and programs must be mounted to address inequalities. These approaches may include more precise targeting, but also greater attention to community-based dimensions of 'interdependence' between individual behaviours, key determinants, and community and institutional resources. Policy-makers who wish to address socioeconomic inequalities in health may favour one of the following approaches. Some view the impact of socioeconomic disadvantage on those groups with the poorest health in the population, such as Aboriginal people and Torres Strait Islanders, as the priority policy goal. Others identify the gap between the health of those groups at the outer ends of the socioeconomic hierarchy (those with the poorest health and those with best health), and see the narrowing of the gap as the goal. Others prefer to focus on the socioeconomic gradient in health that runs across the whole population [ 31 ]. Graham has identified that the last approach widens the policy debate in three ways [ 31 ]. Firstly, it looks for the causes of health inequality in the systematic differences in life chances and opportunities, living standards and lifestyles that are associated with people's unequal positions across the socioeconomic hierarchy, and for the pathways through which they influence health [ 31 , 36 ]. Secondly, as a result, addressing health inequalities becomes a population-wide goal that includes every citizen [ 31 ]. Thirdly, 'reducing health gradients' provides a more comprehensive policy approach: one that encompasses 'remedying disadvantages' and 'narrowing health gaps' within the broader goal of 'equalising health chances across all the socioeconomic groups' [ 31 ]. She also observes that, "improving the health of poor groups and improving their position relative to other groups are necessary elements in a strategy to reduce the socioeconomic gradient. However, neither is sufficient on its own. To reduce the socioeconomic gradient, health in other socioeconomic groups also needs to improve at a faster rate than in the highest socioeconomic group. Thus, policies to ameliorate health disadvantage, to close health gaps and to reduce health gradients need to be pursued together, and not at the expense of each other" [ 31 ]. There is also an urgent need to make health inequalities a research priority for each stage of the life course – not just to monitor the size and extent of the disparities but also to undertake research that will find preventive approaches and further policy interventions that will be effective in reducing them, and that are likely to be implemented by governments and communities.
Discussion
Our analysis indicates that socioeconomic inequalities in the prevalence of chronic diseases and their concomitant risk factors are evident across the Australian population. However, the diseases with substantial disparities across the socioeconomic quintiles are different, for different stages in the life course. Although these results cannot be directly compared with those of previous studies, because of definitional and methodological differences, the recurring finding of inequalities for chronic disease morbidity and risk factor prevalence across the socioeconomic gradient remains a significant concern.The burden in the Australian population attributable to socioeconomic inequality is large, and has far-reaching implications in terms of unnecessary disability and suffering, the loss of potentially economically productive members of society, and increased costs for the health and social care systems [ 35 ]. Despite the expenditure of millions of dollars to prevent and reduce the prevalence of chronic diseases and their risk factors, these inequities have persisted. However, the situation in Australia is by no means unique, for inequalities in these diseases and their risk factors have been observed for most of the developed countries in which they have been studied [ 26 ].What should we be doing differently? There is a growing body of knowledge that will help to provide direction for developing policies to reduce inequities across the population. The socioeconomic environment is a powerful and potentially modifiable factor, and public policy is a key instrument to improve this environment, particularly in areas such as housing, taxation and social security, work environments, urban design, pollution control, educational achievement, and early childhood development [ 34 ].However, attention must be paid to the nature of any action that is taken, to ensure that social and economic inequalities are not increased. Some programs, by their very success, can increase inequality by widening the gap between groups in the population; for example, such programs may be more attractive to those who are already healthier, or not as effective for certain groups with poorer health, less education or more stressful lives. In one smoking cessation initiative, it was found that the prevalence of smoking decreased predominately in those adults with higher education, thus increasing the existing difference with those who were more disadvantaged [ 37 ]. While smoking prevalence in Australia has reduced considerably over the last 20 years, attributes such as lower education and occupational status, unemployment, rented housing, and living in disadvantaged areas reflect a higher probability of reporting tobacco expenditure [ 32 ]. As a result, the tax revenue from the sale of tobacco products is being disproportionately drawn from the poorest households and represents a greater proportion of their household budget [ 32 ].It is also evident that the ways in which systems such as education and health are delivered and structured can increase existing inequality. For example, schooling can be a way of addressing inequality and also a way of reproducing it. It has been suggested that there are two goals for a social justice program in education: to work to eliminate the contribution that the education system makes to the production over time of social inequality in general; and to maximise the positive contributions that the education system makes to reducing social inequality [ 33 ]. Therefore, different approaches and mixes of policies and programs must be mounted to address inequalities. These approaches may include more precise targeting, but also greater attention to community-based dimensions of 'interdependence' between individual behaviours, key determinants, and community and institutional resources.Policy-makers who wish to address socioeconomic inequalities in health may favour one of the following approaches. Some view the impact of socioeconomic disadvantage on those groups with the poorest health in the population, such as Aboriginal people and Torres Strait Islanders, as the priority policy goal. Others identify the gap between the health of those groups at the outer ends of the socioeconomic hierarchy (those with the poorest health and those with best health), and see the narrowing of the gap as the goal. Others prefer to focus on the socioeconomic gradient in health that runs across the whole population [ 31 ].Graham has identified that the last approach widens the policy debate in three ways [ 31 ]. Firstly, it looks for the causes of health inequality in the systematic differences in life chances and opportunities, living standards and lifestyles that are associated with people's unequal positions across the socioeconomic hierarchy, and for the pathways through which they influence health [ 31 , 36 ]. Secondly, as a result, addressing health inequalities becomes a population-wide goal that includes every citizen [ 31 ]. Thirdly, 'reducing health gradients' provides a more comprehensive policy approach: one that encompasses 'remedying disadvantages' and 'narrowing health gaps' within the broader goal of 'equalising health chances across all the socioeconomic groups' [ 31 ].She also observes that, "improving the health of poor groups and improving their position relative to other groups are necessary elements in a strategy to reduce the socioeconomic gradient. However, neither is sufficient on its own. To reduce the socioeconomic gradient, health in other socioeconomic groups also needs to improve at a faster rate than in the highest socioeconomic group. Thus, policies to ameliorate health disadvantage, to close health gaps and to reduce health gradients need to be pursued together, and not at the expense of each other" [ 31 ].There is also an urgent need to make health inequalities a research priority for each stage of the life course – not just to monitor the size and extent of the disparities but also to undertake research that will find preventive approaches and further policy interventions that will be effective in reducing them, and that are likely to be implemented by governments and communities.Conclusions Clearly, any moves to address the impact of chronic disease at a population level must take into account socioeconomic inequalities in prevalence. More research is needed to determine which approaches are effective and why others have failed to have the desired impact, particularly for those who are from socioeconomically disadvantaged areas. Finally, although rates are generally highest at the oldest ages, the development of risk factors for many chronic diseases occurs early in life, and thus, it is essential those health inequities are addressed right across the life course.
Conclusions
Clearly, any moves to address the impact of chronic disease at a population level must take into account socioeconomic inequalities in prevalence. More research is needed to determine which approaches are effective and why others have failed to have the desired impact, particularly for those who are from socioeconomically disadvantaged areas. Finally, although rates are generally highest at the oldest ages, the development of risk factors for many chronic diseases occurs early in life, and thus, it is essential those health inequities are addressed right across the life course.Methods Data sources The ABS conducts the National Health Survey (NHS) on a regular basis, most recently in 2001 [ 22 ]. The NHS collects information from approximately 26,900 people from all States and Territories living in private dwellings, selected at random using a multi-stage area sample of private dwellings. The survey is undertaken across much of Australia, but excludes the 'sparsely settled' areas, which comprised less than 1% of the non-Indigenous population and 25% of the Indigenous population at the 2001 Census: a separate Australia-wide survey of the health of Indigenous people, also conducted in 2001, surveyed these sparsely settled areas. The survey includes self-reported details of health conditions (both acute and long term) and major risk factors, as well as demographic and socioeconomic information about the survey respondent. Respondents were asked if they had been told by a doctor or nurse that they had asthma, cancer, heart and circulatory conditions, and/or diabetes. These conditions, together with injuries and mental health, form the NHPAs [ 27 ]. However, for long term mental health problems, respondents were not asked whether they had been told by a doctor or nurse that they had any mental health problems; thus, the responses may be based on self-diagnosis, rather than diagnosis by a health practitioner [ 22 ]. Respondents were also asked a series of questions about other specific, non-NHPA, conditions, covering eye and sight problems, ear and hearing problems, and arthritis, rheumatism and gout. They were then shown a series of three prompt cards (two with conditions listed, while the third contained more general descriptions of condition types) and asked whether they had any of the conditions shown or conditions similar to those shown or described. In each of these cases, details were recorded for conditions reported as current at the time of the survey; respondents were also asked whether the condition had lasted, or was expected to last, for six months or more. Information was gathered directly from individuals aged 15 years and older. For children up to the age of 15 years, information was provided by proxy, from a parent or guardian. The particular conditions for which data were requested from the ABS for this analysis were: the NHPA conditions (other than injury, which has not been included in this paper, with its focus on chronic disease); plus other 'serious' chronic conditions, in line with the classification developed by Mathers [ 15 ]; and for which sufficient cases were available for analysis by five groups of socioeconomic disadvantage of area (see below for details of the way these groups were constructed). The risk factors used by the ABS were those identified for the NHPA conditions [ 27 ]. The ABS has coded conditions reported by respondents to output disease categories based on ICD-10. Conditions described as 'chronic' in this article include those long-term conditions reported in the NHS, which are commonly recognised by health practitioners as chronic diseases [ 23 ]. The risk factor for 'high risk due to alcohol' reflects the National Health and Medical Research Council's risk levels for harm in the long term from alcohol consumption [ 24 ]. The risk factors for overweight and underweight were calculated from self-reported height and weight information and grouped to reflect World Health Organization (WHO) guidelines [ 25 ]. Given the policy importance of the NHPAs, the 2001 NHS questionnaire underwent significant revisions to more precisely capture information on several of the NHPAs. Consequently, while the quality of the information on NHPAs has been improved from the 1995 NHS, the degree of comparability with previous surveys has been somewhat compromised for many of the major health conditions. Some specific conditions (e.g., diabetes) appear to be comparable between the 1989–90 and 2001 surveys, however, for most groups of conditions based on ICD chapter headings (e.g., all circulatory) the ABS advise that the combined effect of major conceptual changes as well as major classification changes between the 1989–90 and 2001 surveys would make direct comparisons very difficult. This analysis is therefore restricted to the 2001 data. Measurement of socioeconomic status The socioeconomic status (SES) of the address of residence of each survey respondent is available at the Census Collection District (CD) level and was added to the NHS file, as was the quintile of socioeconomic disadvantage of area into which that CD fell at the Census. The measure used to allocate CDs to quintiles was the 1996 Census Index of Relative Socio-Economic Disadvantage (IRSD). The IRSD is one of five Socio-Economic Indexes for Areas produced by the ABS using Principal Components Analysis. It summarises information available from variables collected in the 1996 Population Census including those related to education, occupation, and income. The variables are expressed as percentages of the relevant population. The NHS records were aggregated to the quintiles derived from the Census data, where Quintile 1 comprises the CDs with the highest IRSD scores (highest socioeconomic status, or most advantaged, areas) and Quintile 5 comprises the CDs with the lowest IRSD scores (lowest socioeconomic status, or most disadvantaged areas). Each quintile comprises approximately 20% of CDs. The ABS produced the estimates of the number of people with chronic diseases and risk factors by quintile. The method used resulted in the production of quintiles of varying sizes, ranging from 17.4% of the population in Quintile 5 (most disadvantaged areas) to 22.8% in Quintile 2 and 21.1% in Quintile 1 (most advantaged areas). This is a differential of over five percentage points between Quintile 5 and Quintile 2, or 1,027,030 fewer people in the most disadvantaged areas when compared with Quintile 2 (and 708,980 fewer in Quintile 5 than in Quintile 1). The effect of this lack of precision on the results by quintile is not known. Although, in part, the difference arises as a result of the method used (that is, that the quintiles are based on the Census population, and applied to the NHS population which has a different profile), it may also reflect different response rates to the survey from different socioeconomic groups. The NHS includes other measures that could potentially be used to measure socioeconomic status. For example, income reporting in ABS surveys is known to be incomplete, in particular for low income groups and, in the 2001 NHS, income was only collected for the respondent and their spouse. Information on education is also available, but a single measure has yet to be agreed. Problems in using education include that it is typically completed early in adulthood; it captures neither differential on-the-job training and other career investments made by individuals with similar levels of formal schooling, nor the volatility in economic status during adulthood that has been shown to have adverse implications for health [ 29 ]. The age structure of the population may also influence the indicator: an older population generally has lower education levels than a younger population due to improved access to education over time. Analysis Chronic disease and risk factor rates are expressed as rates per 100,000 population, indirectly age-standardised. The rates were also calculated by age although, given the small sample size of the NHS, only broad age groups (0–14 years, 15–24 years, 25–64 years and 65 years and over) were available. The standard population and quintile populations are the weighted 2001 survey populations from the NHS. The extent of any differential between the quintiles is shown by the rate ratio, which expresses the ratio of the rate in each quintile to the rate in Quintile 1 (the most advantaged areas, with a rate ratio of 1.00).
Methods
Data sources The ABS conducts the National Health Survey (NHS) on a regular basis, most recently in 2001 [ 22 ]. The NHS collects information from approximately 26,900 people from all States and Territories living in private dwellings, selected at random using a multi-stage area sample of private dwellings. The survey is undertaken across much of Australia, but excludes the 'sparsely settled' areas, which comprised less than 1% of the non-Indigenous population and 25% of the Indigenous population at the 2001 Census: a separate Australia-wide survey of the health of Indigenous people, also conducted in 2001, surveyed these sparsely settled areas. The survey includes self-reported details of health conditions (both acute and long term) and major risk factors, as well as demographic and socioeconomic information about the survey respondent. Respondents were asked if they had been told by a doctor or nurse that they had asthma, cancer, heart and circulatory conditions, and/or diabetes. These conditions, together with injuries and mental health, form the NHPAs [ 27 ]. However, for long term mental health problems, respondents were not asked whether they had been told by a doctor or nurse that they had any mental health problems; thus, the responses may be based on self-diagnosis, rather than diagnosis by a health practitioner [ 22 ]. Respondents were also asked a series of questions about other specific, non-NHPA, conditions, covering eye and sight problems, ear and hearing problems, and arthritis, rheumatism and gout. They were then shown a series of three prompt cards (two with conditions listed, while the third contained more general descriptions of condition types) and asked whether they had any of the conditions shown or conditions similar to those shown or described. In each of these cases, details were recorded for conditions reported as current at the time of the survey; respondents were also asked whether the condition had lasted, or was expected to last, for six months or more. Information was gathered directly from individuals aged 15 years and older. For children up to the age of 15 years, information was provided by proxy, from a parent or guardian. The particular conditions for which data were requested from the ABS for this analysis were: the NHPA conditions (other than injury, which has not been included in this paper, with its focus on chronic disease); plus other 'serious' chronic conditions, in line with the classification developed by Mathers [ 15 ]; and for which sufficient cases were available for analysis by five groups of socioeconomic disadvantage of area (see below for details of the way these groups were constructed). The risk factors used by the ABS were those identified for the NHPA conditions [ 27 ]. The ABS has coded conditions reported by respondents to output disease categories based on ICD-10. Conditions described as 'chronic' in this article include those long-term conditions reported in the NHS, which are commonly recognised by health practitioners as chronic diseases [ 23 ]. The risk factor for 'high risk due to alcohol' reflects the National Health and Medical Research Council's risk levels for harm in the long term from alcohol consumption [ 24 ]. The risk factors for overweight and underweight were calculated from self-reported height and weight information and grouped to reflect World Health Organization (WHO) guidelines [ 25 ]. Given the policy importance of the NHPAs, the 2001 NHS questionnaire underwent significant revisions to more precisely capture information on several of the NHPAs. Consequently, while the quality of the information on NHPAs has been improved from the 1995 NHS, the degree of comparability with previous surveys has been somewhat compromised for many of the major health conditions. Some specific conditions (e.g., diabetes) appear to be comparable between the 1989–90 and 2001 surveys, however, for most groups of conditions based on ICD chapter headings (e.g., all circulatory) the ABS advise that the combined effect of major conceptual changes as well as major classification changes between the 1989–90 and 2001 surveys would make direct comparisons very difficult. This analysis is therefore restricted to the 2001 data.
Data sources
The ABS conducts the National Health Survey (NHS) on a regular basis, most recently in 2001 [ 22 ]. The NHS collects information from approximately 26,900 people from all States and Territories living in private dwellings, selected at random using a multi-stage area sample of private dwellings. The survey is undertaken across much of Australia, but excludes the 'sparsely settled' areas, which comprised less than 1% of the non-Indigenous population and 25% of the Indigenous population at the 2001 Census: a separate Australia-wide survey of the health of Indigenous people, also conducted in 2001, surveyed these sparsely settled areas.The survey includes self-reported details of health conditions (both acute and long term) and major risk factors, as well as demographic and socioeconomic information about the survey respondent. Respondents were asked if they had been told by a doctor or nurse that they had asthma, cancer, heart and circulatory conditions, and/or diabetes. These conditions, together with injuries and mental health, form the NHPAs [ 27 ]. However, for long term mental health problems, respondents were not asked whether they had been told by a doctor or nurse that they had any mental health problems; thus, the responses may be based on self-diagnosis, rather than diagnosis by a health practitioner [ 22 ]. Respondents were also asked a series of questions about other specific, non-NHPA, conditions, covering eye and sight problems, ear and hearing problems, and arthritis, rheumatism and gout. They were then shown a series of three prompt cards (two with conditions listed, while the third contained more general descriptions of condition types) and asked whether they had any of the conditions shown or conditions similar to those shown or described. In each of these cases, details were recorded for conditions reported as current at the time of the survey; respondents were also asked whether the condition had lasted, or was expected to last, for six months or more. Information was gathered directly from individuals aged 15 years and older. For children up to the age of 15 years, information was provided by proxy, from a parent or guardian.The particular conditions for which data were requested from the ABS for this analysis were:the NHPA conditions (other than injury, which has not been included in this paper, with its focus on chronic disease); plus other 'serious' chronic conditions, in line with the classification developed by Mathers [ 15 ]; and for which sufficient cases were available for analysis by five groups of socioeconomic disadvantage of area (see below for details of the way these groups were constructed).The risk factors used by the ABS were those identified for the NHPA conditions [ 27 ].The ABS has coded conditions reported by respondents to output disease categories based on ICD-10. Conditions described as 'chronic' in this article include those long-term conditions reported in the NHS, which are commonly recognised by health practitioners as chronic diseases [ 23 ]. The risk factor for 'high risk due to alcohol' reflects the National Health and Medical Research Council's risk levels for harm in the long term from alcohol consumption [ 24 ]. The risk factors for overweight and underweight were calculated from self-reported height and weight information and grouped to reflect World Health Organization (WHO) guidelines [ 25 ].Given the policy importance of the NHPAs, the 2001 NHS questionnaire underwent significant revisions to more precisely capture information on several of the NHPAs. Consequently, while the quality of the information on NHPAs has been improved from the 1995 NHS, the degree of comparability with previous surveys has been somewhat compromised for many of the major health conditions. Some specific conditions (e.g., diabetes) appear to be comparable between the 1989–90 and 2001 surveys, however, for most groups of conditions based on ICD chapter headings (e.g., all circulatory) the ABS advise that the combined effect of major conceptual changes as well as major classification changes between the 1989–90 and 2001 surveys would make direct comparisons very difficult. This analysis is therefore restricted to the 2001 data.Measurement of socioeconomic status The socioeconomic status (SES) of the address of residence of each survey respondent is available at the Census Collection District (CD) level and was added to the NHS file, as was the quintile of socioeconomic disadvantage of area into which that CD fell at the Census. The measure used to allocate CDs to quintiles was the 1996 Census Index of Relative Socio-Economic Disadvantage (IRSD). The IRSD is one of five Socio-Economic Indexes for Areas produced by the ABS using Principal Components Analysis. It summarises information available from variables collected in the 1996 Population Census including those related to education, occupation, and income. The variables are expressed as percentages of the relevant population. The NHS records were aggregated to the quintiles derived from the Census data, where Quintile 1 comprises the CDs with the highest IRSD scores (highest socioeconomic status, or most advantaged, areas) and Quintile 5 comprises the CDs with the lowest IRSD scores (lowest socioeconomic status, or most disadvantaged areas). Each quintile comprises approximately 20% of CDs. The ABS produced the estimates of the number of people with chronic diseases and risk factors by quintile. The method used resulted in the production of quintiles of varying sizes, ranging from 17.4% of the population in Quintile 5 (most disadvantaged areas) to 22.8% in Quintile 2 and 21.1% in Quintile 1 (most advantaged areas). This is a differential of over five percentage points between Quintile 5 and Quintile 2, or 1,027,030 fewer people in the most disadvantaged areas when compared with Quintile 2 (and 708,980 fewer in Quintile 5 than in Quintile 1). The effect of this lack of precision on the results by quintile is not known. Although, in part, the difference arises as a result of the method used (that is, that the quintiles are based on the Census population, and applied to the NHS population which has a different profile), it may also reflect different response rates to the survey from different socioeconomic groups. The NHS includes other measures that could potentially be used to measure socioeconomic status. For example, income reporting in ABS surveys is known to be incomplete, in particular for low income groups and, in the 2001 NHS, income was only collected for the respondent and their spouse. Information on education is also available, but a single measure has yet to be agreed. Problems in using education include that it is typically completed early in adulthood; it captures neither differential on-the-job training and other career investments made by individuals with similar levels of formal schooling, nor the volatility in economic status during adulthood that has been shown to have adverse implications for health [ 29 ]. The age structure of the population may also influence the indicator: an older population generally has lower education levels than a younger population due to improved access to education over time.
Measurement of socioeconomic status
The socioeconomic status (SES) of the address of residence of each survey respondent is available at the Census Collection District (CD) level and was added to the NHS file, as was the quintile of socioeconomic disadvantage of area into which that CD fell at the Census. The measure used to allocate CDs to quintiles was the 1996 Census Index of Relative Socio-Economic Disadvantage (IRSD). The IRSD is one of five Socio-Economic Indexes for Areas produced by the ABS using Principal Components Analysis. It summarises information available from variables collected in the 1996 Population Census including those related to education, occupation, and income. The variables are expressed as percentages of the relevant population.The NHS records were aggregated to the quintiles derived from the Census data, where Quintile 1 comprises the CDs with the highest IRSD scores (highest socioeconomic status, or most advantaged, areas) and Quintile 5 comprises the CDs with the lowest IRSD scores (lowest socioeconomic status, or most disadvantaged areas). Each quintile comprises approximately 20% of CDs.The ABS produced the estimates of the number of people with chronic diseases and risk factors by quintile. The method used resulted in the production of quintiles of varying sizes, ranging from 17.4% of the population in Quintile 5 (most disadvantaged areas) to 22.8% in Quintile 2 and 21.1% in Quintile 1 (most advantaged areas). This is a differential of over five percentage points between Quintile 5 and Quintile 2, or 1,027,030 fewer people in the most disadvantaged areas when compared with Quintile 2 (and 708,980 fewer in Quintile 5 than in Quintile 1). The effect of this lack of precision on the results by quintile is not known. Although, in part, the difference arises as a result of the method used (that is, that the quintiles are based on the Census population, and applied to the NHS population which has a different profile), it may also reflect different response rates to the survey from different socioeconomic groups.The NHS includes other measures that could potentially be used to measure socioeconomic status. For example, income reporting in ABS surveys is known to be incomplete, in particular for low income groups and, in the 2001 NHS, income was only collected for the respondent and their spouse. Information on education is also available, but a single measure has yet to be agreed. Problems in using education include that it is typically completed early in adulthood; it captures neither differential on-the-job training and other career investments made by individuals with similar levels of formal schooling, nor the volatility in economic status during adulthood that has been shown to have adverse implications for health [ 29 ]. The age structure of the population may also influence the indicator: an older population generally has lower education levels than a younger population due to improved access to education over time.Analysis Chronic disease and risk factor rates are expressed as rates per 100,000 population, indirectly age-standardised. The rates were also calculated by age although, given the small sample size of the NHS, only broad age groups (0–14 years, 15–24 years, 25–64 years and 65 years and over) were available. The standard population and quintile populations are the weighted 2001 survey populations from the NHS. The extent of any differential between the quintiles is shown by the rate ratio, which expresses the ratio of the rate in each quintile to the rate in Quintile 1 (the most advantaged areas, with a rate ratio of 1.00).
Analysis
Chronic disease and risk factor rates are expressed as rates per 100,000 population, indirectly age-standardised. The rates were also calculated by age although, given the small sample size of the NHS, only broad age groups (0–14 years, 15–24 years, 25–64 years and 65 years and over) were available. The standard population and quintile populations are the weighted 2001 survey populations from the NHS. The extent of any differential between the quintiles is shown by the rate ratio, which expresses the ratio of the rate in each quintile to the rate in Quintile 1 (the most advantaged areas, with a rate ratio of 1.00).Competing interests The author(s) declare that they have no competing interests.
Competing interests
The author(s) declare that they have no competing interests.Authors' contributions JG conceived the study, and was responsible for its design and coordination. ST participated in the design of the study and performed the statistical analysis. DH participated in the drafting of the manuscript. All authors read and approved the final manuscript.
Authors' contributions
JG conceived the study, and was responsible for its design and coordination. ST participated in the design of the study and performed the statistical analysis. DH participated in the drafting of the manuscript. All authors read and approved the final manuscript.


Background Survival in colorectal cancer may correlate with the degree of systemic inflammatory response to the tumour. Atrial fibrillation may be regarded as an inflammatory complication. We aimed to determine if atrial fibrillation is a prognostic factor in colorectal cancer.
Background
Survival in colorectal cancer may correlate with the degree of systemic inflammatory response to the tumour. Atrial fibrillation may be regarded as an inflammatory complication. We aimed to determine if atrial fibrillation is a prognostic factor in colorectal cancer.Patients and methods A prospective colorectal cancer patient database was cross-referenced with the hospital clinical-coding database to identify patients who had underwent colorectal cancer surgery and were in atrial fibrillation pre- or postoperatively.
Patients and methods
A prospective colorectal cancer patient database was cross-referenced with the hospital clinical-coding database to identify patients who had underwent colorectal cancer surgery and were in atrial fibrillation pre- or postoperatively.Results A total of 175 patients underwent surgery for colorectal cancer over a two-year period. Of these, 13 patients had atrial fibrillation pre- or postoperatively. Atrial fibrillation correlated with worse two-year survival (p = 0.04; log-rank test). However, in a Cox regression analysis, atrial fibrillation was not significantly associated with survival.
Results
A total of 175 patients underwent surgery for colorectal cancer over a two-year period. Of these, 13 patients had atrial fibrillation pre- or postoperatively. Atrial fibrillation correlated with worse two-year survival (p = 0.04; log-rank test). However, in a Cox regression analysis, atrial fibrillation was not significantly associated with survival.Conclusion The presence or development of atrial fibrillation in patients undergoing surgery for colorectal cancer is associated with worse overall survival, however it was not found to be an independent factor in multivariate analysis.
Conclusion
The presence or development of atrial fibrillation in patients undergoing surgery for colorectal cancer is associated with worse overall survival, however it was not found to be an independent factor in multivariate analysis.

Background In general, colorectal cancer patients are three times more likely to be in atrial fibrillation preoperatively than matched controls undergoing non-colorectal cancer surgery [ 1 , 2 ]. It may also occur postoperatively. Recent data suggest that atrial fibrillation may be an inflammatory complication, resulting from the initiation of an inflammatory response to surgery or infection [ 3 - 6 ]. Colorectal cancer patients may have elevated C-reactive protein (CRP) levels [ 7 ] indicating a systemic inflammatory response. Elevated CRP levels may be associated with a worse prognosis in colorectal cancer patients [ 8 ]. Postoperative dysrhythmias may [ 9 ] or may not [ 10 ] be associated with worse survival following surgery for lung cancer. We hypothesised that atrial fibrillation (AF) is an adverse prognostic indicator in patients undergoing surgery for colorectal cancer.
Background
In general, colorectal cancer patients are three times more likely to be in atrial fibrillation preoperatively than matched controls undergoing non-colorectal cancer surgery [ 1 , 2 ]. It may also occur postoperatively. Recent data suggest that atrial fibrillation may be an inflammatory complication, resulting from the initiation of an inflammatory response to surgery or infection [ 3 - 6 ]. Colorectal cancer patients may have elevated C-reactive protein (CRP) levels [ 7 ] indicating a systemic inflammatory response. Elevated CRP levels may be associated with a worse prognosis in colorectal cancer patients [ 8 ]. Postoperative dysrhythmias may [ 9 ] or may not [ 10 ] be associated with worse survival following surgery for lung cancer. We hypothesised that atrial fibrillation (AF) is an adverse prognostic indicator in patients undergoing surgery for colorectal cancer.Patients and methods Patients who underwent a resection for colorectal cancer between 1 st  January 2000 and 31 st  December 2001 in a 600-bed district general hospital in the United Kingdom National Health Service were identified. The hospital serves a population of approximately 230,000. About 90 elective and emergency laparotomies are performed each year for colorectal cancer. Patients were identified from the prospectively maintained colorectal cancer database maintained by the colorectal surgical department. Patients with radiological, endoscopic or clinical examinations suspicious of colorectal cancer are referred to the weekly colorectal multi-disciplinary team (MDT) meeting. In the case of suspicious radiology, the referral to the MDT is made automatically by the radiology department. This avoids the possibility of the responsible clinical firm failing to refer a patient for consideration. Similarly, the pathology department automatically refers any patient in whom histology shows colorectal malignancy. In addition, patients who undergo surgery where a suspicion of colorectal cancer is raised are referred for consideration. The colorectal meeting is attended by the colorectal surgeons, radiologists, pathologists, palliative care physicians and nursing staff. Patients determined to have colorectal cancer by the MDT are entered into the database. The colorectal department periodically compares the database to clinical coding data for patients with colorectal cancer in order to ensure complete data capture. All patients are followed-up regularly by a team of colorectal nurse specialists in a dedicated clinic. Age, sex, mode of presentation (emergency or elective), Dukes stage, postoperative anastomotic leakage and adjuvant therapy were recorded for all patients. The colorectal cancer database was cross-referenced with the hospital clinical-coding database to identify those patients who were in atrial fibrillation at any time before or after their surgery. Patients with colorectal cancer who did not undergo surgery or who only underwent palliative stoma formation were excluded. All patients were followed up for at least two years postoperatively. Overall survival and recurrence-free survival were recorded. Recurrence-free survival was defined as the time interval between operation and first diagnosis of local or distant recurrence. Patients with no recurrence were censored at the time of death from any cause other than cancer or at the time they were last seen by the colorectal team. Characteristics between those with and without AF were compared using the Student t-test and Fisher Exact test for continuous and categorical data respectively. Potential prognostic factors were compared by the log-rank test. Significant prognostic factors identified from the univariate analysis were entered into a multivariate Cox regression model of survival to test for independence. The 5% level was considered significant in the multivariate analysis. Statistical analysis was performed using Statsdirect ®  version 2 (Statsdirect Ltd., UK).
Patients and methods
Patients who underwent a resection for colorectal cancer between 1 st  January 2000 and 31 st  December 2001 in a 600-bed district general hospital in the United Kingdom National Health Service were identified. The hospital serves a population of approximately 230,000. About 90 elective and emergency laparotomies are performed each year for colorectal cancer. Patients were identified from the prospectively maintained colorectal cancer database maintained by the colorectal surgical department. Patients with radiological, endoscopic or clinical examinations suspicious of colorectal cancer are referred to the weekly colorectal multi-disciplinary team (MDT) meeting. In the case of suspicious radiology, the referral to the MDT is made automatically by the radiology department. This avoids the possibility of the responsible clinical firm failing to refer a patient for consideration. Similarly, the pathology department automatically refers any patient in whom histology shows colorectal malignancy. In addition, patients who undergo surgery where a suspicion of colorectal cancer is raised are referred for consideration. The colorectal meeting is attended by the colorectal surgeons, radiologists, pathologists, palliative care physicians and nursing staff. Patients determined to have colorectal cancer by the MDT are entered into the database. The colorectal department periodically compares the database to clinical coding data for patients with colorectal cancer in order to ensure complete data capture. All patients are followed-up regularly by a team of colorectal nurse specialists in a dedicated clinic.Age, sex, mode of presentation (emergency or elective), Dukes stage, postoperative anastomotic leakage and adjuvant therapy were recorded for all patients. The colorectal cancer database was cross-referenced with the hospital clinical-coding database to identify those patients who were in atrial fibrillation at any time before or after their surgery. Patients with colorectal cancer who did not undergo surgery or who only underwent palliative stoma formation were excluded. All patients were followed up for at least two years postoperatively. Overall survival and recurrence-free survival were recorded. Recurrence-free survival was defined as the time interval between operation and first diagnosis of local or distant recurrence. Patients with no recurrence were censored at the time of death from any cause other than cancer or at the time they were last seen by the colorectal team. Characteristics between those with and without AF were compared using the Student t-test and Fisher Exact test for continuous and categorical data respectively. Potential prognostic factors were compared by the log-rank test. Significant prognostic factors identified from the univariate analysis were entered into a multivariate Cox regression model of survival to test for independence. The 5% level was considered significant in the multivariate analysis. Statistical analysis was performed using Statsdirect ®  version 2 (Statsdirect Ltd., UK).Results One hundred and seventy-five patients (M:F = 111:64) who underwent bowel resection for colorectal cancer were identified from the database. Their median age was 74 years (interquartile range 66 to 80 years). Tumour site, Dukes stage and mode of operation (emergency or elective) are summarised in Table  1 . Anastomotic leaks occurred in three patients while another three patients received preoperative radiotherapy. Median follow-up was 2.38 years. There were 60 deaths (42 cancer-specific deaths) during the follow-up period. The remaining 18 patients died from conditions such as pneumonia, pulmonary embolus or myocardial infarction. Cause of death was recorded for all patients in the database. Twenty-eight patients (16%) developed recurrence during postoperative surveillance. The remaining 14 patients who died were noted to have incurable disease at the time of surgery. Table 1 Baseline characteristics of study group No. of patients (%) Age ≥72 years 98 (56%) < 72 years 77 (44%) Gender Male 111 (64%) Female 64 (36%) Site Colon 119 (68%) Rectum 56 (32%) Dukes' Stage A 26 (15%) B 75 (43%) C 57 (33%) D 17 (9%) Presentation Elective 147 (84%) Emergency 28 (16%) Cross-referencing with the clinical coding database identified thirteen patients with a history of atrial fibrillation. Five patients were in AF preoperatively. The remaining eight patients developed postoperative AF. A comparison of baseline characteristics between patients with and without AF is shown in table  2 . The AF was paroxysmal in three patients, persistent but eventually resolved in five and permanent in the remaining five patients. There were seven deaths among the patients with atrial fibrillation: two from recurrent colorectal cancer, four from other causes (pneumonia in two patients, respiratory failure secondary to pulmonary fibrosis in one and left ventricular failure in one) with recurrent cancer present and one non-cancer related death (peri-operative myocardial infarction). Table 2 Comparison of characteristics between patients with AF and those without. Characteristic Sinus rhythm Atrial fibrillation p Male gender 102 9 0.77 Mean age 72 73 0.64 Rectal cancer 52 4 0.99 Elective surgery 139 8 0.05 Pre-operative radiotherapy 2 1 0.22 Anastomotic leak 3 0 0.79 Dukes Stage A 24 2 0.73* B 69 6 C 53 4 D 16 1 *Fisher-Freeman-Halton Exact Test Survival analysis There was no correlation between overall survival and the following variables in the univariate survival analysis: gender, postoperative anastomotic leak, site of tumour (rectal versus colonic) or preoperative radiotherapy. Mode of surgery (emergency or elective), age (<72 years or ≥ 72 years) and Dukes' stage had a significant effect on survival. When patients with atrial fibrillation were compared to those without (Figure  1 ), atrial fibrillation correlated with worse overall survival. Dukes' stage, mode of surgery, age and atrial fibrillation were entered into a Cox regression model overall survival (Table  2 ). Mode of surgery, age and Dukes' stage retained significance but atrial fibrillation did not (Model chi-square 49.6; 7 degrees of freedom; p < 0.0001). There was no significant correlation between atrial fibrillation and recurrence-free survival (p = 0.74). Figure 1 Kaplan-Meier survival curves for patients with a history of atrial fibrillation (1) versus those without (0). P = 0.04 (Log-rank test).
Results
One hundred and seventy-five patients (M:F = 111:64) who underwent bowel resection for colorectal cancer were identified from the database. Their median age was 74 years (interquartile range 66 to 80 years). Tumour site, Dukes stage and mode of operation (emergency or elective) are summarised in Table  1 . Anastomotic leaks occurred in three patients while another three patients received preoperative radiotherapy. Median follow-up was 2.38 years. There were 60 deaths (42 cancer-specific deaths) during the follow-up period. The remaining 18 patients died from conditions such as pneumonia, pulmonary embolus or myocardial infarction. Cause of death was recorded for all patients in the database. Twenty-eight patients (16%) developed recurrence during postoperative surveillance. The remaining 14 patients who died were noted to have incurable disease at the time of surgery.Baseline characteristics of study groupNo. of patients (%)Cross-referencing with the clinical coding database identified thirteen patients with a history of atrial fibrillation. Five patients were in AF preoperatively. The remaining eight patients developed postoperative AF. A comparison of baseline characteristics between patients with and without AF is shown in table  2 . The AF was paroxysmal in three patients, persistent but eventually resolved in five and permanent in the remaining five patients. There were seven deaths among the patients with atrial fibrillation: two from recurrent colorectal cancer, four from other causes (pneumonia in two patients, respiratory failure secondary to pulmonary fibrosis in one and left ventricular failure in one) with recurrent cancer present and one non-cancer related death (peri-operative myocardial infarction).Comparison of characteristics between patients with AF and those without.CharacteristicSinus rhythmAtrial fibrillationp*Fisher-Freeman-Halton Exact TestSurvival analysis There was no correlation between overall survival and the following variables in the univariate survival analysis: gender, postoperative anastomotic leak, site of tumour (rectal versus colonic) or preoperative radiotherapy. Mode of surgery (emergency or elective), age (<72 years or ≥ 72 years) and Dukes' stage had a significant effect on survival. When patients with atrial fibrillation were compared to those without (Figure  1 ), atrial fibrillation correlated with worse overall survival. Dukes' stage, mode of surgery, age and atrial fibrillation were entered into a Cox regression model overall survival (Table  2 ). Mode of surgery, age and Dukes' stage retained significance but atrial fibrillation did not (Model chi-square 49.6; 7 degrees of freedom; p < 0.0001). There was no significant correlation between atrial fibrillation and recurrence-free survival (p = 0.74). Figure 1 Kaplan-Meier survival curves for patients with a history of atrial fibrillation (1) versus those without (0). P = 0.04 (Log-rank test).
Survival analysis
There was no correlation between overall survival and the following variables in the univariate survival analysis: gender, postoperative anastomotic leak, site of tumour (rectal versus colonic) or preoperative radiotherapy. Mode of surgery (emergency or elective), age (<72 years or ≥ 72 years) and Dukes' stage had a significant effect on survival. When patients with atrial fibrillation were compared to those without (Figure  1 ), atrial fibrillation correlated with worse overall survival. Dukes' stage, mode of surgery, age and atrial fibrillation were entered into a Cox regression model overall survival (Table  2 ). Mode of surgery, age and Dukes' stage retained significance but atrial fibrillation did not (Model chi-square 49.6; 7 degrees of freedom; p < 0.0001). There was no significant correlation between atrial fibrillation and recurrence-free survival (p = 0.74).Discussion Tumours stimulate an inflammatory response when they invade or metastasise [ 11 ]. This inflammation may cause a rise in serum levels of inflammatory markers such as CRP. Serum CRP levels correlate with survival in colorectal cancer patients [ 8 ]. Atrial fibrillation may be precipitated or maintained by an inflammatory mechanism [ 3 ]. Thus, we hypothesised that the presence of AF, an inflammatory complication, would be associated with poorer survival in colorectal cancer as it is a manifestation of more advanced disease. Our data demonstrate a significant correlation between atrial fibrillation and survival following surgery for colorectal cancer in univariate analysis. Previous investigators have found that atrial arrhythmias were significantly more common in those patients with a history of cancer. In addition, CRP levels were higher in those patients with a history of cancer. However, there was no independent link between cancer and arrhythmias. This is consistent with inflammation being the causal link between cancer and arrhythmias [ 12 ]. In our cohort, atrial fibrillation was not an independent predictor of survival. Dukes' stage was the strongest predictor of survival. Previous data from colorectal cancer patients suggest the degree of inflammatory response may reflect the degree of disease progression [ 13 ]. The incidence of liver metastases, peritoneal tumour deposits, lymph node metastases and intravascular invasion are higher in patients with elevated CRP levels [ 14 ]. Thus, atrial fibrillation may be a manifestation of systemic inflammation due to more advanced disease and would not be independent of Dukes' stage. Cox regression analysis of our study cohort (Table  3 ) appears to show poor survival in Dukes B patients (Hazard ratio 9.13) compared to Dukes C patients (Hazard ratio 0.15). There was a trend for Dukes B patients to be older (mean age 73.79 years) than Dukes C patients (70.99 years; p = 0.1, student t-test, β = 0.67) and this may account for the reduced survival. There were no differences in the Dukes' stage distribution between patients with and without AF (Table  2 ). However, the small numbers in our series (only thirteen patients with AF) render it underpowered to detect a correlation between the presence of AF and colorectal cancer stage. Table 3 Overall univariate and multivariate survival analysis Variable Univariate p Multivariate p Hazard Ratio 95% Confidence interval of hazard ratio Age (< 72 years versus ≥ 72 years) 0.03 0.003 2.38 1.339 to 4.224 Pre-operative radiotherapy 0.26 - - - Anastomotic leak 0.23 - - - Gender (Male versus female) 0.33 - - - Site of tumour (Rectal versus colonic) 0.87 - - - Emergency surgery <0.0001 0.004 2.35 1.312 to 4.205 Atrial fibrillation 0.04 0.06 2.21 0.979 to 4.985 Dukes' stage (A, B, C, D) <0.0001 <0.0001 Dukes A 0.09 0.032 to 0.267 Dukes B 9.13 4.343 to 19.206 Dukes C 0.15 0.073 to 0.312 Dukes D 1 Reference There are several other limitations to our study. The quality of the clinical coding has not been assessed. Thus, the sensitivity and specificity of our clinical coding department in coding AF is unknown. Only 13 patients (7.4%) developed AF. Previous work suggests that 13% of elective colorectal patients develop postoperative AF [ 15 ]. Thus, it is possible that some patients with unrecorded AF have been incorporated into the control arm of our series. The study cohort has been followed for a relatively short time, only two years. However, most trials of follow-up following surgery for colorectal cancer report a mean time to relapse of approximately 24 months [ 16 ]. We were unable to demonstrate a significant relationship between atrial fibrillation and recurrence-free survival. This may be due to an insufficient sample size. However, atrial fibrillation does correlate with overall survival. The presence of atrial fibrillation may be a clinical marker of poor overall survival in patients undergoing surgery for colorectal cancer.
Discussion
Tumours stimulate an inflammatory response when they invade or metastasise [ 11 ]. This inflammation may cause a rise in serum levels of inflammatory markers such as CRP. Serum CRP levels correlate with survival in colorectal cancer patients [ 8 ]. Atrial fibrillation may be precipitated or maintained by an inflammatory mechanism [ 3 ]. Thus, we hypothesised that the presence of AF, an inflammatory complication, would be associated with poorer survival in colorectal cancer as it is a manifestation of more advanced disease. Our data demonstrate a significant correlation between atrial fibrillation and survival following surgery for colorectal cancer in univariate analysis. Previous investigators have found that atrial arrhythmias were significantly more common in those patients with a history of cancer. In addition, CRP levels were higher in those patients with a history of cancer. However, there was no independent link between cancer and arrhythmias. This is consistent with inflammation being the causal link between cancer and arrhythmias [ 12 ].In our cohort, atrial fibrillation was not an independent predictor of survival. Dukes' stage was the strongest predictor of survival. Previous data from colorectal cancer patients suggest the degree of inflammatory response may reflect the degree of disease progression [ 13 ]. The incidence of liver metastases, peritoneal tumour deposits, lymph node metastases and intravascular invasion are higher in patients with elevated CRP levels [ 14 ]. Thus, atrial fibrillation may be a manifestation of systemic inflammation due to more advanced disease and would not be independent of Dukes' stage. Cox regression analysis of our study cohort (Table  3 ) appears to show poor survival in Dukes B patients (Hazard ratio 9.13) compared to Dukes C patients (Hazard ratio 0.15). There was a trend for Dukes B patients to be older (mean age 73.79 years) than Dukes C patients (70.99 years; p = 0.1, student t-test, β = 0.67) and this may account for the reduced survival. There were no differences in the Dukes' stage distribution between patients with and without AF (Table  2 ). However, the small numbers in our series (only thirteen patients with AF) render it underpowered to detect a correlation between the presence of AF and colorectal cancer stage.Overall univariate and multivariate survival analysisVariableUnivariate pMultivariate pHazard Ratio95% Confidence interval of hazard ratioThere are several other limitations to our study. The quality of the clinical coding has not been assessed. Thus, the sensitivity and specificity of our clinical coding department in coding AF is unknown. Only 13 patients (7.4%) developed AF. Previous work suggests that 13% of elective colorectal patients develop postoperative AF [ 15 ]. Thus, it is possible that some patients with unrecorded AF have been incorporated into the control arm of our series. The study cohort has been followed for a relatively short time, only two years. However, most trials of follow-up following surgery for colorectal cancer report a mean time to relapse of approximately 24 months [ 16 ]. We were unable to demonstrate a significant relationship between atrial fibrillation and recurrence-free survival. This may be due to an insufficient sample size. However, atrial fibrillation does correlate with overall survival. The presence of atrial fibrillation may be a clinical marker of poor overall survival in patients undergoing surgery for colorectal cancer.Competing interest The author(s) declare that they have no competing interests.
Competing interest
The author(s) declare that they have no competing interests.Authors' contribution SRW  conceived the study, performed the statistical analysis and drafted the paper. KMG  and  NJW  performed the literature search and collected the data. TAJ  and  NJK  co-wrote the paper with  SRW . All authors approved the manuscript.
Authors' contribution
SRW  conceived the study, performed the statistical analysis and drafted the paper.SRWKMG  and  NJW  performed the literature search and collected the data.KMGNJWTAJ  and  NJK  co-wrote the paper with  SRW .TAJNJKSRWAll authors approved the manuscript.Funding support None declared
Funding support
None declared