Education, health promotion, and social and lifestyle determinants of health and disease
Lawrence W. Green and Louise Potvin
Specific behaviours and health
Behaviours and disease—the causal links
Behavioural risk factors and the public’s health
Complexity and determinants of behaviours
Socio-economic and cultural factors and health
Socio-economic status and mortality
Socio-economic status and morbidity
Socio-economic status and health-related behaviours
Reasons for relationships between socio-economic status and health
Socio-economic status, gender, and health
Culture and health
Socio-economic status and lifestyle: the merging of perspectives
Relationships among lifestyle factors
Education, socio-economic status, lifestyle, and the health agenda
Education and health
Channels of educational influence on health
Education of those at risk of threats to their health
Education for community development
Education to influence environmental conditions
Interactions of behaviour and environment are pervasive as determinants of health. Technological, engineering, biomedical, legal, and regulatory approaches to public health have sought to control behaviour and to protect people from the environment and from each other. These strategies have declared their victories, only to find new environmental or behavioural problems breaking out somewhere else as causes of ill health. Educational approaches to health provide, at the very least, a palliative solution when technological solutions await development, and a developmental solution when legal or regulatory solutions await an informed and activated electorate. Education enables people to take personal and collective action to protect themselves from environmental threats and to support or resist the development and distribution of technology and the passage of legislation.
In this chapter we attempt to focus on those complex behaviour–environment interactions, called lifestyle, which influence health, and the ways in which education shapes or modifies lifestyle. We use the term lifestyle to refer to any combination of specific practices and environmental conditions reflecting patterns of living influenced by family and social history, culture, and socio-economic circumstances. We know that a discrete behaviour can be influenced directly by health education targeted at individuals and groups. Lifestyle changes more slowly and usually requires some combination of educational, organizational, economic, and environmental interventions in support of changes in both behaviour and conditions of living. This combination of strategies defines health promotion for individuals and communities (Green and Kreuter 1999).
AIDS presents the obvious contemporary example of a disease awaiting a technological solution, and a lifestyle responding slowly to educational, organizational, economic, and environmental interventions and changes. Virtually every public health breakthrough has had an educational process that served the public until the technology was at hand and that helped the diffusion and application of that new technology. Unless, and until, an HIV vaccine is developed, society must depend on behavioural preventive measures to curb the spread of AIDS. Much of the behaviour in question with AIDS is not amenable to legal regulation because of its private nature. Thus education is a primary choice to control the spread of AIDS. The success of health education in filling that gap has been modest but not insignificant depending on the targeted population. Although impossible to attribute to a single programme, the early dramatic changes in sexual practices (general use of condoms) among men in organized urban gay communities appear to have been in response to health education programmes (Petrow 1990; Higgins 1991). Reviews also show evidence of a possible increase in the use of clean needles and safe sexual practices among intravenous drug users (Higgins et al. 1991; Fisher and Fisher 1992). Evidence that health education leads to the regular use of condoms among sexually active adolescents, however, has not held up consistently (Fisher and Fisher 1992). Changing some of the more complex lifestyles associated with AIDS in lower-risk populations will require more population-tailored health promotion interventions that address information–motivation–behavioural needs in the specific environmental contexts of the behaviours.
We start with a reductionist view of specific behaviours as they relate to health and disease, and then progress to the more complex sociocultural aspects inherent in the term lifestyle. Finally, we examine the functions of education in reducing disease and promoting health in populations or communities.
Specific behaviours and health
For many diseases, some behaviours clearly increase the risk of developing disease and can be considered proximal causes of disease. Other behaviours correlate with and precede better health, increased longevity, and decreased disease risk, although the causal link is more tenuous, warranting their inclusion with other more distal determinants. Examining the relationships between specific behaviours and specific indicators of health and disease status provides the foundation for assessing behavioural and lifestyle factors as health determinants.
Behaviours and disease—the causal links
Evidence from observational epidemiological studies, human experimental trials, and animal models, together with potential mechanisms of biological action, lead one to conclude that many behaviours are, in fact, contributing causes (causal risk factors) of specific diseases. The causal link is relatively easy to establish for single-agent communicable diseases, but much more difficult to identify for multiple-cause chronic diseases and conditions (Krieger 1994). We present three examples of evidence supporting causal links between behaviours and coronary heart disease: smoking, diet, and physical activity. These three examples illustrate that even in the absence of direct experimental evidence in humans, strong evidence of other types can be assembled for the steps in a causal chain from behaviour through physiological effects to disease.
Studies of the relationship between smoking and coronary heart disease provide strong evidence of a behaviour as a cause of a chronic disease. A plausible biological model has been available for a long time (Dawber 1960). Observational epidemiological studies, using a variety of designs ranging from cross-populational to cross-sectional to case–control to prospective, have found strong and consistent measures of association, the correct temporal sequence, and a dose–response relationship (Stamler 1992). In addition, randomized trials that include smoking cessation programmes provide experimental evidence for smoking as a cause of coronary heart disease. In both the Multiple Risk Factor Intervention Trial (MRFIT) and a British trial on the effect of smoking reduction, the number of smokers decreased significantly after a smoking cessation programme, and many of them remained abstinent after several years (Ockene et al. 1990; Rose and Colwell 1992). Both studies have shown a decrease in coronary heart disease mortality: 13 per cent after 20 years in the British trial (Rose and Colwell 1992), and 12 per cent after 10.5 years in MRFIT (Ockene et al. 1990). In addition, when smokers at baseline from the experimental and control groups were pooled, quitters had a significant decrease in their risk of mortality from coronary heart disease compared with non-quitters (Ockene et al. 1990; Kuller et al. 1991).
Evidence that consumption of saturated fat and cholesterol are contributing causes of coronary heart disease has come from numerous ecological studies showing a correlation between dietary fat and coronary heart disease mortality and incidence rates (Keys 1970; McGill 1979). Other studies have shown that a high serum cholesterol level increases the risk of coronary heart disease development (Pooling Project Research Group 1978; Stamler et al. 1986; Andersen et al. 1987a), that changes in dietary saturated fat and cholesterol lead to changes in serum cholesterol (Mensink and Katan 1992), and that lowering the serum cholesterol level decreases the occurrence of coronary heart disease (Frick et al. 1987). A direct demonstration of the diet–heart hypothesis by a true experimental study may never occur because of the large sample size, the sustained differential changes needed between control and intervention groups, and the long-term follow-up required for such a trial. However, the strong evidence that exists for each step in a causal chain from diet to coronary heart disease has led to major national recommendations that diet be a first-line approach to reduce blood cholesterol to prevent disease (NHLBI 1990, NHLBI 1993; US Department of Health and Human Services 1991).
The relationship between physical activity and coronary heart disease is the third example. Evidence that physical inactivity is a causal risk factor for coronary heart disease comes from biological effects of exercise on cardiovascular physiology, observational epidemiological studies, and randomized controlled trials of physical activity and physiological coronary heart disease risk factors. The biological effects of exercise training to enhance cardiovascular health are well established (McArdle et al. 1986). Epidemiological evidence shows consistent and relatively strong associations, the correct temporal sequence, and a dose–response relationship between physical activity level and coronary heart disease (Powell et al. 1987; Berlin and Colditz 1990; Blair et al. 1993). Observational epidemiological studies and randomized controlled trials have demonstrated the beneficial effects of physical activity on blood pressure (Arroll and Beaglehole 1992) and on blood lipids and lipoproteins (Haskell 1986; Lokey and Tran 1989), which have, in turn, been causally linked with subsequent coronary heart disease. Strong evidence exists for the steps in a causal chain.
Many causal risk factors are not themselves behaviours, but have determinants that are behaviours; in these cases the behavioural determinants can be considered as indirect risk factors that act earlier in the causal pathway. For example, a combination of high caloric intake and low energy output are the behavioural determinants of obesity (Heath et al. 1991; Helmrich et al. 1991; Stern 1991). Obesity, in turn, has been found in prospective studies to be a risk factor for type 2 (adult-onset) diabetes (Bergstom et al. 1990).
Behaviours contribute to the prognosis of those diseases for which the stage of diagnosis or the compliance with prescribed regimens of treatment or self-care affects outcomes. For example, the prognosis of breast cancer depends on the stage of disease at which the woman obtains medical care, and the prognosis for type 1 (insulin-dependent) diabetes depends on the patient’s compliance with his or her insulin prescriptions. Because behaviour is so central to disease outcomes, a large literature on patient education and patient compliance with medical regimens has been catalogued and subjected to meta-analyses (Mullen et al. 1985; D.G. Simons-Morton et al. 1992; Silagy and Ketteridge 1998).
Behavioural risk factors and the public’s health
Behavioural determinants of health and disease status can be found for almost every disease through behavioural risk factors, or through behavioural factors that influence physiological risk factors, or through behavioural factors that influence treatment and prognosis.
The leading causes of death in developed nations are primarily chronic diseases and injuries. Deaths from infectious diseases in the last two decades have refocused public health attention, particularly on the HIV/AIDS epidemic (National Center for Health Statistics 1993). The 10 leading causes of death in the United States in 1997, their generally accepted behavioural and physiological risk factors (McGinnis and Foege 1993), and some behavioural determinants of the physiological risk factors are listed in Table 1. The list for other developed countries would differ only slightly and for those causes that are further down the list. For example, the three leading causes of death in Canada in 1991 were the same as in the United States, accounting also for a little less than two-thirds of the total number of deaths (Canadian Center for Health Information 1994). World Health Organization (WHO) estimates of the percentage of deaths from the four leading causes for the developed countries in 1980 were as follows (WHO 1990): cardiovascular disease, 48 per cent; cancer, 19 per cent; respiratory diseases, 7.5 per cent; accidents, 7.0 per cent.
Table 1 The 10 leading causes of death in the United States (1997), their generally accepted behavioural (in italics) and physiological risk factors, and the behavioural determinants of the physiological risk factors
The causes of death in developing countries differ markedly. WHO estimates of the four leading causes of death for the developing countries are: respiratory diseases, 21 per cent; infectious and parasitic diseases, 18 per cent; cardiovascular diseases, 16 per cent; perinatal mortality, 7 per cent (WHO 1990). Among the most striking differences in health indicators between the developed and the developing countries are the perinatal–juvenile mortality rates. The infant mortality rates, for example, range from less than 10 per 1000 live births in western European, North American, and other Pacific Rim countries to more than 150 among some developing countries. The probabilities of dying before the age of 5 years range from less than 12 per 1000 live births to more than 280 for the same comparison countries (WHO 1994). UNICEF (1993) estimates that 12.9 million children die each year, almost a quarter of them from diarrhoeal diseases and another 16 per cent from diseases that are preventable by proper immunization.
By 2020, according to WHO estimates, the tobacco epidemic is expected to kill more people than any single disease. Because it is a known or probable determinant of at least 25 diseases, and the most important determinant of some of the leading causes of death, tobacco use will cause nearly 18 per cent of all deaths in developed countries and 11 per cent in developing countries (WHO 1998). The majority of addicted tobacco users began their use while in their teenage years or earlier.
Even when the immediate causes of deaths are specific infectious or toxic agents, behaviours are important contributing determinants of the transmission of those agents. In an analysis involving 66 countries, Hertz et al. (1994) have shown that the three most important predictors of infant mortality rates are percentage of households with sanitation, total literacy rate, and the percentage of households without safe water. Clearly, the major public health needs in developing nations relate to the provision of immunization, access to a sufficient supply of clean water, and the installation of proper sanitation facilities (WHO 1979, 1981, 1986). However, these environmental measures achieve the intended health goals only to the extent that an informed population uses them properly. A report by the World Bank (1993) suggests that the single most important public health policy for developing countries lies in the improvement of the education of young girls. Better-educated women have fewer children, who tend to be healthier and, in turn, are better educated.
Examination of the recent trends in the richest of the developing countries provides evidence that improvement of the socio-economic situation is accompanied by a shift in mortality towards the ‘civilization diseases’ reviewed earlier. For example, in Mexico in 1991 infectious and parasitic diseases were only the fourth leading cause of death, following diseases of the heart (15.8 per cent), malignant neoplasms (10.0 per cent), and accidents (9.5 per cent). Developing countries are now the primary target for market expansion for the multinational tobacco companies. As living standards improve in these countries, deaths from cardiovascular diseases and lung cancer will probably increase considerably. Such trends for lung cancer have been reported for China, India, and Malaysia (Simpson and Ball 1992; WHO 1998).
The role of behavioural risk factors in chronic diseases has been studied primarily in developed nations. Their patterns of mortality make behaviour a legitimate target for health policy (Canada 1974; Epp 1986; McGinnis and Foege 1993). However, it is clear from the shift in causes of death as countries develop and from historical trends in morbidity and mortality in developed nations that attention to behavioural risk factors is warranted early in a nation’s development. Health education needs to accompany environmental and policy interventions as well as economic development and basic education.
When one examines the generally accepted behavioural risk factors for disease, it readily becomes apparent that in developed nations a few categories of behaviours are related to a large proportion of deaths. At least one of the three main behavioural risk factors—smoking, dietary practices, and alcohol use—is causally related to each of the 10 leading causes of death in the United States. Active smoking is a risk factor for coronary heart disease (Manson et al. 1992; Bartecchi et al. 1994), diabetes (Rimm et al. 1993), stroke (Robbins et al. 1994), and adverse pregnancy outcomes, such as low birth weight, premature rupture of membranes, and abruptio placentae (Fox et al. 1994; Mittendorf et al. 1994). Passive smoking (i.e. exposure to environmental tobacco smoke), has been related to lung cancer and other respiratory diseases (Office of Health and Environmental Assessment 1992) in adults, and is an independent risk factor for coronary heart disease (Hertz et al. 1994). Exposure to environmental tobacco smoke in the home has been associated with asthma and other respiratory conditions, and with ear infections in infants and children (Office of Environmental Health Hazard and Assessment 1997)
Dietary factors are related to the development of atherosclerosis and coronary heart disease through serum cholesterol level (Hunninghake et al. 1993) and obesity (Manson et al. 1992). Fat intake is also related to colon cancer and possibly to prostate and breast cancer; fibre intake is associated with colon cancer, and fruit and vegetable intake is related to cancers of the lung, cervix, bladder, oral cavity, oesophagus, stomach, and colon (Austoker 1994a). Through body weight, dietary factors are also associated with diabetes mellitus (Colditz et al. 1990; Helmrich et al. 1991). Alcohol use is related to cirrhosis of the liver and other liver diseases (Anderson et al. 1993), suicide, homicide, and unintentional injuries (Petrakis 1987), congenital anomalies (Ogston and Parry 1992), and cancer of the mouth, pharynx, larynx, oesophagus, and liver (Austoker 1994b). The population attributable risk (i.e. the proportion of the disease in the population that can be attributed to the behavioural factor) can be estimated for many diseases, to the extent that strong evidence of causality exists. For example, the US Centers for Disease Control and Prevention (US CDC 1993) has used estimates of the relative risk and risk factor prevalence, taken from numerous epidemiological studies, to calculate the proportion of disease-specific deaths attributable to smoking in the United States in 1990. The proportion of deaths estimated to be attributable to active smoking or to exposure to environmental tobacco smoke in the United States are shown for various diseases in Table 2. These estimates represented a total of 418 690 deaths (Bartechi et al. 1994). Based on information about the causal associations between behaviours and health, and on the prevalence of health problems, McGinnis and Foege (1993) estimated that, in the United States, 19 per cent of all deaths in 1990 were attributable to smoking and 14 per cent to diet and activity patterns.
Table 2 Percentage of deaths attributable to cigarette smoking (population attributable risk) for selected leading causes of death in the United States in 1990a
Complexity and determinants of behaviours
Despite the implied simplicity in identifying a few major behaviours accounting for the majority of deaths in developed countries, those behaviours are highly complex. Most behavioural risk factors, and health-care behaviours also, are the product of a variety of component behaviours, tasks, or actions. For example, food consumption has been said to confront most people with a chain of behaviours that includes procuring and selecting food, planning menus or selecting from a menu, preparing or ordering foods, and eating with literally hundreds of food-related choices, including where to shop or eat, what to purchase or prepare, how to season food, and with whom to eat (B.G. Simons-Morton et al. 1986). One can identify similar chains of component behaviours and behaviour-related choices for other health behaviours identified in Table 1.
Not only are health behaviours complex, but each behaviour has numerous influences or determinants. Factors that influence behaviours can be grouped into three major categories (Green and Kreuter 1999): predisposing, reinforcing, and enabling. Predisposing factors reside in the individual and include attitudes, values, and beliefs, but these are shaped over time by cultural and social reinforcing factors—the positive or negative feedback on or consequences of behaviour—such as peer acceptance or social disapproval. Enabling factors are generally conditions of the environment that facilitate the behaviour or, alternatively, create barriers to it.
Most behaviours have influences from all three categories. Some of the known and likely influencing factors for the three most important preventive health behaviours plus the important interacting health behaviour, physical activity, are shown in Table 3.
Table 3 Some known and suspected influences on four major behavioural risk factors
The influences on smoking initiation and cessation are numerous (Warner 1986a,b; D.G. Simons-Morton et al. 1991). Predisposing factors include attitudes about smoking and beliefs about and knowledge of the health effects of smoking. Reinforcing social factors include social support, peer influences, and cigarette advertising (providing vicarious reinforcement). Enabling factors include availability and cost of cigarettes.
A variety of factors influence dietary practices (B.G. Simons-Morton et al. 1986; Samuels 1990; Contento et al. 1993). These include both personal and cultural food preferences, perceived social acceptance, social context, availability and convenience of foods, and skills in menu planning, food purchasing, food selection, and food preparation.
Numerous factors influence alcohol use and abuse (Petrakis 1987; Zarek et al. 1987; B.G. Simons-Morton et al. 1990; Villas et al. 1993). Predisposing factors may include expectations about the effects of alcohol, psychological stress and low self-esteem, perceptions of invulnerability to adverse consequences of drinking such as losing one’s job, being a child of an alcoholic, and early drinking experiences. Reinforcing factors include parent and peer influences, and may include advertising and modelling in the visual media. Enabling factors and barriers include availability or non-availability of non-alcoholic drinks, cost of alcoholic beverages, access to alcohol, and supervision of adolescents.
In addition to the three major behavioural risk factors for causes of mortality, every behaviour related to morbidity and well-being also has a variety of influences. Physical inactivity is a good illustrative example. It is not only a risk factor for coronary heart disease (Blair et al. 1993), but is also related to hypertension (Duncan et al. 1985; Arroll and Beaglehole 1992), osteoporosis (Lee 1991), and mental health (Emery and Blumenthal 1991), all of which are prevalent health problems in developed countries. The numerous influences on physical activity include beliefs about the importance of physical activity, attitudes about physical activity, motivation and self-discipline, accessibility of an exercise facility, skills in relapse prevention and goal setting, discomfort or inconvenience of exercise, and family support (D.G. Simons-Morton et al. 1988b; Custer and Doty 1992; Field and Steinfardt 1992).
In addition to the complexity of risk behaviours and their numerous determinants, the performance of each behaviour is interwoven with other behaviours and with socio-economic and cultural factors.
Socio-economic and cultural factors and health
To understand better the lifestyle determinants of health, one must examine the context within which behaviour occurs. That context includes contemporary personal interaction with family and other people and with complex organizations. The cultural context includes the cumulative weight of these interactions over generations, as reflected in values and traditions related to behaviour.
The substantial evidence that socio-economic conditions are associated with health status is reviewed elsewhere. Culture plays an intimate role in the determination of health status, most clearly through health behaviours, but also apparently through traditional patterns of social support (Berkman and Syme 1979). In this section we review representative studies and major reports for an overview of the relationships between socio-economic status and health, socio-economic status and use of health-care services, and culture and health, in order to provide a context for the behavioural factors. This will lead us to the more complex sociocultural–behavioural–environmental construct known as lifestyle.
Education may be the most basic aspect of socio-economic status as presented here. Duncan (1961) described the relationship between the basic components (income, education, and occupation of socio-economic status): ‘Education qualifies the individual for participation in occupational life, and pursuit of an occupation yields him a return in the form of income’.
Socio-economic status and mortality
This relationship is hardly new. Antonovsky (1967) reviewed the literature from the seventeenth century through to the early 1960s on the relationship between socio-economic status and mortality, including over 30 studies primarily from the European countries and the United States. In these studies socio-economic status was measured in a variety of ways, including type of occupation, median rental costs in census tracts, taxpayer status, and indices comprising education, occupation, and median family income. Antonovsky concluded that:
Despite the multiplicity of methods and indices used in the 30-odd studies cited, and despite the variegated populations surveyed, the inescapable conclusion is that [socio-economic] class influences one’s chance of staying alive. Almost without exception, the evidence shows that [socio-economic] classes differ on mortality rates.
People with lower socio-economic status have higher mortality rates. He observed that the greatest difference in mortality rates occurred during the middle years of life (the thirties and forties) which, he conjectured, may be due to differences in preventable (postponable) deaths between those with different socio-economic status.
Table 4 summarizes results from several studies conducted in developed countries, showing a persisting relationship between socio-economic status and mortality. National data show a consistent association between various indicators of socio-economic status and various indicators of mortality and longevity. In the United States in 1960, socio-economic status as measured by educational level (years of schooling completed) and by family income was inversely associated with mortality ratios (Kitagawa and Hauser 1973). In 1982, in the United Kingdom, the Black Report (Black et al. 1982) cited higher 1970 to 1972 mortality rates in occupational groups of lower socio-economic status for both males and females in all age groups. Morris (1979) reported that, in the United Kingdom in 1975 to 1976, all-cause mortality in men continued to be higher for occupational groups of lower socio-economic status. Wilkins et al. (1989) showed that household wealth (as defined by the household income weighted by the number of individuals in the household, the average income of the census tract, and the Statistic Canada low-income cut-off) correlated with the life expectancy at birth for both males and females, and more so for the former. Using data from the 1986 National Mortality Followback Survey and the 1986 National Health Interview Survey, Pappas et al. (1993) showed that the level of income is strongly inversely related to the age-adjusted death rates for men and women, both black and white. All these studies show a persistent gradient. In all these studies, the advantages associated with better socio-economic conditions increase across the whole spectrum of each socio-economic indicator.
Table 4 Results from recent studies of socio-economic status and mortality
Studies comparing data from multiple points in time show different pictures for different countries. The results of Wilkins et al. (1989) show that the gap between the ‘haves’ and the ‘have nots’, as indicated by the age-adjusted death rates, remained fairly stable in Canada between 1971 and 1986. Over a longer period between 1960 and 1986, however, Pappas et al. (1993) have demonstrated an increase in that gap, which is more pronounced for men than for women.
A socio-economic differential for infant and childhood mortality has been observed in many studies. Early childhood death rates (ages 0–5 years) in Southampton, England, from 1977 to 1982 were higher in districts with high unemployment, poor housing, and single-parent families (Robinson and Pinch 1987). In Kentucky, rates in 1982 to 1983, adjusted for a variety of variables, were significantly higher in poor than in non-poor infants during the postneonatal period (Spurlock et al. 1987). In Canada, Wilkins et al. (1989) found that, in 1986, the infant mortality rate in the lowest quintile of wealth was almost double the infant mortality rate in the highest quintile. Leon et al. (1992) found that in Sweden, in 1985 to 1986, the relative risk of neonatal mortality was significantly higher (1.20) for manual compared with non-manual occupational classes; the post-neonatal mortality was also significantly higher (1.38). Even in countries such as Canada and Sweden with equitable health-care systems and welfare policy, people of lower socio-economic classes experience higher mortality.
An inverse socio-economic differential for cause-specific mortality has also been observed for many diseases. The Whitehall Study of British civil servants revealed gradients of mortality rates for coronary heart disease with the lowest rates among high-status occupations and highest mortality rates for the lowest-grade employees. Ischaemic heart disease mortality in the United Kingdom is higher in manual than in non-manual workers (Marmot and McDowall 1986; Pocock et al. 1987), and in New York City cancer mortality rates are higher in lower-income groups (Shai 1986). Wilkins et al. (1989) have calculated the specific age-adjusted mortality rates in 1986 for several diseases for different groups of Canadians defined by their relative wealth. Comparing the rates for people in various quintiles on wealth, they found an excess mortality (1.5 times greater) among the poorest quintile for infectious diseases, lung cancer, uterine cancer, alcoholism for males, obstructive respiratory diseases, cirrhosis, perinatal mortality, pedestrian accidents, suicides, and other types of accidents. Davey Smith et al. (1996) retrieved the mortality data of the 300 000 men enrolled in the MRFIT study during the 1970s. They found a consistent gradient relationship between the median family income of the zip code of residence at the time of enrolment and age-adjusted mortality rates from all causes as well as from cardiovascular diseases, lung cancer, diabetes, respiratory diseases, and cirrhosis.
Thus, it seems that the relationship between socio-economic status and health is not attributable to a threshold effect. On the contrary, those at the top of the hierarchy are better off than those just below them who are themselves better off than the others, and so on down to those at the very bottom. The gradient adheres whether the socio-economic measure is education, income, occupational status, or place of residence.
It was long believed that all the above patterns of socio-economic status are also reflected in white versus non-white differences in mortality. In the United States the black death rate exceeds the white death rate by 50 per cent (Andersen et al. 1987b). However, Keil et al. (1992) have shown that in Charleston, North Carolina, where both groups experienced a similar socio-economic gradient when controlled for socio-economic status, the all-cause mortality rates between 1960 and 1988 were the same for black men and for white men.
Most within-country studies have been conducted in developed nations; however, comparisons between nations provide a global perspective. In general, less developed countries with lower per capita incomes exhibit lower life expectancy and higher infant mortality rates than more developed countries with higher per capita incomes.
A number of recent studies have examined the relationship between mortality rates and various indicators of social inequalities in geographical areas varying in size from metropolitan areas (Lynch et al. 1998) to whole countries (Wilkinson 1992a,Wilkinson 1992 b; Wilkinson and Marmot 1998). These studies consistently showed that those areas where inequalities between those at the top of the social hierarchy and those at the bottom were the largest were also those in which the mortality gradient was the strongest (Wilkinson 1996). Similar findings were found with other indicators of social inequities such as differences in educational status (Kunst and Mackenbach 1994) or differences in social capital (Kawachi et al. 1997). Disparity within a given society, or relative deprivation, seems to be more influential than the absolute level of poverty.
Socio-economic status and morbidity
Morbidity is difficult to estimate at the level of populations. Studies using diagnosed cases in the numerator of a prevalence rate are very rare, and those that exist are restricted to a limited number of diseases. For population studies, more general and accessible indicators such as self-reported health, the number of days of sickness, or disabilities are usually preferred as measures of morbidity. The same gradient has been observed between socio-economic status and mortality across a wide variety of socio-economic and morbidity indicators and in various industrialized countries, as shown in Table 5.
Table 5 Results from recent studies of socio-economic status and morbidity
The results of the 1990 Canadian Health Promotion Survey (Adams 1993) showed that men and women with a higher level of education self-rated their health as excellent or good in a much higher proportion than individuals with a lower education. The same gradient has also been observed for a socio-economic indicator based on the relative wealth of the household. House et al. (1990) reported that individuals with lower socio-economic status, as defined by a combination of income and education values, declared more than twice as many chronic conditions as individuals with higher socio-economic status. In addition, they were able to show that the excess of preventable morbidity in the lower socio-economic strata for middle-aged people is not counterbalanced by an excess delayed morbidity for people past the age of 75 in the high socio-economic strata. The relative protection associated with higher socio-economic status exists for all age groups.
The same gradient between morbidity and socio-economic status has also been observed in Scandinavian countries where the social security and welfare is such that social inequalities are minimized. Among men aged 25 to 74, the age-standardized proportion of people with limiting long-standing illness is more than double in people with elementary education compared with people with higher education. This is true for Finland, Norway, and Sweden and, although to a lower extent, for women as well as men (Lahelma et al. 1994). Two different analyses of the data generated by the Whitehall II study of civil servants in England show a clear association between the grade of the occupation and the percentage of people self-rating their health as average or worse (Marmot et al. 1991) and between the occupation and the odds ratios of recorded long sickness absence from work adjusted for age, several health behaviours, work characteristics, social circumstances, and demographic factors. Bor et al. (1993) observed that, by the age of 5 years, Australian children born in households with lower socio-economic status had poorer general health, were sick more often, experienced a greater number of persistent health conditions, and had more dental problems.
Socio-economic status and health-related behaviours
Many surveys conducted in a wide variety of industrialized countries on numerous behaviours have consistently produced results showing associations between socio-economic status and health behaviours. The poorer and the less educated an individual in developing countries, the more likely he or she is to engage in patterns of behaviours that are not conducive to health. In the Canadian Health Promotion Survey (Health and Welfare Canada 1993), the number of smokers is double in people with elementary schooling compared with people with university degrees, and ranges from 36 per cent among the very poorest to 25 per cent among the richest. The rates were 40 per cent among blue collar workers and 27 per cent among people in managerial/professional positions (Pederson 1993). The Whitehall II study (Marmot et al. 1991) shows a wide gap between the proportions smoking in the highest and the lowest occupational grades. Although the difference is not as great for women, the relationship between smoking and education varies only slightly when adjusted for age, sex, and ethnicity (Winkleby et al. 1990; Shea et al. 1991). Analyses of data generated by the major community trials in cardiovascular disease prevention showed that the dramatic drop in prevalence of smoking over the 1980s was more pronounced for people with higher education compared with people with lower education (Winkleby et al. 1992b; Luepker et al. 1993). The same trend has also been observed in Canada for the period 1985 to 1991 (Millar and Stephens 1993).
Reasons for relationships between socio-economic status and health
There are four general hypotheses about possible mechanisms that can explain the consistent relationships observed between socio-economic indicators and health indicators such as mortality and morbidity (Macintyre 1997) . The first states that the relationship is spurious and arises from the association of both socio-economic and health indicators with underlying genetic predisposition. By showing that the relationship between job status and health persists after adjustment for height and body-mass index, the Whitehall I and Whitehall II studies (Marmot et al. 1984, 1991) have contributed to rendering that hypothesis highly improbable.
The second explanation, referred to as the ‘drift hypothesis’, states that socio-economic conditions deteriorate as a result of poor health rather than the reverse. Results from longitudinal studies, however, demonstrate that this hypothesis alone cannot explain all of the association between socio-economic status and mortality/morbidity. Living conditions, as defined by the relative poverty of the area of residence, are moderately associated with the relative risk of mortality over subsequent years, after controlling for baseline health, health practices, and socio-demographic characteristics (Fox et al. 1986; Haan et al. 1987). It has also been observed that the socio-economic conditions experienced during childhood are independently associated with mortality and with health-affecting factors such as social isolation, health-promoting lifestyles, and working conditions in adult life (Peck 1994).
The third explanation is related to the diminished access to health care associated with poverty. Adler et al. (1994) have made a strong case that, even though access to health care is probably contributing to the association between socio-economic status and health, the socio-economic status–health gradient still persists in countries with universal health insurance coverage, as shown earlier here for Canada and Sweden.
Finally, the fourth and most plausible explanation is that components of socio-economic status are intertwined with crucial features of life that affect health: physical environment, social and cultural environment, development and socialization process, and the health-related behaviours (Adler et al. 1994). Even if this hypothesis of the social determinants of health seems to be most plausible there is a vigorous debate among researchers as to the mechanisms by which social circumstances affect health. Notable are the stress hypothesis (Brunner and Marmot, 1999), the cumulative risk through life course approach (Kuh et al. 1997; Wadsworth 1999), the social cohesion explanation (Wilkinson 1996, 1999; Muntaner and Lynch 1999), the mediator role of health-related behaviours (Green 1970b; Marmot et al. 1978; Stronks et al. 1996; Cavelaars et al. 1997), and the perspective developed by Link and Phelan (1995) and echoed by Potvin and Frohlich (1998). This latter view holds that social categories such as socio-economic status, gender, or race are associated with access to resources, patterns of relationships, and distribution of power that shape health and disease patterns in a population.
Socio-economic status, gender, and health
One important aspect of life circumstances whose relationship with health has been increasingly studied is gender. Research has demonstrated that men’s and women’s experience with health differ markedly (Chapman Walsh et al. 1995) and that these differences cannot be solely attributable to biological determinants related to sexual differentiation (Bird and Fremont 1991; Krieger et al. 1993). The social construct of gender, as opposed to the biological categories of sex, was designed to refer to cultural and social conventions, roles and behaviours assigned to men and women (Krieger 1996). These in turn shape the social, political, cultural, and economical circumstances experienced by men and women. Gender thus attempts to capture this differential experience men and women have of their environment and the possibilities and constraints associated with these differences (Krieger et al. 1993; Potvin and Frohlich 1998). A growing body of research is showing that some of these constraints and possibilities are interacting with the living conditions associated with socio-economic status to shape the health of people. The relationship between gender and health is a complex one.
Until recently research on gender differences and health in the industrialized world has demonstrated a consistent portrait: although women were living longer than men, they had higher rates of morbidity throughout their lives (Verbrugge 1985, 1989; Macintyre 1993). The universality of this picture, however, is now being challenged. Until industrialization, life expectancy was higher for men and still is in some less developed countries. In the Western world the women’s relative advantage in mortality rate over men was increasing for most of the twentieth century (Macintyre and Hunt 1997). Furthermore, it seems that the direction of the differential morbidity in men and women varies according to specific conditions and to phases in the life-cycle (Macintyre et al. 1996; Lahelma et al. 1999). Recent studies examining health inequalities resulting from the interaction between socio-economic status and gender provided insightful results.
The correlation between socio-economic status and health appears to be stronger for men than for women; the gradient of health that parallels the gradient of socio-economic strata is generally steeper when applied to men (Koskinen and Martelin 1994; Stronks et al. 1995; Arber 1997; Arber and Cooper 1999). This interaction was observed for various health indicators such as mortality, body size, activity limitations, overall morbidity, and self-assessed health, and across several socio-economic status measures such as occupation, education, and income (Macintyre and Hunt 1997). There are, however, variations in this interaction effect. Some combinations of disease and socio-economic status indicators, for example, show similar correlation in men and women (Arber 1997). It seems also that the gradient of socio-economic status and health is similar for men and women in their twenties and thirties (Matthews et al. 1999) whereas in people aged 60 and over socio-economic differences in self-assessed health are weaker among women compared with men (Arber and Cooper 1999).
Although making sense of this interaction effect of gender and socio-economic status on health still challenges health sociologists (Macintyre and Hunt 1997), some explanations have been explored. First, differentials in employment status between men and women have been examined. Stronks et al. (1995) concluded that several aspects of the job market might explain the differential gradient between men and women. One aspect is the lower proportion of women in paid employment. A complementary explanation is the differential distribution of working conditions with lower-status occupations being more likely to involve physical health risk for men than women. These differences in occupational opportunities have already been suggested to explain the health differentials between men and women (Ross and Bird 1994). Second, Koskinen and Martelin (1994) have found that the interaction effect between gender and socio-economic status arises totally from the married population. In other marital status groups the relationship between socio-economic status and health is similar for men and women. Observing that the proportion of married women is similar across socio-economic status categories, whereas the proportion of married men increased with socio-economic status, the authors suggest that various factors affecting one’s life situation such as social support may be at play. Third, the same study also pointed to the fact that the causes of death that show the strongest relationship with socio-economic status, such as unintentional injury and violence and diseases of the circulatory system, are also more common among men.
Some common causes of death in women such as breast cancer even show a reverse gradient, affecting more women in higher socio-economic status categories (Koskinen and Martelin 1994). Fourth, Kawachi et al. (1999) have examined the patterns of relationships at the state-aggregated level between indicators of women’s emancipation status and gender-specific health indicators. They found a strong relationship between various indicators of women’s emancipation status and women’s health. Surprisingly, they found an even stronger relationship between indicators of women’s emancipation status and men’s health. In addition to reinforcing the evidence that men’s differences in health are more sensitive to life circumstances than are women’s (Hunt and Annandale 1999), these intriguing results may indicate that men’s health status is more closely related to general inequalities in life circumstances.
Culture and health
Culture appears to play an independent part in health status (Corin 1994). Culture is intimately related to accepted social practices, many of which are in turn related to health and disease.
Epidemiological evidence for the impact of culture on health comes from the ecological studies of diet and coronary heart disease, mentioned earlier, and immigrant studies of cardiovascular disease. The ecological studies show clear cultural differences in both dietary practices and cardiovascular disease consequences. Studies of Japanese men living in Japan and emigrating to California revealed coronary heart disease and stroke rates comparable with those of the country of residence only in subsequent generations, whereas those emigrating to Hawaii had intermediate rates (Keys et al. 1958; Kato et al. 1973). The implications are that, as the Japanese became acculturated, they assumed both the dietary and cardiovascular patterns of the new country. This is a clear argument against the hypothesis that genetic factors have the dominant influence on heart disease, and for the hypothesis that cultural factors play a prominent part. Dietary factors appear to be powerfully influenced by culture (Rozin 1984).
The Roseto study provides another example of the effects of culture on health. Early observations have shown that this ethnically homogeneous Pennsylvania community experienced a significantly lower mortality from myocardial infarction than the nearby community of Bangor despite a higher prevalence of hypertension and obesity and a similar proportion of smokers (Stout et al. 1964; Bruhn 1965; Lynn et al. 1967). These results were attributed to the apparent protective effect of a unique social, ethnic, and family cohesion in the community (Bruhn and Wolf 1979; Bruhn et al. 1982). It was also hypothesized that the ‘Americanization’ of the lifestyle in this originally close-knit traditional Italian community (Bruhn and Wolf 1979) would lead to a loss of their relative protection from myocardial infarction (Lynn et al. 1967). Recent analyses show that, in fact, Rosetans have lost that relative protection over the last two decades (Egolf et al. 1992). This loss was accompanied by an increase in the number of intermarriages of Rosetans with people of non-Italian descent, a decrease in social participation in Roseto, and an increase in the general wealth of the community, as the original Italian-born generation was gradually replaced by their ageing American-born offspring (Lasker et al. 1994).
Socio-economic status and lifestyle: the merging of perspectives
Lifestyle has emerged as a concept in modern discourse to describe in shorthand what Madison Avenue advertising agencies call market segments—groups or types of people differentiated by a set of consumption and other living patterns related to their income, education, occupation, gender, residence, and geopolitical and ethnic identification. This commercialization of the term is not totally unrelated to the social science origins of the concept. In the health field, however, the term has been used more variously. At one extreme, it describes discrete narrowly defined behaviour related to chronic diseases or health enhancement. This usage is associated with elements of individualism (Coreil and Levin 1984–5). At the other extreme, ‘lifestyle’ is used to describe the total social milieu including the ‘psycho-socio-economic environment’ as well as personal health behaviours (Hancock 1986).
As a behavioural concept, lifestyle generally implies more complex, repetitive (if not habitual) patterns of behaviour conditioned by culture and living standards but still under the control of the individual or family within their economic means. The public health application of this behavioural notion of lifestyle has tended to associate it with ‘health-related’ (e.g. food consumption patterns) as distinct from ‘health-directed’ (e.g. diet) behaviour (Steuart 1965; Gottlieb and Green 1987). As a sociopolitical and ecological concept, lifestyle reflects how categories of people sharing similar life circumstances interact with their social context (Rutten 1995; Green et al. 1996; Frohlich and Potvin 1999a). The public health application of this notion of lifestyle has been to seek policies and environmental regulations that would redirect lifestyle or ‘make healthy choices the easier choices’ (Epp 1986; WHO 1986; Green and Kreuter 1999).
Relationships among lifestyle factors
The concept of lifestyle as the interplay between habitual behavioural patterns and sociocultural conditions leads one to put into a broader context the reductionist examinations, presented in the first half of this chapter, of specific behaviours and specific measures of socio-economic status as they relate to health and disease. The dynamic interplay between the specific measures creates a complex and intricate system of social, economic, cultural, and behavioural factors, interwoven with disease risk factors and health status, and influenced by the health-care and physical environments. A simplified scheme of such a system is shown in Fig. 1.
Fig. 1 Some interrelationship in the complex system of lifestyle, environment, and health status.
Although such a complex system is extremely difficult (if not impossible) to study directly, parts of it can be, and have been, studied. Evidence for its existence comes from the relationships presented earlier between specific health behaviours and health, socio-economic factors and health, and culture and health.
Studies of the relationships among health behaviours have not been consistent in showing strong interrelationships between types of health-related behaviours (Green 1970b; Steele and McBroom 1972). The highest correlations observed were generally below 0.20 and were between smoking and alcohol use, alcohol use and exercise (Calnan 1989), and smoking and diet (Blaxter 1990). Given these low correlations, there is very little evidence supporting a one-dimensional concept of health-related behaviours (Calnan 1994). In fact, patterns of health-related behaviours, when observed, tend to vary by socio-economic characteristics such as income and education (Green 1970a; Townsend et al. 1988; Prattala et al. 1994). According to Calnan (1994), recent developments in the area of health-related behaviours have led to a shift in emphasis away from an individualized model that explains behavioural choices solely as a product of knowledge and beliefs. Recent research emphasizes the role of social circumstances in influencing individuals’ behaviours. This implies that income and education are highly influential determinants of the health risk factors.
Nevertheless, findings support the role of education as the most basic component of socio-economic status as a determinant of health risk behaviours. Winkleby et al. (1992a) have systematically compared the relationships between different behaviours and three indicators of socio-economic status: education, income, and occupation. Education stands out as the indicator most consistently and highly correlated with health-related behaviours. Although education level is primarily a function of social, cultural, and economic circumstances, educational influence on lifestyles and health-related behaviours is clearly an important aspect of public health practice.
Education, socio-economic status, lifestyle, and the health agenda
Thus it seems quite clear that at least part of the relationship between socio-economic status and mortality and morbidity indicators is attributable to differences in behaviours and to differences in the environment. These differences in behaviours are strongly associated with the level of education and income. The body of knowledge presented above provides the basis for an ambitious health education agenda. The greatest gains in longevity will come from our efforts to reduce the number of premature preventable deaths among people from the lowest socio-economic classes. Wilkins et al. (1989) showed that, for 1986, 20 per cent of all potential years of life lost before the age of 75 was related to income differences for the urban population of Canada. This becomes as high as 44.5 per cent for injury deaths between the age of 25 and 44, and 39.8 per cent for deaths from circulatory diseases between 45 and 74.
The challenge for health professionals is to support behavioural modifications among the segments of the population with lower socio-economic status. The use of the mass media in educational campaigns is not sufficient to alter the risk patterns of people with lower socio-economic status for whom enabling factors are most problematic (Green and McAlister 1984). Ecological programmes that also address the environmental, organizational, and social aspects of health problems are more likely to succeed (Green et al. 1996; Richard et al. 1996; Green and Kreuter 1999)
Education and health
Education’s powerful and pervasive correlations with health and health-related behaviour (Green 1970, 1972; Pincus et al. 1987) can be seen to operate through behaviour in at least four ways: expanding opportunities for the individual, increasing knowledge of the world and the options it offers, building self-confidence, and increasing specific skills and capabilities. As education advances, so do individuals, families, and the community on each of these dimensions of development. With education come personal, family, or community development, which result in improved health, reduced exposure to environmental threats to health, increased access to health resources, and increased purchasing power to buy primary health-care and advanced medical care.
Channels of educational influence on health
The term education, like the term lifestyle, takes on various meanings depending on the context of its use. As a descriptive characteristic of individuals, it generally refers to years of formal schooling. As an epidemiological and demographic variable, it most typically serves as a surrogate measure of socio-economic status. As a family variable, education of the main earner often stands as an indicator of family’s socio-economic status. Research generally shows, however, that the education of the female head of household is more influential in determining family health and the health behaviour of other family members (Green 1970, 1972; Mechanic 1979; Carmelli et al. 1986; Davis and Robinson 1988).
Education is also used as a term to describe organizations, social institutions, and the status of communities. Whole sectors of the community may be broadly identified as educational, as in ‘the education establishment’ or ‘higher education’. Education is also a function of most social institutions and departments of government. It is in this latter context that the term ‘health education’ is used to describe the educational function of health agencies, but health education also has a place in a variety of different sites such as schools, churches, workplaces, and recreational facilities (Poland et al. 2000). The term health education applies to a wide range of approaches and topics relevant to health, including, for example, education in basic hygiene for children, safety education for children and youth, parent education for young adults, and chronic disease prevention and management in later adulthood. The various forms of health education occur through a variety of channels, some institutional and some interpersonal.
Health education can be defined as the combination of planned learning experiences to facilitate voluntary actions conducive to health (Green et al. 1986; Green and Kreuter 1999, p. 27). The actions may be of individuals to protect or promote their own health, of families to protect or promote the health of their members, or of organizational or community leaders and influential persons to change environmental conditions affecting health (see Chapter 7.3).
Education of those at risk of threats to their health
The assumption, well grounded by decades of experimental research, underlying most health education programmes for people whose health may be at risk is that, if people are provided with some combination of new information, skill, or resources, and reinforcement, they will take actions to protect themselves or to improve themselves. Unfortunately, many of the behavioural changes that people can make to help protect or improve their health are not inherently satisfying or reinforcing. Knowledge and skills in making the change in behaviour are usually easier to introduce through health education than is the reinforcement of the behaviour. In the clinic or the classroom, the health professional or teacher can reward the patient or the student with words of praise and with good marks. This is a critical component of counselling and of formal education. In the community, such direct reinforcement is more difficult to provide or for the individual to find. When community health education programmes depend on educational or communication opportunities through media channels, such as radio, television, or the written word, rather than through direct interpersonal contact, they can strengthen knowledge, beliefs, attitudes, and skills, but they are limited in their ability to reinforce behaviour.
Cognitive models of health education emphasize the informational and affective components of the educational process. The Health Belief Model (Becker 1974), for example, centres on three beliefs that account for most of the variance in predispositions to adopt a recommended health practice. These include a belief in susceptibility (or belief that you could have the disease and not know it in the case of undertaking screening or treatment for conditions such as hypertension), and a belief in the severity of the consequences of not taking action. The third belief influencing action is that the benefits of treatment or intervention will outweigh the costs (including social benefits and costs such as inconvenience, discomfort, or embarrassment). This model has been widely tested and found to have predictive validity (Becker 1974; Harrison et al. 1992).
Even the proponents of the Health Belief Model and other cognitive models that have proved useful in the development of health education interventions acknowledge that the task of behavioural change requires more than changing beliefs, attitudes, and perceptions (Harrison et al. 1992). These factors may produce strong desires to change, but without skills and resources the highly motivated individual will only be frustrated. Frustration leads to a need to deny the prior motivation, which leads to rationalization or other defence mechanisms that erect barriers to future attempts to convince the individual to change. Therefore, simplistic approaches to health education based entirely on information transfer can backfire in two ways. First, a result that is worse than being ineffective in changing behaviour for many people is aroused expectations and motivation that lead to frustration and disappointment when the target behaviour proves out of reach. Second, they may set up defence mechanisms in the disappointed individuals that make subsequent attempts to reach them with health messages more difficult.
A more promising alternative to information-only and fear-arousal approaches to health education is the combination approach referred to in the definition cited above. This approach recognizes that behaviour is complex and has multiple causes and sources of influence variously impinging on it. Motivation must be backed with skills and resources to enable the behavioural change, and with rewards or social support to reinforce it. These additional elements require upstream community organization, environmental changes, and training of professionals, family members, employers, or others to enable and provide social support for the behaviour (Green and Kreuter 1999; McKinley and Marceau 2000).
In addition to the rational model of education influencing cognitive predispositions, and enabling or reinforcing behaviour, an understanding of education influencing health through alternative routes that do not necessarily involve specific behaviour changes has been suggested (Lorig and Laurin 1985). Education may influence the process of social support, which can have a direct influence on health with changing health behaviour (Nuckolls et al. 1972; Berkman and Syme 1979). Education can also increase self-confidence, self-image, or self-efficacy, any of which might have an independent effect on health with or without behavioural change (Ewart et al. 1983; Kaplan et al. 1984).
Education for community development
Education can serve health at yet another level through the community development process or movements such as the Healthy Cities initiatives in Europe and Healthy Communities in North America. Here the function of education can be seen as more circuitous. Education arouses interest and increases the consciousness of the public about local issues or problems. The public then seeks more active participation in debating the priority that should be given to the problem and to optional solutions, and watches more vigilantly the process of governmental or institutional response. All these ways in which the public becomes more active in participating in community affairs have a ripple effect on the community’s ability to solve other problems more effectively (Green 1986). With an active population, public agencies tend to be more responsive, elected and appointed officials tend to be more sensitive to public needs, and community organizations tend to be more co-operative in working with each other than in communities where the public waits for governmental and other organizations to provide all the leadership on health matters (Cottrell 1976; Goeppinger and Baglioni 1985). Community development leads to better schooling, which results in improved levels of education in the community, which comes full circle to the functions of education for health described above.
Education to influence environmental conditions
In addition to the education of people to influence their health behaviours and their socio-economic status, education also can be directed towards those people who have the power and authority to change the physical and health-care environments—environments that can influence health directly or that can enable health behaviours.
Organizations, communities, and governments establish more or less healthful environmental conditions through policies, practices, facilities, and resources. Those conditions are influenced by decision-makers such as managers, department heads, and administrators within organizations, legislators, regulators, enforcers, and agency administrators within local, state, and federal government, and community leaders. Education of the decision-makers is a crucial avenue for facilitating healthful environmental change (D.G. Simons-Morton et al. 1988a).
Approaches to influencing organizations, communities, and governments are often called something else, but always contain educational components. Such approaches include organizational change, consulting, social and political action, community organization, persuasive communication, and political process (e.g. lobbying) (D.G. Simons-Morton et al. 1988a; B.G. Simons-Morton et al. 1995; Green and Kreuter 1999; McKinlay and Marceau 2000).
Lifestyle is the combination of specific practices and environmental conditions reflecting patterns of living. These involve interactions between behaviours, environment, culture, and socio-economic status. Each of the factors that make up and influence lifestyle has been shown to affect health, illness, disability, and mortality. A pivotal variable in mediating these relationships is education. Environmental conditions are also important determinants of health status, and these conditions, in turn, influence and are influenced by behaviour and lifestyle.
Educational level is an integral part of the concept of socio-economic status and a crucial determinant of lifestyle. Education is also an important avenue for influencing specific behaviours, the environment, and the complex system of lifestyle, all of which influence health status. Education is crucial for achieving changes in personal behaviours, organizations, communities, and environments. Education can influence organizational, economic, and environmental supports that can contribute significantly to the protection and promotion of health.
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