Volume 168 Issue 5

Smoking behaviours of Australian adults in 1995: trends and concerns David J Hill, Victoria M White and Michelle M Scollo MJA 1998; 168: 209-213 For editorial comment see Gray Abstract - Introduction - Methods - Results - Discussion - References - Authors' details - - ©MJA1998 Abstract Objectives: To estimate the prevalence of smoking among Australian men and women in 1995 and to examine trends in smoking prevalence in Australia over the past 10 years. Design: A representative sample of adults participated in face-to-face interviews conducted by a large market research company. Participants: 2819 men and 2880 women over the age of 16. Main outcome measure: Self-reported smoking behaviours assessed by standard questions. Results: Overall, 27.1% of men and 23.2% of women were smokers of tobacco (factory-made cigarettes, pipes, cigars or roll-your-own cigarettes). This difference in smoking prevalence of men and women was significant. More men (32.1%) than women (21.7%) were past smokers and more women (53.4%) than men (39.3%) had never been regular smokers. On average, male smokers smoked about 20 factory-made cigarettes a day, while women smoked about 18. Occupation and education levels were inversely related to smoking prevalence. Comparisons with earlier data suggest that the decline in smoking prevalence seen in previous surveys has ceased. However, the number of cigarettes consumed daily decreased between 1992 and 1995. In the period between 1983 and 1989, when per capita expenditure on adult antismoking campaigns rose, smoking prevalence declined, but levelled off thereafter in a period when expenditure on campaigns fell. Conclusion: Failure to find a continuing decline in prevalence of smoking among the Australian population is of great concern and indicates the importance of continuing and extending antismoking programs. Introduction Cigarette smoking is still the most important preventable cause of premature death in Australia. Reducing the prevalence of smoking rightly remains a high priority on Australia's public health agenda.1,2 As such, it is important to collect and publish estimates of the prevalence of smoking and its sociodemographic correlates at regular intervals, as indicators of progress towards controlling the virtual epidemic of tobacco-related disease,3 and as a basis for public decisions on action to take. We have published data on national smoking prevalence for the years 1974,4 1976,5 1980,6 1983,7 1986,8 19899 and 1992,10 using a standard method of data collection. Here we report data for 1995. Methods Survey method: A large market research company carried out the sampling and interviewing as part of an omnibus survey in September/October 1995. Procedures were the same as in our previous surveys. In brief, interviews were conducted on the weekend, with respondents within a census collector's district selected at random within specified strata, including State and rural or urban divisions. Within each collector's district an individual residence was chosen at random for the first contact and the adjacent house was contacted next. Further adjacent households were approached until the required number of interviews for that collector's district were obtained (usually eight from about 200 households in each census collector's district). Response rate: Of the households contacted 47% agreed to participate in the survey, 42% refused, and in the remaining 11% either the residents were too old or ill or could not speak English, or call-back was unsuccessful. The response rate for this survey was similar to the rates for the 1992 and 1989 surveys, but it was lower than response rates in earlier years. Thus, if variable response rates influence reported prevalences, this could only have occurred for the period before 1989; recent comparisons are not confounded by response rates. Data collected: Respondents indicated their smoking status by choosing a category from the following list: Current smoker -- cigarettes only, cigarettes plus cigars or pipes, cigars only (ex-cigarettes), pipes only (ex-cigarettes), cigars only (never cigarettes), or pipes only (never cigarettes); Past smoker -- of cigarettes only, of cigarettes plus pipe or cigar, of cigars or pipes only; and Never smoker -- those who had never smoked regularly. Current smokers of factory-made cigarettes indicated the brand usually smoked, the size of the pack usually bought and the number of cigarette packets usually smoked in a week. Respondents' sex, age (in five-year age groups), country of birth, highest level of education achieved, and occupation were recorded, as well as the occupation of the household's main income earner. Analysis of data: We calculated 95% confidence intervals (95% CI) associated with estimates of smoking prevalence among men and women and the difference between estimates. Confidence intervals around prevalence estimates for particular subgroups (eg, men aged 50-59 years) are not given. We used chi-squared tests of association to examine differences in the prevalence of smoking between groups, and logistic regression analyses to explore whether associations between smoking prevalence, educational level, occupation and, for women, country of birth were independent of associations between age and smoking. We used analyses of variance and t tests to examine whether the mean number of cigarettes smoked differed between groups. Finally, to compare the prevalence of smoking across recent years, we age-standardised the data from the 1995, 1992 and 1989 surveys to the age distribution of the 1986 sample. Results Smoking status: Of the 5699 participants, 51% (2880) were women and 49% (2819) were men. A comparison of the distributions of age, occupation, education and country of birth in the sample data with those in the census data indicated no bias in the socio demographic variables in the dataset. Eighty-two per cent of respondents were interviewed on the first visit to their home. The smoking status of these respondents was similar to that of the more hard-to-reach respondents (those who were interviewed on the second or third contact). Cigarettes dominated tobacco use, with only 1% of men and less than 1% of women indicating that they smoked only pipes or cigars. Only 8% of men and 4% of women had smoked roll-your-own cigarettes in the month before the survey. The smoking status of the males and females in the sample is shown in Table 1. Smoking prevalence: In 1995, the estimated prevalence of smoking among Australian men aged 16 years and over was 27.1% (95% CI, 25.6%-28.7%), while among women the prevalence was estimated to be 23.2% (95% CI, 21.7%-24.7%); this difference was significant (3.9%; 95% CI, 1.6%-6.2%). The prevalence of smoking peaked between the ages of 25 and 29 in men (34.7%) and in women (35%), after which age smoking generally decreased among both men and women. Past smoking or non-smoking: More men (32.1%; 95% CI, 30.4%-33.8%) than women (21.7%; 95% CI, 20.2%-23.2%) were past smokers, a highly significant difference (10.4%; 95% CI, 8.1%-12.7%). More women (53.4%; 95% CI, 51.6%-55.2%) than men (39.3%; 95% CI, 37.5%-41.1%) had never been regular smokers, and again this difference was highly significant (14.1%; 95% CI, 11.5%-16.7%). Men and women differed in their age-related patterns of past smoking. Among men, the proportion of past smokers increased with age to reach a peak of 54.4% in those over 70. Although among women the proportion of past smokers peaked at 26% for those over 70, the proportion of past smokers in the other age groups showed little variation from the overall rate of 21.7%. The proportion who had ever smoked was calculated by adding the percentage of those who had smoked in the past to the percentage of current smokers. Overall, 59.2% (95% CI, 57.4%-61.0%) of men and 44.9% (95% CI, 43.1%-46.7%) of women had smoked at some stage in their life. The association between age and ever having smoked differed for men and women. While among men the proportion who had ever smoked increased with increasing age, for women the proportions of ever smokers began to decrease after the age of 35. Quit proportions: The quit proportion (ie, proportion of ever smokers who had given up smoking in each age and sex group) is also shown in Table 1. The overall quit proportion for men (0.54) was slightly higher than that among women (0.48). However, as Table 1 shows, there is little difference in the quit proportions for men and women in most age groups, except for the 16 to 19 years and 60 to 69 years age groups. Factory-made cigarettes: The mean number of factory-made cigarettes smoked per day by smokers was 19.7 (SD, 11.6) for men and 18.1 (SD, 10.8) for women; this difference was statistically significant (t=2.55, df=1238, P=0.01). In 1995, the cigarettes smoked by women had a lower average tar content than the cigarettes smoked by men (t=5.56, df=1022, P<0.001). The average tar content of cigarettes smoked by men was 8.2 mg (SD, 2.8), while for women it was 7.2 mg (SD, 3.0). Education: As in previous reports of this survey series, the prevalence of smoking and the mean number of cigarettes smoked per day differed for people in various sociodemographic groups. As Table 2 shows, smoking prevalence decreased with increased education, so that only 17.0% of men and 14.2% of women who were university graduates smoked. The association between education level and smoking prevalence was significant for both sexes, but was stronger for men (chi-squared=91.3, df=6, P<0.001) than women (chi-squared=35.5, df=6, P<0.001). The proportions of ex-smokers in the different education levels indicate that quitting was common in all groups. However, the greater proportion of never smokers in the better-educated groups indicates that the lower prevalence of smoking among this group was due to their never having smoked in the first place. For both men and women, smokers with more years of formal education smoked fewer cigarettes per day than did those who had fewer years of education (men: F3,618 = 3.15, P<0.05; women: F3,614=8.92, P<0.001). Occupational levels: Respondents were classified into one of four occupation levels based on the occupation of the household's main income earner. Occupational levels differed in the level of skill required for the job, such that unskilled workers (eg, labourers) were classified as "lower blue collar", while skilled workers (eg, plumbers) were classified as "upper blue collar". As occupation status increased, the prevalence of smoking decreased (Table 2). Among men from "upper white collar" households, 18.7% smoked compared with 40.9% of men from "lower blue collar" households. The association between occupation status and smoking was significant for both men (chi-squared=102.3, df=6, P<0.001) and women (chi-squared=46.8, df=6, P<0.001). Among men, the proportion of ex-smokers was slightly lower among "lower blue collar" households than other groups . Among women, the proportion of ex-smokers was similar among all occupation groups. The proportion of never smokers was greatest among higher occupation levels. This pattern of results indicates that the lower prevalence of smoking seen in higher occupation groups is due to the relatively lower rate of taking up smoking among these groups rather than their greater success at quitting. The differences in the mean number of cigarettes consumed per day between occupation groups was not statistically significant for men (F3,618 = 2.0, P=0.12) or women (F3,614=1.4, P=0.24). Country of birth: The prevalence of smoking was lowest among both men and women born in Asian countries. However, while the prevalence of 19.8% among Asian-born men was not significantly different from that found for groups born elsewhere, the prevalence of smoking among Asian-born women was significantly lower than the prevalence of smoking among women born in Australia (chi-squared=7.2, df=1, P<0.01) or the United Kingdom (chi-squared=6.6, df=1, P<0.01). The number of cigarettes smoked per day showed little variation according to place of birth. The overall association between place of birth and cigarette consumption was not significant for either men (F4,617=1.3, P=0.26) or women (F4,614=1.0, P=0.40). However, Asian-born men consumed significantly fewer cigarettes per day than did men born elsewhere (t=2.14, df=620, P<0.05). Logistic regression analyses of demo graphic data: In separate analyses, age was entered before the predictor variable of education level, occupational status or country of birth (for women only), and in each case the association between smoking prevalence and the predictor variable remained significant. Pack size and number of cigarettes smoked: The cigarette packet size most commonly used was 25 (by 36% of smokers), followed by packets of 30 and 40 (19% each), 50 (17%), 20 (6%) and 35 (4%). The number of cigarettes smoked per day was related to packet size (F5,1205=34.93, P<0.001). The mean number of cigarettes smoked per day by those who used packets of 20 was 13, for packets of 25 it was 16, for packets of 30 it was 17, for packets of 40 the mean was 23, and for packets of 50 it was 25. Except for women from lower blue collar households, the packet of 25 cigarettes was the most popular size for all occupation and education groups. After collapsing pack-size categories and combining sexes, 55% of blue collar smokers used pack sizes of 20, 25 or 30 compared with 68% of white collar smokers, and 32% of white collar smokers used packs of 35, 40 or 50 compared with 45% of blue collar smokers (chi-squared=20.0, df=1, P<0.001). Comparisons with previous years Smoking prevalences in Australia from 1986 to 1995 are shown in Figure 1, with a line of best fit superimposed. Of concern from a public health point of view is that the observed prevalence in 1995 is no lower than that in 1992, necessitating an upward adjustment of the projection to 2001 compared with our projection based on the four triennial surveys up to 1992. It was expected that the national prevalence targets for the year 2000 would be bettered.10 Now the trends suggest they will not be met, either for men or women. The results of the 1995 survey also show 5% more men and 3% more women smoking than would have been expected on the basis of the trends to 1992, and, by extrapolation, this means that in 1995, 356 000 more men and 384 500 more women were smoking in Australia than expected . The mean number of cigarettes smoked by men in 1995 was lower than the 22.1 cigarettes smoked per day by men in 1992 (t=3.39, df=1348, P<0.001). Among women, however, the average number of cigarettes smoked per day in 1995 was not significantly less than the number smoked in 1992 (19.1) (t=1.59, df=1322, P=0.113). The legislative and other activity to restrict advertising and promotion of tobacco, as well as expenditure on adult-directed antismoking campigns, between 1989 and 1995 are outlined in Box 1. Discussion This is the first time in eight successive surveys that the reported prevalence of smoking in men was no lower than the previous survey and the first time since 1983 that this was also the case for prevalence of smoking in women. If these data signal an underlying change and the previously falling prevalence of smoking has indeed stabilised, a major public health response is indicated. Attainment of what is arguably the nation's primary public health target1 -- reducing the prevalence of smoking in men and women to 20% by 2000 -- is clearly under threat. Given that this target was and still may be seen as modest, this would be a major public health failure, as the following calculations show. For every percentage point smoking prevalence in Australia exceeds the national goal, nearly 140 000 people are smoking who, had the goal been met, would not have been. So, if the goal is missed by 3%, which would be the case if the prevalence remains stable, nearly 420 000 more people than expected will be smoking. If they remain smokers, according to estimates of Doll et al,15 210 000 will die prematurely as a result of their smoking. Against the above fairly alarming observations must be set some auspicious trends -- smokers are smoking less and are probably less exposed to inhaled carcinogens and this should flow through to modest public health gains. As well as benefiting themselves, it seems plausible that a lower daily consumption by smokers is reducing the passive exposure of others to cigarette smoke, as forgone cigarettes may be some of those previously smoked at work or in public places where others would be exposed to sidestream smoke. Assuming these data indicate a slow-down or stalling in the previous reduction in smoking prevalence, why has this occurred and what needs to be done? Figure 2 suggests one explanation for influences on smoking prevalence. At times when antismoking activity is high, whether it be in the form of policy or programs, smoking declines, but when these abate prevalence stagnates. Clearly, these data call for more extensive and rigorous analysis which might incorporate, in a multivariate analysis, other factors such as price, regulations and pro-cigarette promotions. This could determine the extent to which program expenditure affects smoking prevalence. The trends are extremely worrying. Stasis in public policy and prevention programs paralleled by static smoking levels suggest the importance of continuing to extend antismoking programs in order to restart the reduction in smoking prevalence. The experience of some Scandinavian countries shows that public health authorities cannot rest on their laurels.16 Much remains to be done (Box 2). References Commonwealth Department of Human Services and Health. Better Health Outcomes for Australians. Canberra: AGPS, 1994. Australian Institute of Health and Welfare. Tobacco use and its health impact in Australia. Canberra: AIHW, 1996. English D, Holman CD, Milne E, et al. The quantification of drug caused morbidity and mortality in Australia. Canberra: Commonwealth Department of Human Services and Health, 1995. Gray N, Hill D. Patterns of tobacco smoking in Australia. Med J Aust 1975; 22: 819-822. Gray NJ, Hill D. Patterns of tobacco smoking in Australia II. Med J Aust 1977; 20: 329-330. Hill D, Gray N. Patterns of tobacco smoking in Australia III. Med J Aust 1982; 1: 23-25. Hill D, Gray N. Australian patterns of tobacco smoking and related health beliefs in 1983. Community Health Stud 1983; 8: 307-316. Hill D. Australian patterns of tobacco smoking in 1986. Med J Aust 1988; 149: 6-10. Hill D, White V, Gray N. Australian patterns of tobacco smoking in 1989. Med J Aust 1991; 154: 797-801. Hill D, White V. Australian adult smoking prevalence in 1992. Aust J Public Health 1995; 19: 305-308. Chapman S, Wooward S. Australian court rules that passive smoking causes lung cancer, asthma attacks and respiratory disease. BMJ 1991; 302: 943-945. Schollam v Dept of Health (NSW). District Court (NSW). 25 May 1992, Case 40830/86. Borland R, Mullins R. The increasing prevalence of workplace smoking bans in Victoria. J Occup Health Safety Aust N Z 1994; 10: 35-40. Borland R, Morand M, Mullins R. Prevalence of workplace smoking bans in Victoria. Aust N Z J Public Health 1997; 21: 694-698. Doll R, Peto R, Wheatley K, et al. Mortality in relation to smoking: 40 years' observations on male British doctors. BMJ 1994; 309: 901-911. Rimpela A. Critical analysis of the Finnish Tobacco Act: implementation and legitimacy 1977-89. Tobacco Control 1992; 1: 285-292. (Received 11 Aug 1997, accepted 27 Jan, 1998) Authors' details Centre for Behavioural Research in Cancer, Anti-Cancer Council of Victoria, Melbourne, VIC. David J Hill, PhD, Director; Victoria M White, MA, Behavioural Scientist; Michelle M Scollo, BBSc, GradDipCommHlth, Public Health Consultant. Reprints will not be available from the authors. Correspondence: David J Hill, Director, Centre for Behavioural Research in Cancer, Anti-Cancer Council of Victoria, 1 Rathdowne Street, Carlton South, VIC 3053. E-mail: davidh AT accv.org.au Readers may print a single copy for personal use. No further reproduction or distribution of the articles should proceed without the permission of the publisher. For permission, contact the Australasian Medical Publishing Company Journalists are welcome to write news stories based on what they read here, but should acknowledge their source as "an article published on the Internet by The Medical Journal of Australia <http://www.mja.com.au>". <URL: http://www.mja.com.au/>

David J Hill · Victoria M White · Michelle M Scollo

Unemployment and young people's health Stephen L Morrell, Richard J Taylor and Charles B Kerr Morrell, Taylor and Kerr, from the University of Sydney's Department of Public Health, review the evidence of an association between unemployment and psychological and physical ill-health in young people aged 15-24 years. Aggregate data show youth unemployment and youth suicide to be strongly associated. Youth unemployment is also associated with psychological symptoms, such as depression and loss of confidence. Effects on physical health have been less extensively studied; however, there is some evidence for an association with raised blood pressure. Finally, the prevalence of lifestyle risk factors (cannabis use and, less consistently, tobacco and alcohol consumption) is higher in unemployed compared with employed young people. (MJA 1998; 168: 236-240) Introduction - Health outcomes and determinants - Is there a remedy? - References - Authors' details - - ©MJA1998 Introduction The postwar boom of 1953-1974 in Australia, when a family could be supported on a single income, inflation was low, and unemployment rates varied between 1% and 5%, was the exception rather than the rule of 20th- century economic life. During every other period of this century between 6% and 10% of the workforce has been without a job.1 From 1966, when the Australian Bureau of Statistics began collating age-specific unemployment rates, until about the mid 1970s, younger and older age groups tended to have about the same unemployment rates, except for 15-19 year olds, whose rates have been consistently higher.2 From the mid 1970s, unemployment rates for people in their early 20s, and, by the 1980s, people in their late 20s to early 30s, also began to surpass those for older age groups; by the 1990s high unemployment affected also the oldest age groups (≥55 years), leaving those 35-54 years the least affected.2 High youth unemployment rates are not simply an artefact of the shrinking pool of 15-19 year olds in the labour market because of greater involvement in further education and training; the official rate in men aged 20-24 years also remains high (15% in July 1997).3 During the 1980s and 1990s unemployment in youth (15-24 years) approached 30%-35%, and in some parts of Australia, such as the Illawarra-Wollongong Statistical District in 1992, official male youth rates exceeded 40%.4 Here, we examine the evidence for unemployment contributing to ill-health in young people (15-24 years of age) (as Mathers and Schofield have done for adults5) and address some of the methodological issues in youth unemployment and health research. The broader effects of unemployment (eg, relationship or family breakdown, income loss or resort to crime) are beyond the scope of our review. These may act as intermediate factors between unemployment and health outcomes. Also, possible effects of unemployment rates on populations are not necessarily the same as their effect on individuals. High unemployment rates can lead people into, for instance, taking jobs they may not like, or, conversely, an individual's experience of unemployment may be ameliorated by living in an area where this has been the norm historically, or worsened where it is the exception. We define unemployment, as distinct from non-employment, as being without paid work, seeking work, and in a position to accept a job if one is offered, in line with official definitions of unemployment.6 The effects of unemployment on health are examined in the categories suicide and parasuicide (attempted suicide), psychological disturbance, physical health, and adverse risk factors for health. We conducted literature searches using MEDLINE and the PsychInfo databases, supplemented by references in key publications. Health outcomes and determinants Parasuicide and suicide Some of the strongest evidence for an association between unemployment and parasuicide (attempted suicide) comes from a study of parasuicide rates in men over 15 years of age in Edinburgh between 1968 and 1982.7 Data on admissions to a treatment centre for poisoning were used to estimate relative risks for parasuicide among unemployed compared with employed men. The relative risk estimates for different years ranged from 9.5 to 25. A strong linear association was found between unemployment duration and relative risk of parasuicide. A study of self-poisoning and self-injury among youth in Oxford found unemployment to be a significant contributing factor in men, along with alcohol and drug abuse, but in women it was relationship break-up.8 Very few psychiatric disorders were reported. A strong secular correlation between aggregate data on suicide and unemployment in Australian men aged 20-24 years, both directly9 and especially in relative terms,10 has been shown to have existed since annual age-specific unemployment figures were first available in 1966. The ratio of suicide rate in men aged 20-24 years to the overall male suicide rate is highly correlated (r=+0.9) with the ratio of the unemployment rate for men in this age group to the overall unemployment rate. This relationship does not apply to women, and remains just as strong up to 1996 as when first reported (Figure).11 Relationships based on aggregate data provide evidence for association, not causation. Moreover, secular associations at the national level break down when smaller geographic areas are analysed (eg, time trends in Scottish suicide rates at regional levels were found not to correlate with corresponding time trends in unemployment levels, despite significant secular correlations at a national level).12 A study in Victoria found higher male suicide rates in non-metropolitan regions with higher levels of unemployment, although it was claimed that a general rise in unemployment was not accompanied by an increase in the suicide rate.13 Biochemical models of mental illness and social factors influencing mental health are sometimes seen as counterposed hypotheses.14 While biological pathways are necessary for relating social phenomena to health outcomes, they are intervening variables, and do not explain the epidemiological variability of suicide rates. Claims have also been made that psychiatric illness is a factor in almost all youth suicides;15 however, previous population-level psychological autopsy studies do not support this.16 There is no population-based evidence specific to contemporary youth in Australia to base these claims on. Psychological disturbance Cohort studies of psychological health and youth unemployment have found deficits in the psychological well-being of unemployed young people, but these are less severe and in some cases different from those experienced by unemployed adults.17,18 For example, an Australian study found that unemployed middle-aged adults had significantly higher levels of psychological disturbance (as measured by the General Health Questionnaire) and lower levels of life satisfaction, but more job involvement, than young unemployed counterparts. However, perceived levels of social support were lower than in younger people.18 Australian cohort studies in the 1980s and 1990s found psychological morbidity associated with unemployment in previously psychologically healthy young people, including school leavers.19 Self-esteem, particularly among school leavers, has been shown to be lower in unemployed and underemployed young people, compared with those in full-time employment, after controlling for known confounders such as job satisfaction, education levels and aptitude, sex, ethnicity, and geographic location.20 One of the largest Australian cohort surveys of young people, the Australian Longitudinal Survey (ALS) of youth (16-25 years of age), conducted by the Federal Department of Employment, Education and Training during the mid to late 1980s, provided evidence for unemployment being causally linked to a 50% increase in risk of psychological disturbance,21 as measured by the 12-item psychological component of the General Health Questionnaire. Respondents were chosen for analysis in such a way as to minimise a "selection effect" from illness predisposing to unemployment. Survey participants analysed had been employed formerly, had no pre-existing psychological morbidity or physical illness, and reported no other life event or situation to account for their psychological disturbance. Psychologically well young men who became unemployed reported feeling unhappy and depressed, whereas young women reported loss of confidence; both complained of not having a useful role in their lives. A reverse of the effect was also found; that is, those who initially were psychologically disturbed and unemployed had similar odds of recovery from psychological disturbance at the succeeding survey point, if by that time they had gained employment, as those who developed psychological disturbance because of unemployment.21 In this particular cohort survey, finding a job was the only consistently effective means of recovery from psychological disturbance. The symmetry of this effect has implications for arguments about the direction of causation between unemployment and psychological disturbance. If the association were solely due to psychologically disturbed individuals self-selecting into unemployment, then re-employment of those who are unemployed and psychologically disturbed would not be associated with recovery from psychological disturbance. That recovery has been shown to occur, and that increased psychological disturbance was associated with job loss, suggest that the psychological health of young people is strongly influenced by their employment status. A number of longitudinal studies of psychological health in youth and unemployment duration have produced conflicting findings. While no evidence of an effect of duration of unemployment was found in the ALS study quoted above,21 a South Australian study in the late 1980s found that psychological ill-health in unemployed youth worsened significantly after nine months of unemployment.22 The effects of unemployment may also have a more generalised influence on the psychological health of populations; that is, the unemployment rate, not necessarily the individual experience of unemployment, could be indirectly responsible for individual psychological ill-health. For example, high unemployment levels can force more people to accept unfavourable working conditions or jobs they would rather not do. An Australian study by Graetz, using data from the ALS, found higher rates of psychological disturbance in employed young people who did not like their job compared with those who did.23 A similar finding emerged from the 10-year cohort study of South Australian school-leavers. Each year young people who were dissatisfied with their jobs were just as psychologically disturbed as those who were unemployed.24 High levels of unemployment also influence job security. The psychological effect of job insecurity on youth was investigated in the United Kingdom.25 Unemployed young people and young people on Youth Opportunity Schemes (YOS) (who are placed either on specific projects or in temporary work with employers) were compared with employed apprentices; the Beck Depression Inventory and the Eysenk Personality Inventory were used. Young people on specific projects were more disillusioned than the temporary work placement youths, but all the YOS participants showed similar feelings of fatalism to the unemployed group (ie, insurmountable outside forces leading to perceived lack of control causing stress), but these feelings were not present in the employed apprentices. This has obvious implications for "work-for-the-dole" schemes and, more generally, for those employed in temporary, or "stop-gap", jobs compared with jobs with some measure of security. Physical health Links between unemployment and various physical symptoms have been documented in cohort studies of adults, and similar physical effects may occur in unemployed young people, but there are few cohort studies of youth. In Australia, the cross-sectional National Health Survey (1989-90) noted that young unemployed people reported fair to poor health (about 65% for men and about 80% for women), and serious chronic illness (62% for males and 35% for females) significantly more often than employed people.26 A five-year prospective study of school leavers (n=1083) in the industrial town of Lulea (northern Sweden) showed evidence of cardiovascular effects of unemployment on youth. The mean increase in systolic blood pressure in long-term unemployed young men was significantly higher than in those who experienced short-term or no unemployment during the study period. No significant differences in diastolic blood pressure were found, and these differences were not found in women.27 A longitudinal study of Irish youth found that unemployed men, but not women, carried less mean body fat compared with those who were employed. The same study found that respiratory function was significantly lower in unemployed women (but not in unemployed men) than those employed.28 These effects could have been due to poverty and undernutrition in the men rather than unemployment, or may have resulted from poverty due to unemployment. A higher prevalence of smoking among unemployed women, possibly related to their unemployment, may have been responsible for their poorer lung function. Despite evidence of an association, the evidence for unemployment actually causing physical ill-health in youth is not strong and, where this exists, factors associated with unemployment, rather than unemployment itself, could also be responsible. Adverse risk factors for health Results of youth unemployment studies and risk factors for health have not been as consistent as those in adults. A Norwegian prospective study of 17-20 year olds (n approx. equal 2000) found higher rates of cannabis use, but not alcohol consumption, among unemployed than employed young people.29 Another study found a positive association with smoking: the odds ratio of taking up smoking among the long-term unemployed (> 20 weeks) was significantly higher than in those with little or no unemployment -- by 50% in men and 100% in women (after adjusting for initial smoking status, socio economic background and education level).30 A study of young people in Scotland (n=1036) found the rate of illegal drug use to be significantly, but weakly, correlated with duration of unemployment, but not alcohol or tobacco use.31 A larger British longitudinal study of young adults found the odds of heavier drinking in young men to be 38% higher among those unemployed for six months or more during the study period compared with those who were not. The same study found that teenage drinking was not significantly associated with subsequent employment experience.32 These cross-sectional studies show an association between unemployment, illicit drug and alcohol use, but do not provide evidence for causation. Is there a remedy? In searching for a remedy for ill-health in young unemployed people it is necessary to look first at whether a "selection effect" is operating -- young people becoming unemployed because of ill-health (Box 1) -- and then to consider the mechanisms by which unemployment could lead to ill-health. Many mechanisms are probably involved in unemployment leading to ill-health in young people: it has a direct material impact on their lives and lifestyles through curtailment of activities, a reduction in disposable income and absence of the social and psychological effects of being in the paid workforce. Sociocultural and psychological moderating and intervening factors, including isolation and perceived lack of purpose in life, can accentuate suicide and parasuicide risk and lead to risk-taking behaviours, with some level of morbidity or even death (accidental injury or poisoning) as a consequence. Health providers, and the health sector, are now, increasingly, focusing on the health needs of the unemployed, and some of the possible strategies are outlined by Harris et al in the final article in this series (MJA 1998; 16 March).36 As with many public health problems, however, solutions often come from outside the domain of health services, although the health sector often plays a large role in advocating such changes. In the past, most public health measures have involved technical or engineering solutions (such as clean water and sewage treatment facilities, immunisation, automobile seat belts), but there is a great deal more inertia when the health problems are the result of direct effects of economic (dis)organisation, and where there is no simple technical magic bullet. As a public health problem whose solution is external to the health system, unemployment challenges health professionals and public health officials either to advocate an economic solution to the problem, or to limit advocacy to more "band-aid" measures (eg, more counselling, more education, more health resources). The young might reply that all they want (and should expect) is a decent job, especially if they are told that they are expected to work. Another approach is to keep young people in the education system for longer, but this still does not address the underlying problems and only delays the inevitable because eventually they must find employment. The financial restructuring of the mid 1980s, although temporarily reducing the unemployment rate a little, also entrenched the trend towards income polarisation which began with the first oil price "shock" of 1974. The income gap between rich and poor has increased since, as has concentration of wealth in the hands of the rich, and it appears that this situation will only worsen as further harsh monetary policies, public sector depopulation, privatisation of essential services, and massive corporate "downsizing" proceed.37 What specifically economic solutions can be advocated? An eminently supportable employment measure is a program of much-needed public investment to maintain and expand physical and social infrastructure which has been left to decay for two decades, and which has not even kept abreast of population increases. Certainly, tightening the income tax system and closing off tax havens is also worthwhile, particularly as Australia is a low tax country compared with many other OECD countries. The most important aspect of the problem, however, is productivity (Box 2). Because there have not been reductions in the working week to match productivity gains to share the work around, youth in the final quarter of the 20th century have paid the price by enduring Depression-level unemployment rates which appear to have had an unfavourable impact on their physical and psychological health. References Withers G, Endres T, Perry L. Australian historical statistics: labour statistics. Canberra: Australian National University, 1985. 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Cullen JH, Ryan GM, Cullen KM, et al. Unemployed youth and health: findings from the pilot phase of a longitudinal study. Soc Sci Med 1987; 25: 133-146. Hammer T. Unemployment and use of drug and alcohol among young people: a longitudinal study in the general population. Br J Addiction 1992; 87: 1571-1581. Hammarstrom A, Janlert U. Unemployment and change of tobacco habits: a study of young people from 16 to 21 years of age. Addiction 1994; 89: 1691-1696. Peck DF, Plant MA. Unemployment and illegal drug use: concordant evidence from a prospective study and national trends. Br Med J (Clin Res Ed) 1986; 293: 929-932. Power C, Estaugh V. Employment and drinking in early adulthood: a longitudinal perspective. Br J Addiction 1990; 85: 487-494. Martikainen PT, Valkonen T. Excess mortality of unemployed men and women during a period of rapidly increasing unemployment. Lancet 1996; 348: 909-912. Claussen B, Bjorndal A, Hjort PF. Health and re-employment in a two year follow up of long term unemployed. J Epidemiol Community Health 1993; 47: 14-18. Arrow JO. Estimating the influence of health as a risk factor on unemployment: a survival analysis of employment durations for workers surveyed in the German Socio-Economic Panel (1984-1990). Soc Sci Med 1996; 42: 1651-1659. Harris E, Webster I, Harris M, Lee P. Unemployment and health: the healthcare system's role. Med J Aust 1998; 168. In press. Rifkin J. The end of work. New York: GP Putnam's and Sons,1995. Norington B. Work's hard yakka but who's complaining? Sydney Morning Herald . Aug 29, 1997. (Received 1 Sep, accepted 22 Dec, 1997) This is the second of three articles in a series on unemployment and health (see also MJA 1998; 168: 177, 178 and 291) Authors' details Department of Public Health and Community Medicine, University of Sydney, Sydney, NSW. Stephen L Morrell, BSc, BA, Senior Research Assistant, and Project Manager; Richard J Taylor, FRCP, FAFPHM, Associate Professor in Public Health; Charles B Kerr, FRACP, FAFPHM, Professor of Preventive and Social Medicine. Reprints will not be available from the authors. Correspondence: Mr S L Morrell, Department of Public Health and Community Medicine, University of Sydney, Sydney, NSW 2006. Readers may print a single copy for personal use. No further reproduction or distribution of the articles should proceed without the permission of the publisher. For permission, contact the Australasian Medical Publishing Company Journalists are welcome to write news stories based on what they read here, but should acknowledge their source as "an article published on the Internet by The Medical Journal of Australia <http://www.mja.com.au>". <URL: http://www.mja.com.au/>

Stephen L Morrell · Richard J Taylor · Charles B Kerr