Variables

The codes used to identify death by suicide in the ICD-8, ICD-9 or ICD-10 classifications (World Health Organization, 1974, 1978, 1992) were E950-E959 (ICD-8 and ICD-9) and X60-X84 (ICD-10).

Educational status and housing tenure were registered. The level of education was first classified according to the national categories of education in each country. The number of educational categories ranged from four (in most countries) to 13 in Belgium. On the basis of the description given by each representative of the data collected in each country, the national categories were converted into International Standard Classification of Education (ISCED) categories (UNESCO, 1997). As several countries provided no information on the group with primary education only (ISCED 1), the ISCED levels 1 and 2 (lower secondary school) were grouped together in the analysis. Education data were not available for Danish people older than 70 years. Education is mostly achieved by early adulthood and is not necessarily consistent with later economic achievement and wealth accumulation over the life cycle. Housing tenure was included to cover such wealth effects and was grouped in two categories: owner and tenant. This variable, however, was available for only six populations, namely those of Norway, Finland, Denmark, England and Wales, Belgium and Turin.

Analysis

Age-standardised mortality rates by educational level and housing tenure were computed for each gender and country. Direct standardisation was applied, using the population of the European Union and Norway of 1995. On the basis of these standardised rates, we computed rate ratios comparing those with a low educational level (ISCED 1 and 2) with those more highly educated (ISCED 3 and above), and tenants with house owners. The formula for the standard error of a relative risk was used to compute standard error (Reference Dawson and TrappDawson & Trapp, 2001). Because differences in suicide inequalities between countries could arise from methodological or sample variations, we tested the homogeneity of inequalities using Cochran's Q statistic, which has a w² distribution with a degree of freedom equal to the number of studies minus 1 (Reference PetittiPetitti, 1994).

Fig. 1

Suicide risk in men: effect of educational level and house ownership status (OR, odds ratio).

We also looked at the cumulative effect of different types of socio-economic disadvantages on suicide levels. We combined education and housing tenure into a single index ranging from 0 (low educational level and tenant) to 4 (high educational level and house owner). The effect of increasing socio-economic disadvantages was assessed for each country through logistic regression, controlling for age.

Main findings

We examined socio-economic risk factors for suicide mortality in ten European populations. Four main findings stand out. Socio-economic inequalities in suicide are pervasive in all male populations, except for that of Turin. Second, inequalities were far less pronounced in women and in some cases even reversed, particularly when educational status was considered. Third, housing tenure seems to be a more important risk factor than education and yields more consistent results between genders. Fourth, our study shows that in most settings, suicide level increases with increasing socio-economic disadvantage.

Limitations

The study has a few limitations which could affect the reliability and comparability of estimates of suicide inequalities. Misclassification could affect the suicide registration. Indeed, there is some evidence that official suicide rates are underestimated (Reference Joseph, Abraham and MuliyilJoseph et al, 2003) because of suicide misclassification. Such misclassification could affect our estimates if countries apply different coding procedures (Reference Jougla, Pavillon and RossollinJougla et al, 1998) and, hence, have different reliability (Reference Rockett and ThomasRockett & Thomas, 1999). We assessed such risk by combining suicide and injuries of undetermined intent (E980-E989, Y10-Y23). The results were relatively robust. When injuries and suicide were added up, the risk of suicide among men of low education compared with men of high education increased from 1.4 to 1.48. This change was more dramatic for the male populations in England and Wales (from 2.67 to 4.10) and in Belgium (from 1.61 to 2.2). The country rankings in educational inequalities among men changed very little, except for Barcelona, which was pushed from eighth position into fourth.

Fig. 2

Suicide risk in women: effect of educational level and house ownership status (OR, odds ratio).

A second group of limitations arises from the differences in the educational system in the countries compared. We devoted considerable effort to obtaining internationally comparable educational categories using the ISCED system. However, the ISCED distribution is not homogeneous and some countries have a low percentage of individuals in the lower secondary educational attainment category (such as Switzerland) or in the categories with upper secondary education and above (England/ Wales and Turin). Sensitivity analysis was performed to assess the impact of the heterogeneity in education distribution. The cross-country analysis was carried out by computing a relative index of inequality on the relative rank of educational status (Reference Mackenbach and KunstMackenbach & Kunst, 1997), which takes into account each educational level individually, with a distinction of about four educational levels in most countries. Inequalities increased and became significant in all male populations but that of Turin. On average, the ranking of countries changed by 1 point. Barcelona became a high-inequality population. The highest inequality level was still seen in Madrid. Turin and Denmark had the smallest inequalities.

The inequalities in England and Wales were higher than those found in a study based on the 1981 population census (Reference Lewis and SloggettLewis & Sloggett, 1998). They are also higher than the figures reported in a psychiatric epidemiological survey (Reference Lewis, Bebbington and BrughaLewis et al, 1998). Discrepancies in source (we used the 1991 population census), in statistical power (we counted 130 suicides for England and Wales v. 581 in the study of Lewis & Sloggett) and follow-up (5 years v. 10 years) may explain such differences in the observed size of inequalities.

Previous studies and interpretation

On three aspects our results are consistent with a previous worldwide meta-analysis of socio-economic risk factors for common mental disorders, a main risk factor for suicide (Reference Lorant, Deliege and EatonLorant et al, 2003). First, in that study low socio-economic status was found to be a significant and positive risk factor of depression in 35 out of 56 studies. Second, as in our study, the association with socio-economic status was unimodal and stronger for economic variable than for educational status. Finally, it was also geographically heterogeneous: greater in the USA than in Europe.

The greater inequality between educational groups found in men as compared with women is consistent with previous individual-level studies (Reference KposowaKposowa, 2000; Reference Blakely, Collings and AtkinsonBlakely et al, 2003). It can be explained by gender differences in health-related and life-threatening behaviours, such as alcohol or drug misuse, which are known risk factors of suicide and are more prevalent among men and among lower socio-economic groups (Reference Kessler, McGonagle and ZhaoKessler et al, 1994; Reference Cavelaars, Kunst and MackenbachCavelaars et al, 1997; Reference McDonough, Williams and HouseMcDonough et al, 1999), although alcohol was not particularly related to socio-economic status in the British survey of psychiatric morbidity. On the other hand, suicide inequalities in women resemble those in men more closely where house ownership is concerned than education: this may be because house ownership is mostly an attribute of the household and thus shared by both spouses, whereas education is an individual attribute, hence more sensitive to gender differences. It could be also that low wealth or income tends to have a similar association with men's and women's overall mortality risk (Reference Duncan, Daly and McDonoughDuncan et al, 2002).

Suicide was more strongly associated with home ownership than with education. This is consistent with previous studies using housing tenure, wealth or car ownership (Reference Lewis and SloggettLewis & Sloggett, 1998; Reference Qin, Mortensen and AgerboQin et al, 2000; Reference Blakely, Collings and AtkinsonBlakely et al, 2003). Several elements may explain such a difference. Housing tenure is an indicator of wealth accumulation and has shown to yield stronger inequality than education (Reference Lorant, Deliege and EatonLorant et al, 2003), presumably because the material pathway (captured by housing tenure) has the edge over the behavioural pathway (captured by educational status). Moreover, psychiatric illness is more likely to affect house ownership than education, because reverse causation is less likely in the latter case. The fact that housing inequalities are greater than educational inequalities indicates that selection factors must not be overlooked and that the roots of inequalities are grasped in the whole life cycle (Reference Power, Matthews and ManorPower et al, 1998). Finally, housing tenure may also be associated with marital status, which has a clear protective effect on suicide (Reference KposowaKposowa, 2000).

Why is there no suicide inequality in Turin, while the largest inequalities were found in Madrid? We can speculate that the outstanding Italian mental care system might play some part. Mental illness is a major risk factor for suicide (Reference Qin, Agerbo and MortensenQin et al, 2003) and is more prevalent among lower socio-economic groups (Reference Lorant, Deliege and EatonLorant et al, 2003). Individuals of lower socio-economic status favour primary mental care over psychiatric specialty care (Reference Alegria, Bijl and LinAlegria et al, 2000). Italy has pioneered an integrated and community-based mental health system, introduced after the reforms of 1978 and the 1994 National Mental Health Plan (Reference BurtiBurti, 2001; Reference Becker and Vázquez-BarqueroBecker & Vázquez-Barquero, 2001). As a consequence, Italy might well be able to tackle suicide inequalities more effectively. A recent assessment of time trends of suicide inequalities in Turin showed that the suicide risk was reduced most in the less educated population groups between the 1970s and the 1990s (Costa et al, personal communication). The link with the reform undergone by the Italian psychiatric system requires further study, however.

The large educational inequalities observed in Madrid (and in some analyses also in Barcelona) might be due to the higher prevalence of drug misuse in these Spanish cities compared with other countries of the European Union (Reference Kraus, Augustin and FrischerKraus et al, 2003). Suicide is a frequent cause of death among drug users in southern Europe (Reference Orti, Domingo-Salvany and MuñozOrti et al, 1996), and there is a strong relationship between parenteral drug use and educational level. The small inequalities in women from Madrid are consistent with the lower prevalence of injecting drug use in women compared with men. Societal inequalities, including inequalities in income, may provide another explanation for the greater inequalities in suicide in Madrid. Spain ranks among the countries with the highest income inquality in Europe. Moreover, large inequalities among Madrid men may also be explained by the low level of suicide prevalence, as rare events tend to concentrate among lower socio-economic groups.

Clinical implications

The pervasive association between socio-economic status and suicide calls for an improvement of access to psychiatric care for lower socio-economic groups. This is relevant because psychiatric disorders seem to be an important pathway in the relationship between socio-economic status and suicide (Reference Qin, Agerbo and MortensenQin et al, 2003) and because of the under-utilisation of speciality mental care among lower socio-economic groups (Reference Alegria, Bijl and LinAlegria et al, 2000). Individuals of lower socio-economic status with a DSM-IV disorder (American Psychiatric Association, 1994) are more likely to receive that care in countries (such as The Netherlands) that have succeeded in integrating mental health within the primary and community care sectors (Reference Alegria, Bijl and LinAlegria et al, 2000). The shift to primary care and community care may thus contribute to reducing socio-economic inequalities in suicide.

CLINICAL IMPLICATIONS

1. ▪ In most settings, men of low socio-economic status are at greater risk of suicide than higher-status groups.

2. ▪ Socio-economic inequalities are smaller and less consistent among women.

3. ▪ In most settings, the more socio-economic disadvantages a person suffers, the higher the risk of suicide.

LIMITATIONS

1. ▪ Misclassification results in an underestimation of national suicide rates, but the bias may not strongly differ by socio-economic status.

2. ▪ Differences in educational systems affect to some extent the national rankings in suicide inequalities.

3. ▪ Low statistical power for some countries challenges comparisons of these countries with others.