Our study shows that income and education predicted return to work among stroke survivors during the first post-stroke years. The results also suggest that income differences between men and women account for women's lower probability to return to work. In general, the study confirms the findings of Lindström et al.  and Howard et al.  who found significantly higher probability of return to work among persons with a higher social class. Both studies also found significant effects of education, but only in bivariate analyses. Income and education have previously been shown not only to be important factors both for identifying individuals and groups at risk of experiencing a stroke, but also for predicting different stroke outcomes; for example, if a stroke is survived, low socioeconomic position is related to higher risk of ending up in a nursing home and to a higher mortality rate [9, 29, 30]. To our knowledge, this is the first study that has examined return to work after stroke in a large population-based sample. Some findings from different countries and different time spans go against these findings [19–23] but it remains unclear why this is the case.
The effect of education on stroke outcome is likely to occur through pathways that involve income. Since education generally precedes income as it is completed early in life, and income is partly the result of educational achievements, education may be conceptualized as a factor underlying the later association between income and return to work. However, there must be other causes for the association between education and return to work, since both education and income, controlled for each other, were significantly associated to the probability of return to work.
Cox et al.  found that people with lower education and income tend to suffer a more severe stroke. We did find a strong association between number of days in in-patient care and return to work. While using days in in-patient care as a proxy and control for stroke severity might include confounding factors such as organisation of health care, its use can still be considered reasonable in the absence of clinical data. The analysis indicates that the relationships studied exist independently of this proxy of stroke severity.
The comparatively positive outcome for stroke survivors who had subarachnoid haemorrhage has been explored earlier and is in line with a previous finding . However, there were no indications that associations between socioeconomic position and return to work differed between different stroke subtypes.
The poorer health condition of persons of lower socioeconomic status may be related to the classes' different lifestyles. For example, in one study, the socioeconomic gradient for the incidence of stroke among middle-aged persons could largely be explained by established risk factors such as smoking and alcohol consumption . Moreover, in a population-based Swedish study on elderly patients with cerebral infarction , the socioeconomic gradient persisted when adjusted for risk factors and acute care variables. Unfortunately, since our study was based on registers there were no health behaviour data and it is not clear whether the relationships found are independent of socioeconomic differences in lifestyle.
The relationships found may be due to socioeconomic-related differences in the amount or quality of the health care received [33, 34]. The findings are valid in the Swedish context, with a general health care system and far-reaching goals of health equity. Sweden is a high-income country representative of a Nordic welfare model with comparatively strong income equity , mandatory health care for all Swedish citizens and a rather strong emphasis on work rehabilitation. There is also a relatively low degree of inequality in regard to educational opportunity . Nevertheless, even in such a context, well-educated persons (and their spouses, relatives and friends) probably have higher expectations and may be in a better position to voice their demands both for cutting-edge health care in connection with the stroke and for subsequent rehabilitation. People with higher income may also be able to pay for private care, e.g. a more elaborate rehabilitation. An OECD study including 21 countries that measured inequity in doctor utilization by income  found, for example, unequal physician utilization favouring better off patients in Sweden.
There are many other contextual factors to address in understanding return to work (compare, for example, Link & Phelan ) and there are structural differences between different sectors of the labour market. Returning to work is strongly linked to the possibility of adjustment at work, and recovery and rehabilitation measures in the work place. For example, the majority of well-educated persons have (higher paid) white-collar professions where it may be easier to find adjusted work tasks than in blue-collar professions.
In our study, men also returned to work somewhat more often and somewhat earlier than women, contrary to the findings of another recent Swedish study showing no significant sex differences in return to work . Not only do men have higher incomes than women , labour market conditions can also be strongly contextual but work setting can also differ based on gender. For example, while there is a relatively high percentage of women in the workforce in Sweden, the country has a strongly gender divided labour market with men mostly working in the private sector and many women working in the public sector. Although our results suggest that income differences between men and women accounted for women being less likely to return to work, the reason for this may still be found in men's and women's different occupations, as well as the fact that women's jobs are generally lower paid .
It is also possible that well-educated and better paid people tend to find their work more stimulating, or perhaps their role in the work place is more important than for people with low pay or less education; they may be more difficult to replace and/or in a better position to voice their demands.
The generalisability of results to other times and countries is unclear. Since we arrived at our contribution by using population-based data, we should at least be able to give strong evidence from Sweden. Nevertheless, even for Sweden, recent policy reforms may affect the generalisability of the results over time. There has been an almost aggressive return-to-work policy in Sweden with activation measures and benefit reductions during recent years. This could possibly change the association between stroke and return to work. It is also possible that such changes do affect groups with different education and income differently.
This study is based on registers generally considered to be of high quality that provide the basic information for Swedish health care development and comparisons (see e.g. ). Very few cases on income and education are missing in our study. Statistics Sweden also takes active part in the methodological discussion on usage of administrative registers (see e.g ). Our study is also in line with the recommendations of a recent review of stroke outcomes suggesting that return-to-work studies should be based on population data and the measurement of paid work . Nevertheless, there are some important limitations in our study design since we do not know the clinical condition of the stroke-affected persons. We avoided using TIA diagnosis due to less reliable data and we excluded those with prior ischemic heart disease, but there might be other co-morbidities or confounding factors due to unmeasured variables such as health behaviour and quality of care. We do not know the type of work returned to, or whether the subjects remained in the labour force after the initial phase of return. Income from work was used as a proxy for work capacity and we only studied stroke survivors who had an income from work prior to the stroke. We tried modelling other income levels (≈ €1100 and ≈ €11000). The proportion of people returning was dependent on the income level chosen, but the socioeconomic pattern remained the same (results not shown). There is also a risk of overestimating return to work in high income groups (and underestimating those in the lowest income group) because our income level was based on the average Swedish income, corresponding to 25 percent of full time work. Someone in the highest income group may work less than 25 percent, but still have an income from work equivalent to the average Swedish income.
By using population data we tried to avoid a selection bias. However, there is a potential bias from fatal events. We have no information about people who did not visit a doctor or died before reaching hospital care. The socioeconomic mortality pattern after a stroke, both before and after discharge from hospital, corresponds to the pattern of not returning to work (based on preliminary analyses from the same data, not shown). Persons with low socioeconomic position are both more likely to die and less likely to return to work. As a consequence, our results could have underestimated the association between socioeconomic position and return to work.
Since the study is based on register data, no information is available on lifestyle factors. We used all relevant covariates available. While this limitation may have resulted in omitted variable bias/unobserved heterogeneity, it was the best possible modelling given the available data.
While the highest income quartile had twice the odds of returning to work compared with the lowest quartile, those with a university education were only 13 percent more likely to return to work than those with an elementary education. This may indicate that economic resources are more decisive than educational ones (e.g. how well-informed someone is), but whether this is the case remains unclear. In the future, it would be interesting to examine more closely the determinants captured by different socioeconomic indicators.