Strengths and limitations
One strength of this study is the sample size, which enabled the analysis of associations between health and both the duration and mode of commuting. Moreover, the public health questionnaire had no specific focus on commuting and health, thus avoiding biased reporting. The data available allowed adjustment for several potential confounders, although there are other confounders that could not be adjusted for, such as shift work, commuting to Denmark and several contextual variables. Contextual variables that have not been addressed in this study include, for example, the availability of different types of work places, and the development of the public transportation system in the vicinity, which are not fully captured by the simple urban/rural dichotomy.
The overall response rate was 56%, and especially low among men, young people, those with a low level of education, low income and persons born outside Sweden . This could mean that there is a lack of representativeness for these population groups, and that the findings of the study are more uncertain for these groups. No information about occupational status was available on individual level for the surveyed population, and we are therefore not able to do a formal analysis of the representativeness of the findings. Lack of time as a reason for not returning the questionnaire could constitute a source of bias in this study, as the commuters that perceive such a lack of time due to long commute duration might also be the ones whose health is also most affected. As this would lead to a lower response rate among individuals with the exposure, long duration commuting, and the outcome, e.g. poor self-rated health, this bias would reduce the observed association between commuting duration and health outcomes. Some of the subjects in this study might have been included twice. Unfortunately, there was no possibility of identifying these from the available data. Considering a population of 1 million and 50000 surveys in each year, 2500 persons might have received the survey twice. Assuming that those that responded in 2004 will respond also in 2008, and that those working > 30 hours/week in 2004 will do that also in 2008, and considering the overall response rate of 56% and inclusion rate of 38%, we would have approximately 500 (2%) duplicates in the dataset. Thus, we consider that a small proportion of subjects responding twice cannot affect the analysis importantly.
Since this is a cross-sectional study it is impossible to say that commuting caused the outcomes, and it is likely that other problems related to health and everyday life affect choices concerning commuting, leading to self-selection bias in our study. Less healthy people could be assumed to be less likely to start, or to continue commuting actively, creating bias away from the null when comparing active with passive transportation. An argument against why self-selection due to prevailing health problems is not the only cause of the association between the health outcomes and commuting is, however, that commuters using car or public transport for more than 30 minutes cover distances that could not be covered by walking or cycling for less than 30 minutes, i.e. there is little possibility to choose an active commuting mode for that distance. Another type of selection bias is also plausible, namely "the healthy commuter effect"; i.e. only those fit enough to endure commuting will start and continue long-distance commuting, whereas those experiencing lower utility or any type of health problems arising from commuting may choose to reduce their commuting time or change mode of commuting to minimize the impact of this strain on their life situation, creating bias towards null. The estimation of the commuting time might be affected by the mood of the respondent; those in a negative mood might be more likely to exaggerate the commute duration, i.e. dependent misclassification leading to increased estimates of association. A stressful life situation, in which it is perceived to be necessary to commute by car instead of the public transportation system , could be associated with negative health outcomes, meaning that there is reversed causation.
Having the financial security required for commuting long distances by car and the freedom to choose where to live, regardless of workplace location and public transport services, could also be associated with good health in a way that has not been completely adjusted for in our analysis. By considering the descriptive data, it is evident that long-duration car commuters are a relatively homogeneous and distinctive group, being male, well-paid and working overtime on jobs associated with high psychological demands and a high level of control. Another factor that should be considered is the availability of green environments close to one's home, which has been shown to be related to better general wellbeing  and vitality among women . Green environments are more likely to be available in rural areas where long-distance car commuting is common.
The question about sleep might measure more of the subjective perception of the quality aspect of sleep than the objective quantity aspect. Thus this outcome variable might be more of a stress proxy than a measure of restricted time opportunities for sleep/recovery, which however is a potential consequence of everyday long-duration commuting. Both the questions concerning sleep and stress ask for subjective perceptions, and therefore there is nothing which can be objectively measured in order to estimate the validity of these questions. Several possible ways of coding SF-36 exist; we chose to use one which has been shown to be related to flattening of the diurnal cortisol profile in a sample of a working Swedes . GHQ12 as a measurement of mental health is a short, but robust, measure of mental health . The use of self-reported health in medical research is widespread, and poor self-reported health has been found to be associated with premature death . Ettema et. al.  recently presented an extensive discussion on the use of subjective well-being as an outcome in transportation research. The 7-grade scale used in the present study has been found slightly more sensitive than alternative 5-grade scales .
The proportion of workers absent due to sickness less than 0, 5, 7 or 14 days per year is used as an indicator of a low sickness absence at workplaces in the public sector in Sweden .The analysis of the association between commuting and sickness absence was sensitive to the cut-off value used to classify high sickness absence. Different lengths of sickness absence in the previous year might reflect different types of negative health events, with long periods potentially caused by periods of actual disease, whereas few or short periods are more likely to also be influenced by "non-disease" factors. These "non-disease" factors could be commuting-related (such as the difficulties of getting to work when ill ), individual (e.g. the perception of need to stay at home when ill) and work-related (such as the possibilities to work while ill or work from home those days). This complexity could also be the explanation of the weakly decreasing trend we see for sickness absence with increasing commuting time: being generally more well-educated, long-distance commuters are more likely to have non-manual jobs that they partly can do from home, and thereby not have to be "absent" from work when feeling ill. Further research is necessary to elucidate the complex relationship between commuting and sickness absence, why the relationship between commuting and sickness absence presented in this study should only be seen as a preliminary estimate.
In a previous study of the association between commuting and utility it was argued incorrect to adjust for overtime and income, as these factors might constitute a compensation for commuting . However, we believe that it is important to adjust for overtime in order to separate the association between commuting and health from other work-related factors or choices. Income is adjusted for since we consider the choice of commuting mode, where income could be a decisive factor. Another work-related factor we chose to adjust for is the psychosocial work environment (job strain) which can be expected to differ between short- and long-distance commuters. The current regulations concerning unemployment benefits in Sweden do not allow an unemployed person to restrict his or her job search geographically (until 2007, unemployed people were allowed to restrict their job search to the area/region in which they lived during the first 100 days of unemployment)  which could lead to longer commuting times among those with a history of unemployment. Family situation could constitute an important factor when choosing home and work location; for example, having children could motivate parents to commute in order to be able to raise their children in what they consider to be a good living environment . However, parents, and perhaps especially single parents, would probably choose to avoid long commutes, if possible. Financial stress was included as financial problems could restrict the choice of work and home locations and mode of commuting. The residential location as conceptualised through the urban/rural dichotomy, is a factor that clearly affects commuting possibilities; those residing in an urban area have a wider selection of nearby workplaces than those living in rural areas, which could lead to generally shorter commutes in urban areas. Also, the availability of public transport is generally better in urban areas.
Although there are limitations to this study, we still consider it an important contribution to better understanding of the complex relationship between commuting and health. The study material is large, and we show associations with various health outcomes after adjustment for several covariates, congruent with findings from previous studies. Future research using more rigorous data gathering methods, and able to take into account additional, especially contextual covariates, could provide stronger evidence. Especially, longitudinal studies are needed.
Although there was no significant departure from multiplicative interaction between commuting time and mode for any of the health outcomes, the suggested shapes of the associations between commuting time and the health outcomes were nevertheless different between public and car commuters.
The generally concave downwards association between car commuting time and health outcomes could be the result of the "healthy commuter effect" discussed above, or because those who need to use their car to commute short distances have poor health or are facing stressful life situation  in comparison to those being able to choose active commuting or public transport. We should also consider the possibility that long-distance car commuting may not actually be particularly harmful to health, especially in this geographical area, where a > 1-hour car commute does not imply > 1 hour of intense rush-hour traffic, but in most cases some tranquil countryside driving, which may offer the possibility of relaxation and give a feeling of flow . Indeed, commuting a shorter distance by car may involve just as much time in stressful car driving, or even more than long-distance commutes, as these shorter journeys may be completely within urban or suburban areas.
A potential explanation of the generally more linearly dose-dependent association between commuting time and health outcomes among public transport commuters is that longer commuting times on public transport imply changes between buses or trains, and thus a higher risk of unpredictable and uncontrollable delays when commuting, which are potential causes of commuting-associated stress [9, 10]. Long-distance commuting using public transport system could mean having to adjust one's everyday life according to bus or train timetables [1, 46], which results in inflexibility and loss of control, with potentially negative effects on health.
Thus, fundamental demographic and socioeconomic factors lead to confounding of the relationships between commuting and health, and it is necessary to handle this complexity when studying such associations in future studies. We investigated whether additional, more specific factors, related to work, home and family conditions, would act as confounders, and included these in additional models. This generally lowered the ORs between commuting and health outcomes, especially for public transport commuting, although only slightly.