Air pollution linked to mental illness in children and stroke

Air pollution has been linked to both mental illness in children and stroke in two new studies. The National Institute for Stroke and Applied Neurosciences in New Zealand showed that such pollution was linked to almost a third of the years of healthy existence destroyed globally by stroke. Air pollution from fine particulate matter was the seventh major risk and household air pollution was the eight. Meanwhile, a new Swedish study has shown that even minor increases in air pollution can cause significant increases in psychiatric illness in children. The study is the first to provide definitive evidence of a long-suspected link between air pollution and cognitive and mental health problems. Air pollution in many UK cities is illegally high and is believed to cause 40,000 premature deaths annually.

In this longitudinal study of Swedish children and adolescents, neighbourhood air pollution concentration was associated with dispensed medications for certain psychiatric disorders, after adjusting for individual-level and group-level characteristics. The association was present in three out of four counties within Sweden. To put our findings in perspective, the association seemed to be present even at annual levels of NO 2 lower than 15 µg/m 3 , which can be compared with the WHO guideline and EU air quality standard of 40 µg/m 3 . However, a finer spatial resolution would have resulted in a wider concentration range especially for NO 2 . This is one of a small number of studies to consider the association between air pollution and mental health, and the first to do so in children. There are several studies suggesting associations between air pollution and autism spectrum disorders 26–32 and cognitive function in children, 23–25 , 33 and this study adds to evidence from them that air pollution may have detrimental effects on the brains of children and adolescents. Furthermore, this is the first study to use a whole population, and to use nationwide register-based data on dispensed medications as an indicator for mental health. Most of the existing evidence for a link between air pollution and mental health comes from short-term studies in adults, where daily fluctuations in air pollution concentrations are compared with the daily number of mental health events or symptoms. 8–16 Furthermore, associations between longer term exposure to air pollution and anxiety and stress were recently reported in two ageing cohorts. 6 , 7

Limitations and strengths of the study

There are some weaknesses and strengths of the study that should be mentioned. First, any dispensed medication in the group N05 is a very crude measure of mental health. N05 consists of neuroleptics (antipsychotic medications), ataractics and sleeping pills (a broad group of sedative medications including hydroxyzine and melatonin-based medications). Furthermore, the N05 group of medications includes antipsychotic medications, which most often are used for children with acute psychosis, children who are aggressive and acting out, children with severe neuropsychiatric disorders, children with strong anxiety or children where a bipolar condition is suspected. The majority of medications dispensed in the N05 group, however, concern children with different states of anxiety and children to whom sleeping pills are prescribed. The range of mental health problems in individuals with an event during follow-up may thus vary from mild to very severe. Also, the accordance (kappa-values) between a dispensed medication consecutive years was generally low. A dispensed medication for an individual a certain year thus did not necessarily indicated a high probability to dispense a medication the following year. This may indicate that the outcome measure as a marker of general mental health could be questioned. Access to more detailed data on type of dispensed medications would have been desirable, but we did not have access to such data. Given the results of this study, we hope to be able to get access to detailed Swedish register data on dispensed medications in the future. Moreover, we decided to dichotomise the outcome, although we could have used the number of dispensed medications per year as a continuous outcome to get a better estimate of a potential dose–response association. We decided against that since the distribution of the number of dispensed medications was much skewed, with some outliers that seemed unrealistic. Despite the crude measure of mental health in this study, we consider the outcome to be an indicator for mental health in children and adolescents in general. A major strength of the data is that they cover the entire population; thus, the whole population of the study area can be seen as a cohort. We have access to register-based data, where dispensed medications of all Swedes are registered. Selection bias and recall bias therefore do not have to be considered in our setting. Another strength of this study is the longitudinal approach, which increases the validity of the results compared to cross-sectional studies. All individuals with an event 1.5 years before start of follow-up (1 January 2007) were excluded. It would have been desirable to base the exclusion criteria on the lifetime history of dispensed medications, but the register for dispensed medications did not start until 1 July 2005; thus, no data on earlier dispensed medications were available. Individuals with a dispensed medication the year and a half before start of follow-up were excluded from the analysis, but the agreement between a dispensed medication subsequent years was rather low. Therefore, an unknown proportion of the events are not incident events. The study is therefore prone to some of the limitations of a cross-sectional study; for example, reverse causation may be an explanation of our findings. However, delaying follow-up to 1 January 2008 and the sensitivity analyses restricting the sample to only urban areas do not suggest reverse causation to be a major explanation of our findings.

As expected, individuals who had been dispensed N05 during follow-up were much more likely to have parents who had been dispensed the same medication since the start date of the register (eg, 36% of the mothers compared to 22% of the mothers for individuals who had not been dispensed N05 during follow-up). We excluded children whose parents had been dispensed a medication for N05 for several reasons: (1) parental mental health problems might influence where the family resides, and therefore also the pollution levels, (2) parental inclination to dispense medication is correlated with children's dispensed medication and (3) parental mental health problems may directly influence children's mental health and an eventual association with air pollution may therefore be explained by the parental mental health status. Adjusting for parental medication for N05 is therefore not straightforward, and we decided to exclude these individuals to get ‘cleaner’ data, although this might have resulted in an underestimation of the true effect, as well as in a decreased generalisability. Despite that, we consider the exclusion of parents with a dispensed N05 medication to be one of the major strengths of the study.

Exposure measurement error should be considered in our study. We used neighbourhood exposure models with the resolution 1 km2. Exposure contrasts within cities are therefore smoothed out in our data, which may have led to an underestimation of risk. However, the model has been validated, and we were able to use the same model in all four counties within Sweden, which is a major advantage. We used annual mean pollutant concentrations at the home address for the year of study inclusion as a marker for long-term exposure to air pollution. The modelled concentrations were based on the year 2010. The underlying assumption of this study is thus that spatial contrasts in pollutant concentrations were rather similar across follow-up. Another potential source of exposure measurement error is that modelled exposure is not necessarily a good marker for actual exposure.42 It is likely that the exposure measurement error would have led to a bias towards the null, but we have not attempted to quantify that bias. In future studies, we will have access to more detailed data on exposure to air pollution, in terms of geographical and temporal resolution and in terms of source-specific exposures.

Another strength of the study is that we have enough statistical power to analyse data in the four counties separately. The sometimes heterogeneous results between the areas raise important questions. For example, the risk estimates seemed generally close to one (no association) in Skåne, but were generally quite homogeneous in the other three counties. Excluding areas where air pollution levels >15 µg/m3 (which basically means excluding the main city centres) resulted in a statistically significant HR associated with NO 2 in Skåne of 1.16. A possible reason for the results in Skåne is that the exposure model was not as valid in Skåne as in the other counties. For example, it is known that ozone is difficult to model in Skåne,43 possibly due to local production of ozone in the summertime, which could skew the results. The discrepancy between Skåne and the other three counties could also be due to differences in terms of demography, immigration, heterogeneity in dispensed N05 between different socioeconomic groups or in associations between socioeconomy and residential air pollution levels, not fully accounted for in the statistical models. We have previously observed that patterns between socioeconomy and air pollution in Skåne seem to be complex,44 but crude analyses suggest that such patterns can also be complex in other parts of the study area.

The crude HRs were close to one, but they are not meaningful unless adjusted for age. Age, in fact, seemed to be the only variable with a substantial influence on the HRs, but potential residual confounding due to socioeconomy should nevertheless be mentioned as an alternative explanation of our findings, although an additional analysis adjusting for parental income did not change the estimates. It is important to fully adjust for socioeconomy when investigating mental health outcomes in children and adolescents.37 It should be noted that while all primary and in-patient care for children and adolescents is completely free of charge for the individual (funded by tax), medications are not free up to a certain annual amount (of around €200). Dispensation of medication is therefore more sensitive to socioeconomic status than, for example, outcomes based on hospital visits or diagnoses. Since air pollution concentrations are also associated with socioeconomic status, the importance of adjusting fully for socioeconomic status in our setting cannot be stressed enough. We used parental educational level and SAMS area educational level as main indicators for socioeconomy, and we also adjusted for body mass index and smoking during early pregnancy since those variables also capture socioeconomic status to a large extent. It would have been desirable to have other data on socioeconomy and lifestyle factors, but the data on socioeconomy are limited to what can be found in the Swedish nationwide registers. However, the HRs did not seem especially sensitive to inclusion or exclusion of the socioeconomic indicators we had access to. We therefore think that the probability for residual confounding from socioeconomy to explain our findings is rather small. Residual confounding could also be present with respect to environmental factors not accounted for in our study, for example, traffic-related noise (which hypothetically could be associated with the outcome). Unfortunately, we had no access to models on traffic-related noise, and thus could not rule out noise as an alternative explanation of our findings.