The effects of pollutants on the health of animals and humans is well described in the literature, and the roles of temperature, precipitation, and other meteorological parameters on pollution exposure levels are also well known
]. Despite this knowledge, meteorological variables are relatively infrequently considered in environmental disease models, even when studies are conducted over large spatial scales. Omitting these variables may lead to bias due to confounding or interactions between pollutants and environmental parameters. Several years ago, Waldman et al.
] published that the prevalence of autism in the U.S. Pacific Northwest was correlated to the amount of annual precipitation in the county. We used the autism data from Waldman et al.
] to further explore the relationship between the environment, pollutants, and autism. By including several measures of local pollution, and a possible route of exposure for pollutants in our regression model we provide a plausible explanation for the association between precipitation and autism reported by Waldman et al.
]. Our study suggests precipitation was more strongly associated with occurrence of autism in counties where the drinking water was primarily derived from surface sources (Figure
). In fact our model suggests the relationship between precipitation and autism is greatly reduced when countries do not derive their drinking water from surface sources (Figure
). This suggests the relationship between precipitation and autism may be linked to drinking water. There are a number of biologically plausible explanations for the interaction between precipitation and drinking water source that should be investigated.
There have been numerous studies on the deposition of semi-volatile organic pollutants and heavy metals in rain and snow
] that establish the biological plausibility that areas of heavy precipitation may also have higher levels of contaminants in their surface water. The fact that the association between autism and precipitation was partially dependent on the amount of drinking water derived from surface sources suggests a possible route of exposure for environmental contaminants. Although direct deposition of pollutants by precipitation is one way that surface water may be contaminated, it is also possible that precipitation increases run-off from agricultural, industrial, and urban sources in local surface waters
]. Further, precipitation may also result in the agitation of contaminated sediments in surface waters, resulting in the disassociation and re-suspension of pollutants into the water column. In some areas around wastewater treatment plants the levels of some pollutants, including estrogenic compounds and heavy metals, can be higher in sediment than in the water column
], so agitation of these sediments could increase the release of pollutants.
We attempted to evaluate whether agricultural activity was associated with the proportion of autism in a county by including the variable crop density in our model, but it was not statistically significant (Table
). We also evaluated whether or not the effect of agriculture was dependent on the level of precipitation (i.e. via run-off) by including an interaction term, but it was also not significant. Further, urban pollution was also not significantly associated with autism in our study (Table
), but it is possible that by including variables such as drinking water source and air emissions, which are sometimes correlated to population density, we explained some of the variation in autism that would have been attributed to rural or urban areas.
We did observe a positive association between the EPA’s risk of neurological disease, which was based on 23 air emission parameters, and autism in counties with low HDD (or the warmer counties in our study) (Table
). Air temperature affects the partitioning of air pollutants between solid and gaseous states
], therefore it is possible that exposure to air emissions varies in direct relation to air temperature. Heating degree-days was used to represent temperature because it provides an estimate of the duration or extent of cold weather in an area (annual sum of degrees Celsius required to attain 18.3°C when the air temperature is less than 18.3°C). In relatively warm counties (i.e. lower HDD), we detected a positive association between the EPA’s risk of neurological disease from air emissions and the prevalence of autism (Figure
). A similar positive association between exposure to heavy metal and chlorinated solvents air emissions and autism was previously reported for a smaller geographical area in California and in Texas
]. Interestingly, this relationship was consistent across the Pacific Northwest only in areas without extreme cold temperatures. The significant interaction term in our model provides a possible explanation for the occasional conflicting results reported in different studies on air emissions and autism
], and suggests temperature may affect the relationship between air emissions and disease.
Our study was also consistent with other observational studies with regard to the association between autism and socioeconomic status. We found a negative association between unemployment rate and autism, which is consistent with other recent studies that have found the prevalence of autism is higher in families that have a higher socioeconomic status (or lower unemployment)
]. Durkin et al.
] suggest this may reflect access to services.
The state of residence was significantly associated with the county level prevalence of autism, and explained a large proportion of the variance. In fact, almost half of the explained variation in autism prevalence in this study was found to be attributable to the state where the county was located (R2
= 0.724 vs. R2
= 0.440). The state of residence where the diagnosis was made may capture many different factors that exist among the different states including access to services, but given the subjective nature of the diagnosis of autism, it likely reflects the differences in the diagnostic and classification criteria. To reduce the variation in the diagnostic criteria, it is recommended that, in the future, a standard definition and system of classification be used to assure consistency in the identification of individuals with autism. The Autism and Developmental Disorders Monitoring (ADDM) network has a system to measure, compare and contrast, and monitor autism rates across selected areas in the U.S. using well defined standards, but county level ADDM data are limited to 762 counties (out of over 3000)
] and are not publicly available.
Testing our model with a larger dataset such as that of the ADDM would also permit for the evaluation of more complicated interactions between possible sources of contaminants and potential modifiers of these contaminants and routes of exposure. Given the biological plausibility for these intricate relationships (i.e. drinking water source by precipitation by source of pollution), and the preliminary findings from this study that suggests basic interactions exist (i.e. precipitation and drinking water as well as air emissions and HDD), it is important that future studies test these hypotheses.
Another biologically plausible explanation for the relationship between the precipitation/drinking water source interaction term and county level autism that should be further explored is that rain may be correlated to depression which may be correlated to the usage of psychotropic pharmaceuticals in a county. We were not able to confirm this relationship because data on use of these pharmaceuticals were not available to us at the county level. However, a strong positive association was detected between a county’s suicide rate and its autism rate, after controlling for possible confounders such as the county’s unemployment rate (Table
). Further, when this variable was excluded from the model (data not shown) the model’s coefficient for precipitation increased, which suggests that suicide rate may explain some of the variation previously attributed to precipitation. A link between depression and autism would be consistent with the hyperserotonin hypothesis proposed by Whitaker-Azmitia
] and other recent studies involving model organisms
Other limitations of the study, besides the small sample size and the crude approaches used for some measures of pollution, were those that are common to most ecological types of observational studies. These include the inability to control for all possible confounding variables and the potential for lag time bias that may have resulted in misclassification of the exposure status because it occurred several years before the children were diagnosed with autism. Lastly, it could not be concluded that the association between the percentage of drinking water derived from surface water sources and the prevalence of autism at county level translates to the individuals (the actual exposures of autistic and non-autistic children in our study were unknown). Therefore, while the associations detected can only be applied to the county level, these results suggest possible areas where further research should be conducted to establish whether the risk factors identified at the county level extend to the individual.