We observe three clusters of conceptions of children later diagnosed with autism at exactly the same time of year for three consecutive years: 1994, 1995 and 1996. We do not detect any clusters in 1992–1993 or 1997–1999. We posit that an unknown etiological driver (or a combination of multiple drivers) caused the observed seasonal patterning of risk from 1994 to 1996. It is difficult to know whether this driver was present in 1992–1993, since the numbers of cases in these years are exceedingly small, averaging approximately 30 to 40 cases in a 30 day period. Thus, even if clusters (and thereby the driver(s)) were present, they may not have been detected due to lack of statistical power. In contrast, for the later period with no clusters, 1997–1999, the numbers of cases are sufficiently large (). The absence of seasonality during this period may be due to the disappearance of the etiological driver(s) or due to the introduction of other etiological drivers in other parts of the year that prevent the detection of a cluster.
What etiological drivers could be implicated by the observed seasonal patterning of conceptions of children later diagnosed with autism? Prenatal exposure to various risk factors or the absence of protective factors during specific gestational periods could explain some of the pattern we observe. However, since a specific exposure has not been established, we cannot estimate an exact timeframe within gestation. It is generally established that exposures during the embryonic stage, which consists of the first two trimesters of gestation, cause increased postnatal risk of neurodevelopmental and psychiatric conditions, including autism 
. These exposures could be seasonally patterned, and a number of such exposures have been hypothesized 
Exposure to a certain strain of flu during the cluster years could explain the presence of clusters in some years and not in others. Exposure to flu during gestation has been associated with increased risk of schizophrenia in ecological studies 
. However, the critical timing of such an exposure is somewhat uncertain. Only one study has related serologic measures of maternal influenza to schizophrenia. This study found that the largest association occurred during early gestation 
. For mothers who conceive in November, the first trimester coincides with the California flu season 
. Our analyses did not detect a difference in gross temporality of the flu season for the cluster versus non-cluster years. However, detailed analyses at the individual level that incorporate the specific virus type and blood viral load through serum antibody analysis could better address such hypotheses 
Negative deficit schizophrenia, the presentation of which is similar to autism, has been found to have an excess of July births 
. This form of schizophrenia has been linked to the Borna virus, which could be infectious or activated seasonally. For autism, an association with the Borna virus is supported by animal models but not by human testing 
. Links between maternal viral exposure and autism in children are an active area of study 
Meanwhile, various studies have found an excess of births of children later diagnosed with autism in the summer 
. While children with autism are significantly more likely to be born preterm, we would nevertheless expect an excess risk of conceiving a child later diagnosed with autism in November to result in an excess of births of children later diagnosed with autism in the summer. For children conceived in November, the second trimester coincides with the pollen season 
, and in Northern California, maternal asthma in the second trimester is associated with an increased risk of autism 
. Pollen seasons vary in intensity from season to season and year to year, which could be in accordance with the observed disappearance of seasonality.
Some scholars propose that a prenatal lack of vitamin D increases the risk of autism and other neurological deficits 
. In humans, most required vitamin D is synthesized in the skin from sunlight, and much of our study area has insolation above the national average. However, vitamin D production is mediated by a number of other factors, including age, skin tone, individual behavior and pollution 
. Pollution is exacerbated in the winter in the Los Angeles metro area, where a large proportion of our cases were born 
. Furthermore, our analyses of geographically segmented data support the possibility that the observed temporal patterning was driven by those in Southern California, metro areas. Therefore, environmental pollution, in combination with local meteorological conditions varying in certain years, could be a plausible driver of the observed seasonality pattern.
Our observations are consistent with the above theories, and any number of the etiological agents mentioned could be contributing to the observed clusters. It is important to note that the driver(s) of seasonality is not likely a critical driver of increased prevalence, even if it plays a modest role in increasing incidence, since autism prevalence keeps rising in the years in which seasonality was not detected. The identification of temporal clusters for autism can complement other findings in molecular biology and epidemiology, point to new mechanisms for greater study, and reject competing explanations. Note that theories suggesting that parents of children with autism select different months in which to have children are not supported by the results reported here. In contrast, the identification of seasonality may provide some support for an etiological role of influenza in the first trimester and/or of increased asthma in the second trimester of pregnancy. Studies from other jurisdictions using comparable definitions of autism and comparable methods need to be conducted in order to identify whether the patterns observed in California are unique.
The fact that our study utilizes data from California limits the generalizability of our results. In addition, we are unable to examine seasonality patterns beyond 2000 with the present dataset. Although our approach does not explicitly correct for increasing autism prevalence, our clusters are not due to this trend, since they are only present in the first half of the study period. Still, our use of a scan statistic for a disorder with increasing prevalence is of a slightly experimental nature, since it is not a common practice. While the DDS serves the vast majority of children with autism in California, it is not possible to determine whether children with autism who do not utilize DDS services or those who were not successfully matched to the BMF display a different pattern of seasonality.
There is some evidence of seasonality in the timing of conceptions of children later diagnosed with autism. Significant excesses of risk are found in November in the years 1994, 1995 and 1996. These significant excesses of risk are not explained by known autism risk factors, such as parental education, age, socio-economic status or child’s sex, nor are they artifacts of planned conceptions by parents. The pattern of increased risk decreases from 1994 to 1996, and is not found in 1997, 1998 or 1999. Searches for environmental drivers of autism should, therefore, allow for the possibility of the existence of seasonally patterned yet temporally anisotropic risk factors.