The study/cluster was adjusted for parental age at birth. Did you actually read it? The cluster just happens to line up perfectly with the high gain microwave broadcast antenna capital of the world...https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2835822/
This article identifies significant high-risk clusters of autism based on residence at birth in California for children born from 1993 through 2001. These clusters are geographically stable. Children born in a primary cluster are at four times greater risk for autism than children living in other parts of the state. This is comparable to the difference between males and females and twice the risk estimated for maternal age over 40. In every year roughly 3% of the new caseload of autism in California arises from the primary cluster we identify – a small zone 20km by 50km. We identify a set of secondary clusters that support the existence of the primary clusters. The identification of robust spatial clusters indicates that autism does not arise from a global treatment and indicates that important drivers of increased autism prevalence are located at the local level.
Kulldorff’s Spatial Scan Statistic (Kulldorff 1997), is implemented by the SaTScan Software (Kulldorff 2006). Kulldorff’s Spatial Scan Statistic (Kulldorff 1997) reduces the problem of cluster detection to a problem of maximum likelihood estimation over geographical space. Through a single hypothesis test over space, the method identifies a region, where the distribution of cases relative to controls (or the expected number of cases) is most likely to be consistent with a significant excess of risk.
In the absence of any known risk factors, the expected number of cases is calculated by multiplying the California case rate with the control population in the circle. If there are any known risk factors that are not spatially random, then an indirect standardization (Armstrong 1969, Julious et al. 2001) can be done. We standardize (Julious et al. 2001, Armstrong 1969) for parental age, a spatially structured risk factor. We thus calculate what the expected number of cases in a neighborhood (block group or ZCTA) would be if the rates in a given age group in the neighborhood were the same as observed for that age group in all of California. For each birth, we use a single categorical parental age variable for the adjustment process. The categorical parental age variable is derived from a continuous parental age variable, by grouping the continuous variable into 35 and older and 34 and younger age categories. We analyzed the data for each year from 1993 to 2001 individually searching for non-overlapping clusters of high and low risk of sole autism in California.