Posts Tagged ‘autism’

Autism Spectrum Disorder (ASD) in Indiana schoolchildren appears to follow the same trend as that reported by the CDC with roughly two percent of the children enrolled in Indiana Public Schools reported as having been diagnosed with an ASD.

For my research methods and stats modeling class this semester, we are required to write a research proposal. For this project I chose to look at autism rates in Indiana due to having the data readily at hand from an earlier request to the Indiana Department of Education (IDOE), and also because I am taking an Intro to Epidemiology class. The idea is to take a geospatial look at the distribution of children enrolled in the Indiana Public School system that have been reported as having been diagnosed with an ASD. I was able to get a polygon shapefile of the school districts in Indiana and joined that with the table sent to me by IDOE. The data from IDOE only shows school district and a count of children reported to have an ASD. In the reporting, if a school district had less than 5 children, no data was reported. For these districts, the null value was replaced with 2.5, which is half the minimum reported value. This is the same technique used by other projects I have worked on. This allowed me to create a quick map of the “observed” ASD rates reported for each school district as shown in Figure 1.

Figure 1 - Observed ASD by School District

Figure 1 – Observed ASD by School District

This map shows the reported values, without any adjustment, just the raw numbers. It shows clusters of higher rates around Marion, Allen, St Joseph, and Vigo counties. But are there really clusters in these areas? We need to look deeper into the data and adjust these raw numbers for enrollment, as schools with significantly higher numbers of students, should naturally reflect a higher number of children with an ASD. So in Figure 2 we have the same data mapped out using the crude rate of number of children diagnosed per 1000 enrolled children per school district.

Figure 2 - Crude ASD rate per 1000 Enrolled Students

Figure 2 – Crude ASD rate per 1000 Enrolled Students

Now we see much less variation across the state, but there is still what appears to be a clustering in several areas. The very dark blue up near Ft Wayne and the other nearly as dark near Evansville are outliers and very well could be an incorrectly entered counts. I would need to recheck these with the IDOE to verify. So, is this the whole story? Well, it could be, but there is another technique we can apply to look even deeper at the data. We can use an empirical Bayesian estimation method. This method takes into account the ASD rate and variance of the surrounding school districts to adjust the value for each district. We do this because we really want to see what the dispersion is across the state without the manmade district lines. In Figure 3 we see the result of the Bayesian analysis.

Figure 3 - Bayes Adjusted ASD rate

Figure 3 – Bayes Adjusted ASD rate

Now we see a relatively even and fairly random pattern, meaning that ASD is fairly evenly dispersed across the state. The effect of the two previously reported outliers is clearly evident and needs to be addressed as to whether the data was reported accurately for those two locations. If I were doing this as a thesis/dissertation, I would contact the IDOE and we could compared the reported values with the previous year’s values to see if it is a significant change.

The idea for this post is to show how important it is to look critically at charts, graphs and maps that are presented in reports, and especially in the media, as to how they present the data. Are they showing us raw numbers, crude rates, or something else? The crude rate is very often used as it is quick, easy, and does a good job of reflecting the data, whereas the Bayesian method is much more time consuming and therefore costly, even though it does the best job of reporting data like this as it looks at the data more spatially. It all depends on the needs and questions the research is trying to answer.

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