Autism: It is not a disaster

Believe it or not, people who are mentally ill are more likely to be victims of violence than perpetrators of violence. This doesn’t make much sense to people because we want to believe that someone who kidnaps, rapes, and murders a person has to be deranged. A “normal” mind can’t possibly do something so horrible, right?

Even worse, a lot of people are quick to point out that a criminal — especially a young criminal — was kind of “quirky” or maybe had “autism or something” instead of waiting for the facts to come through on a case. I believe that it’s our own attempt to justify what happened and to tell ourselves that we would never do something like that. Because, deep down, we’re afraid to be monsters ourselves.

Don’t deny it. It’s true.

Furthermore, autism and other neurological disorders are not mental health problems. You wouldn’t walk up to someone with cerebral palsy and say that they’re “crazy,” would you? Likewise, you wouldn’t say that Muhammad Ali, who has Parkinson’s, is more likely to commit violence than someone who is neurotypical. Would you? Nevertheless, for a very long time, children with cerebral palsy or autism have been treated as being “crazy” or “quirky,” and mass shooters as possibly being autistic (with the implication that said autism was the cause for their violence).

And so we come to yesterday’s news that “1 in 50 children have autism.” From Dr. Willingham’s post:

“According to the CDC, hidden within these numbers is the finding that most of the increase from 2007 to now occurred in school-aged children. In other words, given that it’s possible to diagnose autism as early as age 18 months and usually by age 5, many of these new autism diagnoses were in children who received them relatively later. Children who were, therefore, walking around for quite a few years with autism that went unrecognized … and uncounted. That fits with the idea that a lot of the increase in autism we’ve seen in the last decade has much to do with greater awareness and identification.”

The anti-vaccine blogs are already chomping at the bits at what this new prevalence number means, totally misunderstanding the meaning of the data. (I’m not surprised, are you?) Not only that, but they have their dire predictions:

“Any expressions of concern from anybody with the power to do something about this disaster? No . And the press, as usual is soft pedaling the findings. Fifteen years ago the autism rate was 1 in 10,000, 12 year ago it was I in 2,500, 10 years ago it was 1 in 1000, and so on. When President Obama was elected in 2008 the official rate was 1 in 150, then it went to 1 in 88 and now it is 1 in 50. Where is it going to stop?”

It will never stop. We will get to 100% saturation. Every child will be autistic.
I’m joking, of course. The prevalence rate will remain the same as it has always been. Our estimate of it will even itself out and approach the prevalence rate and remain there. This is because our ability to do surveillance for autism is improving. The identification of cases by healthcare providers is improving. People with autism are coming forward and demanding to be counted. Our elected leaders are devoting more resources to ways to assist people with autism to lead long and productive lives. These are all good things.
It is not a disaster.
What is a disaster is that people who call themselves “advocates” for children and adults with autism continue to say and do things that actually harm people with autism and other neurological disorders. They call it a “disaster” to have a child with autism, or they say that they “lost” their child to autism. They then write that their children are monsters or have monsters inside them. And we’re supposed to just stand back and be understanding because we don’t have children or children who are autistic? We’re supposed to agree that it’s a “disaster” when all rationality says that it’s not and that children with autism can and will grow up to be productive citizens who even appear on CSPAN as advocates of people with similar neurological disabilities?
No, we’re not. I won’t. And I hope you won’t either.
Advertisements

How Many Was That Again?

Have you ever noticed that reports of case counts from public health sources usually have the word “reported” included in them? You have, haven’t you? Well, have you ever wondered why that is so?

Click to enlarge

The reason for that is because of the inherent nature of epidemiological surveillance and the barriers to getting an exact case count for every single disease or condition out there. Some of these issues with surveillance make for an overestimation of the number of cases. Other issues make for an underestimation of the number of cases. In all cases, it is highly unlikely that you are seeing the true number of cases in any report from public health.

Does that make these reports not useful or even – as some will claim – “manipulated” in any way? Not necessarily, and let me tell you why…

CASE DEFINITIONS
The first thing you need to understand in analyzing descriptive data presented to you from public health sources is the case definition being used in counting cases. A case definition is usually presented in terms of person, place, and time. For example, a case of Salmonella food poisoning may be defined as “anyone with a stool culture positive for Salmonella who ate avocados in Pittsburgh in the week of December 8 to 15″. That’s pretty specific, right?

Case definitions can also be very broad, like saying that a case of Salmonella food poisoning is “anyone with gastrointestinal disease with an onset of December 10 to 17”. This definition would surely bring up many more cases than the cases from the previous, more stringent case definition. So you can see why you need to know exactly what defines a case.

DIAGNOSTIC TOOLS
Likewise, you need to know what diagnostic tools are being used to define a case. In our example above, we used a stool culture to define the specific case definition and a clinical description of “gastrointestinal disease” to define the second. When being presented with data, make sure that you know what diagnostic tool – or tools – was (were) used. It makes a big difference.

For example, in the late 1970’s and early 1980’s, we had very little with regards to technology to isolate the Human Immunodeficiency Virus (HIV). So an HIV infection had to progress to Acquired Immune Deficiency Syndrome (AIDS) – a collection of signs and symptoms of the deterioration of the immune system – in order to define a case of HIV infection. AIDS itself was very broad at first, and the definition then was refined. As more and more diagnostic tools have been made available, the case definition of HIV and AIDS has changed. Where the presence of an opportunistic infection was once enough to diagnose a person with AIDS, there are now lab tests to look at the white blood cell counts and diagnose earlier in order to intervene and treat earlier.

AUTISM
The example with HIV/AIDS above is true of autism as well. It used to be that there was no uniform diagnosis for autism – or any of the conditions that fall within the autism spectrum. Children were either “hyper”, or “retarded”, or “slow”, or had some other condition. As medical science began to understand what it meant to be on the autism spectrum, the definition of someone with autism changed, leading to better recognition of cases and a subsequent rise in the prevalence – the underlying rate of disease in a population – that we see now.

Incidentally, the case definition for autism became more sensitive and specific – and thus more accurate – around the same time that vaccines began to be more abundant and more recommended. This lead to the misperception that vaccines raised the rates of autism and not the better diagnostic tools. But that is for a whole other discussion.

BETTER SURVEILLANCE
It goes without saying that an improvement in surveillance methods also leads to a change in the number of cases observed and counted. For example, infant mortality reporting has gotten better as more and more health care providers in the United States are able to report infant deaths electronically. Health departments at all levels of government are more active in their surveillance of cases by surveying hospitals, clinics, and even midwives on the survival numbers of infants. So you can see how this extra effort to count the deaths that were previously not reported has led to the belief that the infant mortality rate in the country has increased.

Other countries don’t have the same systems as we do in the United States. As a result, their infant mortality rates are different – even lower –  than those observed here. Is it true, then, that the US is failing in controlling infant mortality compared to countries with less resources? Nope. It’s all in how we’ve been counting the numbers. Apples to apples, the rates are much better in the United States, where expectant mothers have better access to prenatal care and children are – for the most part – born in medical facilities capable of caring for them if they are in trouble.

CONCLUSION
So here is what you do when you compare two rates of a single disease either across time, across location, or even across populations of people. You need to make sure that the case definitions of both datasets are comparable and as close to matching as possible. Otherwise, you really are comparing apples to oranges. You also need to look at the diagnostic methods used for each dataset. There is no use in comparing one dataset whose cases were diagnosed based on symptoms – a subjective way of diagnosing – and another dataset whose cases were diagnosed by a lab – an objective way of diagnosing. Finally, you need to look at the surveillance system that collected these data and make sure that the systems for both sets of data are – yet again – comparable. If one relied on providers reporting cases while the other went out and looked for cases, then – yet again – you will find yourself comparing apples to oranges.