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Hi! and welcome to What Works Wednesdays where historically a success story from clinical files is shared. With all the buzz about the latest “research” on getting the flu while pregnant and the supposed link to autism, it seems logical to help readers better understand research so they can interpret findings themselves. If readers know how to read research, then they are better able to know if an intervention works (or if the conclusions from a study are flawed or misinterpreted).

What is Research?

  • “work undertaken systematically to increase the stock of knowledge” (Wikipedia.org)
  • “diligent and systematic inquiry or investigation into a subject in order to discover or revise facts, theories, applications, etc.” (dictionary.com)

Most scientists conduct research by utilizing the scientific method. The scientific method requires the development of a hypothesis (which is usually formed from observation or reading other research), conducting the experiment, gathering results, and determining if the results support the original hypothesis.

Different Types of Research

Using the scientific method, scientists design different types of studies. These study types include:

  1. Experiments. In experimental studies, researchers recruit participants and assign them to treatment groups. Researchers can study one or more treatments and participants may receive some treatments or they may receive a placebo or no treatment at all. Usually, researchers measure one or more important variables before the study and they measure the variable(s) again after the study.
  2. Single Subject Experimental Studies. In these studies (most often conducted by behavior analysts), researchers recruit participants who are observed and measured carefully for a period of time before receiving treatment. Researchers then implement treatment while continuing to observe and measure carefully.
  3. Correlational studies. In these studies, researchers use existing data sets (e.g., collected for some other purpose) or they recruit participants. Researchers gather a wide range of information on each participant (e.g., age, SES, education, health history). Participants do not generally receive treatments or interventions of any kind.
  4. Qualitative studies. In qualitative studies, researchers occasional recruit participants but at times they enroll participants with whom they are already familiar. In qualitative studies, researchers study one or more individuals or one or more groups (e.g., one class). Researchers carefully study the participant and take copious notes. Researchers may interview the participants and they may use focus groups to better understand some of the issues. If a treatment is provided, the researcher continues to carefully study the participants to document the participants’ responses to the treatment.

Conclusions Based on Study Type

Researchers must use caution when drawing conclusions about their studies. Researchers who use well-designed experimental designs can draw cause-effect conclusions. For example, a researcher can enroll a bunch of smokers in a study. Some of the smokers receive a behavioral treatment, some of the smokers receive nicotine patches, and other participants receive both. At the end of the study (if the researchers have conducted the study carefully), the researchers will be able to say that one or more methods is successful at helping smokers quit.

Similarly, in a single subject experimental study, researchers can demonstrate if a treatment changes behavior. Again, the study must be carefully designed and conducted but it is possible to draw cause-effect conclusions. For example, a researcher could study 3 smokers. The researcher would observe the smokers and collect data. One smoker could receive treatment. While she is being studied, the other smokers would still be studied. After the first smoker quits successfully, the next smoker would receive treatment. He would continue to be studied as would the non-treated smoker. Finally, when the last smoker receives treatment, researchers continue to observe him. If the researchers successfully help all 3 participants quit smoking (and the study is carefully designed and carried out), they will be able to say that the treatment caused the behavior change.

Correlational versus Causal

Correlational studies are designed to determine if any relationships exist between variables. Researchers could gather data on 1,000 people from an existing data base. They could sort the data into smokers and non-smokers. They could run a simple data analysis to see if smokers have other tendencies (e.g., like to go to race car events, like to drink socially, and so forth). Researchers may not conclude causal relationships from their studies. They are only able to conclude that a relationship exists. Of more importance is the strength of the relationship. For example, if researchers ran an analysis on the relationship between giving birth to a child and gender, they would find a very strong (almost perfect) relationship between giving birth and being a female. If a weak relationship exists between variables it is more likely due to chance.

Go Forth and Read

In these days of social media, spin rooms, and media crazed talk shows, very poorly designed studies are being presented to the public without appropriate interpretation of the study or its results. If you are interested in reading a few examples of this, check previous posts here and here.

In summary, don’t believe everything you read about the “latest scientific study” unless you read the study itself. When you read the actual study, what you find may actually surprise you.

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One of the popular topics this week has been the discussion about children outgrowing their autism diagnosis. If you have missed these discussions, you may catch yourself up by visiting here, here, and here.

Before we jump in to the original article on which all of this discussion is based, we would like to point out that we have already discussed how children with autism can recover from autism. If you missed that post, you may read it here.

Ok, so what is all this talk about “outgrowing autism”?

We prefer to go to the original source to make sure people are accurately reporting what was published. Heather A. Close, Li-Ching Lee, Christopher N. Kaufmann, and Andrew W. Zimmerman authored the paper. The journal Pediatrics published the paper which is available online now and will be available in hard copy in February 2012.

Purpose

The authors set out to describe characteristics and co-occurring conditions in young children, children, and adolescents.The authors also stated that they wanted to describe how characteristics and conditions may cause a change in the diagnosis of ASD.

Participants

The authors actually sought participants who either: a) currently had an autism spectrum disorder (ASD) diagnosis; or b) who had an ASD diagnosis in the past but no longer carry such a diagnosis. Interestingly, the authors did not speak to any of the participants directly. Rather, they pulled the data from a database that was collected as part of the 2007 National Survey of Children’s Health.

Method

To complete the study, the authors opened the 2007 database and retrieved answers to two questions.

  1. Has a doctor or other health care provider ever told you that the child had ASD?
  2. Does that child currently have autism or ASD?

Once the authors retrieved the data for parents who responded to the two questions, the authors created two groups with the data. One group with a current diagnosis of ASD and a second group of children who do not have an ASD diagnosis but whose parents were once told that the child had ASD.

The authors then ran a number of statistical analyses with the data based on three age ranges of children.

  1. young children aged 3–5
  2. children aged 6–11
  3. adolescents aged 12–17

The authors also examined the data set to determine if any of the following conditions co-occurred in the children:

  • attention-deficit/hyperactivity disorder
  • learning disability
  • developmental delay
  • speech problems
  • hearing problems
  • anxiety
  • depression
  • behavioral or conduct problems
  • seizures or epilepsy

Results

Finding #1: Young children ages 3-5 with a current diagnosis of ASD were more likely to have a current learning disability. Might we add here that learning disabilities are often not diagnosed until age 8 or 9. Thus, we find it hard to believe that children ages 3-5 were diagnosed with learning disabilities. Perhaps they had developmental delays but the authors did not state that.

Finding 2: Young children ages 3-5 with a current ASD diagnosis were more likely to have current co-occurring conditions than children without a current ASD diagnosis.

Finding 3: Children ages 6-11 with a current diagnosis of ASD were more likely to have a past speech problem and current anxiety.

Finding 4: Adolescents age 12-17 with a current diagnosis of ASD were more likely to have current speech problems and 10 times more likely to have current seizures or epilepsy. Note: We would hope that the children have current speech problems since the definition of ASD diagnosis requires that speech problems be present!

Comments

Honestly, it is surprising that such a low-quality study would find itself in a prestigious peer-reviewed journal such as Pediatrics. It is even more surprising that it would receive subsequent attention from the press. This type of study hardly qualifies as a master’s thesis, let alone a study that is covered in national news.

The authors did not conduct an experiment. The authors did not verify if the children actually had autism. The authors did not review records to determine if a diagnosis existed in the child’s history. The authors based their entire paper on someone else’s data set.

The original data set the authors utilized is also full of issues. The authors noted that the majority of respondents were white, non-Hispanic, and that most of them had health insurance for at least one year. Not to mention that at 61% of the respondents had incomes over 200 times above the poverty level. Thus, the study results are skewed towards white, middle and upper-class families.

Finally, the authors based all of their conclusions and findings on parent report. Please do not think that we do not believe parents. We do. However, a parent may think their child has ADHD or learning problems but that does not mean that an actual diagnosis of ADHD or learning disability exists. Thus, when the authors discuss how other conditions may impact the autism diagnosis, they are merely speculating as their study did not prove, or control for the other conditions.

The bottom line here is that children in this study who had a history of ASD, may or may not have even had an ASD to begin with. The authors failed to verify this information. Moreover, the authors failed to ask if the children received intensive early intervention or biomedical intervention that may have resulted in recovery from autism.

Finally, the authors never stated that children outgrow autism. The authors themselves state that the children who no longer have a diagnosis could have been:

  • misdiagnosed in the first place
  • responsive to early intervention
  • or they may have experienced developmental changes

So, we are asking you to please check the facts before spreading rumors. Children do not outgrow autism.

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Each week, we try to review a research article. This week, instead of reviewing an article, we are going to review a type of research. We are doing this because lately, news outlets have highlighted various studies about autism (e.g., autism and prematurity, autism and diabetes, and autism and IVF to name a few). While these findings are certainly interesting, we should point out that all of these studies are correlational rather than causal. Specifically, prematurity does not cause autism but rather it is associated with or correlated to autism.

A correlational study is designed to measure behaviors, outcomes, or characteristics within a sample and then determine through the use of statistics if any relationships exist between or among the variables. This is usually done by surveying a large group of people and comparing the results from within the group. For example, IVF, or in-vitro fertilization has been shown to be associated with autism.We are sure you are not surprised that IVF is also associated with older mothers. Other factors that should be considered when studying IVF include the rate of prematurity, complications with pregnancy, low birth weight. You see, if a family is using IVF, by nature, the parents had difficulty getting pregnant naturally. Thus, other factors may be at play. These factors may impact the later development, labor, and delivery of the fetus. As the author of the study, Dr. Zachor, pointed out, “mothers in her study who had IVF tended to be older — with a median age of 32.6 years. Also significantly, nearly 4% of the children with autism were born prematurely, and about 5% of those had a low birth weight. In the general population, only about 1% of all newborns are delivered with a low birth weight.” Thus, we should use caution when interpreting the results of this study. Other, more salient factors, may be linked to autism. I would want to know how many of those babies had other complications (e.g., reflux, ear infections, fevers) and how many of those babies also had autism.

Another study recently demonstrated that prematurity increased the risk of autism. Before reading the study, think about all of the factors regarding “preemies”. We would expect preemies to have longer hospitalizations when compared to full-term babies. When babies live in hospitals, they are more likely to be exposed to germs and other illnesses that they would not experience in the safety of their home. Preemies are more likely to have disturbed sleep patterns. Hospital lights, noises, and other disruptions make it difficult to sleep comfortably. Preemies are also more likely to have brain bleeds. Finally, preemies are also more likely to weigh less. None of these factors alone (hospitalization, disturbed sleep, low birth weight) cause autism just as being premature doesn’t cause autism. However, the fact that a child is born prematurely increases the likelihood of other complications. More interesting to us is what other issues did those babies have (e.g., reflux, ear infections, fevers) and were those babies also more likely to develop autism?

So your take away today is this: When you read studies, pay careful attention to the nature of the study. Is it a correlational study where researchers have merely conducted a series of statistical analyses and reported a finding? If so, dig deeper to find other variables that may be the real culprit and continue to push scientists to design experimental prospective studies so that we may learn the real causes of autism.

With that, we are off to sip on a glass of wine and ponder the latest research on alcohol consumption and breast cancer.

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