By Alex Denning
To explain my experience as a graduate research assistant at the Health Communication Research Center (HCRC) it takes some background and a few stories. So here it goes.
I came in to the HCRC as a public health student with no background in journalism or even health communication and was pretty nervous about my position as a “biostatistician” in a center full of journalists. In my role at the Center, I would analyze and interpret data and help translate findings to the journalists covering the stories. But, with the support of the others at the HCRC I got the confidence to give advice, help analyze, or correct data points when needed. A lot of this confidence has come from the experiences of actually working with surveys and health releases.
One of the most difficult parts of my job was helping out with surveys. Surveys are an important way of finding out information about different topics, but if they are not done with “due diligence” they can turn out to be a huge mess. Many times there would be great ideas for questions that needed to be answered but there needs to be some deep thought about how to ask the question. When people read questions, there could be many different ways they interpret them. Take for instance the question “how large was your community growing up?” The possible answers are rural, urban, or suburban. This question may seem easy to answer, but there could be many different ways that the respondents could answer this question. Some people may think that their community was urban while others could answer suburban. To improve this question, a population range could be specified for each category so there is no difference in answering the question. . If the survey has not been well thought-out it can lead to issues when it comes down to the data analysis.
Overall, I think the best part about my job was working with data for the health releases. I know this sounds extremely nerdy, but many times the data points are the meat of the story. Although there are a ton of data sets available sometimes there can be issues with putting the data into words or even correctly using the data. Sometimes in small counties in Missouri there are not enough occurrences or events of some disease or health outcome. This can lead to the problem of statistical significance because there were not enough events to make the numbers stable. Statistical significance means, in the most basic sense, that the result or event was unlikely to have happened by chance alone. For example, take some death data from one Missouri county:
| 2007 | 2008 | |||
| Number | Rate | Number | Rate | |
| Cause of Death Tuberculosis | 0 | @0 | 1 | @4.3 |
From looking at this table above it looks like from 2007 to 2008 there were 4.3 times as many deaths from tuberculosis in Bates County but really since the numbers are not stable (the @ sign tells you the numbers are unstable) we can’t make a statistically valid claim about this problem. In other words, we are going to have to find a different data source to help understand the problem of tuberculosis in Bates County. By bringing my background in statistics and basic epidemiology we have been able to create better releases with data that actually means something.
The experience that I have had at the HCRC has benefited me in many ways by giving me a different side of public health that most students in my cohort will not get. Through this experience at the HCRC, I have improved my skills on communicating with journalists and hopefully I can bring some of the insights from journalists back into the public health field.