How Research Affects Health Policy Development

Instructor: Amanda Robison-Chadwell

Amanda is currently the Public Health Director in Texas and has been teaching in public health at Rasmussen University and has a PhD in Public Health.

In this lesson you'll learn how research affects health policy development. We'll examine challenges scientists face in communicating their research as well as briefly touch on scientific studies.

Researchers Engaging Policy Makers

News reporters talk a lot about health care in politics, and you might wonder where policy makers get their information as it relates to making decisions. Well, the answer is, it's complicated.

In an ideal world, policy makers would be informed on how to address health care issues by experts in the field who base their suggestions on rigorous scientific studies. This isn't to say that they aren't…sort of. One challenge is the expectation of fast turnaround, and when it comes to scientific studies, especially rigorous ones, fast turnaround is difficult to do. Researchers know that the best studies are often not only expensive but also take time, and waiting for a quality study to answer their questions isn't really on the top of the priority list for policy makers.

Often then, compilations of studies like meta-analysis (a statistical review of several similar experiments or studies) and systematic review (a review of literature from scientific studies related to a specific research question) are the best ways to get quality information to policy makers without spending a lot of time doing a large-scale study. This is assuming there have been a number of studies done on the subject already.

Let's thread a hypothetical example. Say a team of epidemiologists has been asked to justify introducing a smoking ordinance in a community. First, the team would likely compile research and gather data on the burden of smoking-related diseases in the community. The research demonstrates a consensus that not only is smoking dangerous, but also that secondhand smoke should be avoided. This would then justify an ordinance that would prevent smoking in public places.

Communicating with Policy Makers

For a scientist, communicating research is often challenging. Try reading academic research articles and you'll find that they are typically filled with a lot of complex language that is often difficult to articulate to non-scientists. There are ways to deal with the challenge however.

First, our epidemiologists would want to compile as many studies on tobacco use as possible and they might say something like 'We have reviewed 107 studies in the last 5 years and 96% of findings show that comprehensive smoking ordinances reduce the risk of smoking-related illness.' This is a very simple statement that gets right to the point.

From there it is all about answering questions and offering some very basic data that is understandable to the general public. It's important to remember that scientific language may not be clear to non-scientists, so be clear and explain uncommon terms. Some epidemiologists in this situation may use charts and graphs, which easily showcase comparisons and trends and display information in a meaningful and impactful way.

Providing Information When There Hasn't Been Thorough Research

What about when that policy maker says 'How dangerous are electronic cigarettes and should we include them in the ordinance?' Research is being conducted on e-cigarettes, but because they are fairly new, the dangers aren't well known. In this case, our epidemiologists would probably recommend including them in the ordinance 'just in case.' As far as whether or not they are a significant risk, it's hard to say due to the lack of research.

In some situations researchers may opt to do a quick study, such as a case report (or several) or a case control study, to answer the question as quickly as they can with some kind of scientific evidence (because something is better than nothing). What we have to remember about these less reliable studies is that they may have issues with bias (a distortion of the results based on sampling or some other flaw in a research study) and generalizability (the notion that a study can or cannot be applied to an entire population).

For example, if a study only included people 20-30 years old in a single race/ethnic group, then we can't reasonably apply it to the entire U.S. population, among other potential issues. This is why researchers might make recommendations to policy makers with the understanding that those recommendations may change as more and better research on the topic is done.

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