Understanding Bias
When reviewing data on heart attacks, you might wonder, "Why did the trials have only 38% female participation?" This question uncovers a deeper issue rooted in historical, regulatory, and social factors that continue to affect medical research today.
Historical Context: The Thalidomide Tragedy
In the 1950s, a drug called thalidomide was prescribed to pregnant women in Europe and Canada to alleviate morning sickness. Unfortunately, it caused severe birth defects, leading to its removal from the market. In response to this tragedy, the U.S. Food and Drug Administration (FDA) recommended in 1977 that all women who could become pregnant be excluded from early-stage clinical trials. The intention was to protect women from potential harm. However, this well-meaning recommendation inadvertently put women at risk by limiting scientific understanding of how drugs affect women's bodies.
The FDA reversed its exclusionary recommendations in the 1990s. Today, government-funded clinical trials are required to include women and other minority groups. However, these trials don't need to ensure that these groups are represented in proportions reflective of the general population. Furthermore, the majority of drug trials in the U.S. are not government-funded and thus are not bound by these requirements.
Another factor influencing trial participation and public perception is media representation. On television and in movies, heart attacks are typically depicted as a man's experience, characterized by clutching at the chest or arm. This portrayal overlooks the reality that women also suffer heart attacks and often do not exhibit the same symptoms.
Consider this: In the top 20 “heart attack” movies listed on IMDb, only two feature heart attacks in women. One of these incidents is fake, and the other is a disguised murder. Effectively, there are no realistic portrayals of women experiencing heart attacks in these popular films.
Connecting Data Literacy and Media Influence
At first glance, linking data literacy to TV heart attacks might seem far-fetched. However, sound scientific practice involves examining bias and controlling variables wherever possible. This includes questioning who is represented in the data and who is not.
Key Questions for Practicing Data Literacy
When analyzing any dataset, especially in medical research, you should always ask:
- Who participated in the data collection?
- Who is left out of the data?
- Who created the data?
Conclusion
Addressing bias in medical research is crucial for ensuring that the findings are applicable to everyone. Historical regulations and ongoing media representations have contributed to the underrepresentation of women in heart attack data. By asking critical questions about data participation and creation, you can become more data literate and advocate for more inclusive research practices. Understanding these biases helps in improving medical treatments and outcomes for all genders, fostering a more equitable healthcare system.