Much of the inequality in healthcare funding comes down to a simple bias: men are considered default humans. This bias has ancient roots. For many peoples in the ancient world, humanity was defined by the male body (the “one-sex model”). Female bodies were simply warped, inverted versions of those male bodies, “defective” men. Therefore women were just men who had gone wrong. Galen described the uterus as an inward-twisted penis. Men were considered to be “active” while women were “passive,” making the male body a more worthy and interesting object of research.
Today, we know that there is more than one sex. Yet much of medical research still considers the male body to be the default form of the human experience. Medical researchers are not necessarily aware of this bias; it is not explicit. They might use different words to refer to their choices to exclude women from studies, but these excuses are not well-founded.
One common excuse is that the menstrual cycle makes women too “variable” to be effective study subjects. For example, a drug trial, say for a heart condition, might result in unfavorable data on average because the drug is ineffective during ovulation. The poor results from the study participants who were ovulating during the drug trial would drag down the overall average. The drug might not be approved even though it works for anyone who is not ovulating.
This is obviously bad public policy. Science aside, if a large percent of the population undergoes hormonal fluctuations, it is essential to gather data on the drug’s effectiveness at different hormonal stages. Refusing to study these participants is simply lazy. Even from a scientific perspective there is no justification. It is easy to control statistically for different hormonal stages: the study can record where each participant is in their menstrual cycle. Male hormones fluctuate over the course of each day and over the course of their lives, but the time of day at which they took the drug does not affect the outcome of the study, and the age of the participant is easy to control for. These same phenomena are present in the female sample, but on different timescales. Many researchers treat the male hormonal timescale as the human default, and the female as aberrant, but there is no scientific basis for this.
Researchers’ biases about default humans have led to age-based divisions in medicine (“child” and “adult”) where the category “adult” simply means “male.” For example, drug doses and vaccines are often standardized for all adults, even while they are fine-tuned for smaller categories among children. There is a separate Pfizer COVID-19 vaccine dose for children under age 5 (3 micrograms), 5-11 (10 micrograms), and over 12 (30 micrograms). Meanwhile, all adults are provided the same dosage, even though many men’s bodyweights are double many women’s! Nevertheless, researchers do not consider women’s reactions to drugs when apportioning dosages, even while women experience worse side effects than men in 90% of clinical trials.
Results of these kinds of bias are insidious and far-reaching. They extend out of the realm of medicine. Until recently, all crash test dummies were designed based on the “50th percentile male” body, meant to reflect some sort of average human. Obviously, the 50th percentile male is unlikely to reflect the 50th percentile human: the dummy is likely to be taller, heavier, differently proportioned, etc., than the average adult human overall. In response to this criticism, car companies included a “female” dummy in their crash tests. This “female” dummy was identical to the “male” but was slightly smaller.
Because women, on average, have different body shapes, fat distribution, densities, and centers of mass than the average adult (the average adult women has a lower center of mass than the adult male, affecting her balance), this crash test dummy still does not reflect the outcome of a human woman in a car crash. As a trivial example, seatbelts are not designed to be maximally effective for people with breasts; they are designed specifically to be maximally effective for people without breasts (i.e., the shape of the current dummies). As a result of this crash test dummy bias, women are on average 47% more likely to be seriously injured in car crashes to this day—and 17% more likely to die.
The biases of male scientists have led to poor data collection, which in turn has led to deaths. Women’s health is under-researched and underfunded due in part to erroneous assumptions and in part to absurd laziness.
Beery AK, Zucker I. Sex bias in neuroscience and biomedical research. Neurosci Biobehav Rev. 2011 Jan;35(3):565-72. doi: 10.1016/j.neubiorev.2010.07.002. Epub 2010 Jul 8. PMID: 20620164; PMCID: PMC3008499.
Khan, Muhammad & Shahid, Izza & Siddiqi, Tariq & Khan, Safi & Warraich, Haider & Greene, Stephen & Butler, Javed & Michos, Erin. (2020). Ten‐Year Trends in Enrollment of Women and Minorities in Pivotal Trials Supporting Recent US Food and Drug Administration Approval of Novel Cardiometabolic Drugs. Journal of the American Heart Association. 9. 10.1161/JAHA.119.015594.