1. Women were routinely excluded from medical research because of andronormativity.Which led to women being underrepresented in medical research historically.

There is this idea that prior to 1993 when the NIH created the revitalization act,there was wholesale and routine exclusion of women from clinical trials because researchers just wanted to focus on men. However, the reality is more complex than the simplistic narrative that women were just excluded because we don’t care about them. Now there is a grain of truth to the idea that women were excluded from clinical trials, the FDA released guidelines in 1977 recommending that women of childbearing potential are excluded from phase l and early phase ll clinical trials.The fact that these guidelines exist to some people is evidence of women’s health being neglected because they are women, but when you look at what the guidelines say,it becomes clear that the FDA’s goal was not to exclude women because they thought it wasn’t important to include them in clinical trials.

They are instead taking a cautious approach to including women in clinical trials due to what happened with the thalidomide scandal.They wanted to ensure another tragedy like that doesn’t happen again. This is why they only recommend not including women of childbearing potential in the early phases of clinical trials where they are trying to ensure that it is safe, and find out the appropriate dosage to use. They make sure that there is at least some evidence of this drug being safe to administer in a specific dosage before risking giving it to a woman who is pregnant or may become pregnant. Phase ll and lll of the clinical trials test both safety and effectiveness with a much larger sample of people.

The document also makes it clear that these are not mandatory requirements for continuing clinical trials or getting new drugs approved. These are recommendations, some people interpreted this guideline as most women being banned from clinical trials altogether. However, that is not what the guideline says. So part of the reason why this idea came about is because of people misinterpreting and/or misrepresenting what the guideline actually says. Usually because they get their information from secondary sources instead of looking at the actual guideline. When you look at the guideline it is clear that the FDA is not against women being included in clinical trials, or just disinterested in studying women. They tried to strike a balance between safety and inclusion.

Inaddition to this, people also believe that women were underrepresented in clinical trials due to them being routinely excluded. This perception of women being underrepresented in clinical trials is what led multiple government agencies to create national offices dedicated to women’s health. Perception is not necessarily reality though. The truth is that there is no evidence of women being underrepresented in clinical trials overall. This study shows that women were a slight majority of participants in clinical trials and epidemiological studies, and another study also shows no evidence of women being routinely excluded from clinical trials. Some women may have been excluded in trials for certain reasons, but overall there was no pervasive bias against women that lead to them being excluded. Some people have pointed out the fact that women are underrepresented in clinical trials for certain diseases like heart disease and aids, then claimed that this is evidence of disregard for women’s health.

However, there are actually reasonable explanations for why less women were in these trials that have nothing to do with sexism. Heart disease was thought to only really affect men for a while due to a limitation of the Framingham study were they only included people under 68 in their sample. The problem with this is that heart attacks and heart disease tends to affect women later on in life. So the study appeared to show that these cardiovascular health issues were not really a problem for women which is why they were excluded from some of the earlier trials. Another reason why women may be exclude from heart diseases trials is due to them having more comorbidities and taking more medications, because they tend to get the disease later on life when they are older and sicker. These are extra variables to take into account which makes it more difficult to study heart disease interventions in women.

When it comes to aids, the disease was thought to only affect gay men for a while, because the first cases of the disease were in gay men. More women were included in clinical trials once it was recognized that they get the disease as well. This is all discussed in more detail in this article.Also, women were not even the only ones to be underrepresented in clinical trials for certain diseases. As the studies show, men were underrepresented in clinical trials on cancer, reproduction, and sex hormones, so this is obviously not a gendered issue. So overall, what we can take away from all this information is that women were not routinely excluded from clinical trials on the basis of their sex because of anti-female bias. When women were excluded there was usually a reasonable explanation for it. Some seemingly arbitrary cases of exclusion happen for both men and women.

2. Women get worse health care treatment due to gender bias.

Another claim that gets made a lot by feminists is that there is widespread anti-female bias in health care leading to worse treatment for women. The evidence to support this claim typically comes from studies showing that women receive less of some form of medical treatment compared to men. This could be painkillers, referrals, medical procedures, etc. The mainstream media has made this claim and has cited these studies as proof that women’s pain is not taken as seriously as men’s pain. A perfect example of this can be found in this episode of The last week tonight show with John Oliver.In this episode John Oliver cites multiples studies to back up this claim, if you were to just look at this episode you might think there is widespread bias against women in health care, but if you dig deeper and do research on this topic you may come to a different conclusion. Let’s look at the studies that John Oliver cited. The first claim he made about women being less likely to be referred for knee replacements then men came from a quote in an article by The New York Times, I was not able to find the study to support this claim, because there was no reference to it listed below or in the article, so I will skip this claim.

The next claim he made regarding life-saving interventions comes from this study.This study was a prospective cohort study meaning that men and women who were admitted into the hospital were simply observed overtime to see what treatments they received. According to the study they were actually able to account for some relevant factors like comorbidities, severity of illness, and diagnosis, but there were other relevant factors that they could not account for like presentation of disease, race, type of medical insurance, patient preference, etc.The researchers acknowledges this limitation in the study and says this.

Our study had several limitations. Because it was observational, we were unable to establish causal relations. We also were unable to determine population prevalence rates for all ICU admission diagnoses and, thus, could not explore reasons for different admission rates among men and women for all conditions. Similarly, information was not available for all critically ill patients in all ICUs in Ontario. However, we used a broad spectrum of teaching and community hospitals and ICUs within a research network well known for its ability to capture valid and detailed information at the patient level. There may have been variables that we were unable to measure that had important effects on the reported associations.

This study cannot be used to claim that there is a bias against women in health care which leads to them getting inferior treatment, but it is being presented as if it can. The next study is about gender disparities in receiving painkillers for abdominal pain. The study found that women received less opioid analgesics then men. This study also suffers from some limitations that prevents the researchers from making a definitive claim of gender bias. This is why they use more conservative language by claiming that gender bias is a possible explanation for the disparities.

Gender bias is a possible explanation for oligoanalgesia in women who present to the ED with acute abdominal pain. Standardized protocols for analgesic administration may ameliorate this discrepancy.

One major issue with this study is that they did not record follow-up pain scores which may have influenced analgesia administration. They were also unable to control for the severity of illness, and comorbidities. Another issues is that they could not determine whether or not patients refused treatment. Also, other factors that are very relevant for this particular study are reasons for why the health care professionals did not prescribe opioid analgesics. Health care professionals may be hesitant to prescribe opioids because it can lead to addiction. If a patient has a history of opioid addiction then that may be a reason to not administer opioids.Lastly, they also did not measure nonanalgesic medications to treat abdominal pain. Below is a quote from the study.

We also did not measure the use of Non analgesic medications to treat abdominal pain (e.g., H2 receptor antagonists, proton pump inhibitors, gastrointestinal ‘‘cocktails’’).

So again, this study cannot be used to claim that there is gender bias against women in health care. Lastly, you can read this article addressing the claims made about heart attacks if you want.

This is something that you will see a lot with these studies that people cite to claim gender bias. There are some important variables that are not controlled for in these studies. This is because health care treatment is such a complex thing that is influenced by so many factors that it is essentially impossible to create a study that controls for all of them, especially when you consider unobservable variables, like the way the patient communicates with their health care provider. This is one of the main reasons why I don’t think there is sufficient evidence to claim that there is gender bias in health care. There are so many things that can influence treatment. Age, race, painscore, diagnosis, comorbidities, hospital overcrowding, patient preference, insurance, location, contraindications based on test results, etc. So as you can see, health care treatment is very complex and there are a lot of factors to consider. Another issue that could potentially lead to more women being misdiagnosed or having negative experiences in healthcare, is the fact that the prevalence of autoimmune diseases is higher in women compared to men. Autoimmune diseases are difficult to diagnose, because the symptoms overlap with symptoms of other diseases.

This can lead to women being misdiagnosed more then men or feeling like their being gaslit by doctors. There could also be differences in the presentation of some conditions in men and women that may contribute to either sex being relatively more likely to be underdiagnosed or misdiagnosed. For example, girls may be better at masking their autism which can lead to them being under diagnosed, while boys may show more obvious signs of dysfunction. Additionally, studies don’t consistently show that women are given less treatment. There are multiples studies showing no difference and there are also studies showing that men were given less treatment. You might have seen feminist cite this research article or this one to prove that there is pervasive bias against women in health care. However, if you look at the studies cited in these papers not only do they suffer from some major limitations which includes not controlling for the many variables that I listed above, but they are also cherry picked studies that only show women getting less painkillers. Here are some other studies with different results.

Gender-Associated Differences in Emergency Department Pain Management

The patients were evaluated by 84 providers, 60 of them male. According to the providers surveyed, female patients described more pain than did male patients (P<.01) and were perceived by providers to experience more pain (P =.03). Female patients received more medications (P<.01) and were less likely to receive no medication (P =.01). Female patients also received more potent analgesics (P =.03). Linear and logistic regression analysis showed that patient perception of pain was the strongest predictor of the number and strength of medications given; patient gender was not a predictor.

Impact of Physician and Patient Gender on Pain Management in the Emergency Department

Analgesic administration rates were not significantly different for female and male patients (63% vs 57%, P = 0.08), although females presenting with severe pain (NRS ≥8) were more likely to receive analgesics (74% vs 64%, P = 0.02).

No gender-related bias in acute musculoskeletal pain management in the emergency department

Pain management measures (ie, analgesia administration, waiting time for analgesia, pain relief and patients' satisfaction) were prospectively assessed in 328 patients (150 women and 178 men, average age 36±18 years) who were treated in our ED for acute musculoskeletal pain. Patients' subjective pain rating on arrival were similar for men and women (59±24 mm vs 61±26 mm, respectively; p=0.47). Interestingly, physicians using the same scale assessed the women's pain level to be higher than that of men (75±25 mm vs 63±22 mm, respectively; p<0.001) and higher than that of women's subjective pain rating (75±25 mm vs 61±26 mm respectively; p<0.001). Nevertheless, the rates of analgesia administration, waiting time for analgesia, pain relief and patient satisfaction were similar for both genders. Physicians' own gender did not affect analgesic care.

Prevalence and treatment of pain in adults admitted to Italian hospitals

The probability of receiving analgesic treatment was higher for women (OR ¼ 1.33, 95%CI: 1.14–1.54) and significantly lower for general medicine wards compared to surgical wards (OR ¼ 0.55, 95%CI: 0.45–0.64), while it was unrelated both to the patient’s self-reported pain and to the level of pain assessed by the nurse (Table 4).

Do Gender and Race Affect Decisions About Pain Management?

No overall differences with respect to patient gender or race were found in decisions to treat or in the maximum permitted doses.

Disparities in Analgesia and Opioid Prescribing Practices for Patients With Musculoskeletal Pain in the Emergency Department

No gender or financial status disparities were found.

Pain in the Emergency Department with One-Week Follow-Up of Pain Resolution

Women were no less likely to have received analgesics in the ED (24%, versus 22% for men) or to not be prescribed analgesics (33%, versus 35% for men), although they were more likely (60%, versus 39% for men) to have taken analgesics in the week following the ED visit (χ2[1]=7.490, P<0.004).

Some other studies have been cited to claim that women get inadequate treatment for heart attacks, because they receive less aggressive treatment then men. However,one of the major confounders that is not adjusted for is age. Women tend to be older then men when they get heart attacks. Older people regardless of gender also receive less aggressive treatment. This could possibly be due to greater potential risk because older patients tend to be sicker and more frail. This paper cites tons of studies that find differences in treatment with men receiving more aggressive treatment, and tons of other studies that show little to no difference in treatment. The studies that show little to no difference control for age, while the others don’t. The exception to this was with bypass surgeries were the results were mixed even after age was adjusted for. The researchers suggests this could be due to some other factors.

Proposed explanations for gender differences include possible sex-related differences in anatomy (e.g., blood vessel size), operative risk and suitability for percutaneous coronary intervention (PCI) versus bypass surgery (Rathore et al. 2003; Bertoni et al. 2004; Jacobs and Eckel 2005; Barrett-Connor 2007).

I think this paper illustrates the point I’m trying to make. There are many factors that influence health care treatment. Therefore, I don’t think people can claim gender bias based on these observational studies.There is another paper reviewing 90 studies on gender differences in stroke treatment that claim to this conclusion.

Sex differences in stroke treatment and outcome are small, with no unequivocal proof of sex discrimination. Women have less favorable functional outcome because of higher age at stroke onset and more severe strokes.

Aside from observational studies, there are some experimental studies using vignettes and virtual human patients. Vignette studies are studies where participants are presented with a hypothetical scenario, and then are asked what they think about it. The results of vignette studies are mixed. Some show no difference, while others show that women are perceived as being in more pain. In virtual human patient studies participants are shown videos of a virtual patient expressing pain, and they are asked what they think about it. These studies mostly show that women are perceived as being in more pain.

Virtual human patient studies

The influence of sex, race, and age on pain assessment and treatment decisions using virtual human technology: a cross-national comparison

Additional research has consistently shown that patient demographic characteristics such as sex,race, and age may influence the pain assessment and treatment decisions of health care trainees and professionals.Citation8–Citation14 Virtual human (VH) technology has been used to demonstrate that females are perceived as experiencing higher pain intensityCitation8,Citation9,Citation12–Citation15 and are more likely to be recommended medical treatmentCitation8,Citation9,Citation12–Citation14.

SEX AND RACE DIFFERENCES IN RATING OTHERS’ PAIN, PAIN-RELATED NEGATIVE MOOD, PAIN COPING, AND RECOMMENDING MEDICAL HELP

Both male and female participants rated pain intensity for female VHs as significantly higher than that for male VHs, F (1, 73) = 4.92, p < 0.05. Both Caucasian and African American participants rated pain intensity for female VHs significantly higher than that for male VHs, F (1, 73) = 6.93, p < 0.05.

A main effect for sex of VHs on sex of participants was found. Both male and female participants’ ratings for recommending medical help were significantly higher for female VHs than that for male VHs, F (1, 73) = 5.98, p < 0.05.

Pain Assessment and Treatment Decisions for Virtual Human Patients

Participants assessed VH patients who were male as having greater pain intensity than those who were female, F(1, 90)=11.74, p=0.001, partial η2=0.12. Table 2 presents the results for pain intensity.

There were no significant findings for pain treatment ratings. Table 2 presents the nonsignificant results for willingness to administer opioid analgesics.

Vignette studies

This is a systematic review of 5 studies.4 of the 5 studies are vignette studies, and 1 is observational

Patient satisifaction surveys

Additionally, the 2018 study about gender norms in health care is a theory-guided literature review. Which means that it is a paper that is designed to only look at studies with gender disparities, and studies about gender norms in health care. They mention this in the paper.

This review was theory-guided with a preunderstanding that gendered norms exist in health care, which has influenced the selection of our search terms. Our directed literature search might be criticized as it potentially excluded studies that did not find/report gender differences. However, the aim of this study was not to prove if gendered norms in health care exist, which earlier research already has shown [2, 3, 13], but to collect and analyze gendered norms and gender bias as described in pain literature and deepen the knowledge about them.

This paper also doesn’t give us an idea to what extent this actually affects treatment in general. Studies that look into gender norms are qualitative, they have to be to get the in-depth information that is needed to know more about gender norms. The downside of this is that the generalizability is very limited with qualitative studies. I think if we are going to really try to understand how pervasive the issue is, some sort of quantitative survey would be more useful. The best thing that I could think of is patient satisfaction surveys. These surveys are meant to give you an idea of the quality of healthcare services that patients receive .Lets look at some of them.

These first two are from Gallup, overall men and women showed no difference in patient satisfaction scores. There were small differences in some measures, but no difference overall.

Gender Comparisons: Patient Satisfaction and Loyalty

Healthcare Satisfaction: Men vs. Women

The next source is a systematic review. This paper found that women had slightly lower odds of submit a patient satisfaction score that was higher than men’s. The odds ratio was 0.98 which means they were 2% less likely to submit a higher score. The author said this difference was not likely to be relevant, and that this systematic review alleviates any concerns that gender might impact patient satisfaction scores.Next, we have the 2022 kff women’s health survey. This survey showed that women were more likely to have at least one negative interaction in the past two years, but the difference is not huge.38% of women reported having at least one negative interaction in the past two years, compared to 32% of men.
Lastly, here is a report titled “Gender Disparities In Health Care in Medicare advantage". I thought it would be a good idea to include this one because alot older people with health conditions use Medicare. According to the report, the patient experience was similar for men and women across all 8 patient experience measures.

Even though these surveys also can’t control for the various factor that influence treatment, at least they can give us any idea of how health care services are for men and women in general. I think these surveys cast doubt on claims of pervasive misogyny in health care. So to summarize, there are three lines of evidence used to support the claim that women are mistreated in health care, observational studies, virtual human patient studies, and vignette studies. Observational studies show mixed results, and do not account for many important variables, some of which are unobservable. This means that they are not adequate to support the claim that women are being mistreated because their women. Virtual human patients studies are probably the strongest evidence, because they are the best simulation of a real world interaction, while also still having experimental control. The participants get to see the virtual human patients facial expressions like they would see with a person in real life. It is not exactly the same of course, but it is the closest to a real world situation. Most of the virtual human patient studies I found showed that women were actually perceived as being in more pain, and were more likely to be recommended medical treatment.

I was only able to find one study showing that men are perceived as having greater pain intensity, but there was no difference in pain treatment rating or willingness to administer opioid analgesics. Now there could be studies that I overlooked, but given the amount of studies that I found showing that women were perceived to be in more pain. I think that it is safe to say that the results of virtual humans studies are likely to be at the very least mixed. Then there are the vignette studies where participants are just shown written hypothetical scenarios. The problem with this is that text based vignettes lack ecological validity, because reading a hypothetical scenario is very different from dealing with a patient in real life. The results of the vignette studies were mixed. Another thing that I noticed that is worth mentioning is that a lot the vignette studies and virtual human patient studies had small unrepresentative samples. This severally limits the generalizability of these studies. There is also little to no difference in patient satisfaction between men and women.

Overall, I don’t think there is sufficient evidence to claim that men or women are dealing with widespread mistreated in health care settings due to there sex. I think that it is a given that there will be some legitimate cases of sexism in health care, but i don’t think there is sufficient evidence to claim that there is pervasive sexism against women in health care.

3. Women are routinely over medicated, because drugs are not tested on women.

Whenever I see this claim being made, this is the source that is cited. However, this is kind of misleading because the study that is cited isn’t conclusive, and they even acknowledge that they have not definitively proven that women are being routinely over medicated.

Among the 27 drugs for which VB ratios were the only format of adverse event data, sex-biased PKs predicted sex-biased VigiBase ADR reporting ratios in 74% of instances (20 of 27 drugs were PK–VB ratio “concordant,” Table 2). In all but a few instances, VigiBase contained thousands of ADR reports, but as noted above, the number of men and women treated with each drug was not specified, nor are the links between the drug and specific ADRs known. It remains likely that some ADR sex differences in the VigiBase reflect unequal numbers of women and men treated with a given drug.

There are some other studies that analyze the proportion of women included in clinical trials for drugs, as well the subgroup analysis of drugs, and the safety and efficacy of those drugs for men and women. Here are those studies linked below. Gender differences in clinical registration trials: is there a real problem?

Our data showed that, overall, women are studied in adequate proportions, and that some type of gender subgroup analysis is performed for most drugs that are approved. The subgroup analyses on efficacy showed that the majority of drugs are equally effective in males and females. While there was a higher proportion of females with side effects compared with males, these differences were relatively small, and likely to be of little clinical significance. It is important to realize that gender difference is one of many variables that cause variability in drug response for efficacy and/or safety in any target population. Other factors include weight, age, genotype, phenotype, ethnicity, hormonal status, fasting conditions, polymorphisms of metabolizing enzymes, receptor expression and sensitivity, co-medication interactions, co-morbidities, pregnancy status, gut microbiome status 18-22. Many of these factors are known to induce substantially more variability than gender if they are distributed heterogeneously in the target population .

Sex based subgroup differences in randomized controlled trials: empirical evidence from Cochrane meta-analyses

Our empirical evaluation of statistically significant sex-treatment interactions from the CDSR revealed only eight (7%) statistically significant sex-treatment interactions among 109 topics. This is not much beyond what would be expected by chance alone. With only eight statistically significant interactions, it is likely that the number of false positives outnumbered the number of true positives. Also, certain reviews had more than one topic, which could lead to an overlap of topics with non-independent data. However, even when we selected only one topic for each review or allowed for multiple comparisons with one outcome per review, the statistically significant sex-treatment interactions would still be uncommon (4/41 (10%) and 7/61 (12%), respectively), not far from what is expected from chance.

Differences in Efficacy and Safety of Pharmaceutical Treatments between Men and Women: An Umbrella Review

Findings, based on 59 studies and data of more than 250,000 patients suggested that for the majority of drugs no substantial differences in efficacy and safety exist between men and women. Some clinically important exceptions, however, were apparent: women experienced substantially lower response rates with newer antiemetics than men (45% vs. 58%; relative risk 1.49, 95% confidence interval 1.35–1.64); men had higher rates of sexual dysfunction than women while on paroxetine for major depressive disorder; women discontinued lovastatin more frequently than men because of adverse events. Overall, for the majority of drugs sex does not appear to be a factor that has to be taken into consideration when choosing a drug treatment.

So, overall this evidence goes against the claim of women being routinely overmedicated, and also shows that drugs are tested on women. Most drugs seem be about equally safe and effective for men and women, with some drugs being either worse for women or men. Now when it comes how many adverse drug reactions are reported, it is true that women report more ADR’s then men, but men seem to report more serious and fatal ADR’s.

Reported adverse drug reactions in women and men

We find that female ADR reports outnumber male ADR reports across the globe, in all adult ages and by all available reporter types. Male reports however, to a larger extent, more often contain serious and fatal ADRs than female reports.

However, this is not solid evidence that women are being routinely overmedicated. There are so many factors that can cause the disparity, one of them being the use of contraceptive, which the study did control for, and this reduced the proportion of women reporting ADR’s. The fact that men report more serious and fatal ADR’s could also suggest that women are more likely to report ADR’s in general even if there not as serious. There are numerous reasons for why this could be the case, and the study suggests a few reasons. The main takeaway though, is that this is far from definitive proof. This is just information from a database that people can report to, with no way to actually prove causation.

Lastly, feminist will mention the drug zolpidem as a prime example of androcentrism in medical research, but even this has been widely contested.

4. Women’s health is underfunded.

This claim originated from this study.Which was then discussed in this nature article.In the study, the total numbers of Daly’s for a disease is compared to the NIH funding for that disease to determine if the funding for the disease is commensurate with the disease burden, or if it is underfunded or overfunded. Then they look at the proportion of men and women with the disease and categorize the disease as either gender neutral, female semi-dominant, female dominant, male semi-dominant, or male-dominant. If a disease is underfunded and female/male dominant it is consider male favoring or female favoring. Disease are consider gender neutral if the proportion of people with the disease are 50-55% of one sex, disease that are semi-dominant range from 55-60%,and dominant disease are disease that are at 60% or higher.In the study they also count the total number of male-favoring, and female-favoring diseases. This is determined by looking if the male or female dominant disease is either underfunded or over-funded. So for example, a male dominant disease that is over-funded is consider male-favoring, but a male dominant disease that is underfunded is consider female-favoring.

So that pretty much sums up the methodology. I think that there are multiples issues with this study that invalidates it. One of the major issues is that this study doesn’t take severity into account when determine if women’s health is underfunded as a whole. Instead the total number of female-favoring and male-favoring diseases are just counted up, and then it gives a percentage of the amount that is male-favoring. I think this is too simplistic. For example why should a headache be counted equally compared to liver cancer. Shouldn’t the underfunding of liver cancer be given more weight, because it is a more severe health condition? Another, issue that I see is with the actual measure that is used. The DALY works by adding the total of numbers of years lost and years of disability together to create this number that represents the overall disease burden for a disease. This seems fine, until you consider the fact that this would mean allocating more funds to people with diseases that are less severe for them as individual patients. There is a good example of what I’m talking about in this paper discussing whether or not health research funding should be proportional to disease burden.

To give a concrete example, according to the Institute for Health Metrics and Evaluation, the total DALY burden attributable to lung cancer in some HICs is similar to that attributable to low back pain (Institute for Health Metrics and Evaluation, 2021b). Both are the cause of very substantial disability and—in the case of lung cancer—many early deaths. But the prevalence of low back pain is several orders of magnitude greater than the prevalence of lung cancer. Low back pain is just not as bad for an individual patient as lung cancer (this is like Scenario 4). If the arguments I have given so far are correct, this suggests that, conditional on scientific opportunity, more funding should be allocated to lung cancer research than to low back pain research.

The next issue that I have with this article is that it does not take into account the many other factors besides disease burden that determines funding. One of them being scientific opportunity. Which is the benefit that you can expect for the amount of money you allocate for a particular disease. This is actually mentioned in the paper under the section titled “ Disease burden as a funding criterion and NIH funding priorities". So I think I have shown that health research funding is more complex then what this study presents, and when you are comparing different disease there are many factors that need to be considered. This study uses an indirect and oversimplified way of determining commensurate funding. Which is why I don’t think that it actually proves that women’s health is underfunded. So what would be a better way to compare women’s health funding to men’s health funding? We can look at this report by the office of research on women’s health. If you look at pg. 112-117 you can see charts showing the NIH budget for health conditions. In total, funding for women's health is twice that of men’s health. The charts show the budget for men and women’s health for each disease. There are some massive disparities in sex-specific funding in some diseases where men are affected just as much or more affected than women. Some examples are lung cancer, stroke, and suicide. I think this a more apt comparison, because you can compare funding for the same diseases.

5.Women have worse post-operative outcomes with male surgeons(Misogyny!)

This is study that is cited to backup this claim. This is an observational study that only finds an association between male surgeon & female patient sex discordance and worse post-operative outcomes. They cannot determines the reasons for why this happens, but there are various reasons why this could be the case, besides hostile discrimination against women. Below are some good critiques of this study, and the conclusions that people draw from them.

Avfm

Comment on study

Conclusion: The evidence for medical misogyny is often either inconclusive or invalid.

Other relevant sources

There is still no women’s health crisis

Sex bias myth in medicine

Letter to White House on boys and men’s health