Addressing Gender and Implicit Biases in Medical Practice

Show Notes for Episode Twelve of seX & whY: Sex and Gender Differences in CPR Part 3

Host: Jeannette Wolfe Guest: Dr Justin Morgenstern

Here is a link to Justin Morgenstern’s awesome First10EM blog site where you can find an excellent review of the two papers that we discussed today: Perman’s DNR paper and Huded’s Cleveland Clinic Study on gender gaps in 30 day survival after ST elevation myocardial infarctions.

Here are some take home points for this podcast: 

• We don’t know what we don’t study and when we don’t consider sex and gender as legitimate variables, we can inadvertently miss opportunities to improve the health of all of our patients.
• There appears to be lots of sex-based differences in cardiac electrophysiology
  • females are more prone to AV nodal re-entrant arrhythmias, sick sinus syndrome, prolonged QTc and postural orthostatic tachycardia syndrome
  • males are more prone to AV block, early repolarization, Brugada’s syndrome, accessory pathway-mediated arrythmias, idiopathy ventricular arrhythmias and dangerous arrythmias associated with arrhythmogenic right ventricular cardiomyopathies

• In many ways, biological sex represents a much “cleaner” variable to study in that most of us have a sex specific chromosomal pairing and hormonal cocktail that allows us to be more easily placed into a binary male or female category.
• Biological sex differences are often detected and treated by tweaking technology- adjusting the results of a blood test or using a different type of imaging modality to account for sex based physiologically differences.
  • Biological sex is akin to the variable of age- its importance is related to context. Although a 15 year and 50-year-old may get the same evaluation for an ankle sprain they should not get the same evaluation for chest pain. Similarly, how females and males react to any particular treatment may or may not be associated with a clinically important difference.
  • As the science of earnestly studying males and females side by side is still so new, we are just beginning to understand where differences actually exist and in what contexts they are clinically relevant.

• As the influence of gender can be quite subtle and often involves many touchpoints, recognizing and fixing gender-based differences can be challenging. For example, here is how an individual’s gender might influence what happens to them if they have a heart attack.
  • Whether they live alone
  • If and when they call an ambulance
  • If they come in by car, how quickly they are triaged
  • Where they are geographically placed in the department
  • How they describe their symptoms
  • How their symptoms are perceived by providers (which in turn may be confounded by provider gender)
  • How quickly an EKG is done
  • How comfortable they are with procedural consent
  • How quickly they go to the cath lab
  • When and what type of medications they are prescribed
  • Who they are referred to for follow up
  • Whether they are compliant with their new meds or appointments
  • Whether they are referred to and participate in cardiac rehab

• Currently, I suspect that most of us in medicine would likely acknowledge that there are some legitimate examples out there of gender and race- based health inequities. The next step, however, requires an acknowledgement that those inequities are not just happening somewhere else, but that they have also likely creeped into our own practices. This can be difficult because it directly threatens our explicit belief that we deliver “the same” excellent care to all of our patients.
• Recognizing and mitigating gender disparities, especially those related to implicit bias, requires deep self-reflection along with an individual and organizational commitment to actually want things to change.
• Solutions include wide-spread “no-blame” educational forums and the development of technical safeguards to help reduce unintentional bias. For example, the creation of default “opt in” disease specific order sets and operational checklists.

Here is a table that shows outcome data from Bosson’s JAHA paper from LA County data base that we briefly mentioned on the podcast.  

Men

Women

CPR

41%

39%

shockable

35%

22%

STEMI

32%

23%

Cath

25%

11%

TTM

40%

33%

Survival/CPC 1-2

24%

16%

Other studies discussed.

European study that examined sex-differences in atrial fibrillation study

Danish study on cardiac arrests in people less than 35 with 2 to one ratio of men to women

Korean eunuch study suggesting that a historical lineage of castrated males outlived several socioeconomically matched peers, supporting the concept of a disposable soma theory.

Cleveland Clinic informational sheet on arrhythmias in women

Study that suggests more women than men die or go to hospice after an intracranial hemorrhage and brings up idea of gender-based differences in “social capital” contributing to this difference

EOL choices in advanced cancer patients showing gender differences in palliative care and DNR preferences