top of page
Erin Li

Breaking Down the Bias: Examining Gender Disparities in Medical Data Collection

Medical databases are systems in which researchers input clinical and laboratory data. Data taken from these information systems are often used to develop treatments and gather conclusions on the demographics of certain diseases. 


In the past, medical databases have excluded female participants, taking data from males and generalizing the conclusions to apply to females as well. This practice can be life-threatening in that underrepresented populations are put at a disadvantage due to lack of knowledge of their particular demographic. Females make up over half the global population and a significant proportion of the patient population but have longer wait times than men for diagnosis and treatment. They are also more likely to be misdiagnosed or mistakenly discharged during serious medical events. 


Inaccurate information from medical databases can lead to misdiagnosis or mistreatment which can be fatal in many cases. This unequal representation has also prevented significant advancements in management and diagnosis of diseases. Lack of female representation can cause patients to suffer inappropriate, ineffective, or harmful treatments, creating a significant deficit to women’s health. Women are often reported of having different symptoms, which leads to false diagnosis, making them at greater risk for suffering adverse events and overall mortality. 


In drug studies, women have been excluded for fear of safety issues, but this misrepresented data base now leads to little known information about effects of certain drugs on women, which can lead to unexpected and fatal side effects. Medicine that has been developed using database evidence often only includes evidence taken from men during clinical trials. When women aren’t included in these clinical trials, they aren’t provided with a different gender-specific medicine but are expected to consume medicine that was developed using data from men without consideration of factors that contribute to women’s health. 


Beyond gender disparities, inequalities in databases also affect minority ethnicities. There have been significant differences in Hispanic populations related to health, access to care, mental health, cancer and cancer screening, low birthweight, asthma, and cardiovascular health. Many medical databases contain information largely based on populations of caucasian males. Information from these databases can not be generalized to all populations and ethnicities. For more accurate diagnosis and treatment, ethnicity and demographic-specific databases should be generated and include a diverse representation of patients. With a more well-represented medical database, more specific information can be applied to certain demographics that were previously unequally represented, so many ineffective treatments and fatalities can be avoided.




References

Annette Flanagin, R. N. (2021, August 17). Updated guidance on the reporting of race and ethnicity in medical and science journals. JAMA. Retrieved November 29, 2022, from https://jamanetwork.com/journals/jama/fullarticle/2783090

Holdcroft, A. (2007, January). Gender bias in research: How does it affect evidence based medicine? Journal of the Royal Society of Medicine. Retrieved November 29, 2022, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1761670/

John P. A. Ioannidis, M. D. (2021, February 16). Recalibrating the use of race in Medical Research. JAMA. Retrieved November 29, 2022, from https://jamanetwork.com/journals/jama/fullarticle/2775794 


5 views0 comments

Recent Posts

See All

Comments


bottom of page