When physicians and pregnant patients make decisions about whether to pursue a vaginal birth or cesarean, there are many factors at play. While vaginal birth can have health benefits for both parent and child, there are significant safety risks. In order to minimize these risks, physicians use predictive models to determine how likely patients are to have successful vaginal births after cesareans (VBAC). For many years, these predictive models included race as a variable. This decision recently came under fire, and the Maternal Fetal Medicine Unit (MFMU) published a calculator that did not include race as a variable but still predicted VBAC success with high accuracy. A large body of work in machine learning has highlighted that supposedly de-biased systems often re-code sensitive variables like race in terms of proxy variables. In order to determine if this was the case in this calculator, we replicated their formula, then found base-rate statistics of all the input variables for three different racial groups: Black, White, and Asian. We found that the distribution of VBAC probabilities for our simulated patients from these three groups was indeed significantly different from each other. Further, the predicted VBAC rates increased as a function of societal marginalization: Black patients were 47.6% likely to have a successful VBAC, Asian patients had a 48.6% probability, and White patients had a 49.4% probability. While these values are all within a few percentage points of each other, the differences in these simulated distributions show how there may still be underlying disparities in the maternal healthcare system.
Competing Interest StatementThe authors have declared no competing interest.
Funding StatementThis study was funded by one of the authors
Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
The study used ONLY openly available human data that were originally located at: 1. https://www.census.gov/newsroom/press-kits/2020/population-estimates-detailed.html 2. https://www.nber.org/research/data/vital-statistics-natality-birth-data 3. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4618667/ 4. https://link.springer.com/article/10.1007/s40615-020-00842-3/tables/2 5. https://elischolar.library.yale.edu/cgi/viewcontent.cgi?article=3401&context=ymtdl 6. https://www.researchgate.net/publication/256448154_Racial_and_ethnic_differences_in_primary_unscheduled_cesarean_deliveries_among_low-risk_primiparous_women_at_an_academic_medical_center_A_retrospective_cohort_study 7. https://www.cdc.gov/nchs/products/databriefs/db289.htm
I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.
Yes
I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
Yes
I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.
Yes
留言 (0)