Drivers of Racial Differences in C-Sections
Lissandro Botelho
Expert in Environmental Economics | Public Administration & Sustainability | Innovation in Research & Policy
Corredor-Waldron, Currie, and Schnell (2024) investigate racial disparities in cesarean section rates, examining the interplay of medical necessity, provider discretion, and systemic factors in maternal health outcomes.
The study utilizes data from 993,165 births in New Jersey from 2008 to 2017. The authors employ a random forest algorithm to predict C-section risk based on maternal health factors. This machine learning approach captures non-linear relationships and interactions among risk factors, offering advantages over traditional logistic regression models.
The research focuses on unscheduled deliveries, minimizing the influence of maternal preferences and elective procedures. This approach enables the examination of medical decision-making under time pressure and uncertainty.
Findings reveal that Black mothers with unscheduled deliveries have a 24.8% higher likelihood of undergoing a C-section compared to white mothers. This disparity persists after controlling for observable risk factors, sociodemographic characteristics, and fixed effects for hospitals and individual providers.
The study's essential contribution lies in its use of variation in the costs of performing unscheduled C-sections. By analyzing how the racial gap changes when obstetrical units face capacity constraints due to concurrent scheduled C-sections, the authors provide evidence that provider discretion, rather than unobserved medical risk, drives the persistent disparity.
The research demonstrates that additional C-sections performed on low-risk women when hospitals are unconstrained negatively impact maternal and infant health outcomes. This finding underscores the clinical significance of the observed disparities and suggests potential for targeted interventions.
The study's implications extend beyond obstetrics, addressing equity in healthcare delivery and bias in clinical decision-making. It shows how provider discretion can lead to differential treatment among patients with similar observable risk factors, challenging the conventional understanding of health disparity drivers.
Corredor-Waldron, Currie, and Schnell's work demonstrate economic analysis's capacity to illuminate social phenomena. Their findings prompt reconsideration of how historical injustices and cultural misunderstandings may shape patient-provider interactions in healthcare settings. As the field incorporates artificial intelligence and machine learning tools for clinical decision support, this study raises questions about ensuring these technologies mitigate, rather than exacerbate, existing disparities.
The research contributes to academic literature and guides policymakers and healthcare providers in addressing racial disparities in maternal health outcomes. By identifying provider discretion as a key factor in differential treatment, the study suggests interventions such as standardizing care protocols, enhancing cultural competency training, and implementing decision support tools to mitigate unconscious biases.
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This work exemplifies health economics research's potential to effect change. It prompts a reconsideration of equitable healthcare delivery foundations, presenting a vision of obstetric care that serves all mothers and infants, irrespective of race or background.
The methodology includes controls for maternal characteristics, provider selection, and potential bias. The authors estimate specifications to explore the importance of observable characteristics and provider selection. They use variations in the costs of ordering unscheduled C-sections due to fluctuations in capacity to examine the role of differences in unobserved medical risk.
The study finds that even when treated by the same physician in the same hospital, Black mothers with unscheduled deliveries are 20.1% more likely than observationally similar white mothers to deliver by C-section. The racial disparity shrinks when the costs of ordering an unscheduled C-section increase due to concurrent scheduled C-sections.
Analysis of maternal and fetal health impacts suggests differences in unobserved health risks do not explain the racial disparity in delivery method. Reductions in unscheduled C-sections among low-risk Black mothers when costs rise do not negatively affect health outcomes while preventing marginal C-sections in low-risk mothers has positive health effects for infants of both races.
The authors investigate the role of physician gender and race, finding no evidence that female physicians are less likely to treat patients differently. They find suggestive evidence that Black doctors are less likely to perform additional C-sections on Black mothers. However, these analyses are limited by the small number of Black physicians in the sample.
In conclusion, the results indicate that provider discretion significantly explains racial differences in C-section rates. The study calls for further research to determine whether this differential treatment reflects a lack of care, communication barriers, cultural misunderstandings, or other factors to address the disparity effectively.
Reference ??
Corredor-Waldron, A., Currie, J., & Schnell, M. (2024). Drivers of Racial Differences in C-Sections. National Bureau of Economic Research.?https://www.nber.org/papers/w32891