Racial and Ethnic Differences in Cesarean Delivery Across Insurance Types, United States, 2014-2024

This population-based study of over 41 million U.S. births from 2014 to 2024 reveals that racial and ethnic disparities in cesarean delivery rates persist independently of insurance status, with non-Hispanic Black women facing the highest risks across all coverage types and the most significant absolute disparities observed among the uninsured.

The Gist

The Big Picture: A National Snapshot

Imagine the United States as a massive stadium filled with over 41 million mothers giving birth between 2014 and 2024. About 1 in 3 of these mothers (32%) had a C-section (a surgical birth), while the other 2 out of 3 had a vaginal birth.

For a decade, the total number of C-sections stayed pretty steady, like a thermostat set to a constant temperature. But if you look closer at the different groups of people in the stadium, you see that the "temperature" isn't the same for everyone. Some groups are getting "hotter" (more C-sections) than others, and the reason isn't just medical—it's also about who they are (race/ethnicity) and how they pay for their care (insurance).

The Two Main Ingredients: Race and Insurance

The researchers wanted to see how two main ingredients mixed together to change the outcome:

1. Race/Ethnicity: Who the mother is.
2. Insurance Type: How she pays (Private insurance, Medicaid, or Uninsured/Self-pay).

Think of this like a recipe. If you change the ingredients, the taste of the cake changes. The study found that the "taste" (C-section rate) changed depending on which combination of race and insurance you used.

The Key Findings (The "Flavor" of the Data)

1. The "Black Woman" Effect

No matter what kind of insurance a Non-Hispanic Black woman had, she was more likely to have a C-section than a Non-Hispanic White woman with the same insurance.

• The Analogy: Imagine two runners in a race. Even if they are wearing the same shoes (same insurance) and running on the same track (same hospital), the Black runner is statistically more likely to be told to stop and take a different path (surgery) than the White runner.
• The Shocking Detail: This difference was biggest for Black women who had no insurance. They had the highest chance of a C-section of anyone in the study. It's like the "double penalty" of being Black and uninsured.

2. The "Insurance" Effect

Insurance matters a lot.

• Private Insurance: Women with private insurance (like employer plans) had higher C-section rates than those on Medicaid or no insurance.
• The Analogy: Think of private insurance like a "VIP pass" at a theme park. The VIPs get more rides (interventions) and more attention, sometimes leading to more surgeries. Medicaid and the uninsured often get fewer interventions, perhaps because they have less access to the "VIP" level of care or face different pressures.

3. The "Mix" (Interaction)

This is the most important part of the study. The researchers found that insurance changes how race affects C-sections.

• The Analogy: Imagine race and insurance are two different colors of paint.
  • If you mix Black + Private, you get a very dark shade (high C-section rate).
  • If you mix Black + Uninsured, you get the darkest shade of all (the highest rate).
  • If you mix White + Uninsured, you get a much lighter shade (lower rate).
• The Takeaway: The gap between Black and White women is widest when they are uninsured. Having insurance helps, but it doesn't completely erase the gap. The system treats Black women differently than White women, regardless of whether they have a VIP pass or a basic ticket.

Why Does This Happen? (The "Why" Behind the Numbers)

The paper suggests this isn't just about biology. It's about structural issues:

• Bias: Doctors might subconsciously think Black women are in more danger or need surgery sooner than White women.
• Access: Uninsured women might wait too long to get help, leading to emergencies that require surgery.
• Money: Hospitals might be incentivized to do more surgeries for private insurance patients because it pays better.

The Bottom Line

Even though the national average of C-sections hasn't changed much over the last 10 years, inequality is hiding in plain sight.

• The Metaphor: Imagine a school where the average test score is 80%. That sounds fine. But if you look closer, you see that Group A is scoring 95% and Group B is scoring 65%. The "average" hides the fact that Group B is being left behind.
• The Conclusion: This study shows that in the US, your race and your bank account (insurance) still dictate how you give birth. To fix this, we can't just look at the "average" birth rate; we have to fix the specific rules and biases that make Black women and uninsured women face higher risks of surgery.

In short: The system is like a bumpy road. Everyone is driving the same car (pregnancy), but some drivers (Black women, especially those uninsured) are hitting more potholes (C-sections) than others, and the type of car insurance they have changes how bumpy the ride gets.

Technical Summary

1. Problem Statement

Cesarean delivery (CD) accounts for nearly one-third of all births in the United States and is associated with significant maternal morbidity and healthcare costs. While persistent racial disparities in CD rates are well-documented (with Non-Hispanic Black women consistently experiencing higher rates than Non-Hispanic White women), the structural factors driving these differences remain incompletely understood.

A critical gap in existing literature is the lack of contemporary, national-level analysis examining the interaction between race/ethnicity and insurance status. Previous studies often relied on older datasets, focused solely on relative measures (odds ratios), or failed to determine whether insurance coverage modifies racial disparities. This study aims to quantify how insurance type (Private, Medicaid, Uninsured) intersects with race/ethnicity to influence CD rates, moving beyond independent associations to understand effect modification.

2. Methodology

• Study Design: Population-based retrospective cohort study.
• Data Source: United States Vital Statistics Natality files covering the period from January 1, 2014, to December 31, 2024.
• Study Population:
  • Inclusion: All singleton live births in the U.S. during the study period.
  • Exclusion: Multifetal gestations and records with missing data on mode of delivery, race/ethnicity, or insurance status.
  • Final Sample: 41,543,568 deliveries.
• Variables:
  • Primary Outcome: Cesarean delivery (Yes vs. No).
  • Primary Exposures:
    • Race/Ethnicity: Non-Hispanic White (reference), Non-Hispanic Black, Hispanic, Non-Hispanic Asian, Non-Hispanic AIAN, Non-Hispanic NHOPI, Non-Hispanic Multiracial, and Unknown.
    • Insurance Status: Private, Medicaid (reference in some models), Uninsured (self-pay), Other, and Unknown.
  • Covariates: Maternal age, marital status, education, parity, prior CD, pre-pregnancy hypertension, gestational hypertension/preeclampsia, diabetes, smoking, prenatal care utilization, and year of delivery.
• Statistical Analysis:
  • Multivariable Logistic Regression: Used to estimate Adjusted Odds Ratios (aOR) and 95% Confidence Intervals (CIs).
  • Interaction Terms: Included to test for effect modification between race/ethnicity and insurance status.
  • Marginal Effects: Post-estimation calculations using the delta method to derive Adjusted Predicted Probabilities and Absolute Risk Differences (RD). This allows for the quantification of disparities in absolute terms (percentage points) rather than just relative odds.
  • Software: Stata.
  • Sensitivity Analyses: Models re-estimated excluding women with prior cesarean deliveries and restricting to term births.

3. Key Contributions

• Temporal Scope: Utilizes the most recent decade of national data (2014–2024), providing a contemporary view of obstetric trends post-pandemic and post-policy shifts.
• Interaction Analysis: Explicitly models the interaction between race and insurance, demonstrating that the effect of race on CD is not uniform across insurance types.
• Dual Metric Approach: Combines relative measures (Odds Ratios) with absolute measures (Risk Differences and Predicted Probabilities), offering a more nuanced understanding of the magnitude of disparities for policy and clinical interpretation.
• Granularity: Analyzes detailed racial/ethnic categories (including AIAN, NHOPI, and Multiracial) across distinct insurance strata.

4. Key Results

A. National Trends

• Overall CD rates remained stable at approximately 32.0% (13.3 million cases) throughout the decade.
• Vaginal delivery remained the predominant mode (~68%).
• A modest increase in CD rates was observed after 2020, though overall patterns remained consistent.

B. Independent Associations

• Race: After adjustment, Non-Hispanic Black women had significantly higher odds of CD compared to Non-Hispanic White women (aOR 1.22; 95% CI 1.22–1.23). Hispanic women also had slightly higher odds (aOR 1.07).
• Insurance: Compared to uninsured women, those with Private insurance had the highest odds of CD (aOR 1.59), followed by Medicaid beneficiaries (aOR 1.39).

C. Interaction Effects (Race × Insurance)

Significant effect modification was observed. The disparity in CD rates between racial groups varied significantly by insurance type:

• Uninsured Population: Disparities were most pronounced. Non-Hispanic Black women who were uninsured had the highest odds of CD compared to their White counterparts (aOR 1.83). Non-Hispanic Asian women also showed elevated odds in this group (aOR 1.89).
• Privately Insured Population: Disparities persisted but were attenuated compared to the uninsured. Non-Hispanic Black women still had higher odds (aOR 1.19) compared to White women.

D. Predicted Probabilities and Absolute Risk Differences

• Highest Probability: Privately insured Non-Hispanic Black women had the highest predicted probability of CD (49.5%).
• Lowest Probability: Uninsured Non-Hispanic White women had one of the lowest probabilities (34.6%).
• Absolute Risk Differences (RD):
  • Uninsured: The largest absolute disparity was found among uninsured Non-Hispanic Black women, who had a 12.3 percentage point higher risk of CD compared to uninsured Non-Hispanic White women (RD 0.123).
  • Private Insurance: The disparity narrowed but remained significant, with Non-Hispanic Black women having a 5.6 percentage point higher risk (RD 0.056).
  • Hispanic Women: Showed elevated risk among the uninsured (RD 0.084) but slightly lower risk compared to White women among the privately insured (RD -0.012).

5. Significance and Implications

• Structural Inequities: The findings suggest that national stability in CD rates masks substantial heterogeneity. The intersection of race and insurance creates a "double burden" for marginalized groups, particularly uninsured Non-Hispanic Black women.
• Mechanisms of Disparity: The results imply that factors beyond clinical indication—such as structural racism, implicit bias in provider decision-making, differential access to high-quality care, and reimbursement incentives (higher intervention rates in private insurance)—drive these disparities.
• Policy Recommendations:
  • Equity-Focused Interventions: Policies to reduce unnecessary C-sections must specifically target racial and insurance-based inequities, not just aggregate rates.
  • Access Expansion: Expanding prenatal and intrapartum care access for uninsured and underinsured populations may mitigate the extreme disparities observed in the uninsured group.
  • Standardization: Implementation of standardized labor management protocols and accountability metrics stratified by race and insurance type could reduce unwarranted variation.
• Limitations: As an observational study using birth certificate data, causal inference is limited. The data lacks granular clinical details (e.g., labor progression, specific indications for CD, provider-level factors) and relies on self-reported or administrative insurance status which may not capture coverage continuity.

Conclusion: The study concludes that while aggregate CD rates in the U.S. have stabilized, racial and ethnic disparities persist and are significantly modified by insurance coverage. The largest inequities are concentrated among uninsured populations, highlighting the critical role of structural and coverage-related factors in obstetric care outcomes.