We examined the impact of the rst year of the COVID-19 pandemic on unmet healthcare need among New Yorkers and potential differences by race/ethnicity and health insurance. Data from the Community Health Survey, collected in 2018, 2019, and 2020, were merged to compare unmet healthcare need within the past 12 months during the pandemic versus the two years before the pandemic. Simple and multivariable logistic regression models evaluated change in unmet healthcare need overall. We assessed whether race/ethnicity or health insurance status modi ed the association. Overall, 12% of New Yorkers (N = 27,660) experienced unmet healthcare during the three-year period. In univariate and multivariate models, the rst year of the pandemic was not associated with change in unmet healthcare need (OR = 1.04, p = 0.548; OR = 1.03, p = 0.699, respectively). No interaction between the rst year of the pandemic and race/ethnicity (p-values range: 0.081-0.893) but there was signi cant interaction with health insurance status (interaction P = 0.009). Stratifying on health insurance status, those uninsured had borderline signi cant lower odds of unmet healthcare need during 2020 compared to the two years prior (OR = 0.72, p = 0.051) while those with insurance had a slight increase that was not signi cant (OR = 1.12, p = 0.143). Unmet healthcare need among New Yorkers during the rst year of the pandemic did not differ signi cantly from the two years prior. Federal pandemic relief funding, which offered no-cost COVID-19 testing and care to all, irrespective of health insurance or legal status, may have helped equalized access to healthcare.

We examined the impact of the first year of the COVID-19 pandemic on unmet healthcare need among New Yorkers and potential differences by race/ethnicity and health insurance. Data from the Community Health Survey, collected in 2018, 2019, and 2020, were merged to compare unmet healthcare need within the past 12 months during the pandemic versus the two years before the pandemic. Simple and multivariable logistic regression models evaluated change in unmet healthcare need overall. We assessed whether race/ethnicity or health insurance status modified the association. Overall, 12% of New Yorkers (N = 27,660) experienced unmet healthcare during the three-year period. In univariate and multivariate models, the first year of the pandemic was not associated with change in unmet healthcare need (OR = 1.04, p = 0.548; OR = 1.03, p = 0.699, respectively). No interaction between the first year of the pandemic and race/ethnicity (p-values range: 0.081–0.893) but there was significant interaction with health insurance status (interaction P = 0.009). Stratifying on health insurance status, those uninsured had borderline significant lower odds of unmet healthcare need during 2020 compared to the two years prior (OR = 0.72, p = 0.051) while those with insurance had a slight increase that was not significant (OR = 1.12, p = 0.143). Unmet healthcare need among New Yorkers during the first year of the pandemic did not differ significantly from the two years prior. Federal pandemic relief funding, which offered no-cost COVID-19 testing and care to all, irrespective of health insurance or legal status, may have helped equalized access to healthcare.

Background Racial disparities exist in maternal morbidity and mortality, with most of these events occurring in healthy pregnant people. A known driver of these outcomes is unplanned cesarean birth. Less understood is to what extent maternal presenting race/ethnicity is associated with unplanned cesarean birth in healthy laboring people, and if there are differences by race/ethnicity in intrapartum decision-making prior to cesarean birth. Methods This secondary analysis of the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b) dataset involved nulliparas with no significant health complications at pregnancy onset who had a trial of labor at ≥ 37 weeks with a singleton, non-anomalous fetus in cephalic presentation (N = 5,095). Logistic regression models were used to examine associations between participant-identified presenting race/ethnicity and unplanned cesarean birth. Participant-identified presenting race/ethnicity was used to capture the influence of racism on participant’s healthcare experiences. Results Unplanned cesarean birth occurred in 19.6% of labors. Rates were significantly higher among Black- (24.1%) and Hispanic- (24.7%) compared to white-presenting participants (17.4%). In adjusted models, white participants had 0.57 (97.5% CI [0.45–0.73], p < 0.001) lower odds of unplanned cesarean birth compared to Black-presenting participants, while Hispanic-presenting had similar odds as Black-presenting people. The primary indication for cesarean birth among Black- and Hispanic- compared to white-presenting people was non-reassuring fetal heart rate in the setting of spontaneous labor onset. Conclusions Among healthy nulliparas with a trial of labor, white-presenting compared to Black or Hispanic-presenting race/ethnicity was associated with decreased odds of unplanned cesarean birth, even after adjustment for pertinent clinical factors. Future research and interventions should consider how healthcare providers’ perception of maternal race/ethnicity may bias care decisions, leading to increased use of surgical birth in low-risk laboring people and racial disparities in birth outcomes.