ABSTRACT
BACKGROUND: Pregnant women less frequently receive COVID-19 vaccination and are at increased risk for adverse pregnancy outcomes from COVID-19. OBJECTIVE: This study aimed to first, describe the vaccination status, treatment, and outcomes of hospitalized, symptomatic pregnant women with COVID-19, and second, estimate whether treatment differs by pregnancy status among treatment-eligible (ie, requiring supplemental oxygen per National Institutes of Health guidelines at the time of the study) women. STUDY DESIGN: From January to November 2021, the COVID-19-Associated Hospitalization Surveillance Network completed medical chart abstraction for a probability sample of 2715 hospitalized women aged 15 to 49 years with laboratory-confirmed SARS-CoV-2 infection. Of these, 1950 women had symptoms of COVID-19 on admission, and 336 were pregnant. We calculated weighted prevalence estimates of demographic and clinical characteristics, vaccination status, and outcomes among pregnant women with symptoms of COVID-19 on admission. We used propensity score matching to estimate prevalence ratios and 95% confidence intervals of treatment-eligible patients who received remdesivir or systemic steroids by pregnancy status. RESULTS: Among 336 hospitalized pregnant women with symptomatic COVID-19, 39.6% were non-Hispanic Black, 24.8% were Hispanic or Latino, and 61.9% were aged 25 to 34 years. Among those with known COVID-19 vaccination status, 92.9% were unvaccinated. One-third (32.7%) were treatment-eligible. Among treatment-eligible pregnant women, 74.1% received systemic steroids and 61.4% received remdesivir. Among those that were no longer pregnant at discharge (n=180), 5.4% had spontaneous abortions and 3.5% had stillbirths. Of the 159 live births, 29.0% were preterm. Among a propensity score-matched cohort of treatment-eligible hospitalized women of reproductive age, pregnant women were less likely than nonpregnant women to receive remdesivir (prevalence ratio, 0.82; 95% confidence interval, 0.69-0.97) and systemic steroids (prevalence ratio, 0.80; 95% confidence interval, 0.73-0.87). CONCLUSION: Most hospitalized pregnant patients with symptomatic COVID-19 were unvaccinated. Hospitalized pregnant patients were less likely to receive recommended remdesivir and systemic steroids compared with similar hospitalized nonpregnant women. Our results underscore the need to identify opportunities for improving COVID-19 vaccination, implementation of treatment of pregnant women, and the inclusion of pregnant women in clinical trials.
ABSTRACT
PURPOSE: Passively generated cell-phone location ("mobility") data originally intended for commercial use has become frequently used in epidemiologic research, notably during the COVID-19 pandemic to study the impact of physical-distancing recommendations on aggregate population behavior (e.g., average daily mobility). Given the opaque nature of how individuals are selected into these datasets, researchers have cautioned that their use may give rise to selection bias, yet little guidance exists for assessing this potential threat to validity in mobility-data research. Through an example analysis of cell-phone-derived mobility data, we present a set of conditions to guide the assessment of selection bias in measures comparing aggregate mobility patterns over time and between groups. METHODS: We specifically consider bias in measures comparing group-level mobility in the same group (difference, ratio, percent difference) and between groups (difference in differences, ratio of ratios, ratio of percent differences). We illustrate no-bias conditions in these measures through an example comparing block-group-level mobility between income groups in United States metro areas before (January 1st-March 10, 2020) and after (March 11th-April 19th, 2020) the day COVID-19 was declared a pandemic. RESULTS: Within-group contrasts describing mobility over time, especially for the higher-income decile, were expected to be most resistant to bias during the example study period. CONCLUSIONS: The presented conditions can be used to assess the susceptibility to selection bias of group-level measures comparing mobility. Importantly, they can be used even without knowledge of the degree of bias in each group at each time point. We further highlight links between no-bias principles originating in epidemiology and economics, showing that certain assumptions (e.g., parallel trends) can apply to biases beyond their original application.