RESUMEN
Objectives: To compare pregnancy loss rates, preterm birth rates and gestational age at delivery in women vaccinated against COVID-19 during pregnancy vs. those unvaccinated. Method(s): Data were captured from Dorsata Prenatal, an electronic medical record (EMR) system that captures obstetrical data for tens of thousands of pregnancies annually. Patients who delivered between February 11, 2021-June 2, 2022, were included. The vaccinated group included women who had at least one COVID-19 vaccination documented in their EMR between 30 days prior to pregnancy and delivery. The unvaccinated group included women without a COVID-19 vaccination documented. The primary outcome measure was gestational age (GA) at delivery. We analyzed the data using chi-square tests, with significance set at p<0.01. Result(s): A total of 51,994 pregnant women were identified-7,947 (15.3%) in the vaccinated group and 44,047 (84.7%) in the unvaccinated group. Vaccination rate varied by race (Asian: 19.7%;White: 17.3%;Black: 11.2%, P<0.001), ethnicity (Latino: 8.6%;Not-Latino: 18.7%;P<0.001), marital status (Married: 19.2%;Single: 8.8%;P<0.001), mother's age (>=35 years: 20.0%;<35 years 14.2%;P<0.001), and region (Northeast: 19.2%;South: 15.2%;West: 9.1%;P<0.001). The vaccinated group had significantly lower rate of preterm delivery (Gestational Age [GA]<37 weeks;vaccinated: 7.8% vs. unvaccinated: 9.6%;P<0.001), and significantly lower rates of pregnancy loss (GA<20 weeks;vaccinated: 1.1% vs. unvaccinated: 4.1%;P<0.001). Conclusion(s): This is one of the largest real-world studies to date in women who received the COVID-19 vaccination during pregnancy. Vaccination rates varied significantly across race/ethnicity. Vaccinated patients had lower preterm delivery and pregnancy loss rates compared with unvaccinated patients.Copyright © 2023
RESUMEN
Objectives: The One Health Approach (OHA) involves a collaborative, multisectoral, multidisciplinary framework to address public health challenges and achieve optimal health outcomes. OHA recognizes the interconnection between people, animals, plants, and their shared environment. This M-CERSI project amplifies the OHA by amplifying and synergizing different disciplines (e.g., social, and behavioral sciences, machine learning, and artificial intelligence options) with expertise from various FDA centers, offices, and academia to harness narrative COVID-19 unstructured publicly available data. Method(s): Human curation and machine learning techniques are augmented with social and behavioral science methods and input by subject matter experts, across four sequential components. First, the collection of publicly available data from various FDA input and output sources. Second, the systematic narrowing of scope of inclusion to public comments submitted to Regulations.gov in response to COVID-19 related meetings and dockets. Third, the extraction of approximately 140,000 comments using computing methods and the newly available OpenGSA Application Programming Interface (API). Fourth, preprocessing and analysis to generate insights using a machine learning technique, topic modeling, combined with human curation techniques. Result(s): Results included the determination of a structure whereby public comment groupings can be parsed into meaningful subsets. Integrative analysis via human curation and computing methods yielded insights into public opinion as well as producing machine learning models that may be applied to future datasets. These results highlight the value of building a multidisciplinary OHA framework. Conclusion(s): This multidisciplinary research collaboration supports FDA's regulatory public health mission and the OHA, effectively reducing silos and leveraging expertise across the scientific spectrum. This approach can be implemented to provide ongoing, timely and accurate information across stakeholder groups. The next phase of research will apply discovered insights to design focus group sample populations, contrast emerging themes, and develop clear messaging that is responsive to public interests and concerns. Copyright © 2022