BACKGROUND: Pregnant women with coronavirus disease 2019 (COVID-19) are at increased risk for severe illness compared with nonpregnant women. Data to assess risk factors for illness severity among pregnant women with COVID-19 are limited. This study aimed to determine risk factors associated with COVID-19 illness severity among pregnant women with SARS-CoV-2 infection. METHODS: Pregnant women with SARS-CoV-2 infection confirmed by molecular testing were reported during March 29, 2020-March 5, 2021 through the Surveillance for Emerging Threats to Mothers and Babies Network (SET-NET). Criteria for illness severity (asymptomatic, mild, moderate-to-severe, or critical) were adapted from National Institutes of Health and World Health Organization criteria. Crude and adjusted risk ratios for moderate-to-severe or critical COVID-19 illness were calculated for selected demographic and clinical characteristics. RESULTS: Among 7,950 pregnant women with SARS-CoV-2 infection, moderate-to-severe or critical COVID-19 illness was associated with age 25 years and older, healthcare occupation, pre-pregnancy obesity, chronic lung disease, chronic hypertension, and pregestational diabetes mellitus. Risk of moderate-to-severe or critical illness increased with the number of underlying medical or pregnancy-related conditions. CONCLUSIONS: Older age and having underlying medical conditions were associated with increased risk of moderate-to-severe or critical COVID-19 illness among pregnant women. This information might help pregnant women understand their risk for moderate-to-severe or critical COVID-19 illness and inform targeted public health messaging.

BACKGROUND: Evidence on risk for adverse outcomes from COVID-19 among pregnant women is still emerging. We examined the association between COVID-19 at delivery and adverse pregnancy outcomes, maternal complications, and severe illness, whether these associations differ by race/ethnicity; and described discharge status by COVID-19 diagnosis and maternal complications. METHODS: Data from 703 hospitals in the Premier Healthcare Database during March-September 2020 were included. Adjusted risk ratios overall and stratified by race/ethnicity were estimated using Poisson regression with robust standard errors. Proportion not discharged home was calculated by maternal complications, stratified by COVID-19 diagnosis. RESULTS: Among 489,471 delivery hospitalizations, 6,550 (1.3%) had a COVID-19 diagnosis. In adjusted models, COVID-19 was associated with increased risk for: acute respiratory distress syndrome (adjusted risk ratio [aRR] = 34.4), death (aRR = 17.0), sepsis (aRR = 13.6), mechanical ventilation (aRR = 12.7), shock (aRR = 5.1), intensive care unit admission (aRR = 3.6), acute renal failure (aRR = 3.5), thromboembolic disease (aRR = 2.7), adverse cardiac event/outcome (aRR = 2.2) and preterm labor with preterm delivery (aRR = 1.2). Risk for any maternal complications or for any severe illness did not significantly differ by race/ethnicity. Discharge status did not differ by COVID-19; however, among women with concurrent maternal complications, a greater proportion of those with (versus without) COVID-19 were not discharged home. CONCLUSIONS: These findings emphasize the importance of implementing recommended mitigation strategies to reduce risk for SARS-CoV-2 infection and further inform counseling and clinical care for pregnant women during the COVID-19 pandemic.

BACKGROUND: Many pregnant persons in the United States are receiving messenger RNA (mRNA) coronavirus disease 2019 (Covid-19) vaccines, but data are limited on their safety in pregnancy. METHODS: From December 14, 2020, to February 28, 2021, we used data from the "v-safe after vaccination health checker" surveillance system, the v-safe pregnancy registry, and the Vaccine Adverse Event Reporting System (VAERS) to characterize the initial safety of mRNA Covid-19 vaccines in pregnant persons. RESULTS: A total of 35,691 v-safe participants 16 to 54 years of age identified as pregnant. Injection-site pain was reported more frequently among pregnant persons than among nonpregnant women, whereas headache, myalgia, chills, and fever were reported less frequently. Among 3958 participants enrolled in the v-safe pregnancy registry, 827 had a completed pregnancy, of which 115 (13.9%) resulted in a pregnancy loss and 712 (86.1%) resulted in a live birth (mostly among participants with vaccination in the third trimester). Adverse neonatal outcomes included preterm birth (in 9.4%) and small size for gestational age (in 3.2%); no neonatal deaths were reported. Although not directly comparable, calculated proportions of adverse pregnancy and neonatal outcomes in persons vaccinated against Covid-19 who had a completed pregnancy were similar to incidences reported in studies involving pregnant women that were conducted before the Covid-19 pandemic. Among 221 pregnancy-related adverse events reported to the VAERS, the most frequently reported event was spontaneous abortion (46 cases). CONCLUSIONS: Preliminary findings did not show obvious safety signals among pregnant persons who received mRNA Covid-19 vaccines. However, more longitudinal follow-up, including follow-up of large numbers of women vaccinated earlier in pregnancy, is necessary to inform maternal, pregnancy, and infant outcomes.

INTRODUCTION: Public health responses often lack the infrastructure to capture the impact of public health emergencies on pregnant women and infants, with limited mechanisms for linking pregnant women with their infants nationally to monitor long-term effects. In 2019, the Centers for Disease Control and Prevention (CDC), in close collaboration with state, local, and territorial health departments, began a 5-year initiative to establish population-based mother-baby linked longitudinal surveillance, the Surveillance for Emerging Threats to Mothers and Babies Network (SET-NET). OBJECTIVES: The objective of this report is to describe an expanded surveillance approach that leverages and modernizes existing surveillance systems to address the impact of emerging health threats during pregnancy on pregnant women and their infants. METHODS: Mother-baby pairs are identified through prospective identification during pregnancy and/or identification of an infant with retrospective linking to maternal information. All data are obtained from existing data sources (e.g., electronic medical records, vital statistics, laboratory reports, and health department investigations and case reporting). RESULTS: Variables were selected for inclusion to address key surveillance questions proposed by CDC and health department subject matter experts. General variables include maternal demographics and health history, pregnancy and infant outcomes, maternal and infant laboratory results, and child health outcomes up to the second birthday. Exposure-specific modular variables are included for hepatitis C, syphilis, and Coronavirus Disease 2019 (COVID-19). The system is structured into four relational datasets (maternal, pregnancy outcomes and birth, infant/child follow-up, and laboratory testing). DISCUSSION: SET-NET provides a population-based mother-baby linked longitudinal surveillance approach and has already demonstrated rapid adaptation to COVID-19. This innovative approach leverages existing data sources and rapidly collects data and informs clinical guidance and practice. These data can help to reduce exposure risk and adverse outcomes among pregnant women and their infants, direct public health action, and strengthen public health systems.

Studies suggest that pregnant women might be at increased risk for severe illness associated with coronavirus disease 2019 (COVID-19) (1,2). This report provides updated information about symptomatic women of reproductive age (15-44 years) with laboratory-confirmed infection with SARS-CoV-2, the virus that causes COVID-19. During January 22-October 3, CDC received reports through national COVID-19 case surveillance or through the National Notifiable Diseases Surveillance System (NNDSS) of 1,300,938 women aged 15-44 years with laboratory results indicative of acute infection with SARS-CoV-2. Data on pregnancy status were available for 461,825 (35.5%) women with laboratory-confirmed infection, 409,462 (88.7%) of whom were symptomatic. Among symptomatic women, 23,434 (5.7%) were reported to be pregnant. After adjusting for age, race/ethnicity, and underlying medical conditions, pregnant women were significantly more likely than were nonpregnant women to be admitted to an intensive care unit (ICU) (10.5 versus 3.9 per 1,000 cases; adjusted risk ratio [aRR] = 3.0; 95% confidence interval [CI] = 2.6-3.4), receive invasive ventilation (2.9 versus 1.1 per 1,000 cases; aRR = 2.9; 95% CI = 2.2-3.8), receive extracorporeal membrane oxygenation (ECMO) (0.7 versus 0.3 per 1,000 cases; aRR = 2.4; 95% CI = 1.5-4.0), and die (1.5 versus 1.2 per 1,000 cases; aRR = 1.7; 95% CI = 1.2-2.4). Stratifying these analyses by age and race/ethnicity highlighted disparities in risk by subgroup. Although the absolute risks for severe outcomes for women were low, pregnant women were at increased risk for severe COVID-19-associated illness. To reduce the risk for severe illness and death from COVID-19, pregnant women should be counseled about the importance of seeking prompt medical care if they have symptoms and measures to prevent SARS-CoV-2 infection should be strongly emphasized for pregnant women and their families during all medical encounters, including prenatal care visits. Understanding COVID-19-associated risks among pregnant women is important for prevention counseling and clinical care and treatment.

Pregnant women with coronavirus disease 2019 (COVID-19) are at increased risk for severe illness and might be at risk for preterm birth (1-3). The full impact of infection with SARS-CoV-2, the virus that causes COVID-19, in pregnancy is unknown. Public health jurisdictions report information, including pregnancy status, on confirmed and probable COVID-19 cases to CDC through the National Notifiable Diseases Surveillance System.* Through the Surveillance for Emerging Threats to Mothers and Babies Network (SET-NET), 16 jurisdictions collected supplementary information on pregnancy and infant outcomes among 5,252 women with laboratory-confirmed SARS-CoV-2 infection reported during March 29-October 14, 2020. Among 3,912 live births with known gestational age, 12.9% were preterm (<37 weeks), higher than the reported 10.2% among the general U.S. population in 2019 (4). Among 610 infants (21.3%) with reported SARS-CoV-2 test results, perinatal infection was infrequent (2.6%) and occurred primarily among infants whose mother had SARS-CoV-2 infection identified within 1 week of delivery. Because the majority of pregnant women with COVID-19 reported thus far experienced infection in the third trimester, ongoing surveillance is needed to assess effects of infections in early pregnancy, as well the longer-term outcomes of exposed infants. These findings can inform neonatal testing recommendations, clinical practice, and public health action and can be used by health care providers to counsel pregnant women on the risks of SARS-CoV-2 infection, including preterm births. Pregnant women and their household members should follow recommended infection prevention measures, including wearing a mask, social distancing, and frequent handwashing when going out or interacting with others or if there is a person within the household who has had exposure to COVID-19.(†).

Pregnant women might be at increased risk for severe coronavirus disease 2019 (COVID-19) (1,2). The COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) (3) collects data on hospitalized pregnant women with laboratory-confirmed SARS-CoV-2, the virus that causes COVID-19; to date, such data have been limited. During March 1-August 22, 2020, approximately one in four hospitalized women aged 15-49 years with COVID-19 was pregnant. Among 598 hospitalized pregnant women with COVID-19, 54.5% were asymptomatic at admission. Among 272 pregnant women with COVID-19 who were symptomatic at hospital admission, 16.2% were admitted to an intensive care unit (ICU), and 8.5% required invasive mechanical ventilation. During COVID-19-associated hospitalizations, 448 of 458 (97.8%) completed pregnancies resulted in a live birth and 10 (2.2%) resulted in a pregnancy loss. Testing policies based on the presence of symptoms might miss COVID-19 infections during pregnancy. Surveillance of pregnant women with COVID-19, including those with asymptomatic infections, is important to understand the short- and long-term consequences of COVID-19 for mothers and newborns. Identifying COVID-19 in women during birth hospitalizations is important to guide preventive measures to protect pregnant women, parents, newborns, other patients, and hospital personnel. Pregnant women and health care providers should be made aware of the potential risks for severe COVID-19 illness, adverse pregnancy outcomes, and ways to prevent infection.

In 2016, Centers for Disease Control and Prevention (CDC) established surveillance of pregnant women with Zika virus infection and their infants in the U.S. states, territories, and freely associated states. To identify cases of Zika-associated birth defects, subject matter experts review data reported from medical records of completed pregnancies to identify findings that meet surveillance case criteria (manual review). The volume of reported data increased over the course of the Zika virus outbreak in the Americas, challenging the resources of the surveillance system to conduct manual review. Machine learning was explored as a possible method for predicting case status. Ensemble models (using machine learning algorithms including support vector machines, logistic regression, random forests, k-nearest neighbors, gradient boosted trees, and decision trees) were developed and trained using data collected from January 2016-October 2017. Models were developed separately, on data from the U.S. states, non-Puerto Rico territories, and freely associated states (referred to as the U.S. Zika Pregnancy and Infant Registry [USZPIR]) and data from Puerto Rico (referred to as the Zika Active Pregnancy Surveillance System [ZAPSS]) due to differences in data collection and storage methods. The machine learning models demonstrated high sensitivity for identifying cases while potentially reducing volume of data for manual review (USZPIR: 96% sensitivity, 25% reduction in review volume; ZAPSS: 97% sensitivity, 50% reduction in review volume). Machine learning models show potential for identifying cases of Zika-associated birth defects and for reducing volume of data for manual review, a potential benefit in other public health emergency response settings.

As of June 16, 2020, the coronavirus disease 2019 (COVID-19) pandemic has resulted in 2,104,346 cases and 116,140 deaths in the United States.* During pregnancy, women experience immunologic and physiologic changes that could increase their risk for more severe illness from respiratory infections (1,2). To date, data to assess the prevalence and severity of COVID-19 among pregnant U.S. women and determine whether signs and symptoms differ among pregnant and nonpregnant women are limited. During January 22-June 7, as part of COVID-19 surveillance, CDC received reports of 326,335 women of reproductive age (15-44 years) who had positive test results for SARS-CoV-2, the virus that causes COVID-19. Data on pregnancy status were available for 91,412 (28.0%) women with laboratory-confirmed infections; among these, 8,207 (9.0%) were pregnant. Symptomatic pregnant and nonpregnant women with COVID-19 reported similar frequencies of cough (>50%) and shortness of breath (30%), but pregnant women less frequently reported headache, muscle aches, fever, chills, and diarrhea. Chronic lung disease, diabetes mellitus, and cardiovascular disease were more commonly reported among pregnant women than among nonpregnant women. Among women with COVID-19, approximately one third (31.5%) of pregnant women were reported to have been hospitalized compared with 5.8% of nonpregnant women. After adjusting for age, presence of underlying medical conditions, and race/ethnicity, pregnant women were significantly more likely to be admitted to the intensive care unit (ICU) (aRR = 1.5, 95% confidence interval [CI] = 1.2-1.8) and receive mechanical ventilation (aRR = 1.7, 95% CI = 1.2-2.4). Sixteen (0.2%) COVID-19-related deaths were reported among pregnant women aged 15-44 years, and 208 (0.2%) such deaths were reported among nonpregnant women (aRR = 0.9, 95% CI = 0.5-1.5). These findings suggest that among women of reproductive age with COVID-19, pregnant women are more likely to be hospitalized and at increased risk for ICU admission and receipt of mechanical ventilation compared with nonpregnant women, but their risk for death is similar. To reduce occurrence of severe illness from COVID-19, pregnant women should be counseled about the potential risk for severe illness from COVID-19, and measures to prevent infection with SARS-CoV-2 should be emphasized for pregnant women and their families.

OBJECTIVE: To inform the current coronavirus disease 2019 (COVID-19) outbreak, we conducted a systematic literature review of case reports of Middle East respiratory syndrome coronavirus (MERS-CoV), severe acute respiratory syndrome coronavirus (SARS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, during pregnancy and summarized clinical presentation, course of illness, and pregnancy and neonatal outcomes. DATA SOURCES: We searched MEDLINE and ClinicalTrials.gov from inception to April 23, 2020. METHODS OF STUDY SELECTION: We included articles reporting case-level data on MERS-CoV, SARS-CoV, and SARS-CoV-2 infection in pregnant women. Course of illness, indicators of severe illness, maternal health outcomes, and pregnancy outcomes were abstracted from included articles. TABULATION, INTEGRATION, AND RESULTS: We identified 1,328 unique articles, and 1,253 articles were excluded by title and abstract review. We completed full-text review on 75, and 29 articles were excluded by full-text review. Among 46 publications reporting case-level data, eight described 12 cases of MERS-CoV infection, seven described 17 cases of SARS-CoV infection, and 31 described 98 cases of SARS-CoV-2 infection. Clinical presentation and course of illness ranged from asymptomatic to severe fatal disease, similar to the general population of patients. Severe morbidity and mortality among women with MERS-CoV, SARS-CoV, or SARS-CoV-2 infection in pregnancy and adverse pregnancy outcomes, including pregnancy loss, preterm delivery, and laboratory evidence of vertical transmission, were reported. CONCLUSION: Understanding whether pregnant women may be at risk for adverse maternal and neonatal outcomes from severe coronavirus infections is imperative. Data from case reports of SARS-CoV, MERS-CoV, and SAR-CoV-2 infections during pregnancy are limited, but they may guide early public health actions and clinical decision-making for COVID-19 until more rigorous and systematically collected data are available. The capture of critical data is needed to better define how this infection affects pregnant women and neonates. This review was not registered with PROSPERO.

Globally, approximately 170,000 confirmed cases of coronavirus disease 2019 (COVID-19) caused by the 2019 novel coronavirus (SARS-CoV-2) have been reported, including an estimated 7,000 deaths in approximately 150 countries (1). On March 11, 2020, the World Health Organization declared the COVID-19 outbreak a pandemic (2). Data from China have indicated that older adults, particularly those with serious underlying health conditions, are at higher risk for severe COVID-19-associated illness and death than are younger persons (3). Although the majority of reported COVID-19 cases in China were mild (81%), approximately 80% of deaths occurred among adults aged ≥60 years; only one (0.1%) death occurred in a person aged ≤19 years (3). In this report, COVID-19 cases in the United States that occurred during February 12-March 16, 2020 and severity of disease (hospitalization, admission to intensive care unit [ICU], and death) were analyzed by age group. As of March 16, a total of 4,226 COVID-19 cases in the United States had been reported to CDC, with multiple cases reported among older adults living in long-term care facilities (4). Overall, 31% of cases, 45% of hospitalizations, 53% of ICU admissions, and 80% of deaths associated with COVID-19 were among adults aged ≥65 years with the highest percentage of severe outcomes among persons aged ≥85 years. In contrast, no ICU admissions or deaths were reported among persons aged ≤19 years. Similar to reports from other countries, this finding suggests that the risk for serious disease and death from COVID-19 is higher in older age groups.

Importance: Zika virus (ZIKV) infection during pregnancy is a cause of congenital microcephaly and severe fetal brain defects, and it has been associated with other adverse pregnancy and birth outcomes. Objective: To estimate the number of pregnant women infected with ZIKV in Puerto Rico and the number of associated congenital microcephaly cases. Design, Setting, and Participants: We conducted a modeling study from April to July 2016. Using parameters derived from published reports, outcomes were modeled probabilistically using Monte Carlo simulation. We used uncertainty distributions to reflect the limited information available for parameter values. Given the high level of uncertainty in model parameters, interquartile ranges (IQRs) are presented as primary results. Outcomes were modeled for pregnant women in Puerto Rico, which currently has more confirmed ZIKV cases than any other US location. Exposure: Zika virus infection in pregnant women. Main Outcomes and Measures: Number of pregnant women infected with ZIKV and number of congenital microcephaly cases. Results: We estimated an IQR of 5900 to 10300 pregnant women (median, 7800) might be infected during the initial ZIKV outbreak in Puerto Rico. Of these, an IQR of 100 to 270 infants (median, 180) may be born with microcephaly due to congenital ZIKV infection from mid-2016 to mid-2017. In the absence of a ZIKV outbreak, an IQR of 9 to 16 cases (median, 12) of congenital microcephaly are expected in Puerto Rico per year. Conclusions and Relevance: The estimate of 5900 to 10300 pregnant women that might be infected with ZIKV provides an estimate for the number of infants that could potentially have ZIKV-associated adverse outcomes. Including baseline cases of microcephaly, we estimated that an IQR of 110 to 290 total cases of congenital microcephaly, mostly attributable to ZIKV infection, could occur from mid-2016 to mid-2017 in the absence of effective interventions. The primary limitation in this analysis is uncertainty in model parameters. Multivariate sensitivity analyses indicated that the cumulative incidence of ZIKV infection and risk of microcephaly given maternal infection in the first trimester were the primary drivers of both magnitude and uncertainty in the estimated number of microcephaly cases. Increased information on these parameters would lead to more precise estimates. Nonetheless, the results underscore the need for urgent actions being undertaken in Puerto Rico to prevent congenital ZIKV infection and prepare for affected infants.

BACKGROUND: Antiretroviral interventions are used to reduce HIV viral replication and prevent mother-to-child transmission. Viral suppression relies on adherence to antiretrovirals. METHODS: A two-phase study was conducted using data from the Breastfeeding, Antiretrovirals and Nutrition study. We included mothers randomized to 28 weeks of postpartum antiretrovirals with ≥1 plasma or breastmilk specimen. All mothers who transmitted HIV to their infants from 2-28 weeks (n=31) and 15% of mothers who did not (n=232) were included. Adherence was measured by pill count [categorized as poor (0-80%), partial (81-98%) and near perfect (>98%)]. Associations between adherence and breastmilk RNA were assessed using mixed effects models. Cox models were used to estimate associations between breastmilk RNA and HIV transmission. Using Monte Carlo simulation, we estimated the number of transmissions that would occur had everyone randomized to maternal ARVs been 90% and 100% adherent. RESULTS: Partial or near perfect antiretroviral adherence significantly reduced the odds of having detectable (≥40 copies/ml) breastmilk RNA, compared to poor adherence (OR 0.23, 95% CI 0.08-0.67; OR 0.36, 95% CI 0.16-0.81, respectively). Detectable breastmilk RNA was associated with increased breastmilk transmission, compared to undetectable breastmilk RNA (HR 3.8, 95% CI 1.2-12.1). All transmitting mothers had ≥1 plasma viral load specimen >100 copies/ml. An estimated similar number of transmissions would occur with 90% adherence compared with 100%. CONCLUSIONS: Helping patients adhere to antiretrovirals throughout breastfeeding is important for realizing the full potential of recommended antiretroviral interventions to prevent mother-to-child HIV transmission. Maintaining plasma viral load <100 copies/ml may prevent breastmilk transmission.

Mother-to-child transmission (MTCT) is the most important mode of HIV-1 acquisition among infants and children and it can occur in utero, intrapartum and postnatally through breastfeeding. Great progress has been made in preventing MTCT through use of antiretroviral regimens during gestation, labor/delivery and breastfeeding. The mechanisms of MTCT, however, are multifactorial and remain incompletely understood. This review focuses on select host factors affecting MTCT, in particular genetic factors, coexisting infections, behavioral factors and nutrition. Whereas much emphasis has been placed on decreasing maternal HIV-1 viral load, an important determinant of MTCT, through use of antiretroviral agents, complementary focus on overall maternal health is often neglected. By addressing coinfections in mothers and infants, improving the mother's nutritional status and modifying risky behaviors and practices, not only is maternal and child health improved, but a direct benefit in reducing MTCT can be derived. The study of genetic variations in susceptibility to HIV-1 infection is rapidly evolving, and the future is likely to bring revolutionary changes in HIV-1 prevention by enhancing natural resistance to infection and by individually tailoring pharmacologic regimens. (2011 Future Medicine Ltd.)