A population-based matched cohort study of major congenital anomalies following COVID-19 vaccination and SARS-CoV-2 infection.

Evidence on associations between COVID-19 vaccination or SARS-CoV-2 infection and the risk of congenital anomalies is limited. Here we report a national, population-based, matched cohort study using linked electronic health records from Scotland (May 2020-April 2022) to estimate the association between COVID-19 vaccination and, separately, SARS-CoV-2 infection between six weeks pre-conception and 19 weeks and six days gestation and the risk of [1] any major congenital anomaly and [2] any non-genetic major congenital anomaly. Mothers vaccinated in this pregnancy exposure period mostly received an mRNA vaccine (73.7% Pfizer-BioNTech BNT162b2 and 7.9% Moderna mRNA-1273). Of the 6731 babies whose mothers were vaccinated in the pregnancy exposure period, 153 had any anomaly and 120 had a non-genetic anomaly. Primary analyses find no association between any vaccination and any anomaly (adjusted Odds Ratio [aOR] = 1.01, 95% Confidence Interval [CI] = 0.83-1.24) or non-genetic anomalies (aOR = 1.00, 95% CI = 0.81-1.22). Primary analyses also find no association between SARS-CoV-2 infection and any anomaly (aOR = 1.02, 95% CI = 0.66-1.60) or non-genetic anomalies (aOR = 0.94, 95% CI = 0.57-1.54). Findings are robust to sensitivity analyses. These data provide reassurance on the safety of vaccination, in particular mRNA vaccines, just before or in early pregnancy.

COVID-19 in pregnancy-what study designs can we use to assess the risk of congenital anomalies in relation to COVID-19 disease, treatment and vaccination?

BACKGROUND: The COVID-19 pandemic has accelerated pregnancy outcome research, but little attention has been given specifically to the risk of congenital anomalies (CA) and first trimester exposures. OBJECTIVES: We reviewed the main data sources and study designs used internationally, particularly in Europe, for CA research, and their strengths and limitations for investigating COVID-19 disease, medications and vaccines. POPULATION: We classify research designs based on four data sources: a) spontaneous adverse event reporting, where study subjects are positive for both exposure and outcome, b) pregnancy exposure registries, where study subjects are positive for exposure, c) congenital anomaly registries, where study subjects are positive for outcome and d) population healthcare data where the entire population of births is included, irrespective of exposure and outcome. STUDY DESIGN: Each data source allows different study designs, including case series, exposed pregnancy cohorts (with external comparator), ecological studies, case-control studies and population cohort studies (with internal comparator). METHODS: The quality of data sources for CA studies is reviewed in relation to criteria including diagnostic accuracy of CA data, size of study population, inclusion of terminations of pregnancy for foetal anomaly, inclusion of first trimester COVID-19-related exposures and use of an internal comparator group. Multinational collaboration models are reviewed. RESULTS: Pregnancy exposure registries have been the main design for COVID-19 pregnancy studies, but lack detail regarding first trimester exposures relevant to CA, or a suitable comparator group. CA registries present opportunities for improving diagnostic accuracy in COVID-19 research, especially when linked to other data sources. Availability of inpatient hospital medication use in population healthcare data is limited. More use of ongoing mother-baby linkage systems would improve research efficiency. Multinational collaboration delivers statistical power. CONCLUSIONS: Challenges and opportunities exist to improve research on CA in relation to the COVID-19 pandemic and future pandemics.

Macrolide and lincosamide antibiotic exposure in the first trimester of pregnancy and risk of congenital anomaly: A European case-control study.

This study investigated the risk of congenital heart defects (CHD) and other congenital anomalies (CA) associated with first trimester use of macrolide antibiotics (mainly erythromycin, spiramycin, clarithromycin and azithromycin) and lincosamides (clindamycin) using a case-malformed control design. Data included 145,936 babies with a CA diagnosis (livebirths, stillbirths and terminations of pregnancy for CA) from 15 population-based EUROCAT registries in 13 European countries, covering 9 million births 1995-2012. Cases were babies with CHD, anencephaly, orofacial clefts, genital and limb reduction anomalies associated with antibiotic exposure in the literature. Controls were babies with other CA or genetic conditions. Main outcomes were odds ratios adjusted (AOR) for maternal age and registry, with 95 % Confidence Intervals (95 %CI). Macrolide and lincosamide exposure was recorded for 307 and 28 cases, 72 and 4 non-genetic controls, 57 and 7 genetic controls, respectively. AOR for CHD was not significantly raised (AOR 0.94, 95 %CI: 0.70-1.26 vs non-genetic controls; AOR 1.01, 95 %CI: 0.73-1.41 vs genetic controls), nor significantly raised for any specific macrolide. The risk of atrioventricular septal defect was significantly raised with exposure to any macrolide (AOR 2.98; 95 %CI: 1.48-6.01), erythromycin (AOR 3.68, 95 %CI: 1.28-10.61), and azithromycin (AOR 4.50, 95 %CI: 1.30-15.58). Erythromycin, clarithromycin, azithromycin, and clindamycin were associated with an increased risk of at least one other CA. Further research is needed on the risk of specific CA associated with macrolide and lincosamide use in the first trimester, particularly relevant for the potential use of azithromycin in the treatment of COVID-19.

Accounting for Underreporting in Mathematical Modeling of Transmission and Control of COVID-19 in Iran

Iran has been the country most affected by the outbreak of SARS-CoV-2 in the Middle East. With a relatively high case fatality ratio and limited testing capacity, the number of confirmed cases reported is suspected to suffer from significant underreporting. Therefore, understanding the transmission dynamics of COVID-19 and assessing the effectiveness of the interventions that have taken place in Iran while accounting for the uncertain level of underreporting is of critical importance. In this paper, we developed a compartmental transmission model to estimate the time-dependent effective reproduction number since the beginning of the outbreak in Iran. We associate the variations in the effective reproduction number with a timeline of interventions and national events. The estimation method accounts for the underreporting due to low case ascertainment. Our estimates of the effective reproduction number ranged from 0.66 to 1.73 between February and April 2020, with a median of 1.16. We estimate a reduction in the effective reproduction number during this period, from 1.73 (95% CI 1.60-1.87) on 1 March 2020 to 0.69 (95% CI 0.68-0.70) on 15 April 2020, due to various non-pharmaceutical interventions. The series of non-pharmaceutical interventions and the public compliance that took place in Iran are found to be effective in slowing down the speed of the spread of COVID-19. However, we argue that if the impact of underreporting is overlooked, the estimated transmission and control dynamics could mislead public health decisions, policy makers, and the general public.