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ImportanceUsing provisional or opportunistic data, three nationwide studies (The Netherlands, USA, and Denmark) have identified a reduction in preterm or extremely preterm births during periods of COVID-19 restrictions. However, these findings have been challenged as none of the studies accounted for perinatal deaths.ObjectiveTo determine whether the reduction in extremely preterm births, observed in Denmark during the COVID-19 lockdown, could be a result of an increase in number of perinatal deaths; and to assess the impact of extended COVID-19 restrictions on the prevalence of preterm birth and perinatal death.DesignThe study is a nationwide Danish register-based prevalence proportion study using detailed data to capture all births (induced abortions were excluded) throughout Denmark. We assessed the prevalence of stillbirth, preterm birth, and early neonatal death.SettingPopulation-based studyParticipantsAll singleton pregnancies delivered in Denmark, between February 27, and September 30, 2015-2020. COVID-19 lockdown was studied in 31,164 births and the extended period of COVID-19 restrictions in 214,862 births.ExposureCOVID-19 restrictions broadly (February 27, – September 30, 2020) or COVID-19 lockdown specifically (March 12, – April 14, 2020).Main OutcomePrevalence of preterm births, stillbirths and early neonatal deaths across the periods under study.ResultsThe extremely preterm birth rate was reduced (OR 0.27, 95% CI 0.07 to 0.86) during the strict lockdown period, while perinatal mortality was not significantly different. During the extended period of restrictions, the extremely preterm birth rate was marginally reduced, and a significant reduction in the stillbirth rate (OR 0.69, 0.50 to 0.95) was observed. No changes in early neonatal mortality rates were found.Conclusion and RelevanceStillbirth and extremely preterm birth rates were reduced in Denmark during the period of COVID-19 restrictions and lockdown, respectively, suggesting that aspects of these containment and control measures confer an element of protection. The present observational study does not allow for causal inference; however, the results support design of studies to ascertain whether behavioural or social changes for pregnant women may improve pregnancy outcomes.FundingNoneKey pointsQuestionCan changes in stillbirth and early neonatal mortality rates during the COVID-19 lockdown explain the reduction in extremely preterm birth seen in Denmark?FindingsIn this nationwide register-based study that included data pertaining to 245,999 pregnancies, the statistically significant reduction in extremely preterm birth during the COVID-19 lockdown was confirmed. The stillbirth and early neonatal mortality rates were unchanged during lockdown, while the stillbirth rate was reduced over the extended period of COVID-19 restrictions.MeaningThe reduction in extremely preterm births seen during the COVID-19 lockdown in Denmark is not a result of increased stillbirth or early neonatal death.

Objectives To explore the impact of COVID-19 lockdown on premature birth rates in Denmark Design Nationwide register-based prevalence proportion study. Participants 31,180 live singleton infants born in Denmark between March 12, and April 14, from 2015 to 2020 Main outcome measures The Main outcome measure was the odds ratio of premature birth, per preterm category, during the lockdown period compared with the calendar match period in the five previous years. Results A total of 31 180 newborns were included in the study period, of these 58 were born extremely premature (gestational age below 28 weeks). The distribution of gestational ages was significantly different (p = 0.004) during the lockdown period compared to the previous five years. The extremely premature birth rate during the lockdown was significantly lower than the corresponding mean rate for the same dates in the previous years (odds ratio 0.09 [95 % CI 0.01 - 0.04], p < 0.001). No significant difference between the lockdown and previous years was found for other gestational age categories. Conclusions The birth rate of extremely premature infants decreased significantly (~90 % reduction) during the Danish nationwide lockdown from a stable rate in the preceding five years. The reasons for this decrease are unclear. Identification of possible causal mechanisms might stimulate changes in clinical practice. Ideally, some cases of extreme prematurity are preventable which may decrease infant morbidity and mortality.

Identification of individuals at risk of developing disease comorbidities represents an important task in tackling the growing personal and societal burdens associated with chronic diseases. We employed machine learning techniques to investigate to what extent data from longitudinal, nationwide Danish health registers can be used to predict individuals at high risk of developing type 2 diabetes (T2D) comorbidities. Leveraging logistic regression-, random forest- and gradient boosting models and register data spanning hospitalizations, drug prescriptions and contacts with primary care contractors from >200,000 individuals newly diagnosed with T2D, we predicted five-year risk of heart failure (HF), myocardial infarction (MI), stroke (ST), cardiovascular disease (CVD) and chronic kidney disease (CKD). For HF, MI, CVD, and CKD, register-based models outperformed a reference model leveraging canonical individual characteristics by achieving area under the receiver operating characteristic curve improvements of 0.06, 0.03, 0.04, and 0.07, respectively. The top 1,000 patients predicted to be at highest risk exhibited observed incidence ratios exceeding 4.99, 3.52, 1.97 and 4.71 respectively. In summary, prediction of T2D comorbidities utilizing Danish registers led to consistent albeit modest performance improvements over reference models, suggesting that register data could be leveraged to systematically identify individuals at risk of developing disease comorbidities.