COVID-19 vaccination in pregnant women in Sweden and Norway.

Vaccines against SARS-CoV-2 are highly effective in preventing severe disease and mortality. Although pregnant women are at increased risk of severe COVID-19, vaccination uptake among pregnant women varies. We used the Swedish and Norwegian population-based health registries to identify pregnant women and to investigate background characteristics associated with not being vaccinated. In this study of 164 560 women giving birth between May 2021 and May 2022, 78% in Sweden and 87% in Norway have been vaccinated with at least one dose at delivery. Not being vaccinated while being pregnant was associated with age below 30 years, low education and income level, birth region other than Scandinavia, smoking during pregnancy, not living with a partner, and gestational diabetes. These results can assist health authorities develop targeted vaccination information to diminish vaccination inequality and prevent severe disease in vulnerable groups.

Association of SARS-CoV-2 Vaccination During Pregnancy With Pregnancy Outcomes.

Importance: Data about the safety of vaccines against SARS-CoV-2 during pregnancy are limited. Objective: To examine the risk of adverse pregnancy outcomes after vaccination against SARS-CoV-2 during pregnancy. Design, Setting, and Participants: This registry-based retrospective cohort study included 157 521 singleton pregnancies ending after 22 gestational weeks from January 1, 2021, until January 12, 2022 (Sweden), or January 15, 2022 (Norway). The Pregnancy Register in Sweden and the Medical Birth Registry of Norway were linked to vaccination and other registries for identification of exposure and background characteristics. Exposures: Data on mRNA vaccines-BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna)-and 1 viral vector vaccine-AZD1222 (AstraZeneca)-were collected from national vaccination registries. Main Outcomes and Measures: The risk of preterm birth and stillbirth was evaluated using Cox regression models, with gestational day as the time metric and vaccination as a time-dependent exposure variable. The risk of small for gestational age, low Apgar score, and neonatal care admission was evaluated using logistic regression. Random-effects meta-analysis was used to combine results between countries. Results: Among the 157 521 singleton births included in the study (103 409 in Sweden and 54 112 in Norway), the mean maternal age at the time of delivery was 31 years, and 28 506 (18%) were vaccinated against SARS-CoV-2 (12.9% with BNT162b2, 4.8% with mRNA-1273, and 0.3% with AZD1222) while pregnant. A total of 0.7%, 8.3%, and 9.1% of individuals delivering were vaccinated during the first, second, and third trimester, respectively. Vaccination against SARS-CoV-2 was not significantly associated with increased risk of preterm birth (6.2 vs 4.9 per 10 000 pregnancy days; adjusted hazard ratio [aHR], 0.98 [95% CI, 0.91 to 1.05]; I2 = 0%; P for heterogeneity = .60), stillbirth (2.1 vs 2.4 per 100 000 pregnancy days; aHR, 0.86 [95% CI, 0.63 to 1.17]), small for gestational age (7.8% vs 8.5%; difference, -0.6% [95% CI, -1.3% to 0.2%]; adjusted OR [aOR], 0.97 [95% CI, 0.90 to 1.04]), low Apgar score (1.5% vs 1.6%; difference, -0.05% [95% CI, -0.3% to 0.1%]; aOR, 0.97 [95% CI, 0.87 to 1.08]), or neonatal care admission (8.5% vs 8.5%; difference, 0.003% [95% CI, -0.9% to 0.9%]; aOR, 0.97 [95% CI, 0.86 to 1.10]). Conclusions and Relevance: In this population-based study conducted in Sweden and Norway, vaccination against SARS-CoV-2 during pregnancy, compared with no SARS-CoV-2 vaccination during pregnancy, was not significantly associated with an increased risk of adverse pregnancy outcomes. The majority of the vaccinations were with mRNA vaccines during the second and third trimesters of pregnancy, which should be considered in interpreting the findings.

The comparative cardiovascular and renal effectiveness of sodium-glucose co-transporter-2 inhibitors and glucagon-like peptide-1 receptor agonists: A Scandinavian cohort study.

AIM: To assess the comparative cardiovascular and renal effectiveness of sodium-glucose co-transporter-2 (SGLT2) inhibitors versus glucagon-like peptide-1 (GLP-1) receptor agonists in routine clinical practice. MATERIALS AND METHODS: A cohort study of nationwide registers from Sweden, Denmark, and Norway, including 87 525 new users of SGLT2 inhibitors and 63 921 new users of GLP-1 receptor agonists, was conducted using data from 2013-2018. Co-primary outcomes, analysed using an intention-to-treat exposure definition, were major adverse cardiovascular events (MACE; myocardial infarction, stroke, and cardiovascular death), heart failure (hospitalization or death because of heart failure), and serious renal events (renal replacement therapy, hospitalization for renal events, and death from renal causes). RESULTS: Use of SGLT2 inhibitors versus GLP-1 receptor agonists was associated with a higher risk of MACE (adjusted incidence rate: 15.2 vs. 14.4 events per 1000 person-years; HR 1.07 [95% CI 1.01-1.15]), a similar risk of heart failure (6.0 vs. 6.0 events per 1000 person-years; HR 1.02 [0.92-1.12]), and a lower risk of serious renal events (2.9 vs. 4.0 events per 1000 person-years; HR 0.76 [0.66-0.87]). In as-treated analyses, the HR (95% CI) was 1.11 (1.00-1.24) for MACE, 0.88 (0.74-1.04) for heart failure, and 0.60 (0.47-0.77) for serious renal events. In secondary outcome analyses, use of SGLT2 inhibitors versus GLP-1 receptor agonists was not associated with statistically significant differences for the risk of myocardial infarction (HR 1.09 [95% CI 1.00-1.19]), cardiovascular death (HR 0.97 [95% CI 0.84-1.12]), death from renal causes (HR 0.75 [95% CI 0.41-1.35]), or any cause death (HR 1.01 [95% CI 0.94-1.09]), while the risk of stroke was higher (HR 1.14 [95% CI 1.03-1.26]), and the risk of renal replacement therapy (HR 0.74 [95% CI 0.56-0.97]) and hospitalization for renal events (HR 0.75 [95% CI 0.65-0.88]) were lower among users of SGLT2 inhibitors. CONCLUSIONS: Use of SGLT2 inhibitors versus GLP-1 receptor agonists was associated with a similar risk of heart failure and a lower risk of serious renal events, while use of GLP-1 receptor agonists versus SGLT2 inhibitors was associated with a slightly lower risk of MACE. In as-treated analyses, the associations with MACE and serious renal events increased in magnitude, and the HR for heart failure tended towards a protective association for SGLT2 inhibitors.

Using instrumental variables to estimate the attributable fraction.

In order to design efficient interventions aimed to improve public health, policy makers need to be provided with reliable information of the health burden of different risk factors. For this purpose, we are interested in the proportion of cases that could be prevented had some harmful exposure been eliminated from the population, i.e. the attributable fraction. The attributable fraction is a causal measure; thus, to estimate the attributable fraction from observational data, we have to make appropriate adjustment for confounding. However, some confounders may be unobserved, or even unknown to the investigator. A possible solution to this problem is to use instrumental variable analysis. In this work, we present how the attributable fraction can be estimated with instrumental variable methods based on the two-stage estimator or the G-estimator. One situation when the problem of unmeasuredconfounding may be particularly severe is when assessing the effect of low educational qualifications on coronary heart disease. By using Mendelian randomization, a special case of instrumental variable analysis, it has been claimed that low educational qualifications is a causal risk factor for coronary heart disease. We use Mendelian randomization to estimate the causal risk ratio and causal odds ratio of low educational qualifications as a risk factor for coronary heart disease with data from the UK Biobank. We compare the two-stage and G-estimator as well as the attributable fraction based on the two estimators. The plausibility of drawing causal conclusion in this analysis is thoroughly discussed and alternative genetic instrumental variables are tested.

On the relationship between the heritability and the attributable fraction.

Heritability is the most commonly used measure of genetic contribution to disease outcomes. Being the fraction of the variance of latent trait liability attributable to genetic factors, heritability of binary traits is a difficult technical concept that is sometimes misinterpreted as the more-easily understandable concept of attributable fraction. In this paper we use the liability threshold model to describe the analytical relationship between heritability and attributable fraction. Towards this end, we consider a hypothetical intervention that is aimed to reduce the genetic risk of the disease for a specified target group of the population. We show how the relation between the heritability and the attributable fraction depends on the disease prevalence, the intervention effect and the size of the target group. We use two real examples to illustrate the practical implications of our theoretical results.

Regression standardization and attributable fraction estimation with between-within frailty models for clustered survival data.

The between-within frailty model has been proposed as a viable analysis tool for clustered survival time outcomes. Previous research has shown that this model gives consistent estimates of the exposure-outcome hazard ratio in the presence of unmeasured cluster-constant confounding, which the ordinary frailty model does not, and that estimates obtained from the between-within frailty model are often more efficient than estimates obtained from the stratified Cox proportional hazards model. In this paper, we derive novel estimation techniques for regression standardization with between-within frailty models. We also show how between-within frailty models can be used to estimate the attributable fraction function, which is a generalization of the attributable fraction for survival time outcomes. We illustrate the proposed methods by analyzing a large cohort on preterm birth and attention deficit hyperactivity disorder. To facilitate use of the proposed methods, we provide R code for all analyses.

Model-based estimation of the attributable fraction for cross-sectional, case-control and cohort studies using the R package AF.

The attributable fraction (or attributable risk) is a widely used measure that quantifies the public health impact of an exposure on an outcome. Even though the theory for AF estimation is well developed, there has been a lack of up-to-date software implementations. The aim of this article is to present a new R package for AF estimation with binary exposures. The package AF allows for confounder-adjusted estimation of the AF for the three major study designs: cross-sectional, (possibly matched) case-control and cohort. The article is divided into theoretical sections and applied sections. In the theoretical sections we describe how the confounder-adjusted AF is estimated for each specific study design. These sections serve as a brief but self-consistent tutorial in AF estimation. In the applied sections we use real data examples to illustrate how the AF package is used. All datasets in these examples are publicly available and included in the AF package, so readers can easily replicate all analyses.

A Note on the Noncollapsibility of Rate Differences and Rate Ratios.

It is well known that the odds ratio is noncollapsible, in the sense that conditioning on a covariate that is related to the outcome typically changes the size of the odds ratio, even if this covariate is unrelated to the exposure. The risk difference and risk ratio do not have this peculiar property; we say that the risk difference and risk ratio are collapsible. However, noncollapsibility is not unique for the odds ratio; the rate difference and rate ratio are generally noncollapsible as well. This may seem paradoxical, since the rate can be viewed as a risk per unit time, and thus one would naively suspect that the rate difference/ratio should inherit collapsibility from the risk difference/ratio. Adding to the confusion, it was recently shown that the exposure coefficient in the Aalen additive hazards model is collapsible. This may seem to contradict the fact that the rate difference is generally noncollapsible, since the exposure coefficient in the Aalen additive hazards model is a rate difference. In this article, we use graphical arguments to explain why the rate difference/ratio does not inherit collapsibility from the risk difference/ratio. We also explain when and why the exposure coefficient in the Aalen additive hazards model is collapsible.