The burden of nausea and vomiting during pregnancy: severe impacts on quality of life, daily life functioning and willingness to become pregnant again – results from a cross-sectional study

Background: Though nausea and vomiting is very common during pregnancy, no studies have investigated the impact of this condition on the women’s daily lives in a Scandinavian population. The aim of this study was to describe the burden of nausea and vomiting during pregnancy (NVP) on global quality of life, daily life functioning and willingness to become pregnant again according to the severity of NVP symptoms. Methods: This study is a cross-sectional population-based study conducted in Norway. Pregnant women and mothers with children <1 year of age with current or prior NVP were eligible to participate. Data were collected through an anonymous on-line questionnaire accessible from November 10th, 2014 to January 31st, 2015. Severity of NVP was measured using the 24-h Pregnancy Unique Quantification of Emesis Scale (PUQE). Associations between severity of NVP, daily life functioning and willingness to become pregnant again were tested using chisquare tests. Associations with global quality of life measured in terms of the Quality of Life Scale (QOLS) were estimated using generalized linear models and reported as unstandardized regression coefficients (β) with 95% confidence intervals (CI). Results: 712 women with NVP were included in the study. NVP was significantly associated with several characteristics, including daily life functioning, quality of life and willingness to become pregnant again. The negative impact was greater the more severe the symptoms were, although considerable adverse effects were also seen among women with mild and moderate NVP symptoms. Over one fourth of the women with severe NVP considered terminating the pregnancy due to NVP, and three in four considered not to get pregnant again. Severity of NVP remained significantly associated with reduced global quality of life when adjusting for maternal characteristics and illnesses with β (95% CI) = −10.9 (−16.9, −4.9) for severe versus mild NVP. Conclusions: NVP as measured by PUQE had a major impact on various aspects of the women’s lives, including global quality of life and willingness to become pregnant again.publishedVersio

Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review

AimTo perform a systematic review on the use of Artificial Intelligence (AI) techniques for predicting COVID-19 hospitalization and mortality using primary and secondary data sources.Study eligibility criteriaCohort, clinical trials, meta-analyses, and observational studies investigating COVID-19 hospitalization or mortality using artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded.Data sourcesArticles recorded in Ovid MEDLINE from 01/01/2019 to 22/08/2022 were screened.Data extractionWe extracted information on data sources, AI models, and epidemiological aspects of retrieved studies.Bias assessmentA bias assessment of AI models was done using PROBAST.ParticipantsPatients tested positive for COVID-19.ResultsWe included 39 studies related to AI-based prediction of hospitalization and death related to COVID-19. The articles were published in the period 2019-2022, and mostly used Random Forest as the model with the best performance. AI models were trained using cohorts of individuals sampled from populations of European and non-European countries, mostly with cohort sample size &lt;5,000. Data collection generally included information on demographics, clinical records, laboratory results, and pharmacological treatments (i.e., high-dimensional datasets). In most studies, the models were internally validated with cross-validation, but the majority of studies lacked external validation and calibration. Covariates were not prioritized using ensemble approaches in most of the studies, however, models still showed moderately good performances with Area under the Receiver operating characteristic Curve (AUC) values &gt;0.7. According to the assessment with PROBAST, all models had a high risk of bias and/or concern regarding applicability.ConclusionsA broad range of AI techniques have been used to predict COVID-19 hospitalization and mortality. The studies reported good prediction performance of AI models, however, high risk of bias and/or concern regarding applicability were detected

Medication safety in pregnancy : results from the MoBa study

This article summarizes the results of several of our studies on medication safety in pregnancy based on the Norwegian Mother and Child Cohort Study (MoBa). Medications investigated include antidepressants, NSAIDs, codeine, triptans, paracetamol and certain herbals. A major advantage of these studies is that MoBa has information on prescribed medications, over-the-counter medications and herbal medications. Moreover, MoBa enables the possibility of including a disease comparison group, and long-term follow-up into childhood. The size of MoBa enables designs like the sibling-design, which offers important advantages over studies comparing unrelated individuals. The possibility of linking MoBa to nationwide registries like the NorPD and the National Patient Registry enables validation of medication exposures and childhood diagnosis. Pharmacoepidemiological studies are vital to our understanding of the safety of medications in pregnancy, but great care must be taken in the analysis and interpretation of observational data to avoid problems of confounding and bias

Women's perception of risks of adverse fetal pregnancy outcomes: a large-scale multinational survey

Objective To determine pregnant women and new mothers’ perception of risks in pregnancy. Design, settings and participants This was a large-scale multinational survey including 9113 pregnant women and new mothers from 18 countries in Europe, North America and Australia. Main outcomes Risk perception scores (0–10) for harmful effects to the fetus were derived for: (1) medicines (over-the-counter medicine and prescribed medicine), (2) food substances (eggs and blue veined cheese), (3) herbal substances (ginger and cranberries) (4) alcohol and tobacco, and (5) thalidomide. Results Overall, 80% (6453/8131) of women perceived the risk of giving birth to a child with a birth defect to be ≤5 of 100 births. The women rated cranberries and ginger least harmful (mean risk perception scores 1.1 and 1.5 of 10, respectively) and antidepressants, alcohol, smoking and thalidomide as most harmful (7.6, 8.6, 9.2 and 9.4 out of 10, respectively). The perception varied with age, level of education, pregnancy status, profession and geographical region. Noticeably, 70% had not heard about thalidomide, but of those who had (2692/9113), the risk perception scores were 0.4–0.5 points lower in women below 25 years compared to women aged 26–30 years. Conclusions In general, women perceived the risks of giving birth to a child with birth defects low, but there were substantial disparities between women's perceived risks and the actual risks when it comes to over-the-counter agents against nausea and prescribed medication. The study revealed that few women knew of thalidomide, suggesting that the general awareness among women of the teratogenic effects of thalidomide is declining, but it has left a general scepticism about safety of medication in pregnancy. This may have some severe consequences if women are left without medical treatments in pregnancy

Medical care contact for infertility and related medication use during pregnancy – a european, cross-sectional web-based study

Aims: The aim of the study was two-fold: i) to determine the prevalence of medical care contact for infertility in European countries; ii) to map overall and long-term/chronic medication use during pregnancy in women who sought medical care due to infertility. Methods: This is a sub-study of the Multinational Medication Use in Pregnancy Study, a cross-sectional, web-based study conducted from October 2011 to February 2012. We included 8097 participants from Europe who were pregnant or new mothers. We collected data on overall and long-term/chronic medication use, medical care seeking due to infertility, and whether women eventually conceived spontaneously or with the aid of infertility treatment. Results: Medical care contact for infertility was lower in Western Europe (prevalence estimate: 10.0-15.3%), compared with Northern (15.2-17.5%) or Eastern (17.4-20.9%), but Poland had the lowest estimate (8.0%). Overall, 660 (8.2%) women sought medical care due to infertility but conceived spontaneously; 548 (6.8%) conceived aided by fertility treatment, and 6889 (85.0%) women did not seek help. Use of any medication was comparable across the three groups (range 80.4-82.5%), but women seeking help for infertility (21.8-24.6%) took more often long-term/chronic medications than women who did not (14.8%). Conclusion: Medical care contacts for infertility varies greatly across European countries. Women who had medical contact due to infertility used more often chronic medications in pregnancy than women who did not, pointing to more co-morbidities and risk pregnancies

Learning the effects of psychotropic drugs during pregnancy using real-world safety data: a paradigm shift toward modern pharmacovigilance

The growing evidence on psychotropic drug safety in pregnancy has been possible thanks to the increasing availability of real-world data, i.e. data not collected in conventional randomised controlled trials. Use of these data is a key to establish psychotropic drug effects on foetal, child, and maternal health. Despite the inherent limitations and pitfalls of observational data, these can still be informative after a critical appraisal of the collective body of evidence has been done. By valuing real-world safety data, and making these a larger part of the regulatory decision-making process, we move toward a modern pregnancy pharmacovigilance. The recent uptake of real-world safety data by health authorities has set the basis for an important paradigm shift, which is integrating such data into drug labelling. The recent safety assessment of sodium valproate in pregnant and childbearing women is probably one of the first examples of modern pregnancy pharmacovigilance

Analyzing Missing Data in Perinatal Pharmacoepidemiology Research: Methodological Considerations to Limit the Risk of Bias

Pharmacoepidemiological studies on the safety of medication during pregnancy are all susceptible to missing data (ie, data that should have been recorded but for some reason were not). Missing data are ubiquitous, irrespective of the data source used. Bias can arise when incomplete data on confounders, outcome measures, pregnancy duration, or even cohort selection criteria are used to estimate prenatal exposure effects that would be obtained from the fully observed data, if these were available for each mother–child dyad. This commentary describes general missing data mechanisms and methods, and illustrates how missing data were handled in recent medication in pregnancy research, according to the utilized data source. We further present one applied example on missing data analysis within MoBa (the Norwegian Mother, Father and Child Cohort Study), and finally illustrate how the causal diagram framework can be helpful in assessing risk of bias due to missing data in perinatal pharmacoepidemiology research. We recommend that applied researchers limit missing data during data collection, carefully diagnose missingness, apply strategies for missing data mitigation under different assumptions, and finally include evaluations of robustness results under these assumptions. Following this set of recommendations can aid future perinatal pharmacoepidemiology research in avoiding the problems that result from failure to consider this important source of bias