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Concerns over the safety of non-steroidal anti-inflammatory drug (NSAID) use during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have been raised. We studied whether use of NSAIDs was associated with adverse outcomes and mortality during SARS-CoV-2 infection. We conducted a population-based cohort study using Danish administrative and health registries. We included individuals who tested positive for SARS-CoV-2 during the period 27 February 2020 to 29 April 2020. NSAID users (defined as individuals having filled a prescription for NSAIDs up to 30 days before the SARS-CoV-2 test) were matched to up to 4 non-users on calendar week of the test date and propensity scores based on age, sex, relevant comorbidities, and use of selected prescription drugs. The main outcome was 30-day mortality, and NSAID users were compared to non-users using risk ratios (RRs) and risk differences (RDs). Secondary outcomes included hospitalization, intensive care unit (ICU) admission, mechanical ventilation, and acute renal replacement therapy. A total of 9,236 SARS-CoV-2 PCR-positive individuals were eligible for inclusion. The median age in the study cohort was 50 years, and 58% were female. Of these, 248 (2.7%) had filled a prescription for NSAIDs, and 535 (5.8%) died within 30 days. In the matched analyses, treatment with NSAIDs was not associated with 30-day mortality (RR 1.02, 95% CI 0.57 to 1.82, p = 0.95; RD 0.1%, 95% CI -3.5% to 3.7%, p = 0.95), risk of hospitalization (RR 1.16, 95% CI 0.87 to 1.53, p = 0.31; RD 3.3%, 95% CI -3.4% to 10%, p = 0.33), ICU admission (RR 1.04, 95% CI 0.54 to 2.02, p = 0.90; RD 0.2%, 95% CI -3.0% to 3.4%, p = 0.90), mechanical ventilation (RR 1.14, 95% CI 0.56 to 2.30, p = 0.72; RD 0.5%, 95% CI -2.5% to 3.6%, p = 0.73), or renal replacement therapy (RR 0.86, 95% CI 0.24 to 3.09, p = 0.81; RD -0.2%, 95% CI -2.0% to 1.6%, p = 0.81). The main limitations of the study are possible exposure misclassification, as not all individuals who fill an NSAID prescription use the drug continuously, and possible residual confounding by indication, as NSAIDs may generally be prescribed to healthier individuals due to their side effects, but on the other hand may also be prescribed for early symptoms of severe COVID-19. Use of NSAIDs was not associated with 30-day mortality, hospitalization, ICU admission, mechanical ventilation, or renal replacement therapy in Danish individuals who tested positive for SARS-CoV-2. The European Union electronic Register of Post-Authorisation Studies EUPAS34734.

Large language models have received enormous attention recently with some studies demonstrating their potential clinical value, despite not being trained specifically for this domain. We aimed to investigate whether ChatGPT, a language model optimized for dialogue, can answer frequently asked questions about diabetes. We conducted a closed e-survey among employees of a large Danish diabetes center. The study design was inspired by the Turing test and non-inferiority trials. Our survey included ten questions with two answers each. One of these was written by a human expert, while the other was generated by ChatGPT. Participants had the task to identify the ChatGPT-generated answer. Data was analyzed at the question-level using logistic regression with robust variance estimation with clustering at participant level. In secondary analyses, we investigated the effect of participant characteristics on the outcome. A 55% non-inferiority margin was pre-defined based on precision simulations and had been published as part of the study protocol before data collection began. Among 311 invited individuals, 183 participated in the survey (59% response rate). 64% had heard of ChatGPT before, and 19% had tried it. Overall, participants could identify ChatGPT-generated answers 59.5% (95% CI: 57.0, 62.0) of the time, which was outside of the non-inferiority zone. Among participant characteristics, previous ChatGPT use had the strongest association with the outcome (odds ratio: 1.52 (1.16, 2.00), p = 0.003). Previous users answered 67.4% (61.7, 72.7) of the questions correctly, versus non-users’ 57.6% (54.9, 60.3). Participants could distinguish between ChatGPT-generated and human-written answers somewhat better than flipping a fair coin, which was against our initial hypothesis. Rigorously planned studies are needed to elucidate the risks and benefits of integrating such technologies in routine clinical practice.

Observational studies on corona virus disease 2019 (COVID-19) vaccines compare event rates in vaccinated and unvaccinated person time using Poisson or Cox regression. In Cox regression, the chosen time scale needs to account for the time-varying incidence of severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infection and COVID-19 vaccination.We aimed to quantify bias in person-time based methods, with and without adjustment for calendar time, using simulations and empirical data analysis. We simulated 500,000 individuals who were followed for 365 days, and a point exposure resembling COVID-19 vaccination (cumulative incidence 80%). We generated an effectiveness outcome, emulating the incidence of severe acute respiratory syndrome corona virus 2 infection in Denmark during 2021 (risk 10%), and a safety outcome with seasonal variation (myocarditis, risk 1/5,000). Incidence rate ratios (IRRs) were set to 0.1 for effectiveness and 5.0 for safety outcomes. IRRs and hazard ratios (HRs) were estimated using Poisson and Cox regression with a time under observation scale, and a calendar time scale. Bias was defined as estimated IRR or HR−true IRR. Further, we obtained estimates for both outcomes using data from the Danish health registries. Unadjusted IRRs (biaseffectivenes +0.16; biassafety −2.09) and HRs estimated using a time-under-observation scale (+0.28;-2.15) were biased. Adjustment for calendar time reduced bias in Cox (+0.03; +0.33) and Poisson regression (0.00; −0.28). Cox regression using a calendar time scale was least biased (0.00, +0.12). When analyzing empirical data, adjusted Poisson and Cox regression using a calendar time scale yielded estimates in accordance with existing evidence. Lack of adjustment for the time-varying incidence of COVID-19 related outcomes may severely bias estimates.

Beneficial effects of dopamine agonist treatment on migraine have been reported but remain to be properly tested. The aim of this study was to examine the effect of cabergoline as preventive treatment for migraine. In a double-blind, placebo-controlled pilot study, 36 adults with ≥  6 monthly migraine days were enrolled at Aarhus University Hospital. Following a 28-days baseline period, participants were randomized to receive cabergoline 0.5 mg or placebo once weekly for 12 weeks as add-on treatment. An electronic headache diary was completed daily, and headache questionnaires and blood tests were collected at baseline and following the treatment period. Primary outcome was change in monthly migraine days. The trial was registered with ClinicalTrials.gov (NCT05525611). Of 101 assessed participants, 36 were enrolled. Baseline monthly migraine days were 13.6 (4.1) in the cabergoline group and 14.0 (5.3) in the placebo group. No significant overall difference in the reduction of monthly migraine days was observed. However, among participants with episodic migraine (n = 20), the mean (SE) reduction in monthly migraine days from baseline to the last 28 days of the treatment period was -5.4 (1.3) with cabergoline compared to -1.8 (0.9) with placebo (p = 0.04) [odds ratio: 0.79 (95% CI 0.65 - 0.95), p = 0.014]. In participants with chronic migraine (n = 13), the reduction in monthly migraine days was not significantly different in the two groups. Patients' global impression of change significantly improved after cabergoline treatment as compared to placebo in the entire group of participants (p = 0.006). The number of participants with episodic migraine achieving ≥  50% reduction in monthly migraine days tended to increase after cabergoline (p = 0.07). Adverse effects were reported by seven participants on cabergoline and four on placebo, none of which were serious. Cabergoline significantly reduced monthly migraine days in episodic migraine without serious adverse effects, supporting further investigation into the use of cabergoline for migraine prevention.

Observational cohort studies are used to evaluate the effectiveness of screening mammography in women offered screening. Because screening mammography has no effect on causes of death other than breast cancer (BC), cohort studies should show reductions in the risk of BC death substantially greater than possible reductions in the risk of all-cause death. We assessed the risk of BC deaths and of all-cause (or of nonBC) deaths associated with screening mammography attendance reported in cohort studies. Cohort studies published from 2002 to 2022 on women invited to screening mammography were searched in PubMed, Web of Sciences, Scopus, and in review articles. Random effect meta-analyses were performed using relative risks (RRs) of death between women who attended screening compared to women who did not attend screening. Eighteen cohort studies were identified, nine that reported RRs of BC deaths only, five that reported RRs of all-cause deaths only, and four that reported RRs for both BC deaths and all-cause deaths. The latter four cohort studies reported 12–76 times more all-cause deaths than BC deaths. The random-effect summary of RR for BC mortality in screening attendees vs nonattendees was 0.55 (95% CI: 0.50–0.60) in 13 cohort studies. The summary of RR for all-cause mortality was 0.54 (0.50–0.58) in 10 cohort studies. In the four cohort studies that evaluated both outcomes, the summary of RRs were 0.63 (0.43–0.83) for BC mortality and of 0.54 (0.44–0.64) for all-cause mortality. The similar relative reductions in breast- and all-cause (or nonBC) mortality indicates that screening mammography attendance is an indicator of characteristics associated with a lower risk of dying from any cause, including from BC, which observational studies have falsely interpreted as a screening effect. [Display omitted] •Observational studies are used for evaluating the effectiveness of screening programmes.•Breast cancer accounts for 2%–3% of all causes of death in women.•Screening mammography has no effect on causes of death other than breast cancer.•Screening leads to same reductions in breast cancer and of all-cause death risk.•Similar risk reductions implies that mortality risk reductions are due to biases.

The E value method deals with unmeasured confounding, a key source of bias in observational studies. The E value method is described and its use is shown in a worked example of a meta-analysis examining the association between the use of antidepressants in pregnancy and the risk of miscarriage.

PurposeIn pharmacoepidemiology, correctly defining the exposure period of pharmacological treatment is a challenging step when information on the time in treatment is missing or incomplete.MethodsIn this review, we describe several methods for defining exposure to pharmacological treatments using secondary data sources that lack such information.Results and conclusionSeveral methods for assessing the duration of redeemed prescriptions and combining them into temporal sequences are available. We present a set of considerations to make researchers aware of the potentials and pitfalls of these methods that may aid in minimizing biases in research using these methods. Additionally, we highlight that, to date, there is no one-size-fits-all solution. Thus, the choice of method should be based on their area of applicability combined with a careful mapping to the research scenario under investigation.

One-fourth of women experience substantially higher weight years after childbirth. We examined weight change from prepregnancy to 18 months postpartum according to subsequent maternal risk of hypertension and cardiovascular disease (CVD). We conducted a cohort study of 47,966 women with a live-born singleton within the Danish National Birth Cohort (DNBC; 1997-2002). Interviews during pregnancy and 6 and 18 months postpartum provided information on height, gestational weight gain (GWG), postpartum weights, and maternal characteristics. Information on pregnancy complications, incident hypertension, and CVD was obtained from the National Patient Register. Using Cox regression, we estimated adjusted hazard ratios (HRs; 95% confidence interval [CI]) for hypertension and CVD through 16 years of follow-up. During this period, 2,011 women were diagnosed at the hospital with hypertension and 1,321 with CVD. The women were on average 32.3 years old (range 18.0-49.2) at start of follow-up, 73% had a prepregnancy BMI <25, and 27% a prepregnancy BMI ≥25. Compared with a stable weight (±1 BMI unit), weight gains from prepregnancy to 18 months postpartum of >1-2 and >2 BMI units were associated with 25% (10%-42%), P = 0.001 and 31% (14%-52%), P < 0.001 higher risks of hypertension, respectively. These risks were similar whether weight gain presented postpartum weight retention or a new gain from 6 months to 18 months postpartum and whether GWG was below, within, or above the recommendations. For CVD, findings differed according to prepregnancy BMI. In women with normal-/underweight, weight gain >2 BMI units and weight loss >1 BMI unit were associated with 48% (17%-87%), P = 0.001 and 28% (6%-55%), P = 0.01 higher risks of CVD, respectively. Further, weight loss >1 BMI unit combined with a GWG below recommended was associated with a 70% (24%-135%), P = 0.001 higher risk of CVD. No such increased risks were observed among women with overweight/obesity (interaction by prepregnancy BMI, P = 0.01, 0.03, and 0.03, respectively). The limitations of this observational study include potential confounding by prepregnancy metabolic health and self-reported maternal weights, which may lead to some misclassification. Postpartum weight retention/new gain in all mothers and postpartum weight loss in mothers with normal-/underweight may be associated with later adverse cardiovascular health.