GP consultations for menstrual disorders after COVID-19 vaccination - A self-controlled cohort study based on routine healthcare data from the Netherlands.

INTRODUCTION: Several studies described that COVID-19 vaccinations can cause menstrual disorders. Our study aimed to describe whether this also resulted in more general practitioner (GP) consultations for menstrual disorders after COVID-19 vaccination, based on a large cohort study. METHODS: A retrospective self-controlled cohort study was performed including vaccinated women in 2021 aged 12-49 years from two large, representative GP databases in the Netherlands. Incidence rates and incidence rate ratio's (IRR) were calculated using Poisson regression, adjusting for SARS-CoV-2 infection as time-varying confounder. The exposed period was set at maximum six months after each COVID-19 vaccination and the non-exposed period was defined as all-time outside the exposed period. RESULTS: The cohort included 631,802 women, of which 18,986 (3 %) consulted the GP for a menstrual disorder during 2021. Increased GP consultations were observed among 12-14 year olds for amenorrhea/hypomenorrhea/oligomenorrhea (IRR: 1.85, 95 % CI: 1.30-2.65) and irregular/frequent menstruation (IRR: 1.33, 95 % CI: 1.06-1.69) after COVID-19 vaccination in general, and after Pfizer/BioNTech vaccination (IRR: 1.87, 95 % CI: 1.31-2.67 for amenorrhea/hypomenorrhea/oligomenorrhea and IRR: 1.35, 95 % CI: 1.06-1.70 for irregular/frequent menstruation). Persons from this age group were in general also vaccinated with Pfizer/BioNTech. No increase in the frequency of GP consultations were observed for older age groups, other vaccine brands, and potential risk groups. CONCLUSION: For the majority of women, no increased GP consultations for menstrual disorders was found. Solely for the youngest age group (12-14 year olds) increased GP consultations for specific types of menstrual disorders was found after Pfizer/BioNTech vaccination.

SARS-CoV-2 transmitters have more than three times higher viral loads than non-transmitters - Practical use of viral load for disease control.

BACKGROUND: Quantitative results of SARS-CoV-2 testing reported as viral load copies/mL can provide valuable information, but are rarely used in practice. We analyze whether viral load in the upper respiratory tract is correlated with transmission and disease course and how this information can be used in practice. STUDY DESIGN: Municipal Health Service (MHS) and clinical patients ≥18 years tested positive for SARS-CoV-2 with RT-PCR between June 1 and September 25, 2020 were included. Transmission was defined as an index having at least one contact tested positive. Test delay was defined as the time between symptom onset and SARS-CoV-2 testing. RESULTS: 683 patients were included (656 MHS and 27 clinical patients). The viral load was considerably lower among clinical patients compared to MHS patients: median log10 copies/mL 2.51 (IQR -1.52 - 6.46) vs 4.92 (IQR -0.54 - 8.26), p < 0.0001. However, the test delay was higher for clinical patients (median 7 [IQR 2 - 19] vs 3 [IQR 0 - 26] days, p < 0.0001). SARS-CoV-2 transmitters showed much higher viral loads than non-transmitters (log10 copies/mL 5.23 [IQR -0.52 - 8.26] vs 4.65 [IQR -0.72 - 8.00], p < 0.0001), but not for those with a test delay > 7 days. Higher viral loads were significantly correlated with older age and with more (severe) COVID-19 related symptoms. CONCLUSION: Indexes that transmitted SARS-CoV-2 had more than three times higher viral loads than non-transmitters. Viral load information can be useful during source and contact tracing to prioritize indexes with highest risk of transmission, taking into account the test delay.

Identification of patients at high risk for Clostridium difficile infection: development and validation of a risk prediction model in hospitalized patients treated with antibiotics.

To develop and validate a prediction model for Clostridium difficile infection (CDI) in hospitalized patients treated with systemic antibiotics, we performed a case-cohort study in a tertiary (derivation) and secondary care hospital (validation). Cases had a positive Clostridium test and were treated with systemic antibiotics before suspicion of CDI. Controls were randomly selected from hospitalized patients treated with systemic antibiotics. Potential predictors were selected from the literature. Logistic regression was used to derive the model. Discrimination and calibration of the model were tested in internal and external validation. A total of 180 cases and 330 controls were included for derivation. Age >65 years, recent hospitalization, CDI history, malignancy, chronic renal failure, use of immunosuppressants, receipt of antibiotics before admission, nonsurgical admission, admission to the intensive care unit, gastric tube feeding, treatment with cephalosporins and presence of an underlying infection were independent predictors of CDI. The area under the receiver operating characteristic curve of the model in the derivation cohort was 0.84 (95% confidence interval 0.80-0.87), and was reduced to 0.81 after internal validation. In external validation, consisting of 97 cases and 417 controls, the model area under the curve was 0.81 (95% confidence interval 0.77-0.85) and model calibration was adequate (Brier score 0.004). A simplified risk score was derived. Using a cutoff of 7 points, the positive predictive value, sensitivity and specificity were 1.0%, 72% and 73%, respectively. In conclusion, a risk prediction model was developed and validated, with good discrimination and calibration, that can be used to target preventive interventions in patients with increased risk of CDI.